Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = \"../hover_net/CoNSeP\"\n",
    "save_data_dir = \"./CoNSeP\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Color Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('Stain_Normalization')\n",
    "import matplotlib.pyplot as plt\n",
    "import glob,os\n",
    "from Stain_Normalization import stainNorm_Vahadane\n",
    "%matplotlib inline\n",
    "\n",
    "target_im_path = \"../hover_net/CoNSeP/Train/Images/train_6.png\"\n",
    "target_im = plt.imread(target_im_path)\n",
    "train_option = \"Train\"\n",
    "# train_option = \"Test\"\n",
    "\n",
    "os.makedirs(save_data_dir, exist_ok=True)\n",
    "os.makedirs(os.path.join(save_data_dir, train_option), exist_ok=True)\n",
    "\n",
    "for im_file_path in glob.glob(os.path.join(data_dir, train_option,'Images','*.png')):\n",
    "# im_file_path = \n",
    "    _, file_name = os.path.split(im_file_path)\n",
    "    save_file_name = file_name[:-4]+\".png\"\n",
    "    \n",
    "    save_norm_dir = os.path.join(save_data_dir, train_option, 'Norms')\n",
    "    os.makedirs(save_norm_dir, exist_ok = True)\n",
    "    save_norm_file_path = os.path.join(save_norm_dir, save_file_name)\n",
    "    \n",
    "    if not os.path.exists(save_norm_file_path):\n",
    "        im = plt.imread(im_file_path)[...,:3]\n",
    "\n",
    "        # plt.imshow(im)\n",
    "\n",
    "        cn = stainNorm_Vahadane.Normalizer()\n",
    "        cn.fit(target_im)\n",
    "        im_norm = cn.transform(im)\n",
    "        plt.imsave(save_norm_file_path, im_norm)\n",
    "#     print(save_norm_file_path)\n",
    "\n",
    "# fig,axes = plt.subplots(1,2)\n",
    "# axes[0].imshow(im)\n",
    "# axes[1].imshow(im_norm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extract Points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io as sio\n",
    "import numpy as np\n",
    "\n",
    "labels_file = glob.glob(os.path.join(data_dir, train_option,'Labels','*.mat'))[0]\n",
    "labels = sio.loadmat(labels_file)\n",
    "inst_map = labels['inst_map']\n",
    "type_map = labels['type_map']\n",
    "inst_type = labels['inst_type']\n",
    "inst_centroid = labels['inst_centroid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage.segmentation import find_boundaries\n",
    "from skimage.filters import gaussian\n",
    "from segmentation._watershed import watershed\n",
    "\n",
    "SMOOTH = 1e-6\n",
    "\n",
    "def validate_obj_params(obj_params=dict()):\n",
    "    if not type(obj_params) is dict:\n",
    "        raise TypeError('The obj_params is expected to be a dictionary not a {}'.format(type(obj_params)))\n",
    "    if not 'sigma' in obj_params.keys():\n",
    "        obj_params['sigma'] = 0.0\n",
    "    if not 'compactness' in obj_params.keys():\n",
    "        obj_params['compactness'] = 0.0\n",
    "    if not 'alpha' in obj_params.keys():\n",
    "        obj_params['alpha'] = 1.0\n",
    "    return obj_params\n",
    "\n",
    "def generate_dist(image, points, obj_params = dict()):\n",
    "    obj_params = validate_obj_params(obj_params)\n",
    "    points = np.array(points)\n",
    "    pl = np.zeros(image.shape[:2])\n",
    "    pl[points[:,1].round().astype(int),points[:,0].round().astype(int)] = np.arange(1,len(points)+1)\n",
    "    image = gaussian(image,obj_params['sigma'], multichannel=True)\n",
    "    \n",
    "    return watershed(image, pl, compactness = obj_params['compactness'], method=True)\n",
    "\n",
    "def generate_obj(image, points, obj_params = dict()):\n",
    "    obj_params = validate_obj_params(obj_params)\n",
    "    reg_split, dist = generate_dist(image,points,obj_params)\n",
    "    bkg = find_boundaries(reg_split)\n",
    "#     dist[bkg] = np.inf\n",
    "    return np.exp(-obj_params['alpha']*dist**2), bkg, reg_split\n",
    "\n",
    "def iou_numpy(outputs: np.array, labels: np.array):\n",
    "    outputs = outputs.round().astype(bool)\n",
    "    labels = labels.round().astype(bool)\n",
    "    intersection = (outputs & labels).sum()\n",
    "    union = (outputs | labels).sum()\n",
    "    \n",
    "    iou = (intersection + SMOOTH) / (union + SMOOTH)\n",
    "    \n",
    "    return 1-iou\n",
    "\n",
    "def cross_entropy_numpy(outputs: np.array, labels: np.array):\n",
    "    return np.mean(-labels*np.log(SMOOTH+outputs)-(1-labels)*np.log(SMOOTH+1-outputs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shutil\n",
    "train_dir = os.path.join(save_data_dir,'Train')\n",
    "train_norm_dir = os.path.join(save_data_dir,'Train','Norms')\n",
    "os.makedirs(train_norm_dir, exist_ok = True)\n",
    "\n",
    "for train_norm_path in glob.glob(os.path.join(train_dir,'*_norm.png')):\n",
    "    new_train_norm_path = os.path.join(train_norm_dir, os.path.split(train_norm_path)[-1][:-9]+'.png')\n",
    "    shutil.move(train_norm_path, new_train_norm_path)\n",
    "#     print(new_train_norm_path)\n",
    "# glob.glob(os.path.join(save_data_dir, 'Train', '*.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "def black_box_function(sigma, compactness, alpha):\n",
    "    obj_params = dict()\n",
    "    obj_params['sigma'] = sigma\n",
    "    obj_params['compactness'] = compactness\n",
    "    obj_params['alpha'] = alpha\n",
    "    loss = 0.0\n",
    "    for im_file_path in glob.glob(os.path.join(save_data_dir, 'Train', 'Norms', 'train_*.png')):\n",
    "        im = plt.imread(im_file_path)\n",
    "        file_name = os.path.split(im_file_path)[-1][:-4]\n",
    "        labels = sio.loadmat(os.path.join(data_dir, 'Train', 'Labels', file_name+'.mat'))\n",
    "        points = labels['inst_centroid']\n",
    "        mask = labels['inst_map']>0\n",
    "        obj, bkg = generate_obj(im, points, obj_params)\n",
    "#         import pdb\n",
    "#         pdb.set_trace()\n",
    "        loss += cross_entropy_numpy(obj,mask.astype(float))+iou_numpy(obj,mask)\n",
    "    \n",
    "    return -loss\n",
    "\n",
    "# test_im = plt.imread(os.path.join(data_dir, train_option, 'Images', 'test_1.png'))\n",
    "# test_labels = sio.loadmat(os.path.join(data_dir, train_option, 'Labels', 'test_1.mat'))\n",
    "# test_points = test_labels['inst_centroid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|   iter    |  target   |   alpha   | compac... |   sigma   |\n",
      "-------------------------------------------------------------\n",
      "| \u001b[0m 1       \u001b[0m | \u001b[0m-21.78   \u001b[0m | \u001b[0m 4.17    \u001b[0m | \u001b[0m 0.000720\u001b[0m | \u001b[0m 0.000343\u001b[0m |\n",
      "| \u001b[95m 2       \u001b[0m | \u001b[95m-20.26   \u001b[0m | \u001b[95m 3.023   \u001b[0m | \u001b[95m 0.000146\u001b[0m | \u001b[95m 0.277   \u001b[0m |\n",
      "| \u001b[0m 3       \u001b[0m | \u001b[0m-25.01   \u001b[0m | \u001b[0m 1.863   \u001b[0m | \u001b[0m 0.000345\u001b[0m | \u001b[0m 1.19    \u001b[0m |\n",
      "| \u001b[95m 4       \u001b[0m | \u001b[95m-18.68   \u001b[0m | \u001b[95m 5.388   \u001b[0m | \u001b[95m 0.000419\u001b[0m | \u001b[95m 2.056   \u001b[0m |\n",
      "| \u001b[0m 5       \u001b[0m | \u001b[0m-18.77   \u001b[0m | \u001b[0m 2.045   \u001b[0m | \u001b[0m 0.000878\u001b[0m | \u001b[0m 0.08216 \u001b[0m |\n",
      "| \u001b[95m 6       \u001b[0m | \u001b[95m-17.25   \u001b[0m | \u001b[95m 6.705   \u001b[0m | \u001b[95m 0.000417\u001b[0m | \u001b[95m 1.676   \u001b[0m |\n",
      "| \u001b[0m 7       \u001b[0m | \u001b[0m-41.61   \u001b[0m | \u001b[0m 1.404   \u001b[0m | \u001b[0m 0.000198\u001b[0m | \u001b[0m 2.402   \u001b[0m |\n",
      "| \u001b[95m 8       \u001b[0m | \u001b[95m-17.17   \u001b[0m | \u001b[95m 9.683   \u001b[0m | \u001b[95m 0.000313\u001b[0m | \u001b[95m 2.077   \u001b[0m |\n",
      "| \u001b[0m 9       \u001b[0m | \u001b[0m-31.81   \u001b[0m | \u001b[0m 8.764   \u001b[0m | \u001b[0m 0.000894\u001b[0m | \u001b[0m 0.2551  \u001b[0m |\n",
      "| \u001b[0m 10      \u001b[0m | \u001b[0m-65.5    \u001b[0m | \u001b[0m 0.3905  \u001b[0m | \u001b[0m 0.000169\u001b[0m | \u001b[0m 2.634   \u001b[0m |\n",
      "| \u001b[0m 11      \u001b[0m | \u001b[0m-17.4    \u001b[0m | \u001b[0m 6.271   \u001b[0m | \u001b[0m 0.000417\u001b[0m | \u001b[0m 1.794   \u001b[0m |\n",
      "| \u001b[0m 12      \u001b[0m | \u001b[0m-17.37   \u001b[0m | \u001b[0m 6.578   \u001b[0m | \u001b[0m 0.000420\u001b[0m | \u001b[0m 1.901   \u001b[0m |\n",
      "| \u001b[0m 13      \u001b[0m | \u001b[0m-17.69   \u001b[0m | \u001b[0m 9.727   \u001b[0m | \u001b[0m 0.000243\u001b[0m | \u001b[0m 2.285   \u001b[0m |\n",
      "| \u001b[95m 14      \u001b[0m | \u001b[95m-17.06   \u001b[0m | \u001b[95m 9.877   \u001b[0m | \u001b[95m 0.000402\u001b[0m | \u001b[95m 2.015   \u001b[0m |\n",
      "| \u001b[0m 15      \u001b[0m | \u001b[0m-17.44   \u001b[0m | \u001b[0m 6.49    \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 1.629   \u001b[0m |\n",
      "| \u001b[0m 16      \u001b[0m | \u001b[0m-20.09   \u001b[0m | \u001b[0m 9.791   \u001b[0m | \u001b[0m 0.0     \u001b[0m | \u001b[0m 1.898   \u001b[0m |\n",
      "| \u001b[0m 17      \u001b[0m | \u001b[0m-19.94   \u001b[0m | \u001b[0m 9.554   \u001b[0m | \u001b[0m 0.000664\u001b[0m | \u001b[0m 1.412   \u001b[0m |\n",
      "| \u001b[0m 18      \u001b[0m | \u001b[0m-19.31   \u001b[0m | \u001b[0m 4.887   \u001b[0m | \u001b[0m 0.000841\u001b[0m | \u001b[0m 2.937   \u001b[0m |\n",
      "| \u001b[0m 19      \u001b[0m | \u001b[0m-17.73   \u001b[0m | \u001b[0m 10.0    \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 2.17    \u001b[0m |\n",
      "| \u001b[0m 20      \u001b[0m | \u001b[0m-17.76   \u001b[0m | \u001b[0m 9.824   \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 2.129   \u001b[0m |\n",
      "| \u001b[0m 21      \u001b[0m | \u001b[0m-18.91   \u001b[0m | \u001b[0m 6.771   \u001b[0m | \u001b[0m 0.000547\u001b[0m | \u001b[0m 1.15    \u001b[0m |\n",
      "| \u001b[0m 22      \u001b[0m | \u001b[0m-30.12   \u001b[0m | \u001b[0m 1.408   \u001b[0m | \u001b[0m 0.000139\u001b[0m | \u001b[0m 0.9505  \u001b[0m |\n",
      "| \u001b[0m 23      \u001b[0m | \u001b[0m-19.3    \u001b[0m | \u001b[0m 2.74    \u001b[0m | \u001b[0m 0.000764\u001b[0m | \u001b[0m 0.2006  \u001b[0m |\n",
      "| \u001b[0m 24      \u001b[0m | \u001b[0m-21.59   \u001b[0m | \u001b[0m 9.549   \u001b[0m | \u001b[0m 0.0     \u001b[0m | \u001b[0m 2.193   \u001b[0m |\n",
      "| \u001b[0m 25      \u001b[0m | \u001b[0m-23.27   \u001b[0m | \u001b[0m 5.982   \u001b[0m | \u001b[0m 0.000112\u001b[0m | \u001b[0m 0.3503  \u001b[0m |\n",
      "| \u001b[0m 26      \u001b[0m | \u001b[0m-17.13   \u001b[0m | \u001b[0m 6.136   \u001b[0m | \u001b[0m 0.000557\u001b[0m | \u001b[0m 1.961   \u001b[0m |\n",
      "| \u001b[95m 27      \u001b[0m | \u001b[95m-16.72   \u001b[0m | \u001b[95m 9.719   \u001b[0m | \u001b[95m 0.000592\u001b[0m | \u001b[95m 2.272   \u001b[0m |\n",
      "| \u001b[0m 28      \u001b[0m | \u001b[0m-17.58   \u001b[0m | \u001b[0m 9.711   \u001b[0m | \u001b[0m 0.000243\u001b[0m | \u001b[0m 2.203   \u001b[0m |\n",
      "| \u001b[0m 29      \u001b[0m | \u001b[0m-16.75   \u001b[0m | \u001b[0m 9.67    \u001b[0m | \u001b[0m 0.000673\u001b[0m | \u001b[0m 2.273   \u001b[0m |\n",
      "| \u001b[0m 30      \u001b[0m | \u001b[0m-17.13   \u001b[0m | \u001b[0m 9.835   \u001b[0m | \u001b[0m 0.000645\u001b[0m | \u001b[0m 2.054   \u001b[0m |\n",
      "| \u001b[0m 31      \u001b[0m | \u001b[0m-17.26   \u001b[0m | \u001b[0m 9.895   \u001b[0m | \u001b[0m 0.000720\u001b[0m | \u001b[0m 2.069   \u001b[0m |\n",
      "| \u001b[0m 32      \u001b[0m | \u001b[0m-36.86   \u001b[0m | \u001b[0m 2.3     \u001b[0m | \u001b[0m 6.479e-0\u001b[0m | \u001b[0m 2.112   \u001b[0m |\n",
      "| \u001b[0m 33      \u001b[0m | \u001b[0m-17.01   \u001b[0m | \u001b[0m 6.626   \u001b[0m | \u001b[0m 0.000592\u001b[0m | \u001b[0m 1.685   \u001b[0m |\n",
      "| \u001b[0m 34      \u001b[0m | \u001b[0m-17.2    \u001b[0m | \u001b[0m 6.183   \u001b[0m | \u001b[0m 0.000511\u001b[0m | \u001b[0m 1.905   \u001b[0m |\n",
      "| \u001b[0m 35      \u001b[0m | \u001b[0m-22.17   \u001b[0m | \u001b[0m 8.392   \u001b[0m | \u001b[0m 0.000418\u001b[0m | \u001b[0m 0.8883  \u001b[0m |\n",
      "| \u001b[0m 36      \u001b[0m | \u001b[0m-16.95   \u001b[0m | \u001b[0m 6.65    \u001b[0m | \u001b[0m 0.000599\u001b[0m | \u001b[0m 1.736   \u001b[0m |\n",
      "| \u001b[0m 37      \u001b[0m | \u001b[0m-17.06   \u001b[0m | \u001b[0m 6.582   \u001b[0m | \u001b[0m 0.000497\u001b[0m | \u001b[0m 1.809   \u001b[0m |\n",
      "| \u001b[0m 38      \u001b[0m | \u001b[0m-17.04   \u001b[0m | \u001b[0m 6.709   \u001b[0m | \u001b[0m 0.000589\u001b[0m | \u001b[0m 1.673   \u001b[0m |\n",
      "| \u001b[0m 39      \u001b[0m | \u001b[0m-17.6    \u001b[0m | \u001b[0m 6.744   \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 1.63    \u001b[0m |\n",
      "| \u001b[0m 40      \u001b[0m | \u001b[0m-17.2    \u001b[0m | \u001b[0m 6.585   \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 1.73    \u001b[0m |\n",
      "| \u001b[0m 41      \u001b[0m | \u001b[0m-17.23   \u001b[0m | \u001b[0m 6.627   \u001b[0m | \u001b[0m 0.000430\u001b[0m | \u001b[0m 1.81    \u001b[0m |\n",
      "| \u001b[0m 42      \u001b[0m | \u001b[0m-39.2    \u001b[0m | \u001b[0m 1.032   \u001b[0m | \u001b[0m 5.715e-0\u001b[0m | \u001b[0m 1.109   \u001b[0m |\n",
      "| \u001b[0m 43      \u001b[0m | \u001b[0m-21.23   \u001b[0m | \u001b[0m 4.369   \u001b[0m | \u001b[0m 0.000193\u001b[0m | \u001b[0m 0.2273  \u001b[0m |\n",
      "| \u001b[0m 44      \u001b[0m | \u001b[0m-17.24   \u001b[0m | \u001b[0m 9.742   \u001b[0m | \u001b[0m 0.000750\u001b[0m | \u001b[0m 2.077   \u001b[0m |\n",
      "| \u001b[0m 45      \u001b[0m | \u001b[0m-17.41   \u001b[0m | \u001b[0m 6.558   \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 1.653   \u001b[0m |\n",
      "| \u001b[0m 46      \u001b[0m | \u001b[0m-16.87   \u001b[0m | \u001b[0m 9.702   \u001b[0m | \u001b[0m 0.000426\u001b[0m | \u001b[0m 2.245   \u001b[0m |\n",
      "| \u001b[0m 47      \u001b[0m | \u001b[0m-18.35   \u001b[0m | \u001b[0m 9.731   \u001b[0m | \u001b[0m 0.000142\u001b[0m | \u001b[0m 2.138   \u001b[0m |\n",
      "| \u001b[0m 48      \u001b[0m | \u001b[0m-17.05   \u001b[0m | \u001b[0m 6.741   \u001b[0m | \u001b[0m 0.000665\u001b[0m | \u001b[0m 1.674   \u001b[0m |\n",
      "| \u001b[0m 49      \u001b[0m | \u001b[0m-16.91   \u001b[0m | \u001b[0m 6.593   \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 1.856   \u001b[0m |\n",
      "| \u001b[0m 50      \u001b[0m | \u001b[0m-18.84   \u001b[0m | \u001b[0m 2.547   \u001b[0m | \u001b[0m 0.000845\u001b[0m | \u001b[0m 0.392   \u001b[0m |\n",
      "| \u001b[0m 51      \u001b[0m | \u001b[0m-17.03   \u001b[0m | \u001b[0m 6.584   \u001b[0m | \u001b[0m 0.000874\u001b[0m | \u001b[0m 1.727   \u001b[0m |\n",
      "| \u001b[0m 52      \u001b[0m | \u001b[0m-19.5    \u001b[0m | \u001b[0m 6.597   \u001b[0m | \u001b[0m 0.000107\u001b[0m | \u001b[0m 1.709   \u001b[0m |\n",
      "| \u001b[95m 53      \u001b[0m | \u001b[95m-16.71   \u001b[0m | \u001b[95m 8.109   \u001b[0m | \u001b[95m 0.000747\u001b[0m | \u001b[95m 2.669   \u001b[0m |\n",
      "| \u001b[0m 54      \u001b[0m | \u001b[0m-19.09   \u001b[0m | \u001b[0m 6.545   \u001b[0m | \u001b[0m 0.000150\u001b[0m | \u001b[0m 1.75    \u001b[0m |\n",
      "| \u001b[0m 55      \u001b[0m | \u001b[0m-17.44   \u001b[0m | \u001b[0m 9.875   \u001b[0m | \u001b[0m 0.000759\u001b[0m | \u001b[0m 2.032   \u001b[0m |\n",
      "| \u001b[0m 56      \u001b[0m | \u001b[0m-19.45   \u001b[0m | \u001b[0m 6.648   \u001b[0m | \u001b[0m 0.000103\u001b[0m | \u001b[0m 1.692   \u001b[0m |\n",
      "| \u001b[0m 57      \u001b[0m | \u001b[0m-16.86   \u001b[0m | \u001b[0m 9.699   \u001b[0m | \u001b[0m 0.000432\u001b[0m | \u001b[0m 2.267   \u001b[0m |\n",
      "| \u001b[0m 58      \u001b[0m | \u001b[0m-16.97   \u001b[0m | \u001b[0m 6.601   \u001b[0m | \u001b[0m 0.000975\u001b[0m | \u001b[0m 1.812   \u001b[0m |\n",
      "| \u001b[0m 59      \u001b[0m | \u001b[0m-17.06   \u001b[0m | \u001b[0m 6.625   \u001b[0m | \u001b[0m 0.000554\u001b[0m | \u001b[0m 1.661   \u001b[0m |\n",
      "| \u001b[0m 60      \u001b[0m | \u001b[0m-17.14   \u001b[0m | \u001b[0m 6.563   \u001b[0m | \u001b[0m 0.000470\u001b[0m | \u001b[0m 1.719   \u001b[0m |\n",
      "| \u001b[0m 61      \u001b[0m | \u001b[0m-19.31   \u001b[0m | \u001b[0m 9.731   \u001b[0m | \u001b[0m 9.722e-0\u001b[0m | \u001b[0m 2.24    \u001b[0m |\n",
      "| \u001b[0m 62      \u001b[0m | \u001b[0m-52.33   \u001b[0m | \u001b[0m 0.422   \u001b[0m | \u001b[0m 0.000657\u001b[0m | \u001b[0m 2.974   \u001b[0m |\n",
      "| \u001b[0m 63      \u001b[0m | \u001b[0m-17.91   \u001b[0m | \u001b[0m 9.716   \u001b[0m | \u001b[0m 0.000179\u001b[0m | \u001b[0m 2.098   \u001b[0m |\n",
      "| \u001b[0m 64      \u001b[0m | \u001b[0m-36.41   \u001b[0m | \u001b[0m 1.086   \u001b[0m | \u001b[0m 0.000815\u001b[0m | \u001b[0m 2.924   \u001b[0m |\n",
      "| \u001b[0m 65      \u001b[0m | \u001b[0m-19.09   \u001b[0m | \u001b[0m 8.717   \u001b[0m | \u001b[0m 0.000178\u001b[0m | \u001b[0m 1.256   \u001b[0m |\n",
      "| \u001b[0m 66      \u001b[0m | \u001b[0m-50.7    \u001b[0m | \u001b[0m 0.7191  \u001b[0m | \u001b[0m 0.000156\u001b[0m | \u001b[0m 2.032   \u001b[0m |\n",
      "| \u001b[0m 67      \u001b[0m | \u001b[0m-17.16   \u001b[0m | \u001b[0m 6.159   \u001b[0m | \u001b[0m 0.000535\u001b[0m | \u001b[0m 1.934   \u001b[0m |\n",
      "| \u001b[0m 68      \u001b[0m | \u001b[0m-21.95   \u001b[0m | \u001b[0m 6.608   \u001b[0m | \u001b[0m 3.655e-0\u001b[0m | \u001b[0m 1.672   \u001b[0m |\n",
      "| \u001b[0m 69      \u001b[0m | \u001b[0m-18.23   \u001b[0m | \u001b[0m 6.642   \u001b[0m | \u001b[0m 0.000206\u001b[0m | \u001b[0m 1.652   \u001b[0m |\n",
      "| \u001b[0m 70      \u001b[0m | \u001b[0m-22.13   \u001b[0m | \u001b[0m 2.573   \u001b[0m | \u001b[0m 0.000741\u001b[0m | \u001b[0m 1.983   \u001b[0m |\n",
      "| \u001b[0m 71      \u001b[0m | \u001b[0m-16.96   \u001b[0m | \u001b[0m 8.116   \u001b[0m | \u001b[0m 0.000637\u001b[0m | \u001b[0m 2.66    \u001b[0m |\n",
      "| \u001b[0m 72      \u001b[0m | \u001b[0m-17.13   \u001b[0m | \u001b[0m 6.599   \u001b[0m | \u001b[0m 0.000909\u001b[0m | \u001b[0m 1.708   \u001b[0m |\n",
      "| \u001b[0m 73      \u001b[0m | \u001b[0m-17.29   \u001b[0m | \u001b[0m 9.709   \u001b[0m | \u001b[0m 0.000304\u001b[0m | \u001b[0m 2.26    \u001b[0m |\n",
      "| \u001b[0m 74      \u001b[0m | \u001b[0m-20.6    \u001b[0m | \u001b[0m 8.112   \u001b[0m | \u001b[0m 0.000194\u001b[0m | \u001b[0m 2.667   \u001b[0m |\n",
      "| \u001b[0m 75      \u001b[0m | \u001b[0m-20.02   \u001b[0m | \u001b[0m 4.061   \u001b[0m | \u001b[0m 9.966e-0\u001b[0m | \u001b[0m 0.5279  \u001b[0m |\n",
      "| \u001b[0m 76      \u001b[0m | \u001b[0m-17.11   \u001b[0m | \u001b[0m 6.627   \u001b[0m | \u001b[0m 0.000506\u001b[0m | \u001b[0m 1.659   \u001b[0m |\n",
      "| \u001b[0m 77      \u001b[0m | \u001b[0m-16.9    \u001b[0m | \u001b[0m 6.626   \u001b[0m | \u001b[0m 0.000584\u001b[0m | \u001b[0m 1.815   \u001b[0m |\n",
      "| \u001b[0m 78      \u001b[0m | \u001b[0m-22.53   \u001b[0m | \u001b[0m 9.702   \u001b[0m | \u001b[0m 1.307e-0\u001b[0m | \u001b[0m 2.245   \u001b[0m |\n",
      "| \u001b[0m 79      \u001b[0m | \u001b[0m-17.98   \u001b[0m | \u001b[0m 5.057   \u001b[0m | \u001b[0m 0.000989\u001b[0m | \u001b[0m 1.207   \u001b[0m |\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| \u001b[0m 80      \u001b[0m | \u001b[0m-16.9    \u001b[0m | \u001b[0m 8.222   \u001b[0m | \u001b[0m 0.000476\u001b[0m | \u001b[0m 2.179   \u001b[0m |\n",
      "| \u001b[0m 81      \u001b[0m | \u001b[0m-16.81   \u001b[0m | \u001b[0m 4.583   \u001b[0m | \u001b[0m 0.000969\u001b[0m | \u001b[0m 1.707   \u001b[0m |\n",
      "| \u001b[0m 82      \u001b[0m | \u001b[0m-20.56   \u001b[0m | \u001b[0m 3.932   \u001b[0m | \u001b[0m 0.000683\u001b[0m | \u001b[0m 0.4146  \u001b[0m |\n",
      "| \u001b[0m 83      \u001b[0m | \u001b[0m-19.82   \u001b[0m | \u001b[0m 1.345   \u001b[0m | \u001b[0m 0.000938\u001b[0m | \u001b[0m 0.02841 \u001b[0m |\n",
      "| \u001b[0m 84      \u001b[0m | \u001b[0m-19.33   \u001b[0m | \u001b[0m 3.18    \u001b[0m | \u001b[0m 0.000806\u001b[0m | \u001b[0m 1.882   \u001b[0m |\n",
      "| \u001b[0m 85      \u001b[0m | \u001b[0m-31.4    \u001b[0m | \u001b[0m 1.768   \u001b[0m | \u001b[0m 0.000209\u001b[0m | \u001b[0m 1.592   \u001b[0m |\n",
      "| \u001b[0m 86      \u001b[0m | \u001b[0m-18.37   \u001b[0m | \u001b[0m 8.254   \u001b[0m | \u001b[0m 0.000119\u001b[0m | \u001b[0m 1.675   \u001b[0m |\n",
      "| \u001b[0m 87      \u001b[0m | \u001b[0m-18.77   \u001b[0m | \u001b[0m 7.102   \u001b[0m | \u001b[0m 0.000456\u001b[0m | \u001b[0m 2.747   \u001b[0m |\n",
      "| \u001b[0m 88      \u001b[0m | \u001b[0m-19.38   \u001b[0m | \u001b[0m 5.114   \u001b[0m | \u001b[0m 0.000710\u001b[0m | \u001b[0m 2.799   \u001b[0m |\n",
      "| \u001b[0m 89      \u001b[0m | \u001b[0m-16.8    \u001b[0m | \u001b[0m 5.957   \u001b[0m | \u001b[0m 0.000950\u001b[0m | \u001b[0m 1.782   \u001b[0m |\n",
      "| \u001b[0m 90      \u001b[0m | \u001b[0m-18.37   \u001b[0m | \u001b[0m 8.311   \u001b[0m | \u001b[0m 0.000840\u001b[0m | \u001b[0m 1.612   \u001b[0m |\n",
      "| \u001b[0m 91      \u001b[0m | \u001b[0m-44.93   \u001b[0m | \u001b[0m 0.3018  \u001b[0m | \u001b[0m 0.000242\u001b[0m | \u001b[0m 0.3366  \u001b[0m |\n",
      "| \u001b[0m 92      \u001b[0m | \u001b[0m-20.49   \u001b[0m | \u001b[0m 7.926   \u001b[0m | \u001b[0m 0.000140\u001b[0m | \u001b[0m 2.377   \u001b[0m |\n",
      "| \u001b[0m 93      \u001b[0m | \u001b[0m-19.07   \u001b[0m | \u001b[0m 6.743   \u001b[0m | \u001b[0m 0.000738\u001b[0m | \u001b[0m 1.194   \u001b[0m |\n",
      "| \u001b[0m 94      \u001b[0m | \u001b[0m-20.62   \u001b[0m | \u001b[0m 3.9     \u001b[0m | \u001b[0m 3.529e-0\u001b[0m | \u001b[0m 0.4646  \u001b[0m |\n",
      "| \u001b[0m 95      \u001b[0m | \u001b[0m-54.74   \u001b[0m | \u001b[0m 0.2542  \u001b[0m | \u001b[0m 3.611e-0\u001b[0m | \u001b[0m 0.08947 \u001b[0m |\n",
      "| \u001b[0m 96      \u001b[0m | \u001b[0m-20.67   \u001b[0m | \u001b[0m 3.908   \u001b[0m | \u001b[0m 0.000184\u001b[0m | \u001b[0m 0.251   \u001b[0m |\n",
      "| \u001b[0m 97      \u001b[0m | \u001b[0m-24.54   \u001b[0m | \u001b[0m 4.776   \u001b[0m | \u001b[0m 0.000210\u001b[0m | \u001b[0m 2.447   \u001b[0m |\n",
      "| \u001b[0m 98      \u001b[0m | \u001b[0m-16.83   \u001b[0m | \u001b[0m 7.893   \u001b[0m | \u001b[0m 0.000716\u001b[0m | \u001b[0m 2.656   \u001b[0m |\n",
      "| \u001b[0m 99      \u001b[0m | \u001b[0m-24.35   \u001b[0m | \u001b[0m 1.517   \u001b[0m | \u001b[0m 0.000944\u001b[0m | \u001b[0m 1.603   \u001b[0m |\n",
      "| \u001b[0m 100     \u001b[0m | \u001b[0m-20.68   \u001b[0m | \u001b[0m 4.841   \u001b[0m | \u001b[0m 0.000415\u001b[0m | \u001b[0m 2.342   \u001b[0m |\n",
      "| \u001b[0m 101     \u001b[0m | \u001b[0m-22.46   \u001b[0m | \u001b[0m 8.911   \u001b[0m | \u001b[0m 0.000819\u001b[0m | \u001b[0m 1.103   \u001b[0m |\n",
      "| \u001b[0m 102     \u001b[0m | \u001b[0m-19.2    \u001b[0m | \u001b[0m 2.783   \u001b[0m | \u001b[0m 0.000781\u001b[0m | \u001b[0m 1.526   \u001b[0m |\n",
      "| \u001b[0m 103     \u001b[0m | \u001b[0m-23.09   \u001b[0m | \u001b[0m 6.391   \u001b[0m | \u001b[0m 9.914e-0\u001b[0m | \u001b[0m 0.4305  \u001b[0m |\n",
      "| \u001b[0m 104     \u001b[0m | \u001b[0m-17.28   \u001b[0m | \u001b[0m 6.621   \u001b[0m | \u001b[0m 0.000862\u001b[0m | \u001b[0m 1.635   \u001b[0m |\n",
      "| \u001b[0m 105     \u001b[0m | \u001b[0m-17.86   \u001b[0m | \u001b[0m 8.156   \u001b[0m | \u001b[0m 0.000429\u001b[0m | \u001b[0m 2.635   \u001b[0m |\n",
      "| \u001b[0m 106     \u001b[0m | \u001b[0m-18.21   \u001b[0m | \u001b[0m 3.986   \u001b[0m | \u001b[0m 0.000656\u001b[0m | \u001b[0m 0.8526  \u001b[0m |\n",
      "| \u001b[0m 107     \u001b[0m | \u001b[0m-31.82   \u001b[0m | \u001b[0m 0.6276  \u001b[0m | \u001b[0m 0.000867\u001b[0m | \u001b[0m 0.9589  \u001b[0m |\n",
      "| \u001b[0m 108     \u001b[0m | \u001b[0m-23.48   \u001b[0m | \u001b[0m 4.063   \u001b[0m | \u001b[0m 0.000523\u001b[0m | \u001b[0m 2.855   \u001b[0m |\n",
      "| \u001b[0m 109     \u001b[0m | \u001b[0m-17.29   \u001b[0m | \u001b[0m 8.377   \u001b[0m | \u001b[0m 0.000323\u001b[0m | \u001b[0m 1.834   \u001b[0m |\n",
      "| \u001b[0m 110     \u001b[0m | \u001b[0m-22.04   \u001b[0m | \u001b[0m 2.95    \u001b[0m | \u001b[0m 8.506e-0\u001b[0m | \u001b[0m 0.4673  \u001b[0m |\n",
      "| \u001b[0m 111     \u001b[0m | \u001b[0m-17.25   \u001b[0m | \u001b[0m 9.003   \u001b[0m | \u001b[0m 0.000814\u001b[0m | \u001b[0m 2.007   \u001b[0m |\n",
      "| \u001b[0m 112     \u001b[0m | \u001b[0m-46.0    \u001b[0m | \u001b[0m 0.563   \u001b[0m | \u001b[0m 0.000673\u001b[0m | \u001b[0m 2.604   \u001b[0m |\n",
      "| \u001b[0m 113     \u001b[0m | \u001b[0m-39.38   \u001b[0m | \u001b[0m 0.7729  \u001b[0m | \u001b[0m 0.000843\u001b[0m | \u001b[0m 2.645   \u001b[0m |\n",
      "| \u001b[95m 114     \u001b[0m | \u001b[95m-16.69   \u001b[0m | \u001b[95m 8.185   \u001b[0m | \u001b[95m 0.000867\u001b[0m | \u001b[95m 2.853   \u001b[0m |\n",
      "| \u001b[0m 115     \u001b[0m | \u001b[0m-18.68   \u001b[0m | \u001b[0m 6.885   \u001b[0m | \u001b[0m 0.000581\u001b[0m | \u001b[0m 1.22    \u001b[0m |\n",
      "| \u001b[0m 116     \u001b[0m | \u001b[0m-25.73   \u001b[0m | \u001b[0m 9.533   \u001b[0m | \u001b[0m 0.000339\u001b[0m | \u001b[0m 0.6476  \u001b[0m |\n",
      "| \u001b[0m 117     \u001b[0m | \u001b[0m-19.72   \u001b[0m | \u001b[0m 3.889   \u001b[0m | \u001b[0m 0.000478\u001b[0m | \u001b[0m 0.4791  \u001b[0m |\n",
      "| \u001b[0m 118     \u001b[0m | \u001b[0m-16.98   \u001b[0m | \u001b[0m 7.788   \u001b[0m | \u001b[0m 0.000945\u001b[0m | \u001b[0m 2.008   \u001b[0m |\n",
      "| \u001b[0m 119     \u001b[0m | \u001b[0m-19.3    \u001b[0m | \u001b[0m 6.453   \u001b[0m | \u001b[0m 0.000204\u001b[0m | \u001b[0m 1.976   \u001b[0m |\n",
      "| \u001b[0m 120     \u001b[0m | \u001b[0m-26.77   \u001b[0m | \u001b[0m 9.24    \u001b[0m | \u001b[0m 2.198e-0\u001b[0m | \u001b[0m 0.437   \u001b[0m |\n",
      "| \u001b[0m 121     \u001b[0m | \u001b[0m-22.33   \u001b[0m | \u001b[0m 1.761   \u001b[0m | \u001b[0m 0.000195\u001b[0m | \u001b[0m 0.4149  \u001b[0m |\n",
      "| \u001b[0m 122     \u001b[0m | \u001b[0m-27.15   \u001b[0m | \u001b[0m 9.042   \u001b[0m | \u001b[0m 5.37e-05\u001b[0m | \u001b[0m 0.4016  \u001b[0m |\n",
      "| \u001b[0m 123     \u001b[0m | \u001b[0m-17.61   \u001b[0m | \u001b[0m 7.165   \u001b[0m | \u001b[0m 0.000565\u001b[0m | \u001b[0m 2.601   \u001b[0m |\n",
      "| \u001b[0m 124     \u001b[0m | \u001b[0m-16.93   \u001b[0m | \u001b[0m 9.427   \u001b[0m | \u001b[0m 0.000523\u001b[0m | \u001b[0m 2.044   \u001b[0m |\n",
      "| \u001b[0m 125     \u001b[0m | \u001b[0m-20.17   \u001b[0m | \u001b[0m 6.72    \u001b[0m | \u001b[0m 0.000202\u001b[0m | \u001b[0m 2.247   \u001b[0m |\n",
      "| \u001b[0m 126     \u001b[0m | \u001b[0m-17.37   \u001b[0m | \u001b[0m 9.916   \u001b[0m | \u001b[0m 0.000401\u001b[0m | \u001b[0m 1.857   \u001b[0m |\n",
      "| \u001b[0m 127     \u001b[0m | \u001b[0m-17.92   \u001b[0m | \u001b[0m 5.67    \u001b[0m | \u001b[0m 0.000300\u001b[0m | \u001b[0m 1.326   \u001b[0m |\n",
      "| \u001b[0m 128     \u001b[0m | \u001b[0m-20.03   \u001b[0m | \u001b[0m 3.188   \u001b[0m | \u001b[0m 0.000799\u001b[0m | \u001b[0m 2.062   \u001b[0m |\n",
      "| \u001b[0m 129     \u001b[0m | \u001b[0m-18.24   \u001b[0m | \u001b[0m 2.426   \u001b[0m | \u001b[0m 0.000886\u001b[0m | \u001b[0m 0.986   \u001b[0m |\n",
      "| \u001b[0m 130     \u001b[0m | \u001b[0m-23.15   \u001b[0m | \u001b[0m 7.092   \u001b[0m | \u001b[0m 0.000160\u001b[0m | \u001b[0m 2.781   \u001b[0m |\n",
      "| \u001b[0m 131     \u001b[0m | \u001b[0m-23.27   \u001b[0m | \u001b[0m 8.135   \u001b[0m | \u001b[0m 8.928e-0\u001b[0m | \u001b[0m 0.6155  \u001b[0m |\n",
      "| \u001b[0m 132     \u001b[0m | \u001b[0m-21.43   \u001b[0m | \u001b[0m 4.994   \u001b[0m | \u001b[0m 0.000160\u001b[0m | \u001b[0m 0.4103  \u001b[0m |\n",
      "| \u001b[0m 133     \u001b[0m | \u001b[0m-32.23   \u001b[0m | \u001b[0m 9.786   \u001b[0m | \u001b[0m 0.000617\u001b[0m | \u001b[0m 0.2714  \u001b[0m |\n",
      "| \u001b[0m 134     \u001b[0m | \u001b[0m-44.57   \u001b[0m | \u001b[0m 0.4048  \u001b[0m | \u001b[0m 0.000532\u001b[0m | \u001b[0m 1.317   \u001b[0m |\n",
      "| \u001b[0m 135     \u001b[0m | \u001b[0m-18.23   \u001b[0m | \u001b[0m 4.773   \u001b[0m | \u001b[0m 0.000975\u001b[0m | \u001b[0m 2.729   \u001b[0m |\n",
      "| \u001b[0m 136     \u001b[0m | \u001b[0m-18.39   \u001b[0m | \u001b[0m 2.177   \u001b[0m | \u001b[0m 0.000871\u001b[0m | \u001b[0m 0.652   \u001b[0m |\n",
      "| \u001b[0m 137     \u001b[0m | \u001b[0m-17.21   \u001b[0m | \u001b[0m 7.788   \u001b[0m | \u001b[0m 0.000554\u001b[0m | \u001b[0m 2.545   \u001b[0m |\n",
      "| \u001b[0m 138     \u001b[0m | \u001b[0m-22.08   \u001b[0m | \u001b[0m 1.197   \u001b[0m | \u001b[0m 0.000859\u001b[0m | \u001b[0m 0.6138  \u001b[0m |\n",
      "| \u001b[0m 139     \u001b[0m | \u001b[0m-32.56   \u001b[0m | \u001b[0m 1.998   \u001b[0m | \u001b[0m 0.0003  \u001b[0m | \u001b[0m 2.22    \u001b[0m |\n",
      "| \u001b[0m 140     \u001b[0m | \u001b[0m-63.51   \u001b[0m | \u001b[0m 0.2075  \u001b[0m | \u001b[0m 0.000549\u001b[0m | \u001b[0m 2.54    \u001b[0m |\n",
      "| \u001b[0m 141     \u001b[0m | \u001b[0m-21.51   \u001b[0m | \u001b[0m 4.068   \u001b[0m | \u001b[0m 0.000734\u001b[0m | \u001b[0m 0.3134  \u001b[0m |\n",
      "| \u001b[0m 142     \u001b[0m | \u001b[0m-23.78   \u001b[0m | \u001b[0m 2.399   \u001b[0m | \u001b[0m 1.796e-0\u001b[0m | \u001b[0m 0.6036  \u001b[0m |\n",
      "| \u001b[0m 143     \u001b[0m | \u001b[0m-23.38   \u001b[0m | \u001b[0m 2.642   \u001b[0m | \u001b[0m 0.000493\u001b[0m | \u001b[0m 1.827   \u001b[0m |\n",
      "| \u001b[0m 144     \u001b[0m | \u001b[0m-17.6    \u001b[0m | \u001b[0m 6.698   \u001b[0m | \u001b[0m 0.000669\u001b[0m | \u001b[0m 1.467   \u001b[0m |\n",
      "| \u001b[0m 145     \u001b[0m | \u001b[0m-17.17   \u001b[0m | \u001b[0m 4.095   \u001b[0m | \u001b[0m 0.000847\u001b[0m | \u001b[0m 1.603   \u001b[0m |\n",
      "| \u001b[0m 146     \u001b[0m | \u001b[0m-23.72   \u001b[0m | \u001b[0m 0.8381  \u001b[0m | \u001b[0m 0.000918\u001b[0m | \u001b[0m 0.2123  \u001b[0m |\n",
      "| \u001b[0m 147     \u001b[0m | \u001b[0m-27.14   \u001b[0m | \u001b[0m 5.291   \u001b[0m | \u001b[0m 0.000141\u001b[0m | \u001b[0m 2.787   \u001b[0m |\n",
      "| \u001b[0m 148     \u001b[0m | \u001b[0m-16.97   \u001b[0m | \u001b[0m 7.95    \u001b[0m | \u001b[0m 0.000524\u001b[0m | \u001b[0m 1.853   \u001b[0m |\n",
      "| \u001b[0m 149     \u001b[0m | \u001b[0m-21.41   \u001b[0m | \u001b[0m 5.836   \u001b[0m | \u001b[0m 0.000538\u001b[0m | \u001b[0m 0.6325  \u001b[0m |\n",
      "| \u001b[0m 150     \u001b[0m | \u001b[0m-19.68   \u001b[0m | \u001b[0m 6.799   \u001b[0m | \u001b[0m 0.000444\u001b[0m | \u001b[0m 0.9946  \u001b[0m |\n",
      "| \u001b[0m 151     \u001b[0m | \u001b[0m-37.77   \u001b[0m | \u001b[0m 0.3052  \u001b[0m | \u001b[0m 0.000755\u001b[0m | \u001b[0m 0.2457  \u001b[0m |\n",
      "| \u001b[0m 152     \u001b[0m | \u001b[0m-25.47   \u001b[0m | \u001b[0m 9.966   \u001b[0m | \u001b[0m 0.000955\u001b[0m | \u001b[0m 0.9957  \u001b[0m |\n",
      "| \u001b[0m 153     \u001b[0m | \u001b[0m-35.38   \u001b[0m | \u001b[0m 0.5245  \u001b[0m | \u001b[0m 0.000787\u001b[0m | \u001b[0m 1.015   \u001b[0m |\n",
      "| \u001b[0m 154     \u001b[0m | \u001b[0m-27.49   \u001b[0m | \u001b[0m 2.138   \u001b[0m | \u001b[0m 0.000145\u001b[0m | \u001b[0m 1.294   \u001b[0m |\n",
      "| \u001b[0m 155     \u001b[0m | \u001b[0m-17.39   \u001b[0m | \u001b[0m 5.759   \u001b[0m | \u001b[0m 0.000831\u001b[0m | \u001b[0m 2.548   \u001b[0m |\n",
      "| \u001b[0m 156     \u001b[0m | \u001b[0m-24.86   \u001b[0m | \u001b[0m 2.6     \u001b[0m | \u001b[0m 0.000874\u001b[0m | \u001b[0m 2.893   \u001b[0m |\n",
      "| \u001b[0m 157     \u001b[0m | \u001b[0m-24.08   \u001b[0m | \u001b[0m 9.392   \u001b[0m | \u001b[0m 0.000176\u001b[0m | \u001b[0m 0.7121  \u001b[0m |\n",
      "| \u001b[0m 158     \u001b[0m | \u001b[0m-18.7    \u001b[0m | \u001b[0m 4.583   \u001b[0m | \u001b[0m 0.000821\u001b[0m | \u001b[0m 0.8961  \u001b[0m |\n",
      "| \u001b[0m 159     \u001b[0m | \u001b[0m-20.17   \u001b[0m | \u001b[0m 4.287   \u001b[0m | \u001b[0m 0.000741\u001b[0m | \u001b[0m 2.677   \u001b[0m |\n",
      "| \u001b[95m 160     \u001b[0m | \u001b[95m-16.69   \u001b[0m | \u001b[95m 8.505   \u001b[0m | \u001b[95m 0.000871\u001b[0m | \u001b[95m 2.268   \u001b[0m |\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| \u001b[0m 161     \u001b[0m | \u001b[0m-22.17   \u001b[0m | \u001b[0m 5.234   \u001b[0m | \u001b[0m 1.341e-0\u001b[0m | \u001b[0m 0.365   \u001b[0m |\n",
      "| \u001b[0m 162     \u001b[0m | \u001b[0m-17.29   \u001b[0m | \u001b[0m 5.332   \u001b[0m | \u001b[0m 0.000619\u001b[0m | \u001b[0m 1.899   \u001b[0m |\n",
      "| \u001b[0m 163     \u001b[0m | \u001b[0m-25.54   \u001b[0m | \u001b[0m 1.76    \u001b[0m | \u001b[0m 0.000604\u001b[0m | \u001b[0m 1.597   \u001b[0m |\n",
      "| \u001b[0m 164     \u001b[0m | \u001b[0m-17.45   \u001b[0m | \u001b[0m 9.777   \u001b[0m | \u001b[0m 0.00024 \u001b[0m | \u001b[0m 1.999   \u001b[0m |\n",
      "| \u001b[0m 165     \u001b[0m | \u001b[0m-19.43   \u001b[0m | \u001b[0m 9.931   \u001b[0m | \u001b[0m 0.000245\u001b[0m | \u001b[0m 1.33    \u001b[0m |\n",
      "| \u001b[0m 166     \u001b[0m | \u001b[0m-139.8   \u001b[0m | \u001b[0m 0.007014\u001b[0m | \u001b[0m 0.000121\u001b[0m | \u001b[0m 1.473   \u001b[0m |\n",
      "| \u001b[0m 167     \u001b[0m | \u001b[0m-41.52   \u001b[0m | \u001b[0m 1.351   \u001b[0m | \u001b[0m 0.000306\u001b[0m | \u001b[0m 2.776   \u001b[0m |\n",
      "| \u001b[0m 168     \u001b[0m | \u001b[0m-20.59   \u001b[0m | \u001b[0m 7.161   \u001b[0m | \u001b[0m 0.000400\u001b[0m | \u001b[0m 0.9028  \u001b[0m |\n",
      "| \u001b[0m 169     \u001b[0m | \u001b[0m-24.21   \u001b[0m | \u001b[0m 4.893   \u001b[0m | \u001b[0m 4.216e-0\u001b[0m | \u001b[0m 1.786   \u001b[0m |\n",
      "| \u001b[0m 170     \u001b[0m | \u001b[0m-25.14   \u001b[0m | \u001b[0m 6.988   \u001b[0m | \u001b[0m 0.000307\u001b[0m | \u001b[0m 0.3921  \u001b[0m |\n",
      "| \u001b[0m 171     \u001b[0m | \u001b[0m-19.29   \u001b[0m | \u001b[0m 4.992   \u001b[0m | \u001b[0m 0.000217\u001b[0m | \u001b[0m 0.7609  \u001b[0m |\n",
      "| \u001b[0m 172     \u001b[0m | \u001b[0m-19.02   \u001b[0m | \u001b[0m 6.745   \u001b[0m | \u001b[0m 8.054e-0\u001b[0m | \u001b[0m 1.193   \u001b[0m |\n",
      "| \u001b[0m 173     \u001b[0m | \u001b[0m-23.69   \u001b[0m | \u001b[0m 2.566   \u001b[0m | \u001b[0m 0.000691\u001b[0m | \u001b[0m 2.207   \u001b[0m |\n",
      "| \u001b[0m 174     \u001b[0m | \u001b[0m-29.67   \u001b[0m | \u001b[0m 2.343   \u001b[0m | \u001b[0m 0.000234\u001b[0m | \u001b[0m 1.93    \u001b[0m |\n",
      "| \u001b[0m 175     \u001b[0m | \u001b[0m-18.2    \u001b[0m | \u001b[0m 9.924   \u001b[0m | \u001b[0m 0.000192\u001b[0m | \u001b[0m 1.582   \u001b[0m |\n",
      "| \u001b[0m 176     \u001b[0m | \u001b[0m-23.39   \u001b[0m | \u001b[0m 8.393   \u001b[0m | \u001b[0m 0.000711\u001b[0m | \u001b[0m 0.8872  \u001b[0m |\n",
      "| \u001b[0m 177     \u001b[0m | \u001b[0m-23.28   \u001b[0m | \u001b[0m 6.14    \u001b[0m | \u001b[0m 0.000233\u001b[0m | \u001b[0m 0.4099  \u001b[0m |\n",
      "| \u001b[0m 178     \u001b[0m | \u001b[0m-18.76   \u001b[0m | \u001b[0m 3.846   \u001b[0m | \u001b[0m 0.000309\u001b[0m | \u001b[0m 0.7661  \u001b[0m |\n",
      "| \u001b[0m 179     \u001b[0m | \u001b[0m-28.17   \u001b[0m | \u001b[0m 4.236   \u001b[0m | \u001b[0m 0.000144\u001b[0m | \u001b[0m 2.514   \u001b[0m |\n",
      "| \u001b[0m 180     \u001b[0m | \u001b[0m-19.38   \u001b[0m | \u001b[0m 4.867   \u001b[0m | \u001b[0m 0.000833\u001b[0m | \u001b[0m 2.933   \u001b[0m |\n",
      "| \u001b[0m 181     \u001b[0m | \u001b[0m-18.41   \u001b[0m | \u001b[0m 8.485   \u001b[0m | \u001b[0m 0.000147\u001b[0m | \u001b[0m 1.425   \u001b[0m |\n",
      "| \u001b[0m 182     \u001b[0m | \u001b[0m-44.76   \u001b[0m | \u001b[0m 0.4931  \u001b[0m | \u001b[0m 0.000558\u001b[0m | \u001b[0m 1.772   \u001b[0m |\n",
      "| \u001b[0m 183     \u001b[0m | \u001b[0m-19.98   \u001b[0m | \u001b[0m 3.558   \u001b[0m | \u001b[0m 0.000459\u001b[0m | \u001b[0m 1.675   \u001b[0m |\n",
      "| \u001b[0m 184     \u001b[0m | \u001b[0m-19.93   \u001b[0m | \u001b[0m 3.518   \u001b[0m | \u001b[0m 0.000232\u001b[0m | \u001b[0m 0.393   \u001b[0m |\n",
      "| \u001b[0m 185     \u001b[0m | \u001b[0m-18.34   \u001b[0m | \u001b[0m 5.878   \u001b[0m | \u001b[0m 0.000532\u001b[0m | \u001b[0m 2.385   \u001b[0m |\n",
      "| \u001b[0m 186     \u001b[0m | \u001b[0m-17.08   \u001b[0m | \u001b[0m 9.876   \u001b[0m | \u001b[0m 0.000525\u001b[0m | \u001b[0m 2.014   \u001b[0m |\n",
      "| \u001b[0m 187     \u001b[0m | \u001b[0m-17.63   \u001b[0m | \u001b[0m 9.427   \u001b[0m | \u001b[0m 0.000931\u001b[0m | \u001b[0m 2.043   \u001b[0m |\n",
      "| \u001b[0m 188     \u001b[0m | \u001b[0m-18.75   \u001b[0m | \u001b[0m 9.253   \u001b[0m | \u001b[0m 0.000741\u001b[0m | \u001b[0m 1.611   \u001b[0m |\n",
      "| \u001b[0m 189     \u001b[0m | \u001b[0m-16.97   \u001b[0m | \u001b[0m 9.825   \u001b[0m | \u001b[0m 0.000387\u001b[0m | \u001b[0m 2.131   \u001b[0m |\n",
      "| \u001b[0m 190     \u001b[0m | \u001b[0m-17.29   \u001b[0m | \u001b[0m 6.582   \u001b[0m | \u001b[0m 0.000416\u001b[0m | \u001b[0m 1.807   \u001b[0m |\n",
      "| \u001b[0m 191     \u001b[0m | \u001b[0m-18.84   \u001b[0m | \u001b[0m 9.254   \u001b[0m | \u001b[0m 0.000764\u001b[0m | \u001b[0m 1.609   \u001b[0m |\n",
      "| \u001b[0m 192     \u001b[0m | \u001b[0m-49.27   \u001b[0m | \u001b[0m 0.1388  \u001b[0m | \u001b[0m 0.000746\u001b[0m | \u001b[0m 0.2606  \u001b[0m |\n",
      "| \u001b[0m 193     \u001b[0m | \u001b[0m-17.37   \u001b[0m | \u001b[0m 9.004   \u001b[0m | \u001b[0m 0.000872\u001b[0m | \u001b[0m 2.01    \u001b[0m |\n",
      "| \u001b[0m 194     \u001b[0m | \u001b[0m-20.56   \u001b[0m | \u001b[0m 3.03    \u001b[0m | \u001b[0m 0.000772\u001b[0m | \u001b[0m 2.039   \u001b[0m |\n",
      "| \u001b[0m 195     \u001b[0m | \u001b[0m-19.27   \u001b[0m | \u001b[0m 3.39    \u001b[0m | \u001b[0m 0.000315\u001b[0m | \u001b[0m 1.089   \u001b[0m |\n",
      "| \u001b[0m 196     \u001b[0m | \u001b[0m-17.16   \u001b[0m | \u001b[0m 6.653   \u001b[0m | \u001b[0m 0.000953\u001b[0m | \u001b[0m 1.731   \u001b[0m |\n",
      "| \u001b[0m 197     \u001b[0m | \u001b[0m-17.63   \u001b[0m | \u001b[0m 8.648   \u001b[0m | \u001b[0m 0.000672\u001b[0m | \u001b[0m 1.739   \u001b[0m |\n",
      "| \u001b[0m 198     \u001b[0m | \u001b[0m-21.13   \u001b[0m | \u001b[0m 3.931   \u001b[0m | \u001b[0m 0.000960\u001b[0m | \u001b[0m 0.4149  \u001b[0m |\n",
      "| \u001b[0m 199     \u001b[0m | \u001b[0m-22.3    \u001b[0m | \u001b[0m 5.144   \u001b[0m | \u001b[0m 6.76e-05\u001b[0m | \u001b[0m 1.708   \u001b[0m |\n",
      "| \u001b[0m 200     \u001b[0m | \u001b[0m-19.88   \u001b[0m | \u001b[0m 3.283   \u001b[0m | \u001b[0m 0.000418\u001b[0m | \u001b[0m 0.2134  \u001b[0m |\n",
      "| \u001b[0m 201     \u001b[0m | \u001b[0m-35.33   \u001b[0m | \u001b[0m 1.282   \u001b[0m | \u001b[0m 3.599e-0\u001b[0m | \u001b[0m 0.9943  \u001b[0m |\n",
      "| \u001b[0m 202     \u001b[0m | \u001b[0m-18.0    \u001b[0m | \u001b[0m 9.252   \u001b[0m | \u001b[0m 0.000187\u001b[0m | \u001b[0m 1.613   \u001b[0m |\n",
      "| \u001b[0m 203     \u001b[0m | \u001b[0m-31.87   \u001b[0m | \u001b[0m 8.971   \u001b[0m | \u001b[0m 0.000824\u001b[0m | \u001b[0m 0.1121  \u001b[0m |\n",
      "| \u001b[0m 204     \u001b[0m | \u001b[0m-16.77   \u001b[0m | \u001b[0m 6.884   \u001b[0m | \u001b[0m 0.000969\u001b[0m | \u001b[0m 2.749   \u001b[0m |\n",
      "| \u001b[0m 205     \u001b[0m | \u001b[0m-18.67   \u001b[0m | \u001b[0m 4.583   \u001b[0m | \u001b[0m 0.000260\u001b[0m | \u001b[0m 0.8984  \u001b[0m |\n",
      "| \u001b[0m 206     \u001b[0m | \u001b[0m-16.87   \u001b[0m | \u001b[0m 9.671   \u001b[0m | \u001b[0m 0.000783\u001b[0m | \u001b[0m 2.274   \u001b[0m |\n",
      "| \u001b[0m 207     \u001b[0m | \u001b[0m-26.06   \u001b[0m | \u001b[0m 2.343   \u001b[0m | \u001b[0m 0.000214\u001b[0m | \u001b[0m 1.431   \u001b[0m |\n",
      "| \u001b[0m 208     \u001b[0m | \u001b[0m-26.91   \u001b[0m | \u001b[0m 4.868   \u001b[0m | \u001b[0m 0.000219\u001b[0m | \u001b[0m 2.934   \u001b[0m |\n",
      "| \u001b[0m 209     \u001b[0m | \u001b[0m-20.01   \u001b[0m | \u001b[0m 3.888   \u001b[0m | \u001b[0m 0.000130\u001b[0m | \u001b[0m 0.4801  \u001b[0m |\n",
      "| \u001b[0m 210     \u001b[0m | \u001b[0m-16.92   \u001b[0m | \u001b[0m 6.598   \u001b[0m | \u001b[0m 0.000924\u001b[0m | \u001b[0m 1.81    \u001b[0m |\n",
      "| \u001b[0m 211     \u001b[0m | \u001b[0m-26.85   \u001b[0m | \u001b[0m 2.027   \u001b[0m | \u001b[0m 0.000501\u001b[0m | \u001b[0m 1.889   \u001b[0m |\n",
      "| \u001b[0m 212     \u001b[0m | \u001b[0m-22.27   \u001b[0m | \u001b[0m 4.369   \u001b[0m | \u001b[0m 0.000748\u001b[0m | \u001b[0m 0.2258  \u001b[0m |\n",
      "| \u001b[0m 213     \u001b[0m | \u001b[0m-29.48   \u001b[0m | \u001b[0m 7.36    \u001b[0m | \u001b[0m 0.000977\u001b[0m | \u001b[0m 0.101   \u001b[0m |\n",
      "| \u001b[0m 214     \u001b[0m | \u001b[0m-17.0    \u001b[0m | \u001b[0m 9.71    \u001b[0m | \u001b[0m 0.000377\u001b[0m | \u001b[0m 2.26    \u001b[0m |\n",
      "| \u001b[0m 215     \u001b[0m | \u001b[0m-46.68   \u001b[0m | \u001b[0m 0.9718  \u001b[0m | \u001b[0m 0.000113\u001b[0m | \u001b[0m 1.934   \u001b[0m |\n",
      "| \u001b[0m 216     \u001b[0m | \u001b[0m-17.05   \u001b[0m | \u001b[0m 6.703   \u001b[0m | \u001b[0m 0.000567\u001b[0m | \u001b[0m 1.674   \u001b[0m |\n",
      "| \u001b[0m 217     \u001b[0m | \u001b[0m-41.26   \u001b[0m | \u001b[0m 0.7408  \u001b[0m | \u001b[0m 0.000720\u001b[0m | \u001b[0m 2.572   \u001b[0m |\n",
      "| \u001b[0m 218     \u001b[0m | \u001b[0m-34.85   \u001b[0m | \u001b[0m 1.627   \u001b[0m | \u001b[0m 9.864e-0\u001b[0m | \u001b[0m 1.505   \u001b[0m |\n",
      "| \u001b[0m 219     \u001b[0m | \u001b[0m-19.12   \u001b[0m | \u001b[0m 6.261   \u001b[0m | \u001b[0m 0.000809\u001b[0m | \u001b[0m 1.137   \u001b[0m |\n",
      "| \u001b[0m 220     \u001b[0m | \u001b[0m-23.46   \u001b[0m | \u001b[0m 5.942   \u001b[0m | \u001b[0m 1.367e-0\u001b[0m | \u001b[0m 1.813   \u001b[0m |\n",
      "| \u001b[0m 221     \u001b[0m | \u001b[0m-16.87   \u001b[0m | \u001b[0m 9.713   \u001b[0m | \u001b[0m 0.000432\u001b[0m | \u001b[0m 2.201   \u001b[0m |\n",
      "| \u001b[0m 222     \u001b[0m | \u001b[0m-19.31   \u001b[0m | \u001b[0m 4.581   \u001b[0m | \u001b[0m 0.000119\u001b[0m | \u001b[0m 0.9028  \u001b[0m |\n",
      "| \u001b[0m 223     \u001b[0m | \u001b[0m-34.91   \u001b[0m | \u001b[0m 1.852   \u001b[0m | \u001b[0m 0.00043 \u001b[0m | \u001b[0m 2.88    \u001b[0m |\n",
      "| \u001b[0m 224     \u001b[0m | \u001b[0m-18.39   \u001b[0m | \u001b[0m 8.645   \u001b[0m | \u001b[0m 0.000966\u001b[0m | \u001b[0m 1.739   \u001b[0m |\n",
      "| \u001b[0m 225     \u001b[0m | \u001b[0m-19.78   \u001b[0m | \u001b[0m 3.897   \u001b[0m | \u001b[0m 0.000387\u001b[0m | \u001b[0m 0.465   \u001b[0m |\n",
      "| \u001b[0m 226     \u001b[0m | \u001b[0m-19.54   \u001b[0m | \u001b[0m 2.767   \u001b[0m | \u001b[0m 0.000370\u001b[0m | \u001b[0m 0.1941  \u001b[0m |\n",
      "| \u001b[0m 227     \u001b[0m | \u001b[0m-16.82   \u001b[0m | \u001b[0m 6.627   \u001b[0m | \u001b[0m 0.000696\u001b[0m | \u001b[0m 1.811   \u001b[0m |\n",
      "| \u001b[0m 228     \u001b[0m | \u001b[0m-17.35   \u001b[0m | \u001b[0m 5.274   \u001b[0m | \u001b[0m 0.000830\u001b[0m | \u001b[0m 1.374   \u001b[0m |\n",
      "| \u001b[0m 229     \u001b[0m | \u001b[0m-37.87   \u001b[0m | \u001b[0m 1.272   \u001b[0m | \u001b[0m 6.502e-0\u001b[0m | \u001b[0m 1.312   \u001b[0m |\n",
      "| \u001b[0m 230     \u001b[0m | \u001b[0m-20.78   \u001b[0m | \u001b[0m 5.118   \u001b[0m | \u001b[0m 0.000402\u001b[0m | \u001b[0m 0.5422  \u001b[0m |\n",
      "| \u001b[0m 231     \u001b[0m | \u001b[0m-17.02   \u001b[0m | \u001b[0m 7.788   \u001b[0m | \u001b[0m 0.000965\u001b[0m | \u001b[0m 2.007   \u001b[0m |\n",
      "| \u001b[0m 232     \u001b[0m | \u001b[0m-17.03   \u001b[0m | \u001b[0m 9.827   \u001b[0m | \u001b[0m 0.000358\u001b[0m | \u001b[0m 2.132   \u001b[0m |\n",
      "| \u001b[0m 233     \u001b[0m | \u001b[0m-17.07   \u001b[0m | \u001b[0m 9.712   \u001b[0m | \u001b[0m 0.000875\u001b[0m | \u001b[0m 2.259   \u001b[0m |\n",
      "| \u001b[0m 234     \u001b[0m | \u001b[0m-17.32   \u001b[0m | \u001b[0m 9.712   \u001b[0m | \u001b[0m 0.000288\u001b[0m | \u001b[0m 2.203   \u001b[0m |\n",
      "| \u001b[0m 235     \u001b[0m | \u001b[0m-24.26   \u001b[0m | \u001b[0m 7.666   \u001b[0m | \u001b[0m 0.000755\u001b[0m | \u001b[0m 0.7124  \u001b[0m |\n",
      "| \u001b[0m 236     \u001b[0m | \u001b[0m-17.33   \u001b[0m | \u001b[0m 6.707   \u001b[0m | \u001b[0m 0.000945\u001b[0m | \u001b[0m 1.677   \u001b[0m |\n",
      "| \u001b[0m 237     \u001b[0m | \u001b[0m-16.79   \u001b[0m | \u001b[0m 6.272   \u001b[0m | \u001b[0m 0.000759\u001b[0m | \u001b[0m 1.79    \u001b[0m |\n",
      "| \u001b[0m 238     \u001b[0m | \u001b[0m-19.49   \u001b[0m | \u001b[0m 5.112   \u001b[0m | \u001b[0m 0.000694\u001b[0m | \u001b[0m 2.796   \u001b[0m |\n",
      "| \u001b[0m 239     \u001b[0m | \u001b[0m-18.5    \u001b[0m | \u001b[0m 9.71    \u001b[0m | \u001b[0m 0.000143\u001b[0m | \u001b[0m 2.201   \u001b[0m |\n",
      "| \u001b[0m 240     \u001b[0m | \u001b[0m-23.79   \u001b[0m | \u001b[0m 1.596   \u001b[0m | \u001b[0m 0.000906\u001b[0m | \u001b[0m 1.555   \u001b[0m |\n",
      "| \u001b[0m 241     \u001b[0m | \u001b[0m-19.78   \u001b[0m | \u001b[0m 9.251   \u001b[0m | \u001b[0m 1.766e-0\u001b[0m | \u001b[0m 1.614   \u001b[0m |\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| \u001b[0m 242     \u001b[0m | \u001b[0m-17.35   \u001b[0m | \u001b[0m 9.005   \u001b[0m | \u001b[0m 0.000862\u001b[0m | \u001b[0m 2.008   \u001b[0m |\n",
      "| \u001b[0m 243     \u001b[0m | \u001b[0m-17.21   \u001b[0m | \u001b[0m 9.428   \u001b[0m | \u001b[0m 0.000756\u001b[0m | \u001b[0m 2.043   \u001b[0m |\n",
      "| \u001b[0m 244     \u001b[0m | \u001b[0m-28.13   \u001b[0m | \u001b[0m 8.724   \u001b[0m | \u001b[0m 0.000148\u001b[0m | \u001b[0m 0.05505 \u001b[0m |\n",
      "| \u001b[0m 245     \u001b[0m | \u001b[0m-18.55   \u001b[0m | \u001b[0m 3.654   \u001b[0m | \u001b[0m 0.000559\u001b[0m | \u001b[0m 1.521   \u001b[0m |\n",
      "| \u001b[0m 246     \u001b[0m | \u001b[0m-17.04   \u001b[0m | \u001b[0m 6.601   \u001b[0m | \u001b[0m 0.000526\u001b[0m | \u001b[0m 1.708   \u001b[0m |\n",
      "| \u001b[0m 247     \u001b[0m | \u001b[0m-16.79   \u001b[0m | \u001b[0m 9.71    \u001b[0m | \u001b[0m 0.000471\u001b[0m | \u001b[0m 2.262   \u001b[0m |\n",
      "| \u001b[0m 248     \u001b[0m | \u001b[0m-28.14   \u001b[0m | \u001b[0m 2.059   \u001b[0m | \u001b[0m 0.000622\u001b[0m | \u001b[0m 2.411   \u001b[0m |\n",
      "| \u001b[0m 249     \u001b[0m | \u001b[0m-17.31   \u001b[0m | \u001b[0m 6.643   \u001b[0m | \u001b[0m 0.000916\u001b[0m | \u001b[0m 1.657   \u001b[0m |\n",
      "| \u001b[0m 250     \u001b[0m | \u001b[0m-17.61   \u001b[0m | \u001b[0m 8.307   \u001b[0m | \u001b[0m 0.000462\u001b[0m | \u001b[0m 1.609   \u001b[0m |\n",
      "| \u001b[0m 251     \u001b[0m | \u001b[0m-20.84   \u001b[0m | \u001b[0m 6.546   \u001b[0m | \u001b[0m 5.146e-0\u001b[0m | \u001b[0m 1.75    \u001b[0m |\n",
      "| \u001b[0m 252     \u001b[0m | \u001b[0m-18.97   \u001b[0m | \u001b[0m 9.926   \u001b[0m | \u001b[0m 0.000614\u001b[0m | \u001b[0m 1.582   \u001b[0m |\n",
      "| \u001b[0m 253     \u001b[0m | \u001b[0m-18.45   \u001b[0m | \u001b[0m 6.621   \u001b[0m | \u001b[0m 0.000177\u001b[0m | \u001b[0m 1.634   \u001b[0m |\n",
      "| \u001b[0m 254     \u001b[0m | \u001b[0m-17.45   \u001b[0m | \u001b[0m 6.623   \u001b[0m | \u001b[0m 0.000967\u001b[0m | \u001b[0m 1.633   \u001b[0m |\n",
      "| \u001b[0m 255     \u001b[0m | \u001b[0m-24.61   \u001b[0m | \u001b[0m 1.886   \u001b[0m | \u001b[0m 0.000654\u001b[0m | \u001b[0m 1.661   \u001b[0m |\n",
      "| \u001b[0m 256     \u001b[0m | \u001b[0m-25.46   \u001b[0m | \u001b[0m 8.539   \u001b[0m | \u001b[0m 0.000610\u001b[0m | \u001b[0m 0.6626  \u001b[0m |\n",
      "| \u001b[0m 257     \u001b[0m | \u001b[0m-20.35   \u001b[0m | \u001b[0m 9.875   \u001b[0m | \u001b[0m 1.78e-05\u001b[0m | \u001b[0m 2.032   \u001b[0m |\n",
      "| \u001b[0m 258     \u001b[0m | \u001b[0m-17.25   \u001b[0m | \u001b[0m 6.49    \u001b[0m | \u001b[0m 0.000441\u001b[0m | \u001b[0m 1.627   \u001b[0m |\n",
      "| \u001b[0m 259     \u001b[0m | \u001b[0m-20.66   \u001b[0m | \u001b[0m 9.731   \u001b[0m | \u001b[0m 4.05e-05\u001b[0m | \u001b[0m 2.238   \u001b[0m |\n",
      "| \u001b[0m 260     \u001b[0m | \u001b[0m-23.18   \u001b[0m | \u001b[0m 1.754   \u001b[0m | \u001b[0m 0.000173\u001b[0m | \u001b[0m 0.5063  \u001b[0m |\n",
      "| \u001b[0m 261     \u001b[0m | \u001b[0m-26.16   \u001b[0m | \u001b[0m 8.107   \u001b[0m | \u001b[0m 1.997e-0\u001b[0m | \u001b[0m 2.668   \u001b[0m |\n",
      "| \u001b[0m 262     \u001b[0m | \u001b[0m-18.06   \u001b[0m | \u001b[0m 9.682   \u001b[0m | \u001b[0m 0.000161\u001b[0m | \u001b[0m 2.078   \u001b[0m |\n",
      "| \u001b[0m 263     \u001b[0m | \u001b[0m-20.98   \u001b[0m | \u001b[0m 4.288   \u001b[0m | \u001b[0m 0.000641\u001b[0m | \u001b[0m 2.679   \u001b[0m |\n",
      "| \u001b[0m 264     \u001b[0m | \u001b[0m-18.52   \u001b[0m | \u001b[0m 4.585   \u001b[0m | \u001b[0m 0.000685\u001b[0m | \u001b[0m 0.8967  \u001b[0m |\n",
      "| \u001b[0m 265     \u001b[0m | \u001b[0m-20.07   \u001b[0m | \u001b[0m 6.884   \u001b[0m | \u001b[0m 6.056e-0\u001b[0m | \u001b[0m 1.221   \u001b[0m |\n",
      "| \u001b[0m 266     \u001b[0m | \u001b[0m-16.8    \u001b[0m | \u001b[0m 9.709   \u001b[0m | \u001b[0m 0.000466\u001b[0m | \u001b[0m 2.261   \u001b[0m |\n",
      "| \u001b[0m 267     \u001b[0m | \u001b[0m-20.95   \u001b[0m | \u001b[0m 3.911   \u001b[0m | \u001b[0m 0.000565\u001b[0m | \u001b[0m 0.2525  \u001b[0m |\n",
      "| \u001b[0m 268     \u001b[0m | \u001b[0m-22.29   \u001b[0m | \u001b[0m 5.116   \u001b[0m | \u001b[0m 0.000407\u001b[0m | \u001b[0m 2.798   \u001b[0m |\n",
      "| \u001b[0m 269     \u001b[0m | \u001b[0m-17.24   \u001b[0m | \u001b[0m 5.758   \u001b[0m | \u001b[0m 0.000881\u001b[0m | \u001b[0m 2.549   \u001b[0m |\n",
      "| \u001b[0m 270     \u001b[0m | \u001b[0m-30.93   \u001b[0m | \u001b[0m 9.805   \u001b[0m | \u001b[0m 0.000474\u001b[0m | \u001b[0m 0.3673  \u001b[0m |\n",
      "| \u001b[0m 271     \u001b[0m | \u001b[0m-30.34   \u001b[0m | \u001b[0m 0.9089  \u001b[0m | \u001b[0m 0.000811\u001b[0m | \u001b[0m 1.369   \u001b[0m |\n",
      "| \u001b[0m 272     \u001b[0m | \u001b[0m-30.77   \u001b[0m | \u001b[0m 9.77    \u001b[0m | \u001b[0m 0.000482\u001b[0m | \u001b[0m 0.3789  \u001b[0m |\n",
      "| \u001b[0m 273     \u001b[0m | \u001b[0m-17.91   \u001b[0m | \u001b[0m 8.503   \u001b[0m | \u001b[0m 0.000278\u001b[0m | \u001b[0m 2.269   \u001b[0m |\n",
      "| \u001b[0m 274     \u001b[0m | \u001b[0m-19.95   \u001b[0m | \u001b[0m 5.667   \u001b[0m | \u001b[0m 4.908e-0\u001b[0m | \u001b[0m 1.323   \u001b[0m |\n",
      "| \u001b[0m 275     \u001b[0m | \u001b[0m-16.92   \u001b[0m | \u001b[0m 9.714   \u001b[0m | \u001b[0m 0.000725\u001b[0m | \u001b[0m 2.2     \u001b[0m |\n",
      "| \u001b[0m 276     \u001b[0m | \u001b[0m-16.82   \u001b[0m | \u001b[0m 9.066   \u001b[0m | \u001b[0m 0.000838\u001b[0m | \u001b[0m 2.246   \u001b[0m |\n",
      "| \u001b[0m 277     \u001b[0m | \u001b[0m-18.55   \u001b[0m | \u001b[0m 3.181   \u001b[0m | \u001b[0m 0.000967\u001b[0m | \u001b[0m 1.883   \u001b[0m |\n",
      "| \u001b[0m 278     \u001b[0m | \u001b[0m-42.25   \u001b[0m | \u001b[0m 1.174   \u001b[0m | \u001b[0m 0.000173\u001b[0m | \u001b[0m 2.031   \u001b[0m |\n",
      "| \u001b[0m 279     \u001b[0m | \u001b[0m-21.5    \u001b[0m | \u001b[0m 5.11    \u001b[0m | \u001b[0m 0.000472\u001b[0m | \u001b[0m 2.802   \u001b[0m |\n",
      "| \u001b[0m 280     \u001b[0m | \u001b[0m-32.18   \u001b[0m | \u001b[0m 3.403   \u001b[0m | \u001b[0m 0.000187\u001b[0m | \u001b[0m 2.875   \u001b[0m |\n",
      "| \u001b[95m 281     \u001b[0m | \u001b[95m-16.64   \u001b[0m | \u001b[95m 8.921   \u001b[0m | \u001b[95m 0.000630\u001b[0m | \u001b[95m 2.291   \u001b[0m |\n",
      "| \u001b[0m 282     \u001b[0m | \u001b[0m-34.0    \u001b[0m | \u001b[0m 9.894   \u001b[0m | \u001b[0m 0.000934\u001b[0m | \u001b[0m 0.1656  \u001b[0m |\n",
      "| \u001b[0m 283     \u001b[0m | \u001b[0m-20.37   \u001b[0m | \u001b[0m 5.839   \u001b[0m | \u001b[0m 0.000577\u001b[0m | \u001b[0m 0.7858  \u001b[0m |\n",
      "| \u001b[0m 284     \u001b[0m | \u001b[0m-16.96   \u001b[0m | \u001b[0m 5.878   \u001b[0m | \u001b[0m 0.000859\u001b[0m | \u001b[0m 2.384   \u001b[0m |\n",
      "| \u001b[0m 285     \u001b[0m | \u001b[0m-19.66   \u001b[0m | \u001b[0m 9.93    \u001b[0m | \u001b[0m 0.000338\u001b[0m | \u001b[0m 1.331   \u001b[0m |\n",
      "| \u001b[0m 286     \u001b[0m | \u001b[0m-16.93   \u001b[0m | \u001b[0m 6.649   \u001b[0m | \u001b[0m 0.000627\u001b[0m | \u001b[0m 1.737   \u001b[0m |\n",
      "| \u001b[0m 287     \u001b[0m | \u001b[0m-19.24   \u001b[0m | \u001b[0m 2.02    \u001b[0m | \u001b[0m 0.000592\u001b[0m | \u001b[0m 0.4459  \u001b[0m |\n",
      "| \u001b[0m 288     \u001b[0m | \u001b[0m-16.9    \u001b[0m | \u001b[0m 6.59    \u001b[0m | \u001b[0m 0.000985\u001b[0m | \u001b[0m 1.853   \u001b[0m |\n",
      "| \u001b[0m 289     \u001b[0m | \u001b[0m-21.41   \u001b[0m | \u001b[0m 6.702   \u001b[0m | \u001b[0m 1.318e-0\u001b[0m | \u001b[0m 1.677   \u001b[0m |\n",
      "| \u001b[0m 290     \u001b[0m | \u001b[0m-17.68   \u001b[0m | \u001b[0m 9.877   \u001b[0m | \u001b[0m 0.000848\u001b[0m | \u001b[0m 2.027   \u001b[0m |\n",
      "| \u001b[0m 291     \u001b[0m | \u001b[0m-16.93   \u001b[0m | \u001b[0m 9.002   \u001b[0m | \u001b[0m 0.000583\u001b[0m | \u001b[0m 2.006   \u001b[0m |\n",
      "| \u001b[0m 292     \u001b[0m | \u001b[0m-22.29   \u001b[0m | \u001b[0m 9.742   \u001b[0m | \u001b[0m 0.000706\u001b[0m | \u001b[0m 1.168   \u001b[0m |\n",
      "| \u001b[0m 293     \u001b[0m | \u001b[0m-16.93   \u001b[0m | \u001b[0m 6.585   \u001b[0m | \u001b[0m 0.000716\u001b[0m | \u001b[0m 1.728   \u001b[0m |\n",
      "| \u001b[0m 294     \u001b[0m | \u001b[0m-18.63   \u001b[0m | \u001b[0m 8.646   \u001b[0m | \u001b[0m 8.923e-0\u001b[0m | \u001b[0m 1.738   \u001b[0m |\n",
      "| \u001b[0m 295     \u001b[0m | \u001b[0m-18.36   \u001b[0m | \u001b[0m 5.693   \u001b[0m | \u001b[0m 0.000457\u001b[0m | \u001b[0m 2.154   \u001b[0m |\n",
      "| \u001b[0m 296     \u001b[0m | \u001b[0m-23.34   \u001b[0m | \u001b[0m 6.451   \u001b[0m | \u001b[0m 2.422e-0\u001b[0m | \u001b[0m 1.974   \u001b[0m |\n",
      "| \u001b[0m 297     \u001b[0m | \u001b[0m-17.08   \u001b[0m | \u001b[0m 6.708   \u001b[0m | \u001b[0m 0.000747\u001b[0m | \u001b[0m 1.676   \u001b[0m |\n",
      "| \u001b[0m 298     \u001b[0m | \u001b[0m-17.72   \u001b[0m | \u001b[0m 8.306   \u001b[0m | \u001b[0m 0.000558\u001b[0m | \u001b[0m 1.611   \u001b[0m |\n",
      "| \u001b[0m 299     \u001b[0m | \u001b[0m-30.38   \u001b[0m | \u001b[0m 9.755   \u001b[0m | \u001b[0m 0.000277\u001b[0m | \u001b[0m 0.2849  \u001b[0m |\n",
      "| \u001b[0m 300     \u001b[0m | \u001b[0m-17.87   \u001b[0m | \u001b[0m 9.252   \u001b[0m | \u001b[0m 0.000359\u001b[0m | \u001b[0m 1.612   \u001b[0m |\n",
      "| \u001b[0m 301     \u001b[0m | \u001b[0m-19.07   \u001b[0m | \u001b[0m 3.831   \u001b[0m | \u001b[0m 0.000746\u001b[0m | \u001b[0m 2.113   \u001b[0m |\n",
      "| \u001b[0m 302     \u001b[0m | \u001b[0m-26.64   \u001b[0m | \u001b[0m 4.134   \u001b[0m | \u001b[0m 0.000319\u001b[0m | \u001b[0m 2.905   \u001b[0m |\n",
      "| \u001b[0m 303     \u001b[0m | \u001b[0m-18.75   \u001b[0m | \u001b[0m 9.552   \u001b[0m | \u001b[0m 0.000151\u001b[0m | \u001b[0m 1.413   \u001b[0m |\n",
      "| \u001b[0m 304     \u001b[0m | \u001b[0m-17.42   \u001b[0m | \u001b[0m 6.691   \u001b[0m | \u001b[0m 0.000855\u001b[0m | \u001b[0m 1.597   \u001b[0m |\n",
      "| \u001b[0m 305     \u001b[0m | \u001b[0m-22.57   \u001b[0m | \u001b[0m 5.319   \u001b[0m | \u001b[0m 0.000132\u001b[0m | \u001b[0m 0.2178  \u001b[0m |\n",
      "| \u001b[0m 306     \u001b[0m | \u001b[0m-18.6    \u001b[0m | \u001b[0m 6.744   \u001b[0m | \u001b[0m 0.000169\u001b[0m | \u001b[0m 1.192   \u001b[0m |\n",
      "| \u001b[0m 307     \u001b[0m | \u001b[0m-19.22   \u001b[0m | \u001b[0m 6.772   \u001b[0m | \u001b[0m 0.000675\u001b[0m | \u001b[0m 1.149   \u001b[0m |\n",
      "| \u001b[0m 308     \u001b[0m | \u001b[0m-16.9    \u001b[0m | \u001b[0m 6.597   \u001b[0m | \u001b[0m 0.000906\u001b[0m | \u001b[0m 1.809   \u001b[0m |\n",
      "| \u001b[0m 309     \u001b[0m | \u001b[0m-23.33   \u001b[0m | \u001b[0m 6.589   \u001b[0m | \u001b[0m 2.867e-0\u001b[0m | \u001b[0m 1.852   \u001b[0m |\n",
      "| \u001b[0m 310     \u001b[0m | \u001b[0m-17.59   \u001b[0m | \u001b[0m 6.557   \u001b[0m | \u001b[0m 0.000323\u001b[0m | \u001b[0m 1.652   \u001b[0m |\n",
      "| \u001b[0m 311     \u001b[0m | \u001b[0m-18.05   \u001b[0m | \u001b[0m 9.876   \u001b[0m | \u001b[0m 0.000976\u001b[0m | \u001b[0m 2.029   \u001b[0m |\n",
      "| \u001b[0m 312     \u001b[0m | \u001b[0m-17.51   \u001b[0m | \u001b[0m 6.882   \u001b[0m | \u001b[0m 0.000697\u001b[0m | \u001b[0m 2.751   \u001b[0m |\n",
      "| \u001b[0m 313     \u001b[0m | \u001b[0m-20.19   \u001b[0m | \u001b[0m 3.581   \u001b[0m | \u001b[0m 0.000823\u001b[0m | \u001b[0m 2.412   \u001b[0m |\n",
      "| \u001b[0m 314     \u001b[0m | \u001b[0m-17.33   \u001b[0m | \u001b[0m 8.223   \u001b[0m | \u001b[0m 0.000362\u001b[0m | \u001b[0m 2.18    \u001b[0m |\n",
      "| \u001b[0m 315     \u001b[0m | \u001b[0m-20.07   \u001b[0m | \u001b[0m 8.114   \u001b[0m | \u001b[0m 0.000224\u001b[0m | \u001b[0m 2.658   \u001b[0m |\n",
      "| \u001b[0m 316     \u001b[0m | \u001b[0m-16.77   \u001b[0m | \u001b[0m 5.957   \u001b[0m | \u001b[0m 0.000828\u001b[0m | \u001b[0m 1.783   \u001b[0m |\n",
      "| \u001b[0m 317     \u001b[0m | \u001b[0m-19.62   \u001b[0m | \u001b[0m 6.772   \u001b[0m | \u001b[0m 0.000818\u001b[0m | \u001b[0m 1.148   \u001b[0m |\n",
      "| \u001b[0m 318     \u001b[0m | \u001b[0m-25.99   \u001b[0m | \u001b[0m 7.426   \u001b[0m | \u001b[0m 0.000197\u001b[0m | \u001b[0m 0.3312  \u001b[0m |\n",
      "| \u001b[0m 319     \u001b[0m | \u001b[0m-17.2    \u001b[0m | \u001b[0m 6.488   \u001b[0m | \u001b[0m 0.000832\u001b[0m | \u001b[0m 1.626   \u001b[0m |\n",
      "| \u001b[0m 320     \u001b[0m | \u001b[0m-17.13   \u001b[0m | \u001b[0m 9.876   \u001b[0m | \u001b[0m 0.000339\u001b[0m | \u001b[0m 2.015   \u001b[0m |\n",
      "| \u001b[0m 321     \u001b[0m | \u001b[0m-19.74   \u001b[0m | \u001b[0m 9.698   \u001b[0m | \u001b[0m 8.194e-0\u001b[0m | \u001b[0m 2.269   \u001b[0m |\n",
      "| \u001b[0m 322     \u001b[0m | \u001b[0m-18.66   \u001b[0m | \u001b[0m 4.585   \u001b[0m | \u001b[0m 0.000408\u001b[0m | \u001b[0m 1.706   \u001b[0m |\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| \u001b[0m 323     \u001b[0m | \u001b[0m-19.6    \u001b[0m | \u001b[0m 2.003   \u001b[0m | \u001b[0m 0.000719\u001b[0m | \u001b[0m 0.8652  \u001b[0m |\n",
      "| \u001b[0m 324     \u001b[0m | \u001b[0m-17.28   \u001b[0m | \u001b[0m 7.949   \u001b[0m | \u001b[0m 0.000855\u001b[0m | \u001b[0m 1.852   \u001b[0m |\n",
      "| \u001b[95m 325     \u001b[0m | \u001b[95m-16.61   \u001b[0m | \u001b[95m 8.504   \u001b[0m | \u001b[95m 0.000732\u001b[0m | \u001b[95m 2.272   \u001b[0m |\n",
      "| \u001b[0m 326     \u001b[0m | \u001b[0m-16.75   \u001b[0m | \u001b[0m 6.448   \u001b[0m | \u001b[0m 0.000982\u001b[0m | \u001b[0m 1.908   \u001b[0m |\n",
      "| \u001b[0m 327     \u001b[0m | \u001b[0m-21.22   \u001b[0m | \u001b[0m 5.989   \u001b[0m | \u001b[0m 0.000279\u001b[0m | \u001b[0m 2.509   \u001b[0m |\n",
      "| \u001b[0m 328     \u001b[0m | \u001b[0m-40.0    \u001b[0m | \u001b[0m 0.583   \u001b[0m | \u001b[0m 0.000460\u001b[0m | \u001b[0m 1.273   \u001b[0m |\n",
      "| \u001b[0m 329     \u001b[0m | \u001b[0m-17.23   \u001b[0m | \u001b[0m 9.83    \u001b[0m | \u001b[0m 0.000295\u001b[0m | \u001b[0m 2.128   \u001b[0m |\n",
      "| \u001b[0m 330     \u001b[0m | \u001b[0m-16.96   \u001b[0m | \u001b[0m 6.926   \u001b[0m | \u001b[0m 0.000534\u001b[0m | \u001b[0m 2.036   \u001b[0m |\n",
      "| \u001b[0m 331     \u001b[0m | \u001b[0m-25.5    \u001b[0m | \u001b[0m 9.707   \u001b[0m | \u001b[0m 0.000840\u001b[0m | \u001b[0m 0.9106  \u001b[0m |\n",
      "| \u001b[0m 332     \u001b[0m | \u001b[0m-17.82   \u001b[0m | \u001b[0m 6.584   \u001b[0m | \u001b[0m 0.000289\u001b[0m | \u001b[0m 1.729   \u001b[0m |\n",
      "| \u001b[0m 333     \u001b[0m | \u001b[0m-28.71   \u001b[0m | \u001b[0m 3.8     \u001b[0m | \u001b[0m 7.886e-0\u001b[0m | \u001b[0m 2.097   \u001b[0m |\n",
      "| \u001b[0m 334     \u001b[0m | \u001b[0m-17.06   \u001b[0m | \u001b[0m 9.683   \u001b[0m | \u001b[0m 0.000659\u001b[0m | \u001b[0m 2.075   \u001b[0m |\n",
      "| \u001b[0m 335     \u001b[0m | \u001b[0m-18.72   \u001b[0m | \u001b[0m 6.447   \u001b[0m | \u001b[0m 0.000236\u001b[0m | \u001b[0m 1.907   \u001b[0m |\n",
      "| \u001b[0m 336     \u001b[0m | \u001b[0m-20.96   \u001b[0m | \u001b[0m 3.909   \u001b[0m | \u001b[0m 0.000574\u001b[0m | \u001b[0m 0.2527  \u001b[0m |\n",
      "| \u001b[0m 337     \u001b[0m | \u001b[0m-27.1    \u001b[0m | \u001b[0m 8.917   \u001b[0m | \u001b[0m 0.000615\u001b[0m | \u001b[0m 0.5749  \u001b[0m |\n",
      "| \u001b[0m 338     \u001b[0m | \u001b[0m-19.82   \u001b[0m | \u001b[0m 3.518   \u001b[0m | \u001b[0m 0.000337\u001b[0m | \u001b[0m 0.393   \u001b[0m |\n",
      "| \u001b[0m 339     \u001b[0m | \u001b[0m-20.17   \u001b[0m | \u001b[0m 1.755   \u001b[0m | \u001b[0m 0.000470\u001b[0m | \u001b[0m 0.1238  \u001b[0m |\n",
      "| \u001b[0m 340     \u001b[0m | \u001b[0m-17.01   \u001b[0m | \u001b[0m 9.717   \u001b[0m | \u001b[0m 0.000650\u001b[0m | \u001b[0m 2.099   \u001b[0m |\n",
      "| \u001b[0m 341     \u001b[0m | \u001b[0m-17.01   \u001b[0m | \u001b[0m 6.601   \u001b[0m | \u001b[0m 0.000997\u001b[0m | \u001b[0m 1.809   \u001b[0m |\n",
      "| \u001b[0m 342     \u001b[0m | \u001b[0m-17.58   \u001b[0m | \u001b[0m 8.778   \u001b[0m | \u001b[0m 0.000233\u001b[0m | \u001b[0m 1.84    \u001b[0m |\n",
      "| \u001b[0m 343     \u001b[0m | \u001b[0m-16.81   \u001b[0m | \u001b[0m 8.11    \u001b[0m | \u001b[0m 0.000703\u001b[0m | \u001b[0m 2.672   \u001b[0m |\n",
      "| \u001b[0m 344     \u001b[0m | \u001b[0m-21.49   \u001b[0m | \u001b[0m 3.446   \u001b[0m | \u001b[0m 0.000212\u001b[0m | \u001b[0m 1.392   \u001b[0m |\n",
      "| \u001b[0m 345     \u001b[0m | \u001b[0m-27.02   \u001b[0m | \u001b[0m 7.677   \u001b[0m | \u001b[0m 0.000284\u001b[0m | \u001b[0m 0.09333 \u001b[0m |\n",
      "| \u001b[0m 346     \u001b[0m | \u001b[0m-18.6    \u001b[0m | \u001b[0m 4.583   \u001b[0m | \u001b[0m 0.000283\u001b[0m | \u001b[0m 0.9016  \u001b[0m |\n",
      "| \u001b[0m 347     \u001b[0m | \u001b[0m-26.52   \u001b[0m | \u001b[0m 3.188   \u001b[0m | \u001b[0m 0.000235\u001b[0m | \u001b[0m 2.063   \u001b[0m |\n",
      "| \u001b[0m 348     \u001b[0m | \u001b[0m-39.69   \u001b[0m | \u001b[0m 2.973   \u001b[0m | \u001b[0m 6.261e-0\u001b[0m | \u001b[0m 2.931   \u001b[0m |\n",
      "| \u001b[0m 349     \u001b[0m | \u001b[0m-17.16   \u001b[0m | \u001b[0m 7.435   \u001b[0m | \u001b[0m 0.000647\u001b[0m | \u001b[0m 1.711   \u001b[0m |\n",
      "| \u001b[0m 350     \u001b[0m | \u001b[0m-17.17   \u001b[0m | \u001b[0m 5.692   \u001b[0m | \u001b[0m 0.000699\u001b[0m | \u001b[0m 2.155   \u001b[0m |\n",
      "| \u001b[0m 351     \u001b[0m | \u001b[0m-17.1    \u001b[0m | \u001b[0m 6.587   \u001b[0m | \u001b[0m 0.000931\u001b[0m | \u001b[0m 1.729   \u001b[0m |\n",
      "| \u001b[0m 352     \u001b[0m | \u001b[0m-17.02   \u001b[0m | \u001b[0m 7.949   \u001b[0m | \u001b[0m 0.000667\u001b[0m | \u001b[0m 1.852   \u001b[0m |\n",
      "| \u001b[0m 353     \u001b[0m | \u001b[0m-17.9    \u001b[0m | \u001b[0m 7.644   \u001b[0m | \u001b[0m 0.00034 \u001b[0m | \u001b[0m 1.429   \u001b[0m |\n",
      "| \u001b[0m 354     \u001b[0m | \u001b[0m-31.74   \u001b[0m | \u001b[0m 1.554   \u001b[0m | \u001b[0m 0.000453\u001b[0m | \u001b[0m 1.995   \u001b[0m |\n",
      "| \u001b[0m 355     \u001b[0m | \u001b[0m-22.18   \u001b[0m | \u001b[0m 9.879   \u001b[0m | \u001b[0m 0.000881\u001b[0m | \u001b[0m 1.284   \u001b[0m |\n",
      "| \u001b[0m 356     \u001b[0m | \u001b[0m-18.79   \u001b[0m | \u001b[0m 4.583   \u001b[0m | \u001b[0m 0.000223\u001b[0m | \u001b[0m 0.8969  \u001b[0m |\n",
      "| \u001b[0m 357     \u001b[0m | \u001b[0m-17.37   \u001b[0m | \u001b[0m 9.877   \u001b[0m | \u001b[0m 0.000255\u001b[0m | \u001b[0m 2.03    \u001b[0m |\n",
      "| \u001b[0m 358     \u001b[0m | \u001b[0m-23.18   \u001b[0m | \u001b[0m 6.583   \u001b[0m | \u001b[0m 0.000124\u001b[0m | \u001b[0m 2.478   \u001b[0m |\n",
      "| \u001b[0m 359     \u001b[0m | \u001b[0m-19.44   \u001b[0m | \u001b[0m 3.556   \u001b[0m | \u001b[0m 0.000530\u001b[0m | \u001b[0m 1.676   \u001b[0m |\n",
      "| \u001b[0m 360     \u001b[0m | \u001b[0m-17.01   \u001b[0m | \u001b[0m 9.742   \u001b[0m | \u001b[0m 0.000380\u001b[0m | \u001b[0m 2.078   \u001b[0m |\n",
      "| \u001b[0m 361     \u001b[0m | \u001b[0m-20.46   \u001b[0m | \u001b[0m 4.583   \u001b[0m | \u001b[0m 1.651e-0\u001b[0m | \u001b[0m 0.9013  \u001b[0m |\n",
      "| \u001b[0m 362     \u001b[0m | \u001b[0m-18.06   \u001b[0m | \u001b[0m 9.872   \u001b[0m | \u001b[0m 0.000963\u001b[0m | \u001b[0m 2.015   \u001b[0m |\n",
      "| \u001b[0m 363     \u001b[0m | \u001b[0m-20.45   \u001b[0m | \u001b[0m 3.028   \u001b[0m | \u001b[0m 0.000948\u001b[0m | \u001b[0m 2.299   \u001b[0m |\n",
      "| \u001b[0m 364     \u001b[0m | \u001b[0m-17.8    \u001b[0m | \u001b[0m 5.692   \u001b[0m | \u001b[0m 0.000549\u001b[0m | \u001b[0m 2.154   \u001b[0m |\n",
      "| \u001b[0m 365     \u001b[0m | \u001b[0m-18.5    \u001b[0m | \u001b[0m 6.91    \u001b[0m | \u001b[0m 0.000690\u001b[0m | \u001b[0m 1.301   \u001b[0m |\n",
      "| \u001b[0m 366     \u001b[0m | \u001b[0m-17.22   \u001b[0m | \u001b[0m 7.433   \u001b[0m | \u001b[0m 0.000400\u001b[0m | \u001b[0m 1.712   \u001b[0m |\n",
      "| \u001b[0m 367     \u001b[0m | \u001b[0m-19.02   \u001b[0m | \u001b[0m 5.275   \u001b[0m | \u001b[0m 0.000159\u001b[0m | \u001b[0m 1.375   \u001b[0m |\n",
      "| \u001b[0m 368     \u001b[0m | \u001b[0m-17.06   \u001b[0m | \u001b[0m 9.839   \u001b[0m | \u001b[0m 0.000361\u001b[0m | \u001b[0m 2.056   \u001b[0m |\n",
      "| \u001b[0m 369     \u001b[0m | \u001b[0m-19.29   \u001b[0m | \u001b[0m 3.384   \u001b[0m | \u001b[0m 0.000764\u001b[0m | \u001b[0m 1.94    \u001b[0m |\n",
      "| \u001b[0m 370     \u001b[0m | \u001b[0m-17.73   \u001b[0m | \u001b[0m 9.426   \u001b[0m | \u001b[0m 0.000975\u001b[0m | \u001b[0m 2.047   \u001b[0m |\n",
      "| \u001b[0m 371     \u001b[0m | \u001b[0m-17.54   \u001b[0m | \u001b[0m 3.934   \u001b[0m | \u001b[0m 0.000771\u001b[0m | \u001b[0m 1.096   \u001b[0m |\n",
      "| \u001b[0m 372     \u001b[0m | \u001b[0m-17.09   \u001b[0m | \u001b[0m 6.65    \u001b[0m | \u001b[0m 0.000502\u001b[0m | \u001b[0m 1.691   \u001b[0m |\n",
      "| \u001b[0m 373     \u001b[0m | \u001b[0m-18.32   \u001b[0m | \u001b[0m 3.105   \u001b[0m | \u001b[0m 0.000548\u001b[0m | \u001b[0m 0.7795  \u001b[0m |\n",
      "| \u001b[0m 374     \u001b[0m | \u001b[0m-19.17   \u001b[0m | \u001b[0m 2.429   \u001b[0m | \u001b[0m 0.000625\u001b[0m | \u001b[0m 0.9859  \u001b[0m |\n",
      "| \u001b[0m 375     \u001b[0m | \u001b[0m-21.05   \u001b[0m | \u001b[0m 9.003   \u001b[0m | \u001b[0m 1.365e-0\u001b[0m | \u001b[0m 2.014   \u001b[0m |\n",
      "| \u001b[0m 376     \u001b[0m | \u001b[0m-16.64   \u001b[0m | \u001b[0m 8.923   \u001b[0m | \u001b[0m 0.000748\u001b[0m | \u001b[0m 2.294   \u001b[0m |\n",
      "| \u001b[0m 377     \u001b[0m | \u001b[0m-17.92   \u001b[0m | \u001b[0m 9.732   \u001b[0m | \u001b[0m 0.000205\u001b[0m | \u001b[0m 2.241   \u001b[0m |\n",
      "| \u001b[95m 378     \u001b[0m | \u001b[95m-16.6    \u001b[0m | \u001b[95m 9.305   \u001b[0m | \u001b[95m 0.000929\u001b[0m | \u001b[95m 2.671   \u001b[0m |\n",
      "| \u001b[0m 379     \u001b[0m | \u001b[0m-16.86   \u001b[0m | \u001b[0m 6.582   \u001b[0m | \u001b[0m 0.000625\u001b[0m | \u001b[0m 1.806   \u001b[0m |\n",
      "| \u001b[0m 380     \u001b[0m | \u001b[0m-17.75   \u001b[0m | \u001b[0m 6.59    \u001b[0m | \u001b[0m 0.000331\u001b[0m | \u001b[0m 1.855   \u001b[0m |\n",
      "| \u001b[0m 381     \u001b[0m | \u001b[0m-27.28   \u001b[0m | \u001b[0m 5.691   \u001b[0m | \u001b[0m 8.317e-0\u001b[0m | \u001b[0m 2.154   \u001b[0m |\n",
      "| \u001b[0m 382     \u001b[0m | \u001b[0m-22.64   \u001b[0m | \u001b[0m 6.454   \u001b[0m | \u001b[0m 0.000895\u001b[0m | \u001b[0m 0.7404  \u001b[0m |\n",
      "| \u001b[0m 383     \u001b[0m | \u001b[0m-23.88   \u001b[0m | \u001b[0m 6.884   \u001b[0m | \u001b[0m 0.000138\u001b[0m | \u001b[0m 2.752   \u001b[0m |\n",
      "| \u001b[0m 384     \u001b[0m | \u001b[0m-16.96   \u001b[0m | \u001b[0m 9.686   \u001b[0m | \u001b[0m 0.000422\u001b[0m | \u001b[0m 2.074   \u001b[0m |\n",
      "| \u001b[0m 385     \u001b[0m | \u001b[0m-21.63   \u001b[0m | \u001b[0m 3.188   \u001b[0m | \u001b[0m 0.000552\u001b[0m | \u001b[0m 1.986   \u001b[0m |\n",
      "| \u001b[0m 386     \u001b[0m | \u001b[0m-18.94   \u001b[0m | \u001b[0m 9.254   \u001b[0m | \u001b[0m 0.000800\u001b[0m | \u001b[0m 1.613   \u001b[0m |\n",
      "| \u001b[0m 387     \u001b[0m | \u001b[0m-22.43   \u001b[0m | \u001b[0m 3.838   \u001b[0m | \u001b[0m 0.000618\u001b[0m | \u001b[0m 2.717   \u001b[0m |\n",
      "| \u001b[0m 388     \u001b[0m | \u001b[0m-19.61   \u001b[0m | \u001b[0m 3.034   \u001b[0m | \u001b[0m 0.000926\u001b[0m | \u001b[0m 2.039   \u001b[0m |\n",
      "| \u001b[0m 389     \u001b[0m | \u001b[0m-29.38   \u001b[0m | \u001b[0m 2.783   \u001b[0m | \u001b[0m 3.952e-0\u001b[0m | \u001b[0m 1.528   \u001b[0m |\n",
      "| \u001b[0m 390     \u001b[0m | \u001b[0m-20.56   \u001b[0m | \u001b[0m 4.993   \u001b[0m | \u001b[0m 2.218e-0\u001b[0m | \u001b[0m 0.7606  \u001b[0m |\n",
      "| \u001b[0m 391     \u001b[0m | \u001b[0m-16.88   \u001b[0m | \u001b[0m 6.274   \u001b[0m | \u001b[0m 0.000968\u001b[0m | \u001b[0m 1.793   \u001b[0m |\n",
      "| \u001b[0m 392     \u001b[0m | \u001b[0m-17.18   \u001b[0m | \u001b[0m 9.725   \u001b[0m | \u001b[0m 0.000946\u001b[0m | \u001b[0m 2.285   \u001b[0m |\n",
      "| \u001b[0m 393     \u001b[0m | \u001b[0m-19.1    \u001b[0m | \u001b[0m 4.157   \u001b[0m | \u001b[0m 0.000419\u001b[0m | \u001b[0m 1.672   \u001b[0m |\n",
      "| \u001b[0m 394     \u001b[0m | \u001b[0m-24.92   \u001b[0m | \u001b[0m 8.55    \u001b[0m | \u001b[0m 2.269e-0\u001b[0m | \u001b[0m 2.607   \u001b[0m |\n",
      "| \u001b[0m 395     \u001b[0m | \u001b[0m-16.79   \u001b[0m | \u001b[0m 6.273   \u001b[0m | \u001b[0m 0.000795\u001b[0m | \u001b[0m 1.792   \u001b[0m |\n",
      "| \u001b[0m 396     \u001b[0m | \u001b[0m-20.79   \u001b[0m | \u001b[0m 7.626   \u001b[0m | \u001b[0m 0.000255\u001b[0m | \u001b[0m 2.823   \u001b[0m |\n",
      "| \u001b[0m 397     \u001b[0m | \u001b[0m-16.75   \u001b[0m | \u001b[0m 8.185   \u001b[0m | \u001b[0m 0.000825\u001b[0m | \u001b[0m 2.854   \u001b[0m |\n",
      "| \u001b[0m 398     \u001b[0m | \u001b[0m-17.65   \u001b[0m | \u001b[0m 8.514   \u001b[0m | \u001b[0m 0.000227\u001b[0m | \u001b[0m 1.784   \u001b[0m |\n",
      "| \u001b[0m 399     \u001b[0m | \u001b[0m-25.88   \u001b[0m | \u001b[0m 2.685   \u001b[0m | \u001b[0m 0.000556\u001b[0m | \u001b[0m 2.447   \u001b[0m |\n",
      "| \u001b[0m 400     \u001b[0m | \u001b[0m-19.83   \u001b[0m | \u001b[0m 5.757   \u001b[0m | \u001b[0m 0.000436\u001b[0m | \u001b[0m 2.551   \u001b[0m |\n",
      "| \u001b[0m 401     \u001b[0m | \u001b[0m-18.25   \u001b[0m | \u001b[0m 3.985   \u001b[0m | \u001b[0m 0.000517\u001b[0m | \u001b[0m 0.8533  \u001b[0m |\n",
      "| \u001b[0m 402     \u001b[0m | \u001b[0m-17.95   \u001b[0m | \u001b[0m 5.274   \u001b[0m | \u001b[0m 0.000320\u001b[0m | \u001b[0m 1.374   \u001b[0m |\n",
      "| \u001b[0m 403     \u001b[0m | \u001b[0m-16.88   \u001b[0m | \u001b[0m 7.531   \u001b[0m | \u001b[0m 0.000625\u001b[0m | \u001b[0m 1.866   \u001b[0m |\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| \u001b[0m 404     \u001b[0m | \u001b[0m-17.09   \u001b[0m | \u001b[0m 6.624   \u001b[0m | \u001b[0m 0.000506\u001b[0m | \u001b[0m 1.681   \u001b[0m |\n",
      "| \u001b[0m 405     \u001b[0m | \u001b[0m-24.55   \u001b[0m | \u001b[0m 5.758   \u001b[0m | \u001b[0m 0.000144\u001b[0m | \u001b[0m 2.548   \u001b[0m |\n",
      "| \u001b[0m 406     \u001b[0m | \u001b[0m-20.0    \u001b[0m | \u001b[0m 3.347   \u001b[0m | \u001b[0m 0.000756\u001b[0m | \u001b[0m 0.362   \u001b[0m |\n",
      "| \u001b[0m 407     \u001b[0m | \u001b[0m-22.56   \u001b[0m | \u001b[0m 8.924   \u001b[0m | \u001b[0m 1.847e-0\u001b[0m | \u001b[0m 2.291   \u001b[0m |\n",
      "| \u001b[0m 408     \u001b[0m | \u001b[0m-16.88   \u001b[0m | \u001b[0m 9.827   \u001b[0m | \u001b[0m 0.000489\u001b[0m | \u001b[0m 2.132   \u001b[0m |\n",
      "| \u001b[0m 409     \u001b[0m | \u001b[0m-17.2    \u001b[0m | \u001b[0m 6.694   \u001b[0m | \u001b[0m 0.000664\u001b[0m | \u001b[0m 1.598   \u001b[0m |\n",
      "| \u001b[0m 410     \u001b[0m | \u001b[0m-17.02   \u001b[0m | \u001b[0m 9.875   \u001b[0m | \u001b[0m 0.000468\u001b[0m | \u001b[0m 2.033   \u001b[0m |\n",
      "| \u001b[0m 411     \u001b[0m | \u001b[0m-17.65   \u001b[0m | \u001b[0m 8.647   \u001b[0m | \u001b[0m 0.000681\u001b[0m | \u001b[0m 1.739   \u001b[0m |\n",
      "| \u001b[0m 412     \u001b[0m | \u001b[0m-28.22   \u001b[0m | \u001b[0m 9.144   \u001b[0m | \u001b[0m 0.000338\u001b[0m | \u001b[0m 0.433   \u001b[0m |\n",
      "| \u001b[0m 413     \u001b[0m | \u001b[0m-17.0    \u001b[0m | \u001b[0m 9.73    \u001b[0m | \u001b[0m 0.000373\u001b[0m | \u001b[0m 2.137   \u001b[0m |\n",
      "| \u001b[0m 414     \u001b[0m | \u001b[0m-17.98   \u001b[0m | \u001b[0m 5.672   \u001b[0m | \u001b[0m 0.000284\u001b[0m | \u001b[0m 1.326   \u001b[0m |\n",
      "| \u001b[0m 415     \u001b[0m | \u001b[0m-17.28   \u001b[0m | \u001b[0m 6.743   \u001b[0m | \u001b[0m 0.000809\u001b[0m | \u001b[0m 1.629   \u001b[0m |\n",
      "| \u001b[0m 416     \u001b[0m | \u001b[0m-27.64   \u001b[0m | \u001b[0m 3.187   \u001b[0m | \u001b[0m 0.000184\u001b[0m | \u001b[0m 2.061   \u001b[0m |\n",
      "| \u001b[0m 417     \u001b[0m | \u001b[0m-19.23   \u001b[0m | \u001b[0m 6.264   \u001b[0m | \u001b[0m 6.412e-0\u001b[0m | \u001b[0m 1.137   \u001b[0m |\n",
      "| \u001b[0m 418     \u001b[0m | \u001b[0m-16.82   \u001b[0m | \u001b[0m 6.632   \u001b[0m | \u001b[0m 0.000725\u001b[0m | \u001b[0m 1.81    \u001b[0m |\n",
      "| \u001b[0m 419     \u001b[0m | \u001b[0m-19.22   \u001b[0m | \u001b[0m 2.425   \u001b[0m | \u001b[0m 0.000617\u001b[0m | \u001b[0m 0.9872  \u001b[0m |\n",
      "| \u001b[0m 420     \u001b[0m | \u001b[0m-24.22   \u001b[0m | \u001b[0m 6.14    \u001b[0m | \u001b[0m 0.000220\u001b[0m | \u001b[0m 2.95    \u001b[0m |\n",
      "| \u001b[0m 421     \u001b[0m | \u001b[0m-18.64   \u001b[0m | \u001b[0m 7.166   \u001b[0m | \u001b[0m 0.000411\u001b[0m | \u001b[0m 2.602   \u001b[0m |\n",
      "| \u001b[0m 422     \u001b[0m | \u001b[0m-42.64   \u001b[0m | \u001b[0m 0.593   \u001b[0m | \u001b[0m 0.000953\u001b[0m | \u001b[0m 2.882   \u001b[0m |\n",
      "| \u001b[0m 423     \u001b[0m | \u001b[0m-21.46   \u001b[0m | \u001b[0m 4.26    \u001b[0m | \u001b[0m 0.000743\u001b[0m | \u001b[0m 0.3943  \u001b[0m |\n",
      "| \u001b[0m 424     \u001b[0m | \u001b[0m-18.74   \u001b[0m | \u001b[0m 9.876   \u001b[0m | \u001b[0m 8.982e-0\u001b[0m | \u001b[0m 2.029   \u001b[0m |\n",
      "| \u001b[0m 425     \u001b[0m | \u001b[0m-21.25   \u001b[0m | \u001b[0m 1.929   \u001b[0m | \u001b[0m 0.000566\u001b[0m | \u001b[0m 0.987   \u001b[0m |\n",
      "| \u001b[0m 426     \u001b[0m | \u001b[0m-16.83   \u001b[0m | \u001b[0m 6.626   \u001b[0m | \u001b[0m 0.000810\u001b[0m | \u001b[0m 1.815   \u001b[0m |\n",
      "| \u001b[0m 427     \u001b[0m | \u001b[0m-17.41   \u001b[0m | \u001b[0m 8.378   \u001b[0m | \u001b[0m 0.000285\u001b[0m | \u001b[0m 1.837   \u001b[0m |\n",
      "| \u001b[0m 428     \u001b[0m | \u001b[0m-18.64   \u001b[0m | \u001b[0m 9.718   \u001b[0m | \u001b[0m 0.000147\u001b[0m | \u001b[0m 2.273   \u001b[0m |\n",
      "| \u001b[0m 429     \u001b[0m | \u001b[0m-18.54   \u001b[0m | \u001b[0m 4.584   \u001b[0m | \u001b[0m 0.000731\u001b[0m | \u001b[0m 0.9031  \u001b[0m |\n",
      "| \u001b[0m 430     \u001b[0m | \u001b[0m-21.75   \u001b[0m | \u001b[0m 3.037   \u001b[0m | \u001b[0m 0.000619\u001b[0m | \u001b[0m 2.038   \u001b[0m |\n",
      "| \u001b[0m 431     \u001b[0m | \u001b[0m-26.63   \u001b[0m | \u001b[0m 2.483   \u001b[0m | \u001b[0m 0.000664\u001b[0m | \u001b[0m 2.675   \u001b[0m |\n",
      "| \u001b[0m 432     \u001b[0m | \u001b[0m-17.15   \u001b[0m | \u001b[0m 8.775   \u001b[0m | \u001b[0m 0.000438\u001b[0m | \u001b[0m 1.839   \u001b[0m |\n",
      "| \u001b[0m 433     \u001b[0m | \u001b[0m-20.9    \u001b[0m | \u001b[0m 3.932   \u001b[0m | \u001b[0m 2.437e-0\u001b[0m | \u001b[0m 0.414   \u001b[0m |\n",
      "| \u001b[0m 434     \u001b[0m | \u001b[0m-17.11   \u001b[0m | \u001b[0m 6.488   \u001b[0m | \u001b[0m 0.000731\u001b[0m | \u001b[0m 1.627   \u001b[0m |\n",
      "| \u001b[0m 435     \u001b[0m | \u001b[0m-17.73   \u001b[0m | \u001b[0m 9.714   \u001b[0m | \u001b[0m 0.000234\u001b[0m | \u001b[0m 2.259   \u001b[0m |\n",
      "| \u001b[0m 436     \u001b[0m | \u001b[0m-18.09   \u001b[0m | \u001b[0m 5.675   \u001b[0m | \u001b[0m 0.000256\u001b[0m | \u001b[0m 1.323   \u001b[0m |\n",
      "| \u001b[0m 437     \u001b[0m | \u001b[0m-17.87   \u001b[0m | \u001b[0m 8.309   \u001b[0m | \u001b[0m 0.000646\u001b[0m | \u001b[0m 1.615   \u001b[0m |\n",
      "| \u001b[0m 438     \u001b[0m | \u001b[0m-21.1    \u001b[0m | \u001b[0m 4.156   \u001b[0m | \u001b[0m 0.000228\u001b[0m | \u001b[0m 1.672   \u001b[0m |\n",
      "| \u001b[0m 439     \u001b[0m | \u001b[0m-27.53   \u001b[0m | \u001b[0m 3.063   \u001b[0m | \u001b[0m 0.000250\u001b[0m | \u001b[0m 2.18    \u001b[0m |\n",
      "| \u001b[0m 440     \u001b[0m | \u001b[0m-17.95   \u001b[0m | \u001b[0m 3.936   \u001b[0m | \u001b[0m 0.000484\u001b[0m | \u001b[0m 1.098   \u001b[0m |\n",
      "| \u001b[0m 441     \u001b[0m | \u001b[0m-18.53   \u001b[0m | \u001b[0m 4.578   \u001b[0m | \u001b[0m 0.000320\u001b[0m | \u001b[0m 0.8996  \u001b[0m |\n",
      "| \u001b[0m 442     \u001b[0m | \u001b[0m-16.98   \u001b[0m | \u001b[0m 9.727   \u001b[0m | \u001b[0m 0.000679\u001b[0m | \u001b[0m 2.137   \u001b[0m |\n",
      "| \u001b[0m 443     \u001b[0m | \u001b[0m-17.16   \u001b[0m | \u001b[0m 6.71    \u001b[0m | \u001b[0m 0.000830\u001b[0m | \u001b[0m 1.676   \u001b[0m |\n",
      "| \u001b[0m 444     \u001b[0m | \u001b[0m-25.02   \u001b[0m | \u001b[0m 5.111   \u001b[0m | \u001b[0m 0.000243\u001b[0m | \u001b[0m 2.795   \u001b[0m |\n",
      "| \u001b[0m 445     \u001b[0m | \u001b[0m-17.24   \u001b[0m | \u001b[0m 9.778   \u001b[0m | \u001b[0m 0.000636\u001b[0m | \u001b[0m 1.998   \u001b[0m |\n",
      "| \u001b[0m 446     \u001b[0m | \u001b[0m-19.12   \u001b[0m | \u001b[0m 9.834   \u001b[0m | \u001b[0m 7.168e-0\u001b[0m | \u001b[0m 2.053   \u001b[0m |\n",
      "| \u001b[0m 447     \u001b[0m | \u001b[0m-17.68   \u001b[0m | \u001b[0m 4.018   \u001b[0m | \u001b[0m 0.000643\u001b[0m | \u001b[0m 1.069   \u001b[0m |\n",
      "| \u001b[0m 448     \u001b[0m | \u001b[0m-23.42   \u001b[0m | \u001b[0m 3.83    \u001b[0m | \u001b[0m 0.000300\u001b[0m | \u001b[0m 2.112   \u001b[0m |\n",
      "| \u001b[0m 449     \u001b[0m | \u001b[0m-17.04   \u001b[0m | \u001b[0m 6.74    \u001b[0m | \u001b[0m 0.000617\u001b[0m | \u001b[0m 1.673   \u001b[0m |\n",
      "| \u001b[0m 450     \u001b[0m | \u001b[0m-39.51   \u001b[0m | \u001b[0m 0.6719  \u001b[0m | \u001b[0m 6.685e-0\u001b[0m | \u001b[0m 0.5656  \u001b[0m |\n",
      "| \u001b[0m 451     \u001b[0m | \u001b[0m-83.62   \u001b[0m | \u001b[0m 0.1519  \u001b[0m | \u001b[0m 0.000146\u001b[0m | \u001b[0m 2.512   \u001b[0m |\n",
      "| \u001b[0m 452     \u001b[0m | \u001b[0m-18.41   \u001b[0m | \u001b[0m 4.579   \u001b[0m | \u001b[0m 0.000573\u001b[0m | \u001b[0m 0.8984  \u001b[0m |\n",
      "| \u001b[0m 453     \u001b[0m | \u001b[0m-17.33   \u001b[0m | \u001b[0m 9.878   \u001b[0m | \u001b[0m 0.000264\u001b[0m | \u001b[0m 2.03    \u001b[0m |\n",
      "| \u001b[0m 454     \u001b[0m | \u001b[0m-17.24   \u001b[0m | \u001b[0m 6.559   \u001b[0m | \u001b[0m 0.000431\u001b[0m | \u001b[0m 1.653   \u001b[0m |\n",
      "| \u001b[0m 455     \u001b[0m | \u001b[0m-16.87   \u001b[0m | \u001b[0m 4.879   \u001b[0m | \u001b[0m 0.000941\u001b[0m | \u001b[0m 1.999   \u001b[0m |\n",
      "| \u001b[0m 456     \u001b[0m | \u001b[0m-16.72   \u001b[0m | \u001b[0m 9.701   \u001b[0m | \u001b[0m 0.000576\u001b[0m | \u001b[0m 2.265   \u001b[0m |\n",
      "| \u001b[0m 457     \u001b[0m | \u001b[0m-21.44   \u001b[0m | \u001b[0m 9.53    \u001b[0m | \u001b[0m 9.533e-0\u001b[0m | \u001b[0m 2.684   \u001b[0m |\n",
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      "| \u001b[0m 459     \u001b[0m | \u001b[0m-21.24   \u001b[0m | \u001b[0m 9.713   \u001b[0m | \u001b[0m 1.971e-0\u001b[0m | \u001b[0m 2.202   \u001b[0m |\n",
      "| \u001b[0m 460     \u001b[0m | \u001b[0m-20.62   \u001b[0m | \u001b[0m 1.344   \u001b[0m | \u001b[0m 0.000717\u001b[0m | \u001b[0m 0.02926 \u001b[0m |\n",
      "| \u001b[0m 461     \u001b[0m | \u001b[0m-17.47   \u001b[0m | \u001b[0m 9.827   \u001b[0m | \u001b[0m 0.000245\u001b[0m | \u001b[0m 2.134   \u001b[0m |\n",
      "| \u001b[0m 462     \u001b[0m | \u001b[0m-22.71   \u001b[0m | \u001b[0m 7.221   \u001b[0m | \u001b[0m 8.544e-0\u001b[0m | \u001b[0m 2.379   \u001b[0m |\n",
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      "| \u001b[0m 464     \u001b[0m | \u001b[0m-17.14   \u001b[0m | \u001b[0m 8.914   \u001b[0m | \u001b[0m 0.000948\u001b[0m | \u001b[0m 2.149   \u001b[0m |\n",
      "| \u001b[0m 465     \u001b[0m | \u001b[0m-17.8    \u001b[0m | \u001b[0m 9.741   \u001b[0m | \u001b[0m 0.000972\u001b[0m | \u001b[0m 2.079   \u001b[0m |\n",
      "| \u001b[0m 466     \u001b[0m | \u001b[0m-18.42   \u001b[0m | \u001b[0m 4.774   \u001b[0m | \u001b[0m 0.000933\u001b[0m | \u001b[0m 2.729   \u001b[0m |\n",
      "| \u001b[0m 467     \u001b[0m | \u001b[0m-25.26   \u001b[0m | \u001b[0m 7.116   \u001b[0m | \u001b[0m 0.000937\u001b[0m | \u001b[0m 0.6008  \u001b[0m |\n",
      "| \u001b[0m 468     \u001b[0m | \u001b[0m-20.11   \u001b[0m | \u001b[0m 6.588   \u001b[0m | \u001b[0m 0.000108\u001b[0m | \u001b[0m 1.857   \u001b[0m |\n",
      "| \u001b[0m 469     \u001b[0m | \u001b[0m-19.24   \u001b[0m | \u001b[0m 8.776   \u001b[0m | \u001b[0m 5.917e-0\u001b[0m | \u001b[0m 1.839   \u001b[0m |\n",
      "| \u001b[0m 470     \u001b[0m | \u001b[0m-23.32   \u001b[0m | \u001b[0m 5.215   \u001b[0m | \u001b[0m 0.000510\u001b[0m | \u001b[0m 0.09189 \u001b[0m |\n",
      "| \u001b[0m 471     \u001b[0m | \u001b[0m-19.73   \u001b[0m | \u001b[0m 6.883   \u001b[0m | \u001b[0m 0.000972\u001b[0m | \u001b[0m 1.22    \u001b[0m |\n",
      "| \u001b[0m 472     \u001b[0m | \u001b[0m-18.27   \u001b[0m | \u001b[0m 4.23    \u001b[0m | \u001b[0m 0.000342\u001b[0m | \u001b[0m 1.076   \u001b[0m |\n",
      "| \u001b[0m 473     \u001b[0m | \u001b[0m-17.08   \u001b[0m | \u001b[0m 7.891   \u001b[0m | \u001b[0m 0.000624\u001b[0m | \u001b[0m 2.654   \u001b[0m |\n",
      "| \u001b[0m 474     \u001b[0m | \u001b[0m-18.3    \u001b[0m | \u001b[0m 4.063   \u001b[0m | \u001b[0m 0.000395\u001b[0m | \u001b[0m 1.265   \u001b[0m |\n",
      "| \u001b[0m 475     \u001b[0m | \u001b[0m-17.64   \u001b[0m | \u001b[0m 7.428   \u001b[0m | \u001b[0m 0.000800\u001b[0m | \u001b[0m 1.627   \u001b[0m |\n",
      "| \u001b[0m 476     \u001b[0m | \u001b[0m-17.06   \u001b[0m | \u001b[0m 9.776   \u001b[0m | \u001b[0m 0.000476\u001b[0m | \u001b[0m 1.999   \u001b[0m |\n",
      "| \u001b[0m 477     \u001b[0m | \u001b[0m-18.03   \u001b[0m | \u001b[0m 9.25    \u001b[0m | \u001b[0m 0.000171\u001b[0m | \u001b[0m 1.618   \u001b[0m |\n",
      "| \u001b[0m 478     \u001b[0m | \u001b[0m-31.42   \u001b[0m | \u001b[0m 8.881   \u001b[0m | \u001b[0m 0.000824\u001b[0m | \u001b[0m 0.3353  \u001b[0m |\n",
      "| \u001b[0m 479     \u001b[0m | \u001b[0m-18.28   \u001b[0m | \u001b[0m 8.506   \u001b[0m | \u001b[0m 0.000236\u001b[0m | \u001b[0m 2.268   \u001b[0m |\n",
      "| \u001b[0m 480     \u001b[0m | \u001b[0m-19.11   \u001b[0m | \u001b[0m 6.262   \u001b[0m | \u001b[0m 7.895e-0\u001b[0m | \u001b[0m 1.137   \u001b[0m |\n",
      "| \u001b[0m 481     \u001b[0m | \u001b[0m-21.4    \u001b[0m | \u001b[0m 6.105   \u001b[0m | \u001b[0m 0.000239\u001b[0m | \u001b[0m 0.5866  \u001b[0m |\n",
      "| \u001b[0m 482     \u001b[0m | \u001b[0m-20.58   \u001b[0m | \u001b[0m 9.735   \u001b[0m | \u001b[0m 0.00018 \u001b[0m | \u001b[0m 2.96    \u001b[0m |\n",
      "| \u001b[0m 483     \u001b[0m | \u001b[0m-17.43   \u001b[0m | \u001b[0m 6.924   \u001b[0m | \u001b[0m 0.000410\u001b[0m | \u001b[0m 2.034   \u001b[0m |\n",
      "| \u001b[0m 484     \u001b[0m | \u001b[0m-34.38   \u001b[0m | \u001b[0m 0.4126  \u001b[0m | \u001b[0m 0.000794\u001b[0m | \u001b[0m 0.4637  \u001b[0m |\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| \u001b[0m 485     \u001b[0m | \u001b[0m-17.15   \u001b[0m | \u001b[0m 7.163   \u001b[0m | \u001b[0m 0.000677\u001b[0m | \u001b[0m 2.602   \u001b[0m |\n",
      "| \u001b[0m 486     \u001b[0m | \u001b[0m-19.88   \u001b[0m | \u001b[0m 7.786   \u001b[0m | \u001b[0m 0.000229\u001b[0m | \u001b[0m 2.546   \u001b[0m |\n",
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      "| \u001b[0m 489     \u001b[0m | \u001b[0m-16.85   \u001b[0m | \u001b[0m 6.271   \u001b[0m | \u001b[0m 0.000931\u001b[0m | \u001b[0m 1.794   \u001b[0m |\n",
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      "| \u001b[0m 492     \u001b[0m | \u001b[0m-17.43   \u001b[0m | \u001b[0m 9.737   \u001b[0m | \u001b[0m 0.000838\u001b[0m | \u001b[0m 2.079   \u001b[0m |\n",
      "| \u001b[0m 493     \u001b[0m | \u001b[0m-17.25   \u001b[0m | \u001b[0m 4.58    \u001b[0m | \u001b[0m 0.000666\u001b[0m | \u001b[0m 1.557   \u001b[0m |\n",
      "| \u001b[0m 494     \u001b[0m | \u001b[0m-19.52   \u001b[0m | \u001b[0m 4.791   \u001b[0m | \u001b[0m 0.000282\u001b[0m | \u001b[0m 1.727   \u001b[0m |\n",
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      "| \u001b[0m 500     \u001b[0m | \u001b[0m-18.23   \u001b[0m | \u001b[0m 5.672   \u001b[0m | \u001b[0m 0.000227\u001b[0m | \u001b[0m 1.324   \u001b[0m |\n",
      "| \u001b[0m 501     \u001b[0m | \u001b[0m-19.54   \u001b[0m | \u001b[0m 9.727   \u001b[0m | \u001b[0m 9.399e-0\u001b[0m | \u001b[0m 2.286   \u001b[0m |\n",
      "| \u001b[0m 502     \u001b[0m | \u001b[0m-18.28   \u001b[0m | \u001b[0m 4.229   \u001b[0m | \u001b[0m 0.000339\u001b[0m | \u001b[0m 1.075   \u001b[0m |\n",
      "| \u001b[0m 503     \u001b[0m | \u001b[0m-19.4    \u001b[0m | \u001b[0m 5.739   \u001b[0m | \u001b[0m 6.39e-05\u001b[0m | \u001b[0m 1.015   \u001b[0m |\n",
      "| \u001b[0m 504     \u001b[0m | \u001b[0m-23.58   \u001b[0m | \u001b[0m 8.118   \u001b[0m | \u001b[0m 7.275e-0\u001b[0m | \u001b[0m 2.658   \u001b[0m |\n",
      "| \u001b[0m 505     \u001b[0m | \u001b[0m-16.82   \u001b[0m | \u001b[0m 9.71    \u001b[0m | \u001b[0m 0.000630\u001b[0m | \u001b[0m 2.201   \u001b[0m |\n",
      "| \u001b[0m 506     \u001b[0m | \u001b[0m-16.82   \u001b[0m | \u001b[0m 9.709   \u001b[0m | \u001b[0m 0.000454\u001b[0m | \u001b[0m 2.26    \u001b[0m |\n",
      "| \u001b[0m 507     \u001b[0m | \u001b[0m-21.55   \u001b[0m | \u001b[0m 9.702   \u001b[0m | \u001b[0m 2.113e-0\u001b[0m | \u001b[0m 2.267   \u001b[0m |\n",
      "| \u001b[0m 508     \u001b[0m | \u001b[0m-17.1    \u001b[0m | \u001b[0m 6.147   \u001b[0m | \u001b[0m 0.000912\u001b[0m | \u001b[0m 2.667   \u001b[0m |\n",
      "| \u001b[0m 509     \u001b[0m | \u001b[0m-16.92   \u001b[0m | \u001b[0m 6.596   \u001b[0m | \u001b[0m 0.000925\u001b[0m | \u001b[0m 1.808   \u001b[0m |\n",
      "| \u001b[0m 510     \u001b[0m | \u001b[0m-16.84   \u001b[0m | \u001b[0m 6.632   \u001b[0m | \u001b[0m 0.000803\u001b[0m | \u001b[0m 1.809   \u001b[0m |\n",
      "=============================================================\n"
     ]
    }
   ],
   "source": [
    "from bayes_opt import BayesianOptimization\n",
    "from haven import haven_utils as hu\n",
    "\n",
    "pbounds = {'compactness':(0,0.001), 'alpha':(0,10), 'sigma':(0,3)}\n",
    "\n",
    "optimizer = BayesianOptimization(\n",
    "    f=black_box_function,\n",
    "    pbounds=pbounds,\n",
    "    verbose=2, # verbose = 1 prints only when a maximum is observed, verbose = 0 is silent\n",
    "    random_state=1,\n",
    ")\n",
    "\n",
    "\n",
    "optimizer.maximize(\n",
    "    init_points=10,\n",
    "    n_iter=500,\n",
    ")\n",
    "\n",
    "hu.save_json('objectness_params_0.json', optimizer.max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 51.2763466 , 855.21712538, 277.10416667, 423.08      ,\n",
       "       968.0037037 , 878.95522388, 122.38076923, 935.8203125 ,\n",
       "       180.66942149, 407.73333333, 255.7605042 , 952.81465517,\n",
       "       296.22510823, 170.6826087 , 270.64628821, 131.46052632,\n",
       "       785.26431718, 334.27753304, 231.30136986, 921.2718894 ,\n",
       "        53.65277778, 916.92957746, 880.54976303, 128.03827751,\n",
       "       299.37320574, 679.96634615, 762.43478261, 733.66990291,\n",
       "       618.26108374,  65.9800995 , 733.2039801 , 744.48      ,\n",
       "       300.9       , 939.96373057, 561.42105263, 262.99468085,\n",
       "       315.9027027 , 102.96216216, 732.04347826,  35.96174863,\n",
       "       156.02747253, 326.97802198, 982.8258427 , 212.9039548 ,\n",
       "       211.31428571, 495.79651163,  16.19883041, 705.42941176,\n",
       "       291.07647059,  91.41176471, 352.91566265, 244.42771084,\n",
       "       479.96875   , 153.03125   , 134.43037975, 898.76433121,\n",
       "       864.37748344,  32.52027027, 179.18243243, 231.35416667,\n",
       "       862.88888889, 705.95      , 373.84558824,   4.32592593,\n",
       "       175.85496183, 445.796875  , 168.30952381,  78.49565217,\n",
       "       352.61904762, 308.5       , 996.625     ])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "point_centroids[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/envs/TomProject/lib/python3.6/site-packages/ipykernel_launcher.py:42: UserWarning: ./CoNSeP/Train/Regions/train_21.tif is a low contrast image\n",
      "/home/ubuntu/anaconda3/envs/TomProject/lib/python3.6/site-packages/ipykernel_launcher.py:41: UserWarning: ./CoNSeP/Train/GTs/train_5.tif is a low contrast image\n",
      "/home/ubuntu/anaconda3/envs/TomProject/lib/python3.6/site-packages/ipykernel_launcher.py:42: UserWarning: ./CoNSeP/Train/Regions/train_5.tif is a low contrast image\n",
      "/home/ubuntu/anaconda3/envs/TomProject/lib/python3.6/site-packages/ipykernel_launcher.py:41: UserWarning: ./CoNSeP/Train/GTs/train_25.tif is a low contrast image\n",
      "/home/ubuntu/anaconda3/envs/TomProject/lib/python3.6/site-packages/ipykernel_launcher.py:42: UserWarning: ./CoNSeP/Train/Regions/train_25.tif is a low contrast image\n"
     ]
    }
   ],
   "source": [
    "import skimage.io as skio\n",
    "import scipy.io as siio\n",
    "from haven import haven_utils as hu\n",
    "import numpy as np\n",
    "import glob,os\n",
    "\n",
    "option = 'Train'\n",
    "\n",
    "obj_params = hu.load_json('objectness_params_0.json')\n",
    "\n",
    "train_obj_dir = os.path.join(save_data_dir, option, 'Objs')\n",
    "train_bkg_dir = os.path.join(save_data_dir, option, 'Bkgs')\n",
    "train_pts_dir = os.path.join(save_data_dir, option, 'Pts')\n",
    "train_gts_dir = os.path.join(save_data_dir, option, 'GTs')\n",
    "train_rgs_dir = os.path.join(save_data_dir, option, 'Regions')\n",
    "os.makedirs(train_obj_dir, exist_ok = True)\n",
    "os.makedirs(train_bkg_dir, exist_ok = True)\n",
    "os.makedirs(train_pts_dir, exist_ok = True)\n",
    "os.makedirs(train_gts_dir, exist_ok = True)\n",
    "os.makedirs(train_rgs_dir, exist_ok = True)\n",
    "for im_file_path in glob.glob(os.path.join(save_data_dir, option, 'Norms', option.lower()+'_*.png')):\n",
    "    points_in_type = dict()\n",
    "    im = skio.imread(im_file_path)\n",
    "    file_name = os.path.split(im_file_path)[-1][:-4]\n",
    "    labels = siio.loadmat(os.path.join(data_dir, option, 'Labels', file_name+'.mat'))\n",
    "    points = labels['inst_centroid']\n",
    "    points_type = labels['inst_type']\n",
    "    for i in np.arange(1,8):\n",
    "        points_in_type[str(i)] = points[np.where(points_type==i)[0],:].tolist()\n",
    "    mask = labels['type_map']\n",
    "    obj, bkg, reg_split = generate_obj(im, points, obj_params)\n",
    "    type_region_label = reg_split.copy()\n",
    "    for point_type, point_centroids in points_in_type.items():\n",
    "        if not len(point_centroids)==0:\n",
    "            point_centroids = np.array(point_centroids,dtype = int)\n",
    "            region_indexes = reg_split[point_centroids[:,1],point_centroids[:,0]]\n",
    "            type_region_label[np.isin(reg_split, region_indexes)] = int(point_type)\n",
    "    obj[bkg] = 0\n",
    "    type_region_label[bkg] = 0\n",
    "    skio.imsave(os.path.join(train_obj_dir, file_name+'.tif'), obj)\n",
    "    skio.imsave(os.path.join(train_bkg_dir, file_name+'.tif'), bkg.astype(float))\n",
    "    skio.imsave(os.path.join(train_gts_dir, file_name+'.tif'), mask.astype(float))\n",
    "    skio.imsave(os.path.join(train_rgs_dir, file_name+'.tif'), type_region_label.astype(float))\n",
    "    hu.save_json(os.path.join(train_pts_dir, file_name+'.json'), points_in_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f214c0615f8>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(1,2,figsize=(20,10))\n",
    "axes[0].imshow(skio.imread('./CoNSeP/Train/GTs/train_5.tif'),vmax = 7, vmin = 0)\n",
    "axes[1].imshow(skio.imread('./CoNSeP/Train/Regions/train_5.tif'),vmax = 7, vmin = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes = []\n",
    "for labels_file_path in glob.glob(os.path.join(data_dir, 'Train', 'Labels', '*.mat')):\n",
    "    labels = siio.loadmat(labels_file_path)\n",
    "    classes += np.unique(labels[\"inst_type\"]).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_mask = skio.imread(os.path.join(train_obj_dir, 'train_5.tif'))\n",
    "test_im = skio.imread(os.path.join(data_dir, 'Train', 'Images', 'train_5.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f047883be48>"
      ]
     },
     "execution_count": 15,
     "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": [
    "plt.imshow(test_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = 'test'\n",
    "test is 'test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "moving ../hover_net/CoNSeP/Test/Overlay/test_1.png to ../hover_net/CoNSeP/Validation/Overlay/validation_1.png\n",
      "moving ../hover_net/CoNSeP/Test/Overlay/test_2.png to ../hover_net/CoNSeP/Validation/Overlay/validation_2.png\n",
      "moving ../hover_net/CoNSeP/Test/Overlay/test_3.png to ../hover_net/CoNSeP/Validation/Overlay/validation_3.png\n",
      "moving ../hover_net/CoNSeP/Test/Overlay/test_4.png to ../hover_net/CoNSeP/Validation/Overlay/validation_4.png\n",
      "moving ../hover_net/CoNSeP/Test/Overlay/test_5.png to ../hover_net/CoNSeP/Validation/Overlay/validation_5.png\n",
      "moving ../hover_net/CoNSeP/Test/Overlay/test_6.png to ../hover_net/CoNSeP/Validation/Overlay/validation_6.png\n",
      "moving ../hover_net/CoNSeP/Test/Overlay/test_7.png to ../hover_net/CoNSeP/Validation/Overlay/validation_7.png\n"
     ]
    }
   ],
   "source": [
    "import os,glob\n",
    "import shutil\n",
    "\n",
    "val_dir = '../hover_net/CoNSeP/Validation'\n",
    "tst_dir = '../hover_net/CoNSeP/Test'\n",
    "os.makedirs(val_dir, exist_ok = True)\n",
    "\n",
    "object_types = [os.path.split(path)[-1] for path in glob.glob('../hover_net/CoNSeP/Test/*')]\n",
    "\n",
    "for i in range(7):\n",
    "    for object_ in object_types:\n",
    "        if object_ in 'Labels':\n",
    "            suffix = 'mat'\n",
    "        else:\n",
    "            suffix = 'png'\n",
    "            \n",
    "        os.makedirs(os.path.join(val_dir, object_), exist_ok = True)\n",
    "        src_path = os.path.join(tst_dir, object_, 'test_{0}.{1}'.format(i+1, suffix))\n",
    "        if os.path.exists(src_path):\n",
    "            tgt_path = os.path.join(val_dir, object_, 'validation_{0}.{1}'.format(i+1, suffix))\n",
    "            print('moving {0} to {1}'.format(src_path,tgt_path))\n",
    "            shutil.move(src_path, tgt_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../hover_net/CoNSeP/Test/Pts/test_7.json'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "src_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "moving ../hover_net/CoNSeP/Test/Labels/test_8.mat to ../hover_net/CoNSeP/Test/Labels/test_1.mat\n",
      "moving ../hover_net/CoNSeP/Test/Overlay/test_8.png to ../hover_net/CoNSeP/Test/Overlay/test_1.png\n",
      "moving ../hover_net/CoNSeP/Test/Images/test_8.png to ../hover_net/CoNSeP/Test/Images/test_1.png\n",
      "moving ../hover_net/CoNSeP/Test/Labels/test_9.mat to ../hover_net/CoNSeP/Test/Labels/test_2.mat\n",
      "moving ../hover_net/CoNSeP/Test/Overlay/test_9.png to ../hover_net/CoNSeP/Test/Overlay/test_2.png\n",
      "moving ../hover_net/CoNSeP/Test/Images/test_9.png to ../hover_net/CoNSeP/Test/Images/test_2.png\n",
      "moving ../hover_net/CoNSeP/Test/Labels/test_10.mat to ../hover_net/CoNSeP/Test/Labels/test_3.mat\n",
      "moving ../hover_net/CoNSeP/Test/Overlay/test_10.png to ../hover_net/CoNSeP/Test/Overlay/test_3.png\n",
      "moving ../hover_net/CoNSeP/Test/Images/test_10.png to ../hover_net/CoNSeP/Test/Images/test_3.png\n",
      "moving ../hover_net/CoNSeP/Test/Labels/test_11.mat to ../hover_net/CoNSeP/Test/Labels/test_4.mat\n",
      "moving ../hover_net/CoNSeP/Test/Overlay/test_11.png to ../hover_net/CoNSeP/Test/Overlay/test_4.png\n",
      "moving ../hover_net/CoNSeP/Test/Images/test_11.png to ../hover_net/CoNSeP/Test/Images/test_4.png\n",
      "moving ../hover_net/CoNSeP/Test/Labels/test_12.mat to ../hover_net/CoNSeP/Test/Labels/test_5.mat\n",
      "moving ../hover_net/CoNSeP/Test/Overlay/test_12.png to ../hover_net/CoNSeP/Test/Overlay/test_5.png\n",
      "moving ../hover_net/CoNSeP/Test/Images/test_12.png to ../hover_net/CoNSeP/Test/Images/test_5.png\n",
      "moving ../hover_net/CoNSeP/Test/Labels/test_13.mat to ../hover_net/CoNSeP/Test/Labels/test_6.mat\n",
      "moving ../hover_net/CoNSeP/Test/Overlay/test_13.png to ../hover_net/CoNSeP/Test/Overlay/test_6.png\n",
      "moving ../hover_net/CoNSeP/Test/Images/test_13.png to ../hover_net/CoNSeP/Test/Images/test_6.png\n",
      "moving ../hover_net/CoNSeP/Test/Labels/test_14.mat to ../hover_net/CoNSeP/Test/Labels/test_7.mat\n",
      "moving ../hover_net/CoNSeP/Test/Overlay/test_14.png to ../hover_net/CoNSeP/Test/Overlay/test_7.png\n",
      "moving ../hover_net/CoNSeP/Test/Images/test_14.png to ../hover_net/CoNSeP/Test/Images/test_7.png\n"
     ]
    }
   ],
   "source": [
    "import os,glob\n",
    "import shutil\n",
    "\n",
    "val_dir = '../hover_net/CoNSeP/Validation'\n",
    "tst_dir = '../hover_net/CoNSeP/Test'\n",
    "os.makedirs(val_dir, exist_ok = True)\n",
    "\n",
    "object_types = [os.path.split(path)[-1] for path in glob.glob('../hover_net/CoNSeP/Test/*')]\n",
    "\n",
    "for i in range(7):\n",
    "    for object_ in object_types:\n",
    "        if object_ in 'Labels':\n",
    "            suffix = 'mat'\n",
    "        else:\n",
    "            suffix = 'png'\n",
    "            \n",
    "        os.makedirs(os.path.join(val_dir, object_), exist_ok = True)\n",
    "        src_path = os.path.join(tst_dir, object_, 'test_{0}.{1}'.format(i+8, suffix))\n",
    "        if os.path.exists(src_path):\n",
    "            tgt_path = os.path.join(tst_dir, object_, 'test_{0}.{1}'.format(i+1, suffix))\n",
    "            print('moving {0} to {1}'.format(src_path,tgt_path))\n",
    "            shutil.move(src_path, tgt_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "test = torch.rand(4,3,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.nn import functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_bool = test>0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[False, False, False, False, False],\n",
       "         [ True, False, False, False,  True],\n",
       "         [False,  True, False,  True, False]],\n",
       "\n",
       "        [[False, False, False,  True,  True],\n",
       "         [ True, False,  True, False, False],\n",
       "         [ True, False,  True,  True, False]],\n",
       "\n",
       "        [[ True,  True, False, False,  True],\n",
       "         [False, False, False, False,  True],\n",
       "         [False, False, False,  True,  True]],\n",
       "\n",
       "        [[False, False, False, False,  True],\n",
       "         [ True, False, False,  True,  True],\n",
       "         [ True, False, False, False,  True]]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "~test_bool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io as siio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "types = []\n",
    "for mat_file in glob.glob('../hover_net/CoNSeP/**/Labels/*.mat'):\n",
    "    test = siio.loadmat(mat_file)\n",
    "    types.append(np.unique(test['type_map']).tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['__header__', '__version__', '__globals__', 'inst_map', 'type_map', 'inst_type', 'inst_centroid'])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "types_ = []\n",
    "for type_ in types:\n",
    "    types_+= type_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " [0.0, 1.0, 2.0, 4.0, 5.0],\n",
       " [0.0, 2.0, 3.0, 4.0, 5.0, 7.0],\n",
       " [0.0, 1.0, 2.0, 4.0, 5.0],\n",
       " [0.0, 2.0, 3.0, 5.0],\n",
       " [0.0, 1.0, 2.0, 5.0, 7.0],\n",
       " [0.0, 2.0, 5.0, 6.0],\n",
       " [0.0, 1.0, 2.0, 4.0, 5.0],\n",
       " [0.0, 1.0, 2.0, 4.0, 5.0]]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "types"
   ]
  }
 ],
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