lidar-scene-flow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "from collections import Counter\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import cv2\n",
    "import PIL.Image as Image\n",
    "\n",
    "from utils.util_flow import load_calib_cam_to_cam, read_calib_file\n",
    "from dataloader.depthloader import disparity_loader, triangulation\n",
    "from utils.flowlib import read_flow, flow_to_image\n",
    "from utils.util_flow import write_flow, readPFM, save_pfm\n",
    "\n",
    "from dataloader import kitti15list_val_lidar as DA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper functions\n",
    "def load_velodyne_points(file_name):\n",
    "    # adapted from https://github.com/hunse/kitti\n",
    "    points = np.fromfile(file_name, dtype=np.float32).reshape(-1, 4)\n",
    "    points[:, 3] = 1.0  # homogeneous\n",
    "    return points\n",
    "\n",
    "def sub2ind(matrixSize, rowSub, colSub):\n",
    "    m, n = matrixSize\n",
    "    return rowSub * (n-1) + colSub - 1\n",
    "\n",
    "def ext_depth(line, kitti_dir):\n",
    "    \"\"\"\n",
    "    extract depth from point clouds in two consequtive frames\n",
    "    \"\"\"\n",
    "    date, seq, fr = line.split(' ')\n",
    "    cam = 2\n",
    "    calib_dir = '%s/%s/'%(kitti_dir,date)\n",
    "    cam2cam = read_calib_file(calib_dir + 'calib_cam_to_cam.txt')\n",
    "    velo2cam = read_calib_file(calib_dir + 'calib_velo_to_cam.txt')\n",
    "    velo2cam = np.hstack((velo2cam['R'].reshape(3,3), velo2cam['T'][..., np.newaxis]))\n",
    "    velo2cam = np.vstack((velo2cam, np.array([0, 0, 0, 1.0])))\n",
    "\n",
    "    # compute projection matrix velodyne->image plane\n",
    "    R_cam2rect = np.eye(4)\n",
    "    R_cam2rect[:3,:3] = cam2cam['R_rect_00'].reshape(3,3)\n",
    "    P_rect = cam2cam['P_rect_0'+str(cam)].reshape(3,4)\n",
    "    P_velo2im = np.dot(np.dot(P_rect, R_cam2rect), velo2cam)\n",
    "    \n",
    "    # depth1\n",
    "    velo = load_velodyne_points('%s/%s/%s/velodyne_points/data/%s.bin'%(kitti_dir,date,seq,fr))\n",
    "    velo = velo[velo[:, 0] >= 0, :]\n",
    "    # project the points to the camera\n",
    "    velo_pts_im = np.dot(P_velo2im, velo.T).T\n",
    "    velo_pts_im[:, :2] = velo_pts_im[:,:2] / velo_pts_im[:,2][..., np.newaxis]\n",
    "    velo_pts_im[:, 2] = velo[:, 0]\n",
    "\n",
    "    # check if in bounds\n",
    "    # use minus 1 to get the exact same value as KITTI matlab code\n",
    "    velo_pts_im[:, 0] = np.round(velo_pts_im[:,0]) - 1\n",
    "    velo_pts_im[:, 1] = np.round(velo_pts_im[:,1]) - 1\n",
    "    val_inds = (velo_pts_im[:, 0] >= 0) & (velo_pts_im[:, 1] >= 0)\n",
    "    val_inds = val_inds & (velo_pts_im[:,0] < shape[1]) & (velo_pts_im[:,1] < shape[0])\n",
    "    velo_pts_im = velo_pts_im[val_inds, :]\n",
    "\n",
    "    # project to image\n",
    "    depth = np.zeros((shape))\n",
    "    depth[velo_pts_im[:, 1].astype(np.int), velo_pts_im[:, 0].astype(np.int)] = velo_pts_im[:, 2]\n",
    "\n",
    "    # find the duplicate points and choose the closest depth\n",
    "    inds = sub2ind(depth.shape, velo_pts_im[:, 1], velo_pts_im[:, 0])\n",
    "    dupe_inds = [item for item, count in Counter(inds).items() if count > 1]\n",
    "    for dd in dupe_inds:\n",
    "        pts = np.where(inds==dd)[0]\n",
    "        x_loc = int(velo_pts_im[pts[0], 0])\n",
    "        y_loc = int(velo_pts_im[pts[0], 1])\n",
    "        depth[y_loc, x_loc] = velo_pts_im[pts, 2].min()\n",
    "    depth[depth<0] = 0 \n",
    "    depth1=depth.copy()\n",
    "\n",
    "    # depth2\n",
    "    velo = load_velodyne_points('%s/%s/%s/velodyne_points/data/%010d.bin'%(kitti_dir,date,seq,int(fr)+1))\n",
    "    velo = velo[velo[:, 0] >= 0, :]\n",
    "    # project the points to the camera\n",
    "    velo_pts_im = np.dot(P_velo2im, velo.T).T\n",
    "    velo_pts_im[:, :2] = velo_pts_im[:,:2] / velo_pts_im[:,2][..., np.newaxis]\n",
    "    velo_pts_im[:, 2] = velo[:, 0]\n",
    "\n",
    "    # check if in bounds\n",
    "    # use minus 1 to get the exact same value as KITTI matlab code\n",
    "    velo_pts_im[:, 0] = np.round(velo_pts_im[:,0]) - 1\n",
    "    velo_pts_im[:, 1] = np.round(velo_pts_im[:,1]) - 1\n",
    "    val_inds = (velo_pts_im[:, 0] >= 0) & (velo_pts_im[:, 1] >= 0)\n",
    "    val_inds = val_inds & (velo_pts_im[:,0] < shape[1]) & (velo_pts_im[:,1] < shape[0])\n",
    "    velo_pts_im = velo_pts_im[val_inds, :]\n",
    "\n",
    "    # project to image\n",
    "    depth = np.zeros((shape))\n",
    "    depth[velo_pts_im[:, 1].astype(np.int), velo_pts_im[:, 0].astype(np.int)] = velo_pts_im[:, 2]\n",
    "\n",
    "    # find the duplicate points and choose the closest depth\n",
    "    inds = sub2ind(depth.shape, velo_pts_im[:, 1], velo_pts_im[:, 0])\n",
    "    dupe_inds = [item for item, count in Counter(inds).items() if count > 1]\n",
    "    for dd in dupe_inds:\n",
    "        pts = np.where(inds==dd)[0]\n",
    "        x_loc = int(velo_pts_im[pts[0], 0])\n",
    "        y_loc = int(velo_pts_im[pts[0], 1])\n",
    "        depth[y_loc, x_loc] = velo_pts_im[pts, 2].min()\n",
    "    depth[depth<0] = 0\n",
    "    depth2=depth.copy()\n",
    "    \n",
    "    return depth1,depth2\n",
    "\n",
    "# scene flow dataset to raw dataset mapping\n",
    "with open('/data/gengshay/kitti_scene/devkit/mapping/train_mapping.txt') as f:\n",
    "    lines = [i.strip() if len(i.strip())>0 else None for i in f.readlines()]\n",
    "    \n",
    "frame_to_lidar = {}\n",
    "for it,l in enumerate(lines):\n",
    "    frame_to_lidar[it] = l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/ssd/kitti_scene/training/image_2/000113_10.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-3-22b051606f36>:24: RuntimeWarning: invalid value encountered in true_divide\n",
      "  gt_logdc = np.log(gt_disp0/gt_disp1)\n",
      "<ipython-input-3-22b051606f36>:57: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  gt_ttc = 0.1/(1-np.exp(gt_logdc))\n",
      "<ipython-input-3-22b051606f36>:58: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  ttc = 0.1/(1-np.exp(logdc))\n",
      "/home/gengshay/code/expansion/dataloader/depthloader.py:51: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  depth = bl*fl / disp # 450px->15mm focal length\n",
      "<ipython-input-3-22b051606f36>:80: RuntimeWarning: invalid value encountered in subtract\n",
      "  gt_f3d = gt_P1-gt_P0\n",
      "<ipython-input-3-22b051606f36>:82: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  P0 = triangulation(bl*fl/d, x0, y0, bl=bl, fl = fl, cx = cx, cy = cy)\n",
      "<ipython-input-3-22b051606f36>:83: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  P1 = triangulation(bl*fl/d/np.exp(logdc), x0 + flow[:,:,0], y0 + flow[:,:,1], bl=bl, fl = fl, cx = cx, cy = cy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/ssd/kitti_scene/training/image_2/000114_10.png\n",
      "/ssd/kitti_scene/training/image_2/000115_10.png\n",
      "/ssd/kitti_scene/training/image_2/000116_10.png\n",
      "/ssd/kitti_scene/training/image_2/000117_10.png\n",
      "/ssd/kitti_scene/training/image_2/000118_10.png\n",
      "/ssd/kitti_scene/training/image_2/000119_10.png\n",
      "/ssd/kitti_scene/training/image_2/000120_10.png\n",
      "/ssd/kitti_scene/training/image_2/000121_10.png\n",
      "/ssd/kitti_scene/training/image_2/000122_10.png\n",
      "/ssd/kitti_scene/training/image_2/000123_10.png\n",
      "/ssd/kitti_scene/training/image_2/000124_10.png\n",
      "/ssd/kitti_scene/training/image_2/000125_10.png\n",
      "/ssd/kitti_scene/training/image_2/000126_10.png\n",
      "/ssd/kitti_scene/training/image_2/000127_10.png\n",
      "/ssd/kitti_scene/training/image_2/000128_10.png\n",
      "/ssd/kitti_scene/training/image_2/000129_10.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-3-22b051606f36>:24: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  gt_logdc = np.log(gt_disp0/gt_disp1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/ssd/kitti_scene/training/image_2/000130_10.png\n",
      "/ssd/kitti_scene/training/image_2/000131_10.png\n",
      "/ssd/kitti_scene/training/image_2/000132_10.png\n",
      "/ssd/kitti_scene/training/image_2/000141_10.png\n",
      "/ssd/kitti_scene/training/image_2/000142_10.png\n",
      "/ssd/kitti_scene/training/image_2/000143_10.png\n",
      "/ssd/kitti_scene/training/image_2/000144_10.png\n",
      "/ssd/kitti_scene/training/image_2/000145_10.png\n",
      "/ssd/kitti_scene/training/image_2/000146_10.png\n",
      "/ssd/kitti_scene/training/image_2/000147_10.png\n",
      "/ssd/kitti_scene/training/image_2/000148_10.png\n",
      "/ssd/kitti_scene/training/image_2/000149_10.png\n",
      "/ssd/kitti_scene/training/image_2/000150_10.png\n",
      "/ssd/kitti_scene/training/image_2/000155_10.png\n",
      "/ssd/kitti_scene/training/image_2/000157_10.png\n",
      "/ssd/kitti_scene/training/image_2/000158_10.png\n",
      "/ssd/kitti_scene/training/image_2/000159_10.png\n",
      "/ssd/kitti_scene/training/image_2/000160_10.png\n",
      "/ssd/kitti_scene/training/image_2/000161_10.png\n",
      "/ssd/kitti_scene/training/image_2/000162_10.png\n",
      "/ssd/kitti_scene/training/image_2/000163_10.png\n",
      "/ssd/kitti_scene/training/image_2/000164_10.png\n",
      "/ssd/kitti_scene/training/image_2/000168_10.png\n",
      "/ssd/kitti_scene/training/image_2/000169_10.png\n",
      "/ssd/kitti_scene/training/image_2/000199_10.png\n"
     ]
    }
   ],
   "source": [
    "prefix='./precomputed/'\n",
    "kitti_raw_path='/data/gengshay/KITTI' # replace with yours\n",
    "kitti_sf_path='/ssd/kitti_scene/training/' # replace with yours\n",
    "modelname='lidar-kitti-train'\n",
    "\n",
    "method='expansion' # over {expansion, flownet3, osf, prsm}\n",
    "\n",
    "dc_err = []\n",
    "ttc_err1 = []\n",
    "ttc_err2 = []\n",
    "ttc_err5 = []\n",
    "f3d_epe = []\n",
    "f3d_out = []\n",
    "tt = []\n",
    "test_left_img, test_right_img ,flow_paths= DA.dataloader(kitti_sf_path)\n",
    "for fp in sorted(test_left_img):\n",
    "    print(fp)\n",
    "    fid = fp.split('/')[-1].split('_')[0]\n",
    "\n",
    "    # load ground-truth\n",
    "    gt_disp0 = disparity_loader('%s/disp_occ_0/%s_10.png'%(kitti_sf_path,fid))\n",
    "    gt_disp1 = disparity_loader('%s/disp_occ_1/%s_10.png'%(kitti_sf_path,fid))\n",
    "    gt_flow = read_flow('%s/flow_occ/%s_10.png'%(kitti_sf_path,fid))\n",
    "    gt_logdc = np.log(gt_disp0/gt_disp1)\n",
    "    d1mask = gt_disp0>0\n",
    "    d2mask = gt_disp1>0\n",
    "    flmask = np.logical_and(np.logical_and(d1mask,d2mask),gt_flow[:,:,2])\n",
    "    \n",
    "    if method=='expansion':\n",
    "        disp0 = disparity_loader('%s/%s/2015/%s_10_disp.pfm'%(prefix,modelname,fid))\n",
    "        disp1 = disparity_loader('%s/%s/2015/%s_11_disp.pfm'%(prefix,modelname,fid))\n",
    "        flow = read_flow('%s/%s/2015/%s_10.png'%(prefix,modelname,fid))\n",
    "    elif method=='flownet3':\n",
    "        disp0 = disparity_loader('/data/gengshay/sceneflow_results/dn-css-ft/disp_0/%s_10.png'%(fid))\n",
    "        disp1 = disparity_loader('/data/gengshay/sceneflow_results/dn-css-ft/disp_1/%s_10.png'%(fid))\n",
    "        flow = read_flow('/data/gengshay/sceneflow_results/dn-css-ft/flow/%s_10.png'%(fid))\n",
    "    elif method=='osf':\n",
    "        disp0 = disparity_loader('/data/gengshay/sceneflow_results/osf/disp_0/%s_10.png'%(fid))\n",
    "        disp1 = disparity_loader('/data/gengshay/sceneflow_results/osf/disp_1/%s_10.png'%(fid))\n",
    "        flow = read_flow('/data/gengshay/sceneflow_results/osf/flow/%s_10.png'%(fid))\n",
    "    elif method=='prsm':\n",
    "        disp0 = disparity_loader('/home/gengshay/code/SF/PRSM/solvePWRSMulti/2Frames/disp_0/%s_10.png'%(fid))\n",
    "        disp1 = disparity_loader('/home/gengshay/code/SF/PRSM/solvePWRSMulti/2Frames/disp_1/%s_10.png'%(fid))\n",
    "        flow = read_flow('/home/gengshay/code/SF/PRSM/solvePWRSMulti/2Frames/flow/%s_10.png'%(fid))\n",
    "    logdc = np.clip(np.log(disp0/np.clip(disp1,1e-8,np.inf)),-np.log(2),np.log(2))\n",
    "        \n",
    "    shape = d1mask.shape\n",
    "    x0,y0=np.meshgrid(range(shape[1]),range(shape[0]))\n",
    "    x0=x0.astype(np.float32)\n",
    "    y0=y0.astype(np.float32)\n",
    "    \n",
    "    \n",
    "    # metrics for expansion and time-to-contact\n",
    "    dcerr = np.abs(gt_logdc-logdc)\n",
    "    dcerr[~flmask]=0\n",
    "    \n",
    "    gt_ttc = 0.1/(1-np.exp(gt_logdc))\n",
    "    ttc = 0.1/(1-np.exp(logdc))\n",
    "    ttcmask = np.logical_and(flmask, gt_ttc>0)\n",
    "    ttcerr1 = (gt_ttc<1) != (ttc<1)\n",
    "    ttcerr2 = (gt_ttc<2) != (ttc<2)\n",
    "    ttcerr5 = (gt_ttc<5) != (ttc<5)\n",
    "    ttcerr1[~ttcmask]=0\n",
    "    ttcerr2[~ttcmask]=0\n",
    "    ttcerr5[~ttcmask]=0\n",
    "\n",
    "    \n",
    "    # metrics for lidar scene flow \n",
    "    d,_ = ext_depth(frame_to_lidar[int(fid)], kitti_raw_path)\n",
    "    dmask = np.logical_and(d>0,flmask)\n",
    "    \n",
    "    ints = load_calib_cam_to_cam('%s/calib/%s.txt'%(kitti_sf_path,fid))\n",
    "    fl = ints['K_cam2'][0,0]\n",
    "    bl = ints['b20']-ints['b30']\n",
    "    cx = ints['K_cam2'][0,2]\n",
    "    cy = ints['K_cam2'][1,2]\n",
    "\n",
    "    gt_P0 = triangulation(gt_disp0, x0, y0, bl=bl, fl = fl, cx = cx, cy = cy)\n",
    "    gt_P1 = triangulation(gt_disp1, x0 + gt_flow[:,:,0], y0 + gt_flow[:,:,1], bl=bl, fl = fl, cx = cx, cy = cy)\n",
    "    gt_f3d = gt_P1-gt_P0\n",
    "    \n",
    "    P0 = triangulation(bl*fl/d, x0, y0, bl=bl, fl = fl, cx = cx, cy = cy)\n",
    "    P1 = triangulation(bl*fl/d/np.exp(logdc), x0 + flow[:,:,0], y0 + flow[:,:,1], bl=bl, fl = fl, cx = cx, cy = cy)\n",
    "    f3d = P1-P0\n",
    "    \n",
    "    f3depe = np.linalg.norm((gt_f3d-f3d)[:3],ord=None, axis=0).reshape(shape)\n",
    "    f3depe[~dmask]=0\n",
    "    f3dout = (np.logical_or(f3depe > 0.3, f3depe / np.linalg.norm((gt_f3d)[:3],ord=None, axis=0).reshape(shape)  > 0.1))\n",
    "    f3dout[~dmask]=0\n",
    "    \n",
    "    \n",
    "    dc_err.append(dcerr[flmask])\n",
    "    ttc_err1.append(ttcerr1[ttcmask])\n",
    "    ttc_err2.append(ttcerr2[ttcmask])\n",
    "    ttc_err5.append(ttcerr5[ttcmask])\n",
    "    f3d_epe.append(f3depe[dmask])\n",
    "    f3d_out.append(f3dout[dmask])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dc:0.006120\n",
      "ttc-err-1:3.736111\n",
      "ttc-err-2:5.339045\n",
      "ttc-err-5:4.702143\n",
      "f3d-mean:0.120488\n",
      "f3d-std:0.334676\n",
      "f3d-out:0.359701\n"
     ]
    }
   ],
   "source": [
    "print('dc:%f'%np.concatenate(dc_err).mean())\n",
    "print('ttc-err-1:%f'%(np.concatenate(ttc_err1).mean()*100))\n",
    "print('ttc-err-2:%f'%(np.concatenate(ttc_err2).mean()*100))\n",
    "print('ttc-err-5:%f'%(np.concatenate(ttc_err5).mean()*100))\n",
    "print('f3d-mean:%f'%np.concatenate(f3d_epe).mean())\n",
    "print('f3d-std:%f'%np.concatenate(f3d_epe).std())\n",
    "print('f3d-out:%f'%np.concatenate(f3d_out).astype(float).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizations\n",
    "plt.figure(figsize=(8,3)); plt.axis('off')\n",
    "plt.imshow(disp0,cmap='gray')\n",
    "plt.figure(figsize=(8,3)); plt.axis('off')\n",
    "plt.imshow(disp1,cmap='gray',vmax=100,vmin=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f0b3023c970>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,3)); plt.axis('off')\n",
    "plt.imshow(logdc,cmap='Spectral',vmax=0.05,vmin=-0.3)\n",
    "plt.figure(figsize=(8,3)); plt.axis('off')\n",
    "plt.imshow(dcerr,cmap='gray',vmin=0,vmax=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f0b35797a60>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,3)); plt.axis('off')\n",
    "plt.imshow(f3depe,vmax=0.5, cmap='gray')\n",
    "plt.figure(figsize=(8,3)); plt.axis('off')\n",
    "plt.imshow(f3dout,vmax=0.5, cmap='gray')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}