{
 "cells": [
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   "metadata": {},
   "source": [
    "# ★★ 2-Star Battery Data\n",
    "\n",
    "This notebook demonstrates how to process raw battery cycler data into a **structured, machine-readable tabular format**. This corresponds to the **second star** in the Five-Star Battery Data framework.\n",
    "\n",
    "---\n",
    "\n",
    "## What does two-star mean?\n",
    "\n",
    "In the 5-Star Battery Data framework, 2-star data is:\n",
    "- **Structured** — each row represents one observation, and each column represents one variable.\n",
    "- **Machine-readable** — stored in formats like CSV or Parquet that software tools can easily parse.\n",
    "- **Clearly labeled** — with SI-compliant names and units (e.g., `Voltage / V`, not `Voltage (V)`).\n",
    "- **Standardized** — follows community conventions such as the **Battery Data Format (BDF)**.\n",
    "\n",
    "This notebook helps you transform messy or proprietary outputs into **clean, standardized tables** that are ready for analysis, publication, and reuse.\n",
    "\n",
    "---\n",
    "\n",
    "## Watch\n",
    "\n",
    "<div style=\"position: relative; padding-bottom: 56.25%; height: 0; overflow: hidden; max-width: 100%;\">\n",
    "  <iframe src=\"https://www.youtube.com/embed/_z6Xtbx8QwM\" title=\"Star 2: Structured Data\"\n",
    "          frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\"\n",
    "          allowfullscreen style=\"position: absolute; top: 0; left: 0; width: 100%; height: 100%;\"></iframe>\n",
    "</div>\n",
    "\n",
    "---\n",
    "\n",
    "## Why is this important?\n",
    "\n",
    "Raw battery data is often inconsistent or difficult to process, which hinders reuse. By transforming it into a standardized, structured table:\n",
    "- It becomes easier to **index, search, filter, and visualize**.\n",
    "- It can be reliably used by **humans and machines** alike.\n",
    "- It aligns with **FAIR data principles** and sets the stage for semantic enrichment in 3-star and 4-star datasets.\n",
    "\n",
    "---\n",
    "\n",
    "## What we will do\n",
    "\n",
    "In this notebook, we will:\n",
    "1. **Read** the raw cycler data file  \n",
    "2. **Process** it to align with the BDF standard by:\n",
    "   - Renaming columns using consistent, SI-style labels  \n",
    "   - Converting timestamps to UNIX time  \n",
    "   - Ensuring consistent units  \n",
    "3. **Visualize** the cleaned time-series data  \n",
    "4. **Serialize** it to a BDF-compliant CSV format  \n",
    "\n",
    "This is an essential step toward making your data FAIR and ready for semantic annotation in the next stars.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9946c27",
   "metadata": {},
   "source": [
    "## 1. Read the cycler data file\n",
    "\n",
    "In this step, we read the csv data file that has been exported from the cycler into a pandas dataframe and explore its content.\n",
    "\n",
    "> **ℹ️ Note on file formats**  \n",
    "> \n",
    "> Cycler hardware often exports data files in a proprietary binary format. It is important for broader usability that this is converted into an open data format (e.g. csv, txt, parquet, etc.) as soon as possible to avoid proprietary software dependencies. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d37a1d28",
   "metadata": {},
   "outputs": [
    {
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       "<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>DataPoint</th>\n",
       "      <th>Cycle Index</th>\n",
       "      <th>Step Index</th>\n",
       "      <th>Step Type</th>\n",
       "      <th>Time(s)</th>\n",
       "      <th>Total Time(s)</th>\n",
       "      <th>Current(A)</th>\n",
       "      <th>Voltage(V)</th>\n",
       "      <th>Capacity(Ah)</th>\n",
       "      <th>Spec. Cap.(mAh/g)</th>\n",
       "      <th>...</th>\n",
       "      <th>Contact resistance(mO)</th>\n",
       "      <th>Module start-stop switch</th>\n",
       "      <th>SOC/DOD(%)</th>\n",
       "      <th>LgD</th>\n",
       "      <th>V1(V)</th>\n",
       "      <th>V2(V)</th>\n",
       "      <th>V3(V)</th>\n",
       "      <th>T1(?)</th>\n",
       "      <th>T2(?)</th>\n",
       "      <th>T3(?)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rest</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.8022</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Close</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rest</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.8022</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Close</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rest</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.8022</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Close</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rest</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.8022</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Close</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rest</td>\n",
       "      <td>30.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.8022</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Close</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   DataPoint  Cycle Index  Step Index Step Type  Time(s)  Total Time(s)  \\\n",
       "0          1            1           1      Rest     0.00           0.00   \n",
       "1          2            1           1      Rest     0.01           0.01   \n",
       "2          3            1           1      Rest    10.00          10.00   \n",
       "3          4            1           1      Rest    20.00          20.00   \n",
       "4          5            1           1      Rest    30.00          30.00   \n",
       "\n",
       "   Current(A)  Voltage(V)  Capacity(Ah)  Spec. Cap.(mAh/g)  ...  \\\n",
       "0         0.0      3.8022           0.0                0.0  ...   \n",
       "1         0.0      3.8022           0.0                0.0  ...   \n",
       "2         0.0      3.8022           0.0                0.0  ...   \n",
       "3         0.0      3.8022           0.0                0.0  ...   \n",
       "4         0.0      3.8022           0.0                0.0  ...   \n",
       "\n",
       "   Contact resistance(mO)  Module start-stop switch  SOC/DOD(%)  LgD  V1(V)  \\\n",
       "0                     0.0                     Close         0.0  NaN    0.0   \n",
       "1                     0.0                     Close         0.0  NaN    0.0   \n",
       "2                     0.0                     Close         0.0  NaN    0.0   \n",
       "3                     0.0                     Close         0.0  NaN    0.0   \n",
       "4                     0.0                     Close         0.0  NaN    0.0   \n",
       "\n",
       "   V2(V)   V3(V)  T1(?)  T2(?)  T3(?)  \n",
       "0    0.0  0.0002   26.2   26.2   26.4  \n",
       "1    0.0  0.0002   26.2   26.2   26.4  \n",
       "2    0.0  0.0003   26.2   26.2   26.4  \n",
       "3    0.0  0.0002   26.2   26.2   26.4  \n",
       "4    0.0  0.0002   26.0   26.2   26.4  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import python package dependencies\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pytz\n",
    "\n",
    "# Load raw data from the parent directory\n",
    "file_name = 'raw_data_output_from_cycler.csv'\n",
    "raw_data = pd.read_csv(file_name)\n",
    "raw_data.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8636452c",
   "metadata": {},
   "source": [
    "The raw data contains 34 columns, corresponding to a variety of properties. We will now process it into a community standard format to support broader re-use and interoperability. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ce82d0d",
   "metadata": {},
   "source": [
    "## Process the data to align with the BDF standard\n",
    "\n",
    "The **Battery Data Format (BDF)** is an open community standard for structuring battery test data in a clear, consistent, and machine-readable way. It defines how to name fields, express units, and organize data so it can be reliably processed, interpreted, and shared across research and industry. By aligning with BDF, we ensure this dataset is easy to work with using both automated tools and human-readable analysis.\n",
    "\n",
    "The raw data contains many quantities that are redundant or can be derived from other fundamental quantities. For BDF conversion, we take only the core subset of measured quantities. It is important to balance the need for archiving complete information with the desire to reduce file size by removing redundant quantities. The BDF defines a minimum set of required quantities, along with a broader set of recommended and optional quantities. It is up to the data curator to determine how much detail is necessary.\n",
    "\n",
    "| Original Label       | BDF Preferred Label         | Description                                                                 |\n",
    "|----------------------|-----------------------------|-----------------------------------------------------------------------------|\n",
    "| Total Time(s)        | Test Time / s              | Elapsed time since the start of the test, recorded in millisecond.         |\n",
    "| Voltage(V)           | Voltage / V                 | Instantaneous voltage measured across the test object, in volt.            |\n",
    "| Current(A)           | Current / A                 | Instantaneous current applied to or from the test object, in ampere.       |\n",
    "| – (generated)        | Unix Time / s              | Timestamp of the measurement in Unix time (second since epoch).       |\n",
    "| – (generated)        | Step Count / 1              | Sequential index of the current step in the test program, increasing monotonically. |\n",
    "| T1(?)                | Temperature 1 / °C          | Measured temperature at sensor 1 on the test object, in degree Celsius.    |\n",
    "| T2(?)                | Temperature 2 / °C          | Measured temperature at sensor 2 on the test object, in degree Celsius.    |\n",
    "| T3(?)                | Temperature 3 / °C          | Measured temperature at sensor 3 on the test object, in degree Celsius.    |\n",
    "  \n",
    "  \n",
    "Now we will process the data from the raw file to align with BDF recommendations. More information on the BDF is available [here](https://github.com/battery-data-alliance/battery-data-format). \n",
    "  \n",
    "\n",
    "> **ℹ️ Unit notation**  \n",
    ">\n",
    "> The use of a forward slash (`/`) to separate the variable name from the unit (e.g., `Voltage / V`) follows recommendations from the **International System of Units (SI)** and **IUPAC**. This style reflects the **algebraic nature of physical quantities**, where each variable is the product of a numeric value and a unit. It improves clarity, avoids ambiguity in composite units, and aligns with standard scientific conventions in data labeling and dimensional analysis.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "95ddd85f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract total test time\n",
    "test_time_s = np.maximum.accumulate(raw_data['Total Time(s)'])\n",
    "\n",
    "# Extract temperatures\n",
    "temperature_1 = raw_data['T1(?)'] if 'T1(?)' in raw_data else np.nan\n",
    "temperature_2 = raw_data['T2(?)'] if 'T2(?)' in raw_data else np.nan\n",
    "temperature_3 = raw_data['T3(?)'] if 'T3(?)' in raw_data else np.nan\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b814674",
   "metadata": {},
   "source": [
    "> **ℹ️ Unix time**  \n",
    ">\n",
    "> **Unix time** (also called *epoch time* or *POSIX time*) is a widely used standard for representing points in time. It counts the duration that has elapsed since **00:00:00 UTC on January 1, 1970**. Using Unix time makes it easy to sort, compare, and align timestamps across different systems and time zones, making it ideal for machine-readable datasets and time-series analysis.  \n",
    "\n",
    "\n",
    "In the following block, we parse the string with the time-stamp to convert it to Unix time. The experiment was conducted in the CET timezone, which is first converted to UTC and then to Unix time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a2b65eeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define CET (standard time only — no daylight savings)\n",
    "cet = pytz.timezone('CET')  # Assumes winter time only (UTC+1, no DST correction)\n",
    "\n",
    "# Parse and localize 'Date' to CET, then convert to UTC\n",
    "df_datetime = pd.to_datetime(raw_data['Date'], format='%Y-%m-%d %H:%M:%S')\n",
    "df_datetime = df_datetime.dt.tz_localize(cet).dt.tz_convert('UTC')\n",
    "\n",
    "# Convert to Unix time in seconds\n",
    "unix_time_s = df_datetime.astype('int64') / 1e9"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fda17bbe",
   "metadata": {},
   "source": [
    "Finally, the converted data with BDF compliant headers is compiled into a new dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3914be8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assemble the BDF-compliant DataFrame\n",
    "bdf_data = pd.DataFrame({\n",
    "    'Test Time / s': test_time_s,\n",
    "    'Current / A': raw_data['Current(A)'],\n",
    "    'Voltage / V': raw_data['Voltage(V)'],\n",
    "    'Unix Time / s': unix_time_s,\n",
    "    'Step Count / 1': raw_data['Step Index'],\n",
    "    'Temperature 1 / °C': temperature_1,\n",
    "    'Temperature 2 / °C': temperature_2,\n",
    "    'Temperature 3 / °C': temperature_3\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eeea842",
   "metadata": {},
   "source": [
    "## 3. Visualize the data\n",
    "\n",
    "To verify data quality and provide insight into the test process, we visualize key quantities such as voltage, current, and temperature over time.\n",
    "\n",
    "These plots help:\n",
    "- Detect anomalies or noise in the raw signal\n",
    "- Understand test conditions and dynamics\n",
    "- Confirm that the data has been correctly aligned and serialized\n",
    "\n",
    "This step is especially important before sharing or analyzing the dataset further.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f1c4980e",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Voltage, Current, and all Temperatures vs Total Time in one figure\n",
    "time_h = bdf_data['Test Time / s'] / 3600\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Voltage\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.plot(time_h, bdf_data['Voltage / V'], label='Voltage / V', color='blue')\n",
    "plt.ylabel('Voltage / V')\n",
    "plt.title('Voltage vs Total Time')\n",
    "plt.grid(True)\n",
    "\n",
    "# Current\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.plot(time_h, bdf_data['Current / A'], label='Current / A', color='orange')\n",
    "plt.ylabel('Current / A')\n",
    "plt.title('Current vs Total Time')\n",
    "plt.grid(True)\n",
    "\n",
    "# Temperatures (1, 2, 3)\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.plot(time_h, bdf_data['Temperature 1 / °C'], label='Temperature 1 / °C', color='green')\n",
    "plt.plot(time_h, bdf_data['Temperature 2 / °C'], label='Temperature 2 / °C', color='red')\n",
    "plt.plot(time_h, bdf_data['Temperature 3 / °C'], label='Temperature 3 / °C', color='purple')\n",
    "plt.xlabel('Total Time / h')\n",
    "plt.ylabel('Temperature / °C')\n",
    "plt.title('Temperatures vs Total Time')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00e2d33d",
   "metadata": {},
   "source": [
    "## Serialize the cleaned data to a BDF-compliant CSV\n",
    "The final step is to serialize the structured data into a **CSV file** that complies with the [BDF](https://github.com/battery-data-alliance/battery-data-format) standard.\n",
    "\n",
    "This means:\n",
    "- The file uses **BDF preferred column labels**  \n",
    "- The **units adhere to BDF recommendations**\n",
    "- All values are **machine-readable** and **unambiguously defined**  \n",
    "\n",
    "The output file can now be indexed, visualized, or combined with metadata and shared as part of a reproducible dataset.\n",
    "\n",
    "```\n",
    "structured_battery_data.bdf.csv\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6ed35d47",
   "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>Test Time / s</th>\n",
       "      <th>Current / A</th>\n",
       "      <th>Voltage / V</th>\n",
       "      <th>Unix Time / s</th>\n",
       "      <th>Step Count / 1</th>\n",
       "      <th>Temperature 1 / °C</th>\n",
       "      <th>Temperature 2 / °C</th>\n",
       "      <th>Temperature 3 / °C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.8022</td>\n",
       "      <td>1.729688e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.8022</td>\n",
       "      <td>1.729688e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.8022</td>\n",
       "      <td>1.729688e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.8022</td>\n",
       "      <td>1.729688e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.8022</td>\n",
       "      <td>1.729688e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Test Time / s  Current / A  Voltage / V  Unix Time / s  Step Count / 1  \\\n",
       "0           0.00          0.0       3.8022   1.729688e+09               1   \n",
       "1           0.01          0.0       3.8022   1.729688e+09               1   \n",
       "2          10.00          0.0       3.8022   1.729688e+09               1   \n",
       "3          20.00          0.0       3.8022   1.729688e+09               1   \n",
       "4          30.00          0.0       3.8022   1.729688e+09               1   \n",
       "\n",
       "   Temperature 1 / °C  Temperature 2 / °C  Temperature 3 / °C  \n",
       "0                26.2                26.2                26.4  \n",
       "1                26.2                26.2                26.4  \n",
       "2                26.2                26.2                26.4  \n",
       "3                26.2                26.2                26.4  \n",
       "4                26.0                26.2                26.4  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save to CSV or display preview\n",
    "bdf_data.to_csv(\"structured_battery_data.bdf.csv\", index=False)\n",
    "bdf_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fc9198f",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "In this notebook, you learned how to transform raw battery cycler output into a clean, structured, and machine-readable dataset that meets the **2-star criteria** in the Five-Star Battery Data framework.\n",
    "\n",
    "| Step                    | What You Did                                                                 |\n",
    "|-------------------------|------------------------------------------------------------------------------|\n",
    "| Load raw data           | Imported time-series data from a cycler-generated `.csv` or similar file     |\n",
    "| Clean & transform       | Renamed fields, standardized units, and computed missing information         |\n",
    "| Align with BDF          | Applied best practices from the Battery Data Format, including column naming |\n",
    "| Generate timestamps     | Converted human-readable dates to Unix time in seconds (UTC)                |\n",
    "| Visualize data          | Plotted voltage, current, and temperature to assess data integrity           |\n",
    "| Export cleaned dataset  | Serialized the result into a structured `.csv` following BDF conventions     |\n",
    "\n",
    "By following this workflow, your dataset is now:\n",
    "- **Structured and machine-readable**\n",
    "- **Consistently labeled using SI and community standards**\n",
    "- **Ready for analysis, semantic metadata, and publication**\n",
    "\n",
    "This notebook gives you a practical and reusable pattern for preparing battery test data that can be confidently shared, understood, and reused by others.\n"
   ]
  },
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   "cell_type": "markdown",
   "id": "46c02077",
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    "---\n",
    "\n",
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/b/b7/Flag_of_Europe.svg\" alt=\"EU Flag\" width=\"100\"/>\n",
    "\n",
    "**This work has received funding from the European Union under the Horizon Europe programme.**  \n",
    "Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the granting authority can be held responsible for them."
   ]
  }
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