rul-prediction-using-long-short-term-memory-lstm (4) (1).ipynb

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     "text": [
      "/kaggle/input/nasa-cmaps/CMaps/RUL_FD002.txt\n",
      "/kaggle/input/nasa-cmaps/CMaps/test_FD003.txt\n",
      "/kaggle/input/nasa-cmaps/CMaps/Damage Propagation Modeling.pdf\n",
      "/kaggle/input/nasa-cmaps/CMaps/readme.txt\n",
      "/kaggle/input/nasa-cmaps/CMaps/train_FD003.txt\n",
      "/kaggle/input/nasa-cmaps/CMaps/test_FD004.txt\n",
      "/kaggle/input/nasa-cmaps/CMaps/train_FD004.txt\n",
      "/kaggle/input/nasa-cmaps/CMaps/x.txt\n",
      "/kaggle/input/nasa-cmaps/CMaps/test_FD002.txt\n",
      "/kaggle/input/nasa-cmaps/CMaps/train_FD001.txt\n",
      "/kaggle/input/nasa-cmaps/CMaps/train_FD002.txt\n",
      "/kaggle/input/nasa-cmaps/CMaps/RUL_FD001.txt\n",
      "/kaggle/input/nasa-cmaps/CMaps/RUL_FD004.txt\n",
      "/kaggle/input/nasa-cmaps/CMaps/RUL_FD003.txt\n",
      "/kaggle/input/nasa-cmaps/CMaps/test_FD001.txt\n",
      "/kaggle/input/nasa-cmaps/cmaps/CMaps/RUL_FD002.txt\n",
      "/kaggle/input/nasa-cmaps/cmaps/CMaps/test_FD003.txt\n",
      "/kaggle/input/nasa-cmaps/cmaps/CMaps/Damage Propagation Modeling.pdf\n",
      "/kaggle/input/nasa-cmaps/cmaps/CMaps/readme.txt\n",
      "/kaggle/input/nasa-cmaps/cmaps/CMaps/train_FD003.txt\n",
      "/kaggle/input/nasa-cmaps/cmaps/CMaps/test_FD004.txt\n",
      "/kaggle/input/nasa-cmaps/cmaps/CMaps/train_FD004.txt\n",
      "/kaggle/input/nasa-cmaps/cmaps/CMaps/x.txt\n",
      "/kaggle/input/nasa-cmaps/cmaps/CMaps/test_FD002.txt\n",
      "/kaggle/input/nasa-cmaps/cmaps/CMaps/train_FD001.txt\n",
      "/kaggle/input/nasa-cmaps/cmaps/CMaps/train_FD002.txt\n",
      "/kaggle/input/nasa-cmaps/cmaps/CMaps/RUL_FD001.txt\n",
      "/kaggle/input/nasa-cmaps/cmaps/CMaps/RUL_FD004.txt\n",
      "/kaggle/input/nasa-cmaps/cmaps/CMaps/RUL_FD003.txt\n",
      "/kaggle/input/nasa-cmaps/cmaps/CMaps/test_FD001.txt\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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     "text": [
      "Tensorflow version:  2.6.4\n",
      "Numpy version:  1.21.6\n",
      "Pandas version:  1.3.5\n",
      "Scikit-learn version:  1.0.2\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.models import Sequential\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import os\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "np.random.seed(34)\n",
    "print(\"Tensorflow version: \", tf.__version__)\n",
    "print(\"Numpy version: \", np.__version__)\n",
    "print(\"Pandas version: \", pd.__version__)\n",
    "print(\"Scikit-learn version: \", sklearn.__version__)"
   ]
  },
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   "source": [
    "columns=[\"id\",\"cycle\",\"op1\",\"op2\",\"op3\",\"sensor1\",\"sensor2\",\"sensor3\",\"sensor4\",\"sensor5\",\"sensor6\",\"sensor7\",\"sensor8\",\n",
    "         \"sensor9\",\"sensor10\",\"sensor11\",\"sensor12\",\"sensor13\",\"sensor14\",\"sensor15\",\"sensor16\",\"sensor17\",\"sensor18\",\"sensor19\"\n",
    "         ,\"sensor20\",\"sensor21\"]"
   ]
  },
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   "source": [
    "train_data = pd.read_csv(\"../input/nasa-cmaps/CMaps/train_FD001.txt\", sep= \"\\s+\", header = None,names=columns )"
   ]
  },
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     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "train_data. head() "
   ]
  },
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   "source": [
    "def add_rul(g):\n",
    "    g['RUL'] = max(g['cycle']) - g['cycle']\n",
    "    return g\n",
    "\n",
    "train = train_data.groupby('id').apply(add_rul)"
   ]
  },
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       "      <td>39.06</td>\n",
       "      <td>23.4190</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.15</td>\n",
       "      <td>1591.82</td>\n",
       "      <td>1403.14</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>2388.07</td>\n",
       "      <td>8131.49</td>\n",
       "      <td>8.4318</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>23.4236</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.0043</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1587.99</td>\n",
       "      <td>1404.20</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8133.23</td>\n",
       "      <td>8.4178</td>\n",
       "      <td>0.03</td>\n",
       "      <td>390</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.95</td>\n",
       "      <td>23.3442</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1582.79</td>\n",
       "      <td>1401.87</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>8133.83</td>\n",
       "      <td>8.3682</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.88</td>\n",
       "      <td>23.3739</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.0019</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.37</td>\n",
       "      <td>1582.85</td>\n",
       "      <td>1406.22</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>2388.04</td>\n",
       "      <td>8133.80</td>\n",
       "      <td>8.4294</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.90</td>\n",
       "      <td>23.4044</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
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       "   id  cycle     op1     op2    op3  sensor1  sensor2  sensor3  sensor4  \\\n",
       "0   1      1 -0.0007 -0.0004  100.0   518.67   641.82  1589.70  1400.60   \n",
       "1   1      2  0.0019 -0.0003  100.0   518.67   642.15  1591.82  1403.14   \n",
       "2   1      3 -0.0043  0.0003  100.0   518.67   642.35  1587.99  1404.20   \n",
       "3   1      4  0.0007  0.0000  100.0   518.67   642.35  1582.79  1401.87   \n",
       "4   1      5 -0.0019 -0.0002  100.0   518.67   642.37  1582.85  1406.22   \n",
       "\n",
       "   sensor5  ...  sensor13  sensor14  sensor15  sensor16  sensor17  sensor18  \\\n",
       "0    14.62  ...   2388.02   8138.62    8.4195      0.03       392      2388   \n",
       "1    14.62  ...   2388.07   8131.49    8.4318      0.03       392      2388   \n",
       "2    14.62  ...   2388.03   8133.23    8.4178      0.03       390      2388   \n",
       "3    14.62  ...   2388.08   8133.83    8.3682      0.03       392      2388   \n",
       "4    14.62  ...   2388.04   8133.80    8.4294      0.03       393      2388   \n",
       "\n",
       "   sensor19  sensor20  sensor21  RUL  \n",
       "0     100.0     39.06   23.4190  191  \n",
       "1     100.0     39.00   23.4236  190  \n",
       "2     100.0     38.95   23.3442  189  \n",
       "3     100.0     38.88   23.3739  188  \n",
       "4     100.0     38.90   23.4044  187  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "train.head() "
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       "      <td>1403.14</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.28</td>\n",
       "      <td>2388.07</td>\n",
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       "      <td>1404.20</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.42</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8133.23</td>\n",
       "      <td>8.4178</td>\n",
       "      <td>0.03</td>\n",
       "      <td>390</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.95</td>\n",
       "      <td>23.3442</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1582.79</td>\n",
       "      <td>1401.87</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.86</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>8133.83</td>\n",
       "      <td>8.3682</td>\n",
       "      <td>0.03</td>\n",
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       "      <td>100.0</td>\n",
       "      <td>38.88</td>\n",
       "      <td>23.3739</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.0019</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.37</td>\n",
       "      <td>1582.85</td>\n",
       "      <td>1406.22</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.19</td>\n",
       "      <td>2388.04</td>\n",
       "      <td>8133.80</td>\n",
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       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.90</td>\n",
       "      <td>23.4044</td>\n",
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       "   id  cycle     op1     op2    op3  sensor1  sensor2  sensor3  sensor4  \\\n",
       "0   1      1 -0.0007 -0.0004  100.0   518.67   641.82  1589.70  1400.60   \n",
       "1   1      2  0.0019 -0.0003  100.0   518.67   642.15  1591.82  1403.14   \n",
       "2   1      3 -0.0043  0.0003  100.0   518.67   642.35  1587.99  1404.20   \n",
       "3   1      4  0.0007  0.0000  100.0   518.67   642.35  1582.79  1401.87   \n",
       "4   1      5 -0.0019 -0.0002  100.0   518.67   642.37  1582.85  1406.22   \n",
       "\n",
       "   sensor5  ...  sensor12  sensor13  sensor14  sensor15  sensor16  sensor17  \\\n",
       "0    14.62  ...    521.66   2388.02   8138.62    8.4195      0.03       392   \n",
       "1    14.62  ...    522.28   2388.07   8131.49    8.4318      0.03       392   \n",
       "2    14.62  ...    522.42   2388.03   8133.23    8.4178      0.03       390   \n",
       "3    14.62  ...    522.86   2388.08   8133.83    8.3682      0.03       392   \n",
       "4    14.62  ...    522.19   2388.04   8133.80    8.4294      0.03       393   \n",
       "\n",
       "   sensor18  sensor19  sensor20  sensor21  \n",
       "0      2388     100.0     39.06   23.4190  \n",
       "1      2388     100.0     39.00   23.4236  \n",
       "2      2388     100.0     38.95   23.3442  \n",
       "3      2388     100.0     38.88   23.3739  \n",
       "4      2388     100.0     38.90   23.4044  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " train_data.head() "
   ]
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   "id": "55735c46",
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x2160 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnt_train=train_data[[\"id\", \"cycle\"]].groupby(\"id\").max().sort_values(by=\"cycle\", ascending=True)\n",
    "cnt_ind = [str(i) for i in cnt_train.index.to_list()]\n",
    "cnt_val = list(cnt_train.cycle.values)\n",
    "\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "fig, ax = plt.subplots(figsize=(12, 30))\n",
    "rects = ax.barh(cnt_ind, cnt_val)\n",
    "ax.set_title(\"Number of Cycles of Different Engines\", fontsize=22)\n",
    "ax.set_ylabel(\"Engine Id\", fontsize=18)\n",
    "ax.set_xlabel(\"Number of Cycles\", fontsize=18)\n",
    "ax.grid(True)\n",
    "\n",
    "for rect in rects:\n",
    "    width = rect.get_width()\n",
    "    plt.text(1.05*rect.get_width(), rect.get_y()+0.5*rect.get_height(),\n",
    "             '%d' % int(width),\n",
    "             ha='center', va='center')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5d1fee63",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-01T21:15:45.137777Z",
     "iopub.status.busy": "2022-07-01T21:15:45.137497Z",
     "iopub.status.idle": "2022-07-01T21:15:45.611843Z",
     "shell.execute_reply": "2022-07-01T21:15:45.610940Z"
    },
    "papermill": {
     "duration": 0.486174,
     "end_time": "2022-07-01T21:15:45.614059",
     "exception": false,
     "start_time": "2022-07-01T21:15:45.127885",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+Pp43ZcqUGPsNHTpUP/zwgz788ENdvXpVy5cvV7ly5ST9X4i9detWTZ06Vf3797fNCetpCFy6dGnt3LlTa9euVZ06dSzaQ0NDtXr1aqVOnVrNmjUzb3/V51xcP9ecOXNq5MiRWrJkiYKCgpQpU6YY+0VERKhFixYxfrfZSkJeH1FRUerWrZtu3bql999/X99//73574xjx46pfv36unz58kvV8fPPP2vAgAEaMGCAObDdvHmz6tevr2nTpumjjz6yWO9q+vTpmjRpkgoUKKCZM2daTH9fvny52rdvry5dumjv3r0p7gqRAGyPkVUA3ljp0qXTn3/+qeLFiysiIkJr167V0KFD1ahRI+XOnVsBAQFavHjxSx+7du3aMRbYdnJy0ujRo+Xg4GC+GldsunbtavFHqyR99NFHSp8+vU6ePGmehihJW7Zs0d69e5U+fXp98803Fh92CxQooH79+sV5PxUqVIg1bCtVqpS6dOmix48fxzkNwdHRUd9//32MP+YfPHigmTNnSpJGjRpl8eHQZDLpu+++s/jwaQuurq6SZDFi4NatW5o0aZJcXFw0c+bMGAFc165dFRAQoFOnTlkEN9GhxNy5c2Pcz/r163X58uUYo2zi884778QIqiSpdu3aatSokW7dupVolxV/8OCBpk+fLunpzyI6qJKePhe///57ubi4aOfOnXFeSGDUqFEWV9J76623zN+w//3331bXMnPmTN2+fVv+/v4WH6glqWLFiuYg5qeffrL6mNbq2bNnvJeTf3bE07Oj6Z4fcRIaGqpVq1bF+FD9v//9Tzt27FDp0qU1atQoc1Al/d/j7ObmpoULF8Y6uipTpkwaNWpUjKBKehpESzL/HJ81bdo0RUVF6f3331eaNGmseiwaNGigH374QSaTSVevXtXMmTP1wQcfqHLlyvLy8lL37t117NixGPt99913Cg8P1/Dhwy1CA+np+0z0CJcpU6bEer8JeU+LnqpYvXr1GO8Vb731Vox1BKNHgHbv3j1G24cffqjSpUvr9u3b5vel57Vu3fqVgqpRo0bF+ryKbRpw1apVYx3t+qqvjzJlysTYL3r66PHjx9W5c2dzUCVJadKkMa/V+LLvN/G9pp4P7+N7H/3999/16NEj1apVSxkzZjRvf9XnXFw/1wwZMqhFixZ6+PBhjKnijx8/1q+//ipJLzVCKa7nQvS/uKaXJvR3/v79+81TiJ/9O8Pb2zve3/kvUqJECQ0cONBiZGH58uVVvXp1RUZGWjxXnjx5om+++UbS04Dv+XUa69Wrpw4dOigsLEzz589/6ZoAIC6MrALwRsufP7+Cg4O1fft2rV27Vrt27dLevXsVGhqq7du3a/v27Vq3bt1Lj+jYu3evNm7cqLNnz+revXuKioqSJKVOnVrXr19XaGhorN82xvYHdurUqeXl5aV9+/bp8uXL5oVSN2/eLEnmqUPPa968eZzD9yXpzp07Wrt2rfbv369bt24pPDxc0tNRG9LTDzqxKVq0aKxB17///qu7d+8qW7ZsqlixYoz2QoUKqWDBgvrvv//irOlVRa+t8ewf3Bs3btSDBw8UEBAQ6+gK6ekf5WvWrNHOnTtVu3ZtSU8/XGbLlk379u3TgQMHVLBgQXP/uKauvMiNGze0evVqHTp0SGFhYeapMdHTUY8fP54oozyifxZZs2ZV1apVY7S7urqqVq1a+v333xUSEmKebhjNwcEhxgco6ekHIkkJ+vY++nka1xS+999/X2PHjtW2bdv05MmTOEf4vAx/f3+LwO15z4ZEGTJkMI+mW7BggcVoukWLFunRo0dq0KCBxYfq6GkuDRo0iHX6mLOzs4oXL661a9dq9+7dMdaXqlKlSpwjOEqWLKmSJUvqn3/+0aZNm8yvqQcPHmju3LlKlSqVOnbsaMWj8H/atWunpk2bavXq1dq0aZP27NmjgwcP6s6dO5o3b56WLFmimTNnqmbNmpKevp7Wr18vOzu7GKM6o0WHRHGtA5SQ97QSJUpIerrWkfT0NRg99fp5ERER2r59u6S4X4etW7fWzp07FRISor59+8Zor1+/fqz7WatQoUKxBlPRobk19/Wqr4/YRluZTCZlypRJN2/ejLU9T548khL2On5WfNNxixYtanG7SZMmGjRokDkEfnY0U2zvo4nxnIvv59qlSxdNmzZNM2bM0AcffGB+3S5btkxXrlxRhQoV5OPjE+f+cYnruRDt2feNZ73M7/yAgIBY3zfeffddffLJJwmuXZJq1KgR65dJ3t7eWrduncVzZf/+/bp8+bIKFCgQ52NVvnx5TZkyRTt37jQH7wCQWAirAKQIfn5+8vPzk/T0j+SdO3dq1KhR2rBhg3777TcFBASY10Gyxt27d9WlS5cXTne7fft2rGFVXFfsif7D9OHDh+ZtFy9elCTlypUr1n1MJpPSp08f65o3K1asUK9eveJc8F16GmbFJq4ao+uJLciKlitXLpuGVdHrSz37wSB6odk1a9a8cDrCs9MmU6VKpRYtWmjMmDGaO3eu+dv8uEbZvMiMGTP06aef6v79+3H2iesxT6hLly5Jiv9n4eXlZdH3WR4eHrGuw5U+fXpJStCaRy+qJVeuXEqVKpUePnyomzdvxhkovow2bdrEu0j+81q3bq1Fixbpt99+swir4gono59bQ4YM0ZAhQ+I9dmxTcl90ha5u3bqpa9eumjZtmjmsWrRokW7duqWAgADzzzAhXFxc9O677+rdd9+VJIWFhWn58uX68ssvdfnyZQUGBmr//v1Kmzatbt68aX7/yJs3b4LPT0rYe1qLFi0UHByshQsX6v3335e9vb0KFCigcuXKqWnTpub3aunp6MlHjx4pVapUcd5HfM/x+GqzVt26deNdPNya+3rV18ezoyaf5ezsrJs3b8Y6lTs6AHz2sU+IhHyJkyFDBtWrV0+///67Fi5caA4ujh49qn/++Ufu7u4Wwbgtn3OS5OPjoypVquivv/7S//73P3MwG72OVZcuXaw+t2cl5LlgTa2xvT6inytx7ZMhQ4Y4f+e/yLNT/F5UR/SahYcOHUrQ71QASCyEVQBSnFSpUsnPz08LFy5UtWrVtHfvXq1YsSJBYdXw4cO1atUq+fj4aOjQoSpevLhcXV3Nw/V9fHx0+fJl80ir59l6mpz0dEHozp0768GDB/r444/VtGlT5cqVS87OzkqVKpV++eUX9e7dO84arZ12lBz+/fdfSbKYmhd9VURvb2+VKlUq3v2fb2/VqpXGjBmjhQsXavjw4XJwcNAff/yhhw8fxhhlE5/du3fr448/loODg7788kvVqlVL2bJlU9q0aWVnZ6cvvvhCY8aMifMxf1kv+3yyxfMwKZ7br6pKlSrKnj279u7dax5Nd+zYMe3atSvGh2rp/55b5cuXjzM0jhbbB8wXvZYaNWqkzz77TCtWrNDly5fl4eGhadOmSXq5qUqxyZAhg1q3bq1ChQqpcuXKunHjhrZv366qVauaz8/e3l7vvffeSx0/IT/3VKlSacqUKerTp4/WrFmjbdu2afv27Zo8ebImT56s999/P9aLP7zscysp38tedF8vew4vWhDeCK+7Vq1a6ffff9fcuXPNYVVcF6hIjOfcix7rrl276q+//tK0adNUs2ZNHTx4UFu2bFHWrFljXa/Oll7m5xPfPi97gYCE7Bf9M8qWLZsqV64cb9/npwgCQGIgrAKQYtnb26tSpUrau3dvgr8VjL56z/Tp02OsZXTv3j1duXIl0erMmjWrJOns2bOxtoeGhsb6DeuaNWv04MEDNWjQQJ9//nmM9uhpgIldz4vaXlV4eLj5inrPXgY9emSBr69vgqd15s2bV2XKlNGOHTu0bt061a5d27z2SkKmAC5dutS8OO4HH3wQo/1lH/O4RP8snr98+bOivx2P7msrWbNm1dGjR3X69OlYP9icPXtWkZGRSpMmjdXhn62kSpVKzZs3txhNF/3zju2qj9HPrUaNGr30iIz4pE6dWu3bt9c333yjmTNn6p133tGePXvk5eUV6zTNV1G0aFG5urrqxo0b5vc9V1dXOTk56cGDBxo9erT5qqe25uvra37/jIyM1Nq1a9WlSxfNnj1bTZo0UbVq1ZQpUya99dZbevTokc6ePWue2vaspHqOv4rX6fXxsp4PgQsUKGBey+j599GkeM7VqlVLnp6eWrdunc6cOWMOgNu1a2fVlV2Ti4eHhyRZrGP1rNu3b9v0irvRot/33N3dk+TiFwDwPBZYB/DGsmb0yvnz5yXFnGIRvcZN9DeLz4ueVhfb1Ivff/89UUfORK/bsWbNmlhDqeir1SWkxkePHsW7AHx8ihUrJmdnZ124cMG8tsazDh48qAMHDrzUsa0xfPhwXb58WSaTSW3atDFvr1KlihwdHfXXX3+91B/y0dPIfvvtNx0/flw7d+6MdZRNfOJ7zK9fv67g4OBY93vR8y0uxYoVk4uLiy5evBjrYug3b97U6tWrJT1dbN+Wop+n8+bNi7V9zpw5kp6uL2WED4rRH54XLlyo8PBwLViwwGL7s6KfA9EhqS107NhRjo6OmjlzpiZNmiTp6aiqhI6geNF7T1hYmHkaavT7noODgzlAiQ7ik1qqVKlUq1Yt81py0dOIHRwczNMCo0fpPC86aLT1c/xVvG6vj5cRPaVaevqz+uuvv3Tx4sVYL1CRFM+5VKlSqXPnzoqMjNRPP/2kBQsWyNHRMdYF8I0keqH8NWvW6O7duzHaFy1alCR1lCxZUpkyZdK+ffsS/YsWALAGYRWAN9bUqVPVo0cP/fPPPzHaIiIiNHPmTPMfyc9fFj76G/ojR47EeuzoBaijv6mNtmfPHg0fPvyVa39WuXLlVLhwYYWFhWngwIF6/Pixue3IkSMaPXp0vDUuW7ZMV69eNW8PDw9X//79zaMREipt2rTmkGjgwIEWo9LCwsL0ySefJPo0N+npouSdOnXS+PHjZW9vr59//tm8tpIkZcmSRZ07d1ZYWJhatmypo0ePxjjGvXv3tHDhQovHI1rjxo3l5OSk1atXa/z48ZJiH2UTn+jHfN68eRYfMu7cuaOePXsqLCws1v1e9HyLi5OTkzp06CDp6c/i2cVxHz58qI8//lh3795V6dKlYyyuntjatWundOnSaevWrfr5558t2jZv3qzJkydLUowrmiWXvHnzys/PT1evXtWQIUN04cKFOK/6WK9ePRUrVkybN29Wnz59Yl0D7sqVK3Fejc4aHh4eatCggS5evKgFCxYoTZo0ev/99xN8nObNm+unn36K9Tl+9epV9ejRQ+Hh4cqRI4fKlCljbhswYIAcHR01aNAgLVq0KMZrOCoqSv/88482bNiQ8JN7zm+//WaeyvusmzdvmhfTfnY6Zc+ePSVJP//8c4yrWo4bN047duxQ+vTp1bZt21euzVZet9fHy3o2BI6+El9co1OT4jnXpk0bpU2bVlOnTtWdO3dUt25d88glo6pQoYJ8fX1169Ytffrpp+YLdEjSiRMnzFfoszVHR0f169dPT548UevWrWP9Wyo8PFwrV66M9fctALyq1/OrGwCwwuPHjzV37lzNnTtX7u7uKly4sDJmzKhbt27pwIED5kVMP/rooxhXUqpXr55CQkLUtWtXVa1a1XwVvuHDhytTpkwaMGCA2rVrpy+++EKLFy9W/vz5denSJW3btk1NmzbVtm3b4hzCn1B2dnaaNGmS6tatq7lz52rjxo0qU6aMwsLCtGnTJgUEBOjff/+NcX916tRRkSJFtG/fPpUsWVLly5dXmjRptH37dt2+fVvdunUzj+BIqM8++0xbt27V3r17VaJECVWsWFH29vbatGmTMmTIoNq1a79w8fm4bNu2TYGBgZKejjQKCwvT0aNHderUKUlPF4gdN26cxRTAaF988YUuX76sP/74Q2XLllXhwoXl5eUlOzs7nT17Vv/9958ePXqkHTt2KEuWLBb7pk+fXvXq1dPChQs1Y8YMSQm/CuD777+vn3/+WXv37lWxYsXk7++vqKgobdmyRalTp9b777+v2bNnx9ivdOnScnd31969e1WlShX5+PjI0dFRfn5+LwwsPv30U+3Zs0chISEqWbKkKlasKCcnJ23dulWXL19Wjhw54rz0e2Jyd3fXzz//rI4dO2rgwIH69ddf5evrq0uXLmnr1q2KjIxU3759E31amyTNmjVLISEhcbZXq1Yt1kXyW7Vqpe3bt5tfB3H9vFOlSqU5c+aoWbNmmjFjhn7//XcVKlRI2bNn18OHD3XixAkdPnxYbm5uateu3UufR7du3cyjJpo0afJS08EuXryoIUOGaOjQofLx8VGePHnk6OioS5cuaffu3Xr06JFMJpOmTZtmXmNPkooXL66ff/5ZvXr1UqdOnTRs2DD5+PgoY8aMun79uvbv369r166pd+/eMa52mFDLli1TYGCgsmXLpsKFCytDhgy6efOmtm7dqnv37qls2bKqV6+euX9AQIB69+6tsWPHqk6dOipbtqyyZs2qgwcP6uDBg0qTJo0mT54c4zVtJMn5+nhZ0e/DcRk8eHCMNdry5MkjPz8/bd++XX/88Ue8F6hIiuecyWTSe++9p19++UWS1Llz55c6TrQVK1bEO829aNGiFhdteBnRv/Pr1aunmTNnasOGDTF+5+/evVvnz5+3uNKpLQQGBurcuXOaMGGCqlevroIFCyp37txKnTq1Ll26pH379unevXv6/fffWbcKQKIjrALwxmrTpo1y5cqlv//+W//8848OHjyoa9euydHRUdmyZVPLli3Vtm1blS1bNsa+Xbt21Z07d7Rw4UKtWbPGfFW0vn37KlOmTGrYsKGWLVumb775Rv/9959OnTqlt99+W0FBQerSpUuMy3q/Kl9fXwUHB+urr77Shg0btGLFCuXKlUsDBgzQRx99pOLFi8fYx8HBQStWrNC3336rFStWKDg4WCaTSRUqVNDAgQO1Y8eOl67HxcVFK1as0HfffadFixZp3bp1ypw5sxo2bKghQ4bos88+e+ljnzp1yhxMpU6dWunTp1e2bNnUpk0b1ahRQ7Vr17b4kP0sR0dHzZgxQ++9955mzZql3bt368CBA3JxcZGHh4eaNm2qOnXqKHfu3LHu37p1a/O0yrhG2cTHZDIpODhYI0eOVHBwsNauXSs3NzfVr19fgwcPNodgz3vrrbf0+++/a8SIEdqxY4f27dunyMhIRUREvDCsSpMmjf744w9Nnz5d8+fPV0hIiB4/fqxcuXKpefPm+uijjywuI29LdevWVXBwsMaOHatNmzZpyZIlcnFxUbVq1dS1a1fzFbkS27Zt22KMuHlWhgwZYv3A3LhxYw0cOFAPHjx44VUfs2fPrg0bNmjWrFn6448/dPDgQe3atUuZMmVS1qxZ1atXL4uA5WWULFnSfJWvl10ba9asWVq/fr3+/vtvHTt2TJs2bdLdu3fl4uKiwoULq1q1aurSpUusV2Ns2rSpSpQooZ9//ll//fWXeZpvlixZVLhwYdWsWVMNGzZ8pXOUno4eypUrl3bs2KE9e/YoNDRUrq6uKlKkiFq1aqX33nsvxmt82LBh8vf315QpU7R7927t2LFDbm5uat68ufr06SMfH59XrsvWkuv18bLimnYZLTAwMNYLCrRu3Vrbt2+X9HTdqPhC16R4zlWtWlW//PKLChQo8MpTRf/77794r3QbFhb2ymGVJBUuXFh//fWX+Xf+8uXL5enpqYEDB6pHjx7KmTOnUqVKlSTrm3311VeqW7eupk+fru3bt2vt2rVKkyaNPDw8FBAQoNq1a8f6dxQAvCq70NDQxJ+rAQAAgARZsWKFWrdurZIlS2r9+vXJXQ7wRmjdurX5y5XEurpmctqyZYvq1KkjX19fbdmyJbnLAQCbYc0qAACAZBYREWFefy56jSYAr+bff//VqlWrlClTJvPi76+Du3fvxrqG4ZEjR9S7d29JUsuWLZO4KgBIWkwDBAAASCazZ8/Wli1btHv3bh0+fFilSpVS48aNk7ss4LX2wQcf6N69e1q3bp0iIyM1ePBgOTs7J3dZVrt69ar8/PyUJ08e5cmTR87Ozjp79qz+/fdfPXnyRJUqVUqU6YYAYGRMAwQAAEgmgYGB+u2332QymVS1alUFBQUZ/mplgNGZTCalSpVKOXPmVOfOnfXBBx8kd0kJcvv2bY0aNUobN27UhQsXdPv2baVNm1YFChRQkyZN1LFjxzjXbgSANwVhFQAAAAAAAAyDNasAAAAAAABgGIRVAAAAAAAAMAzCKgAAAAAAABgGYZWVjh07ltwlvDRqTx7UnjyoPXlQe/Kg9uRB7cmD2pMHtScPak8e1J48qD3pvQ51E1YBAAAAAADAMAirAAAAAAAAYBiEVQAAAAAAADAMwioAAAAAAAAYhkNyFwAAAAAAAGA0ERERunfvXpztadKkUVhYWBJWlDhetW5nZ2c5ONg2TiKsAgAAAAAAeEZERITu3Lkjk8kkOzu7WPu89dZbSpMmTRJX9upepe6oqCiFhoYqXbp0Ng2smAYIAAAAAADwjHv37sUbVKVUdnZ2MplM8Y44SwyEVQAAAAAAAM8hqIpdUjwuhFUAAAAAAAAwDMIqAAAAAAAAGAZhFQAAAAAAACRJZcuWVVBQULLWwNUAAQAAAAAAXsA040KS3l9oh+yJdqw5c+aof//+unAhac/hZTGyCgAA4BljxoxR1apVlTNnTuXJk0fNmzfXwYMHze0REREaOnSoypUrp2zZsil//vzq3Lmzzp0798Jjh4SEqHLlynJ3d1fRokU1ffp0i/YnT55oxIgRKlKkiNzd3VWkSBGNGDFCERER5j4jRoxQ6dKllS1bNnl6eqpBgwbavn174j0AAAAAyYywCgAA4BkhISHq1KmT1qxZo6VLl8rBwUGNGjXSrVu3JEkPHz7U3r171bdvX/3999+aO3euLly4oHfffdciVHre6dOn9d5776lMmTLauHGjPv74Y/Xv319Lliwx9xk7dqymTp2qUaNGaceOHfr66681ZcoUjRkzxtzH29tb3377rbZs2aLVq1fL09NT7777rq5evWq7BwUAALwWNm/erHfeeUfZs2dXrly5VK1aNU2ePFk9e/bUvXv3ZDKZ5OHhYZ7md+3aNbVs2VIeHh4qVKiQZs2alcxn8BTTAAEAAJ6xePFii9uTJk1Srly5tG3bNtWuXVsuLi76888/Lfp8//338vf315EjR1SwYMFYjztjxgx5eHho9OjRkqT8+fNr165dGjdunBo2bChJ2rFjh2rVqqXatWtLkjw9PVW7dm39888/5uM0b97c4rgjR47UrFmztH//flWvXv2Vzh0AALy+IiIi1KpVK7Vp00ZTpkzR48ePtXfvXvn4+CgoKEhffvml9uzZo0ePHilTpkySpB49eujcuXP6888/5eTkpMGDB+vs2bPJfCaMrAIAAIjX3bt3FRkZKZPJFGefO3fuSFK8fXbs2KFq1apZbKtevbr27Nmjx48fS5L8/f0VEhKio0ePSpIOHz6sTZs2qUaNGrEeMzw8XDNnzlT69OlVuHDhBJwVAAB409y5c0dhYWGqVauWcufOrXz58qlZs2YqXLiw0qdPLzs7O7m7uytLlixycXHR8ePHtW7dOo0dO1b+/v4qWrSoJk6cqAcPHiT3qTCyCgAAID4DBw5U4cKFVaZMmVjbw8PD9dlnn6lWrVrKnj3uhVCvXr2qKlWqWGxzc3NTRESEbty4IQ8PD/Xu3Vt3796Vn5+f7O3tFRERob59+6pz584W+61evVqdOnXS/fv35eHhoT/++ENZsmR55XMFAACvr4wZM6pVq1Zq2rSpKleurEqVKqlhw4bKmTNnrP2PHDmiVKlSqWTJkuZtuXLlUtasWZOq5DgxsgoAACAOgwcP1rZt2zRr1izZ29vHaI+IiFDXrl0VFhamCRMmvPL9LV68WPPmzdPUqVP1999/6+eff9bUqVP166+/WvSrWLGiNm3apLVr16p69epq3769Ll++/Mr3DwA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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset_stats = train_data.agg(['mean', 'std']).T[2:]\n",
    "ax = subset_stats.plot.bar(figsize=(18,10), \n",
    "                           y=\"std\")\n",
    "ax.set_title(\"Standard Deviation of Every Sensor From Every Engine\", fontsize=22)\n",
    "ax.set_xlabel(\"Sensor\", fontsize=18)\n",
    "ax.set_ylabel(\"Standard Deviation\", fontsize=18)\n",
    "\n",
    "for p in ax.patches:\n",
    "    ax.annotate(str(round(p.get_height(),3)), (p.get_x() * 1.005, p.get_height() * 1.005))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd803d3c",
   "metadata": {
    "papermill": {
     "duration": 0.009618,
     "end_time": "2022-07-01T21:15:45.633547",
     "exception": false,
     "start_time": "2022-07-01T21:15:45.623929",
     "status": "completed"
    },
    "tags": []
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e5eb55ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-01T21:15:45.652837Z",
     "iopub.status.busy": "2022-07-01T21:15:45.652538Z",
     "iopub.status.idle": "2022-07-01T21:15:45.659181Z",
     "shell.execute_reply": "2022-07-01T21:15:45.658244Z"
    },
    "papermill": {
     "duration": 0.018538,
     "end_time": "2022-07-01T21:15:45.661112",
     "exception": false,
     "start_time": "2022-07-01T21:15:45.642574",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'no_std_sensors = [\\'op3\\', \\'sensor1\\', \\'sensor5\\', \\'sensor10\\',\\n                 \\'sensor16\\', \\'sensor18\\', \\'sensor19\\']\\ntrain = train_data.drop(no_std_sensors, axis=1)\\n#Let\\'s also plot some sensor data over the cycles of some engines.\\n\\ndef plot_ts_sensors(engine_ids, sensor):\\n    sub_df = train[train[\"id\"].isin(engine_ids)][[\"id\", \"cycle\", sensor]]\\n    sub_df = sub_df.pivot(columns=\"id\", index=\"cycle\", values=sensor)\\n    ax = sub_df.plot(figsize=(18,10))\\n    ax.set_title(f\"{sensor}\", fontsize=22, style=\"italic\")\\n    ax.set_xlabel(\"cycle\", fontsize=18)\\n    ax.set_ylabel(\"value\", fontsize=18)\\nplt_sensors = list(train.columns)[2:]\\nnum_ex = 8\\nfor sens in plt_sensors:\\n    plot_ts_sensors(random.sample(range(1, 100), num_ex), sens)\\n'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"no_std_sensors = ['op3', 'sensor1', 'sensor5', 'sensor10',\n",
    "                 'sensor16', 'sensor18', 'sensor19']\n",
    "train = train_data.drop(no_std_sensors, axis=1)\n",
    "#Let's also plot some sensor data over the cycles of some engines.\n",
    "\n",
    "def plot_ts_sensors(engine_ids, sensor):\n",
    "    sub_df = train[train[\"id\"].isin(engine_ids)][[\"id\", \"cycle\", sensor]]\n",
    "    sub_df = sub_df.pivot(columns=\"id\", index=\"cycle\", values=sensor)\n",
    "    ax = sub_df.plot(figsize=(18,10))\n",
    "    ax.set_title(f\"{sensor}\", fontsize=22, style=\"italic\")\n",
    "    ax.set_xlabel(\"cycle\", fontsize=18)\n",
    "    ax.set_ylabel(\"value\", fontsize=18)\n",
    "plt_sensors = list(train.columns)[2:]\n",
    "num_ex = 8\n",
    "for sens in plt_sensors:\n",
    "    plot_ts_sensors(random.sample(range(1, 100), num_ex), sens)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f2439c85",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-01T21:15:45.681929Z",
     "iopub.status.busy": "2022-07-01T21:15:45.681206Z",
     "iopub.status.idle": "2022-07-01T21:15:45.697131Z",
     "shell.execute_reply": "2022-07-01T21:15:45.696312Z"
    },
    "papermill": {
     "duration": 0.028296,
     "end_time": "2022-07-01T21:15:45.699006",
     "exception": false,
     "start_time": "2022-07-01T21:15:45.670710",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def process_targets(data_length, early_rul = None):\n",
    "    \"\"\" \n",
    "    Takes datalength and earlyrul as input and \n",
    "    creates target rul.\n",
    "    \"\"\"\n",
    "    if early_rul == None:\n",
    "        return np.arange(data_length-1, -1, -1)\n",
    "    else:\n",
    "        early_rul_duration = data_length - early_rul\n",
    "        if early_rul_duration <= 0:\n",
    "            return np.arange(data_length-1, -1, -1)\n",
    "        else:\n",
    "            return np.append(early_rul*np.ones(shape = (early_rul_duration,)), np.arange(early_rul-1, -1, -1))\n",
    "def process_input_data_with_targets(input_data, target_data = None, window_length = 1, shift = 1):\n",
    "    \"\"\"Depending on values of window_length and shift, this function generates batchs of data and targets \n",
    "    from input_data and target_data.\n",
    "    \n",
    "    Number of batches = np.floor((len(input_data) - window_length)/shift) + 1\n",
    "    \n",
    "    **We don't check input dimensions uisng exception handling. So readers should be careful while using these\n",
    "    functions. If input data are not of desired dimension, either error occurs or something undesirable is \n",
    "    produced as output.**\n",
    "    \n",
    "    Arguments:\n",
    "        input_data: input data to function (Must be 2 dimensional)\n",
    "        target_data: input rul values (Must be 1D array)s\n",
    "        window_length: window length of data\n",
    "        shift: Distance by which the window moves for next batch. This is closely related to overlap\n",
    "               between data. For example, if window length is 30 and shift is 1, there is an overlap of \n",
    "               29 data points between two consecutive batches.\n",
    "        \n",
    "    \"\"\"\n",
    "    num_batches = np.int(np.floor((len(input_data) - window_length)/shift)) + 1\n",
    "    num_features = input_data.shape[1]\n",
    "    output_data = np.repeat(np.nan, repeats = num_batches * window_length * num_features).reshape(num_batches, window_length,\n",
    "                                                                                                  num_features)\n",
    "    if target_data is None:\n",
    "        for batch in range(num_batches):\n",
    "            output_data[batch,:,:] = input_data[(0+shift*batch):(0+shift*batch+window_length),:]\n",
    "        return output_data\n",
    "    else:\n",
    "        output_targets = np.repeat(np.nan, repeats = num_batches)\n",
    "        for batch in range(num_batches):\n",
    "            output_data[batch,:,:] = input_data[(0+shift*batch):(0+shift*batch+window_length),:]\n",
    "            output_targets[batch] = target_data[(shift*batch + (window_length-1))]\n",
    "        return output_data, output_targets\n",
    "def process_test_data(test_data_for_an_engine, window_length, shift, num_test_windows = 1):\n",
    "    \"\"\" This function takes test data for an engine as first input. The next two inputs\n",
    "    window_length and shift are same as other functins. \n",
    "    \n",
    "    Finally it takes num_test_windows as the last input. num_test_windows sets how many examplles we\n",
    "    want from test data (from last). By default it extracts only the last example.\n",
    "    \n",
    "    The function return last examples and number of last examples (a scaler) as output. \n",
    "    We need the second output later. If we are extracting more than 1 last examples, we have to \n",
    "    average their prediction results. The second scaler halps us do just that.\n",
    "    \"\"\"\n",
    "    max_num_test_batches = np.int(np.floor((len(test_data_for_an_engine) - window_length)/shift)) + 1\n",
    "    if max_num_test_batches < num_test_windows:\n",
    "        required_len = (max_num_test_batches -1)* shift + window_length\n",
    "        batched_test_data_for_an_engine = process_input_data_with_targets(test_data_for_an_engine[-required_len:, :],\n",
    "                                                                          target_data = None,\n",
    "                                                                          window_length = window_length, shift = shift)\n",
    "        return batched_test_data_for_an_engine, max_num_test_batches\n",
    "    else:\n",
    "        required_len = (num_test_windows - 1) * shift + window_length\n",
    "        batched_test_data_for_an_engine = process_input_data_with_targets(test_data_for_an_engine[-required_len:, :],\n",
    "                                                                          target_data = None,\n",
    "                                                                          window_length = window_length, shift = shift)\n",
    "        return batched_test_data_for_an_engine, num_test_windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2ac2a604",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-01T21:15:45.718977Z",
     "iopub.status.busy": "2022-07-01T21:15:45.718698Z",
     "iopub.status.idle": "2022-07-01T21:15:46.115274Z",
     "shell.execute_reply": "2022-07-01T21:15:46.113119Z"
    },
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     "duration": 0.40916,
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     "exception": false,
     "start_time": "2022-07-01T21:15:45.708291",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:33: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:58: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed trianing data shape:  (17731, 30, 14)\n",
      "Processed training ruls shape:  (17731,)\n",
      "Processed test data shape:  (497, 30, 14)\n",
      "True RUL shape:  (100,)\n"
     ]
    }
   ],
   "source": [
    "test_data = pd.read_csv(\"../input/nasa-cmaps/CMaps/test_FD001.txt\", sep = \"\\s+\", header = None,names=columns )\n",
    "true_rul = pd.read_csv(\"../input/nasa-cmaps/CMaps/RUL_FD001.txt\", sep = '\\s+', header = None)\n",
    "\n",
    "window_length = 30\n",
    "shift = 1\n",
    "early_rul = 125           \n",
    "processed_train_data = []\n",
    "processed_train_targets = []\n",
    "\n",
    "# How many test windows to take for each engine. If set to 1 (this is the default), only last window of test data for \n",
    "# each engine is taken. If set to a different number, that many windows from last are taken. \n",
    "# Final output is the average output of all windows.\n",
    "num_test_windows = 5     \n",
    "processed_test_data = []\n",
    "num_test_windows_list = []\n",
    "\n",
    "columns_to_be_dropped =['id','op1','op2','op3', 'sensor1', 'sensor5','sensor6','sensor7', 'sensor10',\n",
    "                 'sensor16', 'sensor18', 'sensor19']\n",
    "\n",
    "train_data_first_column = train_data [\"id\"]\n",
    "test_data_first_column = test_data[\"id\"]\n",
    "\n",
    "# Scale data for all engines\n",
    "scaler = StandardScaler()\n",
    "train_data = scaler.fit_transform(train_data.drop(columns = columns_to_be_dropped))\n",
    "test_data = scaler.transform(test_data.drop(columns = columns_to_be_dropped))\n",
    "\n",
    "train_data = pd.DataFrame(data = np.c_[train_data_first_column, train_data])\n",
    "test_data = pd.DataFrame(data = np.c_[test_data_first_column, test_data])\n",
    "\n",
    "num_train_machines = len(train_data[0].unique())\n",
    "num_test_machines = len(test_data[0].unique())\n",
    "\n",
    "# Process training and test data sepeartely as number of engines in training and test set may be different.\n",
    "# As we are doing scaling for full dataset, we are not bothered by different number of engines in training and test set.\n",
    "\n",
    "# Process trianing data\n",
    "for i in np.arange(1, num_train_machines + 1):\n",
    "    temp_train_data = train_data[train_data[0] == i].drop(columns = [0]).values\n",
    "    \n",
    "    # Verify if data of given window length can be extracted from training data\n",
    "    if (len(temp_train_data) < window_length):\n",
    "        print(\"Train engine {} doesn't have enough data for window_length of {}\".format(i, window_length))\n",
    "        raise AssertionError(\"Window length is larger than number of data points for some engines. \"\n",
    "                             \"Try decreasing window length.\")\n",
    "        \n",
    "    temp_train_targets = process_targets(data_length = temp_train_data.shape[0], early_rul = early_rul)\n",
    "    data_for_a_machine, targets_for_a_machine = process_input_data_with_targets(temp_train_data, temp_train_targets, \n",
    "                                                                                window_length = window_length, shift = shift)\n",
    "    \n",
    "    processed_train_data.append(data_for_a_machine)\n",
    "    processed_train_targets.append(targets_for_a_machine)\n",
    "\n",
    "processed_train_data = np.concatenate(processed_train_data)\n",
    "processed_train_targets = np.concatenate(processed_train_targets)\n",
    "\n",
    "# Process test data\n",
    "for i in np.arange(1, num_test_machines + 1):\n",
    "    temp_test_data = test_data[test_data[0] == i].drop(columns = [0]).values\n",
    "    \n",
    "    # Verify if data of given window length can be extracted from test data\n",
    "    if (len(temp_test_data) < window_length):\n",
    "        print(\"Test engine {} doesn't have enough data for window_length of {}\".format(i, window_length))\n",
    "        raise AssertionError(\"Window length is larger than number of data points for some engines. \"\n",
    "                             \"Try decreasing window length.\")\n",
    "    \n",
    "    # Prepare test data\n",
    "    test_data_for_an_engine, num_windows = process_test_data(temp_test_data, window_length = window_length, shift = shift,\n",
    "                                                             num_test_windows = num_test_windows)\n",
    "    \n",
    "    processed_test_data.append(test_data_for_an_engine)\n",
    "    num_test_windows_list.append(num_windows)\n",
    "\n",
    "processed_test_data = np.concatenate(processed_test_data)\n",
    "true_rul = true_rul[0].values\n",
    "\n",
    "# Shuffle training data\n",
    "index = np.random.permutation(len(processed_train_targets))\n",
    "processed_train_data, processed_train_targets = processed_train_data[index], processed_train_targets[index]\n",
    "\n",
    "print(\"Processed trianing data shape: \", processed_train_data.shape)\n",
    "print(\"Processed training ruls shape: \", processed_train_targets.shape)\n",
    "print(\"Processed test data shape: \", processed_test_data.shape)\n",
    "print(\"True RUL shape: \", true_rul.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a3a411b1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-01T21:15:46.139158Z",
     "iopub.status.busy": "2022-07-01T21:15:46.137561Z",
     "iopub.status.idle": "2022-07-01T21:15:46.161175Z",
     "shell.execute_reply": "2022-07-01T21:15:46.159493Z"
    },
    "papermill": {
     "duration": 0.036795,
     "end_time": "2022-07-01T21:15:46.163856",
     "exception": false,
     "start_time": "2022-07-01T21:15:46.127061",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed train data shape:  (14184, 30, 14)\n",
      "Processed validation data shape:  (3547, 30, 14)\n",
      "Processed train targets shape:  (14184,)\n",
      "Processed validation targets shape:  (3547,)\n"
     ]
    }
   ],
   "source": [
    "processed_train_data, processed_val_data, processed_train_targets, processed_val_targets = train_test_split(processed_train_data,\n",
    "                                                                                                            processed_train_targets,\n",
    "                                                                                                            test_size = 0.2,\n",
    "                                                                                                            random_state = 83)\n",
    "print(\"Processed train data shape: \", processed_train_data.shape)\n",
    "print(\"Processed validation data shape: \", processed_val_data.shape)\n",
    "print(\"Processed train targets shape: \", processed_train_targets.shape)\n",
    "print(\"Processed validation targets shape: \", processed_val_targets.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6d58ed83",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-01T21:15:46.185071Z",
     "iopub.status.busy": "2022-07-01T21:15:46.184762Z",
     "iopub.status.idle": "2022-07-01T21:15:46.191103Z",
     "shell.execute_reply": "2022-07-01T21:15:46.190173Z"
    },
    "papermill": {
     "duration": 0.019011,
     "end_time": "2022-07-01T21:15:46.192998",
     "exception": false,
     "start_time": "2022-07-01T21:15:46.173987",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def create_compiled_model():\n",
    "    model = Sequential([\n",
    "        layers.LSTM(128, input_shape = (window_length, 14), return_sequences=True, activation = \"tanh\"),\n",
    "        layers.LSTM(64, activation = \"tanh\", return_sequences = True),\n",
    "        layers.LSTM(32, activation = \"tanh\"),\n",
    "        layers.Dense(96, activation = \"relu\"),\n",
    "        layers.Dense(128, activation = \"relu\"),\n",
    "        layers.Dense(1)\n",
    "    ])\n",
    "    model.compile(loss = \"mse\", optimizer = tf.keras.optimizers.Adam(learning_rate=0.001))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a555a990",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-01T21:15:46.213800Z",
     "iopub.status.busy": "2022-07-01T21:15:46.212363Z",
     "iopub.status.idle": "2022-07-01T21:16:04.830537Z",
     "shell.execute_reply": "2022-07-01T21:16:04.829290Z"
    },
    "papermill": {
     "duration": 18.63064,
     "end_time": "2022-07-01T21:16:04.832808",
     "exception": false,
     "start_time": "2022-07-01T21:15:46.202168",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-01 21:15:46.305405: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-07-01 21:15:46.412379: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-07-01 21:15:46.413304: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-07-01 21:15:46.415317: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-07-01 21:15:46.415659: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-07-01 21:15:46.416379: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-07-01 21:15:46.417001: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-07-01 21:15:48.518834: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-07-01 21:15:48.519693: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-07-01 21:15:48.520363: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-07-01 21:15:48.520951: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15403 MB memory:  -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n",
      "2022-07-01 21:15:49.576651: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\n",
      "Epoch 00001: LearningRateScheduler setting learning rate to 0.001.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-01 21:15:53.860518: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8005\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "111/111 - 7s - loss: 3216.4961 - val_loss: 406.8273\n",
      "Epoch 2/10\n",
      "\n",
      "Epoch 00002: LearningRateScheduler setting learning rate to 0.001.\n",
      "111/111 - 1s - loss: 214.3869 - val_loss: 155.4617\n",
      "Epoch 3/10\n",
      "\n",
      "Epoch 00003: LearningRateScheduler setting learning rate to 0.001.\n",
      "111/111 - 1s - loss: 155.3125 - val_loss: 150.1575\n",
      "Epoch 4/10\n",
      "\n",
      "Epoch 00004: LearningRateScheduler setting learning rate to 0.001.\n",
      "111/111 - 1s - loss: 130.7495 - val_loss: 126.7408\n",
      "Epoch 5/10\n",
      "\n",
      "Epoch 00005: LearningRateScheduler setting learning rate to 0.001.\n",
      "111/111 - 1s - loss: 117.5070 - val_loss: 121.9381\n",
      "Epoch 6/10\n",
      "\n",
      "Epoch 00006: LearningRateScheduler setting learning rate to 0.0001.\n",
      "111/111 - 1s - loss: 90.0104 - val_loss: 92.9226\n",
      "Epoch 7/10\n",
      "\n",
      "Epoch 00007: LearningRateScheduler setting learning rate to 0.0001.\n",
      "111/111 - 1s - loss: 84.6284 - val_loss: 88.7795\n",
      "Epoch 8/10\n",
      "\n",
      "Epoch 00008: LearningRateScheduler setting learning rate to 0.0001.\n",
      "111/111 - 1s - loss: 81.2258 - val_loss: 85.8233\n",
      "Epoch 9/10\n",
      "\n",
      "Epoch 00009: LearningRateScheduler setting learning rate to 0.0001.\n",
      "111/111 - 1s - loss: 78.0082 - val_loss: 83.2398\n",
      "Epoch 10/10\n",
      "\n",
      "Epoch 00010: LearningRateScheduler setting learning rate to 0.0001.\n",
      "111/111 - 1s - loss: 75.5488 - val_loss: 80.6686\n"
     ]
    }
   ],
   "source": [
    "def scheduler(epoch):\n",
    "    if epoch < 5:\n",
    "        return 0.001\n",
    "    else:\n",
    "        return 0.0001\n",
    "callback = tf.keras.callbacks.LearningRateScheduler(scheduler, verbose = 1)\n",
    "model = create_compiled_model()\n",
    "history = model.fit(processed_train_data, processed_train_targets, epochs = 10,\n",
    "                    validation_data = (processed_val_data, processed_val_targets),\n",
    "                    callbacks = callback,\n",
    "                    batch_size = 128, verbose = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c62074ea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-01T21:16:04.858155Z",
     "iopub.status.busy": "2022-07-01T21:16:04.856694Z",
     "iopub.status.idle": "2022-07-01T21:16:05.711698Z",
     "shell.execute_reply": "2022-07-01T21:16:05.709827Z"
    },
    "papermill": {
     "duration": 0.869883,
     "end_time": "2022-07-01T21:16:05.714176",
     "exception": false,
     "start_time": "2022-07-01T21:16:04.844293",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE:  15.182087192347678\n"
     ]
    }
   ],
   "source": [
    "rul_pred = model.predict(processed_test_data).reshape(-1)\n",
    "preds_for_each_engine = np.split(rul_pred, np.cumsum(num_test_windows_list)[:-1])\n",
    "mean_pred_for_each_engine = [np.average(ruls_for_each_engine, weights = np.repeat(1/num_windows, num_windows)) \n",
    "                             for ruls_for_each_engine, num_windows in zip(preds_for_each_engine, num_test_windows_list)]\n",
    "RMSE = np.sqrt(mean_squared_error(true_rul, mean_pred_for_each_engine))\n",
    "print(\"RMSE: \", RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ac5d84dd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-01T21:16:05.737626Z",
     "iopub.status.busy": "2022-07-01T21:16:05.737131Z",
     "iopub.status.idle": "2022-07-01T21:16:05.791625Z",
     "shell.execute_reply": "2022-07-01T21:16:05.790731Z"
    },
    "papermill": {
     "duration": 0.06848,
     "end_time": "2022-07-01T21:16:05.793752",
     "exception": false,
     "start_time": "2022-07-01T21:16:05.725272",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (Taking only last examples):  15.712150649004318\n"
     ]
    }
   ],
   "source": [
    "tf.keras.models.save_model(model, \"FD001_LSTM_piecewise_RMSE_\"+ str(np.round(RMSE, 4)) + \".h5\")\n",
    "#We will now compute the RMSE by taking only last example of each engine.\n",
    "\n",
    "indices_of_last_examples = np.cumsum(num_test_windows_list) - 1\n",
    "preds_for_last_example = np.concatenate(preds_for_each_engine)[indices_of_last_examples]\n",
    "\n",
    "RMSE_new = np.sqrt(mean_squared_error(true_rul, preds_for_last_example))\n",
    "print(\"RMSE (Taking only last examples): \", RMSE_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "32c39af7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-01T21:16:05.817646Z",
     "iopub.status.busy": "2022-07-01T21:16:05.817176Z",
     "iopub.status.idle": "2022-07-01T21:16:05.823452Z",
     "shell.execute_reply": "2022-07-01T21:16:05.822541Z"
    },
    "papermill": {
     "duration": 0.020334,
     "end_time": "2022-07-01T21:16:05.825497",
     "exception": false,
     "start_time": "2022-07-01T21:16:05.805163",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S-score:  463.7042018340465\n"
     ]
    }
   ],
   "source": [
    "def compute_s_score(rul_true, rul_pred):\n",
    "    \"\"\"\n",
    "    Both rul_true and rul_pred should be 1D numpy arrays.\n",
    "    \"\"\"\n",
    "    diff = rul_pred - rul_true\n",
    "    return np.sum(np.where(diff < 0, np.exp(-diff/13)-1, np.exp(diff/10)-1))\n",
    "s_score = compute_s_score(true_rul, preds_for_last_example)\n",
    "print(\"S-score: \", s_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "88bc973b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-01T21:16:05.849077Z",
     "iopub.status.busy": "2022-07-01T21:16:05.848220Z",
     "iopub.status.idle": "2022-07-01T21:16:06.040084Z",
     "shell.execute_reply": "2022-07-01T21:16:06.038979Z"
    },
    "papermill": {
     "duration": 0.205871,
     "end_time": "2022-07-01T21:16:06.042202",
     "exception": false,
     "start_time": "2022-07-01T21:16:05.836331",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot true and predicted RUL values\n",
    "plt.plot(true_rul, label = \"True RUL\", color = \"red\")\n",
    "plt.plot(preds_for_last_example, label = \"Pred RUL\", color = \"blue\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 40.308043,
   "end_time": "2022-07-01T21:16:09.633486",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-07-01T21:15:29.325443",
   "version": "2.3.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}