tsrv_lstm.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zcF-Dv23N_ON",
        "outputId": "71531d4e-49e1-4c87-d00e-bb13d0ff031d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.5.1->giotto-tda->hepml) (0.30.2)\n",
            "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.5.1->giotto-tda->hepml) (0.10.2)\n",
            "Requirement already satisfied: anyio<4,>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from jupyter-server>=1.8->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.5.1->giotto-tda->hepml) (3.7.1)\n",
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            "Requirement already satisfied: cffi>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.5.1->giotto-tda->hepml) (1.15.1)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.5.1->giotto-tda->hepml) (0.5.1)\n",
            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server>=1.8->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.5.1->giotto-tda->hepml) (1.3.0)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server>=1.8->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.5.1->giotto-tda->hepml) (1.1.3)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.5.1->giotto-tda->hepml) (2.21)\n",
            "Building wheels for collected packages: wget\n",
            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9655 sha256=3202ff3c8e64bf71f204cc6e4f75116b58e2c2a6a26e8d0b52092e980f836586\n",
            "  Stored in directory: /root/.cache/pip/wheels/8b/f1/7f/5c94f0a7a505ca1c81cd1d9208ae2064675d97582078e6c769\n",
            "Successfully built wget\n",
            "Installing collected packages: wget, texttable, watchdog, pathspec, mypy-extensions, jedi, igraph, asttokens, pyflagser, ghapi, black, giotto-ph, execnb, nbdev, giotto-tda, hepml\n",
            "Successfully installed asttokens-2.4.0 black-23.9.1 execnb-0.1.5 ghapi-1.0.4 giotto-ph-0.2.2 giotto-tda-0.6.0 hepml-0.0.12 igraph-0.10.8 jedi-0.19.0 mypy-extensions-1.0.0 nbdev-2.3.12 pathspec-0.11.2 pyflagser-0.4.5 texttable-1.6.7 watchdog-3.0.0 wget-3.2\n",
            "Collecting arrow\n",
            "  Downloading arrow-1.2.3-py3-none-any.whl (66 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.4/66.4 kB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.7.0 in /usr/local/lib/python3.10/dist-packages (from arrow) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7.0->arrow) (1.16.0)\n",
            "Installing collected packages: arrow\n",
            "Successfully installed arrow-1.2.3\n",
            "Collecting keras_lr_finder\n",
            "  Downloading keras_lr_finder-0.1-py2.py3-none-any.whl (3.5 kB)\n",
            "Requirement already satisfied: keras>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from keras_lr_finder) (2.13.1)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->keras_lr_finder) (1.16.0)\n",
            "Installing collected packages: keras_lr_finder\n",
            "Successfully installed keras_lr_finder-0.1\n",
            "Mounted at /content/drive\n",
            "Archive:  drive/MyDrive/jLab Shared Docs/MFC Modeling/stanfordMFCDataset.zip\n",
            "   creating: rocket4/\n",
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          ]
        }
      ],
      "source": [
        "!pip install --upgrade hepml\n",
        "!pip install arrow\n",
        "!pip install keras_lr_finder\n",
        "# reload modules before executing user code\n",
        "#%load_ext autoreload\n",
        "# reload all modules every time before executing Python code\n",
        "#%autoreload 2\n",
        "# render plots in notebook\n",
        "%matplotlib inline\n",
        "import datetime\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import glob\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from hepml.core import plot_regression_tree\n",
        "sns.set(color_codes=True)\n",
        "sns.set_palette(sns.color_palette(\"muted\"))\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_absolute_percentage_error as MAPE\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)\n",
        "\n",
        "!unzip drive/MyDrive/\"jLab Shared Docs\"/\"MFC Modeling\"/stanfordMFCDataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "B7PoS8fwOS-Q"
      },
      "outputs": [],
      "source": [
        "#Load teros data\n",
        "import glob\n",
        "teros_files = glob.glob(\"rocket4/TEROSoutput*.csv\")\n",
        "X = pd.DataFrame()\n",
        "for f in teros_files:\n",
        "  try:\n",
        "    csv = pd.read_csv(f, index_col=False).dropna()\n",
        "    X = pd.concat([X, csv])\n",
        "  except:\n",
        "    continue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "zaaHwFRvXtN4"
      },
      "outputs": [],
      "source": [
        "#Load power data\n",
        "power_files = glob.glob(\"rocket4/soil*.csv\")\n",
        "y = pd.DataFrame()\n",
        "for f in sorted(power_files, key=lambda x: int(x.split('.')[0].split('_')[-1])):\n",
        "#in power_files:\n",
        "  try:\n",
        "    csv = pd.read_csv(f, on_bad_lines='skip', skiprows=10).dropna(how='all')\n",
        "    csv = csv.rename({'Unnamed: 0': 'timestamp'}, axis='columns')\n",
        "    y = pd.concat([y,csv])\n",
        "  except:\n",
        "    continue\n",
        "y[\"timestamp\"] = y[\"timestamp\"].round(decimals = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "ViS0TjC4n6lk"
      },
      "outputs": [],
      "source": [
        "#Convert current to amps, voltage to volts\n",
        "y[\"I1L [10pA]\"] = np.abs(y[\"I1L [10pA]\"] * 10E-12)\n",
        "y[\"V1 [10nV]\"] = np.abs(y[\"V1 [10nV]\"] * 10E-9)\n",
        "y[\"I1H [nA]\"] = np.abs(y[\"I1H [nA]\"] * 10E-9)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "z0aqozsZpSOB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f7c2874-f421-4eeb-fc17-3b568fe127da"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-56-82a78218b4d4>:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.timestamp = df.timestamp.dt.tz_localize('UTC').dt.tz_convert('US/Pacific')\n",
            "<ipython-input-56-82a78218b4d4>:17: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['tsd'] = (df['timestamp'] - df['timestamp'][840344]).dt.days\n"
          ]
        }
      ],
      "source": [
        "#Sort data by timestamp, convert to datetime\n",
        "X = X.sort_values(['timestamp'])\n",
        "y = y.sort_values(['timestamp'])\n",
        "X['timestamp'] = pd.to_datetime(X['timestamp'], unit='s')\n",
        "y['timestamp'] = pd.to_datetime(y['timestamp'], unit='s')\n",
        "\n",
        "#Merge data by timestamp\n",
        "uncut_df = pd.merge_asof(left=X,right=y,direction='nearest',tolerance=pd.Timedelta('1 sec'), on = 'timestamp').dropna(how='all')\n",
        "\n",
        "#Isolate data from cell0\n",
        "df = uncut_df.loc[uncut_df['sensorID'] == 0]\n",
        "\n",
        "#Localize timestamp\n",
        "df.timestamp = df.timestamp.dt.tz_localize('UTC').dt.tz_convert('US/Pacific')\n",
        "\n",
        "#Get time since deployement\n",
        "df['tsd'] = (df['timestamp'] - df['timestamp'][840344]).dt.days"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t4g4n9n-lork"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "npGkrzVu84Lb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ab125525-f04b-4850-8a98-7473b3b5b942"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-57-3623f9981324>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[\"power\"] = np.abs(np.multiply(df.iloc[:, 8], df.iloc[:, 9]))\n",
            "<ipython-input-57-3623f9981324>:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['power'] = df['power']*1E9\n"
          ]
        }
      ],
      "source": [
        "#Use only data from after deployment date\n",
        "#df = df.loc[(df['timestamp'] > '2021-09-24') & (df['timestamp'] < '2021-10-15')] #Future of Clean Computing Graph\n",
        "#df = df.loc[(df['timestamp'] > '2021-06-24') & (df['timestamp'] < '2021-07-02')]\n",
        "df = df.loc[(df['timestamp'] > '2021-06-18')] #Two weeks after deployment\n",
        "#df = df.loc[(df['timestamp'] > '2021-06-25') & (df['timestamp'] < '2021-06-26')] #Small training set\n",
        "\n",
        "#Power drop\n",
        "#df = df.loc[(df['timestamp'] > '2021-11-01') & (df['timestamp'] < '2021-11-22')]\n",
        "\n",
        "#Calculate power\n",
        "df[\"power\"] = np.abs(np.multiply(df.iloc[:, 8], df.iloc[:, 9]))\n",
        "#df[\"power\"] = np.abs(np.multiply(df[\"I1L [10pA]\"], df[\"V1 [10nV]\"]))\n",
        "\n",
        "df['power'] = df['power']*1E9"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"I1L [10pA]\"] = np.abs(df[\"I1L [10pA]\"] * 10E6)\n",
        "df[\"V1 [10nV]\"] = np.abs(df[\"V1 [10nV]\"] * 10E3)\n",
        "df[\"I1H [nA]\"] = np.abs(df[\"I1H [nA]\"] * 10E3)"
      ],
      "metadata": {
        "id": "LA_-scL4kyzh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc6b99ad-28d0-4bfc-c676-57ef6c19287f"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-58-7cff2d179b25>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[\"I1L [10pA]\"] = np.abs(df[\"I1L [10pA]\"] * 10E6)\n",
            "<ipython-input-58-7cff2d179b25>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[\"V1 [10nV]\"] = np.abs(df[\"V1 [10nV]\"] * 10E3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.resample('60min', on='timestamp').mean().iloc[1: , :]"
      ],
      "metadata": {
        "id": "OvyKFJVci_H1"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.rolling('1h', on = df.index).mean().iloc[1: , :]"
      ],
      "metadata": {
        "id": "WvlVuWD79_M-"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Add power time series\n",
        "df['power - 1h'] = df['power'].shift(1).dropna()\n",
        "#df['power - 2h'] = df['power'].shift(2).dropna()\n",
        "#df['previous_power - 3'] = df['power'].shift(3).dropna()\n",
        "#df['previous_power - 4'] = df['power'].shift(4).dropna()\n",
        "\n",
        "#Add teros time series\n",
        "df['EC - 1h'] = df['EC'].shift(1).dropna()\n",
        "df['temp - 1h'] = df['temp'].shift(1).dropna()\n",
        "df['raw_VWC - 1h'] = df['raw_VWC'].shift(1).dropna()\n",
        "\n",
        "#Add voltage and current time series\n",
        "df['V1 - 1h'] = df['V1 [10nV]'].shift(1).dropna()\n",
        "df['I1L - 1h'] = df['I1L [10pA]'].shift(1).dropna()\n",
        "df['I1H - 1h'] = df['I1H [nA]'].shift(1).dropna()\n",
        "df = df.dropna()"
      ],
      "metadata": {
        "id": "axHauqFOoc6s"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Drop data outages\n",
        "df = df.drop(df[(df.index > '2021-11-11') & (df.index < '2021-11-22 01:00:00')].index)\n",
        "df = df[:-1]"
      ],
      "metadata": {
        "id": "1TGxjPBIFIPo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zPn7hOrUMr4m"
      },
      "outputs": [],
      "source": [
        "#Add datetime features\n",
        "#df['year'] = df['timestamp'].dt.isocalendar().year\n",
        "#the month number, e.g. January = 1\n",
        "#df['month'] = df['timestamp'].dt.month\n",
        "#the calendar day, e.g. 13th May = 13\n",
        "#df['day'] = df['timestamp'].dt.day\n",
        "#the hour\n",
        "#df['hour'] = df['timestamp'].dt.hour"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "KCA31poRjAFZ"
      },
      "outputs": [],
      "source": [
        "#Re-split data for training\n",
        "#X = pd.concat([df.iloc[:, 1:2], df.iloc[:, 15:16], df.iloc[:, 17:19], df.iloc[:, 11:15]], axis = 1)#.dropna()\n",
        "#y = df.iloc[:, 10:11]#.dropna()\n",
        "\n",
        "#Creating training and testing sets (without rolling)\n",
        "#X_train, X_test = train_test_split(pd.concat([df.iloc[:, 2:5], df.iloc[:, 13:14], df.iloc[:, 15:19]], axis = 1), test_size=0.3, shuffle=False)\n",
        "#y_train, y_test = train_test_split(df.iloc[:, 14:15], test_size=0.3, shuffle=False)\n",
        "\n",
        "#Creating training and testing sets (with roll)\n",
        "#X_train, X_test = train_test_split(pd.concat([df.iloc[:, 1:2], df.iloc[:, 17:18], df.iloc[:, 19:20], df.iloc[:, 3:4], df.iloc[:, 9:10], df.iloc[:, 13:17]], axis = 1), test_size=0.3, shuffle=False)\n",
        "#y_train, y_test = train_test_split(pd.concat([df.iloc[:, 12:13]], axis = 1), test_size=0.3, shuffle=False)\n",
        "\n",
        "#Creating training and testing sets (with roll)\n",
        "#X_train, X_test = train_test_split(pd.concat([df.iloc[:, 17:21], df.iloc[:, 1:2], df.iloc[:, 21:22], df.iloc[:, 23:24], df.iloc[:, 2:3], df.iloc[:, 4:5], df.iloc[:, 10:11], df.iloc[:, 25:26], df.iloc[:, 15:16], df.iloc[:, 13:15], df.iloc[:, 24:25]], axis = 1), test_size=0.3, shuffle=False)\n",
        "#y_train, y_test = train_test_split(pd.concat([df.iloc[:, 16:17]], axis = 1), test_size=0.3, shuffle=False)\n",
        "\n",
        "#Creating training and testing sets (with roll, hour)\n",
        "#X_train, X_test = train_test_split(pd.concat([df.iloc[:, 15:19], df.iloc[:, 1:2], df.iloc[:, 19:20], df.iloc[:, 21:22], df.iloc[:, 2:3], df.iloc[:, 4:5], df.iloc[:, 10:11], df.iloc[:, 13:14], df.iloc[:, 22:23]], axis = 1), test_size=0.3, shuffle=False)\n",
        "#y_train, y_test = train_test_split(pd.concat([df.iloc[:, 14:15]], axis = 1), test_size=0.3, shuffle=False)\n",
        "\n",
        "#Creating training and testing sets (with roll, no hour)\n",
        "#X_train, X_test = train_test_split(pd.concat([df.iloc[:, 15:19], df.iloc[:, 2:3], df.iloc[:, 20:21], df.iloc[:, 23:24]], axis = 1), test_size=0.3, shuffle=False)\n",
        "#y_train, y_test = train_test_split(pd.concat([df.iloc[:, 14:15]], axis = 1), test_size=0.3, shuffle=False)\n",
        "X = pd.concat([df[\"power - 1h\"], df[\"V1 - 1h\"], df[\"I1L - 1h\"], df[\"EC - 1h\"], df[\"raw_VWC - 1h\"], df[\"temp - 1h\"], df[\"tsd\"]], axis = 1)\n",
        "y = pd.concat([df[\"power\"], df['V1 [10nV]'], df['I1L [10pA]']], axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = X.reset_index(drop=True).values\n",
        "y = y.reset_index(drop=True).values"
      ],
      "metadata": {
        "id": "FCyH7z2oAQs6"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qVXVI59xBSMT",
        "outputId": "0f8b8c86-5921-430d-fe26-15e42ef0a635"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0168539325842696\n",
            "Epoch 1/20\n",
            "21/21 - 2s - loss: 62.3008 - 2s/epoch - 82ms/step\n",
            "Epoch 2/20\n",
            "21/21 - 0s - loss: 35.6629 - 83ms/epoch - 4ms/step\n",
            "Epoch 3/20\n",
            "21/21 - 0s - loss: 29.6625 - 93ms/epoch - 4ms/step\n",
            "Epoch 4/20\n",
            "21/21 - 0s - loss: 27.2748 - 81ms/epoch - 4ms/step\n",
            "Epoch 5/20\n",
            "21/21 - 0s - loss: 24.8475 - 100ms/epoch - 5ms/step\n",
            "Epoch 6/20\n",
            "21/21 - 0s - loss: 22.3534 - 90ms/epoch - 4ms/step\n",
            "Epoch 7/20\n",
            "21/21 - 0s - loss: 20.3337 - 94ms/epoch - 4ms/step\n",
            "Epoch 8/20\n",
            "21/21 - 0s - loss: 22.0031 - 91ms/epoch - 4ms/step\n",
            "Epoch 9/20\n",
            "21/21 - 0s - loss: 18.6411 - 96ms/epoch - 5ms/step\n",
            "Epoch 10/20\n",
            "21/21 - 0s - loss: 19.0437 - 97ms/epoch - 5ms/step\n",
            "Epoch 11/20\n",
            "21/21 - 0s - loss: 17.7815 - 98ms/epoch - 5ms/step\n",
            "Epoch 12/20\n",
            "21/21 - 0s - loss: 16.9060 - 88ms/epoch - 4ms/step\n",
            "Epoch 13/20\n",
            "21/21 - 0s - loss: 17.0502 - 88ms/epoch - 4ms/step\n",
            "Epoch 14/20\n",
            "21/21 - 0s - loss: 16.1042 - 81ms/epoch - 4ms/step\n",
            "Epoch 15/20\n",
            "21/21 - 0s - loss: 15.6109 - 102ms/epoch - 5ms/step\n",
            "Epoch 16/20\n",
            "21/21 - 0s - loss: 17.4077 - 90ms/epoch - 4ms/step\n",
            "Epoch 17/20\n",
            "21/21 - 0s - loss: 17.6616 - 92ms/epoch - 4ms/step\n",
            "Epoch 18/20\n",
            "21/21 - 0s - loss: 16.4132 - 100ms/epoch - 5ms/step\n",
            "Epoch 19/20\n",
            "21/21 - 0s - loss: 14.7573 - 91ms/epoch - 4ms/step\n",
            "Epoch 20/20\n",
            "21/21 - 0s - loss: 13.8288 - 94ms/epoch - 4ms/step\n",
            "21/21 [==============================] - 0s 2ms/step\n",
            "20/20 [==============================] - 0s 3ms/step\n",
            "Train MAPE power:\n",
            " 0.14659813582078265\n",
            "Test MAPE power:\n",
            " 0.16270956718723004\n",
            "Train MAPE voltage:\n",
            " 0.10131761241654876\n",
            "Test MAPE voltage:\n",
            " 0.1424924721850744\n",
            "Train MAPE current:\n",
            " 0.1404875114653175\n",
            "Test MAPE current:\n",
            " 0.16336081795335014\n",
            "2.0168539325842696\n",
            "Epoch 1/20\n",
            "40/40 - 2s - loss: 49.0411 - 2s/epoch - 43ms/step\n",
            "Epoch 2/20\n",
            "40/40 - 0s - loss: 23.6772 - 163ms/epoch - 4ms/step\n",
            "Epoch 3/20\n",
            "40/40 - 0s - loss: 20.4268 - 157ms/epoch - 4ms/step\n",
            "Epoch 4/20\n",
            "40/40 - 0s - loss: 18.4434 - 162ms/epoch - 4ms/step\n",
            "Epoch 5/20\n",
            "40/40 - 0s - loss: 16.9039 - 182ms/epoch - 5ms/step\n",
            "Epoch 6/20\n",
            "40/40 - 0s - loss: 14.5225 - 252ms/epoch - 6ms/step\n",
            "Epoch 7/20\n",
            "40/40 - 0s - loss: 16.3042 - 241ms/epoch - 6ms/step\n",
            "Epoch 8/20\n",
            "40/40 - 0s - loss: 16.4283 - 235ms/epoch - 6ms/step\n",
            "Epoch 9/20\n",
            "40/40 - 0s - loss: 14.4796 - 242ms/epoch - 6ms/step\n",
            "Epoch 10/20\n",
            "40/40 - 0s - loss: 14.0766 - 244ms/epoch - 6ms/step\n",
            "Epoch 11/20\n",
            "40/40 - 0s - loss: 14.0274 - 256ms/epoch - 6ms/step\n",
            "Epoch 12/20\n",
            "40/40 - 0s - loss: 14.2748 - 241ms/epoch - 6ms/step\n",
            "Epoch 13/20\n",
            "40/40 - 0s - loss: 13.4461 - 249ms/epoch - 6ms/step\n",
            "Epoch 14/20\n",
            "40/40 - 0s - loss: 13.2397 - 288ms/epoch - 7ms/step\n",
            "Epoch 15/20\n",
            "40/40 - 0s - loss: 12.7593 - 313ms/epoch - 8ms/step\n",
            "Epoch 16/20\n",
            "40/40 - 0s - loss: 13.1314 - 278ms/epoch - 7ms/step\n",
            "Epoch 17/20\n",
            "40/40 - 0s - loss: 12.8907 - 250ms/epoch - 6ms/step\n",
            "Epoch 18/20\n",
            "40/40 - 0s - loss: 12.5457 - 254ms/epoch - 6ms/step\n",
            "Epoch 19/20\n",
            "40/40 - 0s - loss: 12.3838 - 240ms/epoch - 6ms/step\n",
            "Epoch 20/20\n",
            "40/40 - 0s - loss: 12.0974 - 237ms/epoch - 6ms/step\n",
            "40/40 [==============================] - 0s 3ms/step\n",
            "20/20 [==============================] - 0s 2ms/step\n",
            "Train MAPE power:\n",
            " 0.1507291489779721\n",
            "Test MAPE power:\n",
            " 0.11960454977060185\n",
            "Train MAPE voltage:\n",
            " 0.10228137502159373\n",
            "Test MAPE voltage:\n",
            " 0.07288314306918149\n",
            "Train MAPE current:\n",
            " 0.13283980345571728\n",
            "Test MAPE current:\n",
            " 0.1071383125135506\n",
            "3.0168539325842696\n",
            "Epoch 1/20\n",
            "60/60 - 2s - loss: 34.7938 - 2s/epoch - 30ms/step\n",
            "Epoch 2/20\n",
            "60/60 - 0s - loss: 21.2941 - 243ms/epoch - 4ms/step\n",
            "Epoch 3/20\n",
            "60/60 - 0s - loss: 16.8924 - 281ms/epoch - 5ms/step\n",
            "Epoch 4/20\n",
            "60/60 - 0s - loss: 14.4657 - 271ms/epoch - 5ms/step\n",
            "Epoch 5/20\n",
            "60/60 - 0s - loss: 13.7359 - 257ms/epoch - 4ms/step\n",
            "Epoch 6/20\n",
            "60/60 - 0s - loss: 13.5381 - 246ms/epoch - 4ms/step\n",
            "Epoch 7/20\n",
            "60/60 - 0s - loss: 12.2477 - 247ms/epoch - 4ms/step\n",
            "Epoch 8/20\n",
            "60/60 - 0s - loss: 13.2687 - 238ms/epoch - 4ms/step\n",
            "Epoch 9/20\n",
            "60/60 - 0s - loss: 12.5064 - 245ms/epoch - 4ms/step\n",
            "Epoch 10/20\n",
            "60/60 - 0s - loss: 12.1112 - 259ms/epoch - 4ms/step\n",
            "Epoch 11/20\n",
            "60/60 - 0s - loss: 12.3688 - 232ms/epoch - 4ms/step\n",
            "Epoch 12/20\n",
            "60/60 - 0s - loss: 11.8342 - 242ms/epoch - 4ms/step\n",
            "Epoch 13/20\n",
            "60/60 - 0s - loss: 11.8562 - 274ms/epoch - 5ms/step\n",
            "Epoch 14/20\n",
            "60/60 - 0s - loss: 11.1514 - 238ms/epoch - 4ms/step\n",
            "Epoch 15/20\n",
            "60/60 - 0s - loss: 11.3338 - 273ms/epoch - 5ms/step\n",
            "Epoch 16/20\n",
            "60/60 - 0s - loss: 11.2979 - 254ms/epoch - 4ms/step\n",
            "Epoch 17/20\n",
            "60/60 - 0s - loss: 11.2704 - 276ms/epoch - 5ms/step\n",
            "Epoch 18/20\n",
            "60/60 - 0s - loss: 11.0206 - 245ms/epoch - 4ms/step\n",
            "Epoch 19/20\n",
            "60/60 - 0s - loss: 10.8379 - 253ms/epoch - 4ms/step\n",
            "Epoch 20/20\n",
            "60/60 - 0s - loss: 11.0449 - 258ms/epoch - 4ms/step\n",
            "60/60 [==============================] - 0s 3ms/step\n",
            "20/20 [==============================] - 0s 3ms/step\n",
            "Train MAPE power:\n",
            " 0.13302994021170036\n",
            "Test MAPE power:\n",
            " 0.08547221700028848\n",
            "Train MAPE voltage:\n",
            " 0.13120402199359876\n",
            "Test MAPE voltage:\n",
            " 0.1364344595366152\n",
            "Train MAPE current:\n",
            " 0.141818411072261\n",
            "Test MAPE current:\n",
            " 0.09941153339056509\n",
            "4.01685393258427\n",
            "Epoch 1/20\n",
            "80/80 - 2s - loss: 27.8589 - 2s/epoch - 24ms/step\n",
            "Epoch 2/20\n",
            "80/80 - 0s - loss: 16.2636 - 324ms/epoch - 4ms/step\n",
            "Epoch 3/20\n",
            "80/80 - 0s - loss: 14.6862 - 320ms/epoch - 4ms/step\n",
            "Epoch 4/20\n",
            "80/80 - 0s - loss: 13.8155 - 307ms/epoch - 4ms/step\n",
            "Epoch 5/20\n",
            "80/80 - 0s - loss: 13.0279 - 349ms/epoch - 4ms/step\n",
            "Epoch 6/20\n",
            "80/80 - 0s - loss: 13.2251 - 343ms/epoch - 4ms/step\n",
            "Epoch 7/20\n",
            "80/80 - 0s - loss: 12.1887 - 318ms/epoch - 4ms/step\n",
            "Epoch 8/20\n",
            "80/80 - 0s - loss: 12.0272 - 341ms/epoch - 4ms/step\n",
            "Epoch 9/20\n",
            "80/80 - 0s - loss: 11.9229 - 353ms/epoch - 4ms/step\n",
            "Epoch 10/20\n",
            "80/80 - 0s - loss: 11.5338 - 326ms/epoch - 4ms/step\n",
            "Epoch 11/20\n",
            "80/80 - 0s - loss: 11.4487 - 342ms/epoch - 4ms/step\n",
            "Epoch 12/20\n",
            "80/80 - 0s - loss: 11.2515 - 325ms/epoch - 4ms/step\n",
            "Epoch 13/20\n",
            "80/80 - 0s - loss: 11.2934 - 325ms/epoch - 4ms/step\n",
            "Epoch 14/20\n",
            "80/80 - 0s - loss: 11.0502 - 354ms/epoch - 4ms/step\n",
            "Epoch 15/20\n",
            "80/80 - 0s - loss: 10.8047 - 316ms/epoch - 4ms/step\n",
            "Epoch 16/20\n",
            "80/80 - 0s - loss: 10.3894 - 339ms/epoch - 4ms/step\n",
            "Epoch 17/20\n",
            "80/80 - 0s - loss: 10.5314 - 325ms/epoch - 4ms/step\n",
            "Epoch 18/20\n",
            "80/80 - 0s - loss: 10.6525 - 334ms/epoch - 4ms/step\n",
            "Epoch 19/20\n",
            "80/80 - 0s - loss: 10.0731 - 308ms/epoch - 4ms/step\n",
            "Epoch 20/20\n",
            "80/80 - 0s - loss: 10.0816 - 359ms/epoch - 4ms/step\n",
            "80/80 [==============================] - 0s 2ms/step\n",
            "20/20 [==============================] - 0s 2ms/step\n",
            "Train MAPE power:\n",
            " 0.12907530181926083\n",
            "Test MAPE power:\n",
            " 0.08118827118758516\n",
            "Train MAPE voltage:\n",
            " 0.07315445670100879\n",
            "Test MAPE voltage:\n",
            " 0.04690166578552138\n",
            "Train MAPE current:\n",
            " 0.10859238030439494\n",
            "Test MAPE current:\n",
            " 0.07550909849616466\n",
            "5.01685393258427\n",
            "Epoch 1/20\n",
            "100/100 - 3s - loss: 28.6956 - 3s/epoch - 25ms/step\n",
            "Epoch 2/20\n",
            "100/100 - 1s - loss: 15.7899 - 602ms/epoch - 6ms/step\n",
            "Epoch 3/20\n",
            "100/100 - 1s - loss: 13.3451 - 573ms/epoch - 6ms/step\n",
            "Epoch 4/20\n",
            "100/100 - 1s - loss: 12.8945 - 590ms/epoch - 6ms/step\n",
            "Epoch 5/20\n",
            "100/100 - 1s - loss: 11.3434 - 582ms/epoch - 6ms/step\n",
            "Epoch 6/20\n",
            "100/100 - 1s - loss: 10.7964 - 586ms/epoch - 6ms/step\n",
            "Epoch 7/20\n",
            "100/100 - 1s - loss: 10.6861 - 586ms/epoch - 6ms/step\n",
            "Epoch 8/20\n",
            "100/100 - 0s - loss: 10.4437 - 382ms/epoch - 4ms/step\n",
            "Epoch 9/20\n",
            "100/100 - 0s - loss: 9.8106 - 408ms/epoch - 4ms/step\n",
            "Epoch 10/20\n",
            "100/100 - 0s - loss: 10.1632 - 405ms/epoch - 4ms/step\n",
            "Epoch 11/20\n",
            "100/100 - 0s - loss: 9.7094 - 428ms/epoch - 4ms/step\n",
            "Epoch 12/20\n",
            "100/100 - 0s - loss: 9.5467 - 423ms/epoch - 4ms/step\n",
            "Epoch 13/20\n",
            "100/100 - 0s - loss: 9.6340 - 451ms/epoch - 5ms/step\n",
            "Epoch 14/20\n",
            "100/100 - 0s - loss: 9.5597 - 387ms/epoch - 4ms/step\n",
            "Epoch 15/20\n",
            "100/100 - 0s - loss: 9.4612 - 411ms/epoch - 4ms/step\n",
            "Epoch 16/20\n",
            "100/100 - 0s - loss: 9.6768 - 423ms/epoch - 4ms/step\n",
            "Epoch 17/20\n",
            "100/100 - 0s - loss: 9.1369 - 409ms/epoch - 4ms/step\n",
            "Epoch 18/20\n",
            "100/100 - 0s - loss: 9.3672 - 402ms/epoch - 4ms/step\n",
            "Epoch 19/20\n",
            "100/100 - 0s - loss: 9.3698 - 404ms/epoch - 4ms/step\n",
            "Epoch 20/20\n",
            "100/100 - 0s - loss: 9.0876 - 391ms/epoch - 4ms/step\n",
            "100/100 [==============================] - 0s 2ms/step\n",
            "20/20 [==============================] - 0s 2ms/step\n",
            "Train MAPE power:\n",
            " 0.12885967256321834\n",
            "Test MAPE power:\n",
            " 0.2043600986815189\n",
            "Train MAPE voltage:\n",
            " 0.06752221068699042\n",
            "Test MAPE voltage:\n",
            " 0.15554804679662265\n",
            "Train MAPE current:\n",
            " 0.10773106282616195\n",
            "Test MAPE current:\n",
            " 0.09774968053897533\n",
            "6.01685393258427\n",
            "Epoch 1/20\n",
            "119/119 - 2s - loss: 29.1009 - 2s/epoch - 14ms/step\n",
            "Epoch 2/20\n",
            "119/119 - 0s - loss: 20.6019 - 456ms/epoch - 4ms/step\n",
            "Epoch 3/20\n",
            "119/119 - 0s - loss: 16.4413 - 473ms/epoch - 4ms/step\n",
            "Epoch 4/20\n",
            "119/119 - 0s - loss: 13.9662 - 416ms/epoch - 3ms/step\n",
            "Epoch 5/20\n",
            "119/119 - 0s - loss: 11.4743 - 426ms/epoch - 4ms/step\n",
            "Epoch 6/20\n",
            "119/119 - 1s - loss: 10.5222 - 530ms/epoch - 4ms/step\n",
            "Epoch 7/20\n",
            "119/119 - 1s - loss: 10.1502 - 724ms/epoch - 6ms/step\n",
            "Epoch 8/20\n",
            "119/119 - 1s - loss: 10.0988 - 658ms/epoch - 6ms/step\n",
            "Epoch 9/20\n",
            "119/119 - 1s - loss: 9.8590 - 685ms/epoch - 6ms/step\n",
            "Epoch 10/20\n",
            "119/119 - 1s - loss: 9.4381 - 745ms/epoch - 6ms/step\n",
            "Epoch 11/20\n",
            "119/119 - 1s - loss: 9.3336 - 649ms/epoch - 5ms/step\n",
            "Epoch 12/20\n",
            "119/119 - 1s - loss: 9.0896 - 679ms/epoch - 6ms/step\n",
            "Epoch 13/20\n",
            "119/119 - 1s - loss: 9.0724 - 566ms/epoch - 5ms/step\n",
            "Epoch 14/20\n",
            "119/119 - 0s - loss: 8.8345 - 452ms/epoch - 4ms/step\n",
            "Epoch 15/20\n",
            "119/119 - 0s - loss: 9.0207 - 422ms/epoch - 4ms/step\n",
            "Epoch 16/20\n",
            "119/119 - 0s - loss: 9.0501 - 473ms/epoch - 4ms/step\n",
            "Epoch 17/20\n",
            "119/119 - 0s - loss: 9.1297 - 456ms/epoch - 4ms/step\n",
            "Epoch 18/20\n",
            "119/119 - 0s - loss: 8.8462 - 458ms/epoch - 4ms/step\n",
            "Epoch 19/20\n",
            "119/119 - 0s - loss: 8.8500 - 447ms/epoch - 4ms/step\n",
            "Epoch 20/20\n",
            "119/119 - 0s - loss: 8.9520 - 448ms/epoch - 4ms/step\n",
            "119/119 [==============================] - 0s 2ms/step\n",
            "20/20 [==============================] - 0s 2ms/step\n",
            "Train MAPE power:\n",
            " 0.11327211280524516\n",
            "Test MAPE power:\n",
            " 0.08485194317399772\n",
            "Train MAPE voltage:\n",
            " 0.06414684708457363\n",
            "Test MAPE voltage:\n",
            " 0.038377458687498464\n",
            "Train MAPE current:\n",
            " 0.10420817102348813\n",
            "Test MAPE current:\n",
            " 0.10309041425281622\n",
            "7.01685393258427\n",
            "Epoch 1/20\n",
            "139/139 - 3s - loss: 20.8217 - 3s/epoch - 21ms/step\n",
            "Epoch 2/20\n",
            "139/139 - 1s - loss: 14.6935 - 558ms/epoch - 4ms/step\n",
            "Epoch 3/20\n",
            "139/139 - 1s - loss: 14.7549 - 554ms/epoch - 4ms/step\n",
            "Epoch 4/20\n",
            "139/139 - 1s - loss: 14.2025 - 577ms/epoch - 4ms/step\n",
            "Epoch 5/20\n",
            "139/139 - 1s - loss: 12.0410 - 573ms/epoch - 4ms/step\n",
            "Epoch 6/20\n",
            "139/139 - 1s - loss: 10.3926 - 584ms/epoch - 4ms/step\n",
            "Epoch 7/20\n",
            "139/139 - 1s - loss: 9.9192 - 556ms/epoch - 4ms/step\n",
            "Epoch 8/20\n",
            "139/139 - 1s - loss: 9.5514 - 577ms/epoch - 4ms/step\n",
            "Epoch 9/20\n",
            "139/139 - 1s - loss: 9.1584 - 584ms/epoch - 4ms/step\n",
            "Epoch 10/20\n",
            "139/139 - 1s - loss: 8.9622 - 552ms/epoch - 4ms/step\n",
            "Epoch 11/20\n",
            "139/139 - 1s - loss: 9.0451 - 564ms/epoch - 4ms/step\n",
            "Epoch 12/20\n",
            "139/139 - 1s - loss: 8.8960 - 857ms/epoch - 6ms/step\n",
            "Epoch 13/20\n",
            "139/139 - 1s - loss: 9.0273 - 875ms/epoch - 6ms/step\n",
            "Epoch 14/20\n",
            "139/139 - 1s - loss: 8.7457 - 871ms/epoch - 6ms/step\n",
            "Epoch 15/20\n",
            "139/139 - 1s - loss: 8.7455 - 902ms/epoch - 6ms/step\n",
            "Epoch 16/20\n",
            "139/139 - 1s - loss: 8.7877 - 1s/epoch - 7ms/step\n",
            "Epoch 17/20\n",
            "139/139 - 1s - loss: 8.6236 - 729ms/epoch - 5ms/step\n",
            "Epoch 18/20\n",
            "139/139 - 1s - loss: 8.5754 - 577ms/epoch - 4ms/step\n",
            "Epoch 19/20\n",
            "139/139 - 1s - loss: 8.5745 - 575ms/epoch - 4ms/step\n",
            "Epoch 20/20\n",
            "139/139 - 1s - loss: 8.4347 - 570ms/epoch - 4ms/step\n",
            "139/139 [==============================] - 0s 2ms/step\n",
            "20/20 [==============================] - 0s 2ms/step\n",
            "Train MAPE power:\n",
            " 0.10584470946285308\n",
            "Test MAPE power:\n",
            " 0.11993676630712162\n",
            "Train MAPE voltage:\n",
            " 0.062560865082872\n",
            "Test MAPE voltage:\n",
            " 0.04197553252651557\n",
            "Train MAPE current:\n",
            " 0.09651089256085958\n",
            "Test MAPE current:\n",
            " 0.09040753788333725\n",
            "8.01685393258427\n",
            "Epoch 1/20\n",
            "159/159 - 2s - loss: 21.4241 - 2s/epoch - 14ms/step\n",
            "Epoch 2/20\n",
            "159/159 - 1s - loss: 15.3969 - 655ms/epoch - 4ms/step\n",
            "Epoch 3/20\n",
            "159/159 - 1s - loss: 14.5238 - 665ms/epoch - 4ms/step\n",
            "Epoch 4/20\n",
            "159/159 - 1s - loss: 12.8202 - 632ms/epoch - 4ms/step\n",
            "Epoch 5/20\n",
            "159/159 - 1s - loss: 11.5926 - 652ms/epoch - 4ms/step\n",
            "Epoch 6/20\n",
            "159/159 - 1s - loss: 10.9373 - 649ms/epoch - 4ms/step\n",
            "Epoch 7/20\n",
            "159/159 - 1s - loss: 9.4204 - 640ms/epoch - 4ms/step\n",
            "Epoch 8/20\n",
            "159/159 - 1s - loss: 9.1578 - 726ms/epoch - 5ms/step\n",
            "Epoch 9/20\n",
            "159/159 - 1s - loss: 9.1435 - 1s/epoch - 7ms/step\n",
            "Epoch 10/20\n",
            "159/159 - 1s - loss: 8.6827 - 1s/epoch - 6ms/step\n",
            "Epoch 11/20\n",
            "159/159 - 1s - loss: 8.6832 - 1s/epoch - 7ms/step\n",
            "Epoch 12/20\n",
            "159/159 - 1s - loss: 8.9587 - 1s/epoch - 7ms/step\n",
            "Epoch 13/20\n",
            "159/159 - 1s - loss: 8.5152 - 731ms/epoch - 5ms/step\n",
            "Epoch 14/20\n",
            "159/159 - 1s - loss: 8.8505 - 623ms/epoch - 4ms/step\n",
            "Epoch 15/20\n",
            "159/159 - 1s - loss: 8.6162 - 651ms/epoch - 4ms/step\n",
            "Epoch 16/20\n",
            "159/159 - 1s - loss: 8.5427 - 662ms/epoch - 4ms/step\n",
            "Epoch 17/20\n",
            "159/159 - 1s - loss: 8.4442 - 633ms/epoch - 4ms/step\n",
            "Epoch 18/20\n",
            "159/159 - 1s - loss: 8.5041 - 633ms/epoch - 4ms/step\n",
            "Epoch 19/20\n",
            "159/159 - 1s - loss: 8.4256 - 647ms/epoch - 4ms/step\n",
            "Epoch 20/20\n",
            "159/159 - 1s - loss: 8.5150 - 651ms/epoch - 4ms/step\n",
            "159/159 [==============================] - 0s 2ms/step\n",
            "20/20 [==============================] - 0s 2ms/step\n",
            "Train MAPE power:\n",
            " 0.10343604536064704\n",
            "Test MAPE power:\n",
            " 0.11535927993670322\n",
            "Train MAPE voltage:\n",
            " 0.0728597377006501\n",
            "Test MAPE voltage:\n",
            " 0.06597558625521777\n",
            "Train MAPE current:\n",
            " 0.1251140259660469\n",
            "Test MAPE current:\n",
            " 0.08765599840669998\n",
            "9.01685393258427\n",
            "Epoch 1/20\n",
            "179/179 - 2s - loss: 25.2351 - 2s/epoch - 13ms/step\n",
            "Epoch 2/20\n",
            "179/179 - 1s - loss: 17.3515 - 723ms/epoch - 4ms/step\n",
            "Epoch 3/20\n",
            "179/179 - 1s - loss: 13.3559 - 757ms/epoch - 4ms/step\n",
            "Epoch 4/20\n",
            "179/179 - 1s - loss: 10.7802 - 1s/epoch - 6ms/step\n",
            "Epoch 5/20\n",
            "179/179 - 1s - loss: 10.2820 - 1s/epoch - 6ms/step\n",
            "Epoch 6/20\n",
            "179/179 - 1s - loss: 10.2155 - 1s/epoch - 6ms/step\n",
            "Epoch 7/20\n",
            "179/179 - 1s - loss: 9.4766 - 1s/epoch - 7ms/step\n",
            "Epoch 8/20\n",
            "179/179 - 1s - loss: 9.4097 - 783ms/epoch - 4ms/step\n",
            "Epoch 9/20\n",
            "179/179 - 1s - loss: 9.4058 - 690ms/epoch - 4ms/step\n",
            "Epoch 10/20\n",
            "179/179 - 1s - loss: 9.0834 - 727ms/epoch - 4ms/step\n",
            "Epoch 11/20\n",
            "179/179 - 1s - loss: 9.0413 - 723ms/epoch - 4ms/step\n",
            "Epoch 12/20\n",
            "179/179 - 1s - loss: 9.1023 - 721ms/epoch - 4ms/step\n",
            "Epoch 13/20\n",
            "179/179 - 1s - loss: 8.9645 - 729ms/epoch - 4ms/step\n",
            "Epoch 14/20\n",
            "179/179 - 1s - loss: 8.7850 - 715ms/epoch - 4ms/step\n",
            "Epoch 15/20\n",
            "179/179 - 1s - loss: 8.8700 - 691ms/epoch - 4ms/step\n",
            "Epoch 16/20\n",
            "179/179 - 1s - loss: 8.7209 - 708ms/epoch - 4ms/step\n",
            "Epoch 17/20\n",
            "179/179 - 1s - loss: 8.7287 - 742ms/epoch - 4ms/step\n",
            "Epoch 18/20\n",
            "179/179 - 1s - loss: 8.7244 - 698ms/epoch - 4ms/step\n",
            "Epoch 19/20\n",
            "179/179 - 1s - loss: 8.6841 - 742ms/epoch - 4ms/step\n",
            "Epoch 20/20\n",
            "179/179 - 1s - loss: 8.5426 - 723ms/epoch - 4ms/step\n",
            "179/179 [==============================] - 1s 2ms/step\n",
            "20/20 [==============================] - 0s 2ms/step\n",
            "Train MAPE power:\n",
            " 0.12319416012226175\n",
            "Test MAPE power:\n",
            " 0.12379624240749978\n",
            "Train MAPE voltage:\n",
            " 0.09945456670989174\n",
            "Test MAPE voltage:\n",
            " 0.050366549479955496\n",
            "Train MAPE current:\n",
            " 0.14658775311493524\n",
            "Test MAPE current:\n",
            " 0.10242049739119667\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import TimeSeriesSplit\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM\n",
        "splits = TimeSeriesSplit(n_splits=9)\n",
        "power_mape = []\n",
        "voltage_mape = []\n",
        "current_mape = []\n",
        "for train_index, test_index in splits.split(X):\n",
        "  #Split train and test sets\n",
        "  X_train = X[train_index]\n",
        "  X_test = X[test_index]\n",
        "  y_train = y[train_index]\n",
        "  y_test = y[test_index]\n",
        "  print(len(train_index)/len(test_index))\n",
        "\n",
        "  #reshape data\n",
        "  X_train = X_train.reshape((X_train.shape[0], 1, X_train.shape[1]))\n",
        "  X_test = X_test.reshape((X_test.shape[0], 1, X_test.shape[1]))\n",
        "\n",
        "  # design network\n",
        "  model = Sequential()\n",
        "  model.add(LSTM(200, input_shape=(X_train.shape[1], X_train.shape[2]), activation='relu'))\n",
        "  model.add(Dense(100, activation='relu'))\n",
        "  model.add(Dense(3))\n",
        "  model.compile(loss='mape', optimizer='adam')\n",
        "\n",
        "  batchsize = 18\n",
        "  # fit network\n",
        "  model.fit(X_train, y_train, epochs=20, batch_size=batchsize, verbose=2, shuffle=False)\n",
        "\n",
        "  train_pred = model.predict(X_train, batch_size=batchsize)\n",
        "  test_pred = model.predict(X_test, batch_size=batchsize)\n",
        "\n",
        "  #Evaluate MAPE power\n",
        "  print(\"Train MAPE power:\\n\", MAPE(y_train[:, 0], train_pred[:, 0]))\n",
        "  print(\"Test MAPE power:\\n\", MAPE(y_test[:, 0], test_pred[:, 0]))\n",
        "\n",
        "  #Evaluate MAPE voltage\n",
        "  print(\"Train MAPE voltage:\\n\", MAPE(y_train[:, 1], train_pred[:, 1]))\n",
        "  print(\"Test MAPE voltage:\\n\", MAPE(y_test[:, 1], test_pred[:, 1]))\n",
        "\n",
        "  #Evaluate MAPE current\n",
        "  print(\"Train MAPE current:\\n\", MAPE(y_train[:, 2], train_pred[:, 2]))\n",
        "  print(\"Test MAPE current:\\n\", MAPE(y_test[:, 2], test_pred[:, 2]))\n",
        "\n",
        "  power_mape.append(MAPE(y_test[:, 0], test_pred[:, 0]))\n",
        "  voltage_mape.append(MAPE(y_test[:, 1], test_pred[:, 1]))\n",
        "  current_mape.append(MAPE(y_test[:, 2], test_pred[:, 2]))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.ticker as mtick\n",
        "\n",
        "fig = plt.figure(1, (7,4))\n",
        "ax = fig.add_subplot(1,1,1)\n",
        "\n",
        "\n",
        "t = np.arange(1, 10) * 10\n",
        "ax.plot(t, np.c_[power_mape, voltage_mape, current_mape], label=['power mape', 'voltage mape', 'current mape']);\n",
        "plt.legend();\n",
        "\n",
        "fmt = '%.0f%%' # Format you want the ticks, e.g. '40%'\n",
        "xticks = mtick.FormatStrFormatter(fmt)\n",
        "ax.xaxis.set_major_formatter(xticks)\n",
        "\n",
        "ax.set_ylim(0, 0.6)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "kAOZCIAQUnw1",
        "outputId": "eefda656-9d25-4f9c-d313-851f384780b8"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 0.6)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 840
        },
        "id": "_lZlqgJ6nB4a",
        "outputId": "1c674325-3d48-47c5-b75b-1b7269e8cac8"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           sensorID      raw_VWC       temp          EC  \\\n",
              "timestamp                                                                 \n",
              "2021-06-21 13:00:00-07:00       0.0  2489.510492  30.135985  229.795455   \n",
              "2021-06-21 14:00:00-07:00       0.0  2492.008897  29.340684  228.551331   \n",
              "2021-06-21 15:00:00-07:00       0.0  2494.291212  28.567803  227.094697   \n",
              "2021-06-21 16:00:00-07:00       0.0  2496.541818  27.871212  227.170455   \n",
              "2021-06-21 17:00:00-07:00       0.0  2498.371749  27.298099  225.315589   \n",
              "...                             ...          ...        ...         ...   \n",
              "2022-01-27 19:00:00-08:00       0.0  2695.337045  10.548106  217.628788   \n",
              "2022-01-27 20:00:00-08:00       0.0  2698.127765  10.325379  216.257576   \n",
              "2022-01-27 21:00:00-08:00       0.0  2697.993118  10.384411  217.615970   \n",
              "2022-01-27 22:00:00-08:00       0.0  2696.044508  10.663636  217.882576   \n",
              "2022-01-27 23:00:00-08:00       0.0  2696.067019  10.622642  218.490566   \n",
              "\n",
              "                           I1L_valid  I2L_valid  I1H [nA]  I1L [10pA]  \\\n",
              "timestamp                                                               \n",
              "2021-06-21 13:00:00-07:00        1.0        1.0  3.142128  223.439853   \n",
              "2021-06-21 14:00:00-07:00        1.0        1.0  3.878484  246.563645   \n",
              "2021-06-21 15:00:00-07:00        1.0        1.0  4.286620  244.376587   \n",
              "2021-06-21 16:00:00-07:00        1.0        1.0  3.997649  267.480179   \n",
              "2021-06-21 17:00:00-07:00        1.0        1.0  3.921021  253.983449   \n",
              "...                              ...        ...       ...         ...   \n",
              "2022-01-27 19:00:00-08:00        1.0        1.0  5.738337  222.336954   \n",
              "2022-01-27 20:00:00-08:00        1.0        1.0  5.612758  217.049998   \n",
              "2022-01-27 21:00:00-08:00        1.0        1.0  5.933845  176.277291   \n",
              "2022-01-27 22:00:00-08:00        1.0        1.0  5.977703  172.602389   \n",
              "2022-01-27 23:00:00-08:00        1.0        1.0  5.985500  234.695428   \n",
              "\n",
              "                            V1 [10nV]     V2 [10nV]  ...    I2L [10pA]    tsd  \\\n",
              "timestamp                                            ...                        \n",
              "2021-06-21 13:00:00-07:00  770.964687 -4.624688e+06  ...  3.056341e+06    0.0   \n",
              "2021-06-21 14:00:00-07:00  672.071432 -4.107527e+06  ...  2.803198e+06    0.0   \n",
              "2021-06-21 15:00:00-07:00  599.703486 -3.709883e+06  ...  2.061264e+06    0.0   \n",
              "2021-06-21 16:00:00-07:00  548.710656 -3.392292e+06  ...  2.235990e+06    0.0   \n",
              "2021-06-21 17:00:00-07:00  508.005800 -3.134217e+06  ...  2.011312e+06    0.0   \n",
              "...                               ...           ...  ...           ...    ...   \n",
              "2022-01-27 19:00:00-08:00  141.396883 -3.997810e+06  ...  1.105493e+05  220.0   \n",
              "2022-01-27 20:00:00-08:00  136.364915 -4.445531e+06  ...  5.993731e+05  220.0   \n",
              "2022-01-27 21:00:00-08:00  143.467903 -4.461138e+06  ...  6.542789e+05  220.0   \n",
              "2022-01-27 22:00:00-08:00  145.993928 -4.379870e+06  ...  6.605283e+05  220.0   \n",
              "2022-01-27 23:00:00-08:00  137.368671 -4.305994e+06  ...  6.045255e+05  220.0   \n",
              "\n",
              "                                 power   power - 1h     EC - 1h  temp - 1h  \\\n",
              "timestamp                                                                    \n",
              "2021-06-21 13:00:00-07:00  1713.556321  1707.297550  230.590909  30.975758   \n",
              "2021-06-21 14:00:00-07:00  1657.393367  1713.556321  229.795455  30.135985   \n",
              "2021-06-21 15:00:00-07:00  1438.495990  1657.393367  228.551331  29.340684   \n",
              "2021-06-21 16:00:00-07:00  1460.677195  1438.495990  227.094697  28.567803   \n",
              "2021-06-21 17:00:00-07:00  1285.123753  1460.677195  227.170455  27.871212   \n",
              "...                                ...          ...         ...        ...   \n",
              "2022-01-27 19:00:00-08:00   309.184521   257.279436  216.752852   9.775285   \n",
              "2022-01-27 20:00:00-08:00   283.561699   309.184521  217.628788  10.548106   \n",
              "2022-01-27 21:00:00-08:00   248.878492   283.561699  216.257576  10.325379   \n",
              "2022-01-27 22:00:00-08:00   248.235735   248.878492  217.615970  10.384411   \n",
              "2022-01-27 23:00:00-08:00   318.284082   248.235735  217.882576  10.663636   \n",
              "\n",
              "                           raw_VWC - 1h     V1 - 1h    I1L - 1h  I1H - 1h  \n",
              "timestamp                                                                  \n",
              "2021-06-21 13:00:00-07:00   2486.461667  891.331314  192.348231  2.373069  \n",
              "2021-06-21 14:00:00-07:00   2489.510492  770.964687  223.439853  3.142128  \n",
              "2021-06-21 15:00:00-07:00   2492.008897  672.071432  246.563645  3.878484  \n",
              "2021-06-21 16:00:00-07:00   2494.291212  599.703486  244.376587  4.286620  \n",
              "2021-06-21 17:00:00-07:00   2496.541818  548.710656  267.480179  3.997649  \n",
              "...                                 ...         ...         ...       ...  \n",
              "2022-01-27 19:00:00-08:00   2692.370304  137.658867  188.822504  6.073694  \n",
              "2022-01-27 20:00:00-08:00   2695.337045  141.396883  222.336954  5.738337  \n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-093666a2-4ad3-4c2a-bced-e91455836bf1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(sum(power_mape) / len(power_mape))\n",
        "print(sum(voltage_mape) / len(voltage_mape))\n",
        "print(sum(current_mape) / len(current_mape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OkUXcf5DYVnf",
        "outputId": "4d8996b5-cc01-4671-ac90-487b7863690e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.378423852181619\n",
            "0.3784860140379798\n",
            "0.10790797987554766\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q9AI2jRhe1dh"
      },
      "outputs": [],
      "source": [
        "#Define SMAPE\n",
        "def SMAPE(actual, predicted):\n",
        "    return 1/len(actual) * np.sum(2 * np.abs(predicted-actual) / (np.abs(actual) + np.abs(predicted))*100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R7URo1JEycKl"
      },
      "outputs": [],
      "source": [
        "#reshape data\n",
        "X_train = X_train.values.reshape((X_train.shape[0], 1, X_train.shape[1]))\n",
        "X_test = X_test.values.reshape((X_test.shape[0], 1, X_test.shape[1]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VMEfs0MU0s57"
      },
      "outputs": [],
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM\n",
        "# design network\n",
        "model = Sequential()\n",
        "model.add(LSTM(200, input_shape=(X_train.shape[1], X_train.shape[2]), activation='relu'))\n",
        "model.add(Dense(100, activation='relu'))\n",
        "model.add(Dense(3))\n",
        "model.compile(loss='mape', optimizer='adam')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0jZu1b4EAQaP",
        "outputId": "53aeb624-e843-4a99-92ae-e345227ad7e5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/6\n",
            "1383/1383 [==============================] - 13s 7ms/step - loss: 477.6351\n",
            "Epoch 2/6\n",
            "1383/1383 [==============================] - 11s 8ms/step - loss: 450.8291\n",
            "Epoch 3/6\n",
            "1383/1383 [==============================] - 16s 11ms/step - loss: 336.5304\n",
            "Epoch 4/6\n",
            "1383/1383 [==============================] - 10s 7ms/step - loss: 220.3073\n",
            "Epoch 5/6\n",
            "1383/1383 [==============================] - 10s 7ms/step - loss: 200.5687\n",
            "Epoch 6/6\n",
            "1383/1383 [==============================] - 11s 8ms/step - loss: 247.2008\n"
          ]
        }
      ],
      "source": [
        "from keras_lr_finder import LRFinder\n",
        "# model is a Keras model\n",
        "lr_finder = LRFinder(model)\n",
        "\n",
        "# Train a model with batch size 512 for 5 epochs\n",
        "# with learning rate growing exponentially from 0.0001 to 1\n",
        "lr_finder.find(X_train, y_train, start_lr=0.0001, end_lr=10, batch_size=512, epochs=6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "xXFgIyU8SU_N",
        "outputId": "1fd481e1-8672-42f7-cdf6-85d5a50686c4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the loss, ignore 20 batches in the beginning and 5 in the end\n",
        "lr_finder.plot_loss(n_skip_beginning=20, n_skip_end=5)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U numpy==1.18.5"
      ],
      "metadata": {
        "id": "EpHTDa_GECRH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nNKtIf-yrYmh",
        "outputId": "6aa6a828-51b6-41b2-cd72-57d86d96d8fa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "1023/1023 - 7s - loss: 15.5108 - 7s/epoch - 7ms/step\n",
            "Epoch 2/20\n",
            "1023/1023 - 6s - loss: 7.1860 - 6s/epoch - 6ms/step\n",
            "Epoch 3/20\n",
            "1023/1023 - 5s - loss: 5.9099 - 5s/epoch - 5ms/step\n",
            "Epoch 4/20\n",
            "1023/1023 - 8s - loss: 5.3296 - 8s/epoch - 7ms/step\n",
            "Epoch 5/20\n",
            "1023/1023 - 5s - loss: 4.5943 - 5s/epoch - 5ms/step\n",
            "Epoch 6/20\n",
            "1023/1023 - 5s - loss: 4.9008 - 5s/epoch - 4ms/step\n",
            "Epoch 7/20\n",
            "1023/1023 - 6s - loss: 4.3204 - 6s/epoch - 6ms/step\n",
            "Epoch 8/20\n",
            "1023/1023 - 4s - loss: 4.3870 - 4s/epoch - 4ms/step\n",
            "Epoch 9/20\n",
            "1023/1023 - 4s - loss: 4.6399 - 4s/epoch - 4ms/step\n",
            "Epoch 10/20\n",
            "1023/1023 - 7s - loss: 3.9823 - 7s/epoch - 6ms/step\n",
            "Epoch 11/20\n",
            "1023/1023 - 5s - loss: 3.8555 - 5s/epoch - 4ms/step\n",
            "Epoch 12/20\n",
            "1023/1023 - 7s - loss: 3.7445 - 7s/epoch - 7ms/step\n",
            "Epoch 13/20\n",
            "1023/1023 - 7s - loss: 3.7791 - 7s/epoch - 6ms/step\n",
            "Epoch 14/20\n",
            "1023/1023 - 5s - loss: 3.8024 - 5s/epoch - 5ms/step\n",
            "Epoch 15/20\n",
            "1023/1023 - 9s - loss: 3.6959 - 9s/epoch - 9ms/step\n",
            "Epoch 16/20\n",
            "1023/1023 - 7s - loss: 3.4849 - 7s/epoch - 7ms/step\n",
            "Epoch 17/20\n",
            "1023/1023 - 6s - loss: 3.5151 - 6s/epoch - 6ms/step\n",
            "Epoch 18/20\n",
            "1023/1023 - 5s - loss: 3.6620 - 5s/epoch - 5ms/step\n",
            "Epoch 19/20\n",
            "1023/1023 - 4s - loss: 3.3413 - 4s/epoch - 4ms/step\n",
            "Epoch 20/20\n",
            "1023/1023 - 6s - loss: 3.2481 - 6s/epoch - 6ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x78757ec789a0>"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ],
      "source": [
        "from keras import backend as K\n",
        "#K.set_value(model.optimizer.learning_rate, 0.3)\n",
        "\n",
        "batchsize = 18\n",
        "# fit network\n",
        "model.fit(X_train, y_train, epochs=20, batch_size=batchsize, verbose=2, shuffle=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-ay2aNApzAPZ"
      },
      "outputs": [],
      "source": [
        "model.save(\"lstm_30min\", overwrite=True, save_format=None)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mv lstm_30min 'drive/MyDrive/jLab Shared Docs/MFC Modeling'"
      ],
      "metadata": {
        "id": "9V9dEE2Ma6Ge"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2E_HqbVB8STN",
        "outputId": "2fb7d542-cef1-471c-9bbc-70d5d42984dd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1383/1383 [==============================] - 11s 8ms/step - loss: 42.0569\n",
            "Epoch 2/5\n",
            "1383/1383 [==============================] - 10s 7ms/step - loss: 38.8623\n",
            "Epoch 3/5\n",
            "1383/1383 [==============================] - 10s 8ms/step - loss: 54.7279\n",
            "Epoch 4/5\n",
            "1383/1383 [==============================] - 10s 7ms/step - loss: 85.0554\n",
            "Epoch 5/5\n",
            "1383/1383 [==============================] - 10s 8ms/step - loss: 87.3932\n"
          ]
        }
      ],
      "source": [
        "from keras_lr_finder import LRFinder\n",
        "# model is a Keras model\n",
        "lr_finder = LRFinder(model)\n",
        "\n",
        "# Train a model with batch size 512 for 5 epochs\n",
        "# with learning rate growing exponentially from 0.0001 to 1\n",
        "lr_finder.find(X_train, y_train, start_lr=0.0001, end_lr=1, batch_size=512, epochs=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "vygfDoPi9FmV",
        "outputId": "ba488352-a5d4-4645-fc02-8209250e2e8c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the loss, ignore 20 batches in the beginning and 5 in the end\n",
        "lr_finder.plot_loss(n_skip_beginning=20, n_skip_end=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "7-W1A98bwFsX",
        "outputId": "a8415706-041f-4d13-a9d9-7a0755b10ba2"
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-16b435b16438>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# plot history\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'train'\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      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'test'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\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      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\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;31mNameError\u001b[0m: name 'history' is not defined"
          ]
        }
      ],
      "source": [
        "# plot history\n",
        "plt.plot(history.history['loss'], label='train')\n",
        "plt.plot(history.history['val_loss'], label='test')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EdiUPWZFuMDK",
        "outputId": "92833e5d-b436-4d0b-a704-6fb8d10d985f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1023/1023 [==============================] - 4s 3ms/step\n",
            "1534/1534 [==============================] - 10s 6ms/step\n"
          ]
        }
      ],
      "source": [
        "train_pred = model.predict(X_train, batch_size=batchsize)\n",
        "test_pred = model.predict(X_test, batch_size=batchsize)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "SpaljOytqFgc",
        "outputId": "12e331f1-bfb6-46ba-858c-26c7504a7b37"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 power    V1 [10nV]  I1L [10pA]\n",
              "timestamp                                                      \n",
              "2021-06-21 11:25:00-07:00  1540.283104   915.287393  159.515921\n",
              "2021-06-21 11:30:00-07:00  1565.117849   948.486201  158.836415\n",
              "2021-06-21 11:35:00-07:00  1566.647996   979.510251  156.328818\n",
              "2021-06-21 11:40:00-07:00  1592.579653  1002.552448  156.576172\n",
              "2021-06-21 11:45:00-07:00  1617.058700  1025.352560  157.303444\n",
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              "2021-10-13 04:50:00-07:00   470.489637   230.556085  206.539972\n",
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              "2021-10-13 05:00:00-07:00   478.045636   228.875178  211.483802\n",
              "2021-10-13 05:05:00-07:00   472.836308   228.223723  209.708232\n",
              "2021-10-13 05:10:00-07:00   462.307070   227.565565  205.584954\n",
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              "      <th></th>\n",
              "      <th>power</th>\n",
              "      <th>V1 [10nV]</th>\n",
              "      <th>I1L [10pA]</th>\n",
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              "      <th>2021-06-21 11:25:00-07:00</th>\n",
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              "      <th>2021-06-21 11:30:00-07:00</th>\n",
              "      <td>1565.117849</td>\n",
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              "      <th>2021-06-21 11:35:00-07:00</th>\n",
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              "      <th>2021-10-13 04:50:00-07:00</th>\n",
              "      <td>470.489637</td>\n",
              "      <td>230.556085</td>\n",
              "      <td>206.539972</td>\n",
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              "    <tr>\n",
              "      <th>2021-10-13 04:55:00-07:00</th>\n",
              "      <td>471.200443</td>\n",
              "      <td>229.796365</td>\n",
              "      <td>207.570490</td>\n",
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              "      <th>2021-10-13 05:00:00-07:00</th>\n",
              "      <td>478.045636</td>\n",
              "      <td>228.875178</td>\n",
              "      <td>211.483802</td>\n",
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              "    <tr>\n",
              "      <th>2021-10-13 05:05:00-07:00</th>\n",
              "      <td>472.836308</td>\n",
              "      <td>228.223723</td>\n",
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              "    <tr>\n",
              "      <th>2021-10-13 05:10:00-07:00</th>\n",
              "      <td>462.307070</td>\n",
              "      <td>227.565565</td>\n",
              "      <td>205.584954</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ],
      "source": [
        "y_train"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WUz5rwHexzhE",
        "outputId": "8bfb9063-22de-4311-dd85-651c8b3297e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1550.2512 ,  852.8913 ,  159.65053],\n",
              "       [1590.5591 ,  882.81134,  160.07521],\n",
              "       [1619.4214 ,  914.918  ,  158.24434],\n",
              "       ...,\n",
              "       [ 480.20694,  235.97131,  207.8695 ],\n",
              "       [ 486.98386,  234.64207,  212.15208],\n",
              "       [ 481.8149 ,  234.33087,  210.27222]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t1WpZxpTtWpR",
        "outputId": "23d90b20-66e4-40de-8ff9-b9baba16fd6d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train MAPE power:\n",
            " 0.04112260203500509\n",
            "Test MAPE power:\n",
            " 0.6959302259631488\n",
            "Train MAE power:\n",
            " 21.886273740813685\n",
            "Test MAE power:\n",
            " 36.963306622464586\n"
          ]
        }
      ],
      "source": [
        "#Evaluate SMAPE\n",
        "#print(\"Train SMAPE:\\n\", SMAPE(y_train.values.ravel(), train_pred))\n",
        "#print(\"Test SMAPE:\\n\", SMAPE(y_test.values.ravel(), test_pred))\n",
        "\n",
        "#Evaluate MAPE\n",
        "print(\"Train MAPE power:\\n\", MAPE(y_train['power'].values.ravel(), train_pred[:, 0]))\n",
        "print(\"Test MAPE power:\\n\", MAPE(y_test['power'].values.ravel(), test_pred[:, 0]))\n",
        "\n",
        "#print(\"Train MAPE power:\\n\", MAPE(y_train['power'].shift(1).dropna().values.ravel(), train_pred[:, 0][1:]))\n",
        "#print(\"Test MAPE power:\\n\", MAPE(y_test['power'].shift(1).dropna().values.ravel(), test_pred[:, 0][1:]))\n",
        "\n",
        "#Evalaute MAE\n",
        "print(\"Train MAE power:\\n\", mean_absolute_error(y_train['power'].values.ravel(), train_pred[:, 0]))\n",
        "print(\"Test MAE power:\\n\", mean_absolute_error(y_test['power'].values.ravel(), test_pred[:, 0]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UzE9rgfb9NPC",
        "outputId": "781ea135-73d0-412f-dc33-f93a6f6b6ed8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train MAPE voltage:\n",
            " 0.033086016360312415\n",
            "Test MAPE voltage:\n",
            " 0.972538369007135\n",
            "Train MAE voltage:\n",
            " 10.47202434790737\n",
            "Test MAE voltage:\n",
            " 28.972905840031043\n"
          ]
        }
      ],
      "source": [
        "#Evaluate MAPE\n",
        "print(\"Train MAPE voltage:\\n\", MAPE(y_train['V1 [10nV]'].values.ravel(), train_pred[:, 1]))\n",
        "print(\"Test MAPE voltage:\\n\", MAPE(y_test['V1 [10nV]'].values.ravel(), test_pred[:, 1]))\n",
        "\n",
        "#print(\"Train MAPE voltage:\\n\", MAPE(y_train['V1 [10nV]'].shift(1).dropna().values.ravel(), train_pred[:, 1][1:]))\n",
        "#print(\"Test MAPE voltage:\\n\", MAPE(y_test['V1 [10nV]'].shift(1).dropna().values.ravel(), test_pred[:, 1][1:]))\n",
        "\n",
        "#Evalaute MAE\n",
        "print(\"Train MAE voltage:\\n\", mean_absolute_error(y_train['V1 [10nV]'].values.ravel(), train_pred[:, 1]))\n",
        "print(\"Test MAE voltage:\\n\", mean_absolute_error(y_test['V1 [10nV]'].values.ravel(), test_pred[:, 1]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9DBTLlnd9rOQ",
        "outputId": "dd4a509b-8708-4532-9c3c-f92e7ba11ceb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train MAPE current:\n",
            " 0.03577720777148524\n",
            "Test MAPE current:\n",
            " 0.059244584192332625\n",
            "Train MAE current:\n",
            " 5.861255325509229\n",
            "Test MAE current:\n",
            " 14.964420144702359\n"
          ]
        }
      ],
      "source": [
        "#Evaluate MAPE\n",
        "print(\"Train MAPE current:\\n\", MAPE(y_train['I1L [10pA]'].values.ravel(), train_pred[:, 2]))\n",
        "print(\"Test MAPE current:\\n\", MAPE(y_test['I1L [10pA]'].values.ravel(), test_pred[:, 2]))\n",
        "\n",
        "#print(\"Train MAPE current:\\n\", MAPE(y_train['I1L [10pA]'].shift(1).dropna().values.ravel(), train_pred[:, 2][1:]))\n",
        "#print(\"Test MAPE current:\\n\", MAPE(y_test['I1L [10pA]'].shift(1).dropna().values.ravel(), test_pred[:, 2][1:]))\n",
        "\n",
        "#Evalaute MAE\n",
        "print(\"Train MAE current:\\n\", mean_absolute_error(y_train['I1L [10pA]'].values.ravel(), train_pred[:, 2]))\n",
        "print(\"Test MAE current:\\n\", mean_absolute_error(y_test['I1L [10pA]'].values.ravel(), test_pred[:, 2]))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train['I1L [10pA]'].diff().min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WJrTpCNy5Vel",
        "outputId": "4c277098-f55f-4434-adbd-663b7d7acff8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-192.79895000000002"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "17z8m-jP56T9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train['V1 [10nV]'].diff().min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aQg0tDbc5cGE",
        "outputId": "a453856d-36a9-4891-ed19-c4a04675716d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-647.5048632575757"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train['power'].diff().min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l-9Tfup05jpH",
        "outputId": "c4591d44-f5cb-475b-dfdb-4edfd797ad05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-948.89687981835"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict(np.concatenate((X_train, X_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r3DLmwqzvRMI",
        "outputId": "7170af03-74d4-48ed-ac0f-2a39c5732b6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "120/120 [==============================] - 0s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hNbyxCS0MxfM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e6851a76-ca21-4d22-da1f-53866f31d56b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-127-e78f9a62da96>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  mv1[\"power_pred\"] = predictions[:, 0]\n",
            "<ipython-input-127-e78f9a62da96>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  mv1[\"voltage_pred\"] = predictions[:, 1]\n",
            "<ipython-input-127-e78f9a62da96>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  mv1[\"current_pred\"] = predictions[:, 2]\n"
          ]
        }
      ],
      "source": [
        "mv1 = df\n",
        "mv1[\"power_pred\"] = predictions[:, 0]\n",
        "mv1[\"voltage_pred\"] = predictions[:, 1]\n",
        "mv1[\"current_pred\"] = predictions[:, 2]\n",
        "mv1 = mv1.loc[(mv1.index > '2021-12-12') & (mv1.index < '2021-12-14')]\n",
        "mv2 = mv1"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.iloc[1915:2298]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179
        },
        "id": "CSp809rtDwg5",
        "outputId": "9571a354-b766-4743-a204-f848b16b552b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-fe349046f2d8>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1915\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2298\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mv1 = mv1.loc[(mv1.index > '2021-1-12') & (mv1.index < '2021-12-14')]"
      ],
      "metadata": {
        "id": "RuZUBbNLFw-X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "id": "EOQVwMjsBzh9",
        "outputId": "a64d43bf-7656-460b-9b4c-52511d7bc101"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib as mpl\n",
        "mpl.use('Agg')\n",
        "#mpl.rc('font', **font)\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.gridspec as gridspec\n",
        "import matplotlib.dates as md\n",
        "import datetime\n",
        "import numpy as np\n",
        "from pytz import timezone\n",
        "import pandas as pd\n",
        "import arrow\n",
        "import os\n",
        "from pandas.plotting import register_matplotlib_converters\n",
        "register_matplotlib_converters()\n",
        "%matplotlib inline\n",
        "\n",
        "# Limits for graphs\n",
        "VOLTAGE_LIM = 0.2\n",
        "CURRENT_LIM = 40\n",
        "POWER_LIM = 4\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "plt.close()\n",
        "plt.xlabel(\"Time\")\n",
        "fig, (ax1, ax3) = plt.subplots(2,figsize=(4,2), sharex=True)\n",
        "fig.autofmt_xdate()\n",
        "\n",
        "\n",
        "volt_color= 'tab:blue'\n",
        "\n",
        "amp_color = 'tab:red'\n",
        "\n",
        "\n",
        "volt_color1= 'tab:blue'\n",
        "volt_style1 = 'dashed'\n",
        "volt_color2= 'tab:green'\n",
        "volt_style2 = 'dotted'\n",
        "amp_color1 = 'tab:red'\n",
        "amp_style1='dashed'\n",
        "amp_color2 = 'tab:orange'\n",
        "amp_style2='dashdot'\n",
        "\n",
        "\n",
        "ax3.fmt_xdata = md.DateFormatter('%m-%d')\n",
        "ax3.xaxis.set_major_formatter(md.DateFormatter('%m-%d'))\n",
        "#ax3.set_ylabel(\"Power (uW)\")\n",
        "ax3.set_ylabel(\"Current\")\n",
        "ax3.grid(True)\n",
        "#print('max power: ',max(max(1E6*df['power1']),max(1E6*df['power2'])))\n",
        "#ax3.set_ylim(0, 4)\n",
        "ax3.plot(mv1.index, 1E6*mv2[\"current_pred\"], color=volt_color1, ls = volt_style1)\n",
        "ax3.plot(mv1.index, 1E6*mv2['I1L [10pA]'], color=volt_color2, ls = volt_style2)\n",
        "ax3.legend(['predictions','actual'], loc='upper right', prop={'size': 6})\n",
        "\n",
        "#Plot error\n",
        "#ax3.plot(mv1['timestamp'], mv1['error']/mv1['power'], color=volt_color2, ls = volt_style2)\n",
        "#ax3.legend(['error'], loc='upper right', prop={'size': 6})\n",
        "\n",
        "ax3.tick_params(axis='x', labelsize=6, rotation=0)\n",
        "#ax3.xaxis.set_major_locator(plt.MaxNLocator(6))\n",
        "#ax3.set_xlim(mv.index[0], datetime.date(2020,5,19))\n",
        "for label in ax3.get_xticklabels():\n",
        "    label.set_horizontalalignment('center')\n",
        "\n",
        "plt.tight_layout(pad=0.6, w_pad=0.5, h_pad=0.6)\n",
        "plt.subplots_adjust(hspace=0.15)\n",
        "plt.savefig('twobat.pdf')\n",
        "plt.close()\n",
        "#tot_energy = np.trapz(df['power1'])\n",
        "#tot_energy = np.trapz(df['power2'])\n",
        "#print(tot_energy)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}