1_add-derived-features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a1c07eac-dca9-454f-8dea-2e9695017fa9",
   "metadata": {},
   "source": [
    "# Add Cycle-Level Features\n",
    "We use a few different types of faetures that summarize cycles for the machine learning models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "06d4dc4b-cae5-4af4-b247-e3fef85cc3e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "from batdata.postprocess.integral import CapacityPerCycle, StateOfCharge\n",
    "from batdata.postprocess.cycle_stats import CycleTimes\n",
    "from batdata.data import BatteryDataset\n",
    "from concurrent.futures import ProcessPoolExecutor\n",
    "from functools import partial\n",
    "from pathlib import Path\n",
    "from tqdm import tqdm\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f0070ab-db5c-4ac8-9a2c-5a3666f540a0",
   "metadata": {},
   "source": [
    "Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a503d6ae-daf0-4d23-a1e5-9470b8a4cadb",
   "metadata": {},
   "outputs": [],
   "source": [
    "hdf5_path = './data/hdf5/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16fccd76-bb30-43e1-aaaf-bbf0fbf006d4",
   "metadata": {},
   "source": [
    "## Load in an example cell\n",
    "Start with one to show off how this works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "944ca2d1-7d94-4cdf-aca4-7cbca3a8f003",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = BatteryDataset.from_batdata_hdf('./data/hdf5/refined/batch_B28B_cell_2.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fc8a205-762c-4e9e-aedb-4936c41be363",
   "metadata": {},
   "source": [
    "## Compute the Features\n",
    "Each is described using a different class"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a54771a7-0303-48bf-b378-e9922977c3a2",
   "metadata": {},
   "source": [
    "### Capacity Per Cycle\n",
    "Compute the amount of charge pushed into the battery and the "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b7f5fc2b-c434-4eb5-ab7d-d9af5a3e795d",
   "metadata": {},
   "outputs": [],
   "source": [
    "CapacityPerCycle().add_summaries(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c656a8c6-87b0-422e-8e0e-1feb9f3b3dcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Charge Capacity (A-hr)')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 350x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(3.5, 2.5))\n",
    "\n",
    "ax.plot(dataset.cycle_stats['capacity_charge'])\n",
    "\n",
    "ax.set_xlabel('Cycle')\n",
    "ax.set_ylabel('Charge Capacity (A-hr)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "178c7d37-4811-4e6b-9249-dab587ac8707",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cycle_number</th>\n",
       "      <th>energy_charge</th>\n",
       "      <th>capacity_charge</th>\n",
       "      <th>energy_discharge</th>\n",
       "      <th>capacity_discharge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.336126e-07</td>\n",
       "      <td>1.250629e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.545621</td>\n",
       "      <td>0.420994</td>\n",
       "      <td>1.460777e+00</td>\n",
       "      <td>3.913739e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.539038</td>\n",
       "      <td>0.419249</td>\n",
       "      <td>1.562767e+00</td>\n",
       "      <td>4.219581e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1.517108</td>\n",
       "      <td>0.413389</td>\n",
       "      <td>1.555165e+00</td>\n",
       "      <td>4.194255e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.513609</td>\n",
       "      <td>0.412468</td>\n",
       "      <td>1.534272e+00</td>\n",
       "      <td>4.134041e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cycle_number  energy_charge  capacity_charge  energy_discharge  \\\n",
       "0             0       0.000000         0.000000      4.336126e-07   \n",
       "1             1       1.545621         0.420994      1.460777e+00   \n",
       "2             2       1.539038         0.419249      1.562767e+00   \n",
       "3             3       1.517108         0.413389      1.555165e+00   \n",
       "4             4       1.513609         0.412468      1.534272e+00   \n",
       "\n",
       "   capacity_discharge  \n",
       "0        1.250629e-07  \n",
       "1        3.913739e-01  \n",
       "2        4.219581e-01  \n",
       "3        4.194255e-01  \n",
       "4        4.134041e-01  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.cycle_stats.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c3706f5-741a-41db-aaac-f584d776d5ce",
   "metadata": {},
   "source": [
    "## Run for each cell\n",
    "Load in, create cycle summaries, save back"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2b806c37-58ec-4fe1-8298-da1e517865ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_computers = [CapacityPerCycle(), StateOfCharge(), CycleTimes()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3828279-d32f-4200-868f-9f4a91b0383b",
   "metadata": {},
   "source": [
    "Make a function we can run in parallel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "065446c5-3fb2-4738-8714-c799629d16b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_features(file: Path, computers: list):\n",
    "    \"\"\"Add features to a certain file\"\"\"\n",
    "    from warnings import catch_warnings, filterwarnings\n",
    "    dataset = BatteryDataset.from_batdata_hdf(str(file))\n",
    "    with catch_warnings():\n",
    "        filterwarnings('ignore')\n",
    "        for feature in feature_computers:\n",
    "            feature.compute_features(dataset)\n",
    "    dataset.to_batdata_hdf(str(file), complevel=9)\n",
    "    return file\n",
    "func = partial(add_features, computers=feature_computers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bff61e7a-178d-4e74-be7c-074396f1dc75",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "602it [10:50,  1.08s/it]\n"
     ]
    }
   ],
   "source": [
    "for path in tqdm(Path(hdf5_path).rglob('*.h5')):\n",
    "    func(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "869421e8-207e-487a-8d3a-4c70788a0adc",
   "metadata": {},
   "source": [
    "## Show the improved HDF5 file\n",
    "The features are now part of HDF5 file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "43c10023-e3ca-42f7-b34e-c740ee964909",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = BatteryDataset.from_batdata_hdf('./data/hdf5/refined/batch_B28B_cell_2.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "75535afa-cf0e-486e-8529-290c5d6b090c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cycle_number</th>\n",
       "      <th>energy_charge</th>\n",
       "      <th>capacity_charge</th>\n",
       "      <th>energy_discharge</th>\n",
       "      <th>capacity_discharge</th>\n",
       "      <th>cycle_start</th>\n",
       "      <th>cycle_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.336126e-07</td>\n",
       "      <td>1.250629e-07</td>\n",
       "      <td>0.000</td>\n",
       "      <td>60.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.545621</td>\n",
       "      <td>0.420994</td>\n",
       "      <td>1.460777e+00</td>\n",
       "      <td>3.913739e-01</td>\n",
       "      <td>60.048</td>\n",
       "      <td>156219.450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.539038</td>\n",
       "      <td>0.419249</td>\n",
       "      <td>1.562767e+00</td>\n",
       "      <td>4.219581e-01</td>\n",
       "      <td>156279.498</td>\n",
       "      <td>161755.470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1.517108</td>\n",
       "      <td>0.413389</td>\n",
       "      <td>1.555165e+00</td>\n",
       "      <td>4.194255e-01</td>\n",
       "      <td>318034.968</td>\n",
       "      <td>80239.932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.513609</td>\n",
       "      <td>0.412468</td>\n",
       "      <td>1.534272e+00</td>\n",
       "      <td>4.134041e-01</td>\n",
       "      <td>398274.900</td>\n",
       "      <td>79615.722</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cycle_number  energy_charge  capacity_charge  energy_discharge  \\\n",
       "0             0       0.000000         0.000000      4.336126e-07   \n",
       "1             1       1.545621         0.420994      1.460777e+00   \n",
       "2             2       1.539038         0.419249      1.562767e+00   \n",
       "3             3       1.517108         0.413389      1.555165e+00   \n",
       "4             4       1.513609         0.412468      1.534272e+00   \n",
       "\n",
       "   capacity_discharge  cycle_start  cycle_duration  \n",
       "0        1.250629e-07        0.000          60.048  \n",
       "1        3.913739e-01       60.048      156219.450  \n",
       "2        4.219581e-01   156279.498      161755.470  \n",
       "3        4.194255e-01   318034.968       80239.932  \n",
       "4        4.134041e-01   398274.900       79615.722  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.cycle_stats.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b460e0ad-4ca5-4045-8392-d4d033f542ad",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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