thesis/electrochemical_OCP_EIS_LPR/TAFEL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a39d7464-a111-4ac6-80b0-bcaa20aa81fa",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0dbb86a3-8915-43d6-b821-379ab76b2849",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.optimize\n",
    "from scipy.signal import medfilt\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79273c57-b22e-4b9b-ad97-8b788969a4a6",
   "metadata": {},
   "source": [
    "## Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9a43783f-d1bb-4c79-ae93-843086a00a26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- Data Loading ---\n",
    "\n",
    "def lpr_cor_import(filename):\n",
    "    \"\"\" Import cor file as pandas dataframe.\"\"\"\n",
    "\n",
    "    try: \n",
    "        df = pd.read_csv(\n",
    "            filename,\n",
    "            skiprows=26,\n",
    "            sep='\\s+',\n",
    "            header=None,\n",
    "            names=[\"E\", \"i\", \"T\"],\n",
    "        ) \n",
    "        df.drop(columns=[\"T\"], inplace=True)\n",
    "        df.drop(df.head(150).index, inplace=True)\n",
    "        df[\"E\"] = df[\"E\"]*1e3 # Convert it to mV\n",
    "        \n",
    "    except FileNotFoundError as e:\n",
    "        print(f\"Error: File was not found.\")\n",
    "        print(e.message)\n",
    "        print(e.args)\n",
    "        return None\n",
    "    except Exception as e:\n",
    "        print(f\"Error reading the CSV file: {e}\")\n",
    "        return None   \n",
    "    else:\n",
    "        return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6a2888b4-e151-4fae-97d5-7de521fb7b48",
   "metadata": {},
   "outputs": [],
   "source": [
    "HS1_1 = lpr_cor_import(\"HIPed_Stellite1_LPR/LPR_1.cor\")\n",
    "HS1_2 = lpr_cor_import(\"HIPed_Stellite1_LPR/LPR_2.cor\")\n",
    "HS1_3 = lpr_cor_import(\"HIPed_Stellite1_LPR/LPR_3.cor\")\n",
    "HS1_4 = lpr_cor_import(\"HIPed_Stellite1_LPR/LPR_4.cor\")\n",
    "HS1_5 = lpr_cor_import(\"HIPed_Stellite1_LPR/LPR_5.cor\")\n",
    "HS1_6 = lpr_cor_import(\"HIPed_Stellite1_LPR/LPR_6.cor\")\n",
    "\n",
    "# Keep it in the same cell to keep df reproducible, even if Vi clicks it multiple times\n",
    "area = 2 #cm^2\n",
    "\n",
    "for df in [HS1_1, HS1_2, HS1_3, HS1_4, HS1_5, HS1_6]:\n",
    "    df[\"i\"] = np.abs(df[\"i\"]/area) # Current density\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0ad5c189-9bd6-4291-bdee-8b75fdd5a3cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>E</th>\n",
       "      <th>i</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>-205.474</td>\n",
       "      <td>3.492260e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>-205.464</td>\n",
       "      <td>3.335005e-11</td>\n",
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       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>-205.454</td>\n",
       "      <td>2.995255e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>-205.444</td>\n",
       "      <td>2.772125e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>-205.434</td>\n",
       "      <td>2.682435e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>6.514500e-11</td>\n",
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       "    <tr>\n",
       "      <th>4000</th>\n",
       "      <td>-167.003</td>\n",
       "      <td>6.453200e-11</td>\n",
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       "    <tr>\n",
       "      <th>4001</th>\n",
       "      <td>-167.005</td>\n",
       "      <td>6.450600e-11</td>\n",
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       "    <tr>\n",
       "      <th>4002</th>\n",
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       "      <td>6.511600e-11</td>\n",
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       "    <tr>\n",
       "      <th>4003</th>\n",
       "      <td>-166.998</td>\n",
       "      <td>6.448450e-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3854 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            E             i\n",
       "150  -205.474  3.492260e-11\n",
       "151  -205.464  3.335005e-11\n",
       "152  -205.454  2.995255e-11\n",
       "153  -205.444  2.772125e-11\n",
       "154  -205.434  2.682435e-11\n",
       "...       ...           ...\n",
       "3999 -167.010  6.514500e-11\n",
       "4000 -167.003  6.453200e-11\n",
       "4001 -167.005  6.450600e-11\n",
       "4002 -166.991  6.511600e-11\n",
       "4003 -166.998  6.448450e-11\n",
       "\n",
       "[3854 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HS1_3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee328bd6-4817-41a9-8831-12cda31cb8f1",
   "metadata": {},
   "source": [
    "# Coding part!\n",
    "\n",
    "Separate code into analyze and plotting, with all answers in a dict.\n",
    "Can't believe it took me this long to really do it this way :|"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bec9f9bc-b531-470e-9c27-a89b512e97ba",
   "metadata": {},
   "source": [
    "## Analyze Tafel Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "110a6031-ea23-46cf-8f1a-7c39ec6d88b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.signal import medfilt\n",
    "import warnings\n",
    "\n",
    "def analyze_tafel(data: pd.DataFrame) -> dict:\n",
    "    \"\"\"\n",
    "    Analyzes Tafel data to determine electrochemical parameters.\n",
    "\n",
    "    Args:\n",
    "        data: DataFrame with 'E' (potential) and 'i' (current) columns.\n",
    "\n",
    "    Returns:\n",
    "        A dictionary containing the analysis results.\n",
    "    \"\"\"\n",
    "    INITIAL_KERNEL = 29\n",
    "    FIT_KERNEL = 99\n",
    "\n",
    "    # 1. Estimate Corrosion Potential (Ecorr) using a median filter\n",
    "    ecorr_guess = data['E'].iloc[np.argmin(medfilt(data['i'], kernel_size=INITIAL_KERNEL))]\n",
    "\n",
    "    # 2. Split data into anodic and cathodic regions based on Ecorr\n",
    "    anodic_data = data[data['E'] > ecorr_guess + 12].copy()\n",
    "    cathodic_data = data[data['E'] < ecorr_guess - 12].copy()\n",
    "\n",
    "    if anodic_data.empty or cathodic_data.empty:\n",
    "        warnings.warn(\"No data in anodic or cathodic regions. Ecorr guess may be incorrect.\")\n",
    "        return {}\n",
    "\n",
    "    anodic_data[\"i\"] = medfilt(anodic_data['i'], kernel_size=FIT_KERNEL)\n",
    "    cathodic_data[\"i\"] = medfilt(cathodic_data['i'], kernel_size=FIT_KERNEL)\n",
    "\n",
    "    # 3. Perform linear fit on E vs. log(i) for each branch\n",
    "    p_anodic = np.poly1d(np.polyfit(    \n",
    "        np.log10(anodic_data[\"i\"]), \n",
    "        anodic_data['E'], 1))\n",
    "    p_cathodic = np.poly1d(np.polyfit(\n",
    "        np.log10(cathodic_data[\"i\"]), \n",
    "        cathodic_data['E'], 1))\n",
    "    beta_a = p_anodic[1]\n",
    "    beta_c = -p_cathodic[1]\n",
    "\n",
    "    # 3.5 Find the i_corr according to each branch\n",
    "    # Find the roots of the polynomial p(log(i)) - E_corr = 0\n",
    "    icorr_anodic = np.power(10, ((p_anodic - ecorr_guess).roots)[0])\n",
    "    icorr_cathodic = np.power(10,((p_cathodic - ecorr_guess).roots)[0])\n",
    "    \n",
    "    # --- 4. Calculations ---\n",
    "    temp_kelvin = 25.0 + 273.15\n",
    "    R = 8.314  # Ideal Gas Constant (J/(mol·K))\n",
    "    F = 96485  # Faraday Constant (C/mol)\n",
    "    n = 3      # Number of electrons transferred\n",
    "\n",
    "    alpha_a, alpha_c = None, None\n",
    "    try:\n",
    "        # Calculate charge transfer coefficients\n",
    "        alpha_a = (2.303 * R * temp_kelvin) / (1e-3 * beta_a * n * F)\n",
    "        alpha_c = (2.303 * R * temp_kelvin) / (1e-3 * beta_c * n * F)\n",
    "    except ZeroDivisionError as e:\n",
    "        print(f\"Error during calculation: {e}. A slope was zero.\")\n",
    "\n",
    "    return {\n",
    "        \"ecorr_guess\": ecorr_guess,\n",
    "        \"p_anodic\": p_anodic,\n",
    "        \"p_cathodic\": p_cathodic,\n",
    "        \"anodic_data\": anodic_data,\n",
    "        \"cathodic_data\": cathodic_data,\n",
    "        \"beta_a\": beta_a,\n",
    "        \"beta_c\": beta_c,\n",
    "        \"alpha_a\": alpha_a,\n",
    "        \"alpha_c\": alpha_c,\n",
    "        \"icorr_anodic\": icorr_anodic,\n",
    "        \"icorr_cathodic\": icorr_cathodic,\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34f9a01d-0a09-4054-9eec-b12c512fe29a",
   "metadata": {},
   "source": [
    "## Plot Tafel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "56eba50b-b152-4033-af25-5ec5f2bbb80c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "from matplotlib.ticker import MultipleLocator, AutoMinorLocator\n",
    "import warnings\n",
    "\n",
    "def plot_tafel(\n",
    "    data: pd.DataFrame,\n",
    "    results: dict = None,\n",
    "    save_path: str = None,\n",
    "    fig_format: str = 'png',\n",
    "    column: str = 'single'\n",
    "):\n",
    "    \"\"\"\n",
    "    Generates a publication-quality Tafel plot from experimental data and analysis results.\n",
    "\n",
    "    This function formats the plot for single or double-column layouts typical\n",
    "    in academic journals, including specific font sizes, line styles, and\n",
    "    LaTeX-formatted labels.\n",
    "\n",
    "    Args:\n",
    "        data (pd.DataFrame): The original DataFrame containing experimental data.\n",
    "                             Must include 'E' (Potential in mV) and 'i' (Current in A).\n",
    "        results (dict, optional): A dictionary from an analysis function containing keys\n",
    "                                  like 'ecorr_guess', 'beta_a', 'beta_c', 'p_anodic',\n",
    "                                  'p_cathodic', 'anodic_data', and 'cathodic_data'.\n",
    "        save_path (str, optional): The file path to save the figure (without extension).\n",
    "                                   If None, the plot is displayed instead. Defaults to None.\n",
    "        fig_format (str, optional): The format to save the figure in (e.g., 'png',\n",
    "                                    'tiff', 'svg'). Defaults to 'png'.\n",
    "        column (str, optional): The journal column type for the plot.\n",
    "                                Can be 'single' or 'double'. Defaults to 'single'.\n",
    "    \"\"\"\n",
    "\n",
    "    # --- 1. Validate Input ---\n",
    "    if results is None or not isinstance(results, dict):\n",
    "        warnings.warn(\n",
    "            \"Analysis 'results' dictionary not provided. Plotting experimental data only.\",\n",
    "            UserWarning\n",
    "        )\n",
    "        results = {} # Use an empty dict to avoid errors later\n",
    "\n",
    "    # --- 2. Configure Matplotlib for Publication Quality based on column type ---\n",
    "    if column == 'single':\n",
    "        # Parameters for a single-column figure (~3.5 inches wide)\n",
    "        figsize = (3.5, 3.0)\n",
    "        plot_params = {\n",
    "            'font.size': 8,\n",
    "            'axes.labelsize': 9,\n",
    "            'legend.fontsize': 7,\n",
    "            'xtick.labelsize': 7,\n",
    "            'ytick.labelsize': 7,\n",
    "            'axes.linewidth': 0.8\n",
    "        }\n",
    "        data_line_width = 1.0\n",
    "        fit_line_width = 1.2\n",
    "        ecorr_line_width = 0.7\n",
    "    elif column == 'double':\n",
    "        # Parameters for a double-column figure (~7 inches wide)\n",
    "        figsize = (7, 5)\n",
    "        plot_params = {\n",
    "            'font.size': 10,\n",
    "            'axes.labelsize': 11,\n",
    "            'legend.fontsize': 9,\n",
    "            'xtick.labelsize': 9,\n",
    "            'ytick.labelsize': 9,\n",
    "            'axes.linewidth': 1.0\n",
    "        }\n",
    "        data_line_width = 1.2\n",
    "        fit_line_width = 1.5\n",
    "        ecorr_line_width = 0.8\n",
    "    else:\n",
    "        raise ValueError(\"Invalid column type specified. Choose 'single' or 'double'.\")\n",
    "\n",
    "    # --- 3. Generate Plot within a temporary style context ---\n",
    "    with plt.rc_context(rc=plot_params):\n",
    "        fig, ax = plt.subplots(figsize=figsize, dpi=300)\n",
    "\n",
    "        # --- 3a. Plot Experimental Data as a Line Plot ---        \n",
    "        ax.semilogx(\n",
    "            np.abs(data[\"i\"]),         # x data\n",
    "            data[\"E\"],                 # y data\n",
    "            marker='none', \n",
    "            linestyle='-',\n",
    "            color='black',\n",
    "            linewidth=data_line_width,\n",
    "            label='Experimental Data'\n",
    "        )\n",
    "\n",
    "        # --- 3b. Plot E_corr line and Overpotential Axis ---\n",
    "        if 'ecorr_guess' in results:\n",
    "            ecorr = results[\"ecorr_guess\"]\n",
    "            ax.axhline(\n",
    "                y=ecorr,\n",
    "                color='k',\n",
    "                linestyle='--',\n",
    "                linewidth=ecorr_line_width,\n",
    "                alpha=0.8,\n",
    "                label=f'$E_{{corr}}$ = {ecorr:.1f} mV'\n",
    "            )\n",
    "\n",
    "            #ax.set_ylim(top=-160, bottom=-210) \n",
    "            #ax.set_xlim(left=10**-16) \n",
    "            \n",
    "            # Create a twin axis for overpotential\n",
    "            bx = ax.twinx()\n",
    "            ymin, ymax = ax.get_ylim()\n",
    "            bx.set_ylim(ymin - ecorr, ymax - ecorr)\n",
    "            bx.set_ylabel(\"Overpotential (mV)\")\n",
    "\n",
    "            # Set major ticks to be multiples of 50 mV and add minor ticks\n",
    "            bx.yaxis.set_major_locator(MultipleLocator(5))\n",
    "            bx.yaxis.set_minor_locator(AutoMinorLocator(1))\n",
    "\n",
    "            bx.tick_params(direction='in', which='both')\n",
    "            bx.spines['right'].set_position(('outward', 0))\n",
    "        else:\n",
    "            warnings.warn(\"Cannot plot E_corr line: 'ecorr_guess' not in results.\", UserWarning)\n",
    "\n",
    "        # --- 3c. Plot Tafel Fit Lines ---\n",
    "        required_fit_keys = [\"anodic_data\", \"cathodic_data\", \n",
    "                             'p_anodic', 'p_cathodic', \n",
    "                             'icorr_anodic', 'icorr_cathodic',                              \n",
    "                             'beta_a', 'beta_c']\n",
    "        if all(key in results for key in required_fit_keys):\n",
    "            # ANODIC LINE DOTTED\n",
    "            ax.plot(\n",
    "                np.logspace(\n",
    "                    np.log10(results[\"icorr_anodic\"]),\n",
    "                    np.log10(results[\"anodic_data\"][\"i\"].max()),\n",
    "                    100,\n",
    "                ), \n",
    "                results[\"p_anodic\"](\n",
    "                    np.log10(\n",
    "                        np.logspace(\n",
    "                            np.log10(results[\"icorr_anodic\"]),\n",
    "                            np.log10(results[\"anodic_data\"][\"i\"].max()),\n",
    "                            100,  \n",
    "                        ),\n",
    "                    )\n",
    "                ),\n",
    "                '--', color='#d62728', linewidth=fit_line_width,\n",
    "                label=f'$\\\\beta_a$ = {results[\"beta_a\"]:.1f} mV dec$^{{-1}}$ \\n $\\\\alpha_a$ = {results[\"alpha_a\"]:.2f}'\n",
    "            )\n",
    "            # ANODIC LINE             \n",
    "            ax.plot(\n",
    "                np.logspace(\n",
    "                    np.log10(results[\"anodic_data\"][\"i\"].min()),\n",
    "                    np.log10(results[\"anodic_data\"][\"i\"].max()),\n",
    "                    100,\n",
    "                ), \n",
    "                results[\"p_anodic\"](\n",
    "                    np.log10(               \n",
    "                        np.logspace(\n",
    "                            np.log10(results[\"anodic_data\"][\"i\"].min()),\n",
    "                            np.log10(results[\"anodic_data\"][\"i\"].max()),\n",
    "                            100,\n",
    "                        ), \n",
    "                    )\n",
    "                ),\n",
    "                color='#d62728', linewidth=fit_line_width,\n",
    "            )\n",
    "            \n",
    "            # CATHODIC LINE DOTTED\n",
    "            ax.plot(\n",
    "                np.logspace(\n",
    "                    np.log10(results[\"icorr_cathodic\"]),\n",
    "                    np.log10(results[\"cathodic_data\"][\"i\"].max()),\n",
    "                    100,\n",
    "                ), \n",
    "                results[\"p_cathodic\"](\n",
    "                    np.log10(               \n",
    "                        np.logspace(\n",
    "                            np.log10(results[\"icorr_cathodic\"]),\n",
    "                            np.log10(results[\"cathodic_data\"][\"i\"].max()),\n",
    "                            100,\n",
    "                        ), \n",
    "                    )\n",
    "                ),\n",
    "                '--', color='#1f77b4', linewidth=fit_line_width,\n",
    "                label=f'$\\\\beta_c$ = {results[\"beta_c\"]:.1f} mV dec$^{{-1}}$ \\n $\\\\alpha_c$ = {results[\"alpha_c\"]:.2f}'\n",
    "            )\n",
    "            # CATHODIC LINE \n",
    "            ax.plot(\n",
    "                np.logspace(\n",
    "                    np.log10(results[\"cathodic_data\"][\"i\"].min()),\n",
    "                    np.log10(results[\"cathodic_data\"][\"i\"].max()),\n",
    "                    100,\n",
    "                ), \n",
    "                results[\"p_cathodic\"](\n",
    "                    np.log10(               \n",
    "                        np.logspace(\n",
    "                            np.log10(results[\"cathodic_data\"][\"i\"].min()),\n",
    "                            np.log10(results[\"cathodic_data\"][\"i\"].max()),\n",
    "                            100,\n",
    "                        ), \n",
    "                    )\n",
    "                ),\n",
    "                color='#1f77b4', linewidth=fit_line_width,\n",
    "            )\n",
    "\n",
    "        else:\n",
    "            warnings.warn(\"Cannot plot Tafel fit lines: one or more required keys are missing from results.\", UserWarning)\n",
    "\n",
    "        # --- 3d. Configure Plot Aesthetics ---\n",
    "        ax.set_xlabel('Current Density, |$j$| (A/cm$^2$)')\n",
    "        ax.set_ylabel('Potential, $E$ vs. Ref. (mV)')\n",
    "        ax.tick_params(direction='in', which='both', top=True, right=False)\n",
    "        ax.grid(True, which=\"both\", ls=\"--\", linewidth=0.4, color='gray', alpha=0.6)\n",
    "\n",
    "        # Combine legends and make the frame less prominent\n",
    "        lines, labels = ax.get_legend_handles_labels()\n",
    "        ax.legend(\n",
    "            lines, labels, loc='best',\n",
    "            frameon=True,\n",
    "            framealpha=0.9,\n",
    "            edgecolor='0.7',\n",
    "            borderpad=0.3,\n",
    "            labelspacing=0.4\n",
    "        )\n",
    "\n",
    "        fig.tight_layout()\n",
    "\n",
    "    # --- 4. Finalize and Show/Save Plot ---\n",
    "    if save_path:\n",
    "        plt.savefig(f\"{save_path}.{fig_format}\", format=fig_format, bbox_inches='tight')\n",
    "        print(f\"✅ Plot saved to {save_path}.{fig_format}\")\n",
    "    else:\n",
    "        plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "0651c767-a244-4649-8a82-fcda97ade5b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha_a: 0.53\n",
      "alpha_c: 0.46\n",
      "Sum of alphas: 0.99\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1050x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming 'HS1_6' is your DataFrame loaded with the experimental data\n",
    "# For example:\n",
    "# data = {'E': np.linspace(0, 1, 100), 'i': np.logspace(-6, -2, 100)}\n",
    "# HS1_6 = pd.DataFrame(data)\n",
    "\n",
    "# 1. Analyze the data\n",
    "analysis_results = analyze_tafel(HS1_5)\n",
    "\n",
    "# Print the calculated alpha values\n",
    "if analysis_results:\n",
    "    print(f\"alpha_a: {analysis_results['alpha_a']:.2f}\")\n",
    "    print(f\"alpha_c: {analysis_results['alpha_c']:.2f}\")\n",
    "    if analysis_results['alpha_a'] is not None and analysis_results['alpha_c'] is not None:\n",
    "        print(f\"Sum of alphas: {analysis_results['alpha_a'] + analysis_results['alpha_c']:.2f}\")\n",
    "\n",
    "\n",
    "# 2. Plot the data and the analysis\n",
    "plot_tafel(HS1_5, analysis_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "2f13865b-557a-4fdf-b3d4-a8ffa5c2c8e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "p = analysis_results[\"p_anodic\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "5f19c6e5-d6db-4530-b1e1-59b9fbe8a17a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "poly1d([ 31.77354549, 155.49089313])"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "9c700d08-e08f-4dd8-9737-3b11e1a7af14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(1.6913954688266906e-11)"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(2.73)**((p.roots)[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a9c66d0-3f55-4f4a-a79c-9da787abdfae",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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