tsrv7_lstm15.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jlab-sensing/MFC_Modeling/blob/main/tsrv7_lstm15.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zcF-Dv23N_ON",
        "outputId": "128c0d27-7324-4fa5-934f-4fe6c9df0b73"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "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=9656 sha256=9618ddbd93439ef78a1f7bb832c2df862d05a97853e76128d81187b735d9e7d9\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-2.4.1 black-24.4.2 execnb-0.1.5 ghapi-1.0.5 giotto-ph-0.2.2 giotto-tda-0.6.0 hepml-0.0.12 igraph-0.11.4 jedi-0.19.1 mypy-extensions-1.0.0 nbdev-2.3.20 pathspec-0.12.1 pyflagser-0.4.5 texttable-1.7.0 watchdog-4.0.0 wget-3.2\n",
            "Collecting arrow\n",
            "  Downloading arrow-1.3.0-py3-none-any.whl (66 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.4/66.4 kB\u001b[0m \u001b[31m2.0 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",
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            "  Downloading types_python_dateutil-2.9.0.20240316-py3-none-any.whl (9.7 kB)\n",
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            "Installing collected packages: types-python-dateutil, arrow\n",
            "Successfully installed arrow-1.3.0 types-python-dateutil-2.9.0.20240316\n",
            "Collecting keras_lr_finder\n",
            "  Downloading keras_lr_finder-0.1-py2.py3-none-any.whl (3.5 kB)\n",
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            "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",
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            "  inflating: rocket4/tmp/TEROSoutput-1622682139-f4.csv  \n",
            "  inflating: rocket4/tmp/figs/sensor-0_from-2021-04-13 15:58:01-07:00_to-2021-05-05 14:59:28-07:00.png  \n",
            "  inflating: __MACOSX/rocket4/tmp/figs/._sensor-0_from-2021-04-13 15:58:01-07:00_to-2021-05-05 14:59:28-07:00.png  \n",
            "  inflating: rocket4/tmp/figs/sensor-1_from-2021-04-13 15:58:12-07:00_to-2021-05-05 14:59:38-07:00.png  \n",
            "  inflating: rocket4/tmp/pkls/teros_data.pkl  \n",
            "  inflating: rocket4/tmp/pkls/seen_datasets.txt  \n",
            "  inflating: rocket4/tmp/old/TEROSoutput-1615346184-f1.csv  \n",
            "  inflating: rocket4/tmp/old/soil_20210413-225754_2.csv  \n",
            "  inflating: rocket4/tmp/old/TEROSoutput-1618354681-f2.csv  \n"
          ]
        }
      ],
      "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": null,
      "metadata": {
        "id": "rbq5qg_iJRIG"
      },
      "outputs": [],
      "source": [
        "!pip freeze > requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "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": 3,
      "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": 4,
      "metadata": {
        "id": "ViS0TjC4n6lk"
      },
      "outputs": [],
      "source": [
        "#Convert current to amps, voltage to volts\n",
        "y[\"I1L [10pA]\"] = np.abs(y[\"I1L [10pA]\"] * 1E-11)\n",
        "y[\"V1 [10nV]\"] = np.abs(y[\"V1 [10nV]\"] * 1E-8)\n",
        "y[\"I1H [nA]\"] = np.abs(y[\"I1H [nA]\"] * 1E-9)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "z0aqozsZpSOB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad6ca9db-ac2b-4036-8000-df584f26eea1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-5-38f7a5b5fe7c>: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"
          ]
        }
      ],
      "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')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "CBAfOHB61Jwc"
      },
      "outputs": [],
      "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'] > '2021-06-04')] #Deployment date\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",
        "#Drop data outages\n",
        "df = df.drop(df[(df.timestamp > '2021-11-11') & (df.timestamp < '2021-11-22 01:00:00')].index)\n",
        "df = df.drop(df[(df.timestamp > '2022-01-27')].index)\n",
        "#df = df.set_index('timestamp')\n",
        "df = df[:-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "p8fFsoLNfrFA"
      },
      "outputs": [],
      "source": [
        "df = df.set_index('timestamp')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "9kNxDOBwk7IN"
      },
      "outputs": [],
      "source": [
        "#Get time since deployement\n",
        "df['tsd'] = (df.index - df.index[0]).days\n",
        "df['hour'] = (df.index).hour"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "-Pfyv0fM3te1"
      },
      "outputs": [],
      "source": [
        "#Calculate power\n",
        "df[\"power\"] = np.abs(np.multiply(df.iloc[:, 7], df.iloc[:, 8]))\n",
        "#df[\"power\"] = np.abs(np.multiply(df[\"I1L [10pA]\"], df[\"V1 [10nV]\"]))\n",
        "\n",
        "#Convert to nW\n",
        "df['power'] = df['power']*1E9"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "jA-WVzVh2-lf"
      },
      "outputs": [],
      "source": [
        "#Convert to 10 nanoamps, 10 microvolts\n",
        "df[\"I1L [10pA]\"] = np.abs(df[\"I1L [10pA]\"] * 1E8)\n",
        "df[\"V1 [10nV]\"] = np.abs(df[\"V1 [10nV]\"] * 1E5)\n",
        "df[\"I1H [nA]\"] = np.abs(df[\"I1H [nA]\"] * 1E8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "XxAlm4FXh57m"
      },
      "outputs": [],
      "source": [
        "df = df.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "8sHtXZCVrsQJ"
      },
      "outputs": [],
      "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['power - 3h'] = df['power'].shift(3).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['EC - 2h'] = df['EC'].shift(2).dropna()\n",
        "df['EC - 3h'] = df['EC'].shift(3).dropna()\n",
        "\n",
        "df['temp - 1h'] = df['temp'].shift(1).dropna()\n",
        "df['temp - 2h'] = df['temp'].shift(2).dropna()\n",
        "df['temp - 3h'] = df['temp'].shift(3).dropna()\n",
        "\n",
        "df['raw_VWC - 1h'] = df['raw_VWC'].shift(1).dropna()\n",
        "df['raw_VWC - 2h'] = df['raw_VWC'].shift(2).dropna()\n",
        "df['raw_VWC - 3h'] = df['raw_VWC'].shift(3).dropna()\n",
        "\n",
        "#Add voltage and current time series\n",
        "df['V1 - 1h'] = df['V1 [10nV]'].shift(1).dropna()\n",
        "df['V1 - 2h'] = df['V1 [10nV]'].shift(2).dropna()\n",
        "df['V1 - 3h'] = df['V1 [10nV]'].shift(3).dropna()\n",
        "\n",
        "df['I1L - 1h'] = df['I1L [10pA]'].shift(1).dropna()\n",
        "df['I1L - 2h'] = df['I1L [10pA]'].shift(2).dropna()\n",
        "df['I1L - 3h'] = df['I1L [10pA]'].shift(3).dropna()\n",
        "\n",
        "df['I1H - 1h'] = df['I1H [nA]'].shift(1).dropna()\n",
        "df['I1H - 2h'] = df['I1H [nA]'].shift(2).dropna()\n",
        "df['I1H - 3h'] = df['I1H [nA]'].shift(3).dropna()\n",
        "df = df.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "u4SM1_EvGS6y"
      },
      "outputs": [],
      "source": [
        "#df = df.rename(columns={'power': 'power [μW]'})\n",
        "df = df.rename(columns={'I1L [10pA]': 'I1L [μA]', 'V1 [10nV]' : 'V1 [mV]'})\n",
        "df = df.set_index('timestamp')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "Z5qjf1wD9yhQ"
      },
      "outputs": [],
      "source": [
        "#X = pd.concat([df[\"power - 1h\"], df[\"power - 2h\"], df[\"power - 3h\"], df[\"V1 - 1h\"], df[\"V1 - 2h\"], df[\"V1 - 3h\"], df[\"I1L - 1h\"], df[\"I1L - 2h\"], df[\"I1L - 3h\"],df[\"EC - 1h\"], df[\"EC - 2h\"], df[\"EC - 3h\"], df[\"raw_VWC - 1h\"], df[\"raw_VWC - 2h\"], df[\"raw_VWC - 3h\"], df[\"temp - 1h\"], df[\"temp - 2h\"], df[\"temp - 3h\"], df[\"tsd\"], df[\"hour\"]], axis = 1)\n",
        "X = pd.concat([df[\"power - 1h\"], df[\"power - 2h\"], df[\"power - 3h\"], df[\"V1 - 1h\"], df[\"V1 - 2h\"], df[\"V1 - 3h\"], df[\"I1L - 1h\"], df[\"I1L - 2h\"], df[\"I1L - 3h\"], df[\"tsd\"], df[\"hour\"]], axis = 1)\n",
        "y = pd.concat([df[\"power\"], df['V1 [mV]'], df['I1L [μA]']], axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "239oMsSK72B3"
      },
      "outputs": [],
      "source": [
        "#New runtime calculation\n",
        "import math\n",
        "from dateutil import parser\n",
        "from matplotlib import pyplot as plt\n",
        "from datetime import datetime, timedelta\n",
        "\n",
        "def internal_R_v3(R=2000): #return internal resistance of v3 cells in ohms\n",
        "    #https://www.jstage.jst.go.jp/article/jwet/20/1/20_21-087/_pdf\n",
        "    v0_oc = 48.5e-3 #48.5 mV\n",
        "    v0_cc = 4.8e-3\n",
        "    v0_r = R*((v0_oc/v0_cc)-1)\n",
        "\n",
        "    v1_oc = 43.8e-3\n",
        "    v1_cc = 20.9e-3\n",
        "    v1_r = R*((v1_oc/v1_cc)-1)\n",
        "\n",
        "    v2_oc = 45.2e-3\n",
        "    v2_cc = 23.5e-3\n",
        "    v2_r = R*((v2_oc/v2_cc)-1)\n",
        "\n",
        "    return (v0_r+v1_r+v2_r)/3\n",
        "\n",
        "def internal_R_v0(R=2000): #return internal resistance of v0 cells in ohms\n",
        "    v3_oc = 41.7e-3 #41.7mV\n",
        "    v3_cc = 5.1e-3\n",
        "    v3_r = R*((v3_oc/v3_cc)-1)\n",
        "\n",
        "    v4_oc = 48.7e-3\n",
        "    v4_cc = 16.8e-3\n",
        "    v4_r = R*((v4_oc/v4_cc)-1)\n",
        "\n",
        "    v5_oc = 39.1e-3\n",
        "    v5_cc = 16.9e-3\n",
        "    v5_r = R*((v5_oc/v5_cc)-1)\n",
        "\n",
        "    return (v3_r+v4_r+v5_r)/3\n",
        "\n",
        "def SMFC_current(v, R):\n",
        "    return v/R\n",
        "\n",
        "#MODEL\n",
        "def cap_leakage(E_cap_tn, timestep):\n",
        "    #Spec for KEMET T491\n",
        "    return 0.01e-6 * E_cap_tn * timestep\n",
        "\n",
        "def Matrix_Power(V, R):\n",
        "    #efficiency interpolated from https://www.analog.com/media/en/technical-documentation/data-sheets/ADP5091-5092.pdf\n",
        "    #given I_in = 100 uA and SYS = 3V\n",
        "    #V is the voltage (V) of the SMFC we captured\n",
        "    #R is the resistance (ohms) of the load we used to get that voltage trace\n",
        "    #Eta = -292.25665*V**4 + 784.30311*V**3 - 770.71691*V**2 + 342.00502*V + 15.83307\n",
        "    #Eta = Eta/100\n",
        "    Eta = 0.60\n",
        "    Pmax = (V**2)/R\n",
        "    Pout = Eta*Pmax\n",
        "    #assert((Eta > 0) & (Eta < 1))\n",
        "    #assert(Pout < 12000e-6)\n",
        "    return Pout\n",
        "\n",
        "def update_capEnergy(e0, V_applied, R, C, dt):\n",
        "    # e0: initial energy stored\n",
        "    # V_applied: voltage from SMFC\n",
        "    # R: internal resistance of SMFC\n",
        "    # C: capacitance of capacitor\n",
        "    # dt: time step since last data point\n",
        "    e_cap = e0 + Matrix_Power(V_applied, R)*dt - cap_leakage(e0, dt)\n",
        "    v_cap = math.sqrt(2*e_cap/C)\n",
        "    if e_cap < 0: #Not charging if leakage is greater than energy\n",
        "        e_cap = 0\n",
        "\n",
        "    return e_cap, v_cap #output final e and v\n",
        "\n",
        "def Advanced_energy():\n",
        "    #Now representing \"Advanced\"\n",
        "    #startup time of 2500 ms\n",
        "    t = 2500e-3\n",
        "    e = 2.4 * 128e-3 * t\n",
        "    e_startup = 2.4 * 128e-3 * 5e-3\n",
        "    return e+e_startup\n",
        "\n",
        "def Minimal_energy():\n",
        "    #Now representing \"Minimal\"\n",
        "    t = 0.888e-3 #tentative time\n",
        "    e = 0.9 * 4.8e-3 * t #this uses average current\n",
        "    e_startup = 0#assume negligible, no known startup time given\n",
        "    return  e + e_startup\n",
        "\n",
        "def Analog_energy():\n",
        "    #Now representing Analog\n",
        "    t = 1e-3 #estimated operating time\n",
        "    e = 0.11 * 2.15e-6 * t\n",
        "    e_startup = 0 #analog device, no startup needed :)\n",
        "    return e + e_startup\n",
        "\n",
        "#STEP 3:\n",
        "# For each day:\n",
        "#   on_Minimal, on_Advanced, on_Analog = 0\n",
        "#   For each time step (like every 60 s given our logging freq):\n",
        "#       - Update the energy in our capacitor (put fcn in models.py) given (1) input voltage, (2) time step, (3) capacitance (prob 10 uF), this will be an integral\n",
        "#       - Check if energy is enough to turn on (1) 1 uJ load, (2) 10 uJ load, and (3) 20 uJ load (will tweak later to reflect real energy cost of each system)\n",
        "#       - If so, add to on_Minimal, on_Advanced, and on_Analog and reset capacitor energy to 0 J (might tweak this value)\n",
        "#   Append on_Minimal, on_Advanced, on_Analog to on_Minimal_list, on_Advanced_list, on_Analog_list. This will be a list of how many sensor readings we are able to take with each of these systems every day given the energy we got\n",
        "#STEP 4: Visualize the daily # of readings with 3 bar graphs, y axis is # of readings and x axis is days.\n",
        "#   - Given 3 lists of integer values, plot them on bar graphs\n",
        "\n",
        "def group_util(test_date1, test_date2, N):\n",
        "    diff = (test_date2 - test_date1) / N\n",
        "    return [test_date1 + diff * idx for idx in range(N)] + [test_date2]\n",
        "\n",
        "def oracle_simulate(v_list, C_h):\n",
        "    #Calculate maximum energy\n",
        "    total_E = 0\n",
        "    for i in range(len(v_list) - 1):\n",
        "        t = (v_list.index[i+1] - v_list.index[i]).total_seconds()\n",
        "        if t > 180:\n",
        "          print(\"Discontinuity\")\n",
        "          print(v_list.index[i+1], v_list.index[i])\n",
        "          print(v_list['V1 [mV]'][i+1], v_list['V1 [mV]'][i])\n",
        "          #total_E, ignore = update_capEnergy(total_E, V_applied=(v_list['V1 [mV]'][i+1] + v_list['V1 [mV]'][i])/2, R=internal_R_v0(), C=C_h[0], dt = t)\n",
        "        else:\n",
        "          total_E, ignore = update_capEnergy(total_E, V_applied=max(v_list['V1 [mV]'][i], v_list['V1 [mV]'][i+1]), R=internal_R_v0(), C=C_h[0], dt = t)\n",
        "    print(\"Oracle activations:\", math.floor(total_E/Minimal_energy()))\n",
        "    return(math.floor(total_E/Minimal_energy()))\n",
        "\n",
        "def naive_simulate(t_list, v_list, v_list_naive, v_list_fine, C_h):\n",
        "    # t_list: list of decimal time stamps in unit of days (e.g. 71.85893518518519 day), same length as v_list\n",
        "    # v_list: list of voltage values from SFMC\n",
        "    # C_h: capacitance of the capacitor being filled up by harvester\n",
        "\n",
        "    #assume capacitor is completely discharged at start\n",
        "    e_minimal_stored = 0\n",
        "    e_minimal_stored_theo = 0\n",
        "\n",
        "    #Initialize evaluation metrics\n",
        "    false_act = 0\n",
        "    max_act = 0\n",
        "    pred_act = 0\n",
        "    succ_act = 0\n",
        "\n",
        "    total_E = 0\n",
        "    total_E_naive = 0\n",
        "\n",
        "    #Calculate maximum energy\n",
        "    #for i in range(len(v_list_fine) - 1):\n",
        "    #    t = (v_list_fine.index[i+1] - v_list_fine.index[i]).total_seconds()\n",
        "    #    total_E, ignore = update_capEnergy(total_E, V_applied=v_list_fine['V1 [10nV]'][i], R=internal_R_v0(), C=C_h[0], dt = t)\n",
        "    #print(total_E/Minimal_energy())\n",
        "    v = v_list_naive.mean()\n",
        "    #for each voltage data point\n",
        "    for jj in range(len(v_list) - 1): #last data point was at 71.85893518518519 day\n",
        "        t = (v_list.index[jj+1] - v_list.index[jj]).total_seconds()\n",
        "        if t <= time_frame_seconds:\n",
        "          #Total predicted vs. actual energy stored\n",
        "          #Predict energy stored during scheduled sub-interval\n",
        "          total_E, ignore = update_capEnergy(total_E, V_applied=v_list[jj], R=internal_R_v0(), C=C_h[0], dt = t)\n",
        "          total_E_naive, ignore = update_capEnergy(total_E_naive, V_applied=v, R=internal_R_v0(), C=C_h[0], dt = t)\n",
        "\n",
        "          E_Minimal_pred, v_minimal_pred = update_capEnergy(e_minimal_stored, V_applied=v, R=internal_R_v0(), C=C_h[0], dt = t) #set dt as length of prediction interval, in seconds\n",
        "          pred_act += math.floor(E_Minimal_pred/Minimal_energy()) #Update number of activations predicted\n",
        "          itn = 0\n",
        "          if math.floor(E_Minimal_pred/Minimal_energy()) > 0:\n",
        "              minimal_intervals = [date for date in group_util(v_list.index[jj], v_list.index[jj] + timedelta(seconds=t), math.floor(E_Minimal_pred/Minimal_energy()))]\n",
        "              #Calculate desired interval\n",
        "              int_len = time_frame_seconds /  math.floor(E_Minimal_pred/Minimal_energy())\n",
        "              for i in range(len(minimal_intervals) - 1):\n",
        "                  #Determine actual energy stored during scheduled sub-interval\n",
        "                  start = v_list_fine.index.searchsorted(minimal_intervals[i])\n",
        "                  end =  v_list_fine.index.searchsorted(minimal_intervals[i+1])\n",
        "\n",
        "                  E_Minimal, ignore = update_capEnergy(e_minimal_stored, V_applied=v_list_fine.iloc[start:end]['V1 [mV]'].mean(), R=internal_R_v0(), C=C_h[0], dt = int_len)\n",
        "                  if not math.isnan(v_list_fine.iloc[start:end]['V1 [mV]'].mean()):\n",
        "                    if E_Minimal < Minimal_energy():\n",
        "                        false_act += 1\n",
        "                        e_minimal_stored = max(0, E_Minimal - Minimal_energy())\n",
        "                        itn += 1\n",
        "\n",
        "                    elif E_Minimal >= Minimal_energy():\n",
        "                        succ_act += 1\n",
        "                        e_minimal_stored = max(0, E_Minimal - Minimal_energy())\n",
        "                        itn+= 1\n",
        "\n",
        "                    else:\n",
        "                      print('Error')\n",
        "                      print(e_minimal_stored, v)\n",
        "\n",
        "                  #Unit test\n",
        "                  #else:\n",
        "                  #  print(\"?\")\n",
        "                  #  print(v_list_fine.index[start])\n",
        "                  #  print(v_list_fine.index[end])\n",
        "                  #  print(minimal_intervals[i], minimal_intervals[i+1])\n",
        "\n",
        "              #Unit test\n",
        "              #if itn != math.floor(E_Minimal_pred/Minimal_energy()):\n",
        "              #    print(\"itn not matching\")\n",
        "              #    print(itn, math.floor(E_Minimal_pred/Minimal_energy()))\n",
        "              #    continue\n",
        "\n",
        "          else:\n",
        "              e_minimal_stored, ignore = update_capEnergy(e_minimal_stored, V_applied=v_list[jj], R=internal_R_v0(), C=C_h[0], dt = t)\n",
        "              #Added this\n",
        "              #start = v_list_fine.index.searchsorted(v_list.index[jj])\n",
        "              #end =  v_list_fine.index.searchsorted(v_list.index[jj+1])\n",
        "              #for h in range(start, end):\n",
        "              #    v = v_list_fine.iloc[h]['V1 [mV]']\n",
        "              #    interval_length = ((v_list_fine.index[h+1]) - (v_list_fine.index[h])).total_seconds()\n",
        "              #    E_Minimal, ignore = update_capEnergy(e_minimal_stored, V_applied=v, R=internal_R_v0(), C=C_h[0], dt = interval_length)\n",
        "              #    e_minimal_stored = E_Minimal\n",
        "\n",
        "\n",
        "        else:\n",
        "          print(\"It's over 9000!\", v_list.index[jj], v_list.index[jj+1])\n",
        "\n",
        "    print(\"Naive total_E activations:\", total_E/Minimal_energy())\n",
        "    print(\"Naive total_E_pred activations:\", total_E_naive/Minimal_energy())\n",
        "    return pred_act, false_act, succ_act, total_E_naive\n",
        "\n",
        "def getMax(c_list, input_list):\n",
        "    max_value = max(input_list)\n",
        "    i = [index for index, item in enumerate(input_list) if item == max_value][0]\n",
        "    return i, max_value, c_list[i]\n",
        "\n",
        "\n",
        "#SMFC\n",
        "import csv\n",
        "from collections import defaultdict\n",
        "from scipy.signal import butter, lfilter\n",
        "import matplotlib.pyplot as plt\n",
        "from datetime import datetime\n",
        "\n",
        "def butter_lowpass(cutoff, fs, order=5):\n",
        "        return butter(order, cutoff, fs=fs, btype='low', analog=False)\n",
        "\n",
        "def butter_lowpass_filter(data, cutoff, fs, order=5):\n",
        "    b, a = butter_lowpass(cutoff, fs, order=order)\n",
        "    y = lfilter(b, a, data)\n",
        "    return y\n",
        "\n",
        "def getMFC_data(y_test, test_pred):\n",
        "    unix_time = y_test.index\n",
        "    d0 = unix_time[0]\n",
        "    days = []\n",
        "    for d in unix_time:\n",
        "        day = d\n",
        "        day_from_start = day-d0\n",
        "        decimal_day = day_from_start.total_seconds()/(24 * 3600)\n",
        "        days.append(decimal_day)\n",
        "\n",
        "    return days\n",
        "\n",
        "def simulate(t_list, v_list, v_list_pred, v_list_fine, C_h):\n",
        "    # t_list: list of decimal time stamps in unit of days (e.g. 71.85893518518519 day), same length as v_list\n",
        "    # v_list: list of voltage values from SFMC\n",
        "    # C_h: capacitance of the capacitor being filled up by harvester\n",
        "\n",
        "    #assume capacitor is completely discharged at start\n",
        "    e_minimal_stored = 0\n",
        "    e_minimal_stored_theo = 0\n",
        "\n",
        "    #Initialize evaluation metrics\n",
        "    false_act = 0\n",
        "    max_act = 0\n",
        "    pred_act = 0\n",
        "    succ_act = 0\n",
        "\n",
        "    total_E = 0\n",
        "    total_E_pred = 0\n",
        "\n",
        "    #Calculate maximum energy\n",
        "    #for i in range(len(v_list_fine) - 1):\n",
        "    #    t = (v_list_fine.index[i+1] - v_list_fine.index[i]).total_seconds()\n",
        "    #    total_E, ignore = update_capEnergy(total_E, V_applied=v_list_fine['V1 [10nV]'][i], R=internal_R_v0(), C=C_h[0], dt = t)\n",
        "    #print(total_E/Minimal_energy())\n",
        "    #for each voltage data point\n",
        "    for jj in range(len(v_list) - 1): #last data point was at 71.85893518518519 day\n",
        "        t = (v_list.index[jj+1] - v_list.index[jj]).total_seconds()\n",
        "        total_E, ignore = update_capEnergy(total_E, V_applied=v_list[jj], R=internal_R_v0(), C=C_h[0], dt = t)\n",
        "        total_E_pred, ignore = update_capEnergy(total_E_pred, V_applied=v_list_pred[jj], R=internal_R_v0(), C=C_h[0], dt = t)\n",
        "        if t <= time_frame_seconds:\n",
        "          #Total predicted vs. actual energy stored\n",
        "          #Predict energy stored during scheduled sub-interval\n",
        "          E_Minimal_pred, v_minimal_pred = update_capEnergy(e_minimal_stored, V_applied=v_list_pred[jj], R=internal_R_v0(), C=C_h[0], dt = t) #set dt as length of prediction interval, in seconds\n",
        "          pred_act += math.floor(E_Minimal_pred/Minimal_energy()) #Update number of activations predicted\n",
        "          itn = 0\n",
        "          if math.floor(E_Minimal_pred/Minimal_energy()) > 0:\n",
        "              minimal_intervals = [date for date in group_util(v_list_pred.index[jj], v_list_pred.index[jj] + timedelta(seconds=t), math.floor(E_Minimal_pred/Minimal_energy()))]\n",
        "              #Calculate desired interval\n",
        "              int_len = time_frame_seconds /  math.floor(E_Minimal_pred/Minimal_energy())\n",
        "              for i in range(len(minimal_intervals) - 1):\n",
        "                  #Determine actual energy stored during scheduled sub-interval\n",
        "                  start = v_list_fine.index.searchsorted(minimal_intervals[i])\n",
        "                  end =  v_list_fine.index.searchsorted(minimal_intervals[i+1])\n",
        "                  v = v_list_fine.iloc[start:end]['V1 [mV]'].mean()\n",
        "\n",
        "                  #interval_length = ((v_list_fine.index[end]) - (v_list_fine.index[start])).total_seconds()\n",
        "                  #if interval_length > int_len:\n",
        "                  #  print('interval_length > int_len')\n",
        "                  #  print('interval_length, int_len:', interval_length, int_len)\n",
        "                  #  print(v_list_fine.index[start], v_list_fine.index[end])\n",
        "                  #else:\n",
        "                  #  print('interval_length <= int_len')\n",
        "                  #  print('interval_length, int_len:', interval_length, int_len)\n",
        "                  #  print(v_list_fine.index[start], v_list_fine.index[end])\n",
        "\n",
        "                  E_Minimal, ignore = update_capEnergy(e_minimal_stored, V_applied=v, R=internal_R_v0(), C=C_h[0], dt = int_len)\n",
        "                  if not math.isnan(v_list_fine.iloc[start:end]['V1 [mV]'].mean()):\n",
        "                    if E_Minimal < Minimal_energy():\n",
        "                        false_act += 1\n",
        "                        e_minimal_stored = max(0, E_Minimal - Minimal_energy())\n",
        "                        itn += 1\n",
        "\n",
        "                    elif E_Minimal >= Minimal_energy():\n",
        "                        succ_act += 1\n",
        "                        e_minimal_stored = max(0, E_Minimal - Minimal_energy())\n",
        "                        itn+= 1\n",
        "\n",
        "                    else:\n",
        "                      print('Error')\n",
        "                      print(e_minimal_stored, v)\n",
        "\n",
        "                  #Unit test\n",
        "                  #else:\n",
        "                  #  print(\"?\")\n",
        "                  #  print(v_list_fine.index[start])\n",
        "                  #  print(v_list_fine.index[end])\n",
        "                  #  print(minimal_intervals[i], minimal_intervals[i+1])\n",
        "\n",
        "              #Unit test\n",
        "              #if itn != math.floor(E_Minimal_pred/Minimal_energy()):\n",
        "              #    print(\"itn not matching\")\n",
        "              #    print(itn, math.floor(E_Minimal_pred/Minimal_energy()))\n",
        "              #    continue\n",
        "\n",
        "          else:\n",
        "              e_minimal_stored, ignore = update_capEnergy(e_minimal_stored, V_applied=v_list[jj], R=internal_R_v0(), C=C_h[0], dt = t)\n",
        "              #Added this\n",
        "              #start = v_list_fine.index.searchsorted(v_list.index[jj])\n",
        "              #end =  v_list_fine.index.searchsorted(v_list.index[jj+1])\n",
        "              #for h in range(start, end):\n",
        "              #    v = v_list_fine.iloc[h]['V1 [mV]']\n",
        "              #    interval_length = ((v_list_fine.index[h+1]) - (v_list_fine.index[h])).total_seconds()\n",
        "              #    E_Minimal, ignore = update_capEnergy(e_minimal_stored, V_applied=v, R=internal_R_v0(), C=C_h[0], dt = interval_length)\n",
        "              #    e_minimal_stored = E_Minimal\n",
        "\n",
        "\n",
        "        else:\n",
        "          print(\"It's over 9000!\", v_list.index[jj], v_list.index[jj+1])\n",
        "\n",
        "    print(\"Runtime total_E activations:\", total_E/Minimal_energy())\n",
        "    print(\"Runtime total_E_pred activations:\", total_E_pred/Minimal_energy())\n",
        "    return pred_act, false_act, succ_act, total_E, total_E_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dDKC7-ZPBPAP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12dad7da-1819-437c-b76e-121b27886bbb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm_13 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train set size: 738\n",
            "Test set size: 369\n",
            "Epoch 1/25\n",
            "93/93 - 2s - loss: 92.8421 - mape: 44.8716 - val_loss: 34.3222 - val_mape: 27.3941 - 2s/epoch - 26ms/step\n",
            "Epoch 2/25\n",
            "93/93 - 1s - loss: 94.4899 - mape: 48.0797 - val_loss: 30.3418 - val_mape: 30.3354 - 569ms/epoch - 6ms/step\n",
            "Epoch 3/25\n",
            "93/93 - 1s - loss: 49.5443 - mape: 28.0799 - val_loss: 17.7734 - val_mape: 23.5839 - 614ms/epoch - 7ms/step\n",
            "Epoch 4/25\n",
            "93/93 - 1s - loss: 82.2908 - mape: 48.2910 - val_loss: 28.8575 - val_mape: 25.3370 - 750ms/epoch - 8ms/step\n",
            "Epoch 5/25\n",
            "93/93 - 1s - loss: 49.5364 - mape: 29.3544 - val_loss: 17.8334 - val_mape: 30.9012 - 781ms/epoch - 8ms/step\n",
            "Epoch 6/25\n",
            "93/93 - 1s - loss: 31.2894 - mape: 21.4088 - val_loss: 9.3058 - val_mape: 13.7697 - 914ms/epoch - 10ms/step\n",
            "Epoch 7/25\n",
            "93/93 - 1s - loss: 70.7700 - mape: 38.5768 - val_loss: 34.5575 - val_mape: 24.3778 - 990ms/epoch - 11ms/step\n",
            "Epoch 8/25\n",
            "93/93 - 1s - loss: 55.1192 - mape: 31.7345 - val_loss: 22.6384 - val_mape: 14.0617 - 825ms/epoch - 9ms/step\n",
            "Epoch 9/25\n",
            "93/93 - 1s - loss: 54.3958 - mape: 28.6263 - val_loss: 8.4897 - val_mape: 15.7872 - 1s/epoch - 11ms/step\n",
            "Epoch 10/25\n",
            "93/93 - 1s - loss: 29.8615 - mape: 26.8960 - val_loss: 13.6502 - val_mape: 30.6125 - 994ms/epoch - 11ms/step\n",
            "Epoch 11/25\n",
            "93/93 - 1s - loss: 23.8518 - mape: 16.0861 - val_loss: 9.2908 - val_mape: 18.1182 - 1000ms/epoch - 11ms/step\n",
            "Epoch 12/25\n",
            "93/93 - 1s - loss: 48.4250 - mape: 31.6549 - val_loss: 31.9170 - val_mape: 23.1706 - 1s/epoch - 11ms/step\n",
            "Epoch 13/25\n",
            "93/93 - 1s - loss: 42.3757 - mape: 27.0895 - val_loss: 24.8657 - val_mape: 18.1930 - 873ms/epoch - 9ms/step\n",
            "Epoch 14/25\n",
            "93/93 - 1s - loss: 39.6935 - mape: 40.1069 - val_loss: 8.8633 - val_mape: 11.9967 - 862ms/epoch - 9ms/step\n",
            "Epoch 15/25\n",
            "93/93 - 1s - loss: 28.9653 - mape: 25.2204 - val_loss: 14.1869 - val_mape: 11.5079 - 855ms/epoch - 9ms/step\n",
            "Epoch 16/25\n",
            "93/93 - 1s - loss: 21.8037 - mape: 21.9720 - val_loss: 10.8555 - val_mape: 16.1892 - 890ms/epoch - 10ms/step\n",
            "Epoch 17/25\n",
            "93/93 - 1s - loss: 19.8995 - mape: 17.4875 - val_loss: 6.4578 - val_mape: 9.7364 - 870ms/epoch - 9ms/step\n",
            "Epoch 18/25\n",
            "93/93 - 1s - loss: 36.0096 - mape: 28.0394 - val_loss: 14.1433 - val_mape: 14.3613 - 836ms/epoch - 9ms/step\n",
            "Epoch 19/25\n",
            "93/93 - 1s - loss: 35.4728 - mape: 26.9295 - val_loss: 8.4141 - val_mape: 12.9704 - 817ms/epoch - 9ms/step\n",
            "Epoch 20/25\n",
            "93/93 - 1s - loss: 28.9732 - mape: 18.9213 - val_loss: 7.2233 - val_mape: 10.9170 - 550ms/epoch - 6ms/step\n",
            "Epoch 21/25\n",
            "93/93 - 0s - loss: 26.3208 - mape: 33.9702 - val_loss: 12.3248 - val_mape: 9.5438 - 487ms/epoch - 5ms/step\n",
            "Epoch 22/25\n",
            "93/93 - 0s - loss: 29.9257 - mape: 24.7333 - val_loss: 9.1422 - val_mape: 16.6321 - 496ms/epoch - 5ms/step\n",
            "Epoch 23/25\n",
            "93/93 - 1s - loss: 23.6297 - mape: 20.1994 - val_loss: 9.4698 - val_mape: 8.4386 - 541ms/epoch - 6ms/step\n",
            "Epoch 24/25\n",
            "93/93 - 1s - loss: 28.9835 - mape: 23.2934 - val_loss: 14.0231 - val_mape: 12.3123 - 538ms/epoch - 6ms/step\n",
            "Epoch 25/25\n",
            "93/93 - 0s - loss: 24.7793 - mape: 23.5788 - val_loss: 19.8062 - val_mape: 10.2471 - 484ms/epoch - 5ms/step\n",
            "93/93 [==============================] - 0s 2ms/step\n",
            "47/47 [==============================] - 0s 2ms/step\n",
            "Discontinuity\n",
            "2021-09-24 04:17:21-07:00 2021-08-17 06:37:13-07:00\n",
            "0.04926536 0.0012214300000000001\n",
            "Oracle activations: 34077\n",
            "It's over 9000! 2021-08-17 06:00:00-07:00 2021-09-24 04:00:00-07:00\n",
            "Runtime total_E activations: 94517.94413030626\n",
            "Runtime total_E_pred activations: 94841.57207813354\n",
            "It's over 9000! 2021-08-17 06:00:00-07:00 2021-09-24 04:00:00-07:00\n",
            "Naive total_E activations: 33131.984647044796\n",
            "Naive total_E_pred activations: 39451.51160935354\n",
            "Dataset, train set, and test set size: 1476 738 369\n",
            "Timeframe: 60min\n",
            "Minimal Application\n",
            "Naive vs. DL succesful activations: 1.7354030005845296\n",
            "Maximum possible activations: 34077\n",
            "Predicted activations: 33883\n",
            "Successful activations: 26720, 78.860%\n",
            "Failed activations: 5856, 17.283%\n",
            "Missed activations: 7357, 21.589%\n",
            "Naive predicted activations (usual actual energy average): 46478\n",
            "Naive successful activations (usual actual energy average): 15397, 33.128%\n",
            "Naive failed activations (usual actual energy average): 29769, 64.050%\n",
            "Naive missed activations (usual actual energy average): 18680, 54.817%\n",
            "Voltage overestimation rate: 57.182%\n",
            "Test MAPE power: 0.214548\n",
            "Test MAPE voltage: 0.020245\n",
            "Test MAPE current: 0.045856\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm_14 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train set size: 1474\n",
            "Test set size: 367\n",
            "Epoch 1/25\n",
            "185/185 - 5s - loss: 60.2603 - mape: 38.4296 - val_loss: 11.1471 - val_mape: 13.7928 - 5s/epoch - 25ms/step\n",
            "Epoch 2/25\n",
            "185/185 - 2s - loss: 48.4261 - mape: 28.5431 - val_loss: 11.4490 - val_mape: 17.1655 - 2s/epoch - 9ms/step\n",
            "Epoch 3/25\n",
            "185/185 - 2s - loss: 28.8759 - mape: 24.5468 - val_loss: 10.7630 - val_mape: 10.9080 - 2s/epoch - 9ms/step\n",
            "Epoch 4/25\n",
            "185/185 - 2s - loss: 26.7848 - mape: 19.8416 - val_loss: 6.8829 - val_mape: 17.6176 - 2s/epoch - 9ms/step\n",
            "Epoch 5/25\n",
            "185/185 - 1s - loss: 20.8609 - mape: 21.4794 - val_loss: 4.5295 - val_mape: 3.3898 - 1s/epoch - 8ms/step\n",
            "Epoch 6/25\n",
            "185/185 - 2s - loss: 23.1541 - mape: 28.0360 - val_loss: 8.8443 - val_mape: 4.1253 - 2s/epoch - 8ms/step\n",
            "Epoch 7/25\n",
            "185/185 - 1s - loss: 25.3140 - mape: 30.8213 - val_loss: 7.2962 - val_mape: 10.0083 - 885ms/epoch - 5ms/step\n",
            "Epoch 8/25\n",
            "185/185 - 1s - loss: 21.8787 - mape: 18.6705 - val_loss: 6.0542 - val_mape: 7.2693 - 931ms/epoch - 5ms/step\n",
            "Epoch 9/25\n",
            "185/185 - 1s - loss: 27.8735 - mape: 37.3651 - val_loss: 13.2133 - val_mape: 18.0926 - 1s/epoch - 6ms/step\n",
            "Epoch 10/25\n",
            "185/185 - 1s - loss: 19.1642 - mape: 17.7439 - val_loss: 7.2947 - val_mape: 9.6719 - 1s/epoch - 7ms/step\n",
            "Epoch 11/25\n",
            "185/185 - 1s - loss: 21.6540 - mape: 22.5320 - val_loss: 11.4288 - val_mape: 8.1909 - 1s/epoch - 8ms/step\n",
            "Epoch 12/25\n",
            "185/185 - 2s - loss: 28.3600 - mape: 27.1169 - val_loss: 7.2759 - val_mape: 14.1417 - 2s/epoch - 9ms/step\n",
            "Epoch 13/25\n",
            "185/185 - 1s - loss: 24.5289 - mape: 33.7402 - val_loss: 4.5305 - val_mape: 6.8111 - 1s/epoch - 8ms/step\n",
            "Epoch 14/25\n",
            "185/185 - 2s - loss: 25.1119 - mape: 23.1409 - val_loss: 2.7281 - val_mape: 3.9059 - 2s/epoch - 8ms/step\n",
            "Epoch 15/25\n",
            "185/185 - 2s - loss: 26.1520 - mape: 20.3986 - val_loss: 3.7803 - val_mape: 4.0255 - 2s/epoch - 9ms/step\n",
            "Epoch 16/25\n",
            "185/185 - 2s - loss: 20.6431 - mape: 20.4831 - val_loss: 4.5860 - val_mape: 9.0944 - 2s/epoch - 9ms/step\n",
            "Epoch 17/25\n",
            "185/185 - 2s - loss: 18.5445 - mape: 15.7532 - val_loss: 4.3205 - val_mape: 8.8952 - 2s/epoch - 13ms/step\n",
            "Epoch 18/25\n",
            "185/185 - 1s - loss: 13.4005 - mape: 14.3794 - val_loss: 4.3033 - val_mape: 5.4090 - 1s/epoch - 6ms/step\n",
            "Epoch 19/25\n",
            "185/185 - 1s - loss: 19.4273 - mape: 34.6543 - val_loss: 7.4190 - val_mape: 8.2924 - 1s/epoch - 8ms/step\n",
            "Epoch 20/25\n",
            "185/185 - 2s - loss: 33.0735 - mape: 22.1237 - val_loss: 11.0925 - val_mape: 7.8955 - 2s/epoch - 8ms/step\n",
            "Epoch 21/25\n",
            "185/185 - 1s - loss: 19.5397 - mape: 15.5921 - val_loss: 4.8390 - val_mape: 10.6444 - 857ms/epoch - 5ms/step\n",
            "Epoch 22/25\n",
            "185/185 - 1s - loss: 14.5598 - mape: 12.4192 - val_loss: 2.7714 - val_mape: 3.6061 - 897ms/epoch - 5ms/step\n",
            "Epoch 23/25\n",
            "185/185 - 1s - loss: 11.6697 - mape: 11.2590 - val_loss: 5.2809 - val_mape: 9.6595 - 867ms/epoch - 5ms/step\n",
            "Epoch 24/25\n",
            "185/185 - 1s - loss: 20.8372 - mape: 21.9343 - val_loss: 3.3695 - val_mape: 4.0894 - 1s/epoch - 7ms/step\n",
            "Epoch 25/25\n",
            "185/185 - 1s - loss: 21.2987 - mape: 33.9416 - val_loss: 5.8901 - val_mape: 7.0153 - 926ms/epoch - 5ms/step\n",
            "185/185 [==============================] - 1s 3ms/step\n",
            "46/46 [==============================] - 0s 3ms/step\n",
            "Discontinuity\n",
            "2021-11-22 01:00:02-08:00 2021-11-10 23:59:53-08:00\n",
            "0.0012214300000000001 0.02535399\n",
            "Oracle activations: 10996\n",
            "It's over 9000! 2021-11-10 23:00:00-08:00 2021-11-22 01:00:00-08:00\n",
            "Runtime total_E activations: 23961.512955501297\n",
            "Runtime total_E_pred activations: 22302.8833825961\n",
            "It's over 9000! 2021-11-10 23:00:00-08:00 2021-11-22 01:00:00-08:00\n",
            "Naive total_E activations: 10346.235551135283\n",
            "Naive total_E_pred activations: 14081.987924161704\n",
            "Dataset, train set, and test set size: 2211 1474 367\n",
            "Timeframe: 60min\n",
            "Minimal Application\n",
            "Naive vs. DL succesful activations: 1.6538858321870702\n",
            "Maximum possible activations: 10996\n",
            "Predicted activations: 10527\n",
            "Successful activations: 9619, 91.375%\n",
            "Failed activations: 908, 8.625%\n",
            "Missed activations: 1377, 12.523%\n",
            "Naive predicted activations (usual actual energy average): 15217\n",
            "Naive successful activations (usual actual energy average): 5816, 38.220%\n",
            "Naive failed activations (usual actual energy average): 9401, 61.780%\n",
            "Naive missed activations (usual actual energy average): 5180, 47.108%\n",
            "Voltage overestimation rate: 20.981%\n",
            "Test MAPE power: 0.174871\n",
            "Test MAPE voltage: 0.109852\n",
            "Test MAPE current: 0.038395\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm_15 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train set size: 2209\n",
            "Test set size: 368\n",
            "Epoch 1/25\n",
            "277/277 - 5s - loss: 48.3002 - mape: 33.3477 - val_loss: 17.4032 - val_mape: 27.1394 - 5s/epoch - 17ms/step\n",
            "Epoch 2/25\n",
            "277/277 - 2s - loss: 24.2604 - mape: 19.2581 - val_loss: 12.0980 - val_mape: 20.1520 - 2s/epoch - 8ms/step\n",
            "Epoch 3/25\n",
            "277/277 - 2s - loss: 28.3116 - mape: 26.6973 - val_loss: 9.3127 - val_mape: 17.2693 - 2s/epoch - 8ms/step\n",
            "Epoch 4/25\n",
            "277/277 - 2s - loss: 16.6207 - mape: 16.6373 - val_loss: 5.2450 - val_mape: 9.9086 - 2s/epoch - 8ms/step\n",
            "Epoch 5/25\n",
            "277/277 - 2s - loss: 24.9731 - mape: 23.2754 - val_loss: 4.4118 - val_mape: 8.5880 - 2s/epoch - 8ms/step\n",
            "Epoch 6/25\n",
            "277/277 - 2s - loss: 16.9072 - mape: 15.1878 - val_loss: 5.1698 - val_mape: 6.3969 - 2s/epoch - 7ms/step\n",
            "Epoch 7/25\n",
            "277/277 - 1s - loss: 18.7858 - mape: 19.1985 - val_loss: 2.9429 - val_mape: 7.0447 - 1s/epoch - 5ms/step\n",
            "Epoch 8/25\n",
            "277/277 - 1s - loss: 15.3466 - mape: 16.3312 - val_loss: 5.5860 - val_mape: 8.2078 - 1s/epoch - 4ms/step\n",
            "Epoch 9/25\n",
            "277/277 - 1s - loss: 13.2659 - mape: 21.9569 - val_loss: 3.2046 - val_mape: 13.8179 - 1s/epoch - 5ms/step\n",
            "Epoch 10/25\n",
            "277/277 - 1s - loss: 25.8896 - mape: 19.3015 - val_loss: 10.9954 - val_mape: 29.6083 - 1s/epoch - 4ms/step\n",
            "Epoch 11/25\n",
            "277/277 - 1s - loss: 12.7781 - mape: 11.0058 - val_loss: 4.3147 - val_mape: 8.4792 - 1s/epoch - 5ms/step\n",
            "Epoch 12/25\n",
            "277/277 - 1s - loss: 11.7090 - mape: 13.2847 - val_loss: 2.6538 - val_mape: 8.7439 - 1s/epoch - 5ms/step\n",
            "Epoch 13/25\n",
            "277/277 - 1s - loss: 13.9533 - mape: 14.5796 - val_loss: 2.6909 - val_mape: 5.6570 - 1s/epoch - 4ms/step\n",
            "Epoch 14/25\n",
            "277/277 - 1s - loss: 17.0329 - mape: 26.6467 - val_loss: 3.4641 - val_mape: 9.6109 - 1s/epoch - 5ms/step\n",
            "Epoch 15/25\n",
            "277/277 - 2s - loss: 13.8904 - mape: 13.4809 - val_loss: 3.0745 - val_mape: 7.1194 - 2s/epoch - 7ms/step\n",
            "Epoch 16/25\n",
            "277/277 - 2s - loss: 14.7489 - mape: 16.0517 - val_loss: 6.1036 - val_mape: 18.9562 - 2s/epoch - 7ms/step\n",
            "Epoch 17/25\n",
            "277/277 - 2s - loss: 12.0642 - mape: 11.1691 - val_loss: 5.4090 - val_mape: 12.4423 - 2s/epoch - 8ms/step\n",
            "Epoch 18/25\n",
            "277/277 - 2s - loss: 12.9399 - mape: 13.1685 - val_loss: 4.2081 - val_mape: 7.0845 - 2s/epoch - 8ms/step\n",
            "Epoch 19/25\n",
            "277/277 - 2s - loss: 15.2512 - mape: 20.0898 - val_loss: 3.3071 - val_mape: 7.7445 - 2s/epoch - 8ms/step\n",
            "Epoch 20/25\n",
            "277/277 - 2s - loss: 15.1909 - mape: 23.9501 - val_loss: 2.4993 - val_mape: 7.3903 - 2s/epoch - 8ms/step\n",
            "Epoch 21/25\n",
            "277/277 - 2s - loss: 11.4596 - mape: 10.4931 - val_loss: 2.8378 - val_mape: 5.6071 - 2s/epoch - 6ms/step\n",
            "Epoch 22/25\n",
            "277/277 - 1s - loss: 12.2223 - mape: 14.3400 - val_loss: 2.2858 - val_mape: 4.6414 - 1s/epoch - 5ms/step\n",
            "Epoch 23/25\n",
            "277/277 - 1s - loss: 19.0441 - mape: 12.4193 - val_loss: 3.8700 - val_mape: 8.9762 - 1s/epoch - 5ms/step\n",
            "Epoch 24/25\n",
            "277/277 - 1s - loss: 13.3459 - mape: 18.7518 - val_loss: 11.5676 - val_mape: 36.5414 - 1s/epoch - 5ms/step\n",
            "Epoch 25/25\n",
            "277/277 - 1s - loss: 11.2312 - mape: 12.2413 - val_loss: 8.1549 - val_mape: 38.6395 - 1s/epoch - 4ms/step\n",
            "277/277 [==============================] - 1s 2ms/step\n",
            "46/46 [==============================] - 0s 2ms/step\n",
            "Oracle activations: 13933\n",
            "Runtime total_E activations: 13178.756812865347\n",
            "Runtime total_E_pred activations: 11720.282409202928\n",
            "Naive total_E activations: 13178.756812865347\n",
            "Naive total_E_pred activations: 5153.631019665272\n",
            "Dataset, train set, and test set size: 2945 2209 368\n",
            "Timeframe: 60min\n",
            "Minimal Application\n",
            "Naive vs. DL succesful activations: 1.078383641674781\n",
            "Maximum possible activations: 13933\n",
            "Predicted activations: 13299\n",
            "Successful activations: 13290, 99.932%\n",
            "Failed activations: 9, 0.068%\n",
            "Missed activations: 643, 4.615%\n",
            "Naive predicted activations (usual actual energy average): 13729\n",
            "Naive successful activations (usual actual energy average): 12324, 89.766%\n",
            "Naive failed activations (usual actual energy average): 1405, 10.234%\n",
            "Naive missed activations (usual actual energy average): 1609, 11.548%\n",
            "Voltage overestimation rate: 0.000%\n",
            "Test MAPE power: 0.957739\n",
            "Test MAPE voltage: 0.064011\n",
            "Test MAPE current: 0.008238\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm_16 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train set size: 2943\n",
            "Test set size: 368\n",
            "Epoch 1/25\n",
            "368/368 - 3s - loss: 39.5889 - mape: 32.1929 - val_loss: 17.2010 - val_mape: 21.1732 - 3s/epoch - 9ms/step\n",
            "Epoch 2/25\n",
            "368/368 - 2s - loss: 30.0860 - mape: 26.5905 - val_loss: 4.7438 - val_mape: 11.2391 - 2s/epoch - 4ms/step\n",
            "Epoch 3/25\n",
            "368/368 - 2s - loss: 31.9382 - mape: 26.0176 - val_loss: 3.5959 - val_mape: 9.4153 - 2s/epoch - 4ms/step\n",
            "Epoch 4/25\n",
            "368/368 - 2s - loss: 17.7903 - mape: 17.7703 - val_loss: 4.1922 - val_mape: 5.0029 - 2s/epoch - 4ms/step\n",
            "Epoch 5/25\n",
            "368/368 - 2s - loss: 15.6392 - mape: 16.0982 - val_loss: 4.5338 - val_mape: 6.5330 - 2s/epoch - 6ms/step\n",
            "Epoch 6/25\n",
            "368/368 - 3s - loss: 20.3389 - mape: 22.1430 - val_loss: 5.2772 - val_mape: 5.9701 - 3s/epoch - 7ms/step\n",
            "Epoch 7/25\n",
            "368/368 - 3s - loss: 14.5448 - mape: 16.3466 - val_loss: 7.2017 - val_mape: 14.1339 - 3s/epoch - 7ms/step\n",
            "Epoch 8/25\n",
            "368/368 - 3s - loss: 11.7570 - mape: 13.0282 - val_loss: 10.5188 - val_mape: 4.2816 - 3s/epoch - 8ms/step\n",
            "Epoch 9/25\n",
            "368/368 - 3s - loss: 21.0661 - mape: 18.6430 - val_loss: 8.6843 - val_mape: 9.1919 - 3s/epoch - 8ms/step\n",
            "Epoch 10/25\n",
            "368/368 - 2s - loss: 12.9382 - mape: 16.8515 - val_loss: 7.8499 - val_mape: 12.5820 - 2s/epoch - 6ms/step\n",
            "Epoch 11/25\n",
            "368/368 - 2s - loss: 11.5380 - mape: 14.6768 - val_loss: 5.5183 - val_mape: 4.3497 - 2s/epoch - 4ms/step\n",
            "Epoch 12/25\n",
            "368/368 - 2s - loss: 13.7934 - mape: 16.4472 - val_loss: 3.3569 - val_mape: 3.2518 - 2s/epoch - 5ms/step\n",
            "Epoch 13/25\n",
            "368/368 - 2s - loss: 21.5408 - mape: 20.5043 - val_loss: 14.8281 - val_mape: 4.1915 - 2s/epoch - 4ms/step\n",
            "Epoch 14/25\n",
            "368/368 - 2s - loss: 16.1216 - mape: 17.4160 - val_loss: 8.3042 - val_mape: 7.3806 - 2s/epoch - 4ms/step\n",
            "Epoch 15/25\n",
            "368/368 - 2s - loss: 14.4158 - mape: 15.3904 - val_loss: 3.1604 - val_mape: 9.7027 - 2s/epoch - 4ms/step\n",
            "Epoch 16/25\n",
            "368/368 - 2s - loss: 19.4626 - mape: 23.0305 - val_loss: 4.8643 - val_mape: 6.9860 - 2s/epoch - 6ms/step\n",
            "Epoch 17/25\n",
            "368/368 - 3s - loss: 10.9704 - mape: 12.5839 - val_loss: 4.4741 - val_mape: 14.8513 - 3s/epoch - 7ms/step\n",
            "Epoch 18/25\n",
            "368/368 - 3s - loss: 10.5099 - mape: 11.7502 - val_loss: 2.9563 - val_mape: 10.8392 - 3s/epoch - 7ms/step\n",
            "Epoch 19/25\n",
            "368/368 - 3s - loss: 8.5847 - mape: 12.1791 - val_loss: 2.4908 - val_mape: 4.3097 - 3s/epoch - 8ms/step\n",
            "Epoch 20/25\n",
            "368/368 - 3s - loss: 9.8216 - mape: 15.0730 - val_loss: 4.1048 - val_mape: 11.0871 - 3s/epoch - 8ms/step\n",
            "Epoch 21/25\n",
            "368/368 - 3s - loss: 17.9817 - mape: 17.1863 - val_loss: 6.2939 - val_mape: 4.2408 - 3s/epoch - 7ms/step\n",
            "Epoch 22/25\n",
            "368/368 - 2s - loss: 9.7762 - mape: 11.1780 - val_loss: 2.0111 - val_mape: 5.1072 - 2s/epoch - 4ms/step\n",
            "Epoch 23/25\n",
            "368/368 - 2s - loss: 12.4780 - mape: 16.8075 - val_loss: 2.3443 - val_mape: 6.5442 - 2s/epoch - 5ms/step\n",
            "Epoch 24/25\n",
            "368/368 - 2s - loss: 10.0478 - mape: 11.6179 - val_loss: 1.8187 - val_mape: 4.5456 - 2s/epoch - 4ms/step\n",
            "Epoch 25/25\n",
            "368/368 - 2s - loss: 19.2180 - mape: 19.6175 - val_loss: 4.2962 - val_mape: 9.3762 - 2s/epoch - 5ms/step\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",
        "from keras import backend as K\n",
        "\n",
        "power_mape = []\n",
        "voltage_mape = []\n",
        "current_mape = []\n",
        "\n",
        "E_actual_list = []\n",
        "E_pred_list = []\n",
        "\n",
        "max_act_list = []\n",
        "pred_act_list = []\n",
        "succ_act_list = []\n",
        "\n",
        "pred_act_naive_list = []\n",
        "false_act_naive_list = []\n",
        "succ_act_naive_list = []\n",
        "\n",
        "#Set parameters\n",
        "batchsize = 8\n",
        "time_frame = '60min'\n",
        "time_frame_seconds = 3600\n",
        "n = 0\n",
        "splits = TimeSeriesSplit(n_splits=4)\n",
        "for train_index, test_index in splits.split(X):\n",
        "  n += 1\n",
        "  if n >= 1:\n",
        "    #Split train and test sets\n",
        "    X_train = X.iloc[train_index]\n",
        "    X_test = X.iloc[test_index]\n",
        "    y_train = y.iloc[train_index]\n",
        "    y_test = y.iloc[test_index]\n",
        "\n",
        "    X_valid, X_test = train_test_split(X_test, test_size=0.5, shuffle=False)\n",
        "    y_valid, y_test = train_test_split(y_test, test_size=0.5, shuffle=False)\n",
        "\n",
        "    #Set dataset bounds\n",
        "    train_bound_lower = y_train.index[0]\n",
        "    train_bound_upper = y_train.index[-1]\n",
        "    valid_bound_lower = y_valid.index[0]\n",
        "    valid_bound_upper = y_valid.index[-1]\n",
        "    test_bound_lower = y_test.index[0]\n",
        "    test_bound_upper = y_test.index[-1]\n",
        "\n",
        "    #Resample data\n",
        "    X_train = X_train.resample(time_frame).mean().dropna()\n",
        "    X_valid = X_valid.resample(time_frame).mean().dropna()\n",
        "    X_test = X_test.resample(time_frame).mean().dropna()\n",
        "\n",
        "    y_train = y_train.resample(time_frame).mean().dropna()\n",
        "    y_valid = y_valid.resample(time_frame).mean().dropna()\n",
        "    y_test = y_test.resample(time_frame).mean().dropna()\n",
        "\n",
        "    #Reshape data\n",
        "    X_train = X_train.values.reshape((X_train.shape[0], 1, X_train.shape[1]))\n",
        "    X_valid = X_valid.values.reshape((X_valid.shape[0], 1, X_valid.shape[1]))\n",
        "    X_test = X_test.values.reshape((X_test.shape[0], 1, X_test.shape[1]))\n",
        "\n",
        "    print(\"Train set size:\", len(y_train))\n",
        "    print(\"Test set size:\", len(y_test))\n",
        "\n",
        "\n",
        "\n",
        "    def quantile_loss(y_true, y_pred, quantile = 0.05):\n",
        "      error = y_true - y_pred\n",
        "      return K.mean(K.maximum(quantile * error, (quantile - 1) * error), axis=-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=quantile_loss, metrics=['mape'], optimizer='adam')\n",
        "\n",
        "\n",
        "    # fit network\n",
        "    model.fit(X_train, y_train, epochs=25, validation_data=(X_valid, y_valid), 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",
        "    #Prepare data for runtime simulation\n",
        "    y_test['power pred'] = test_pred[:, 0]\n",
        "    y_test['V1 [mV] pred'] = test_pred[:, 1]\n",
        "    y_test['I1L [μA] pred'] = test_pred[:, 2]\n",
        "\n",
        "    days  = getMFC_data(y_test, test_pred)\n",
        "\n",
        "    v_test = df.loc[(((df.index >= test_bound_lower)) & (df.index <= test_bound_upper))]['V1 [mV]']\n",
        "    v_test = v_test.drop(v_test[(v_test.index > '2021-11-11') & (v_test.index < '2021-11-22 01:00:00')].index)\n",
        "    v_test = pd.DataFrame(v_test)/1E5\n",
        "    v_avg_true = v_test['V1 [mV]'].resample(time_frame).mean().dropna()\n",
        "    v_avg_pred = y_test['V1 [mV] pred']/1E5\n",
        "    C0 = [0.007000000000000006, 0.007000000000000006, 0.007000000000000006]\n",
        "\n",
        "    #Remove first and last entries of averaged data to prevent overestimation of available energy\n",
        "    v_avg_true = v_avg_true[1:][:-1]\n",
        "    v_avg_pred = v_avg_pred[1:][:-1]\n",
        "\n",
        "    #Run oracle model\n",
        "    max_act = oracle_simulate(v_test, C0)\n",
        "\n",
        "    #Call simulate function\n",
        "    pred_act, false_act, succ_act, total_E, total_E_pred = simulate(days, v_avg_true, v_avg_pred, v_test, C0)\n",
        "\n",
        "    #Run naive model\n",
        "    v_valid = df.loc[(((df.index >= valid_bound_lower)) & (df.index < valid_bound_upper))]['V1 [mV]']/1E5\n",
        "    pred_act_naive, false_act_naive, succ_act_naive, total_E = naive_simulate(days, v_avg_true, v_valid, v_test, C0)\n",
        "    print(\"Dataset, train set, and test set size:\", len(y_train) + len(y_valid) + len(y_test), len(y_train), len(y_test))\n",
        "    print('Timeframe:', time_frame)\n",
        "\n",
        "    print('Minimal Application')\n",
        "    print(\"Naive vs. DL succesful activations:\", succ_act/succ_act_naive)\n",
        "    #print('Predicted vs. Actual percent difference: %.3f%%' % ((total_E * 100 / total_E_pred) - 100))\n",
        "    print('Maximum possible activations:', max_act)\n",
        "    print('Predicted activations:', pred_act)\n",
        "    print('Successful activations: %d, %.3f%%' % (succ_act, succ_act * 100/pred_act))\n",
        "    print('Failed activations: %d, %.3f%%' % (false_act, false_act * 100/pred_act))\n",
        "    print('Missed activations: %d, %.3f%%' % (max_act - succ_act, (max_act - succ_act) * 100/max_act))\n",
        "\n",
        "    #Naive model\n",
        "    print('Naive predicted activations (usual actual energy average):', pred_act_naive)\n",
        "    print('Naive successful activations (usual actual energy average): %d, %.3f%%' % (succ_act_naive, succ_act_naive * 100/pred_act_naive))\n",
        "    print('Naive failed activations (usual actual energy average): %d, %.3f%%' % (false_act_naive, false_act_naive * 100/pred_act_naive))\n",
        "    print('Naive missed activations (usual actual energy average): %d, %.3f%%' % (max_act - succ_act_naive, (max_act - succ_act_naive) * 100/max_act))\n",
        "\n",
        "\n",
        "    print('Voltage overestimation rate: %.3f%%' % ((y_test['V1 [mV]'].values <= y_test['V1 [mV] pred']).mean() * 100))\n",
        "    print(\"Test MAPE power: %3f\" %  MAPE(y_test['power'].values.ravel(), y_test['power pred']))\n",
        "    print(\"Test MAPE voltage: %3f\" % MAPE(y_test['V1 [mV]'], y_test['V1 [mV] pred']))\n",
        "    print(\"Test MAPE current: %3f\" % MAPE(y_test['I1L [μA]'], y_test['I1L [μA] pred']))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Minimal_energy()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_9U04gixnzfA",
        "outputId": "a87a265c-783b-458b-c94c-9f2868c73d27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.83616e-06"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install keras_spiking"
      ],
      "metadata": {
        "id": "uPqJxnnrmzZ8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "\n",
        "import keras_spiking\n",
        "\n",
        "energy = keras_spiking.ModelEnergy(model)\n",
        "energy.summary(print_warnings=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5EY3FXcVmxMh",
        "outputId": "fa0a048a-b20e-4a27-b1e9-11641b2c4b1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Layer (type)    |Output shape|Param #|Conn #|Neuron #|J/inf (cpu)\n",
            "----------------|------------|-------|------|--------|-----------\n",
            "lstm_20 (LSTM)  | (None, 200)| 176800|     0|       0|          0\n",
            "dense_40 (Dense)| (None, 100)|  20100| 20000|     100|    0.00017\n",
            "dense_41 (Dense)|   (None, 3)|    303|   300|       0|    2.6e-06\n",
            "=================================================================\n",
            "Total energy per inference [Joules/inf] (cpu): 1.75e-04\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras_spiking/model_energy.py:773: UserWarning: Cannot compute stats for layer of type 'LSTM'.Use `ModelEnergy.register_layer` to register this layer.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "v_avg_true"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mex6Dt0HWetL",
        "outputId": "b6677760-2b3b-4dff-a061-c1a659eb0dc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "timestamp\n",
              "2021-08-12 23:18:00-07:00    0.022662\n",
              "2021-08-12 23:21:00-07:00    0.022647\n",
              "2021-08-12 23:24:00-07:00    0.022891\n",
              "2021-08-12 23:27:00-07:00    0.023224\n",
              "2021-08-12 23:30:00-07:00    0.022799\n",
              "                               ...   \n",
              "2021-10-05 04:30:00-07:00    0.030337\n",
              "2021-10-05 04:33:00-07:00    0.029895\n",
              "2021-10-05 04:36:00-07:00    0.030100\n",
              "2021-10-05 04:39:00-07:00    0.029709\n",
              "2021-10-05 04:42:00-07:00    0.029990\n",
              "Name: V1 [mV], Length: 7357, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "v_avg_true[1:][:-1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CWLKfeOERpo6",
        "outputId": "85c9ce95-43a8-433e-b32c-904eeaab90d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "timestamp\n",
              "2021-12-12 01:51:00-08:00    0.042887\n",
              "2021-12-12 01:54:00-08:00    0.043115\n",
              "2021-12-12 01:57:00-08:00    0.043433\n",
              "2021-12-12 02:00:00-08:00    0.044432\n",
              "2021-12-12 02:03:00-08:00    0.044542\n",
              "                               ...   \n",
              "2021-12-27 08:30:00-08:00    0.011772\n",
              "2021-12-27 08:33:00-08:00    0.011377\n",
              "2021-12-27 08:36:00-08:00    0.011526\n",
              "2021-12-27 08:39:00-08:00    0.011652\n",
              "2021-12-27 08:42:00-08:00    0.011742\n",
              "Freq: 3T, Name: V1 [mV], Length: 7338, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(v_test.index) - 1):\n",
        "  if (v_test.index[i+1] - v_test.index[i]).total_seconds() > 30:\n",
        "    print(v_test.index[i+1], v_test.index[i])\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cBH2eoBlNzG9",
        "outputId": "9ca35e92-4f25-4518-993a-5a835188a38c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-12-25 19:31:52-08:00 2021-12-25 19:30:44-08:00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "v_test.index[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4DAoxCrRPof3",
        "outputId": "e2af66bc-0b0a-457b-d81a-35601ffc0874"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2021-12-12 01:50:03-0800', tz='US/Pacific')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "v_avg_true"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-zATqkJrRkwK",
        "outputId": "75ada025-1da4-4398-dea4-ed678acbc9a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "timestamp\n",
              "2021-12-12 01:48:00-08:00    0.042680\n",
              "2021-12-12 01:51:00-08:00    0.042887\n",
              "2021-12-12 01:54:00-08:00    0.043115\n",
              "2021-12-12 01:57:00-08:00    0.043433\n",
              "2021-12-12 02:00:00-08:00    0.044432\n",
              "                               ...   \n",
              "2021-12-27 08:30:00-08:00    0.011772\n",
              "2021-12-27 08:33:00-08:00    0.011377\n",
              "2021-12-27 08:36:00-08:00    0.011526\n",
              "2021-12-27 08:39:00-08:00    0.011652\n",
              "2021-12-27 08:42:00-08:00    0.011742\n",
              "Freq: 3T, Name: V1 [mV], Length: 7339, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "v_avg_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yXgyEwB-RmcJ",
        "outputId": "1cc1c496-2190-456b-c5e1-74022033284a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "timestamp\n",
              "2021-12-12 01:48:00-08:00    0.034146\n",
              "2021-12-12 01:51:00-08:00    0.034819\n",
              "2021-12-12 01:54:00-08:00    0.034582\n",
              "2021-12-12 01:57:00-08:00    0.036043\n",
              "2021-12-12 02:00:00-08:00    0.036840\n",
              "                               ...   \n",
              "2021-12-27 08:30:00-08:00    0.010847\n",
              "2021-12-27 08:33:00-08:00    0.011536\n",
              "2021-12-27 08:36:00-08:00    0.010602\n",
              "2021-12-27 08:39:00-08:00    0.010739\n",
              "2021-12-27 08:42:00-08:00    0.010621\n",
              "Freq: 3T, Name: V1 [mV] pred, Length: 7339, dtype: float32"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "v_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "21YMQFEpRgXB",
        "outputId": "854d94e4-11dd-4de9-a17c-e0535bdc1a05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            V1 [mV]\n",
              "timestamp                          \n",
              "2021-12-12 01:50:03-08:00  0.041934\n",
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              "2021-12-12 01:50:44-08:00  0.041475\n",
              "2021-12-12 01:50:58-08:00  0.043989\n",
              "...                             ...\n",
              "2021-12-27 08:43:53-08:00  0.014330\n",
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              "2021-12-27 08:44:21-08:00  0.012309\n",
              "2021-12-27 08:44:34-08:00  0.011223\n",
              "2021-12-27 08:44:48-08:00  0.011524\n",
              "\n",
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              "      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",
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              "\n",
              "  <div id=\"id_8fdf4e05-e2e3-4a6a-b4db-2a636f078b99\">\n",
              "    <style>\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
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              "      }\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('v_test')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <script>\n",
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              "        document.querySelector('#id_8fdf4e05-e2e3-4a6a-b4db-2a636f078b99 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('v_test');\n",
              "      }\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "v_test",
              "summary": "{\n  \"name\": \"v_test\",\n  \"rows\": 96965,\n  \"fields\": [\n    {\n      \"column\": \"timestamp\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2021-12-12 01:50:03-08:00\",\n        \"max\": \"2021-12-27 08:44:48-08:00\",\n        \"num_unique_values\": 96965,\n        \"samples\": [\n          \"2021-12-18 21:02:21-08:00\",\n          \"2021-12-22 19:01:15-08:00\",\n          \"2021-12-12 02:12:08-08:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"V1 [mV]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.009604894997887855,\n        \"min\": 0.0005972,\n        \"max\": 0.05107294000000001,\n        \"num_unique_values\": 24881,\n        \"samples\": [\n          0.046569360000000004,\n          0.025350299999999996,\n          0.028609100000000002\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "v_avg_true.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bAs8srbMRHJM",
        "outputId": "07a13a5a-a4be-42ec-cac8-3b65205f7b4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2022-01-11 16:18:00-08:00', '2022-01-11 16:21:00-08:00',\n",
              "               '2022-01-11 16:24:00-08:00', '2022-01-11 16:27:00-08:00',\n",
              "               '2022-01-11 16:30:00-08:00', '2022-01-11 16:33:00-08:00',\n",
              "               '2022-01-11 16:36:00-08:00', '2022-01-11 16:39:00-08:00',\n",
              "               '2022-01-11 16:42:00-08:00', '2022-01-11 16:45:00-08:00',\n",
              "               ...\n",
              "               '2022-01-26 23:30:00-08:00', '2022-01-26 23:33:00-08:00',\n",
              "               '2022-01-26 23:36:00-08:00', '2022-01-26 23:39:00-08:00',\n",
              "               '2022-01-26 23:42:00-08:00', '2022-01-26 23:45:00-08:00',\n",
              "               '2022-01-26 23:48:00-08:00', '2022-01-26 23:51:00-08:00',\n",
              "               '2022-01-26 23:54:00-08:00', '2022-01-26 23:57:00-08:00'],\n",
              "              dtype='datetime64[ns, US/Pacific]', name='timestamp', length=7354, freq='3T')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "v_avg_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6b1goUbeRKW6",
        "outputId": "a1375a5a-f8f7-4f11-d5ef-0102e77b38b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "timestamp\n",
              "2021-12-12 01:48:00-08:00    0.042024\n",
              "2021-12-12 01:51:00-08:00    0.042606\n",
              "2021-12-12 01:54:00-08:00    0.042404\n",
              "2021-12-12 01:57:00-08:00    0.042549\n",
              "2021-12-12 02:00:00-08:00    0.043646\n",
              "                               ...   \n",
              "2021-12-27 08:30:00-08:00    0.011390\n",
              "2021-12-27 08:33:00-08:00    0.011344\n",
              "2021-12-27 08:36:00-08:00    0.010932\n",
              "2021-12-27 08:39:00-08:00    0.011157\n",
              "2021-12-27 08:42:00-08:00    0.011242\n",
              "Freq: 3T, Name: V1 [mV] pred, Length: 7339, dtype: float32"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5fEIWPla0Ry3"
      },
      "outputs": [],
      "source": [
        "X_valid, X_test = train_test_split(X_test, test_size=0.5, shuffle=False)\n",
        "y_valid, y_test = train_test_split(y_test, test_size=0.5, shuffle=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ojiHhleC0MC2"
      },
      "outputs": [],
      "source": [
        "valid_bound_lower = y_valid.index[0]\n",
        "valid_bound_upper = y_valid.index[-1]\n",
        "test_bound_lower = y_test.index[0]\n",
        "test_bound_upper = y_test.index[-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dLNh39-10R2W"
      },
      "outputs": [],
      "source": [
        "time_frame = '60min'\n",
        "\n",
        "X_train = X_train.resample(time_frame).mean()\n",
        "X_valid = X_valid.resample(time_frame).mean()\n",
        "X_test = X_test.resample(time_frame).mean()\n",
        "\n",
        "y_train = y_train.resample(time_frame).mean()\n",
        "y_valid = y_valid.resample(time_frame).mean()\n",
        "y_test = y_test.resample(time_frame).mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-wpNt8fwnMb0"
      },
      "outputs": [],
      "source": [
        "#reshape data\n",
        "X_train = X_train.values.reshape((X_train.shape[0], 1, X_train.shape[1]))\n",
        "X_valid = X_valid.values.reshape((X_valid.shape[0], 1, X_valid.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": "4RjWZdVsm7uP"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.python.ops.numpy_ops import np_config\n",
        "np_config.enable_numpy_behavior()\n",
        "def custom_loss(y_true, y_pred):\n",
        "    residual = (y_true - y_pred*1.11).astype(\"float\")\n",
        "    loss = tf.where(residual < 0, (residual**2) * 1, residual**2)\n",
        "    return loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qMejODgzMaS_"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.python.ops.numpy_ops import np_config\n",
        "np_config.enable_numpy_behavior()\n",
        "def custom_loss(y_true, y_pred):\n",
        "    residual = (y_true - y_pred).astype(\"float\")\n",
        "    loss = tf.math.sqrt(tf.where(residual < 0, (residual**2), residual**2))\n",
        "    return loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ami-GIIJBvyb"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.python.ops.numpy_ops import np_config\n",
        "np_config.enable_numpy_behavior()\n",
        "def custom_loss(y_true, y_pred):\n",
        "    residual = (y_true - y_pred).astype(\"float\")\n",
        "    loss = tf.where(residual < 0, tf.reduce_mean(tf.abs(tf.divide(tf.subtract(y_pred,y_true),(y_true + 1e-10)))) * 100, tf.reduce_mean(tf.abs(tf.divide(tf.subtract(y_pred,y_true),(y_true + 1e-10)))) * 100)\n",
        "    return loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z3yDS6AN6iVZ"
      },
      "outputs": [],
      "source": [
        "def quantile_loss(y_true, y_pred, quantile = 0.75):\n",
        "    error = y_true - y_pred\n",
        "    return K.mean(K.maximum(quantile * error, (quantile - 1) * error), axis=-1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t-3Fo0pWma0i",
        "outputId": "046df085-29d4-4d1c-c093-59e856f09dfd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "144/144 [==============================] - 1s 2ms/step\n",
            "18/18 [==============================] - 0s 2ms/step\n",
            "18/18 [==============================] - 0s 2ms/step\n"
          ]
        }
      ],
      "source": [
        "from keras.models import load_model\n",
        "model = load_model(\"drive/MyDrive/jLab Shared Docs/MFC Modeling/lstm8_60min_quant75\", custom_objects={'quantile_loss': quantile_loss})\n",
        "train_pred = model.predict(X_train)\n",
        "valid_pred = model.predict(X_valid)\n",
        "test_pred = model.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JHzihcpcKFp0"
      },
      "outputs": [],
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import Dropout\n",
        "from keras import Input\n",
        "from keras.models import Model\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(Dropout(0.2))\n",
        "model.add(Dense(100, activation='relu'))\n",
        "model.add(Dense(3))\n",
        "model.compile(loss=quantile_loss, metrics=['mape'], optimizer='adam')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vbVqQlFke364",
        "outputId": "2956c1d7-f0aa-4e0a-c053-f4b430c6f1c4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(2559, 1, 20)"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "A6p_LefYPcsX",
        "outputId": "617c295c-2004-4398-96eb-2023b6587d89"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/25\n",
            "320/320 - 6s - loss: 248.6508 - mape: 18.3042 - val_loss: 91.5237 - val_mape: 13.1559 - 6s/epoch - 20ms/step\n",
            "Epoch 2/25\n",
            "320/320 - 2s - loss: 156.1360 - mape: 18.4219 - val_loss: 79.2564 - val_mape: 11.4511 - 2s/epoch - 6ms/step\n",
            "Epoch 3/25\n",
            "320/320 - 1s - loss: 120.9987 - mape: 13.9169 - val_loss: 75.4047 - val_mape: 12.1010 - 1s/epoch - 4ms/step\n",
            "Epoch 4/25\n",
            "320/320 - 1s - loss: 103.2360 - mape: 11.7955 - val_loss: 81.9642 - val_mape: 16.6948 - 1s/epoch - 4ms/step\n",
            "Epoch 5/25\n",
            "320/320 - 1s - loss: 101.4126 - mape: 10.8723 - val_loss: 78.5507 - val_mape: 15.2109 - 1s/epoch - 4ms/step\n",
            "Epoch 6/25\n",
            "320/320 - 1s - loss: 108.1772 - mape: 11.9389 - val_loss: 76.0652 - val_mape: 14.0007 - 1s/epoch - 4ms/step\n",
            "Epoch 7/25\n"
          ]
        },
        {
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-33-07d88db97184>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mbatchsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# fit network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_valid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_valid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatchsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     66\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m             \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1854\u001b[0m                             \u001b[0mpss_evaluation_shards\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pss_evaluation_shards\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1855\u001b[0m                         )\n\u001b[0;32m-> 1856\u001b[0;31m                     val_logs = self.evaluate(\n\u001b[0m\u001b[1;32m   1857\u001b[0m                         \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_x\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1858\u001b[0m                         \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     66\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m             \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, return_dict, **kwargs)\u001b[0m\n\u001b[1;32m   2283\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautotune_steps_per_execution\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2284\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_steps_per_execution_tuner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2285\u001b[0;31m             for (\n\u001b[0m\u001b[1;32m   2286\u001b[0m                 \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2287\u001b[0m                 \u001b[0mdataset_or_iterator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/engine/data_adapter.py\u001b[0m in \u001b[0;36menumerate_epochs\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1339\u001b[0m         \u001b[0;34m\"\"\"Yields `(epoch, tf.data.Iterator)`.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1340\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_truncate_execution_to_epoch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1341\u001b[0;31m             \u001b[0mdata_iterator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset\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   1342\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initial_epoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_epochs\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   1343\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_insufficient_data\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Set by `catch_stop_iteration`.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/data/ops/dataset_ops.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    498\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecuting_eagerly\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minside_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    499\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolocate_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variant_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 500\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0miterator_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOwnedIterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    501\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    502\u001b[0m       raise RuntimeError(\"`tf.data.Dataset` only supports Python-style \"\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/data/ops/iterator_ops.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, dataset, components, element_spec)\u001b[0m\n\u001b[1;32m    704\u001b[0m             \u001b[0;34m\"When `dataset` is provided, `element_spec` and `components` must \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    705\u001b[0m             \"not be specified.\")\n\u001b[0;32m--> 706\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\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    707\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    708\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_next_call_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/data/ops/iterator_ops.py\u001b[0m in \u001b[0;36m_create_iterator\u001b[0;34m(self, dataset)\u001b[0m\n\u001b[1;32m    743\u001b[0m             self._flat_output_types)\n\u001b[1;32m    744\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterator_resource\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_set_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfulltype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 745\u001b[0;31m       \u001b[0mgen_dataset_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds_variant\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterator_resource\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    746\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    747\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/ops/gen_dataset_ops.py\u001b[0m in \u001b[0;36mmake_iterator\u001b[0;34m(dataset, iterator, name)\u001b[0m\n\u001b[1;32m   3419\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mtld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_eager\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3420\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3421\u001b[0;31m       _result = pywrap_tfe.TFE_Py_FastPathExecute(\n\u001b[0m\u001b[1;32m   3422\u001b[0m         _ctx, \"MakeIterator\", name, dataset, iterator)\n\u001b[1;32m   3423\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "from keras import backend as K\n",
        "#K.set_value(model.optimizer.learning_rate, 0.3)\n",
        "\n",
        "batchsize = 8\n",
        "# fit network\n",
        "model.fit(X_train, y_train, epochs=25, validation_data=(X_valid, y_valid), batch_size=batchsize, verbose=2, shuffle=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m6T3ohA_lJxu"
      },
      "outputs": [],
      "source": [
        "def enable_dropout(model):\n",
        "    for layer in model.layers:\n",
        "        if isinstance(layer, Dropout):\n",
        "            layer.training = True\n",
        "def disable_dropout(model):\n",
        "    for layer in model.layers:\n",
        "        if isinstance(layer, Dropout):\n",
        "            layer.training = False\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cL3HCC1SnP4h"
      },
      "outputs": [],
      "source": [
        "enable_dropout(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dBPDyrWbiSN3"
      },
      "outputs": [],
      "source": [
        "num_samples = 100\n",
        "\n",
        "test_predictions = []\n",
        "for _ in range(num_samples):\n",
        "    test_prediction = model.predict(X_test, verbose=0)\n",
        "    test_predictions.append(test_prediction)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SMnuLKDDijFn"
      },
      "outputs": [],
      "source": [
        "prediction_std = np.std(test_predictions, axis=0)\n",
        "prediction_mean = np.mean(test_predictions, axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i86Tu1C9nuep"
      },
      "outputs": [],
      "source": [
        "lower_bound = prediction_mean - 1.96 * prediction_std\n",
        "upper_bound = prediction_mean + 1.96 * prediction_std"
      ]
    },
    {
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9V9dEE2Ma6Ge",
        "outputId": "21e0aef2-e261-4052-e30d-173e18ca633b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "drive.mount('/content/drive', force_remount=True)\n",
        "model.save(\"lstm8_3min_quant75\", overwrite=True, save_format=None)\n",
        "!mv lstm8_3min_quant75 'drive/MyDrive/jLab Shared Docs/MFC Modeling'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EdiUPWZFuMDK",
        "outputId": "39cd7bf9-1b9f-4146-d32f-41c3b8745ebc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "320/320 [==============================] - 1s 3ms/step\n",
            "69/69 [==============================] - 0s 3ms/step\n",
            "69/69 [==============================] - 0s 3ms/step\n"
          ]
        }
      ],
      "source": [
        "train_pred = model.predict(X_train, batch_size=batchsize)\n",
        "valid_pred = model.predict(X_valid, 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": 356
        },
        "id": "smwPbn4r4vrk",
        "outputId": "54a8afe2-e1b5-4d83-ee79-c1aa99134e5d"
      },
      "outputs": [
        {
          "ename": "ValueError",
          "evalue": "Input contains NaN.",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-198-e4585e9dd3f6>\u001b[0m in \u001b[0;36m<cell line: 9>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m#print(\"Test MAPE power:\\n\", MAPE(y_test['power'].values.ravel(), test_pred))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Train MAPE power:\\n\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMAPE\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'power'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Valid MAPE power:\\n\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMAPE\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_valid\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'power'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Test MAPE power:\\n\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMAPE\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'power'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_absolute_percentage_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m    363\u001b[0m     \u001b[0;36m112589990684262.48\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    364\u001b[0m     \"\"\"\n\u001b[0;32m--> 365\u001b[0;31m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0m\u001b[1;32m    366\u001b[0m         \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultioutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    367\u001b[0m     )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     99\u001b[0m     \"\"\"\n\u001b[1;32m    100\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\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    102\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m    919\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    920\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 921\u001b[0;31m             _assert_all_finite(\n\u001b[0m\u001b[1;32m    922\u001b[0m                 \u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    923\u001b[0m                 \u001b[0minput_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m    159\u001b[0m                 \u001b[0;34m\"#estimators-that-handle-nan-values\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m             )\n\u001b[0;32m--> 161\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg_err\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    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN."
          ]
        }
      ],
      "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))\n",
        "#print(\"Test MAPE power:\\n\", MAPE(y_test['power'].values.ravel(), test_pred))\n",
        "\n",
        "print(\"Train MAPE power:\\n\", MAPE(y_train['power'].values.ravel(), train_pred[:, 0]))\n",
        "print(\"Valid MAPE power:\\n\", MAPE(y_valid['power'].values.ravel(), valid_pred[:, 0]))\n",
        "print(\"Test MAPE power:\\n\", MAPE(y_test['power'].values.ravel(), test_pred[:, 0]))\n",
        "\n",
        "#Scaled\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",
        "#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/",
          "height": 356
        },
        "id": "UzE9rgfb9NPC",
        "outputId": "077d1dfa-0978-4047-e765-c5f8975a2e33"
      },
      "outputs": [
        {
          "ename": "ValueError",
          "evalue": "Input contains NaN.",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-199-a773905087bc>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#Evaluate MAPE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Train MAPE voltage:\\n\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMAPE\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'V1 [mV]'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Valid MAPE voltage:\\n\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMAPE\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_valid\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'V1 [mV]'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Test MAPE voltage:\\n\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMAPE\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'V1 [mV]'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_absolute_percentage_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m    363\u001b[0m     \u001b[0;36m112589990684262.48\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    364\u001b[0m     \"\"\"\n\u001b[0;32m--> 365\u001b[0;31m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0m\u001b[1;32m    366\u001b[0m         \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultioutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    367\u001b[0m     )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     99\u001b[0m     \"\"\"\n\u001b[1;32m    100\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\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    102\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m    919\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    920\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 921\u001b[0;31m             _assert_all_finite(\n\u001b[0m\u001b[1;32m    922\u001b[0m                 \u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    923\u001b[0m                 \u001b[0minput_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m    159\u001b[0m                 \u001b[0;34m\"#estimators-that-handle-nan-values\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m             )\n\u001b[0;32m--> 161\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg_err\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    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN."
          ]
        }
      ],
      "source": [
        "#Evaluate MAPE\n",
        "print(\"Train MAPE voltage:\\n\", MAPE(y_train['V1 [mV]'], train_pred[:, 1]))\n",
        "print(\"Valid MAPE voltage:\\n\", MAPE(y_valid['V1 [mV]'], valid_pred[:, 1]))\n",
        "print(\"Test MAPE voltage:\\n\", MAPE(y_test['V1 [mV]'], 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 [mV]'].values.ravel(), train_pred[:, 1]))\n",
        "#print(\"Test MAE voltage:\\n\", mean_absolute_error(y_test['V1 [mV]'].values.ravel(), test_pred[:, 1]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9DBTLlnd9rOQ",
        "outputId": "4003fa98-9458-41af-b002-69ef00741ec1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Valid MAPE current:\n",
            " 0.035445924693978094\n",
            "Test MAPE current:\n",
            " 0.04266828062561251\n"
          ]
        }
      ],
      "source": [
        "#Evaluate MAPE\n",
        "print(\"Train MAPE current:\\n\", MAPE(y_train['I1L [μA]'], train_pred[:, 2]))\n",
        "print(\"Valid MAPE current:\\n\", MAPE(y_valid['I1L [μA]'], valid_pred[:, 2]))\n",
        "print(\"Test MAPE current:\\n\", MAPE(y_test['I1L [μA]'], 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 [μA]'].values.ravel(), train_pred[:, 2]))\n",
        "#print(\"Test MAE current:\\n\", mean_absolute_error(y_test['I1L [μA]'].values.ravel(), test_pred[:, 2]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y_T21B6a3UyG"
      },
      "outputs": [],
      "source": [
        "#New runtime calculation\n",
        "import math\n",
        "from dateutil import parser\n",
        "from matplotlib import pyplot as plt\n",
        "from datetime import datetime, timedelta\n",
        "\n",
        "def internal_R_v3(R=2000): #return internal resistance of v3 cells in ohms\n",
        "    #https://www.jstage.jst.go.jp/article/jwet/20/1/20_21-087/_pdf\n",
        "    v0_oc = 48.5e-3 #48.5 mV\n",
        "    v0_cc = 4.8e-3\n",
        "    v0_r = R*((v0_oc/v0_cc)-1)\n",
        "\n",
        "    v1_oc = 43.8e-3\n",
        "    v1_cc = 20.9e-3\n",
        "    v1_r = R*((v1_oc/v1_cc)-1)\n",
        "\n",
        "    v2_oc = 45.2e-3\n",
        "    v2_cc = 23.5e-3\n",
        "    v2_r = R*((v2_oc/v2_cc)-1)\n",
        "\n",
        "    return (v0_r+v1_r+v2_r)/3\n",
        "\n",
        "def internal_R_v0(R=2000): #return internal resistance of v0 cells in ohms\n",
        "    v3_oc = 41.7e-3 #41.7mV\n",
        "    v3_cc = 5.1e-3\n",
        "    v3_r = R*((v3_oc/v3_cc)-1)\n",
        "\n",
        "    v4_oc = 48.7e-3\n",
        "    v4_cc = 16.8e-3\n",
        "    v4_r = R*((v4_oc/v4_cc)-1)\n",
        "\n",
        "    v5_oc = 39.1e-3\n",
        "    v5_cc = 16.9e-3\n",
        "    v5_r = R*((v5_oc/v5_cc)-1)\n",
        "\n",
        "    return (v3_r+v4_r+v5_r)/3\n",
        "\n",
        "def SMFC_current(v, R):\n",
        "    return v/R\n",
        "\n",
        "#MODEL\n",
        "def cap_leakage(E_cap_tn, timestep):\n",
        "    #Spec for KEMET T491\n",
        "    return 0.01e-6 * E_cap_tn * timestep\n",
        "\n",
        "def Matrix_Power(V, R):\n",
        "    #efficiency interpolated from https://www.analog.com/media/en/technical-documentation/data-sheets/ADP5091-5092.pdf\n",
        "    #given I_in = 100 uA and SYS = 3V\n",
        "    #V is the voltage (V) of the SMFC we captured\n",
        "    #R is the resistance (ohms) of the load we used to get that voltage trace\n",
        "    #Eta = -292.25665*V**4 + 784.30311*V**3 - 770.71691*V**2 + 342.00502*V + 15.83307\n",
        "    #Eta = Eta/100\n",
        "    Eta = 0.60\n",
        "    Pmax = (V**2)/R\n",
        "    Pout = Eta*Pmax\n",
        "    #assert((Eta > 0) & (Eta < 1))\n",
        "    #assert(Pout < 12000e-6)\n",
        "    return Pout\n",
        "\n",
        "def update_capEnergy(e0, V_applied, R, C, dt):\n",
        "    # e0: initial energy stored\n",
        "    # V_applied: voltage from SMFC\n",
        "    # R: internal resistance of SMFC\n",
        "    # C: capacitance of capacitor\n",
        "    # dt: time step since last data point\n",
        "    e_cap = e0 + Matrix_Power(V_applied, R)*dt - cap_leakage(e0, dt)\n",
        "    v_cap = math.sqrt(2*e_cap/C)\n",
        "    if e_cap < 0: #Not charging if leakage is greater than energy\n",
        "        e_cap = 0\n",
        "\n",
        "    return e_cap, v_cap #output final e and v\n",
        "\n",
        "def Advanced_energy():\n",
        "    #Now representing \"Advanced\"\n",
        "    #startup time of 2500 ms\n",
        "    t = 2500e-3\n",
        "    e = 2.4 * 128e-3 * t\n",
        "    e_startup = 2.4 * 128e-3 * 5e-3\n",
        "    return e+e_startup\n",
        "\n",
        "def Minimal_energy():\n",
        "    #Now representing \"Minimal\"\n",
        "    t = 0.888e-3 #tentative time\n",
        "    e = 0.9 * 4.8e-3 * t #this uses average current\n",
        "    e_startup = 0#assume negligible, no known startup time given\n",
        "    return  e + e_startup\n",
        "\n",
        "def Analog_energy():\n",
        "    #Now representing Analog\n",
        "    t = 1e-3 #estimated operating time\n",
        "    e = 0.11 * 2.15e-6 * t\n",
        "    e_startup = 0 #analog device, no startup needed :)\n",
        "    return e + e_startup\n",
        "\n",
        "#STEP 3:\n",
        "# For each day:\n",
        "#   on_Minimal, on_Advanced, on_Analog = 0\n",
        "#   For each time step (like every 60 s given our logging freq):\n",
        "#       - Update the energy in our capacitor (put fcn in models.py) given (1) input voltage, (2) time step, (3) capacitance (prob 10 uF), this will be an integral\n",
        "#       - Check if energy is enough to turn on (1) 1 uJ load, (2) 10 uJ load, and (3) 20 uJ load (will tweak later to reflect real energy cost of each system)\n",
        "#       - If so, add to on_Minimal, on_Advanced, and on_Analog and reset capacitor energy to 0 J (might tweak this value)\n",
        "#   Append on_Minimal, on_Advanced, on_Analog to on_Minimal_list, on_Advanced_list, on_Analog_list. This will be a list of how many sensor readings we are able to take with each of these systems every day given the energy we got\n",
        "#STEP 4: Visualize the daily # of readings with 3 bar graphs, y axis is # of readings and x axis is days.\n",
        "#   - Given 3 lists of integer values, plot them on bar graphs\n",
        "\n",
        "def group_util(test_date1, test_date2, N):\n",
        "    diff = (test_date2 - test_date1) / N\n",
        "    return [test_date1 + diff * idx for idx in range(N)] + [test_date2]\n",
        "\n",
        "def simulate(t_list, v_list, v_list_pred, v_list_fine, C_h):\n",
        "    # t_list: list of decimal time stamps in unit of days (e.g. 71.85893518518519 day), same length as v_list\n",
        "    # v_list: list of voltage values from SFMC\n",
        "    # C_h: capacitance of the capacitor being filled up by harvester\n",
        "\n",
        "    #assume capacitor is completely discharged at start\n",
        "    e_minimal_stored = 0\n",
        "    e_minimal_stored_theo = 0\n",
        "\n",
        "    #Initialize evaluation metrics\n",
        "    false_act = 0\n",
        "    max_act = 0\n",
        "    pred_act = 0\n",
        "    succ_act = 0\n",
        "\n",
        "    total_E = 0\n",
        "    total_E_pred = 0\n",
        "\n",
        "    #Calculate maximum energy\n",
        "    #for i in range(len(v_list_fine) - 1):\n",
        "    #    t = (v_list_fine.index[i+1] - v_list_fine.index[i]).total_seconds()\n",
        "    #    total_E, ignore = update_capEnergy(total_E, V_applied=v_list_fine['V1 [10nV]'][i], R=internal_R_v0(), C=C_h[0], dt = t)\n",
        "    #print(total_E/Minimal_energy())\n",
        "    total_E = 0\n",
        "    #for each voltage data point\n",
        "    for jj in range(len(t_list) - 1): #last data point was at 71.85893518518519 day\n",
        "        t = (t_list[jj+1] - t_list[jj])*24*3600\n",
        "        #Total predicted vs. actual energy stored\n",
        "        total_E, ignore = update_capEnergy(total_E, V_applied=v_list[jj], R=internal_R_v0(), C=C_h[0], dt = t)\n",
        "        total_E_pred, ignore = update_capEnergy(total_E_pred, V_applied=v_list_pred[jj], R=internal_R_v0(), C=C_h[0], dt = t)\n",
        "\n",
        "        #Predict energy stored during scheduled sub-interval\n",
        "        E_Minimal_pred, v_minimal_pred = update_capEnergy(e_minimal_stored, V_applied=v_list_pred[jj], R=internal_R_v0(), C=C_h[0], dt = t) #set dt as length of prediction interval, in seconds\n",
        "        pred_act += math.floor(E_Minimal_pred/Minimal_energy()) #Update number of activations predicted\n",
        "        if math.floor(E_Minimal_pred/Minimal_energy()) > 0:\n",
        "            minimal_intervals = [date.strftime(\"%Y/%m/%d %H:%M:%S\") for date in group_util(v_list_pred.index[jj], v_list_pred.index[jj] + timedelta(seconds=t), math.floor(E_Minimal_pred/Minimal_energy()))]\n",
        "            for i in range(len(minimal_intervals) - 1):\n",
        "                #Determine actual energy stored during scheduled sub-interval\n",
        "                start = v_list_fine.index.searchsorted(minimal_intervals[i])\n",
        "                end =  v_list_fine.index.searchsorted(minimal_intervals[i+1])\n",
        "                #for h in range(start, end):\n",
        "                #  v = v_list_fine.iloc[h]['V1 [10nV]']\n",
        "                #  interval_length = ((v_list_fine.index[h+1]) - (v_list_fine.index[h])).total_seconds()\n",
        "                #  E_Minimal, ignore = update_capEnergy(e_minimal_stored, V_applied=v, R=internal_R_v0(), C=C_h[0], dt = interval_length)\n",
        "                #  e_minimal_stored = E_Minimal\n",
        "                v = v_list_fine.iloc[start:end]['V1 [mV]'].mean()\n",
        "                interval_length = (parser.parse(minimal_intervals[i+1]) - parser.parse(minimal_intervals[i])).total_seconds()\n",
        "                E_Minimal, v_minimal = update_capEnergy(e_minimal_stored, V_applied=v, R=internal_R_v0(), C=C_h[0], dt = interval_length)\n",
        "                if E_Minimal < Minimal_energy():\n",
        "                    false_act += 1\n",
        "                    e_minimal_stored = max(0, E_Minimal - Minimal_energy())\n",
        "\n",
        "                elif E_Minimal >= Minimal_energy():\n",
        "                    succ_act += 1\n",
        "                    e_minimal_stored = max(0, E_Minimal - Minimal_energy())\n",
        "\n",
        "        else:\n",
        "            e_minimal_stored, ignore = update_capEnergy(e_minimal_stored, V_applied=v_list[jj], R=internal_R_v0(), C=C_h[0], dt = t)\n",
        "            #start = y_valid.index.searchsorted(v_list.index[jj])\n",
        "            #end =  y_valid.index.searchsorted(v_list.index[jj] + timedelta(seconds=t))\n",
        "            #for h in range(start, end):\n",
        "            #  v = v_list_fine.iloc[h]['V1 [10nV]']\n",
        "            #  interval_length = ((v_list_fine.index[h+1]) - (v_list_fine.index[h])).total_seconds()\n",
        "            #  E_Minimal, ignore = update_capEnergy(e_minimal_stored, V_applied=v, R=internal_R_v0(), C=C_h[0], dt = interval_length)\n",
        "            #  e_minimal_stored = E_Minimal\n",
        "\n",
        "    print(total_E/Minimal_energy())\n",
        "    print(total_E_pred/Minimal_energy())\n",
        "    return pred_act, false_act, succ_act, total_E, total_E_pred\n",
        "\n",
        "def oracle_simulate(v_list, C_h):\n",
        "    #Calculate maximum energy\n",
        "    total_E = 0\n",
        "    for i in range(len(v_list) - 1):\n",
        "        t = (v_list.index[i+1] - v_list.index[i]).total_seconds()\n",
        "        total_E, ignore = update_capEnergy(total_E, V_applied=v_list['V1 [mV]'][i], R=internal_R_v0(), C=C_h[0], dt = t)\n",
        "    print(math.floor(total_E/Minimal_energy()))\n",
        "    return(math.floor(total_E/Minimal_energy()))\n",
        "\n",
        "def naive_simulate(v_list, v_list_train, v_list_fine, C_h):\n",
        "    # t_list: list of decimal time stamps in unit of days (e.g. 71.85893518518519 day), same length as v_list\n",
        "    # v_list: list of voltage values from SFMC\n",
        "    # C_h: capacitance of the capacitor being filled up by harvester\n",
        "\n",
        "    #assume capacitor is completely discharged at start\n",
        "    e_minimal_stored = 0\n",
        "    e_minimal_stored_theo = 0\n",
        "\n",
        "    #Initialize evaluation metrics\n",
        "    false_act = 0\n",
        "    pred_act = 0\n",
        "    succ_act = 0\n",
        "\n",
        "    power_list = []\n",
        "    E_tot = 0\n",
        "    total_E = 0\n",
        "\n",
        "    #for i in range(len(v_list_train) - 1):\n",
        "    #    t = (v_list_train.index[i+1] - v_list_train.index[i]).total_seconds()\n",
        "    #    total_E, ignore = update_capEnergy(total_E, V_applied=v_list_train[i], R=internal_R_v0(), C=C_h[0], dt = t)\n",
        "    t = (v_list_fine.index[-1] - v_list_fine.index[0]).total_seconds()\n",
        "    total_E, ignore = update_capEnergy(0, V_applied=v_list_train.mean(), R=internal_R_v0(), C=C_h[0], dt = t)\n",
        "\n",
        "    #Schedule activations with fixed-duty cycle based on total energy\n",
        "    print(total_E)\n",
        "    pred_act = math.floor(total_E/Minimal_energy())\n",
        "    minimal_intervals = [date.strftime(\"%Y/%m/%d %H:%M:%S\") for date in group_util(v_list_fine.index[0], v_list_fine.index[-1], pred_act)]\n",
        "    for i in range(len(minimal_intervals) - 1):\n",
        "        #Determine actual energy stored during scheduled sub-interval\n",
        "        start = v_list_fine.index.searchsorted(minimal_intervals[i])\n",
        "        end =  v_list_fine.index.searchsorted(minimal_intervals[i+1])\n",
        "        v = v_list_fine.iloc[start:end]['V1 [mV]'].mean()\n",
        "        interval_length = (parser.parse(minimal_intervals[i+1]) - parser.parse(minimal_intervals[i])).total_seconds()\n",
        "        E_Minimal, v_minimal = update_capEnergy(e_minimal_stored, V_applied=v, R=internal_R_v0(), C=C_h[0], dt = interval_length)\n",
        "\n",
        "        if (E_Minimal) < Minimal_energy():\n",
        "            false_act += 1\n",
        "            e_minimal_stored = max(0, E_Minimal - Minimal_energy())\n",
        "\n",
        "        elif (E_Minimal) >= Minimal_energy():\n",
        "            succ_act += 1\n",
        "            e_minimal_stored = max(0, E_Minimal - Minimal_energy())\n",
        "\n",
        "    return pred_act, false_act, succ_act, total_E\n",
        "\n",
        "def getMax(c_list, input_list):\n",
        "    max_value = max(input_list)\n",
        "    i = [index for index, item in enumerate(input_list) if item == max_value][0]\n",
        "    return i, max_value, c_list[i]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lA4YLowE-kwp"
      },
      "outputs": [],
      "source": [
        "#SMFC\n",
        "import csv\n",
        "from collections import defaultdict\n",
        "from scipy.signal import butter, lfilter\n",
        "import matplotlib.pyplot as plt\n",
        "from datetime import datetime\n",
        "\n",
        "def butter_lowpass(cutoff, fs, order=5):\n",
        "        return butter(order, cutoff, fs=fs, btype='low', analog=False)\n",
        "\n",
        "def butter_lowpass_filter(data, cutoff, fs, order=5):\n",
        "    b, a = butter_lowpass(cutoff, fs, order=order)\n",
        "    y = lfilter(b, a, data)\n",
        "    return y\n",
        "\n",
        "def getMFC_data(y_test, test_pred):\n",
        "    unix_time = y_test.index\n",
        "    d0 = unix_time[0]\n",
        "    days = []\n",
        "    for d in unix_time:\n",
        "        day = d\n",
        "        day_from_start = day-d0\n",
        "        decimal_day = day_from_start.total_seconds()/(24 * 3600)\n",
        "        days.append(decimal_day)\n",
        "\n",
        "    return days"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_jO53X4j4LWF"
      },
      "outputs": [],
      "source": [
        "#y['timestamp'] = y['timestamp'].dt.tz_localize('UTC').dt.tz_convert('US/Pacific')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KBs2M_N_1T9u"
      },
      "outputs": [],
      "source": [
        "#y_train, y_test = train_test_split(pd.concat([df[\"power\"], df['V1 [mV]'], df['I1L [μA]']], axis = 1), test_size=0.3, shuffle=False)\n",
        "#ignore, y_test = train_test_split(y_test, test_size=0.5, shuffle=False)\n",
        "\n",
        "y_test['power pred'] = test_pred[:, 0]\n",
        "y_test['V1 [mV] pred'] = test_pred[:, 1]\n",
        "y_test['I1L [μA] pred'] = test_pred[:, 2]\n",
        "\n",
        "v_test = df.loc[(((df.index >= test_bound_lower)) & (df.index <= test_bound_upper))]['V1 [mV]']\n",
        "v_test = v_test.drop(v_test[(v_test.index > '2021-11-11') & (v_test.index < '2021-11-22 01:00:00')].index)\n",
        "v_test = pd.DataFrame(v_test)/1E5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "319XDeR5Vhxt",
        "outputId": "42438086-78ee-4226-a807-0f751b3080b5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "5940.023134485983\n",
            "7932.151920007585\n"
          ]
        }
      ],
      "source": [
        "#main\n",
        "#STEP 1: Import voltage values from v3_Data.csv and get filtered average of v3 and v0 cells\n",
        "days  = getMFC_data(y_test, test_pred)\n",
        "\n",
        "v_avg_true = v_test['V1 [mV]'].resample(time_frame).mean()\n",
        "v_avg_pred = y_test['V1 [mV] pred']/1E5\n",
        "\n",
        "#Call simulate function\n",
        "C0 = [0.007000000000000006, 0.007000000000000006, 0.007000000000000006]\n",
        "pred_act, false_act, succ_act, total_E, total_E_pred = simulate(days, v_avg_true, v_avg_pred, v_test, C0)\n",
        "\n",
        "#print(sum(Advanced_true)/sum(Advanced_pred))\n",
        "#print(sum(Minimal_true)/sum(Minimal_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "dHjJGwsH7CgA",
        "outputId": "cc837843-b4e8-4e4e-b96e-0da9755e0893"
      },
      "outputs": [
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              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('v_test')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_f8747598-f011-495b-8f4e-d9b22aa72285 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('v_test');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                            V1 [mV]\n",
              "timestamp                          \n",
              "2022-01-04 00:31:25-08:00  0.019359\n",
              "2022-01-04 00:31:39-08:00  0.017503\n",
              "2022-01-04 00:31:53-08:00  0.017273\n",
              "2022-01-04 00:32:06-08:00  0.018733\n",
              "2022-01-04 00:32:20-08:00  0.019295\n",
              "...                             ...\n",
              "2022-01-26 23:58:52-08:00  0.024386\n",
              "2022-01-26 23:59:06-08:00  0.008550\n",
              "2022-01-26 23:59:19-08:00  0.008552\n",
              "2022-01-26 23:59:33-08:00  0.008208\n",
              "2022-01-26 23:59:47-08:00  0.007138\n",
              "\n",
              "[145448 rows x 1 columns]"
            ]
          },
          "execution_count": 309,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "v_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "VUOr-LwanKm0",
        "outputId": "f1106fcf-2cdb-4334-c21f-53a050070607"
      },
      "outputs": [
        {
          "data": {
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              "      <th>2022-01-04 00:31:25-08:00</th>\n",
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              "      <th>2022-01-04 00:31:39-08:00</th>\n",
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              "      <th>2022-01-04 00:31:53-08:00</th>\n",
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              "      <th>2022-01-04 00:32:20-08:00</th>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>2022-01-26 23:58:52-08:00</th>\n",
              "      <td>0.024386</td>\n",
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              "      <th>2022-01-26 23:59:06-08:00</th>\n",
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              "\n",
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              "\n",
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              "        document.querySelector('#df-72b932a3-5d0e-42fa-8978-275937596907 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-72b932a3-5d0e-42fa-8978-275937596907');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "\n",
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              "\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    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",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    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-f8380359-a8c0-47d1-9fed-5ec568d73ec0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_3252b301-878c-40ae-8ab4-b78b174c85f9\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('v_test')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_3252b301-878c-40ae-8ab4-b78b174c85f9 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('v_test');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                            V1 [mV]\n",
              "timestamp                          \n",
              "2022-01-04 00:31:25-08:00  0.019359\n",
              "2022-01-04 00:31:39-08:00  0.017503\n",
              "2022-01-04 00:31:53-08:00  0.017273\n",
              "2022-01-04 00:32:06-08:00  0.018733\n",
              "2022-01-04 00:32:20-08:00  0.019295\n",
              "...                             ...\n",
              "2022-01-26 23:58:52-08:00  0.024386\n",
              "2022-01-26 23:59:06-08:00  0.008550\n",
              "2022-01-26 23:59:19-08:00  0.008552\n",
              "2022-01-26 23:59:33-08:00  0.008208\n",
              "2022-01-26 23:59:47-08:00  0.007138\n",
              "\n",
              "[145448 rows x 1 columns]"
            ]
          },
          "execution_count": 236,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "v_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DzFyZRdeXqi5",
        "outputId": "97623491-48bc-483f-f010-9e8678f68ee7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "timestamp\n",
              "2021-12-12 01:49:50-08:00    0.043980\n",
              "2021-12-12 01:50:03-08:00    0.041934\n",
              "2021-12-12 01:50:17-08:00    0.043989\n",
              "2021-12-12 01:50:31-08:00    0.042013\n",
              "2021-12-12 01:50:44-08:00    0.041475\n",
              "                               ...   \n",
              "2022-01-04 00:30:03-08:00    0.017420\n",
              "2022-01-04 00:30:17-08:00    0.019538\n",
              "2022-01-04 00:30:31-08:00    0.017284\n",
              "2022-01-04 00:30:44-08:00    0.017385\n",
              "2022-01-04 00:30:58-08:00    0.018693\n",
              "Name: V1 [mV], Length: 145447, dtype: float64"
            ]
          },
          "execution_count": 308,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "v_valid"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q1KUw6O9nOmG",
        "outputId": "e1b26723-0ca4-4d6b-c15c-c9e513a290f3"
      },
      "outputs": [
        {
          "data": {
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              "timestamp\n",
              "2021-12-12 01:49:50-08:00    0.043980\n",
              "2021-12-12 01:50:03-08:00    0.041934\n",
              "2021-12-12 01:50:17-08:00    0.043989\n",
              "2021-12-12 01:50:31-08:00    0.042013\n",
              "2021-12-12 01:50:44-08:00    0.041475\n",
              "                               ...   \n",
              "2022-01-04 00:30:03-08:00    0.017420\n",
              "2022-01-04 00:30:17-08:00    0.019538\n",
              "2022-01-04 00:30:31-08:00    0.017284\n",
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              "2022-01-04 00:30:58-08:00    0.018693\n",
              "Name: V1 [mV], Length: 145447, dtype: float64"
            ]
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          "execution_count": 235,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "v_valid"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "rDUYdMa3bufq",
        "outputId": "1159e82a-736c-43d9-c731-a6db496cfc5f"
      },
      "outputs": [
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              "      <th>2022-01-04 04:00:00-08:00</th>\n",
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              "      <th>...</th>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
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              "            title=\"Generate code using this dataframe.\"\n",
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              "                                power      V1 [mV]     I1L [μA]  power pred  \\\n",
              "timestamp                                                                     \n",
              "2022-01-04 00:00:00-08:00  405.154084  1715.061405  2344.693810  483.075684   \n",
              "2022-01-04 01:00:00-08:00  341.636308  1608.950867  2144.520019  426.893036   \n",
              "2022-01-04 02:00:00-08:00  381.809739  1525.182380  2491.289034  449.889496   \n",
              "2022-01-04 03:00:00-08:00  378.086693  1453.656542  2615.117542  440.493958   \n",
              "2022-01-04 04:00:00-08:00  448.177092  1424.520742  2729.009246  462.500183   \n",
              "...                               ...          ...          ...         ...   \n",
              "2022-01-26 19:00:00-08:00  125.508030   786.610682  1605.716220  175.428802   \n",
              "2022-01-26 20:00:00-08:00  145.646759   796.118125  1831.411269  238.623276   \n",
              "2022-01-26 21:00:00-08:00  269.964851   854.617288  1960.053860  298.504303   \n",
              "2022-01-26 22:00:00-08:00  134.280113   875.723939  1554.202803  234.360672   \n",
              "2022-01-26 23:00:00-08:00  183.901430   881.986966  1879.943966  273.348938   \n",
              "\n",
              "                           V1 [mV] pred  I1L [μA] pred  \n",
              "timestamp                                               \n",
              "2022-01-04 00:00:00-08:00   1789.912842    2676.387939  \n",
              "2022-01-04 01:00:00-08:00   1725.699951    2491.783691  \n",
              "2022-01-04 02:00:00-08:00   1627.041260    2737.216064  \n",
              "2022-01-04 03:00:00-08:00   1548.683105    2832.309326  \n",
              "2022-01-04 04:00:00-08:00   1529.899536    2922.944092  \n",
              "...                                 ...            ...  \n",
              "2022-01-26 19:00:00-08:00    951.307068    1609.442383  \n",
              "2022-01-26 20:00:00-08:00   1108.758667    2140.667236  \n",
              "2022-01-26 21:00:00-08:00   1170.561768    2310.200684  \n",
              "2022-01-26 22:00:00-08:00   1228.170410    1950.415405  \n",
              "2022-01-26 23:00:00-08:00   1186.542725    2221.720703  \n",
              "\n",
              "[552 rows x 6 columns]"
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          "execution_count": 307,
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              "      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-8b9e722f-ab26-47e5-9347-7a2cc1436560 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_7783d3b2-1e63-4b48-851b-3d2b53104d74\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('y_test')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "        document.querySelector('#id_7783d3b2-1e63-4b48-851b-3d2b53104d74 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('y_test');\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                power      V1 [mV]     I1L [μA]  power pred  \\\n",
              "timestamp                                                                     \n",
              "2022-01-04 00:30:00-08:00  405.154084  1715.061405  2344.693810  334.600342   \n",
              "2022-01-04 01:00:00-08:00  290.084847  1632.173939  1791.021038  233.449310   \n",
              "2022-01-04 01:30:00-08:00  393.187770  1585.727795  2498.019000  318.065765   \n",
              "2022-01-04 02:00:00-08:00  364.317906  1550.815644  2318.975258  292.764587   \n",
              "2022-01-04 02:30:00-08:00  399.435097  1499.353443  2664.918183  309.505951   \n",
              "...                               ...          ...          ...         ...   \n",
              "2022-01-26 21:30:00-08:00  189.666397   860.226523  1854.389462  136.552856   \n",
              "2022-01-26 22:00:00-08:00  125.684062   872.375909  1452.308417  104.261116   \n",
              "2022-01-26 22:30:00-08:00  142.876165   879.071970  1656.097189  118.113823   \n",
              "2022-01-26 23:00:00-08:00  124.228592   884.140833  1445.325189  103.180275   \n",
              "2022-01-26 23:30:00-08:00  244.029786   879.816656  2317.880443  174.891159   \n",
              "\n",
              "                           V1 [mV] pred  I1L [μA] pred  \n",
              "timestamp                                               \n",
              "2022-01-04 00:30:00-08:00   1386.479492    2066.960449  \n",
              "2022-01-04 01:00:00-08:00   1414.514038    1632.301636  \n",
              "2022-01-04 01:30:00-08:00   1259.702026    2151.985840  \n",
              "2022-01-04 02:00:00-08:00   1260.300049    2024.656250  \n",
              "2022-01-04 02:30:00-08:00   1181.952271    2226.973877  \n",
              "...                                 ...            ...  \n",
              "2022-01-26 21:30:00-08:00    820.742493    1620.852173  \n",
              "2022-01-26 22:00:00-08:00    823.773987    1322.093384  \n",
              "2022-01-26 22:30:00-08:00    828.011597    1439.502197  \n",
              "2022-01-26 23:00:00-08:00    828.642456    1306.809692  \n",
              "2022-01-26 23:30:00-08:00    788.631836    1963.524292  \n",
              "\n",
              "[1103 rows x 6 columns]"
            ]
          },
          "execution_count": 234,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "3u4xsjD1bxAK",
        "outputId": "f8be4d2b-c45d-4af1-b726-881d19db7f00"
      },
      "outputs": [
        {
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              "type": "dataframe",
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              "      try {\n",
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              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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              "    (() => {\n",
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              "        document.querySelector('#df-825c6b99-def6-4ba1-8db0-4f55eb529ecc button');\n",
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              "\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "\n",
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              "        google.colab.notebook.generateWithVariable('y_valid');\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                 power      V1 [mV]     I1L [μA]\n",
              "timestamp                                                       \n",
              "2021-12-12 01:48:00-08:00  1414.328805  4289.665667  3254.752833\n",
              "2021-12-12 01:51:00-08:00  1327.178421  4288.728385  3020.776846\n",
              "2021-12-12 01:54:00-08:00  1174.357756  4311.537000  2742.442615\n",
              "2021-12-12 01:57:00-08:00   689.089853  4343.296846  1612.600923\n",
              "2021-12-12 02:00:00-08:00   938.844377  4443.161231  2110.218308\n",
              "...                                ...          ...          ...\n",
              "2022-01-04 00:18:00-08:00   437.323979  1802.143143  2334.578714\n",
              "2022-01-04 00:21:00-08:00   387.250253  1727.627846  2257.236385\n",
              "2022-01-04 00:24:00-08:00   310.925951  1788.472769  1640.332385\n",
              "2022-01-04 00:27:00-08:00   613.508125  1775.438154  3414.614385\n",
              "2022-01-04 00:30:00-08:00   498.995127  1827.687500  2600.963500\n",
              "\n",
              "[11015 rows x 3 columns]"
            ]
          },
          "execution_count": 78,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_valid"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "yfZO7__aneW0",
        "outputId": "6c2ba8ff-ca9f-441d-da0b-31247a097c8f"
      },
      "outputs": [
        {
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              "\n",
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            ],
            "text/plain": [
              "                                 power      V1 [mV]     I1L [μA]\n",
              "timestamp                                                       \n",
              "2021-12-12 01:00:00-08:00  1110.313583  4311.206733  2564.759600\n",
              "2021-12-12 02:00:00-08:00  1300.544442  4627.458561  2836.013973\n",
              "2021-12-12 03:00:00-08:00  1252.099795  4729.874121  2649.654299\n",
              "2021-12-12 04:00:00-08:00  1292.220860  4544.956065  2849.791582\n",
              "2021-12-12 05:00:00-08:00  1242.752035  4392.519322  2855.785962\n",
              "...                                ...          ...          ...\n",
              "2022-01-03 20:00:00-08:00   322.122927  2027.706947  1588.639468\n",
              "2022-01-03 21:00:00-08:00   367.964290  2024.127371  1832.313019\n",
              "2022-01-03 22:00:00-08:00   384.467106  1967.737174  1973.739727\n",
              "2022-01-03 23:00:00-08:00   351.081527  1870.863342  1873.352369\n",
              "2022-01-04 00:00:00-08:00   397.646956  1770.018188  2241.944761\n",
              "\n",
              "[552 rows x 3 columns]"
            ]
          },
          "execution_count": 306,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_valid"
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    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "height": 455
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        "id": "hTP4CcQ2bzNo",
        "outputId": "5f5be61d-80fc-419c-bd9e-a4c1cec334a9"
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            "text/plain": [
              "                                  power       V1 [mV]     I1L [μA]\n",
              "timestamp                                                         \n",
              "2021-06-04 00:00:00-07:00  28584.912829  30978.015100  9227.531300\n",
              "2021-06-04 00:03:00-07:00  28593.839842  30979.656923  9229.984538\n",
              "2021-06-04 00:06:00-07:00  28849.177683  30977.880000  9312.927308\n",
              "2021-06-04 00:09:00-07:00  28619.841255  30982.847929  9237.481500\n",
              "2021-06-04 00:12:00-07:00  29504.094723  30977.568000  9524.382000\n",
              "...                                 ...           ...          ...\n",
              "2021-12-12 01:36:00-08:00    977.135332   3808.944923  2579.045846\n",
              "2021-12-12 01:39:00-08:00   1073.137208   3906.020846  2775.596692\n",
              "2021-12-12 01:42:00-08:00   1014.992151   3975.996692  2586.309846\n",
              "2021-12-12 01:45:00-08:00    789.875499   4091.504615  1960.270769\n",
              "2021-12-12 01:48:00-08:00   1007.300235   4164.122500  2425.937500\n",
              "\n",
              "[91737 rows x 3 columns]"
            ]
          },
          "execution_count": 77,
          "metadata": {},
          "output_type": "execute_result"
        }
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      "source": [
        "y_train"
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "height": 455
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        "id": "Ai2YOHI9nmQ5",
        "outputId": "3aa93928-8993-4c3c-b7d7-0e25dccc01a8"
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      "outputs": [
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            "text/plain": [
              "                                  power       V1 [mV]     I1L [μA]\n",
              "timestamp                                                         \n",
              "2021-06-04 00:00:00-07:00  28922.968850  30980.772868  9335.917736\n",
              "2021-06-04 00:30:00-07:00  28986.729424  30988.424229  9354.177374\n",
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              "2021-06-04 01:30:00-07:00  29072.216304  31005.005909  9376.743492\n",
              "2021-06-04 02:00:00-07:00  28935.536294  31016.404030  9329.363417\n",
              "...                                 ...           ...          ...\n",
              "2021-12-11 23:30:00-08:00    561.990648   2146.349098  2636.613500\n",
              "2021-12-12 00:00:00-08:00    593.759197   2287.079250  2608.579364\n",
              "2021-12-12 00:30:00-08:00    666.938247   2648.440908  2548.716076\n",
              "2021-12-12 01:00:00-08:00    725.182712   2955.859864  2501.595386\n",
              "2021-12-12 01:30:00-08:00    910.593167   3858.806782  2380.672828\n",
              "\n",
              "[9174 rows x 3 columns]"
            ]
          },
          "execution_count": 232,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "yNLub0m0iv3R",
        "outputId": "ed7d9bc4-47a9-4af6-a03f-523a38e1ceb1"
      },
      "outputs": [
        {
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            "text/plain": [
              "                                  I1L_valid  I2L_valid  I1H [nA]  \\\n",
              "timestamp                                                          \n",
              "2022-01-04 00:31:25.300000-08:00        1.0        1.0  0.000120   \n",
              "2022-01-04 00:31:26.300000-08:00        1.0        1.0  0.000049   \n",
              "2022-01-04 00:31:27.300000-08:00        1.0        1.0  0.000034   \n",
              "2022-01-04 00:31:28.300000-08:00        1.0        1.0  0.000056   \n",
              "2022-01-04 00:31:29.300000-08:00        1.0        1.0  0.000070   \n",
              "...                                     ...        ...       ...   \n",
              "2022-01-26 23:59:42.400000-08:00        1.0        1.0  0.000007   \n",
              "2022-01-26 23:59:43.400000-08:00        1.0        1.0  0.000064   \n",
              "2022-01-26 23:59:44.400000-08:00        1.0        1.0  0.000096   \n",
              "2022-01-26 23:59:45.400000-08:00        1.0        1.0  0.000067   \n",
              "2022-01-26 23:59:46.400000-08:00        1.0        1.0  0.000062   \n",
              "\n",
              "                                    I1L [10pA]  V1 [10nV]  V2 [10nV]  \\\n",
              "timestamp                                                              \n",
              "2022-01-04 00:31:25.300000-08:00  5.504445e-05   0.019359 -3425303.0   \n",
              "2022-01-04 00:31:26.300000-08:00  1.531339e-05   0.016187 -3097399.0   \n",
              "2022-01-04 00:31:27.300000-08:00  3.158336e-05   0.016241 -2979040.0   \n",
              "2022-01-04 00:31:28.300000-08:00  8.161530e-06   0.016918 -3072919.0   \n",
              "2022-01-04 00:31:29.300000-08:00  4.198890e-06   0.017424 -3150030.0   \n",
              "...                                        ...        ...        ...   \n",
              "2022-01-26 23:59:42.400000-08:00  5.879503e-05   0.006467 -3190176.0   \n",
              "2022-01-26 23:59:43.400000-08:00  2.781560e-06   0.008516 -3507187.0   \n",
              "2022-01-26 23:59:44.400000-08:00  2.845776e-05   0.009704 -3698127.0   \n",
              "2022-01-26 23:59:45.400000-08:00  9.667300e-07   0.008702 -3544396.0   \n",
              "2022-01-26 23:59:46.400000-08:00  5.334230e-06   0.008517 -3516244.0   \n",
              "\n",
              "                                  I2H [nA]  I2L [10pA]        power  \n",
              "timestamp                                                            \n",
              "2022-01-04 00:31:25.300000-08:00   58726.0  -5724244.0  1065.585692  \n",
              "2022-01-04 00:31:26.300000-08:00  140452.0   2394883.0   247.879069  \n",
              "2022-01-04 00:31:27.300000-08:00  172682.0   5277364.0   512.950087  \n",
              "2022-01-04 00:31:28.300000-08:00  142027.0   2522111.0   138.078560  \n",
              "2022-01-04 00:31:29.300000-08:00  127345.0    861362.0    73.163517  \n",
              "...                                    ...         ...          ...  \n",
              "2022-01-26 23:59:42.400000-08:00  191806.0   7617224.0   380.239806  \n",
              "2022-01-26 23:59:43.400000-08:00  122115.0    466108.0    23.686736  \n",
              "2022-01-26 23:59:44.400000-08:00   78291.0  -3679362.0   276.150973  \n",
              "2022-01-26 23:59:45.400000-08:00  118587.0     85215.0     8.412881  \n",
              "2022-01-26 23:59:46.400000-08:00  123344.0    590551.0    45.430890  \n",
              "\n",
              "[1978027 rows x 9 columns]"
            ]
          },
          "execution_count": 305,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_test_fine"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "eeBNHen6nsPX",
        "outputId": "54f2c968-79d2-4424-867d-25e0875f8f15"
      },
      "outputs": [
        {
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              "\n",
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            "text/plain": [
              "                                  I1L_valid  I2L_valid  I1H [nA]  \\\n",
              "timestamp                                                          \n",
              "2022-01-04 00:31:25.300000-08:00        1.0        1.0  0.000120   \n",
              "2022-01-04 00:31:26.300000-08:00        1.0        1.0  0.000049   \n",
              "2022-01-04 00:31:27.300000-08:00        1.0        1.0  0.000034   \n",
              "2022-01-04 00:31:28.300000-08:00        1.0        1.0  0.000056   \n",
              "2022-01-04 00:31:29.300000-08:00        1.0        1.0  0.000070   \n",
              "...                                     ...        ...       ...   \n",
              "2022-01-26 23:59:42.400000-08:00        1.0        1.0  0.000007   \n",
              "2022-01-26 23:59:43.400000-08:00        1.0        1.0  0.000064   \n",
              "2022-01-26 23:59:44.400000-08:00        1.0        1.0  0.000096   \n",
              "2022-01-26 23:59:45.400000-08:00        1.0        1.0  0.000067   \n",
              "2022-01-26 23:59:46.400000-08:00        1.0        1.0  0.000062   \n",
              "\n",
              "                                    I1L [10pA]  V1 [10nV]  V2 [10nV]  \\\n",
              "timestamp                                                              \n",
              "2022-01-04 00:31:25.300000-08:00  5.504445e-05   0.019359 -3425303.0   \n",
              "2022-01-04 00:31:26.300000-08:00  1.531339e-05   0.016187 -3097399.0   \n",
              "2022-01-04 00:31:27.300000-08:00  3.158336e-05   0.016241 -2979040.0   \n",
              "2022-01-04 00:31:28.300000-08:00  8.161530e-06   0.016918 -3072919.0   \n",
              "2022-01-04 00:31:29.300000-08:00  4.198890e-06   0.017424 -3150030.0   \n",
              "...                                        ...        ...        ...   \n",
              "2022-01-26 23:59:42.400000-08:00  5.879503e-05   0.006467 -3190176.0   \n",
              "2022-01-26 23:59:43.400000-08:00  2.781560e-06   0.008516 -3507187.0   \n",
              "2022-01-26 23:59:44.400000-08:00  2.845776e-05   0.009704 -3698127.0   \n",
              "2022-01-26 23:59:45.400000-08:00  9.667300e-07   0.008702 -3544396.0   \n",
              "2022-01-26 23:59:46.400000-08:00  5.334230e-06   0.008517 -3516244.0   \n",
              "\n",
              "                                  I2H [nA]  I2L [10pA]        power  \n",
              "timestamp                                                            \n",
              "2022-01-04 00:31:25.300000-08:00   58726.0  -5724244.0  1065.585692  \n",
              "2022-01-04 00:31:26.300000-08:00  140452.0   2394883.0   247.879069  \n",
              "2022-01-04 00:31:27.300000-08:00  172682.0   5277364.0   512.950087  \n",
              "2022-01-04 00:31:28.300000-08:00  142027.0   2522111.0   138.078560  \n",
              "2022-01-04 00:31:29.300000-08:00  127345.0    861362.0    73.163517  \n",
              "...                                    ...         ...          ...  \n",
              "2022-01-26 23:59:42.400000-08:00  191806.0   7617224.0   380.239806  \n",
              "2022-01-26 23:59:43.400000-08:00  122115.0    466108.0    23.686736  \n",
              "2022-01-26 23:59:44.400000-08:00   78291.0  -3679362.0   276.150973  \n",
              "2022-01-26 23:59:45.400000-08:00  118587.0     85215.0     8.412881  \n",
              "2022-01-26 23:59:46.400000-08:00  123344.0    590551.0    45.430890  \n",
              "\n",
              "[1978027 rows x 9 columns]"
            ]
          },
          "execution_count": 231,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_test_fine"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nkxREnv8mxIG",
        "outputId": "8862a084-0268-4d74-a06e-a0ed33c5a164"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "6023\n"
          ]
        }
      ],
      "source": [
        "max_act = oracle_simulate(v_test, C0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_8FYi1cQipbQ",
        "outputId": "82c3ed85-a516-46f3-f635-4328773c5f24"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.04833582787777644\n"
          ]
        }
      ],
      "source": [
        "v_valid = df.loc[(((df.index >= valid_bound_lower)) & (df.index < valid_bound_upper))]['V1 [mV]']/1E5\n",
        "#v_valid = y.loc[(((y.timestamp >= '2021-06-04 00:12:00')) & (y.timestamp <= '2021-12-12 07:00:00'))].set_index('timestamp')['V1 [10nV]']\n",
        "pred_act_naive, false_act_naive, succ_act_naive, total_E = naive_simulate(v_avg_true, v_valid, v_test, C0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sEE2sfGmWVac",
        "outputId": "0b7e52e3-04b1-4c6d-a7b3-ea5e091b624c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Minimal Application\n",
            "Maximum possible activations: 6023\n",
            "Predicted activations: 7753\n",
            "Successful activations: 63, 0.813%\n",
            "Failed activations: 7677, 99.020%\n",
            "Missed activations: 5960, 98.954%\n",
            "Naive predicted activations (usual actual energy average): 12600\n",
            "Naive successful activations (usual actual energy average): 179, 1.421%\n",
            "Naive failed activations (usual actual energy average): 12421, 98.579%\n",
            "Naive missed activations (usual actual energy average): 5844, 97.028%\n",
            "Voltage overestimation rate: 100.000%\n",
            "Test MAPE power: 0.321821\n",
            "Test MAPE voltage: 0.175783\n",
            "Test MAPE current: 0.124365\n"
          ]
        }
      ],
      "source": [
        "print('Minimal Application')\n",
        "#print('Predicted vs. Actual percent difference: %.3f%%' % ((total_E * 100 / total_E_pred) - 100))\n",
        "print('Maximum possible activations:', max_act)\n",
        "print('Predicted activations:', pred_act)\n",
        "print('Successful activations: %d, %.3f%%' % (succ_act, succ_act * 100/pred_act))\n",
        "print('Failed activations: %d, %.3f%%' % (false_act, false_act * 100/pred_act))\n",
        "print('Missed activations: %d, %.3f%%' % (max_act - succ_act, (max_act - succ_act) * 100/max_act))\n",
        "\n",
        "#Naive model\n",
        "print('Naive predicted activations (usual actual energy average):', pred_act_naive)\n",
        "print('Naive successful activations (usual actual energy average): %d, %.3f%%' % (succ_act_naive, succ_act_naive * 100/pred_act_naive))\n",
        "print('Naive failed activations (usual actual energy average): %d, %.3f%%' % (false_act_naive, false_act_naive * 100/pred_act_naive))\n",
        "print('Naive missed activations (usual actual energy average): %d, %.3f%%' % (max_act - succ_act_naive, (max_act - succ_act_naive) * 100/max_act))\n",
        "\n",
        "\n",
        "print('Voltage overestimation rate: %.3f%%' % ((y_test['V1 [mV]'].values <= y_test['V1 [mV] pred']).mean() * 100))\n",
        "print(\"Test MAPE power: %3f\" %  MAPE(y_test['power'].values.ravel(), y_test['power pred']))\n",
        "print(\"Test MAPE voltage: %3f\" % MAPE(y_test['V1 [mV]'], y_test['V1 [mV] pred']))\n",
        "print(\"Test MAPE current: %3f\" % MAPE(y_test['I1L [μA]'], y_test['I1L [μA] pred']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0xSRT8tMg-yX",
        "outputId": "b7ebf3b2-27d2-402b-bb0b-7fa60d046a4a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "33.3463687150838"
            ]
          },
          "execution_count": 277,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "succ_act/succ_act_naive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sq_q3yCC-xeu",
        "outputId": "cf7f61b8-884a-4076-b81f-31d171f25d85"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predicted vs. Actual Total Energy Percent Difference: 35.316%\n",
            "Test MAPE power:\n",
            " 0.3218212644314396\n",
            "Predicted vs. Actual Total Voltage Percent Difference: 16.417%\n",
            "Test MAPE voltage:\n",
            " 0.17578258388898876\n"
          ]
        }
      ],
      "source": [
        "y_test_fine = y.loc[(y.timestamp.dt.tz_localize('UTC').dt.tz_convert('US/Pacific') >= test_bound_lower) & (y.timestamp.dt.tz_localize('UTC').dt.tz_convert('US/Pacific') <= test_bound_upper)].set_index('timestamp')\n",
        "y_test_fine['power'] = np.abs(np.multiply(y_test_fine.iloc[:, 3], y_test_fine.iloc[:, 4])) * 1E9\n",
        "y_test_fine.index = y_test_fine.index.tz_localize('UTC').tz_convert('US/Pacific')\n",
        "E_actual = 0\n",
        "for i in range(len(y_test_fine) - 1):\n",
        "  t = (y_test_fine.index[i+1] - y_test_fine.index[i]).total_seconds()\n",
        "  E_actual += y_test_fine['power'][i] * t\n",
        "\n",
        "E_pred = 0\n",
        "for i in range(len(y_test) - 1):\n",
        "  t = (y_test.index[i+1] - y_test.index[i]).total_seconds()\n",
        "  E_pred += y_test['power pred'][i] * t\n",
        "\n",
        "print('Predicted vs. Actual Total Energy Percent Difference: %.3f%%' % ((E_pred - E_actual) * 100 / E_actual))\n",
        "print(\"Test MAPE power:\\n\", MAPE(y_test['power'].values.ravel(), y_test['power pred'].values.ravel()))\n",
        "\n",
        "V_actual = y_test['V1 [mV]'].mean()\n",
        "V_pred = y_test['V1 [mV] pred'].mean()\n",
        "print('Predicted vs. Actual Total Voltage Percent Difference: %.3f%%' % ((V_pred - V_actual) * 100 / V_actual))\n",
        "print(\"Test MAPE voltage:\\n\", MAPE(y_test['V1 [mV]'].values.ravel(), y_test['V1 [mV] pred'].values.ravel()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "GFdnhQ2o7VUe",
        "outputId": "ed88464e-6200-4bc1-efb4-1f987fa64709"
      },
      "outputs": [
        {
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              "      <th>power</th>\n",
              "      <th>V1 [mV]</th>\n",
              "      <th>I1L [μA]</th>\n",
              "      <th>power pred</th>\n",
              "      <th>V1 [mV] pred</th>\n",
              "      <th>I1L [μA] pred</th>\n",
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              "      <th>timestamp</th>\n",
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              "    <tr>\n",
              "      <th>2022-01-04 01:15:00-08:00</th>\n",
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              "      <td>1385.678955</td>\n",
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              "    <tr>\n",
              "      <th>2022-01-04 01:18:00-08:00</th>\n",
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              "      <td>210.021912</td>\n",
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              "    <tr>\n",
              "      <th>2022-01-04 01:21:00-08:00</th>\n",
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              "      <th>2022-01-04 01:24:00-08:00</th>\n",
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              "      <th>2022-01-04 01:27:00-08:00</th>\n",
              "      <td>239.763317</td>\n",
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              "      <td>1522.050462</td>\n",
              "      <td>164.683945</td>\n",
              "      <td>1532.057617</td>\n",
              "      <td>1115.846802</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2022-01-26 23:45:00-08:00</th>\n",
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              "      <th>2022-01-26 23:51:00-08:00</th>\n",
              "      <td>189.330058</td>\n",
              "      <td>872.273692</td>\n",
              "      <td>2170.535000</td>\n",
              "      <td>112.064743</td>\n",
              "      <td>815.215576</td>\n",
              "      <td>1204.301392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-26 23:54:00-08:00</th>\n",
              "      <td>178.768529</td>\n",
              "      <td>854.210154</td>\n",
              "      <td>2092.793308</td>\n",
              "      <td>113.209625</td>\n",
              "      <td>809.253662</td>\n",
              "      <td>1216.111694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-26 23:57:00-08:00</th>\n",
              "      <td>541.523919</td>\n",
              "      <td>992.999231</td>\n",
              "      <td>5453.417308</td>\n",
              "      <td>115.885208</td>\n",
              "      <td>800.858826</td>\n",
              "      <td>1252.247437</td>\n",
              "    </tr>\n",
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              "\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "                                power      V1 [mV]     I1L [μA]  power pred  \\\n",
              "timestamp                                                                     \n",
              "2022-01-04 01:15:00-08:00  335.756845  1576.995077  2129.092538  215.395981   \n",
              "2022-01-04 01:18:00-08:00  291.567232  1645.108692  1772.328077  210.021912   \n",
              "2022-01-04 01:21:00-08:00  229.125950  1637.842643  1398.949714  202.120728   \n",
              "2022-01-04 01:24:00-08:00  161.554283  1602.251538  1008.295385  197.954758   \n",
              "2022-01-04 01:27:00-08:00  239.763317  1575.265231  1522.050462  164.683945   \n",
              "...                               ...          ...          ...         ...   \n",
              "2022-01-26 23:45:00-08:00  154.006651   892.816286  1724.953429  106.010529   \n",
              "2022-01-26 23:48:00-08:00  169.263540   805.634462  2100.996769  107.535858   \n",
              "2022-01-26 23:51:00-08:00  189.330058   872.273692  2170.535000  112.064743   \n",
              "2022-01-26 23:54:00-08:00  178.768529   854.210154  2092.793308  113.209625   \n",
              "2022-01-26 23:57:00-08:00  541.523919   992.999231  5453.417308  115.885208   \n",
              "\n",
              "                           V1 [mV] pred  I1L [μA] pred  \n",
              "timestamp                                               \n",
              "2022-01-04 01:15:00-08:00   1545.130371    1385.678955  \n",
              "2022-01-04 01:18:00-08:00   1519.483643    1377.470337  \n",
              "2022-01-04 01:21:00-08:00   1518.516113    1319.136108  \n",
              "2022-01-04 01:24:00-08:00   1520.321777    1291.011108  \n",
              "2022-01-04 01:27:00-08:00   1532.057617    1115.846802  \n",
              "...                                 ...            ...  \n",
              "2022-01-26 23:45:00-08:00    833.119202    1123.842407  \n",
              "2022-01-26 23:48:00-08:00    836.108093    1141.277954  \n",
              "2022-01-26 23:51:00-08:00    815.215576    1204.301392  \n",
              "2022-01-26 23:54:00-08:00    809.253662    1216.111694  \n",
              "2022-01-26 23:57:00-08:00    800.858826    1252.247437  \n",
              "\n",
              "[11015 rows x 6 columns]"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "SwiHiBfWcmd_",
        "outputId": "30932b81-32cf-4275-8b87-654989b85d83"
      },
      "outputs": [
        {
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              "      <td>0.009440</td>\n",
              "      <td>-3658593.0</td>\n",
              "      <td>95588.0</td>\n",
              "      <td>-2428770.0</td>\n",
              "      <td>173.385200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-26 23:59:55.400000-08:00</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000063</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>0.008559</td>\n",
              "      <td>-3519182.0</td>\n",
              "      <td>121359.0</td>\n",
              "      <td>512323.0</td>\n",
              "      <td>40.958741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-26 23:59:56.400000-08:00</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000011</td>\n",
              "      <td>0.000056</td>\n",
              "      <td>0.006535</td>\n",
              "      <td>-3201070.0</td>\n",
              "      <td>191333.0</td>\n",
              "      <td>7312894.0</td>\n",
              "      <td>368.949319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-26 23:59:57.400000-08:00</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000066</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.008544</td>\n",
              "      <td>-3509879.0</td>\n",
              "      <td>121202.0</td>\n",
              "      <td>418370.0</td>\n",
              "      <td>18.522919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-26 23:59:58.400000-08:00</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000106</td>\n",
              "      <td>0.000038</td>\n",
              "      <td>0.010049</td>\n",
              "      <td>-3754186.0</td>\n",
              "      <td>68587.0</td>\n",
              "      <td>-4894369.0</td>\n",
              "      <td>379.546249</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1969155 rows × 9 columns</p>\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      }\n",
              "\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('y_valid')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('y_valid');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                  I1L_valid  I2L_valid  I1H [nA]  I1L [10pA]  \\\n",
              "timestamp                                                                      \n",
              "2022-01-04 03:00:00.600000-08:00        1.0        1.0  0.000023    0.000042   \n",
              "2022-01-04 03:00:01.600000-08:00        1.0        1.0  0.000059    0.000007   \n",
              "2022-01-04 03:00:02.600000-08:00        1.0        1.0  0.000070    0.000005   \n",
              "2022-01-04 03:00:03.700000-08:00        1.0        1.0  0.000092    0.000027   \n",
              "2022-01-04 03:00:04.700000-08:00        1.0        1.0  0.000067    0.000002   \n",
              "...                                     ...        ...       ...         ...   \n",
              "2022-01-26 23:59:54.400000-08:00        1.0        1.0  0.000085    0.000018   \n",
              "2022-01-26 23:59:55.400000-08:00        1.0        1.0  0.000063    0.000005   \n",
              "2022-01-26 23:59:56.400000-08:00        1.0        1.0  0.000011    0.000056   \n",
              "2022-01-26 23:59:57.400000-08:00        1.0        1.0  0.000066    0.000002   \n",
              "2022-01-26 23:59:58.400000-08:00        1.0        1.0  0.000106    0.000038   \n",
              "\n",
              "                                  V1 [10nV]  V2 [10nV]  I2H [nA]  I2L [10pA]  \\\n",
              "timestamp                                                                      \n",
              "2022-01-04 03:00:00.600000-08:00   0.013406 -2812824.0  186261.0   6786788.0   \n",
              "2022-01-04 03:00:01.600000-08:00   0.014486 -2975491.0  141145.0   2463462.0   \n",
              "2022-01-04 03:00:02.600000-08:00   0.015000 -3046359.0  126652.0    943232.0   \n",
              "2022-01-04 03:00:03.700000-08:00   0.015984 -3201192.0   97163.0  -2217798.0   \n",
              "2022-01-04 03:00:04.700000-08:00   0.014866 -3035710.0  136198.0   1489898.0   \n",
              "...                                     ...        ...       ...         ...   \n",
              "2022-01-26 23:59:54.400000-08:00   0.009440 -3658593.0   95588.0  -2428770.0   \n",
              "2022-01-26 23:59:55.400000-08:00   0.008559 -3519182.0  121359.0    512323.0   \n",
              "2022-01-26 23:59:56.400000-08:00   0.006535 -3201070.0  191333.0   7312894.0   \n",
              "2022-01-26 23:59:57.400000-08:00   0.008544 -3509879.0  121202.0    418370.0   \n",
              "2022-01-26 23:59:58.400000-08:00   0.010049 -3754186.0   68587.0  -4894369.0   \n",
              "\n",
              "                                       power  \n",
              "timestamp                                     \n",
              "2022-01-04 03:00:00.600000-08:00  564.015490  \n",
              "2022-01-04 03:00:01.600000-08:00  101.279128  \n",
              "2022-01-04 03:00:02.600000-08:00   67.860226  \n",
              "2022-01-04 03:00:03.700000-08:00  428.043414  \n",
              "2022-01-04 03:00:04.700000-08:00   30.249115  \n",
              "...                                      ...  \n",
              "2022-01-26 23:59:54.400000-08:00  173.385200  \n",
              "2022-01-26 23:59:55.400000-08:00   40.958741  \n",
              "2022-01-26 23:59:56.400000-08:00  368.949319  \n",
              "2022-01-26 23:59:57.400000-08:00   18.522919  \n",
              "2022-01-26 23:59:58.400000-08:00  379.546249  \n",
              "\n",
              "[1969155 rows x 9 columns]"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_valid"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c8ErV5URFCuO",
        "outputId": "2f277322-a325-4733-8bee-3b96a726a3a9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Minimal Application\n",
            "Maximum possible activations: 14525\n",
            "Predicted activations: 15094\n",
            "Successful activations: 14295, 94.707%\n",
            "Failed activations: 654, 4.333%\n",
            "Missed activations: 230, 1.583%\n",
            "Voltage overestimation rate: 21.429%\n",
            "Test MAPE power: 0.397089\n",
            "Test MAPE voltage: 0.192598\n",
            "Test MAPE current: 0.084680\n"
          ]
        }
      ],
      "source": [
        "print('Minimal Application')\n",
        "print('Maximum possible activations:', max_act)\n",
        "#print('Predicted vs. Actual percent difference: %.3f%%' % ((total_E * 100 / total_E_pred) - 100))\n",
        "print('Predicted activations:', pred_act)\n",
        "print('Successful activations: %d, %.3f%%' % (succ_act, succ_act * 100/pred_act))\n",
        "print('Failed activations: %d, %.3f%%' % (false_act, false_act * 100/pred_act))\n",
        "print('Missed activations: %d, %.3f%%' % (max_act - succ_act, (max_act - succ_act) * 100/max_act))\n",
        "\n",
        "#Naive model\n",
        "#print('Naive predicted activations (usual actual energy average):', pred_act_naive)\n",
        "#print('Naive successful activations (usual actual energy average): %d, %.3f%%' % (succ_act_naive, succ_act_naive * 100/pred_act_naive))\n",
        "#print('Naive failed activations (usual actual energy average): %d, %.3f%%' % (false_act_naive, false_act_naive * 100/pred_act_naive))\n",
        "#print('Naive missed activations (usual actual energy average): %d, %.3f%%' % (max_act - succ_act_naive, (max_act - succ_act_naive) * 100/max_act))\n",
        "\n",
        "\n",
        "print('Voltage overestimation rate: %.3f%%' % ((y_test['V1 [mV]'].values <= y_test['V1 [mV] pred']).mean() * 100))\n",
        "print(\"Test MAPE power: %3f\" %  MAPE(y_test['power'].values.ravel(), y_test['power pred']))\n",
        "print(\"Test MAPE voltage: %3f\" % MAPE(y_test['V1 [mV]'], y_test['V1 [mV] pred']))\n",
        "print(\"Test MAPE current: %3f\" % MAPE(y_test['I1L [μA]'], y_test['I1L [μA] pred']))\n",
        "\n",
        "E_actual = 0\n",
        "for i in range(len(y_valid) - 1):\n",
        "  t = (y_valid.index[i+1] - y_valid.index[i]).total_seconds()\n",
        "  E_actual += y_valid['power'][i] * t\n",
        "\n",
        "E_pred = 0\n",
        "for i in range(len(y_test) - 1):\n",
        "  t = (y_test.index[i+1] - y_test.index[i]).total_seconds()\n",
        "  E_pred += y_test['power pred'][i] * t\n",
        "\n",
        "print('Predicted vs. Actual Total Energy Percent Difference: %.3f%%' % ((E_pred - E_actual) * 100 / E_actual))\n",
        "print(\"Test MAPE power:\\n\", MAPE(y_test['power'].values.ravel(), y_test['power pred'].values.ravel()))\n",
        "\n",
        "V_actual = y_test['V1 [mV]'].mean()\n",
        "V_pred = y_test['V1 [mV] pred'].mean()\n",
        "print('Predicted vs. Actual Total Voltage Percent Difference: %.3f%%' % ((V_pred - V_actual) * 100 / V_actual))\n",
        "print(\"Test MAPE voltage:\\n\", MAPE(y_test['V1 [mV]'].values.ravel(), y_test['V1 [mV] pred'].values.ravel()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "IATnXZdnzQUZ",
        "outputId": "4ee8d455-ec86-4321-b3d7-a19d307bfc2c"
      },
      "outputs": [
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              "    async function quickchart(key) {\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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            ],
            "text/plain": [
              "                               timestamp  I1L_valid  I2L_valid  I1H [nA]  \\\n",
              "196268         2021-12-12 07:00:00-08:00        1.0        1.0  0.000061   \n",
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              "196272         2021-12-12 07:00:04-08:00        1.0        1.0  0.000043   \n",
              "...                                  ...        ...        ...       ...   \n",
              "3539322 2022-01-26 23:59:54.400000-08:00        1.0        1.0  0.000085   \n",
              "3539323 2022-01-26 23:59:55.400000-08:00        1.0        1.0  0.000063   \n",
              "3539324 2022-01-26 23:59:56.400000-08:00        1.0        1.0  0.000011   \n",
              "3539325 2022-01-26 23:59:57.400000-08:00        1.0        1.0  0.000066   \n",
              "3539326 2022-01-26 23:59:58.400000-08:00        1.0        1.0  0.000106   \n",
              "\n",
              "         I1L [10pA]  V1 [10nV]  V2 [10nV]  I2H [nA]  I2L [10pA]  \n",
              "196268     0.000005   0.041883 -6778880.0  133394.0   1649628.0  \n",
              "196269     0.000035   0.043325 -6993566.0   79646.0  -3589121.0  \n",
              "196270     0.000013   0.041720 -6785368.0  140830.0   2457455.0  \n",
              "196271     0.000058   0.040247 -6566398.0  198989.0   8161928.0  \n",
              "196272     0.000024   0.041192 -6695528.0  159828.0   4169637.0  \n",
              "...             ...        ...        ...       ...         ...  \n",
              "3539322    0.000018   0.009440 -3658593.0   95588.0  -2428770.0  \n",
              "3539323    0.000005   0.008559 -3519182.0  121359.0    512323.0  \n",
              "3539324    0.000056   0.006535 -3201070.0  191333.0   7312894.0  \n",
              "3539325    0.000002   0.008544 -3509879.0  121202.0    418370.0  \n",
              "3539326    0.000038   0.010049 -3754186.0   68587.0  -4894369.0  \n",
              "\n",
              "[3936643 rows x 9 columns]"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_valid"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CeS3pOWbzNu0"
      },
      "outputs": [],
      "source": [
        "y.loc[(((y.timestamp >= '2021-10-27 11:00:00')) & (y.timestamp <= '2021-12-12 07:00:00'))].set_index('timestamp')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QQS490snzw8K"
      },
      "outputs": [],
      "source": [
        "for i in range(len(y_valid) - 1):\n",
        "  t = (y_valid.index[i+1] - y_valid.index[i]).total_seconds()\n",
        "  E_actual += y_test['power'][i] * t\n",
        "  E_pred += y_test['power pred'][i] * t\n",
        "\n",
        "print(E_actual)\n",
        "print(E_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3g1xdwCn12ni",
        "outputId": "49a2c716-d084-4c52-90b3-1aa127748daf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "timestamp\n",
              "2021-12-12 07:00:00-08:00           0.041883\n",
              "2021-12-12 07:00:01-08:00           0.043325\n",
              "2021-12-12 07:00:02-08:00           0.041720\n",
              "2021-12-12 07:00:03-08:00           0.040247\n",
              "2021-12-12 07:00:04-08:00           0.041192\n",
              "                                      ...   \n",
              "2022-01-26 23:59:54.400000-08:00    0.009440\n",
              "2022-01-26 23:59:55.400000-08:00    0.008559\n",
              "2022-01-26 23:59:56.400000-08:00    0.006535\n",
              "2022-01-26 23:59:57.400000-08:00    0.008544\n",
              "2022-01-26 23:59:58.400000-08:00    0.010049\n",
              "Name: V1 [10nV], Length: 3936643, dtype: float64"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_valid.iloc[:, 4]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hXKvElJB2Oif",
        "outputId": "7d2ce216-f147-4b87-8915-d4cf5de13ec5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "timestamp\n",
              "2021-12-12 07:00:00-08:00            192.545615\n",
              "2021-12-12 07:00:01-08:00           1531.514615\n",
              "2021-12-12 07:00:02-08:00            534.511108\n",
              "2021-12-12 07:00:03-08:00           2335.803141\n",
              "2021-12-12 07:00:04-08:00            991.808967\n",
              "                                       ...     \n",
              "2022-01-26 23:59:54.400000-08:00     173.385200\n",
              "2022-01-26 23:59:55.400000-08:00      40.958741\n",
              "2022-01-26 23:59:56.400000-08:00     368.949319\n",
              "2022-01-26 23:59:57.400000-08:00      18.522919\n",
              "2022-01-26 23:59:58.400000-08:00     379.546249\n",
              "Name: power, Length: 3936643, dtype: float64"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_valid['power']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HW68pdntxnxg",
        "outputId": "573d12ec-b8a5-4aa0-f091-8964908d9acb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predicted vs. Actual Total Energy Percent Difference: -9.901%\n",
            "1250151761.7231832\n",
            "1277751695.589875 1151244183.2244873\n",
            "1277183293.3527315 1150738791.5039062\n",
            "Predicted vs. Actual Total Energy Percent Difference: -7.952%\n",
            "Predicted vs. Actual Total Energy Percent Difference: -9.901%\n",
            "Predicted vs. Actual Total Energy Percent Difference: -9.900%\n",
            "Test MAPE power:\n",
            " 0.1124492428462554\n",
            "Predicted vs. Actual Total Voltage Percent Difference: -12.917%\n",
            "Test MAPE voltage:\n",
            " 0.12150683804099596\n"
          ]
        }
      ],
      "source": [
        "E_actual1 = 0\n",
        "for i in range(len(y_valid) - 1):\n",
        "  t = (y_valid.index[i+1] - y_valid.index[i]).total_seconds()\n",
        "  E_actual1 += y_valid['power'][i] * t\n",
        "\n",
        "E_pred1 = 0\n",
        "for i in range(len(y_test) - 1):\n",
        "  t = (y_test.index[i+1] - y_test.index[i]).total_seconds()\n",
        "  E_pred1 += y_test['power pred'][i] * t\n",
        "\n",
        "E_actual3 = 0\n",
        "E_pred2 = 0\n",
        "t = (y_test.index[-1] - y_test.index[0]).total_seconds()\n",
        "E_actual3 = y_test['power'].mean() * t\n",
        "E_pred2 = y_test['power pred'].mean() * t"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1L1vV3v35WjA",
        "outputId": "c65fc356-c1aa-4575-fdef-c152f7535252"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1250151761.7231832\n",
            "1277751695.589875 1151244183.2244873\n",
            "1277183293.3527315 1150738791.5039062\n",
            "Predicted vs. Actual Total Energy Percent Difference: -7.912%\n",
            "Predicted vs. Actual Total Energy Percent Difference: -9.901%\n",
            "Predicted vs. Actual Total Energy Percent Difference: -9.900%\n",
            "Test MAPE power:\n",
            " 0.1124492428462554\n",
            "Predicted vs. Actual Total Voltage Percent Difference: -12.917%\n",
            "Test MAPE voltage:\n",
            " 0.12150683804099596\n"
          ]
        }
      ],
      "source": [
        "print(E_actual1)\n",
        "print(E_actual2, E_pred1)\n",
        "print(E_actual3, E_pred2)\n",
        "print('Predicted vs. Actual Total Energy Percent Difference: %.3f%%' % ((E_pred1 - E_actual1) * 100 / E_actual1))\n",
        "print('Predicted vs. Actual Total Energy Percent Difference: %.3f%%' % ((E_pred1 - E_actual2) * 100 / E_actual2))\n",
        "print('Predicted vs. Actual Total Energy Percent Difference: %.3f%%' % ((E_pred2 - E_actual3) * 100 / E_actual))\n",
        "print(\"Test MAPE power:\\n\", MAPE(y_test['power'].values.ravel(), y_test['power pred'].values.ravel()))\n",
        "\n",
        "V_actual = y_test['V1 [mV]'].mean()\n",
        "V_pred = y_test['V1 [mV] pred'].mean()\n",
        "print('Predicted vs. Actual Total Voltage Percent Difference: %.3f%%' % ((V_pred - V_actual) * 100 / V_actual))\n",
        "print(\"Test MAPE voltage:\\n\", MAPE(y_test['V1 [mV]'].values.ravel(), y_test['V1 [mV] pred'].values.ravel()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FDjlvbhI2WVd"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "6wLtieQwib8Q",
        "outputId": "4ba2a889-58f5-4fdb-ab01-37a0831e300f"
      },
      "outputs": [
        {
          "data": {
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              "</style>\n",
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              "      <th></th>\n",
              "      <th>power</th>\n",
              "      <th>V1 [mV]</th>\n",
              "      <th>I1L [μA]</th>\n",
              "      <th>power pred</th>\n",
              "      <th>V1 [mV] pred</th>\n",
              "      <th>I1L [μA] pred</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>timestamp</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
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              "    <tr>\n",
              "      <th>2021-12-12 07:00:00-08:00</th>\n",
              "      <td>832.455732</td>\n",
              "      <td>4151.725769</td>\n",
              "      <td>2005.083615</td>\n",
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              "      <th>2021-12-12 07:03:00-08:00</th>\n",
              "      <td>1465.838329</td>\n",
              "      <td>4106.704077</td>\n",
              "      <td>3569.379000</td>\n",
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              "      <td>3985.485352</td>\n",
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              "    <tr>\n",
              "      <th>2021-12-12 07:06:00-08:00</th>\n",
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              "      <td>2023.972615</td>\n",
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              "    <tr>\n",
              "      <th>2021-12-12 07:09:00-08:00</th>\n",
              "      <td>1633.396942</td>\n",
              "      <td>4095.342308</td>\n",
              "      <td>3988.425923</td>\n",
              "      <td>623.106201</td>\n",
              "      <td>3957.334961</td>\n",
              "      <td>1738.401733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-12 07:12:00-08:00</th>\n",
              "      <td>821.786125</td>\n",
              "      <td>4125.783714</td>\n",
              "      <td>1991.830357</td>\n",
              "      <td>681.972229</td>\n",
              "      <td>3924.288574</td>\n",
              "      <td>1953.399048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2022-01-26 23:45:00-08:00</th>\n",
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              "      <th>2022-01-26 23:48:00-08:00</th>\n",
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              "      <th>2022-01-26 23:51:00-08:00</th>\n",
              "      <td>189.330058</td>\n",
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              "    <tr>\n",
              "      <th>2022-01-26 23:54:00-08:00</th>\n",
              "      <td>178.768529</td>\n",
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              "</table>\n",
              "<p>21940 rows × 6 columns</p>\n",
              "</div>\n",
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              "        const element = document.querySelector('#df-2162f49b-fab9-4e1f-a003-424debadb9d1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    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",
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              "\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
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              "    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-cbbaaa3c-99fc-47c7-8e59-340b67463ba0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_c3738405-1b07-4d97-81c3-b42f563ee3bb\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('y_test')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_c3738405-1b07-4d97-81c3-b42f563ee3bb button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('y_test');\n",
              "      }\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                 power      V1 [mV]     I1L [μA]  power pred  \\\n",
              "timestamp                                                                      \n",
              "2021-12-12 07:00:00-08:00   832.455732  4151.725769  2005.083615  687.079834   \n",
              "2021-12-12 07:03:00-08:00  1465.838329  4106.704077  3569.379000  639.677246   \n",
              "2021-12-12 07:06:00-08:00   834.110829  4121.156692  2023.972615  666.963989   \n",
              "2021-12-12 07:09:00-08:00  1633.396942  4095.342308  3988.425923  623.106201   \n",
              "2021-12-12 07:12:00-08:00   821.786125  4125.783714  1991.830357  681.972229   \n",
              "...                                ...          ...          ...         ...   \n",
              "2022-01-26 23:45:00-08:00   154.006651   892.816286  1724.953429  106.010529   \n",
              "2022-01-26 23:48:00-08:00   169.263540   805.634462  2100.996769  107.535858   \n",
              "2022-01-26 23:51:00-08:00   189.330058   872.273692  2170.535000  112.064743   \n",
              "2022-01-26 23:54:00-08:00   178.768529   854.210154  2092.793308  113.209625   \n",
              "2022-01-26 23:57:00-08:00   541.523919   992.999231  5453.417308  115.885208   \n",
              "\n",
              "                           V1 [mV] pred  I1L [μA] pred  \n",
              "timestamp                                               \n",
              "2021-12-12 07:00:00-08:00   3977.874023    1975.119995  \n",
              "2021-12-12 07:03:00-08:00   3985.485352    1786.538696  \n",
              "2021-12-12 07:06:00-08:00   3956.843750    1885.170288  \n",
              "2021-12-12 07:09:00-08:00   3957.334961    1738.401733  \n",
              "2021-12-12 07:12:00-08:00   3924.288574    1953.399048  \n",
              "...                                 ...            ...  \n",
              "2022-01-26 23:45:00-08:00    833.119202    1123.842407  \n",
              "2022-01-26 23:48:00-08:00    836.108093    1141.277954  \n",
              "2022-01-26 23:51:00-08:00    815.215576    1204.301392  \n",
              "2022-01-26 23:54:00-08:00    809.253662    1216.111694  \n",
              "2022-01-26 23:57:00-08:00    800.858826    1252.247437  \n",
              "\n",
              "[21940 rows x 6 columns]"
            ]
          },
          "execution_count": 95,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "4c1Xb8Svh_uU",
        "outputId": "c11e834f-b77a-4191-9fd3-02330471a580"
      },
      "outputs": [
        {
          "data": {
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              "      <th>power</th>\n",
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              "    <tr>\n",
              "      <th>2021-12-12 07:12:00-08:00</th>\n",
              "      <td>821.786125</td>\n",
              "      <td>4125.783714</td>\n",
              "      <td>1991.830357</td>\n",
              "      <td>681.972229</td>\n",
              "      <td>3924.288574</td>\n",
              "      <td>1953.399048</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "\n",
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              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "                                 power      V1 [mV]     I1L [μA]  power pred  \\\n",
              "timestamp                                                                      \n",
              "2021-12-12 07:00:00-08:00   832.455732  4151.725769  2005.083615  687.079834   \n",
              "2021-12-12 07:03:00-08:00  1465.838329  4106.704077  3569.379000  639.677246   \n",
              "2021-12-12 07:06:00-08:00   834.110829  4121.156692  2023.972615  666.963989   \n",
              "2021-12-12 07:09:00-08:00  1633.396942  4095.342308  3988.425923  623.106201   \n",
              "2021-12-12 07:12:00-08:00   821.786125  4125.783714  1991.830357  681.972229   \n",
              "...                                ...          ...          ...         ...   \n",
              "2022-01-26 23:45:00-08:00   154.006651   892.816286  1724.953429  106.010529   \n",
              "2022-01-26 23:48:00-08:00   169.263540   805.634462  2100.996769  107.535858   \n",
              "2022-01-26 23:51:00-08:00   189.330058   872.273692  2170.535000  112.064743   \n",
              "2022-01-26 23:54:00-08:00   178.768529   854.210154  2092.793308  113.209625   \n",
              "2022-01-26 23:57:00-08:00   541.523919   992.999231  5453.417308  115.885208   \n",
              "\n",
              "                           V1 [mV] pred  I1L [μA] pred  \n",
              "timestamp                                               \n",
              "2021-12-12 07:00:00-08:00   3977.874023    1975.119995  \n",
              "2021-12-12 07:03:00-08:00   3985.485352    1786.538696  \n",
              "2021-12-12 07:06:00-08:00   3956.843750    1885.170288  \n",
              "2021-12-12 07:09:00-08:00   3957.334961    1738.401733  \n",
              "2021-12-12 07:12:00-08:00   3924.288574    1953.399048  \n",
              "...                                 ...            ...  \n",
              "2022-01-26 23:45:00-08:00    833.119202    1123.842407  \n",
              "2022-01-26 23:48:00-08:00    836.108093    1141.277954  \n",
              "2022-01-26 23:51:00-08:00    815.215576    1204.301392  \n",
              "2022-01-26 23:54:00-08:00    809.253662    1216.111694  \n",
              "2022-01-26 23:57:00-08:00    800.858826    1252.247437  \n",
              "\n",
              "[21940 rows x 6 columns]"
            ]
          },
          "execution_count": 92,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-QYOHTHDIWQe",
        "outputId": "83df5603-ecb2-4490-a6aa-2245b158a802"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1277183293.3527315"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "E_actual"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qzjwbUI2IYHf",
        "outputId": "ec826320-fe19-485c-c95a-96b26bee05ff"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1426960532.8125"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "E_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "DfEzPx2nIPP1",
        "outputId": "c60096a3-45ac-4f78-e1ce-c44c8d4c00e7"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>power</th>\n",
              "      <th>V1 [mV]</th>\n",
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              "      <th>timestamp</th>\n",
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              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-12-12 07:00:00-08:00</th>\n",
              "      <td>1140.033430</td>\n",
              "      <td>4122.039578</td>\n",
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              "      <td>3594.256836</td>\n",
              "      <td>3097.407715</td>\n",
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              "    <tr>\n",
              "      <th>2021-12-12 08:00:00-08:00</th>\n",
              "      <td>1221.898969</td>\n",
              "      <td>4711.103114</td>\n",
              "      <td>2593.657875</td>\n",
              "      <td>1028.424072</td>\n",
              "      <td>3488.235596</td>\n",
              "      <td>2986.022217</td>\n",
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              "    <tr>\n",
              "      <th>2021-12-12 09:00:00-08:00</th>\n",
              "      <td>1197.535369</td>\n",
              "      <td>4850.168803</td>\n",
              "      <td>2469.059155</td>\n",
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              "    <tr>\n",
              "      <th>2021-12-12 10:00:00-08:00</th>\n",
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              "      <td>2704.230957</td>\n",
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              "    <tr>\n",
              "      <th>2021-12-12 11:00:00-08:00</th>\n",
              "      <td>1155.981317</td>\n",
              "      <td>4568.301205</td>\n",
              "      <td>2530.440235</td>\n",
              "      <td>1164.166016</td>\n",
              "      <td>4091.508301</td>\n",
              "      <td>2930.791504</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2022-01-26 19:00:00-08:00</th>\n",
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              "      <td>1605.716220</td>\n",
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              "    <tr>\n",
              "      <th>2022-01-26 20:00:00-08:00</th>\n",
              "      <td>145.801971</td>\n",
              "      <td>796.118125</td>\n",
              "      <td>1831.411269</td>\n",
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              "      <td>1868.164917</td>\n",
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              "    <tr>\n",
              "      <th>2022-01-26 21:00:00-08:00</th>\n",
              "      <td>167.509591</td>\n",
              "      <td>854.617288</td>\n",
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              "      <td>252.848724</td>\n",
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              "      <td>2053.287598</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-26 22:00:00-08:00</th>\n",
              "      <td>136.105260</td>\n",
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              "<p>1097 rows × 6 columns</p>\n",
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            ],
            "text/plain": [
              "                                 power      V1 [mV]     I1L [μA]   power pred  \\\n",
              "timestamp                                                                       \n",
              "2021-12-12 07:00:00-08:00  1140.033430  4122.039578  2765.702289  1084.565918   \n",
              "2021-12-12 08:00:00-08:00  1221.898969  4711.103114  2593.657875  1028.424072   \n",
              "2021-12-12 09:00:00-08:00  1197.535369  4850.168803  2469.059155  1150.178467   \n",
              "2021-12-12 10:00:00-08:00  1301.111855  4773.818977  2725.515696  1124.334839   \n",
              "2021-12-12 11:00:00-08:00  1155.981317  4568.301205  2530.440235  1164.166016   \n",
              "...                                ...          ...          ...          ...   \n",
              "2022-01-26 19:00:00-08:00   126.307353   786.610682  1605.716220   223.334015   \n",
              "2022-01-26 20:00:00-08:00   145.801971   796.118125  1831.411269   230.588547   \n",
              "2022-01-26 21:00:00-08:00   167.509591   854.617288  1960.053860   252.848724   \n",
              "2022-01-26 22:00:00-08:00   136.105260   875.723939  1554.202803   277.336578   \n",
              "2022-01-26 23:00:00-08:00   165.808607   881.986966  1879.943966   236.008606   \n",
              "\n",
              "                           V1 [mV] pred  I1L [μA] pred  \n",
              "timestamp                                               \n",
              "2021-12-12 07:00:00-08:00   3594.256836    3097.407715  \n",
              "2021-12-12 08:00:00-08:00   3488.235596    2986.022217  \n",
              "2021-12-12 09:00:00-08:00   4165.506836    2804.586426  \n",
              "2021-12-12 10:00:00-08:00   4211.593262    2704.230957  \n",
              "2021-12-12 11:00:00-08:00   4091.508301    2930.791504  \n",
              "...                                 ...            ...  \n",
              "2022-01-26 19:00:00-08:00   1028.856689    1913.050903  \n",
              "2022-01-26 20:00:00-08:00   1068.249512    1868.164917  \n",
              "2022-01-26 21:00:00-08:00   1063.985107    2053.287598  \n",
              "2022-01-26 22:00:00-08:00   1098.136230    2183.248535  \n",
              "2022-01-26 23:00:00-08:00   1122.012695    1853.999268  \n",
              "\n",
              "[1097 rows x 6 columns]"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPwapz3XAsjN",
        "outputId": "fd4d0c50-e7ed-45b6-ca4c-60001c016bea"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predicted vs. Actual Total Voltage Percent Difference: -12.917%\n",
            "Test MAPE voltage:\n",
            " 0.12150683804099596\n"
          ]
        }
      ],
      "source": [
        "V_actual = y_test['V1 [mV]'].mean()\n",
        "V_pred = test_pred[:, 1].mean()\n",
        "print('Predicted vs. Actual Total Voltage Percent Difference: %.3f%%' % ((V_pred - V_actual) * 100 / V_actual))\n",
        "print(\"Test MAPE voltage:\\n\", MAPE(y_test['V1 [mV]'].values.ravel(), test_pred[:, 1]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "tRfNUl__AyJM",
        "outputId": "35bf3995-1286-4976-f11e-cde586b3a8f8"
      },
      "outputs": [
        {
          "data": {
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    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-38b9d05e-d81e-4259-9a12-2a2af159a762 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_bdf483dd-179a-4bb3-8dc3-aad9e7063abf\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('y_test')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_bdf483dd-179a-4bb3-8dc3-aad9e7063abf button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('y_test');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                 power      V1 [mV]     I1L [μA]  power pred  \\\n",
              "timestamp                                                                      \n",
              "2021-12-12 07:00:00-08:00  1140.033430  4122.039578  2765.702289  896.540100   \n",
              "2021-12-12 08:00:00-08:00  1221.898969  4711.103114  2593.657875  855.916443   \n",
              "2021-12-12 09:00:00-08:00  1197.535369  4850.168803  2469.059155  912.042664   \n",
              "2021-12-12 10:00:00-08:00  1301.111855  4773.818977  2725.515696  917.297913   \n",
              "2021-12-12 11:00:00-08:00  1155.981317  4568.301205  2530.440235  967.913086   \n",
              "...                                ...          ...          ...         ...   \n",
              "2022-01-26 19:00:00-08:00   126.307353   786.610682  1605.716220  141.711487   \n",
              "2022-01-26 20:00:00-08:00   145.801971   796.118125  1831.411269  146.581406   \n",
              "2022-01-26 21:00:00-08:00   167.509591   854.617288  1960.053860  173.050766   \n",
              "2022-01-26 22:00:00-08:00   136.105260   875.723939  1554.202803  193.038025   \n",
              "2022-01-26 23:00:00-08:00   165.808607   881.986966  1879.943966  151.267365   \n",
              "\n",
              "                           V1 [mV] pred  I1L [μA] pred  \n",
              "timestamp                                               \n",
              "2021-12-12 07:00:00-08:00   3485.286621    2751.873047  \n",
              "2021-12-12 08:00:00-08:00   3375.262207    2587.294922  \n",
              "2021-12-12 09:00:00-08:00   3871.938721    2408.600098  \n",
              "2021-12-12 10:00:00-08:00   4019.700684    2272.369873  \n",
              "2021-12-12 11:00:00-08:00   3956.411377    2619.130859  \n",
              "...                                 ...            ...  \n",
              "2022-01-26 19:00:00-08:00    859.096497    1549.949341  \n",
              "2022-01-26 20:00:00-08:00    905.779968    1545.485107  \n",
              "2022-01-26 21:00:00-08:00    909.924255    1715.934448  \n",
              "2022-01-26 22:00:00-08:00    915.536255    1838.838013  \n",
              "2022-01-26 23:00:00-08:00    945.966858    1506.462036  \n",
              "\n",
              "[1097 rows x 6 columns]"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xiFog7i3Y18n"
      },
      "outputs": [],
      "source": [
        "freq = 180 #Frequency of device activation in minutes\n",
        "E_event = 768000 #Energy required to power sensing device, in microjoules\n",
        "#E_event = 3.9\n",
        "def metrics(freq, charge_time, E_event, y_test, test_pred):\n",
        "  metrics = y_test\n",
        "  metrics = metrics.drop(columns = ['V1 [mV]', 'I1L [μA]'])\n",
        "  metrics['power'] = metrics['power'] / 100 #convert to microwatts\n",
        "  metrics['power_pred'] = test_pred[:, 0] / 100 #convert to microwatts\n",
        "\n",
        "  metrics['energy_gen'] = metrics['power'] * 3600 #Actual energy generated every hour in microjoules\n",
        "  metrics['energy_pred'] = metrics['power_pred'] * 3600 #Predicted energy generated every hour in microjoules\n",
        "  metrics['activated'] = [0] * len(metrics) #Track if there is enough energy to activate the device at the end of time interval\n",
        "  metrics['activated_pred'] = [0] * len(metrics) #Track if there is enough energy to activate the device at the end of time interval, using predicted values\n",
        "  metrics['E_stored'] = [0] * len(metrics) #Energy stored at the end of time interval\n",
        "  metrics['E_stored_pred'] = [0] * len(metrics) #Predicted energy stored at the end of time interval\n",
        "  E_stored = 0\n",
        "  E_stored_pred = 0\n",
        "  for i in range(len(metrics)):\n",
        "    E_stored_pred = E_stored + metrics['energy_pred'].iloc[i] #Predicted energy stored at the end of an hour\n",
        "    E_stored += metrics['energy_gen'].iloc[i] #Actual energy stored at the end of an hour\n",
        "\n",
        "    metrics['E_stored'].iloc[i] = E_stored\n",
        "    metrics['E_stored_pred'].iloc[i] = E_stored_pred\n",
        "    if E_stored >= E_event:\n",
        "      metrics['activated'].iloc[i] = 1\n",
        "      E_stored -= E_event\n",
        "    if E_stored_pred >= E_event:\n",
        "      metrics['activated_pred'].iloc[i] = 1\n",
        "\n",
        "    #metrics['true_active_pred'] = (metrics['activated'] & metrics['activated_pred'])\n",
        "    #metrics['false_active_pred'] = (metrics['activated'] < metrics['activated_pred']) * 1\n",
        "    #metrics['missed_active'] = (metrics['activated'] > metrics['activated_pred']) * 1\n",
        "  return metrics\n",
        "\n",
        "\n",
        "metrics = metrics(freq, 60, E_event, y_test, test_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "5cM81UImXa5Z",
        "outputId": "d1e5c45b-822f-42a0-c18b-cfbd2b621816"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        },
        {
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-21-e0e40ba3801b>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'drive/MyDrive/jLab Shared Docs/MFC Modeling/Runtime_Simulation'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mmain\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/content/drive/MyDrive/jLab Shared Docs/MFC Modeling/Runtime_Simulation/main.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m#STEP 1: Import voltage values from v3_Data.csv and get filtered average of v3 and v0 cells\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mdays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv0_avg_v\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv3_avg_v\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0mSMFC\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetMFC_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Data/design_iterations/v3_Data.csv'\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      7\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mii\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m#turn everything into unit of volts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mv0_avg_v\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mii\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mv0_avg_v\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mii\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/drive/MyDrive/jLab Shared Docs/MFC Modeling/Runtime_Simulation/SMFC.py\u001b[0m in \u001b[0;36mgetMFC_data\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# each value in each column is appended to a list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\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     22\u001b[0m         \u001b[0mreader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcsv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDictReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# read rows into a dictionary format\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreader\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# read a row as {column1: value1, column2: value2,...}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Data/design_iterations/v3_Data.csv'"
          ]
        }
      ],
      "source": [
        "import sys\n",
        "drive.mount('/content/drive', force_remount=True)\n",
        "sys.path.append('drive/MyDrive/jLab Shared Docs/MFC Modeling/Runtime_Simulation')\n",
        "import main\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r3DLmwqzvRMI",
        "outputId": "0e36e5c6-41e8-4580-c203-c76ef65f09c9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2295/2295 [==============================] - 6s 2ms/step\n"
          ]
        }
      ],
      "source": [
        "predictions = model.predict(np.concatenate((X_train, X_test)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hNbyxCS0MxfM"
      },
      "outputs": [],
      "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 > '2021-11-04') & (mv1.index < '2021-11-23')]\n",
        "mv1 = mv1.loc[(mv1.index > '2021-12-12') & (mv1.index < '2021-12-14')]\n",
        "#mv1 = mv1.loc[(mv1.index > '2021-06-18')]\n",
        "mv2 = mv1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "EOQVwMjsBzh9",
        "outputId": "b5bac3bf-3507-4e0a-be4c-8328eb72573f"
      },
      "outputs": [
        {
          "ename": "KeyError",
          "evalue": "'power_pred'",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\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   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'power_pred'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-32-235a7bc15dd3>\u001b[0m in \u001b[0;36m<cell line: 61>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[0;31m#print('max power: ',max(max(1E6*df['power1']),max(1E6*df['power2'])))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[0;31m#ax3.set_ylim(0, 4)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmv1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmv2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"power_pred\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m1E-3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvolt_color1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvolt_style1\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     62\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmv1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmv2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'power'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m1E-3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvolt_color2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvolt_style2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'predictions'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'actual'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'upper right'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'size'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3807\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   3808\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\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   3809\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'power_pred'"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 400x300 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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",
        "plt.close()\n",
        "plt.xlabel(\"Time\")\n",
        "fig, (ax1, ax2, ax3) = plt.subplots(3,figsize=(4,3), sharex=False)\n",
        "fig.autofmt_xdate()\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",
        "ax1.fmt_xdata = md.DateFormatter('%m-%d')\n",
        "ax1.xaxis.set_major_formatter(md.DateFormatter('%m-%d'))\n",
        "ax1.set_ylabel(\"Power\")\n",
        "ax1.grid(True)\n",
        "\n",
        "ax2.fmt_xdata = md.DateFormatter('%m-%d')\n",
        "ax2.xaxis.set_major_formatter(md.DateFormatter('%m-%d'))\n",
        "ax2.set_ylabel(\"Power\")\n",
        "ax2.grid(True)\n",
        "\n",
        "ax3.fmt_xdata = md.DateFormatter('%m-%d')\n",
        "ax3.xaxis.set_major_formatter(md.DateFormatter('%m-%d'))\n",
        "\n",
        "ax1.set_ylabel(\"Power (μW)\", fontsize=10)\n",
        "ax2.set_ylabel(\"Voltage (mV)\", fontsize=10)\n",
        "ax3.set_ylabel(\"Current (μA)\", fontsize=10)\n",
        "#ax3.set_xlabel(\"Date\", fontsize=10)\n",
        "ax3.grid(True)\n",
        "#print('max power: ',max(max(1E6*df['power1']),max(1E6*df['power2'])))\n",
        "#ax3.set_ylim(0, 4)\n",
        "ax1.plot(mv1.index, mv2[\"power_pred\"] * 1E-3, color=volt_color1, ls = volt_style1)\n",
        "ax1.plot(mv1.index, mv2['power'] * 1E-3, color=volt_color2, ls = volt_style2)\n",
        "ax1.legend(['predictions','actual'], loc='upper right', prop={'size': 6})\n",
        "\n",
        "ax2.plot(mv1.index, mv2[\"voltage_pred\"] * 1E-2, color=volt_color1, ls = volt_style1)\n",
        "ax2.plot(mv1.index, mv2['V1 [mV]'] * 1E-2, color=volt_color2, ls = volt_style2)\n",
        "ax2.legend(['predictions','actual'], loc='upper right', prop={'size': 6})\n",
        "\n",
        "ax3.plot(mv1.index, mv2[\"current_pred\"] * 1E-2, color=volt_color1, ls = volt_style1)\n",
        "ax3.plot(mv1.index, mv2['I1L [μA]'] * 1E-2, color=volt_color2, ls = volt_style2)\n",
        "ax3.legend(['predictions','actual'], loc='upper right', prop={'size': 6})\n",
        "#ax3.legend(['actual'], loc='upper right', prop={'size': 6})\n",
        "\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=2.0)\n",
        "plt.subplots_adjust(hspace=0.50)\n",
        "plt.savefig('twobat.pdf')\n",
        "plt.close()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HJa0_HQqdpUn",
        "outputId": "00dc70d5-f163-4e2e-ac84-436dd57b60ab"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "115/115 [==============================] - 1s 3ms/step\n",
            "115/115 [==============================] - 1s 4ms/step\n",
            "115/115 [==============================] - 1s 4ms/step\n"
          ]
        }
      ],
      "source": [
        "from keras.models import load_model\n",
        "\n",
        "def quantile_loss(y_true, y_pred, quantile = 0.5):\n",
        "    error = y_true - y_pred\n",
        "    return K.mean(K.maximum(quantile * error, (quantile - 1) * error), axis=-1)\n",
        "\n",
        "model = load_model(\"drive/MyDrive/jLab Shared Docs/MFC Modeling/lstm8_60min_quant50\", custom_objects={'quantile_loss': quantile_loss})\n",
        "predictions = model.predict(np.concatenate((X_train, X_valid, X_test)))\n",
        "mv1 = df\n",
        "mv1[\"power_pred_med\"] = predictions[:, 0]\n",
        "mv1[\"voltage_pred_med\"] = predictions[:, 1]\n",
        "mv1[\"current_pred_med\"] = predictions[:, 2]\n",
        "\n",
        "def quantile_loss(y_true, y_pred, quantile = 0.05):\n",
        "    error = y_true - y_pred\n",
        "    return K.mean(K.maximum(quantile * error, (quantile - 1) * error), axis=-1)\n",
        "\n",
        "model = load_model(\"drive/MyDrive/jLab Shared Docs/MFC Modeling/lstm8_60min_quant5\", custom_objects={'quantile_loss': quantile_loss})\n",
        "predictions = model.predict(np.concatenate((X_train, X_valid, X_test)))\n",
        "mv1 = df\n",
        "mv1[\"power_pred_lower\"] = predictions[:, 0]\n",
        "mv1[\"voltage_pred_lower\"] = predictions[:, 1]\n",
        "mv1[\"current_pred_lower\"] = predictions[:, 2]\n",
        "\n",
        "def quantile_loss(y_true, y_pred, quantile = 0.75):\n",
        "    error = y_true - y_pred\n",
        "    return K.mean(K.maximum(quantile * error, (quantile - 1) * error), axis=-1)\n",
        "\n",
        "model = load_model(\"drive/MyDrive/jLab Shared Docs/MFC Modeling/lstm8_60min_quant75\", custom_objects={'quantile_loss': quantile_loss})\n",
        "predictions = model.predict(np.concatenate((X_train, X_valid, X_test)))\n",
        "mv1 = df\n",
        "mv1[\"power_pred_upper\"] = predictions[:, 0]\n",
        "mv1[\"voltage_pred_upper\"] = predictions[:, 1]\n",
        "mv1[\"current_pred_upper\"] = predictions[:, 2]\n",
        "\n",
        "\n",
        "mv1 = mv1.loc[(mv1.index > '2021-12-12') & (mv1.index < '2021-12-14')]\n",
        "mv2 = mv1\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "id": "Myp7Hcfim1Qp",
        "outputId": "6c86a706-6e86-4cbf-dfad-66425f97232b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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",
        "line_width = 0.5\n",
        "plt.title(\"1 hour prediction interval\")\n",
        "\n",
        "plt.close()\n",
        "plt.xlabel(\"Time\")\n",
        "fig, (ax1, ax2, ax3) = plt.subplots(3,figsize=(4,3), sharex=False)\n",
        "fig.autofmt_xdate()\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 = 'dashed'\n",
        "volt_color3= 'tab:red'\n",
        "volt_style3 = 'dashed'\n",
        "volt_color4= 'tab:orange'\n",
        "volt_style4 = 'dashed'\n",
        "\n",
        "#amp_color1 = 'tab:red'\n",
        "#amp_style1='dashed'\n",
        "#amp_color2 = 'tab:orange'\n",
        "#amp_style2='dashdot'\n",
        "\n",
        "ax1.fmt_xdata = md.DateFormatter('%m-%d')\n",
        "ax1.xaxis.set_major_formatter(md.DateFormatter('%m-%d'))\n",
        "ax1.grid(True)\n",
        "\n",
        "ax2.fmt_xdata = md.DateFormatter('%m-%d')\n",
        "ax2.xaxis.set_major_formatter(md.DateFormatter('%m-%d'))\n",
        "ax2.grid(True)\n",
        "\n",
        "ax3.fmt_xdata = md.DateFormatter('%m-%d')\n",
        "ax3.xaxis.set_major_formatter(md.DateFormatter('%m-%d'))\n",
        "ax3.grid(True)\n",
        "\n",
        "ax1.set_ylabel(\"Power (μW)\", fontsize=7.5, labelpad = 2.5)\n",
        "ax2.set_ylabel(\"Voltage (mV)\", fontsize=7.5, labelpad = 2.5)\n",
        "ax3.set_ylabel(\"Current (μA)\", fontsize=7.5, labelpad = 2.5)\n",
        "#ax3.set_xlabel(\"Date\", fontsize=10)\n",
        "\n",
        "ax1.set_ylim(0, 1.5)\n",
        "ax2.set_ylim(0, 60)\n",
        "ax3.set_ylim(0, 40)\n",
        "\n",
        "#ax3.set_xlim([datetime.date(2021, 12, 12), datetime.date(2021, 12, 14)])\n",
        "ax1.set_xlim([mv1.index[0], mv1.index[-1]])\n",
        "ax2.set_xlim([mv1.index[0], mv1.index[-1]])\n",
        "ax3.set_xlim([mv1.index[0], mv1.index[-1]])\n",
        "\n",
        "ax1.plot(mv1.index, mv2[\"power_pred_lower\"] * 1E-3, color='tab:red', ls = volt_style3, linewidth=line_width)\n",
        "ax1.plot(mv1.index, mv2[\"power_pred_upper\"] * 1E-3, color='tab:orange', ls = volt_style4, linewidth=line_width)\n",
        "ax1.plot(mv1.index, mv2[\"power\"] * 1E-3, color='tab:blue', ls = 'solid', linewidth=line_width)\n",
        "ax1.plot(mv1.index, mv2[\"power_pred_med\"] * 1E-3, color='tab:green', ls = volt_style2, linewidth=line_width)\n",
        "ax1.fill_between(mv1.index, mv2[\"power_pred_lower\"] * 1E-3, mv2[\"power_pred_upper\"] * 1E-3, color='grey', alpha=0.5)\n",
        "#ax1.legend(['lower bound', 'upper bound', 'actual', 'median predictions'], loc='lower center', prop={'size': 5.5}, ncol=2)\n",
        "\n",
        "ax2.plot(mv1.index, mv2[\"voltage_pred_lower\"] * 1E-2, color='tab:red', ls = volt_style3, linewidth=line_width)\n",
        "ax2.plot(mv1.index, mv2[\"voltage_pred_upper\"] * 1E-2, color='tab:orange', ls = volt_style4, linewidth=line_width)\n",
        "ax2.plot(mv1.index, mv2['V1 [mV]'] * 1E-2, color='tab:blue', ls = 'solid', linewidth=line_width)\n",
        "ax2.plot(mv1.index, mv2[\"voltage_pred_med\"] * 1E-2, color='tab:green', ls = volt_style2, linewidth=line_width)\n",
        "ax2.fill_between(mv1.index, mv2[\"voltage_pred_lower\"] * 1E-2, mv2[\"voltage_pred_upper\"] * 1E-2, color='grey', alpha=0.5)\n",
        "ax2.legend(['lower bound', 'upper bound', 'ground truth', 'median prediction'], loc='lower left', prop={'size': 6.6}, ncol=2, columnspacing=0.5)\n",
        "\n",
        "ax3.plot(mv1.index, mv2[\"current_pred_lower\"] * 1E-2, color='tab:red', ls = volt_style3, linewidth=line_width)\n",
        "ax3.plot(mv1.index, mv2[\"current_pred_upper\"] * 1E-2, color='tab:orange', ls = volt_style4, linewidth=line_width)\n",
        "ax3.plot(mv1.index, mv2['I1L [μA]'] * 1E-2, color='tab:blue', ls = 'solid', linewidth=line_width)\n",
        "ax3.plot(mv1.index, mv2[\"current_pred_med\"] * 1E-2, color='tab:green', ls = volt_style2, linewidth=line_width)\n",
        "ax3.fill_between(mv1.index, mv2[\"current_pred_lower\"] * 1E-2, mv2[\"current_pred_upper\"] * 1E-2, color='grey', alpha=0.5)\n",
        "#ax3.legend(['lower bound', 'upper bound', 'actual', 'median predictions'], loc='upper right', prop={'size': 5.5}, ncol=1)\n",
        "\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=7.5, rotation=0, pad = 0.1)\n",
        "ax3.set_xticks(list(ax3.get_xticks()) + [ax3.get_xlim()[0], ax3.get_xlim()[1]])\n",
        "for label in ax3.get_xticklabels():\n",
        "    label.set_horizontalalignment('center')\n",
        "\n",
        "ax1.tick_params(axis='y', labelsize=7.5, rotation=0, pad = 0.1)\n",
        "ax2.tick_params(axis='y', labelsize=7.5, rotation=0, pad = 0.1)\n",
        "ax3.tick_params(axis='y', labelsize=7.5, rotation=0, pad = 0.1)\n",
        "\n",
        "plt.tight_layout(pad=0.3, w_pad=0.5, h_pad=0.1)\n",
        "#plt.subplots_adjust(hspace=0.15)\n",
        "plt.savefig('twobat.pdf')\n",
        "plt.close()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "gpuType": "T4",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}