Build_Dataset_KO.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "725c9c6d",
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# Install conda on your Colab environment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ignore this first cell if you are running the notebook in a local environment.\n",
    "\n",
    "One can still run it locally but it will have no effect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 40951,
     "status": "ok",
     "timestamp": 1664526150927,
     "user": {
      "displayName": "Leon Faure",
      "userId": "06483081905364613855"
     },
     "user_tz": -120
    },
    "id": "c4f08880",
    "outputId": "eaae29a9-4739-4b0f-a2a7-56bfa89f0bf0",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Run this cell first - it will install a conda distribution (mamba)\n",
    "# on your Drive then restart the kernel automatically \n",
    "# (don't worry about the crashing/restarting kernel messages)\n",
    "# It HAS to be runned FIRST everytime you use the notebook in colab\n",
    "\n",
    "import os\n",
    "import sys\n",
    "RunningInCOLAB  = 'google.colab' in str(get_ipython())\n",
    "\n",
    "if RunningInCOLAB:\n",
    "    !pip install -q condacolab\n",
    "    import condacolab\n",
    "    condacolab.install()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# Set up your Colab or local environment\n",
    "# Then import libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run this cell in both cases of use (local or Colab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 117260,
     "status": "ok",
     "timestamp": 1664526767265,
     "user": {
      "displayName": "Leon Faure",
      "userId": "06483081905364613855"
     },
     "user_tz": -120
    },
    "id": "BYwheAEcr-ME",
    "outputId": "8ba41a54-6751-4c00-ed1a-938db78cafb6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['README.md', 'Duplicate_Model.ipynb', 'Dataset_experimental', 'Tutorial.ipynb', '.ipynb_checkpoints', '.git', 'Build_Model_RC.ipynb', 'Build_Model_AMN_KO.ipynb', 'biolog_simulations.npy', 'environment_amn_light.yml', 'Build_Experimental.ipynb', 'Reservoir', 'Build_Model_MM.ipynb', 'Dataset_model', 'Figures.ipynb', 'Result', 'Figures', '.gitignore', 'LICENSE', 'Build_Model_ANN_Dense.ipynb', 'Build_Dataset.ipynb', 'Library', 'Dataset_input', 'Functions', 'Build_Dataset_KO.ipynb', 'environment_amn.yml', 'Build_Model_AMN.ipynb', '.DS_Store']\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "RunningInCOLAB  = 'google.colab' in str(get_ipython())\n",
    "\n",
    "if RunningInCOLAB:\n",
    "    \n",
    "    # Check everything is fine with conda in Colab\n",
    "    import condacolab\n",
    "    condacolab.check()\n",
    "    \n",
    "    # Mount your drive environment in the colab runtime\n",
    "    from google.colab import drive\n",
    "    drive.mount('/content/drive',force_remount=True)\n",
    "    \n",
    "    # Change this variable to your path on Google Drive to which the repo has been cloned\n",
    "    # If you followed the colab notebook 'repo_cloning.ipynb', nothing to change here\n",
    "    repo_path_in_drive = '/content/drive/My Drive/Github/amn_release/'\n",
    "    # Change directory to your repo cloned in your drive\n",
    "    DIRECTORY = repo_path_in_drive\n",
    "    os.chdir(repo_path_in_drive)\n",
    "    # Copy the environment given in the environment_amn_light.yml\n",
    "    !mamba env update -n base -f environment_amn_light.yml\n",
    "    \n",
    "    # This is one of the few Colab-compatible font\n",
    "    font = 'Liberation Sans'\n",
    "    \n",
    "else:\n",
    "    \n",
    "    # In this case the local root of the repo is our working directory\n",
    "    DIRECTORY = './'\n",
    "    font = 'arial'\n",
    "\n",
    "# printing the working directory files. One can check you see the same folders and files as in the git webpage.\n",
    "print(os.listdir(DIRECTORY))\n",
    "\n",
    "from Library.Build_Dataset_KO import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KeI_mWAbs7gx"
   },
   "source": [
    "# Generate Training Sets with FBA simulation or experimental data file\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zIkQy4a1SNzx"
   },
   "source": [
    "Below are provided several examples to generate training sets using different metabolic models, or experimental datafiles.\n",
    "\n",
    "We also provide a way to run cobrapy with provided inputs, such as the reservoir computing predictions (see figure 5 of the research paper)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Examples of experimental or manual training set generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This cell provides a way to generate a training set with the same object (parameter) as simulated training sets using Cobra, but only using the experimental data. In this cell, we do not run cobra to generate the training set but instead directly use the experimental data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving...\n",
      "Re-loading...\n",
      "model file name: ./Dataset_model/biolog_iML1515_EXP_UB\n",
      "reduced model: False\n",
      "medium file name: ./Dataset_input/biolog_iML1515_EXP\n",
      "medium bound: UB\n",
      "list of reactions in objective: ['BIOMASS_Ec_iML1515_core_75p37M']\n",
      "method: EXP\n",
      "trainingsize: 17400\n",
      "list of medium reactions: 430\n",
      "list of medium levels: 0\n",
      "list of medium values: 0\n",
      "ratio of variable medium turned on: 0\n",
      "list of measured reactions: 3682\n",
      "Stoichiometric matrix (1877, 3682)\n",
      "Boundary matrix from reactions to medium: (430, 3682)\n",
      "Measurement matrix from reaction to measures: (3682, 3682)\n",
      "Reaction to metabolite matrix: (1877, 3682)\n",
      "Metabolite to reaction matrix: (3682, 1877)\n",
      "Training set X: (17400, 430)\n",
      "Training set Y: (17400, 1)\n",
      "list of genes targeted: 0\n",
      "Training set X for genes: (1, 1)\n",
      "S_int matrix ()\n",
      "S_ext matrix ()\n",
      "Q matrix ()\n",
      "P matrix ()\n",
      "b_int vector ()\n",
      "b_ext vector ()\n",
      "Sb matrix ()\n",
      "c vector ()\n"
     ]
    }
   ],
   "source": [
    "# Generate training set from experimental file (E. coli biolog with gene KOs, ASAP database) for iML1515\n",
    "\n",
    "# What you can change\n",
    "seed = 10\n",
    "np.random.seed(seed=seed)  # seed for random number generator\n",
    "cobraname = 'iML1515_duplicated'  # name of the model\n",
    "mediumbound = 'UB' # a must exact bounds unknown\n",
    "mediumname = 'biolog_iML1515_EXP' # name of experimental file\n",
    "method = 'EXP' # FBA, pFBA or EXP\n",
    "reduce = False # Set at True if you want to reduce the model\n",
    "# End of What you can change\n",
    "\n",
    "# Get data\n",
    "cobrafile = DIRECTORY+'Dataset_input/'+cobraname\n",
    "mediumfile  = DIRECTORY+'Dataset_input/'+mediumname\n",
    "parameter = TrainingSet(cobraname=cobrafile, \n",
    "                        mediumname=mediumfile, mediumbound=mediumbound, mediumsize=430, \n",
    "                        method=method,verbose=False)\n",
    "\n",
    "# Saving file\n",
    "trainingfile  = DIRECTORY+'Dataset_model/'+mediumname+'_'+parameter.mediumbound\n",
    "print(\"Saving...\")\n",
    "parameter.save(trainingfile, no_matrices_LP=True, reduce=reduce)\n",
    "# With this dataset, the generation of LP matrices is prohibitive.\n",
    "# Due to high number of possible exchange reactions, the row_echelon as it is encoded cannot work.\n",
    "\n",
    "# Verifying\n",
    "parameter = TrainingSet()\n",
    "parameter.load(trainingfile)\n",
    "print(\"Re-loading...\")\n",
    "parameter.printout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Running Cobra on a provided dataset set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This cell has a completely different purpose than the rest of the notebook. It serves as a cell running Cobrapy with  provided values as inputs. These inputs are extracted from Reservoir Computing, you can see an example in the notebook `Build_Model_RC.ipynb`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-loading...\n",
      "model file name: ./Dataset_model/biolog_iML1515_EXP_UB\n",
      "reduced model: False\n",
      "medium file name: ./Dataset_input/biolog_iML1515_EXP\n",
      "medium bound: UB\n",
      "list of reactions in objective: ['BIOMASS_Ec_iML1515_core_75p37M']\n",
      "method: EXP\n",
      "trainingsize: 17400\n",
      "list of medium reactions: 430\n",
      "list of medium levels: 0\n",
      "list of medium values: 0\n",
      "ratio of variable medium turned on: 0\n",
      "list of measured reactions: 3682\n",
      "Stoichiometric matrix (1877, 3682)\n",
      "Boundary matrix from reactions to medium: (430, 3682)\n",
      "Measurement matrix from reaction to measures: (3682, 3682)\n",
      "Reaction to metabolite matrix: (1877, 3682)\n",
      "Metabolite to reaction matrix: (3682, 1877)\n",
      "Training set X: (17400, 430)\n",
      "Training set Y: (17400, 1)\n",
      "list of genes targeted: 0\n",
      "Training set X for genes: (1, 1)\n",
      "S_int matrix ()\n",
      "S_ext matrix ()\n",
      "Q matrix ()\n",
      "P matrix ()\n",
      "b_int vector ()\n",
      "b_ext vector ()\n",
      "Sb matrix ()\n",
      "c vector ()\n",
      "0 0.7800 0.0000\n",
      "1 1.4300 0.0000\n",
      "2 1.0400 0.0000\n",
      "3 1.0900 0.0000\n",
      "4 0.9100 0.0000\n",
      "5 0.1200 0.0000\n",
      "6 0.8900 0.0000\n",
      "7 0.9900 0.0000\n",
      "8 1.7900 0.0000\n",
      "9 0.2300 0.0000\n",
      "10 1.4200 0.0000\n",
      "11 0.7100 0.0000\n",
      "12 1.5300 0.0000\n",
      "13 0.2800 0.0000\n",
      "14 1.3300 0.0000\n",
      "15 1.2200 0.0000\n",
      "16 1.3100 0.0000\n",
      "17 1.6000 0.0000\n",
      "18 1.4000 0.0000\n",
      "19 1.3300 0.0000\n",
      "20 1.3900 0.0000\n",
      "21 1.4500 0.0000\n",
      "22 0.1200 0.0000\n",
      "23 0.9800 0.0000\n",
      "24 0.1100 0.0000\n",
      "25 1.3400 0.0000\n",
      "26 0.1200 0.0000\n",
      "27 0.7800 0.0000\n",
      "28 1.5500 0.0000\n",
      "29 1.3300 0.0000\n",
      "30 1.1100 0.0000\n",
      "31 1.6800 0.0000\n",
      "32 1.2000 0.0000\n",
      "33 1.1600 0.0000\n",
      "34 0.8800 0.0000\n",
      "35 0.8800 0.0000\n",
      "36 1.2300 0.0000\n",
      "37 1.4300 0.0000\n",
      "38 0.6300 0.0000\n",
      "39 1.4200 0.0000\n",
      "40 1.8500 0.0000\n",
      "41 1.5700 0.0000\n",
      "42 1.4000 0.0000\n",
      "43 1.1800 0.0000\n",
      "44 0.9200 0.0000\n",
      "45 1.1100 0.0000\n",
      "46 0.6100 0.0000\n",
      "47 0.9600 0.0000\n",
      "48 1.6000 0.0000\n",
      "49 1.2900 0.0000\n",
      "50 0.3400 0.0000\n",
      "51 1.0800 0.0000\n",
      "52 0.8900 0.0000\n",
      "53 0.1400 0.0000\n",
      "54 1.6200 0.0000\n",
      "55 1.3900 0.0000\n",
      "56 0.5600 0.0000\n",
      "57 1.7700 0.0000\n",
      "58 0.1900 0.0000\n",
      "59 1.8500 0.0000\n",
      "60 1.1500 0.0000\n",
      "61 1.0800 0.0000\n",
      "62 1.2300 0.0000\n",
      "63 0.9200 0.0000\n",
      "64 1.0000 0.0000\n",
      "65 2.0300 0.0000\n",
      "66 1.8000 0.0000\n",
      "67 0.1100 0.0000\n",
      "68 1.9400 0.0000\n",
      "69 1.8500 0.0000\n",
      "70 1.6600 0.0000\n",
      "71 1.0300 0.0000\n",
      "72 1.6600 0.0000\n",
      "73 1.6900 0.0000\n",
      "74 0.1500 0.0000\n",
      "75 1.0400 0.0000\n",
      "76 2.1600 0.0000\n",
      "77 0.1000 0.0000\n",
      "78 0.1100 0.0000\n",
      "79 1.7000 0.0000\n",
      "80 1.5600 0.0000\n",
      "81 0.1100 0.0000\n",
      "82 1.8800 0.0000\n",
      "83 1.7500 0.0000\n",
      "84 0.1200 0.0000\n",
      "85 0.1400 0.0000\n",
      "86 0.1100 0.0000\n",
      "87 0.9300 0.0000\n",
      "88 0.8400 0.0000\n",
      "89 0.9600 0.0000\n",
      "90 0.8900 0.0000\n",
      "91 1.4600 0.0000\n",
      "92 0.1200 0.0000\n",
      "93 0.3100 0.0000\n",
      "94 1.4400 0.0000\n",
      "95 1.5100 0.0000\n",
      "96 1.4200 0.0000\n",
      "97 1.5800 0.0000\n",
      "98 1.5800 0.0000\n",
      "99 1.8500 0.0000\n",
      "100 1.2700 0.0000\n",
      "101 1.1500 0.0000\n",
      "102 1.5700 0.0000\n",
      "103 1.5900 0.0000\n",
      "104 1.3100 0.0000\n",
      "105 1.3100 0.0000\n",
      "106 1.7800 0.0000\n",
      "107 0.1200 0.0000\n",
      "108 1.1900 0.0000\n",
      "109 0.8800 0.0000\n",
      "110 1.6500 0.0000\n",
      "111 1.6400 0.0000\n",
      "112 0.3800 0.0000\n",
      "113 0.1100 0.0000\n",
      "114 0.1300 0.0000\n",
      "115 0.1300 0.0000\n",
      "116 1.0900 0.0000\n",
      "117 1.5900 0.0000\n",
      "118 1.7500 0.0000\n",
      "119 0.1800 0.4767\n",
      "120 0.1800 0.4767\n",
      "121 0.1600 0.4767\n",
      "122 0.2000 0.4767\n",
      "123 0.1700 0.4767\n",
      "124 0.1700 0.0003\n",
      "125 0.1900 0.0005\n",
      "126 0.1500 0.4767\n",
      "127 0.1900 0.4767\n",
      "128 0.2400 0.4649\n",
      "129 0.1800 0.4767\n",
      "130 0.1600 0.4767\n",
      "131 0.2000 0.0000\n",
      "132 0.1800 0.0000\n",
      "133 0.1800 0.4767\n",
      "134 0.2000 0.4767\n",
      "135 0.2100 0.4767\n",
      "136 0.1700 0.4766\n",
      "137 0.3200 0.4767\n",
      "138 0.1700 0.4767\n",
      "139 0.1700 0.4767\n",
      "140 0.1800 0.4767\n",
      "141 0.1700 0.4767\n",
      "142 0.2100 0.4767\n",
      "143 0.1600 0.4767\n",
      "144 0.1700 0.4767\n",
      "145 0.1600 0.4767\n",
      "146 0.1700 0.4767\n",
      "147 0.1500 0.4767\n",
      "148 0.1600 0.4767\n",
      "149 0.1600 0.4767\n",
      "150 0.1600 0.4767\n",
      "151 0.1800 0.4767\n",
      "152 0.1800 0.4767\n",
      "153 0.1400 0.4767\n",
      "154 0.1500 0.4767\n",
      "155 0.1600 0.4767\n",
      "156 0.1700 0.4767\n",
      "157 0.1400 0.4767\n",
      "158 0.1700 0.4767\n",
      "159 0.1500 0.4767\n",
      "160 0.1600 0.4767\n",
      "161 0.1300 0.4767\n",
      "162 0.1400 0.4767\n",
      "163 0.1600 0.4767\n",
      "164 0.1700 0.0743\n",
      "165 0.1600 0.4767\n",
      "166 0.1900 0.0002\n",
      "167 0.1900 0.4767\n",
      "168 0.1900 0.4767\n",
      "169 0.1200 0.0263\n",
      "170 0.1800 0.4767\n",
      "171 0.1800 0.4767\n",
      "172 0.1700 0.0002\n",
      "173 0.1800 0.4767\n",
      "174 0.1700 0.4767\n",
      "175 0.2000 0.4767\n",
      "176 0.2600 0.4767\n",
      "177 0.2000 0.0000\n",
      "178 0.2000 0.4767\n",
      "179 0.1600 0.4764\n",
      "180 0.1500 0.4767\n",
      "181 0.2100 0.4767\n",
      "182 0.1600 0.4767\n",
      "183 0.1900 0.0004\n",
      "184 0.1600 0.4767\n",
      "185 0.1900 0.4767\n",
      "186 0.1500 0.4767\n",
      "187 0.1600 0.4767\n",
      "188 0.1800 0.4767\n",
      "189 0.1500 0.4767\n",
      "190 0.1900 0.4767\n",
      "191 0.1600 0.4767\n",
      "192 0.1500 0.4767\n",
      "193 0.1500 0.0000\n",
      "194 0.2100 0.4766\n",
      "195 0.1700 0.4766\n",
      "196 0.1600 0.4767\n",
      "197 0.1400 0.4767\n",
      "198 0.1700 0.4767\n",
      "199 0.1600 0.4767\n",
      "200 0.1700 0.4767\n",
      "201 0.1600 0.4767\n",
      "202 0.1700 0.4767\n",
      "203 0.1800 0.4767\n",
      "204 0.1800 0.0007\n",
      "205 0.1600 0.0007\n",
      "206 0.1600 0.0007\n",
      "207 0.1600 0.4484\n",
      "208 0.1700 0.4484\n",
      "209 0.2000 0.4484\n",
      "210 0.1700 0.4484\n",
      "211 0.1500 0.0025\n",
      "212 0.1600 0.0011\n",
      "213 0.1900 0.0013\n",
      "214 0.1900 0.4767\n",
      "215 0.1500 0.4766\n",
      "216 0.1900 0.4766\n",
      "217 0.1500 0.0000\n",
      "218 0.1800 0.4767\n",
      "219 0.1700 0.0002\n",
      "220 0.1800 0.4767\n",
      "221 0.4300 0.4767\n",
      "222 0.1500 0.4767\n",
      "223 0.1600 0.4767\n",
      "224 0.1900 0.1409\n",
      "225 0.2000 0.4767\n",
      "226 0.1900 0.4767\n",
      "227 0.1600 0.4767\n",
      "228 0.2000 0.4767\n",
      "229 0.1900 0.4767\n",
      "230 0.2100 0.4767\n",
      "231 0.2200 0.4767\n",
      "232 0.2000 0.4767\n",
      "233 0.1700 0.4767\n",
      "234 0.2000 0.4767\n",
      "235 0.1500 0.4767\n",
      "236 0.1900 0.4767\n",
      "237 0.1500 0.4767\n",
      "238 0.1800 0.4767\n",
      "239 1.5100 1.1487\n",
      "240 1.4400 1.1487\n",
      "241 1.5800 1.1487\n",
      "242 1.3500 1.1487\n",
      "243 1.5400 1.1487\n",
      "244 0.3000 0.0003\n",
      "245 1.4900 0.0005\n",
      "246 1.5600 1.1487\n",
      "247 1.4500 1.1487\n",
      "248 1.2000 1.1309\n",
      "249 1.3500 1.1487\n",
      "250 1.3500 1.1487\n",
      "251 1.3500 0.0000\n",
      "252 1.2600 0.0000\n",
      "253 1.7700 1.1487\n",
      "254 1.4000 1.1487\n",
      "255 1.4400 1.1487\n",
      "256 1.0300 1.1486\n",
      "257 1.0200 1.1487\n",
      "258 1.6300 1.1487\n",
      "259 1.4600 1.1487\n",
      "260 1.8400 1.1487\n",
      "261 1.3100 1.1487\n",
      "262 1.3000 1.1487\n",
      "263 1.1000 1.1487\n",
      "264 1.3800 1.1487\n",
      "265 1.3400 1.1487\n",
      "266 1.3600 1.1487\n",
      "267 1.2100 1.1487\n",
      "268 1.2900 1.1487\n",
      "269 1.3400 1.1487\n",
      "270 1.3400 1.1487\n",
      "271 1.2800 1.1487\n",
      "272 1.1300 1.1487\n",
      "273 1.1000 1.1487\n",
      "274 1.0500 1.1487\n",
      "275 1.2100 1.1487\n",
      "276 1.1800 1.1487\n",
      "277 1.1100 1.1487\n",
      "278 1.1300 1.1487\n",
      "279 1.0500 1.1487\n",
      "280 1.1600 1.1487\n",
      "281 1.3500 1.1487\n",
      "282 1.2300 1.1487\n",
      "283 0.9400 1.1487\n",
      "284 1.0700 1.1487\n",
      "285 0.9700 1.1487\n",
      "286 1.2200 0.0002\n",
      "287 1.3700 1.1487\n",
      "288 1.4900 1.1487\n",
      "289 1.0300 0.3833\n",
      "290 1.2300 1.1487\n",
      "291 1.1000 1.1487\n",
      "292 0.6800 0.0002\n",
      "293 1.4100 1.1487\n",
      "294 1.2100 1.1487\n",
      "295 1.5400 1.1487\n",
      "296 1.3000 1.1487\n",
      "297 0.6500 0.0000\n",
      "298 1.2300 1.1486\n",
      "299 1.3800 1.1480\n",
      "300 1.1700 1.1487\n",
      "301 1.4200 1.1487\n",
      "302 1.3900 1.1487\n",
      "303 1.3700 0.0004\n",
      "304 1.2900 1.1465\n",
      "305 1.4300 1.1465\n",
      "306 1.0700 1.1465\n",
      "307 1.6000 1.1487\n",
      "308 1.4200 1.1487\n",
      "309 1.4700 1.1487\n",
      "310 1.3300 1.1487\n",
      "311 1.4100 1.1487\n",
      "312 1.3100 1.1487\n",
      "313 1.2600 0.0000\n",
      "314 1.3000 1.1483\n",
      "315 1.2900 1.1485\n",
      "316 1.2800 1.1487\n",
      "317 1.1700 1.1487\n",
      "318 1.5400 1.1487\n",
      "319 1.4100 1.1487\n",
      "320 1.0700 1.0859\n",
      "321 1.3600 1.1487\n",
      "322 1.2400 1.1487\n",
      "323 1.3400 1.1487\n",
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      "325 0.5100 0.0007\n",
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      "327 1.2500 0.4484\n",
      "328 0.9800 0.4484\n",
      "329 1.2600 0.4484\n",
      "330 1.2500 0.4484\n",
      "331 1.4100 0.4484\n",
      "332 1.4800 0.0011\n",
      "333 0.8400 0.0013\n",
      "334 1.6800 1.1479\n",
      "335 1.3100 1.1486\n",
      "336 1.6500 1.1486\n",
      "337 1.3600 0.0000\n",
      "338 2.0000 1.1487\n",
      "339 1.4900 0.0002\n",
      "340 1.7300 1.1487\n",
      "341 1.2400 1.1487\n",
      "342 1.5400 1.1487\n",
      "343 1.5600 1.1487\n",
      "344 1.6000 0.3350\n",
      "345 1.5000 1.1487\n",
      "346 1.3800 1.1487\n",
      "347 1.5200 1.1487\n",
      "348 1.3300 1.1487\n",
      "349 1.3800 1.1487\n",
      "350 1.3500 1.1487\n",
      "351 1.5400 1.1487\n",
      "352 1.3500 1.1487\n",
      "353 1.2300 1.1487\n",
      "354 1.2100 1.1487\n",
      "355 1.1000 1.1487\n",
      "356 1.3100 1.1487\n",
      "357 1.6200 1.1487\n",
      "358 1.4200 1.1487\n",
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      "361 1.1600 0.8224\n",
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      "367 1.5700 0.8224\n",
      "368 0.2900 0.7979\n",
      "369 1.8700 0.8224\n",
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      "382 1.2100 0.8224\n",
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      "392 1.2800 0.8224\n",
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      "401 1.0400 0.8224\n",
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      "403 1.2700 0.8224\n",
      "404 1.2100 0.8224\n",
      "405 1.2100 0.8224\n",
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      "410 1.4400 0.8224\n",
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      "413 1.6200 0.8224\n",
      "414 1.7300 0.8224\n",
      "415 0.5800 0.8224\n",
      "416 1.9600 0.8224\n",
      "417 0.1500 0.0000\n",
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      "419 1.2700 0.8220\n",
      "420 0.8400 0.8224\n",
      "421 1.3100 0.8224\n",
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      "431 1.5100 0.8224\n",
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      "441 1.8700 0.8224\n",
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      "447 0.9900 0.4484\n",
      "448 1.8500 0.4484\n",
      "449 0.8600 0.4484\n",
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      "473 0.1200 0.8224\n",
      "474 0.1200 0.8224\n",
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      "482 0.9300 1.0825\n",
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      "487 0.1700 1.0603\n",
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      "491 0.1300 0.0000\n",
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      "493 1.1900 1.0825\n",
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      "497 0.5600 1.0825\n",
      "498 1.2900 1.0825\n",
      "499 1.3800 1.0825\n",
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      "502 1.2100 1.0825\n",
      "503 1.1500 1.0825\n",
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      "505 0.8900 1.0825\n",
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      "513 0.7200 1.0825\n",
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      "515 1.5000 1.0825\n",
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      "518 1.2100 1.0825\n",
      "519 1.0900 1.0825\n",
      "520 1.0400 1.0825\n",
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      "523 0.9300 1.0825\n",
      "524 0.5700 1.0825\n",
      "525 0.9700 0.0002\n",
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      "529 1.1600 1.0825\n",
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      "535 1.5400 1.0825\n",
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      "540 1.2300 1.0825\n",
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      "543 1.8400 1.0804\n",
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      "548 1.5400 1.0825\n",
      "549 1.6500 1.0825\n",
      "550 1.5600 1.0825\n",
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      "552 1.4400 0.0000\n",
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      "559 0.9900 1.0195\n",
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      "567 0.7300 0.4484\n",
      "568 0.5500 0.4484\n",
      "569 2.1900 0.4484\n",
      "570 0.8000 0.0011\n",
      "571 0.1500 0.0013\n",
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      "574 1.7900 1.0824\n",
      "575 1.8100 0.0000\n",
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      "579 1.3400 1.0825\n",
      "580 1.8200 1.0825\n",
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      "584 1.9100 1.0825\n",
      "585 0.5200 1.0825\n",
      "586 1.3700 1.0825\n",
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      "588 1.8400 1.0825\n",
      "589 1.9600 1.0825\n",
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      "591 0.1100 1.0825\n",
      "592 0.1200 1.0825\n",
      "593 0.1200 1.0825\n",
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      "598 0.9400 1.1374\n",
      "599 1.0100 1.1374\n",
      "600 0.8500 1.1374\n",
      "601 0.7600 1.1374\n",
      "602 0.1100 0.0003\n",
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      "604 0.9200 1.1374\n",
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      "617 1.0500 1.1374\n",
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      "619 0.1100 1.1374\n",
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      "648 1.1700 1.1374\n",
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      "653 0.5600 1.1374\n",
      "654 1.6200 1.1374\n",
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      "658 1.0400 1.1374\n",
      "659 1.1700 1.1374\n",
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      "664 0.1100 1.1352\n",
      "665 0.7800 1.1374\n",
      "666 1.9600 1.1374\n",
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      "670 1.9800 1.1374\n",
      "671 1.3500 -0.0000\n",
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      "675 0.1100 1.1374\n",
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      "679 1.8700 1.1374\n",
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      "685 0.6200 0.4484\n",
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      "687 0.5400 0.4484\n",
      "688 2.7200 0.4484\n",
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      "690 0.2700 0.0013\n",
      "691 1.3200 1.1367\n",
      "692 1.6400 1.1373\n",
      "693 1.8600 1.1373\n",
      "694 1.8900 0.0000\n",
      "695 0.9200 1.1374\n",
      "696 2.1900 0.0002\n",
      "697 1.2900 1.1374\n",
      "698 1.2900 1.1374\n",
      "699 1.9300 1.1374\n",
      "700 1.6600 1.1374\n",
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      "702 1.4000 1.1374\n",
      "703 1.9600 1.1374\n",
      "704 1.2600 1.1374\n",
      "705 1.5000 1.1374\n",
      "706 1.0300 1.1374\n",
      "707 1.9300 1.1374\n",
      "708 1.8800 1.1374\n",
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      "710 0.1100 1.1374\n",
      "711 0.1200 1.1374\n",
      "712 0.1100 1.1374\n",
      "713 1.2300 1.1374\n",
      "714 1.7800 1.1374\n",
      "715 1.8400 1.1374\n",
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      "717 1.1300 0.8131\n",
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      "724 1.6300 0.8131\n",
      "725 0.1700 0.7880\n",
      "726 1.8500 0.8131\n",
      "727 1.2400 0.8131\n",
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      "731 1.1400 0.8131\n",
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      "734 1.4800 0.8131\n",
      "735 0.3600 0.8131\n",
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      "741 1.4800 0.8131\n",
      "742 1.0300 0.8131\n",
      "743 1.3500 0.8131\n",
      "744 1.8700 0.8131\n",
      "745 1.7500 0.8131\n",
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      "748 1.4400 0.8131\n",
      "749 1.2500 0.8131\n",
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      "751 1.2100 0.8131\n",
      "752 1.3600 0.8131\n",
      "753 1.5300 0.8131\n",
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      "758 1.6400 0.8131\n",
      "759 1.7200 0.8131\n",
      "760 1.1900 0.8131\n",
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      "762 1.2000 0.8131\n",
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      "764 1.6500 0.8131\n",
      "765 1.4000 0.8131\n",
      "766 0.4400 0.2868\n",
      "767 1.3900 0.8131\n",
      "768 0.9700 0.8131\n",
      "769 0.1300 0.0002\n",
      "770 1.6200 0.8131\n",
      "771 1.7500 0.8131\n",
      "772 0.5700 0.8131\n",
      "773 1.7000 0.8131\n",
      "774 0.1500 0.0000\n",
      "775 1.7300 0.8122\n",
      "776 1.2400 0.8126\n",
      "777 0.8300 0.8131\n",
      "778 1.3500 0.8131\n",
      "779 1.2700 0.8131\n",
      "780 1.3100 0.0004\n",
      "781 1.6300 0.8131\n",
      "782 1.3600 0.8131\n",
      "783 0.1100 0.8131\n",
      "784 0.8200 0.8131\n",
      "785 1.7300 0.8131\n",
      "786 1.7400 0.8131\n",
      "787 1.8700 0.8131\n",
      "788 1.5000 0.8131\n",
      "789 1.9400 0.8131\n",
      "790 1.4300 0.0000\n",
      "791 1.4700 0.8128\n",
      "792 1.8600 0.8130\n",
      "793 0.1100 0.8131\n",
      "794 0.1200 0.8131\n",
      "795 1.1900 0.8131\n",
      "796 1.4600 0.8131\n",
      "797 1.5000 0.8131\n",
      "798 1.8300 0.8131\n",
      "799 1.6300 0.8131\n",
      "800 0.1200 0.0007\n",
      "801 0.1700 0.0007\n",
      "802 0.1300 0.0007\n",
      "803 1.5300 0.4484\n",
      "804 1.0000 0.4484\n",
      "805 1.9000 0.4484\n",
      "806 0.8400 0.4484\n",
      "807 1.8500 0.0025\n",
      "808 0.1300 0.0011\n",
      "809 0.2500 0.0013\n",
      "810 1.7000 0.8128\n",
      "811 1.7800 0.8130\n",
      "812 1.7700 0.8130\n",
      "813 2.1700 -0.0000\n",
      "814 1.8200 0.8131\n",
      "815 2.2900 0.0002\n",
      "816 1.7600 0.8131\n",
      "817 1.6400 0.8131\n",
      "818 1.9300 0.8131\n",
      "819 1.5600 0.8131\n",
      "820 1.8100 0.2395\n",
      "821 1.5800 0.8131\n",
      "822 2.0000 0.8131\n",
      "823 0.1500 0.8131\n",
      "824 1.5900 0.8131\n",
      "825 1.2900 0.8131\n",
      "826 2.1900 0.8131\n",
      "827 2.0900 0.8131\n",
      "828 1.6900 0.8131\n",
      "829 0.1900 0.8131\n",
      "830 0.1200 0.8131\n",
      "831 0.1300 0.8131\n",
      "832 1.4900 0.8131\n",
      "833 2.2600 0.8131\n",
      "834 1.9200 0.8131\n",
      "835 1.1800 1.7714\n",
      "836 1.0900 1.7714\n",
      "837 0.8400 1.7715\n",
      "838 1.0900 1.7715\n",
      "839 1.1600 1.7714\n",
      "840 0.2300 0.0003\n",
      "841 1.1900 0.0005\n",
      "842 1.0000 1.7714\n",
      "843 1.1300 1.7714\n",
      "844 1.2800 1.7313\n",
      "845 1.8600 1.7715\n",
      "846 1.1700 1.7715\n",
      "847 1.8200 0.0000\n",
      "848 1.4900 0.0000\n",
      "849 1.5400 1.7715\n",
      "850 1.2800 1.7715\n",
      "851 1.4600 1.7714\n",
      "852 1.0200 1.7714\n",
      "853 1.0400 1.7715\n",
      "854 1.4100 1.7715\n",
      "855 1.2400 1.7715\n",
      "856 1.0700 1.7715\n",
      "857 1.1200 1.7714\n",
      "858 1.2100 1.7715\n",
      "859 1.2000 1.7715\n",
      "860 1.1600 1.7715\n",
      "861 1.2000 1.7715\n",
      "862 1.6300 1.7715\n",
      "863 1.4100 1.7715\n",
      "864 1.2900 1.7715\n",
      "865 1.1800 1.7714\n",
      "866 1.4600 1.7714\n",
      "867 1.2500 1.7714\n",
      "868 1.0200 1.7714\n",
      "869 1.2400 1.7714\n",
      "870 1.2300 1.7714\n",
      "871 1.6300 1.7715\n",
      "872 1.4600 1.7715\n",
      "873 1.5400 1.7715\n",
      "874 0.9800 1.7715\n",
      "875 1.3000 1.7715\n",
      "876 1.2000 1.7714\n",
      "877 1.0600 1.7715\n",
      "878 1.1300 1.7714\n",
      "879 1.2300 1.7715\n",
      "880 1.2000 1.7714\n",
      "881 1.2000 1.7715\n",
      "882 0.8600 0.0002\n",
      "883 1.6100 1.7714\n",
      "884 1.6000 1.7715\n",
      "885 1.1600 0.6294\n",
      "886 1.3000 1.7715\n",
      "887 1.4500 1.7715\n",
      "888 0.5200 0.0002\n",
      "889 1.1400 1.7715\n",
      "890 1.4300 1.7715\n",
      "891 1.1500 1.7715\n",
      "892 1.3700 1.7715\n",
      "893 0.4800 0.0000\n",
      "894 1.4300 1.7715\n",
      "895 1.4100 1.7705\n",
      "896 1.2000 1.7715\n",
      "897 1.5400 1.7715\n",
      "898 1.6700 1.7715\n",
      "899 1.6500 0.0004\n",
      "900 1.6200 1.7715\n",
      "901 1.2100 1.7715\n",
      "902 1.1000 1.7715\n",
      "903 1.2200 1.7714\n",
      "904 1.6700 1.7714\n",
      "905 1.1600 1.7714\n",
      "906 1.2600 1.7714\n",
      "907 1.7900 1.7714\n",
      "908 1.7600 1.7715\n",
      "909 1.3100 0.0000\n",
      "910 1.3900 1.7089\n",
      "911 1.3900 1.7713\n",
      "912 1.3800 1.7715\n",
      "913 1.3200 1.7715\n",
      "914 1.4400 1.7714\n",
      "915 0.8500 1.7715\n",
      "916 1.2300 1.7715\n",
      "917 1.5000 1.7715\n",
      "918 1.7500 1.7715\n",
      "919 1.5600 1.7715\n",
      "920 0.5000 0.0007\n",
      "921 0.7500 0.0007\n",
      "922 0.6900 0.0007\n",
      "923 1.3100 0.4484\n",
      "924 1.3500 0.4484\n",
      "925 1.5700 0.4484\n",
      "926 1.4100 0.4484\n",
      "927 1.0900 0.0025\n",
      "928 0.9800 0.0011\n",
      "929 0.6500 0.0013\n",
      "930 1.2500 1.7634\n",
      "931 1.6600 1.7714\n",
      "932 1.1700 1.7714\n",
      "933 1.5300 0.0000\n",
      "934 0.9400 1.7715\n",
      "935 1.4200 0.0002\n",
      "936 1.7700 1.7715\n",
      "937 1.2100 1.7715\n",
      "938 1.3500 1.7715\n",
      "939 1.3700 1.7715\n",
      "940 1.9500 0.5178\n",
      "941 1.7500 1.7715\n",
      "942 1.5500 1.7715\n",
      "943 1.5500 1.7715\n",
      "944 1.0000 1.7715\n",
      "945 1.4900 1.7715\n",
      "946 1.7000 1.7715\n",
      "947 1.3700 1.7715\n",
      "948 1.4800 1.7715\n",
      "949 1.5200 1.7715\n",
      "950 1.7100 1.7714\n",
      "951 1.4100 1.7714\n",
      "952 1.4600 1.7715\n",
      "953 1.3800 1.7715\n",
      "954 1.5600 1.7715\n",
      "955 1.1000 0.2111\n",
      "956 0.9700 0.2111\n",
      "957 0.8300 0.2112\n",
      "958 0.8500 0.2112\n",
      "959 0.9300 0.2111\n",
      "960 0.3000 0.0003\n",
      "961 0.8900 0.0005\n",
      "962 0.9300 0.2111\n",
      "963 1.1800 0.2111\n",
      "964 0.1500 0.1887\n",
      "965 0.9500 0.2112\n",
      "966 0.8400 0.2112\n",
      "967 1.3000 0.0000\n",
      "968 0.8900 0.0000\n",
      "969 0.9900 0.2112\n",
      "970 0.8700 0.2112\n",
      "971 0.8900 0.2111\n",
      "972 0.8500 0.0000\n",
      "973 0.9000 0.2112\n",
      "974 0.9300 0.2112\n",
      "975 1.0000 0.2112\n",
      "976 0.9100 0.2112\n",
      "977 0.7800 0.0000\n",
      "978 0.7700 0.2112\n",
      "979 0.8000 0.2112\n",
      "980 0.8400 0.2112\n",
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      "982 0.6500 0.2112\n",
      "983 0.7900 0.2112\n",
      "984 0.7300 0.2112\n",
      "985 0.9100 0.2111\n",
      "986 0.9400 0.2111\n",
      "987 1.0200 0.2111\n",
      "988 0.9400 0.2111\n",
      "989 0.6400 0.2111\n",
      "990 0.4400 0.2111\n",
      "991 1.0200 0.2112\n",
      "992 0.9200 0.2112\n",
      "993 0.4700 0.2112\n",
      "994 0.8400 0.2112\n",
      "995 0.9500 0.2112\n",
      "996 0.8300 0.2111\n",
      "997 0.4800 0.2112\n",
      "998 0.5800 0.0000\n",
      "999 0.7700 0.2112\n",
      "1000 0.8400 0.2111\n",
      "1001 0.5300 0.2112\n",
      "1002 0.8800 0.0002\n",
      "1003 0.9000 0.2111\n",
      "1004 0.9200 0.2112\n",
      "1005 0.1300 0.0000\n",
      "1006 0.8500 0.2111\n",
      "1007 0.8600 0.2111\n",
      "1008 0.6500 0.0002\n",
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      "1010 0.9000 0.2112\n",
      "1011 0.7900 0.2112\n",
      "1012 0.8200 0.2112\n",
      "1013 0.4300 0.0000\n",
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      "1015 0.8500 0.2110\n",
      "1016 0.7700 0.2112\n",
      "1017 0.8800 0.2112\n",
      "1018 1.0600 0.2112\n",
      "1019 1.0900 0.0004\n",
      "1020 1.0100 0.2112\n",
      "1021 0.9300 0.2112\n",
      "1022 0.7800 0.2112\n",
      "1023 1.0200 0.2111\n",
      "1024 1.0400 0.2111\n",
      "1025 1.0400 0.2111\n",
      "1026 0.9300 0.0000\n",
      "1027 1.0200 0.2111\n",
      "1028 1.1800 0.2112\n",
      "1029 0.9900 0.0000\n",
      "1030 0.7000 0.2110\n",
      "1031 0.5500 0.2089\n",
      "1032 0.8700 0.2112\n",
      "1033 0.9100 0.2112\n",
      "1034 0.8600 0.2111\n",
      "1035 0.9400 0.2112\n",
      "1036 0.9100 0.2112\n",
      "1037 1.1100 0.2112\n",
      "1038 1.0200 0.2112\n",
      "1039 0.9600 0.2111\n",
      "1040 0.2800 0.0007\n",
      "1041 0.3000 0.0007\n",
      "1042 0.2800 0.0007\n",
      "1043 1.2100 0.2112\n",
      "1044 0.9800 0.2112\n",
      "1045 1.0100 0.2112\n",
      "1046 0.9600 0.2112\n",
      "1047 0.7600 0.0025\n",
      "1048 0.2900 0.0011\n",
      "1049 0.5900 0.0013\n",
      "1050 1.3500 0.2112\n",
      "1051 1.1900 0.2111\n",
      "1052 1.1500 0.2111\n",
      "1053 0.9600 -0.0000\n",
      "1054 1.1400 0.2112\n",
      "1055 1.1800 0.0002\n",
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      "1057 0.9300 0.2112\n",
      "1058 1.1400 0.2112\n",
      "1059 1.0600 0.2112\n",
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      "1061 1.0700 0.2112\n",
      "1062 1.0500 0.0000\n",
      "1063 1.0000 0.2112\n",
      "1064 1.0400 0.2112\n",
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      "1066 1.0300 0.2112\n",
      "1067 1.1400 0.2112\n",
      "1068 1.0000 0.2112\n",
      "1069 1.0200 0.2112\n",
      "1070 0.9900 0.2111\n",
      "1071 1.0200 0.2111\n",
      "1072 1.0300 0.2112\n",
      "1073 1.0800 0.2112\n",
      "1074 1.1300 0.2112\n",
      "1075 0.2800 0.4849\n",
      "1076 0.2500 0.4849\n",
      "1077 0.2400 0.4850\n",
      "1078 0.2200 0.4850\n",
      "1079 0.4400 0.4849\n",
      "1080 0.2500 0.0003\n",
      "1081 0.3100 0.0005\n",
      "1082 0.2700 0.4849\n",
      "1083 0.3400 0.4849\n",
      "1084 0.1500 0.4421\n",
      "1085 0.3100 0.4850\n",
      "1086 0.2000 0.4850\n",
      "1087 0.2200 0.0000\n",
      "1088 0.1900 0.0000\n",
      "1089 0.1700 0.4850\n",
      "1090 0.2200 0.4850\n",
      "1091 0.2000 0.4849\n",
      "1092 0.2000 0.4849\n",
      "1093 0.2100 0.4850\n",
      "1094 0.1600 0.4850\n",
      "1095 0.1500 0.4850\n",
      "1096 0.1900 0.4850\n",
      "1097 0.2000 0.4849\n",
      "1098 0.2200 0.4850\n",
      "1099 0.2100 0.4850\n",
      "1100 0.2100 0.4850\n",
      "1101 0.1700 0.4850\n",
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      "1103 0.1400 0.4850\n",
      "1104 0.1700 0.4850\n",
      "1105 0.1700 0.4849\n",
      "1106 0.2300 0.4849\n",
      "1107 0.1800 0.4849\n",
      "1108 0.1600 0.4849\n",
      "1109 0.1900 0.4849\n",
      "1110 0.1600 0.4849\n",
      "1111 0.1700 0.4850\n",
      "1112 0.2000 0.4850\n",
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      "1114 0.1700 0.4850\n",
      "1115 0.1500 0.4850\n",
      "1116 0.1800 0.4849\n",
      "1117 0.1400 0.4850\n",
      "1118 0.1700 0.4849\n",
      "1119 0.1600 0.4850\n",
      "1120 0.1900 0.4849\n",
      "1121 0.1500 0.4850\n",
      "1122 0.1800 0.0002\n",
      "1123 0.1800 0.4849\n",
      "1124 0.1900 0.4850\n",
      "1125 0.1500 0.0263\n",
      "1126 0.1900 0.4849\n",
      "1127 0.1900 0.4849\n",
      "1128 0.1900 0.0002\n",
      "1129 0.1800 0.4850\n",
      "1130 0.1700 0.4850\n",
      "1131 0.1900 0.4850\n",
      "1132 0.2100 0.4850\n",
      "1133 0.1700 -0.0000\n",
      "1134 0.2500 0.4850\n",
      "1135 0.1800 0.4847\n",
      "1136 0.1900 0.4850\n",
      "1137 0.1900 0.4850\n",
      "1138 0.2700 0.4850\n",
      "1139 0.2100 0.0004\n",
      "1140 0.1700 0.4850\n",
      "1141 0.2400 0.4850\n",
      "1142 0.1900 0.4850\n",
      "1143 0.2100 0.4849\n",
      "1144 0.2200 0.4849\n",
      "1145 0.1700 0.4849\n",
      "1146 0.2600 0.4850\n",
      "1147 0.2400 0.4849\n",
      "1148 0.2000 0.4850\n",
      "1149 0.1800 0.0000\n",
      "1150 0.1600 0.4847\n",
      "1151 0.1800 0.4798\n",
      "1152 0.2200 0.4850\n",
      "1153 0.1900 0.4850\n",
      "1154 0.2500 0.4849\n",
      "1155 0.2400 0.4850\n",
      "1156 0.1800 0.4850\n",
      "1157 0.2100 0.4850\n",
      "1158 0.2200 0.4850\n",
      "1159 0.1900 0.4849\n",
      "1160 0.1600 0.0007\n",
      "1161 0.2000 0.0007\n",
      "1162 0.1700 0.0007\n",
      "1163 0.1900 0.4484\n",
      "1164 0.1600 0.4484\n",
      "1165 0.2200 0.4484\n",
      "1166 0.1700 0.4484\n",
      "1167 0.1700 0.0025\n",
      "1168 0.1400 0.0011\n",
      "1169 0.2400 0.0013\n",
      "1170 0.2400 0.4850\n",
      "1171 0.2500 0.4849\n",
      "1172 0.2000 0.4849\n",
      "1173 0.2400 0.0000\n",
      "1174 0.2100 0.4850\n",
      "1175 0.2600 0.0002\n",
      "1176 0.2200 0.4850\n",
      "1177 0.2000 0.4850\n",
      "1178 0.2000 0.4850\n",
      "1179 0.2400 0.4850\n",
      "1180 0.2500 0.1427\n",
      "1181 0.2400 0.4850\n",
      "1182 0.2400 0.4850\n",
      "1183 0.2300 0.4850\n",
      "1184 0.1900 0.4850\n",
      "1185 0.2300 0.4850\n",
      "1186 0.2700 0.4850\n",
      "1187 0.2800 0.4850\n",
      "1188 0.2000 0.4850\n",
      "1189 0.2300 0.4850\n",
      "1190 0.2400 0.4849\n",
      "1191 0.2200 0.4849\n",
      "1192 0.2100 0.4850\n",
      "1193 0.1600 0.4850\n",
      "1194 0.2100 0.4850\n",
      "1195 0.7100 1.1069\n",
      "1196 0.6300 1.1069\n",
      "1197 0.1500 0.0002\n",
      "1198 0.8200 1.1069\n",
      "1199 1.0200 1.1069\n",
      "1200 0.5400 1.1069\n",
      "1201 0.6500 1.1069\n",
      "1202 0.1500 0.0000\n",
      "1203 0.5800 1.1069\n",
      "1204 0.6600 1.1062\n",
      "1205 0.7600 1.1069\n",
      "1206 1.0400 1.1069\n",
      "1207 0.8200 1.1069\n",
      "1208 1.3900 0.0004\n",
      "1209 0.5300 1.1054\n",
      "1210 0.6400 1.1054\n",
      "1211 0.3000 1.1054\n",
      "1212 0.6300 1.1069\n",
      "1213 0.6400 1.1068\n",
      "1214 0.7800 1.1069\n",
      "1215 0.7000 1.1069\n",
      "1216 1.2800 1.1069\n",
      "1217 0.7100 1.1069\n",
      "1218 1.0700 0.0000\n",
      "1219 0.7100 1.0930\n",
      "1220 0.6000 1.1068\n",
      "1221 0.2600 1.1069\n",
      "1222 0.2300 1.1069\n",
      "1223 0.7100 1.1069\n",
      "1224 0.4700 1.1069\n",
      "1225 0.9100 1.0423\n",
      "1226 0.9100 1.1069\n",
      "1227 1.0700 1.1069\n",
      "1228 0.1200 0.0007\n",
      "1229 0.2100 0.0007\n",
      "1230 0.1400 0.0007\n",
      "1231 0.9100 0.4484\n",
      "1232 0.7900 0.4484\n",
      "1233 0.5700 0.4484\n",
      "1234 0.8000 0.4484\n",
      "1235 0.8000 0.4484\n",
      "1236 0.9500 0.0011\n",
      "1237 0.1200 0.0013\n",
      "1238 1.0700 1.1013\n",
      "1239 0.6800 1.1068\n",
      "1240 0.6600 1.1068\n",
      "1241 0.9600 0.0000\n",
      "1242 0.7100 1.1069\n",
      "1243 1.0000 0.0002\n",
      "1244 0.7600 1.1069\n",
      "1245 0.9000 1.1069\n",
      "1246 0.7000 1.1069\n",
      "1247 0.6000 1.1069\n",
      "1248 0.7300 1.0807\n",
      "1249 1.1000 1.1069\n",
      "1250 0.6100 1.1069\n",
      "1251 0.6400 1.1069\n",
      "1252 0.6900 1.1069\n",
      "1253 0.7100 1.1069\n",
      "1254 0.5800 1.1069\n",
      "1255 0.6400 1.1069\n",
      "1256 0.8500 1.1069\n",
      "1257 0.1400 1.1069\n",
      "1258 0.1400 1.1068\n",
      "1259 0.1400 1.1068\n",
      "1260 0.9300 1.1069\n",
      "1261 0.7700 1.1069\n",
      "1262 0.5400 1.1069\n",
      "1263 0.9300 1.1069\n",
      "1264 1.0200 1.1068\n",
      "1265 0.7000 1.1069\n",
      "1266 0.8100 1.1069\n",
      "1267 0.7800 1.1068\n",
      "1268 0.1200 0.0003\n",
      "1269 0.5200 0.0005\n",
      "1270 0.7400 1.1068\n",
      "1271 0.6700 1.1068\n",
      "1272 0.1800 1.0965\n",
      "1273 0.8700 1.1069\n",
      "1274 0.9700 1.1069\n",
      "1275 1.3100 0.0000\n",
      "1276 0.9300 0.0000\n",
      "1277 0.8300 1.1069\n",
      "1278 1.0500 1.1069\n",
      "1279 0.9300 1.1069\n",
      "1280 1.0600 1.1068\n",
      "1281 0.8300 1.1069\n",
      "1282 0.9100 1.1069\n",
      "1283 0.6300 1.1069\n",
      "1284 0.7300 1.1069\n",
      "1285 0.7500 1.1069\n",
      "1286 0.6400 1.1069\n",
      "1287 0.7200 1.1069\n",
      "1288 0.7700 1.1069\n",
      "1289 0.9000 1.1069\n",
      "1290 0.9000 1.1069\n",
      "1291 0.8700 1.1069\n",
      "1292 0.7400 1.1069\n",
      "1293 0.7200 1.1069\n",
      "1294 0.5400 1.1069\n",
      "1295 0.5600 1.1069\n",
      "1296 0.8400 1.1069\n",
      "1297 0.6300 1.1069\n",
      "1298 0.5600 1.1069\n",
      "1299 0.8700 1.1069\n",
      "1300 0.7800 1.1069\n",
      "1301 0.5800 1.1069\n",
      "1302 0.5500 1.1069\n",
      "1303 0.9800 1.1069\n",
      "1304 0.5700 1.1069\n",
      "1305 0.6400 1.1069\n",
      "1306 0.7800 1.1069\n",
      "1307 0.8100 1.1069\n",
      "1308 0.9400 1.1068\n",
      "1309 0.4500 1.1069\n",
      "1310 0.6000 0.0002\n",
      "1311 0.8300 1.1068\n",
      "1312 0.8800 1.1069\n",
      "1313 0.6900 0.4159\n",
      "1314 0.4500 0.6917\n",
      "1315 0.4800 0.6916\n",
      "1316 0.4300 0.6917\n",
      "1317 0.3700 0.6917\n",
      "1318 0.3600 0.6916\n",
      "1319 0.1000 0.0003\n",
      "1320 0.3400 0.6917\n",
      "1321 0.4100 0.6916\n",
      "1322 0.4700 0.6916\n",
      "1323 0.1400 0.6758\n",
      "1324 0.5800 0.6917\n",
      "1325 0.1500 0.6917\n",
      "1326 0.4800 0.0000\n",
      "1327 0.2100 0.0000\n",
      "1328 0.3600 0.6807\n",
      "1329 0.5000 0.6917\n",
      "1330 0.4000 0.6917\n",
      "1331 0.3400 0.6916\n",
      "1332 0.6000 0.6917\n",
      "1333 0.2200 0.6917\n",
      "1334 0.2000 0.6917\n",
      "1335 0.2600 0.6917\n",
      "1336 0.1900 0.6917\n",
      "1337 0.1700 0.6917\n",
      "1338 0.2300 0.6917\n",
      "1339 0.3400 0.6917\n",
      "1340 0.1900 0.6917\n",
      "1341 0.1600 0.6917\n",
      "1342 0.1400 0.6917\n",
      "1343 0.1600 0.6917\n",
      "1344 0.2600 0.6917\n",
      "1345 0.2400 0.6917\n",
      "1346 0.2500 0.6917\n",
      "1347 0.3000 0.6917\n",
      "1348 0.1400 0.6917\n",
      "1349 0.1300 0.6917\n",
      "1350 0.1700 0.6917\n",
      "1351 0.2000 0.6917\n",
      "1352 0.1200 0.6917\n",
      "1353 0.2200 0.6917\n",
      "1354 0.1500 0.6917\n",
      "1355 0.2400 0.6917\n",
      "1356 0.1300 0.6917\n",
      "1357 0.1300 0.6917\n",
      "1358 0.1600 0.6917\n",
      "1359 0.1900 0.6916\n",
      "1360 0.2200 0.6917\n",
      "1361 0.3100 0.0002\n",
      "1362 0.1800 0.6916\n",
      "1363 0.1700 0.6917\n",
      "1364 0.2200 0.0280\n",
      "1365 0.1800 0.6917\n",
      "1366 0.1600 0.6917\n",
      "1367 0.1300 0.0002\n",
      "1368 0.1700 0.6917\n",
      "1369 0.1900 0.6917\n",
      "1370 0.1500 0.6917\n",
      "1371 0.1700 0.6917\n",
      "1372 0.1400 -0.0000\n",
      "1373 0.3700 0.6917\n",
      "1374 0.3000 0.6913\n",
      "1375 0.1400 0.6917\n",
      "1376 0.2000 0.6917\n",
      "1377 0.3600 0.6917\n",
      "1378 0.2800 0.0004\n",
      "1379 0.3300 0.6917\n",
      "1380 0.2800 0.6917\n",
      "1381 0.1600 0.6917\n",
      "1382 0.3700 0.6917\n",
      "1383 0.3700 0.0000\n",
      "1384 0.4300 0.6917\n",
      "1385 0.3600 0.6917\n",
      "1386 0.4900 0.6917\n",
      "1387 0.4600 0.6917\n",
      "1388 0.2800 0.0000\n",
      "1389 0.1600 0.6913\n",
      "1390 0.1500 0.6917\n",
      "1391 0.1600 0.6917\n",
      "1392 0.2000 0.6917\n",
      "1393 0.3900 0.0000\n",
      "1394 0.4200 0.6917\n",
      "1395 0.1500 0.6917\n",
      "1396 0.3500 0.6917\n",
      "1397 0.4200 0.6901\n",
      "1398 0.1200 0.0007\n",
      "1399 0.1300 0.0007\n",
      "1400 0.1200 0.0007\n",
      "1401 0.3900 0.4484\n",
      "1402 0.4400 0.4484\n",
      "1403 0.4400 0.4484\n",
      "1404 0.4200 0.4484\n",
      "1405 0.2000 0.0025\n",
      "1406 0.1100 0.0011\n",
      "1407 0.1500 0.0013\n",
      "1408 0.3100 0.6917\n",
      "1409 0.2900 0.6916\n",
      "1410 0.2900 0.6916\n",
      "1411 0.3900 0.0000\n",
      "1412 0.1700 0.6917\n",
      "1413 0.2200 0.0002\n",
      "1414 0.2000 0.6917\n",
      "1415 0.2200 0.6917\n",
      "1416 0.4200 0.6917\n",
      "1417 0.4100 0.6917\n",
      "1418 0.3200 0.1915\n",
      "1419 0.3700 0.6917\n",
      "1420 0.2500 0.6917\n",
      "1421 1.8100 0.6917\n",
      "1422 0.2100 0.6917\n",
      "1423 0.2100 0.6917\n",
      "1424 0.2400 0.6917\n",
      "1425 0.2800 0.6917\n",
      "1426 0.1900 0.6917\n",
      "1427 0.1500 0.6917\n",
      "1428 0.2500 0.6916\n",
      "1429 0.1400 0.6916\n",
      "1430 0.2500 0.6917\n",
      "1431 0.1500 0.6917\n",
      "1432 0.2400 0.6917\n",
      "1433 1.3100 1.3074\n",
      "1434 1.8400 1.3074\n",
      "1435 1.5300 1.3074\n",
      "1436 1.6300 1.3074\n",
      "1437 1.5300 1.3074\n",
      "1438 0.1300 0.0003\n",
      "1439 1.5900 1.3074\n",
      "1440 1.3300 1.3074\n",
      "1441 1.6900 1.3074\n",
      "1442 1.2800 1.2719\n",
      "1443 2.0200 1.3074\n",
      "1444 1.4900 1.3074\n",
      "1445 1.6800 0.0000\n",
      "1446 1.4400 0.0000\n",
      "1447 1.7600 1.2828\n",
      "1448 1.7300 1.3074\n",
      "1449 2.1700 1.3074\n",
      "1450 1.8700 1.3074\n",
      "1451 1.6500 1.3074\n",
      "1452 1.9900 1.3074\n",
      "1453 1.6600 1.3074\n",
      "1454 2.1000 1.3074\n",
      "1455 1.9400 1.3074\n",
      "1456 1.7700 1.3074\n",
      "1457 1.4400 1.3074\n",
      "1458 1.9900 1.3074\n",
      "1459 1.5300 1.3074\n",
      "1460 1.3700 1.3074\n",
      "1461 2.1000 1.3074\n",
      "1462 2.4700 1.3074\n",
      "1463 1.4900 1.3074\n",
      "1464 1.6300 1.3074\n",
      "1465 1.5400 1.3074\n",
      "1466 1.8900 1.3074\n",
      "1467 1.7800 1.3074\n",
      "1468 1.6900 1.3074\n",
      "1469 1.7600 1.3074\n",
      "1470 1.8500 1.3074\n",
      "1471 1.3700 1.3074\n",
      "1472 1.7100 1.3074\n",
      "1473 1.9200 1.3074\n",
      "1474 2.0000 1.3074\n",
      "1475 2.0800 1.3074\n",
      "1476 1.6700 1.3074\n",
      "1477 1.7800 1.3074\n",
      "1478 1.8700 1.3074\n",
      "1479 2.1100 1.3074\n",
      "1480 0.9800 0.0002\n",
      "1481 1.9000 1.3074\n",
      "1482 1.8900 1.3074\n",
      "1483 0.3600 0.2031\n",
      "1484 1.9900 1.3074\n",
      "1485 1.8600 1.3074\n",
      "1486 0.1500 0.0002\n",
      "1487 2.2300 1.3074\n",
      "1488 2.2300 1.3074\n",
      "1489 1.9400 1.3074\n",
      "1490 2.4200 1.3074\n",
      "1491 0.1600 -0.0000\n",
      "1492 1.7300 1.3074\n",
      "1493 1.4900 1.3067\n",
      "1494 2.0500 1.3074\n",
      "1495 1.2500 1.3074\n",
      "1496 1.6500 1.3074\n",
      "1497 1.9400 0.0004\n",
      "1498 1.7100 1.3074\n",
      "1499 2.0600 1.3074\n",
      "1500 1.8800 1.3074\n",
      "1501 2.2200 1.3074\n",
      "1502 2.0300 1.3074\n",
      "1503 2.2200 1.3074\n",
      "1504 1.8400 1.3074\n",
      "1505 2.2300 1.3074\n",
      "1506 2.0700 1.3074\n",
      "1507 1.6200 0.0000\n",
      "1508 2.1100 1.3067\n",
      "1509 2.5400 1.3074\n",
      "1510 1.7700 1.3074\n",
      "1511 1.7000 1.3074\n",
      "1512 2.4200 1.3074\n",
      "1513 2.0900 1.3074\n",
      "1514 0.5700 1.3074\n",
      "1515 1.4600 1.3074\n",
      "1516 2.0700 1.3074\n",
      "1517 0.1300 0.0007\n",
      "1518 0.3700 0.0007\n",
      "1519 0.1700 0.0007\n",
      "1520 1.9600 0.4484\n",
      "1521 2.4000 0.4484\n",
      "1522 2.0300 0.4484\n",
      "1523 2.2900 0.4484\n",
      "1524 2.1600 0.0025\n",
      "1525 0.3800 0.0011\n",
      "1526 0.3900 0.0013\n",
      "1527 1.4500 1.3074\n",
      "1528 1.7600 1.3074\n",
      "1529 1.7800 1.3074\n",
      "1530 2.2500 0.0000\n",
      "1531 2.2000 1.3074\n",
      "1532 2.3200 0.0002\n",
      "1533 1.7900 1.3074\n",
      "1534 1.9900 1.3074\n",
      "1535 1.9300 1.3074\n",
      "1536 2.1200 1.3074\n",
      "1537 1.7800 0.3795\n",
      "1538 2.0700 1.3074\n",
      "1539 2.0500 1.3074\n",
      "1540 1.8200 1.3074\n",
      "1541 1.7700 1.3074\n",
      "1542 1.7700 1.3074\n",
      "1543 2.0800 1.3074\n",
      "1544 1.6800 1.3074\n",
      "1545 1.3500 1.3074\n",
      "1546 2.2000 1.3074\n",
      "1547 1.8100 1.3074\n",
      "1548 1.7900 1.3074\n",
      "1549 1.6300 1.3074\n",
      "1550 2.7400 1.3074\n",
      "1551 1.8800 1.3074\n",
      "1552 2.7600 1.5141\n",
      "1553 2.8300 1.5141\n",
      "1554 2.6800 1.5141\n",
      "1555 2.6400 1.5141\n",
      "1556 2.7100 1.5141\n",
      "1557 0.1200 0.0003\n",
      "1558 2.9200 1.5141\n",
      "1559 2.0900 1.5141\n",
      "1560 2.5900 1.5141\n",
      "1561 0.4500 1.4525\n",
      "1562 2.5900 1.5141\n",
      "1563 2.9000 1.5141\n",
      "1564 1.7700 0.0000\n",
      "1565 1.7000 0.0000\n",
      "1566 2.9300 1.4855\n",
      "1567 2.0000 1.5141\n",
      "1568 2.6200 1.5141\n",
      "1569 2.6600 1.5140\n",
      "1570 2.7600 1.5141\n",
      "1571 2.6400 1.5141\n",
      "1572 1.6100 1.5141\n",
      "1573 1.6000 1.5141\n",
      "1574 1.3300 1.5141\n",
      "1575 1.1000 1.5141\n",
      "1576 2.3700 1.5141\n",
      "1577 2.4400 1.5141\n",
      "1578 1.3100 1.5141\n",
      "1579 2.9700 1.5141\n",
      "1580 2.9000 1.5141\n",
      "1581 2.4700 1.5141\n",
      "1582 1.1400 1.5141\n",
      "1583 1.6600 1.5141\n",
      "1584 1.6700 1.5141\n",
      "1585 2.6800 1.5141\n",
      "1586 1.0700 1.5141\n",
      "1587 1.1400 1.5141\n",
      "1588 2.3200 1.5141\n",
      "1589 1.7800 1.5141\n",
      "1590 0.9100 1.5141\n",
      "1591 2.5700 1.5141\n",
      "1592 2.8200 1.5141\n",
      "1593 1.8900 1.5141\n",
      "1594 2.6900 1.5141\n",
      "1595 2.1500 1.5141\n",
      "1596 1.9100 1.5141\n",
      "1597 1.6800 1.5141\n",
      "1598 1.2500 1.5141\n",
      "1599 0.8700 0.0002\n",
      "1600 2.7700 1.5141\n",
      "1601 2.2900 1.5141\n",
      "1602 1.5800 0.2907\n",
      "1603 1.9300 1.5141\n",
      "1604 2.3100 1.5141\n",
      "1605 0.6500 0.0002\n",
      "1606 2.9600 1.5141\n",
      "1607 2.4400 1.5141\n",
      "1608 1.1600 1.5141\n",
      "1609 2.8700 1.5141\n",
      "1610 0.1600 -0.0000\n",
      "1611 2.4900 1.5141\n",
      "1612 2.2000 1.5132\n",
      "1613 1.4700 1.5141\n",
      "1614 1.4400 1.5141\n",
      "1615 2.7700 1.5141\n",
      "1616 3.0400 1.5141\n",
      "1617 2.7400 1.5141\n",
      "1618 2.7800 1.5141\n",
      "1619 1.4900 1.5141\n",
      "1620 2.8400 1.5141\n",
      "1621 2.5800 1.5141\n",
      "1622 2.8700 1.5141\n",
      "1623 2.3300 1.5141\n",
      "1624 2.8600 1.5141\n",
      "1625 2.4300 1.5141\n",
      "1626 1.6200 0.0000\n",
      "1627 2.8000 1.5132\n",
      "1628 2.7400 1.5141\n",
      "1629 1.9900 1.5141\n",
      "1630 1.9700 1.5141\n",
      "1631 2.4100 1.5141\n",
      "1632 2.8700 1.5141\n",
      "1633 0.2000 1.5141\n",
      "1634 1.5800 1.5141\n",
      "1635 2.5600 1.5141\n",
      "1636 0.1300 0.0007\n",
      "1637 0.2300 0.0007\n",
      "1638 0.1300 0.0007\n",
      "1639 2.5700 0.4484\n",
      "1640 2.9000 0.4484\n",
      "1641 2.8100 0.4484\n",
      "1642 2.9700 0.4484\n",
      "1643 2.0000 0.0025\n",
      "1644 0.1400 0.0011\n",
      "1645 0.6400 0.0013\n",
      "1646 1.9100 1.5141\n",
      "1647 2.8200 1.5140\n",
      "1648 2.7800 1.5140\n",
      "1649 2.5900 -0.0000\n",
      "1650 2.9400 1.5141\n",
      "1651 2.7400 0.0002\n",
      "1652 2.7300 1.5141\n",
      "1653 2.2700 1.5141\n",
      "1654 2.8300 1.5141\n",
      "1655 2.9000 1.5141\n",
      "1656 2.8900 0.4382\n",
      "1657 2.8700 1.5141\n",
      "1658 2.7200 1.5141\n",
      "1659 1.5500 1.5141\n",
      "1660 2.8900 1.5141\n",
      "1661 2.5000 1.5141\n",
      "1662 2.7900 1.5141\n",
      "1663 1.8300 1.5141\n",
      "1664 0.2800 1.5141\n",
      "1665 2.7200 1.5141\n",
      "1666 2.1100 1.5141\n",
      "1667 2.3300 1.5141\n",
      "1668 2.8100 1.5141\n",
      "1669 2.7200 1.5141\n",
      "1670 2.8700 1.5141\n",
      "1671 1.7300 1.5118\n",
      "1672 2.1400 1.5118\n",
      "1673 1.7400 1.5118\n",
      "1674 1.8300 1.5118\n",
      "1675 1.6700 1.5117\n",
      "1676 0.1300 0.0003\n",
      "1677 1.8600 1.5118\n",
      "1678 1.7400 1.5118\n",
      "1679 2.0600 1.5118\n",
      "1680 0.9100 1.4502\n",
      "1681 2.5600 1.5118\n",
      "1682 1.4900 1.5118\n",
      "1683 2.0800 0.0000\n",
      "1684 1.5300 0.0000\n",
      "1685 2.8500 1.4833\n",
      "1686 2.2000 1.5118\n",
      "1687 1.4400 1.5118\n",
      "1688 2.5300 1.5117\n",
      "1689 2.5300 1.5118\n",
      "1690 2.0900 1.5118\n",
      "1691 1.3500 1.5118\n",
      "1692 1.4500 1.5118\n",
      "1693 1.8800 1.5118\n",
      "1694 1.1700 1.5118\n",
      "1695 2.7600 1.5118\n",
      "1696 2.5500 1.5118\n",
      "1697 1.7400 1.5118\n",
      "1698 2.6600 1.5118\n",
      "1699 3.0300 1.5118\n",
      "1700 1.4500 1.5118\n",
      "1701 0.5500 1.5118\n",
      "1702 0.7100 1.5118\n",
      "1703 0.5900 1.5118\n",
      "1704 2.4500 1.5118\n",
      "1705 2.7400 1.5118\n",
      "1706 0.6900 1.5118\n",
      "1707 2.2700 1.5118\n",
      "1708 2.7800 1.5118\n",
      "1709 2.2300 1.5118\n",
      "1710 2.6100 1.5118\n",
      "1711 2.8400 1.5118\n",
      "1712 2.7300 1.5118\n",
      "1713 2.9900 1.5118\n",
      "1714 2.9800 1.5118\n",
      "1715 2.8200 1.5118\n",
      "1716 2.6000 1.5118\n",
      "1717 2.6600 1.5118\n",
      "1718 0.6700 0.0002\n",
      "1719 2.3900 1.5117\n",
      "1720 2.3800 1.5118\n",
      "1721 1.0100 0.2907\n",
      "1722 2.1900 1.5118\n",
      "1723 2.8000 1.5118\n",
      "1724 0.1400 0.0002\n",
      "1725 2.4300 1.5118\n",
      "1726 2.7600 1.5118\n",
      "1727 1.6500 1.5118\n",
      "1728 2.9200 1.5118\n",
      "1729 0.1600 -0.0000\n",
      "1730 2.5100 1.5118\n",
      "1731 2.5800 1.5109\n",
      "1732 2.9600 1.5118\n",
      "1733 1.3600 1.5118\n",
      "1734 2.4200 1.5118\n",
      "1735 2.1700 0.0004\n",
      "1736 2.5400 1.5118\n",
      "1737 2.7400 1.5118\n",
      "1738 2.1400 1.5118\n",
      "1739 2.7400 1.5118\n",
      "1740 2.6800 1.5118\n",
      "1741 2.6400 1.5118\n",
      "1742 2.6700 1.5118\n",
      "1743 2.6500 1.5118\n",
      "1744 2.4800 1.5118\n",
      "1745 2.0700 0.0000\n",
      "1746 2.8400 1.5109\n",
      "1747 2.8600 1.5118\n",
      "1748 1.6900 1.5118\n",
      "1749 1.7200 1.5118\n",
      "1750 2.5300 1.5118\n",
      "1751 2.6500 1.5118\n",
      "1752 1.3700 1.5118\n",
      "1753 2.3400 1.5118\n",
      "1754 2.6600 1.5118\n",
      "1755 0.1400 0.0007\n",
      "1756 0.3000 0.0007\n",
      "1757 0.1500 0.0007\n",
      "1758 2.6300 0.4484\n",
      "1759 2.8000 0.4484\n",
      "1760 2.6800 0.4484\n",
      "1761 2.8500 0.4484\n",
      "1762 2.6200 0.0025\n",
      "1763 0.2800 0.0011\n",
      "1764 0.5000 0.0013\n",
      "1765 1.7000 1.5118\n",
      "1766 2.3500 1.5117\n",
      "1767 2.5800 1.5117\n",
      "1768 2.5900 0.0000\n",
      "1769 2.5300 1.5118\n",
      "1770 2.6800 0.0002\n",
      "1771 2.5400 1.5118\n",
      "1772 2.4600 1.5118\n",
      "1773 2.5300 1.5118\n",
      "1774 2.5200 1.5118\n",
      "1775 2.4200 0.4378\n",
      "1776 2.7000 1.5118\n",
      "1777 2.4300 1.5118\n",
      "1778 1.6800 1.5118\n",
      "1779 2.6200 1.5118\n",
      "1780 2.7800 1.5118\n",
      "1781 2.5900 1.5118\n",
      "1782 1.9200 1.5118\n",
      "1783 1.2600 1.5118\n",
      "1784 2.8100 1.5118\n",
      "1785 1.6100 1.5117\n",
      "1786 1.7200 1.5117\n",
      "1787 2.6200 1.5118\n",
      "1788 2.6700 1.5118\n",
      "1789 2.4100 1.5118\n",
      "1790 2.0600 1.0958\n",
      "1791 2.2400 1.0958\n",
      "1792 1.8600 1.0958\n",
      "1793 1.9500 1.0958\n",
      "1794 1.8700 1.0958\n",
      "1795 0.1300 0.0003\n",
      "1796 2.1200 1.0958\n",
      "1797 1.5000 1.0958\n",
      "1798 2.1400 1.0958\n",
      "1799 0.3500 1.0465\n",
      "1800 2.7900 1.0958\n",
      "1801 1.5200 1.0958\n",
      "1802 2.1000 0.0000\n",
      "1803 1.4900 0.0000\n",
      "1804 1.5400 1.0821\n",
      "1805 2.0700 1.0958\n",
      "1806 1.8700 1.0958\n",
      "1807 2.2300 1.0958\n",
      "1808 2.8200 1.0958\n",
      "1809 1.6400 1.0958\n",
      "1810 1.3600 1.0958\n",
      "1811 1.8400 1.0958\n",
      "1812 1.5200 1.0958\n",
      "1813 2.7800 1.0958\n",
      "1814 1.0800 1.0958\n",
      "1815 1.9700 1.0958\n",
      "1816 1.5000 1.0958\n",
      "1817 2.7800 1.0958\n",
      "1818 2.9200 1.0958\n",
      "1819 2.3700 1.0958\n",
      "1820 2.2900 1.0958\n",
      "1821 2.4900 1.0958\n",
      "1822 2.4400 1.0958\n",
      "1823 2.7100 1.0958\n",
      "1824 1.2900 1.0958\n",
      "1825 1.0600 1.0958\n",
      "1826 1.8500 1.0958\n",
      "1827 2.5400 1.0958\n",
      "1828 2.3400 1.0958\n",
      "1829 2.3700 1.0958\n",
      "1830 2.8900 1.0958\n",
      "1831 2.5700 1.0958\n",
      "1832 2.8800 1.0958\n",
      "1833 2.8200 1.0958\n",
      "1834 2.6800 1.0958\n",
      "1835 2.5300 1.0958\n",
      "1836 2.1500 1.0958\n",
      "1837 1.0900 0.0002\n",
      "1838 2.7500 1.0958\n",
      "1839 2.8500 1.0958\n",
      "1840 0.4100 0.1618\n",
      "1841 2.3200 1.0958\n",
      "1842 2.9100 1.0958\n",
      "1843 0.1400 0.0002\n",
      "1844 2.6200 1.0958\n",
      "1845 2.2400 1.0958\n",
      "1846 1.8400 1.0958\n",
      "1847 2.8700 1.0958\n",
      "1848 0.3500 -0.0000\n",
      "1849 2.8200 1.0958\n",
      "1850 2.9500 1.0952\n",
      "1851 2.9400 1.0958\n",
      "1852 1.2700 1.0958\n",
      "1853 2.7800 1.0958\n",
      "1854 2.9800 0.0004\n",
      "1855 2.7700 1.0958\n",
      "1856 2.7800 1.0958\n",
      "1857 1.6800 1.0958\n",
      "1858 2.8600 1.0958\n",
      "1859 2.9200 1.0958\n",
      "1860 2.9900 1.0958\n",
      "1861 2.8900 1.0958\n",
      "1862 2.8700 1.0958\n",
      "1863 2.8000 1.0958\n",
      "1864 1.7900 0.0000\n",
      "1865 2.4900 1.0952\n",
      "1866 2.9000 1.0958\n",
      "1867 1.5200 1.0958\n",
      "1868 1.6200 1.0958\n",
      "1869 2.6600 1.0860\n",
      "1870 2.7900 1.0958\n",
      "1871 1.5800 1.0958\n",
      "1872 2.6400 1.0958\n",
      "1873 2.9400 1.0958\n",
      "1874 0.1200 0.0007\n",
      "1875 1.9400 0.0007\n",
      "1876 0.7900 0.0007\n",
      "1877 2.6600 0.4484\n",
      "1878 2.9000 0.4484\n",
      "1879 2.7600 0.4484\n",
      "1880 2.9900 0.4484\n",
      "1881 2.8800 0.0025\n",
      "1882 2.4300 0.0011\n",
      "1883 0.5800 0.0013\n",
      "1884 1.9200 1.0958\n",
      "1885 2.1600 1.0958\n",
      "1886 2.8600 1.0958\n",
      "1887 2.8500 -0.0000\n",
      "1888 2.6600 1.0958\n",
      "1889 2.9300 0.0002\n",
      "1890 2.7700 1.0958\n",
      "1891 2.6900 1.0958\n",
      "1892 2.7500 1.0958\n",
      "1893 2.7900 1.0958\n",
      "1894 2.6700 0.3172\n",
      "1895 2.8100 1.0958\n",
      "1896 2.5700 1.0958\n",
      "1897 1.7700 1.0958\n",
      "1898 2.9500 1.0958\n",
      "1899 2.9400 1.0958\n",
      "1900 2.7400 1.0958\n",
      "1901 2.2400 1.0958\n",
      "1902 1.3900 1.0958\n",
      "1903 2.7800 1.0958\n",
      "1904 1.6300 1.0958\n",
      "1905 1.5200 1.0958\n",
      "1906 2.9500 1.0958\n",
      "1907 2.7100 1.0958\n",
      "1908 2.7200 1.0958\n",
      "1909 0.9500 0.8787\n",
      "1910 1.1000 0.8787\n",
      "1911 0.9900 0.8787\n",
      "1912 0.9600 0.8787\n",
      "1913 0.9800 0.8787\n",
      "1914 0.1300 0.0003\n",
      "1915 0.7800 0.8787\n",
      "1916 0.8800 0.8787\n",
      "1917 1.7600 0.8787\n",
      "1918 0.1500 0.8351\n",
      "1919 2.1800 0.8787\n",
      "1920 1.4400 0.8787\n",
      "1921 1.9200 0.0000\n",
      "1922 1.3200 0.0000\n",
      "1923 2.3400 0.8627\n",
      "1924 1.9000 0.8787\n",
      "1925 2.7100 0.0000\n",
      "1926 1.9000 0.8787\n",
      "1927 2.9000 0.8787\n",
      "1928 1.2700 0.8787\n",
      "1929 1.0600 0.8787\n",
      "1930 1.1500 0.8787\n",
      "1931 0.8700 0.8787\n",
      "1932 0.8700 0.8787\n",
      "1933 1.5600 0.8787\n",
      "1934 1.1000 0.8787\n",
      "1935 1.2400 0.8787\n",
      "1936 2.1300 0.8787\n",
      "1937 2.9400 0.8787\n",
      "1938 0.5200 0.8787\n",
      "1939 2.2800 0.8787\n",
      "1940 1.9000 0.8787\n",
      "1941 1.5900 0.8787\n",
      "1942 2.3700 0.8787\n",
      "1943 1.3700 0.8787\n",
      "1944 0.1800 0.8787\n",
      "1945 2.0800 0.8787\n",
      "1946 2.2700 0.8787\n",
      "1947 0.2000 0.8787\n",
      "1948 1.9300 0.8787\n",
      "1949 3.0200 0.8787\n",
      "1950 2.6100 0.8787\n",
      "1951 2.0500 0.8787\n",
      "1952 0.9500 0.8787\n",
      "1953 2.2500 0.8787\n",
      "1954 2.0200 0.8787\n",
      "1955 0.2700 0.8787\n",
      "1956 0.5400 0.0002\n",
      "1957 2.4100 0.8787\n",
      "1958 1.8300 0.8787\n",
      "1959 0.2800 0.1167\n",
      "1960 1.1500 0.8787\n",
      "1961 1.6300 0.8787\n",
      "1962 0.1400 0.0002\n",
      "1963 1.8900 0.8787\n",
      "1964 1.6100 0.8787\n",
      "1965 0.9100 0.8787\n",
      "1966 2.8600 0.8787\n",
      "1967 0.1500 -0.0000\n",
      "1968 2.8300 0.8787\n",
      "1969 1.9200 0.8782\n",
      "1970 2.8700 0.8787\n",
      "1971 0.6600 0.8787\n",
      "1972 1.2400 0.8787\n",
      "1973 2.0100 0.0004\n",
      "1974 2.9200 0.8787\n",
      "1975 2.8800 0.8787\n",
      "1976 1.2600 0.8787\n",
      "1977 2.9200 0.8787\n",
      "1978 2.9500 0.8787\n",
      "1979 2.6200 0.8787\n",
      "1980 2.3800 0.8787\n",
      "1981 2.6600 0.8787\n",
      "1982 2.7000 0.8787\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-f81f9785d2ff>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m         \u001b[0;31m# print(inf)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m         \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun_cobra\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparameter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobjective\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'pFBA'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m         \u001b[0;31m# print(parameter.Y[i], Y[i])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%d %.4f %.4f\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/amn_release/Library/Build_Dataset.py\u001b[0m in \u001b[0;36mrun_cobra\u001b[0;34m(model, objective, IN, method, verbose, objective_fraction, cobra_min_flux, gene_KO)\u001b[0m\n\u001b[1;32m    491\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m             \u001b[0msolution\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcobra\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflux_analysis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpfba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 493\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'pFBA'\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    494\u001b[0m             \u001b[0;31m# print(\"found!\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mcobra\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInfeasible\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/AMN/lib/python3.7/site-packages/cobra/flux_analysis/parsimonious.py\u001b[0m in \u001b[0;36mpfba\u001b[0;34m(model, fraction_of_optimum, objective, reactions)\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0madd_pfba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobjective\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobjective\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfraction_of_optimum\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfraction_of_optimum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m         \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslim_optimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m         \u001b[0msolution\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_solution\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreactions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreactions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0msolution\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/AMN/lib/python3.7/site-packages/cobra/core/model.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type, value, traceback)\u001b[0m\n\u001b[1;32m   1226\u001b[0m         \u001b[0;34m\"\"\"Pop the top context manager and trigger the undo functions\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1227\u001b[0m         \u001b[0mcontext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_contexts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1228\u001b[0;31m         \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1229\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1230\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprefix_existing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobjective\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"left\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/AMN/lib/python3.7/site-packages/cobra/util/context.py\u001b[0m in \u001b[0;36mreset\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     40\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_history\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m             \u001b[0mentry\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_history\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m             \u001b[0mentry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/AMN/lib/python3.7/site-packages/cobra/util/solver.py\u001b[0m in \u001b[0;36mreset\u001b[0;34m()\u001b[0m\n\u001b[1;32m    204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mreset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m             \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobjective\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreverse_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    207\u001b[0m             \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobjective\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdirection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreverse_value\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdirection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/AMN/lib/python3.7/site-packages/optlang/glpk_interface.py\u001b[0m in \u001b[0;36mobjective\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m    624\u001b[0m         \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproblem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    625\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_objective\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 626\u001b[0;31m             \u001b[0mvariables\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobjective\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    627\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mvariable\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    628\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mvariable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_index\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/AMN/lib/python3.7/site-packages/optlang/interface.py\u001b[0m in \u001b[0;36mvariables\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    463\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    464\u001b[0m         \u001b[0;34m\"\"\"Variables in constraint/objective's expression.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 465\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpression\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0matoms\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mVariable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    466\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    467\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_canonicalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpression\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/AMN/lib/python3.7/site-packages/optlang/interface.py\u001b[0m in \u001b[0;36mexpression\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    458\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mexpression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    459\u001b[0m         \u001b[0;34m\"\"\"The mathematical expression defining the objective/constraint.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 460\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_expression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    462\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/AMN/lib/python3.7/site-packages/optlang/glpk_interface.py\u001b[0m in \u001b[0;36m_get_expression\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    321\u001b[0m                         \u001b[0;32myield\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msymbolics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoeff\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    322\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 323\u001b[0;31m             \u001b[0mexpression\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msymbolics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msymbolics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mterm\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mterm\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mterm_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    324\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_expression\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpression\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproblem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_objective_offset\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    325\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_expression_expired\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/AMN/lib/python3.7/site-packages/optlang/glpk_interface.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    321\u001b[0m                         \u001b[0;32myield\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msymbolics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoeff\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    322\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 323\u001b[0;31m             \u001b[0mexpression\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msymbolics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msymbolics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mterm\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mterm\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mterm_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    324\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_expression\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpression\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproblem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_objective_offset\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    325\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_expression_expired\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/AMN/lib/python3.7/site-packages/optlang/glpk_interface.py\u001b[0m in \u001b[0;36mterm_generator\u001b[0;34m()\u001b[0m\n\u001b[1;32m    319\u001b[0m                     \u001b[0mcoeff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mglp_get_obj_coef\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproblem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproblem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    320\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mcoeff\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0.\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 321\u001b[0;31m                         \u001b[0;32myield\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msymbolics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoeff\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    322\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m             \u001b[0mexpression\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msymbolics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msymbolics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mterm\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mterm\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mterm_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# This cell run FBA on a provided dataset and compute R2 between\n",
    "# provided objective and calculated objective\n",
    "# R2 = 1 when the training set was generated by FBA, but may be different than 1\n",
    "# when the training set is an experimental one\n",
    "# For experimental training set medium input fluxes can be scaled by a value\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# What you can change \n",
    "seed = 10\n",
    "np.random.seed(seed=seed)\n",
    "mediumname = 'biolog_iML1515_EXP_UB' # for running Cobra with RC predictions as inputs\n",
    "# End of What you can change\n",
    "\n",
    "# Get data\n",
    "trainingfile = DIRECTORY+'Dataset_model/'+mediumname\n",
    "parameter = TrainingSet()\n",
    "parameter.load(trainingfile)\n",
    "print(\"Re-loading...\")\n",
    "parameter.printout()\n",
    "scaler_list = [21] # test different scalers\n",
    "\n",
    "# regression cobra vs. true values\n",
    "L = parameter.X.shape[0]\n",
    "\n",
    "all_res = []\n",
    "\n",
    "for scaler in scaler_list:\n",
    "    Y = {}\n",
    "    for i in range(L):\n",
    "        inf = {r: 0 for r in parameter.medium}\n",
    "        for j in range(len(parameter.medium)):\n",
    "            #print(j, parameter.medium[j],parameter.X[i,j], len(parameter.model.reactions))\n",
    "            eps = 1.0e-4 if parameter.X[i,j] < 1.0e-4 else 0\n",
    "            inf[parameter.medium[j]] = scaler * parameter.X[i,j] + eps\n",
    "\n",
    "        # print(inf)\n",
    "        out,Y[i] = run_cobra(parameter.model, parameter.objective, inf, method='pFBA', verbose=False)\n",
    "        # print(parameter.Y[i], Y[i])\n",
    "        print(\"%d %.4f %.4f\" % (i, parameter.Y[i], Y[i]))\n",
    "\n",
    "    Y = list(Y.values())\n",
    "    r2 = r2_score(parameter.Y[0:L], Y[0:L], multioutput='variance_weighted')\n",
    "\n",
    "    all_res.append((Y, r2))\n",
    "    print('scaler %.2f R2 %.4f ' % (scaler, r2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lfaure/miniconda3/envs/AMN/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set_theme(palette=\"colorblind\", font_scale=50)\n",
    "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
    "sns.regplot(parameter.Y, all_res[0][0], fit_reg=0, marker='.')\n",
    "sns.lineplot([min(parameter.Y.ravel()), max(parameter.Y.ravel())], [min(parameter.Y.ravel()), max(parameter.Y.ravel())], color='y')\n",
    "plt.savefig(\"RegPerf_cobra.png\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.11 ('AMN')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  },
  "vscode": {
   "interpreter": {
    "hash": "a6c18e9696c1974a25314c7f50070d762f9d347da5760adda9ec45ed5120fd09"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}