notebooks/01_cond_factors/spt_processing.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SPT Data Processing\n",
    "\n",
    "© 2023 Daniel F. Ruiz, Exneyder A. Montoya-Araque y Universidad EAFIT.\n",
    "\n",
    "This notebook can be interactively run in [Google - Colab](https://colab.research.google.com/github/AppliedMechanics-EAFIT/EAFIT_slope_stability/blob/main/notebooks/01_cond_factors/spt_processing.ipynb).\n",
    "\n",
    "This notebook was developed following the procedure by {cite:t}`Gonzalez1999_ParametrosSPT` for estimating the shear strength parameters from the Standard Penetration Test data ([paper in Spanish](https://www.scg.org.co/wp-content/uploads/2021/08/ESTIMATIVOS-DE-PARAMETROS-DE-RESISTENCIA-CON-SPT-1.pdf))."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Required modules and global setup for plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from scipy.optimize import curve_fit, minimize\n",
    "from ipywidgets import widgets as wgt\n",
    "from IPython import get_ipython\n",
    "from IPython.display import display\n",
    "\n",
    "if 'google.colab' in str(get_ipython()):\n",
    "    print('Running on CoLab. Installing the required modules...')\n",
    "    from subprocess import run\n",
    "    # run('pip install ipympl', shell=True);\n",
    "    from google.colab import output, files\n",
    "    output.enable_custom_widget_manager()\n",
    "else:\n",
    "    import tkinter as tk\n",
    "    from tkinter.filedialog import askopenfilename\n",
    "\n",
    "# Figures setup\n",
    "# %matplotlib widget\n",
    "mpl.rcParams.update({\n",
    "    \"font.family\": \"serif\",\n",
    "    \"font.serif\": [\"Computer Modern Roman\", \"cmr\", \"cmr10\", \"DejaVu Serif\"],  # or \n",
    "    \"mathtext.fontset\": \"cm\",  # Use Computer Modern fonts for math\n",
    "    \"axes.formatter.use_mathtext\": True,  # Use mathtext for axis labels\n",
    "    \"axes.unicode_minus\": False,   # Use standard minus sign instead of a unicode character\n",
    "})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Funciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def x_from_y(x_coord, y_coord, target_y):\n",
    "    for i in range(len(y_coord)):\n",
    "        if y_coord[i] == target_y:  # if target_y coincides with a node\n",
    "            if i < len(y_coord) and y_coord[i+1] == y_coord[i]:  # if on a horizontal segment:\n",
    "                return 0.5 * (x_coord[i] + x_coord[i+1])  # return midpoint in x\n",
    "            else:\n",
    "                return x_coord[i]\n",
    "        elif i > 0 and y_coord[i-1] < target_y < y_coord[i]:\n",
    "            # Interpolate x-value when the target y-value lies between two points\n",
    "            x1, x2 = x_coord[i-1], x_coord[i]\n",
    "            y1, y2 = y_coord[i-1], y_coord[i]\n",
    "            return x1 + (x2 - x1) * (target_y - y1) / (y2 - y1)\n",
    "    return None  # Return None if y-value not found within the range\n",
    "\n",
    "def compute_stresses(df, mat_depths, 𝛾_moist, 𝛾_sat, wt_depth=None):\n",
    "    𝛾_w = 9.81  # unit weight of water [kN/m³]\n",
    "    # Copying arrays\n",
    "    mat_depths = mat_depths.copy()\n",
    "    𝛾_moist = 𝛾_moist.copy()\n",
    "    𝛾_sat = 𝛾_sat.copy()\n",
    "    # Find an appropiate index for locating the watertable\n",
    "    if wt_depth in mat_depths:\n",
    "        idx_wt = mat_depths.index(wt_depth)\n",
    "    elif wt_depth >= mat_depths[-1] or wt_depth is None:\n",
    "        wt_depth = mat_depths[-1]\n",
    "        idx_wt = len(mat_depths) - 1\n",
    "    else:\n",
    "        for i in range(len(mat_depths)):\n",
    "            if wt_depth <= mat_depths[i]:\n",
    "                idx_wt = i\n",
    "                break\n",
    "    # Insert the new value at the appropriate index\n",
    "    mat_depths.insert(idx_wt, wt_depth)\n",
    "    𝛾_moist.insert(idx_wt, 𝛾_moist[idx_wt])\n",
    "    𝛾_sat.insert(idx_wt, 𝛾_sat[idx_wt])\n",
    "    # Create an unified unit weight vector\n",
    "    𝛾_s = 𝛾_moist[: idx_wt + 1] + 𝛾_sat[idx_wt + 1 :]\n",
    "    # Create vector of thicknesses\n",
    "    mat_depths.insert(0, 0)  # insert zero for the first layer\n",
    "    thickness = np.diff(mat_depths)  # thickness of each soil layer [m]\n",
    "    # Create a vector for unit weigth of water (zero above wt, and 𝛾_w below)\n",
    "    𝛾_w = np.full_like(𝛾_s, 𝛾_w)\n",
    "    𝛾_w[: idx_wt + 1] = 0\n",
    "    # Compute vertical stresses and water pressure at boundaries\n",
    "    𝜎_v = np.insert(np.cumsum(thickness * 𝛾_s), 0, 0)\n",
    "    p_w = np.insert(np.cumsum(thickness * 𝛾_w), 0, 0)\n",
    "    𝜎_v_eff = 𝜎_v - p_w\n",
    "    df['Sigma_eff_(kPa)'] = [\n",
    "        x_from_y(x_coord=𝜎_v_eff, y_coord=mat_depths, target_y=z)\n",
    "        for z in df['Profundidad_(m)'].to_numpy()\n",
    "    ]\n",
    "    return\n",
    "\n",
    "def compute_corrected_N(df, perfo_diam, field_test_energy):\n",
    "    # Stress level correction\n",
    "    n, 𝜎_ref = 0.5, 95.76  # reference stress level = 1 tsf(short) to kPa\n",
    "    # df['Cn'] = (𝜎_ref / df['Sigma_eff_(kPa)']) ** n  # Liao y Whitman (1986)\n",
    "    df['Cn'] = 2 / (1 + df['Sigma_eff_(kPa)']/𝜎_ref)  # Skempton (1986)\n",
    "    # Correction factor to reach an standard energy level of 60%\n",
    "    df['n1_E60'] = field_test_energy / 60\n",
    "    # Correction factor due to the length of the drill rod\n",
    "    n2 = np.empty_like(df['Profundidad_(m)'], dtype=float)\n",
    "    depth = df['Profundidad_(m)'].to_numpy()\n",
    "    n2[depth >= 10] = 1\n",
    "    n2[depth < 10] = 0.95\n",
    "    n2[depth < 6] = 0.85\n",
    "    n2[depth < 4] = 0.75\n",
    "    df['n2'] = n2\n",
    "    # Correction factor due to the cassing\n",
    "    mat_type = np.array([int(d[0]) for d in df['Descripción'].to_list()])\n",
    "    n3 = np.ones_like(df['Profundidad_(m)'], dtype=float)\n",
    "    n3[mat_type == 1] = 0.8\n",
    "    n3[mat_type == 2] = 0.8\n",
    "    n3[mat_type == 3] = 0.9\n",
    "    n3[mat_type == 4] = 0.8\n",
    "    n3[mat_type == 5] = 0.85\n",
    "    n3[mat_type == 6] = 0.9\n",
    "    n3[df['Revestimiento_(m)'] < df['Profundidad_(m)']] = 1\n",
    "    df['n3'] = n3\n",
    "    # Correction factor due to the hole diameter\n",
    "    if perfo_diam <= 120:\n",
    "        df['n4'] = 1\n",
    "    elif perfo_diam <= 150:\n",
    "        df['n4'] = 1.05\n",
    "    elif perfo_diam > 150:\n",
    "        df['n4'] = 1.15\n",
    "    # N corrected\n",
    "    df['N45'] = np.int64(df['N_campo'] * df['Cn'] * df['n2'] * df['n3'] * df['n4'] * field_test_energy / 45)\n",
    "    df['N55'] = np.int64(df['N_campo'] * df['Cn'] * df['n2'] * df['n3'] * df['n4'] * field_test_energy / 55)\n",
    "    df['N60'] = np.int64(df['N_campo'] * df['Cn'] * df['n2'] * df['n3'] * df['n4'] * field_test_energy / 60)\n",
    "\n",
    "def compute_correlations(df, corr_𝜙='f'):\n",
    "    '''\n",
    "    Correlations for the friction angle based on the letters accompanying the\n",
    "    formulas (7) and (8) in Gonzalez (1999)\n",
    "    '''\n",
    "    # Equivalent friction angle\n",
    "    if corr_𝜙 == 'a':  # Peck\n",
    "        df['𝜙_eq'] = 28.5 + 0.25 * df['N45']\n",
    "    elif corr_𝜙 == 'b':  # Peck, Hanson & Thornburn\n",
    "        df['𝜙_eq'] = 26.25 * (2 - np.exp(-1 * df['N45']/62))\n",
    "    elif corr_𝜙 == 'c':  # Kishida\n",
    "        df['𝜙_eq'] = 15 + (12.5 * df['N45']) ** 0.5\n",
    "    elif corr_𝜙 == 'd':  # Schmertmann\n",
    "        df['𝜙_eq'] = np.arctan((df['N45'] / 43.3)**0.34)\n",
    "    elif corr_𝜙 == 'e':  # Japan National Railway (JNR)\n",
    "        df['𝜙_eq'] = 27 + 0.1875 * df['N45']\n",
    "    elif corr_𝜙 == 'f':  # Japan Road Bureau (JRB)\n",
    "        df['𝜙_eq'] = 15 + (9.375 * df['N45']) ** 0.5\n",
    "    # Equivalent shear strength \n",
    "    df['𝜏_eq'] = df['Sigma_eff_(kPa)'] * np.tan(np.deg2rad(df['𝜙_eq']))\n",
    "    # Undrained shear strength based on Schmertmann (1975)\n",
    "    df['Su_(kPa)'] = df['N60'] / 15 * 95.76\n",
    "    # Put nan to 'Su_(kPa)' where fines are < 50% or isnan\n",
    "    df.loc[df['Finos_(%)'] < 50, 'Su_(kPa)'] = np.nan\n",
    "    df.loc[df['Finos_(%)'].isna(), 'Su_(kPa)'] = np.nan\n",
    "    # Elasticity modulus\n",
    "    mat_type = np.array([int(d[0]) for d in df['Descripción'].to_list()])\n",
    "    elastic_mod = np.ones_like(df['Profundidad_(m)'])\n",
    "    elastic_mod[mat_type == 1] = (250 * (df['N55'] + 15))[mat_type == 1]\n",
    "    elastic_mod[mat_type == 2] = (500 * (df['N55'] + 15))[mat_type == 2]\n",
    "    elastic_mod[mat_type == 3] = (40000 + (df['N55'] * 1050))[mat_type == 3]\n",
    "    mask_4a = np.logical_and(mat_type == 4, df['N55'] <= 15)\n",
    "    elastic_mod[mask_4a] = (600 * (df['N55'] + 6))[mask_4a]\n",
    "    mask_4b = np.logical_and(mat_type == 4, df['N55'] > 15)\n",
    "    elastic_mod[mask_4b] = (2000 + (600 * (df['N55'] + 6)))[mask_4b]\n",
    "    elastic_mod[mat_type == 5] = (320 * (df['N55'] + 15))[mat_type == 5]\n",
    "    elastic_mod[mat_type == 6] = (300 * (df['N55'] + 6))[mat_type == 6]\n",
    "    df['Es_(kPa)'] = elastic_mod\n",
    "\n",
    "\n",
    "def complete_table(df, mat_depths, 𝛾_moist, 𝛾_sat, wt_depth=None, perfo_diam=75,\n",
    "                   field_test_energy=60, corr_𝜙='f'):\n",
    "    compute_stresses(df, mat_depths, 𝛾_moist, 𝛾_sat, wt_depth)\n",
    "    compute_corrected_N(df, perfo_diam, field_test_energy)\n",
    "    compute_correlations(df, corr_𝜙='f')\n",
    "\n",
    "def f_mc(x, m, b):  # Linear function for Mohr-Coulomb envelope with non-zero intercept\n",
    "    return m * x + b\n",
    "\n",
    "def f_mc_b0(x, m):  # Linear function for Mohr-Coulomb envelope with zero intercept forced if negative\n",
    "    return m * x\n",
    "\n",
    "\n",
    "palette = mpl.colors.ListedColormap(['#4477AA', '#EE6677', '#228833', '#CCBB44', '#66CCEE', '#AA3377', '#BBBBBB'])\n",
    "palette = mpl.colors.ListedColormap(['#004488', '#DDAA33', '#BB5566', '#6699CC', '#EECC66', '#EE99AA'])\n",
    "\n",
    "\n",
    "def plot_processing(df, mat_depths, wt_depth, plot_su=True, figsize=None):\n",
    "    if figsize is None:\n",
    "        figsize = [9, 6]\n",
    "    fig, axs = plt.subplot_mosaic([['A', 'B', 'B'], ['A', '.', '.']],\n",
    "                              layout='constrained', figsize=figsize)\n",
    "    # Set Dark2 as the default color cycle\n",
    "    # plt.style.use('seaborn-darkgrid')\n",
    "    plt.rcParams.update({'axes.prop_cycle': plt.cycler(color=palette.colors)})\n",
    "\n",
    "    # Create a twin axis for Su in axs['A'] if plot_su is True\n",
    "    if plot_su:\n",
    "        ax_twin = axs['B'].twinx()\n",
    "\n",
    "    # Plotting the fine fraction content\n",
    "    if 'Finos_(%)' in df.columns:\n",
    "        if not df['Finos_(%)'].isna().all():  # check if it's not full of nan\n",
    "            axs['A'].plot(df['Finos_(%)'], df['Profundidad_(m)'], ls=\"\",\n",
    "                        marker=\"|\", mfc=\"w\", mec=\"k\", ms=10, mew=2.5, label=\"Fine fraction [%]\")\n",
    "    for i, mat in enumerate(df['Material'].unique()):\n",
    "        df_mat = df[df['Material'] == mat]\n",
    "        # Plotting N values vs depth\n",
    "        axs['A'].plot(df_mat['N_campo'], df_mat['Profundidad_(m)'], ls=\"\", marker=\"o\", ms=6,\n",
    "                      mfc=mpl.colors.to_rgba(f\"C{i}\", 0.8), mec=mpl.colors.to_rgba('k', 1), mew=.75)#, label=mat)\n",
    "        # Plotting shear strength vs sigma_v\n",
    "        axs['B'].plot(df_mat['Sigma_eff_(kPa)'], df_mat['𝜏_eq'], ls=\"\", marker=\"o\", ms=6,\n",
    "                      mfc=mpl.colors.to_rgba(f\"C{i}\", 0.8), mec=mpl.colors.to_rgba('k', 1), mew=.75, label=mat)\n",
    "        if len(df_mat) == 1:\n",
    "            phi = df_mat['𝜙_eq'].values[0]\n",
    "            m, b = np.tan(np.deg2rad(phi)), 0.0\n",
    "            axs['B'].plot(df_mat['Sigma_eff_(kPa)'], m * df_mat['Sigma_eff_(kPa)'] + b,\n",
    "                        ls=':', c=f\"C{i}\", label=f\"$\\\\phi'={phi:.1f}^\\\\circ$\")\n",
    "        else:\n",
    "            m, b = curve_fit(f_mc, df_mat['Sigma_eff_(kPa)'], df_mat['𝜏_eq'])[0]\n",
    "            if b < 0:\n",
    "                m, b = curve_fit(f_mc_b0, df_mat['Sigma_eff_(kPa)'], df_mat['𝜏_eq'])[0][0], 0.0\n",
    "            # phi = np.rad2deg(m)\n",
    "            phi = np.rad2deg(np.arctan(m))  # Correction by O. Perafán (2025/04/19)\n",
    "            axs['B'].plot(df_mat['Sigma_eff_(kPa)'], m * df_mat['Sigma_eff_(kPa)'] + b,\n",
    "                        ls=':', c=f\"C{i}\", label=f\"$\\\\phi'={phi:.1f}^\\\\circ$, $c'={b:.1f}$ kPa\")\n",
    "        # Plotting Su values vs depth in a twin axis if plot_su is True\n",
    "        if plot_su and not np.isnan(df_mat['Su_(kPa)'].mean()):\n",
    "            label = f\"Average $S_\\\\mathrm{{u}} = {df_mat['Su_(kPa)'].mean():.1f}$ kPa\"\n",
    "            ax_twin.plot(df_mat['Sigma_eff_(kPa)'], df_mat['Su_(kPa)'], ls=\"\", marker=\"_\", ms=7,\n",
    "                         mec=mpl.colors.to_rgba(f\"C{i}\", 1), mew=2.5)#, label=label)\n",
    "            # Fake plot in the original axis to show the legend\n",
    "            axs['B'].plot([], [], ls=\"\", marker=\"_\", ms=7, mec=mpl.colors.to_rgba(f\"C{i}\", 1),\n",
    "                          mew=2.5, label=label)\n",
    "    for d in mat_depths:\n",
    "        axs['A'].axhline(d, color=\"k\", ls=\"--\", lw=1.25)\n",
    "    axs['A'].axhline(0, color=\"g\", ls=\"-\", lw=3.0, label=\"Terrain surface\")\n",
    "    axs['A'].axhline(np.nan, color=\"k\", ls=\"--\", lw=1.25, label=\"Material boundary\")\n",
    "    axs['A'].axhline(y=wt_depth, ls=\"-\", color=\"XKCD:electric blue\", lw=1.25, label=\"Watertable\")\n",
    "\n",
    "    # Plot setup\n",
    "    axs['A'].invert_yaxis()\n",
    "    # invert y-axis for depth for the twin axis\n",
    "    if plot_su:\n",
    "        ax_twin.set_ylabel(\"$S_\\\\mathrm{u}$  [kPa]\")\n",
    "        ax_twin.spines[\"right\"].set_linewidth(1.5)\n",
    "    axs['A'].set(xlabel=\"N$_\\\\mathrm{SPT}$ &  Fine fraction [%]\", ylabel=\"Depth [m]\", xlim=[-5, 105])\n",
    "    axs['B'].set(xlabel=\"$\\\\sigma'$  [kPa]\", ylabel=\"$\\\\tau'$  [kPa]\")\n",
    "    for ax in axs.values():\n",
    "        ax.xaxis.set_label_position(\"top\")\n",
    "        ax.xaxis.tick_top()\n",
    "        ax.spines[\"top\"].set_linewidth(1.5)\n",
    "        ax.spines[\"left\"].set_linewidth(1.5)\n",
    "        ax.grid(True, ls=\"--\", color=\"silver\")\n",
    "    # axs['A'].legend(loc=\"upper right\")\n",
    "    fig.legend(loc='upper center', bbox_to_anchor=(0.7, 0.45), ncol=1, handlelength=2.5)\n",
    "    # Manual legend for plot A \n",
    "    handles = [mpl.lines.Line2D([0], [0], marker=\"o\", mec=\"k\", mfc=\"0.5\", ms=6, lw=0, label=\"N$_\\\\mathrm{SPT}$\"),\n",
    "               mpl.lines.Line2D([0], [0], marker=\"|\", mec=\"k\", ms=10, mew=2.5, lw=0, label=\"Fine\\nfraction\")]\n",
    "    axs['A'].legend(handles=handles, loc=\"upper right\", fontsize=\"small\")\n",
    "    # Manual legend for plot B\n",
    "    handles = [mpl.lines.Line2D([0], [0], marker=\"o\", mec=\"k\", mfc=\"0.5\", ms=6, ls=':', label=\"$\\\\phi'-c'$ correlation\"),\n",
    "               mpl.lines.Line2D([0], [0], marker=\"_\", mec=\"k\", ms=7, mew=2.5, lw=0, label=f\"$S_\\\\mathrm{{u}}$ correlation\")]\n",
    "    axs['B'].legend(handles=handles, loc=\"upper left\", fontsize=\"small\", handlelength=2.5)\n",
    "    fig.canvas.header_visible = False\n",
    "    fig.canvas.toolbar_position = 'bottom'\n",
    "    plt.show()\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "testing_data = True  # Set to False to use the GUI to load the data from an external file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Non-tabulated data\n",
    "mat_depths = [7, 12.5, 18]  # bottom depth of each material [m]\n",
    "𝛾_moist = [18.50, 16.5, 16.00]  # Total/moist/bulk unit weight of each soil layer [kN/m³]\n",
    "𝛾_sat = [19.50, 17.0, 17.50]  # Saturated unit weight of each soil layer [kN/m³]\n",
    "wt_depth = 10  # watertable depth [m]\n",
    "perfo_diam = 75  # [mm]\n",
    "field_test_energy = 45  # [%]\n",
    "corr_𝜙 = 'f'  # JRB\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eamontoyaa/.pyenv/versions/3.11.11/envs/EAFIT-3p11-env/lib/python3.11/site-packages/openpyxl/worksheet/_reader.py:329: UserWarning: Data Validation extension is not supported and will be removed\n",
      "  warn(msg)\n"
     ]
    },
    {
     "data": {
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>Material</th>\n",
       "      <th>Descripción</th>\n",
       "      <th>Profundidad_(m)</th>\n",
       "      <th>Revestimiento_(m)</th>\n",
       "      <th>N_campo</th>\n",
       "      <th>Finos_(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dep. de vertiente</td>\n",
       "      <td>6 - Limos, suelos finogranulares</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dep. de vertiente</td>\n",
       "      <td>6 - Limos, suelos finogranulares</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dep. de vertiente</td>\n",
       "      <td>6 - Limos, suelos finogranulares</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dep. de vertiente</td>\n",
       "      <td>6 - Limos, suelos finogranulares</td>\n",
       "      <td>4.25</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dep. de vertiente</td>\n",
       "      <td>6 - Limos, suelos finogranulares</td>\n",
       "      <td>5.25</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Horizonte IC - Migmatita - Sup</td>\n",
       "      <td>5 - Arena limosa y/o arcillosa</td>\n",
       "      <td>7.25</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Horizonte IC - Migmatita - Sup</td>\n",
       "      <td>5 - Arena limosa y/o arcillosa</td>\n",
       "      <td>8.25</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Horizonte IC - Migmatita - Sup</td>\n",
       "      <td>5 - Arena limosa y/o arcillosa</td>\n",
       "      <td>10.25</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Horizonte IC - Migmatita - Sup</td>\n",
       "      <td>5 - Arena limosa y/o arcillosa</td>\n",
       "      <td>11.25</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Horizonte IC - Migmatita - Sup</td>\n",
       "      <td>5 - Arena limosa y/o arcillosa</td>\n",
       "      <td>12.25</td>\n",
       "      <td>6</td>\n",
       "      <td>33</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Horizonte IC - Migmatita - Inf</td>\n",
       "      <td>3 - Arena densa a dura</td>\n",
       "      <td>13.25</td>\n",
       "      <td>6</td>\n",
       "      <td>42</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Horizonte IC - Migmatita - Inf</td>\n",
       "      <td>3 - Arena densa a dura</td>\n",
       "      <td>14.25</td>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Horizonte IC - Migmatita - Inf</td>\n",
       "      <td>3 - Arena densa a dura</td>\n",
       "      <td>15.25</td>\n",
       "      <td>6</td>\n",
       "      <td>74</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Horizonte IC - Migmatita - Inf</td>\n",
       "      <td>3 - Arena densa a dura</td>\n",
       "      <td>16.25</td>\n",
       "      <td>6</td>\n",
       "      <td>33</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Horizonte IC - Migmatita - Inf</td>\n",
       "      <td>3 - Arena densa a dura</td>\n",
       "      <td>17.25</td>\n",
       "      <td>6</td>\n",
       "      <td>63</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Material                       Descripción  \\\n",
       "0                Dep. de vertiente  6 - Limos, suelos finogranulares   \n",
       "1                Dep. de vertiente  6 - Limos, suelos finogranulares   \n",
       "2                Dep. de vertiente  6 - Limos, suelos finogranulares   \n",
       "3                Dep. de vertiente  6 - Limos, suelos finogranulares   \n",
       "4                Dep. de vertiente  6 - Limos, suelos finogranulares   \n",
       "5   Horizonte IC - Migmatita - Sup    5 - Arena limosa y/o arcillosa   \n",
       "6   Horizonte IC - Migmatita - Sup    5 - Arena limosa y/o arcillosa   \n",
       "7   Horizonte IC - Migmatita - Sup    5 - Arena limosa y/o arcillosa   \n",
       "8   Horizonte IC - Migmatita - Sup    5 - Arena limosa y/o arcillosa   \n",
       "9   Horizonte IC - Migmatita - Sup    5 - Arena limosa y/o arcillosa   \n",
       "10  Horizonte IC - Migmatita - Inf            3 - Arena densa a dura   \n",
       "11  Horizonte IC - Migmatita - Inf            3 - Arena densa a dura   \n",
       "12  Horizonte IC - Migmatita - Inf            3 - Arena densa a dura   \n",
       "13  Horizonte IC - Migmatita - Inf            3 - Arena densa a dura   \n",
       "14  Horizonte IC - Migmatita - Inf            3 - Arena densa a dura   \n",
       "\n",
       "    Profundidad_(m)  Revestimiento_(m)  N_campo  Finos_(%)  \n",
       "0              0.25                  0        4        NaN  \n",
       "1              1.25                  0       12       18.0  \n",
       "2              2.25                  0       11       35.0  \n",
       "3              4.25                  0        9        NaN  \n",
       "4              5.25                  0       12       23.0  \n",
       "5              7.25                  6       12        5.0  \n",
       "6              8.25                  6       23        NaN  \n",
       "7             10.25                  6       19        0.0  \n",
       "8             11.25                  6       16        1.0  \n",
       "9             12.25                  6       33       10.0  \n",
       "10            13.25                  6       42        0.0  \n",
       "11            14.25                  6       60        2.0  \n",
       "12            15.25                  6       74        NaN  \n",
       "13            16.25                  6       33        5.0  \n",
       "14            17.25                  6       63        NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tabulated data\n",
    "if testing_data:\n",
    "    url = \"https://raw.githubusercontent.com/eamontoyaa/data4testing/main/spt/\"\n",
    "    df = pd.read_excel(f\"{url}spt_processing_input_2.xlsx\")\n",
    "elif testing_data is False and 'google.colab' in str(get_ipython()):\n",
    "    file = files.upload()\n",
    "    df = pd.read_excel(list(file.values())[0])\n",
    "else:  # GUI for file selection from local machine if not in CoLab\n",
    "    tk.Tk().withdraw() # part of the import if you are not using other tkinter functions\n",
    "    file = askopenfilename()\n",
    "    df = pd.read_excel(file)\n",
    "\n",
    "# # df.loc[4, 'Finos_(%)'] = 55\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing the data and completing the table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Material</th>\n",
       "      <th>Descripción</th>\n",
       "      <th>Profundidad_(m)</th>\n",
       "      <th>Revestimiento_(m)</th>\n",
       "      <th>N_campo</th>\n",
       "      <th>Finos_(%)</th>\n",
       "      <th>Sigma_eff_(kPa)</th>\n",
       "      <th>Cn</th>\n",
       "      <th>n1_E60</th>\n",
       "      <th>n2</th>\n",
       "      <th>n3</th>\n",
       "      <th>n4</th>\n",
       "      <th>N45</th>\n",
       "      <th>N55</th>\n",
       "      <th>N60</th>\n",
       "      <th>𝜙_eq</th>\n",
       "      <th>𝜏_eq</th>\n",
       "      <th>Su_(kPa)</th>\n",
       "      <th>Es_(kPa)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dep. de vertiente</td>\n",
       "      <td>6 - Limos, suelos finogranulares</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.6250</td>\n",
       "      <td>1.907855</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>21.846532</td>\n",
       "      <td>1.854226</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dep. de vertiente</td>\n",
       "      <td>6 - Limos, suelos finogranulares</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>18.0</td>\n",
       "      <td>23.1250</td>\n",
       "      <td>1.610969</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>26.456439</td>\n",
       "      <td>11.507756</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dep. de vertiente</td>\n",
       "      <td>6 - Limos, suelos finogranulares</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>35.0</td>\n",
       "      <td>41.6250</td>\n",
       "      <td>1.394039</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>25.155048</td>\n",
       "      <td>19.547364</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dep. de vertiente</td>\n",
       "      <td>6 - Limos, suelos finogranulares</td>\n",
       "      <td>4.25</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.6250</td>\n",
       "      <td>1.098260</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>23.660254</td>\n",
       "      <td>34.448930</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dep. de vertiente</td>\n",
       "      <td>6 - Limos, suelos finogranulares</td>\n",
       "      <td>5.25</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>23.0</td>\n",
       "      <td>97.1250</td>\n",
       "      <td>0.992923</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>24.682458</td>\n",
       "      <td>44.636487</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Horizonte IC - Migmatita - Sup</td>\n",
       "      <td>5 - Arena limosa y/o arcillosa</td>\n",
       "      <td>7.25</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>5.0</td>\n",
       "      <td>133.6250</td>\n",
       "      <td>0.834928</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>24.185587</td>\n",
       "      <td>60.013056</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7040.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Horizonte IC - Migmatita - Sup</td>\n",
       "      <td>5 - Arena limosa y/o arcillosa</td>\n",
       "      <td>8.25</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.1250</td>\n",
       "      <td>0.778901</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>27.624381</td>\n",
       "      <td>78.564814</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Horizonte IC - Migmatita - Sup</td>\n",
       "      <td>5 - Arena limosa y/o arcillosa</td>\n",
       "      <td>10.25</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180.7975</td>\n",
       "      <td>0.692514</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>26.039701</td>\n",
       "      <td>88.335964</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Horizonte IC - Migmatita - Sup</td>\n",
       "      <td>5 - Arena limosa y/o arcillosa</td>\n",
       "      <td>11.25</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>187.9875</td>\n",
       "      <td>0.674966</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>24.682458</td>\n",
       "      <td>86.394869</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Horizonte IC - Migmatita - Sup</td>\n",
       "      <td>5 - Arena limosa y/o arcillosa</td>\n",
       "      <td>12.25</td>\n",
       "      <td>6</td>\n",
       "      <td>33</td>\n",
       "      <td>10.0</td>\n",
       "      <td>195.1775</td>\n",
       "      <td>0.658286</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>29.031215</td>\n",
       "      <td>108.327703</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Horizonte IC - Migmatita - Inf</td>\n",
       "      <td>3 - Arena densa a dura</td>\n",
       "      <td>13.25</td>\n",
       "      <td>6</td>\n",
       "      <td>42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202.7425</td>\n",
       "      <td>0.641603</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>30.612495</td>\n",
       "      <td>119.961266</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Horizonte IC - Migmatita - Inf</td>\n",
       "      <td>3 - Arena densa a dura</td>\n",
       "      <td>14.25</td>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "      <td>2.0</td>\n",
       "      <td>210.4325</td>\n",
       "      <td>0.625489</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>33.624581</td>\n",
       "      <td>139.941185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Horizonte IC - Migmatita - Inf</td>\n",
       "      <td>3 - Arena densa a dura</td>\n",
       "      <td>15.25</td>\n",
       "      <td>6</td>\n",
       "      <td>74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>218.1225</td>\n",
       "      <td>0.610165</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>36</td>\n",
       "      <td>33</td>\n",
       "      <td>35.539596</td>\n",
       "      <td>155.812814</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Horizonte IC - Migmatita - Inf</td>\n",
       "      <td>3 - Arena densa a dura</td>\n",
       "      <td>16.25</td>\n",
       "      <td>6</td>\n",
       "      <td>33</td>\n",
       "      <td>5.0</td>\n",
       "      <td>225.8125</td>\n",
       "      <td>0.595573</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>28.346348</td>\n",
       "      <td>121.823230</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Horizonte IC - Migmatita - Inf</td>\n",
       "      <td>3 - Arena densa a dura</td>\n",
       "      <td>17.25</td>\n",
       "      <td>6</td>\n",
       "      <td>63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233.5025</td>\n",
       "      <td>0.581664</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>29</td>\n",
       "      <td>27</td>\n",
       "      <td>33.371173</td>\n",
       "      <td>153.798027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Material                       Descripción  \\\n",
       "0                Dep. de vertiente  6 - Limos, suelos finogranulares   \n",
       "1                Dep. de vertiente  6 - Limos, suelos finogranulares   \n",
       "2                Dep. de vertiente  6 - Limos, suelos finogranulares   \n",
       "3                Dep. de vertiente  6 - Limos, suelos finogranulares   \n",
       "4                Dep. de vertiente  6 - Limos, suelos finogranulares   \n",
       "5   Horizonte IC - Migmatita - Sup    5 - Arena limosa y/o arcillosa   \n",
       "6   Horizonte IC - Migmatita - Sup    5 - Arena limosa y/o arcillosa   \n",
       "7   Horizonte IC - Migmatita - Sup    5 - Arena limosa y/o arcillosa   \n",
       "8   Horizonte IC - Migmatita - Sup    5 - Arena limosa y/o arcillosa   \n",
       "9   Horizonte IC - Migmatita - Sup    5 - Arena limosa y/o arcillosa   \n",
       "10  Horizonte IC - Migmatita - Inf            3 - Arena densa a dura   \n",
       "11  Horizonte IC - Migmatita - Inf            3 - Arena densa a dura   \n",
       "12  Horizonte IC - Migmatita - Inf            3 - Arena densa a dura   \n",
       "13  Horizonte IC - Migmatita - Inf            3 - Arena densa a dura   \n",
       "14  Horizonte IC - Migmatita - Inf            3 - Arena densa a dura   \n",
       "\n",
       "    Profundidad_(m)  Revestimiento_(m)  N_campo  Finos_(%)  Sigma_eff_(kPa)  \\\n",
       "0              0.25                  0        4        NaN           4.6250   \n",
       "1              1.25                  0       12       18.0          23.1250   \n",
       "2              2.25                  0       11       35.0          41.6250   \n",
       "3              4.25                  0        9        NaN          78.6250   \n",
       "4              5.25                  0       12       23.0          97.1250   \n",
       "5              7.25                  6       12        5.0         133.6250   \n",
       "6              8.25                  6       23        NaN         150.1250   \n",
       "7             10.25                  6       19        0.0         180.7975   \n",
       "8             11.25                  6       16        1.0         187.9875   \n",
       "9             12.25                  6       33       10.0         195.1775   \n",
       "10            13.25                  6       42        0.0         202.7425   \n",
       "11            14.25                  6       60        2.0         210.4325   \n",
       "12            15.25                  6       74        NaN         218.1225   \n",
       "13            16.25                  6       33        5.0         225.8125   \n",
       "14            17.25                  6       63        NaN         233.5025   \n",
       "\n",
       "          Cn  n1_E60    n2   n3  n4  N45  N55  N60       𝜙_eq        𝜏_eq  \\\n",
       "0   1.907855    0.75  0.75  1.0   1    5    4    4  21.846532    1.854226   \n",
       "1   1.610969    0.75  0.75  1.0   1   14   11   10  26.456439   11.507756   \n",
       "2   1.394039    0.75  0.75  1.0   1   11    9    8  25.155048   19.547364   \n",
       "3   1.098260    0.75  0.85  1.0   1    8    6    6  23.660254   34.448930   \n",
       "4   0.992923    0.75  0.85  1.0   1   10    8    7  24.682458   44.636487   \n",
       "5   0.834928    0.75  0.95  1.0   1    9    7    7  24.185587   60.013056   \n",
       "6   0.778901    0.75  0.95  1.0   1   17   13   12  27.624381   78.564814   \n",
       "7   0.692514    0.75  1.00  1.0   1   13   10    9  26.039701   88.335964   \n",
       "8   0.674966    0.75  1.00  1.0   1   10    8    8  24.682458   86.394869   \n",
       "9   0.658286    0.75  1.00  1.0   1   21   17   16  29.031215  108.327703   \n",
       "10  0.641603    0.75  1.00  1.0   1   26   22   20  30.612495  119.961266   \n",
       "11  0.625489    0.75  1.00  1.0   1   37   30   28  33.624581  139.941185   \n",
       "12  0.610165    0.75  1.00  1.0   1   45   36   33  35.539596  155.812814   \n",
       "13  0.595573    0.75  1.00  1.0   1   19   16   14  28.346348  121.823230   \n",
       "14  0.581664    0.75  1.00  1.0   1   36   29   27  33.371173  153.798027   \n",
       "\n",
       "    Su_(kPa)  Es_(kPa)  \n",
       "0        NaN    3000.0  \n",
       "1        NaN    5100.0  \n",
       "2        NaN    4500.0  \n",
       "3        NaN    3600.0  \n",
       "4        NaN    4200.0  \n",
       "5        NaN    7040.0  \n",
       "6        NaN    8960.0  \n",
       "7        NaN    8000.0  \n",
       "8        NaN    7360.0  \n",
       "9        NaN   10240.0  \n",
       "10       NaN   63100.0  \n",
       "11       NaN   71500.0  \n",
       "12       NaN   77800.0  \n",
       "13       NaN   56800.0  \n",
       "14       NaN   70450.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_table(df, mat_depths, 𝛾_moist, 𝛾_sat, wt_depth, perfo_diam, field_test_energy, corr_𝜙)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the processed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_processing(df, mat_depths, wt_depth)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "EAFIT-env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "ef8c03790f2d0da309ee3dc20d7c1e218681fdbff540ce14f81a0ed1380bf99b"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}