notebooks/02_trigg_factors/infinite_slope_rainfall_spatial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rainfall as a triggering factor in a spatial domain\n",
    "\n",
    "© 2024 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/02_trigg_factors/infinite_slope_rainfall_spatial.ipynb).\n",
    "\n",
    "This notebook runs the model ``TRIGRS`` (Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Analysis), developed by {cite:t}`Baum_etal_2002_TRIGRSFortranProgram` and {cite:t}`Baum_etal_2008_TRIGRSFortranProgram`, and parallelized later by {cite:t}`Alvioli_Baum_2016_ParallelizationTRIGRSModel`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Required modules and global setup for plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import subprocess\n",
    "import textwrap\n",
    "import itertools\n",
    "import requests\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\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",
    "    subprocess.run('pip install ipympl', shell=True);\n",
    "    subprocess.run('pip install palettable', shell=True);\n",
    "    subprocess.run('pip install pynewmarkdisp', shell=True);\n",
    "    from google.colab import output, files\n",
    "    output.enable_custom_widget_manager()\n",
    "    \n",
    "else:\n",
    "    import tkinter as tk\n",
    "    from tkinter.filedialog import askopenfilename\n",
    "\n",
    "from pynewmarkdisp.spatial import get_hillshade\n",
    "\n",
    "# Figures setup\n",
    "%matplotlib widget\n",
    "# plt.style.use(\"default\")\n",
    "mpl.rcParams.update(\n",
    "    {\n",
    "        'figure.constrained_layout.use': True,\n",
    "        'interactive': False,\n",
    "        \"text.usetex\": False,  # Use mathtext, not LaTeX\n",
    "        \"font.family\": \"DejaVu Serif\",  # Use the Computer modern font\n",
    "        \"mathtext.fontset\": \"cm\",\n",
    "        \"axes.formatter.use_mathtext\": True,\n",
    "        \"axes.unicode_minus\": False,\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a folder called TRIGRS in the current working directory if it doesn't exist\n",
    "workdir = os.getcwd()\n",
    "exec_dir = os.path.join(workdir, \"TRIGRS_2\")\n",
    "os.makedirs(f\"{exec_dir}\", exist_ok=True)  \n",
    "\n",
    "# Create a folder inside TRIGRS to store the ascii files\n",
    "inputs_dir = os.path.join(exec_dir, \"inputs\")\n",
    "outputs_dir = os.path.join(exec_dir, \"outputs\")\n",
    "os.makedirs(f\"{inputs_dir}\", exist_ok=True)\n",
    "os.makedirs(f\"{outputs_dir}\", exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tpx_in_maker(tpx_inputs):\n",
    "    tpx_in_template = textwrap.dedent(\n",
    "        f\"\"\"\\\n",
    "        TopoIndex 1.0.15; Name of project (up to 255 characters)\n",
    "        TopoIndex analysis - Project: {tpx_inputs[\"proj_name\"]}\n",
    "        Flow-direction numbering scheme (ESRI=1, TopoIndex=2)\n",
    "        {tpx_inputs[\"flow_dir_scheme\"]}\n",
    "        Exponent, Number of iterations\n",
    "        {tpx_inputs[\"exponent_weight_fact\"]}, {tpx_inputs[\"iterarions\"]}\n",
    "        Name of elevation grid file\n",
    "        TRIGRS_2/inputs/dem.asc\n",
    "        Name of direction grid\n",
    "        TRIGRS_2/inputs/directions.asc\n",
    "        Save listing of D8 downslope neighbor cells (TIdsneiList_XYZ)?  Enter T (.true.) or F (.false.)\n",
    "        T\n",
    "        Save grid of D8 downslope neighbor cells (***TIdscelGrid_XYZ)? Enter T (.true.) or F (.false.)\n",
    "        T\n",
    "        Save cell index number grid (TIcelindxGrid_XYZ)?  Enter T (.true.) or F (.false.)\n",
    "        T\n",
    "        Save list of cell number and corresponding index number (***TIcelindxList_XYZ)? Enter T (.true.) or F (.false.)\n",
    "        T\n",
    "        Save flow-direction grid remapped from ESRI to TopoIndex (TIflodirGrid_XYZ)? Enter T (.true.) or F (.false.)\n",
    "        T\n",
    "        Save grid of points on ridge crests (TIdsneiList_XYZ)? Enter T (.true.) or F (.false.); Sparse (T) or dense (F)?\n",
    "        F,T\n",
    "        ID code for output files? (8 characters or less)\n",
    "        {tpx_inputs[\"proj_name\"]}\"\"\"\n",
    "    )\n",
    "    with open(\"tpx_in.txt\", \"w\") as file:\n",
    "        file.write(tpx_in_template)\n",
    "    return\n",
    "\n",
    "\n",
    "def run_topoidx(tpx_inputs):\n",
    "    tpx_in_maker(tpx_inputs)  # Generating initializing files\n",
    "    subprocess.call([\"chmod +x ./TRIGRS_2/Exe_TopoIndex\"], shell=True)  # Change permission\n",
    "    subprocess.call([\"./TRIGRS_2/Exe_TopoIndex\"])  # Running Fortran executable\n",
    "    # Moving log file to outputs folder and renaming it\n",
    "    os.rename(os.path.join(workdir, \"TopoIndexLog.txt\"), os.path.join(outputs_dir, f\"TopoIndexLog_{tpx_inputs['proj_name']}.txt\"))\n",
    "    os.rename(os.path.join(workdir, \"tpx_in.txt\"), os.path.join(exec_dir, f\"tpx_in_{tpx_inputs['proj_name']}.txt\"))\n",
    "    return\n",
    "\n",
    "\n",
    "def tr_in_maker(trg_inputs):\n",
    "    # Create text block for geotechnical parameters of each zone\n",
    "    geoparams = \"\"\n",
    "    for zone in trg_inputs[\"geoparams\"].keys():\n",
    "        z = trg_inputs[\"geoparams\"][zone]\n",
    "        txt_zone = \"\".join(\n",
    "            [\n",
    "                f\"zone, {zone}\\n\",\n",
    "                \"cohesion, phi, uws, diffus, K-sat, Theta-sat, Theta-res, Alpha\\n\",\n",
    "                f'{z[\"c\"]}, {z[\"φ\"]}, {z[\"γ_sat\"]}, {z[\"D_sat\"]}, {z[\"K_sat\"]}, {z[\"θ_sat\"]}, {z[\"θ_res\"]}, {z[\"α\"]}\\n',\n",
    "            ]\n",
    "        )\n",
    "        # print(txt_zone)\n",
    "        geoparams = str().join([geoparams, txt_zone])\n",
    "    # Update rifil entry with placeholder for each file\n",
    "    trg_inputs[\"rifil\"] = str().join(\n",
    "        [f\"TRIGRS_2/inputs/ri{n}.asc\\n\" for n in range(1, len(trg_inputs[\"capt\"]))]\n",
    "    )\n",
    "\n",
    "    # Template\n",
    "    trg_in_template = (\n",
    "        textwrap.dedent(\n",
    "            f\"\"\"\\\n",
    "        Project: {trg_inputs['proj_name']}\n",
    "        TRIGRS, version 2.1.00c, (meter-kilogram-second-degrees units)\n",
    "        tx, nmax, mmax, zones\n",
    "        {trg_inputs['tx']}, {trg_inputs['nmax']}, {trg_inputs['mmax']}, {trg_inputs['zones']}\n",
    "        nzs, zmin, uww, nper t\n",
    "        {trg_inputs['nzs']}, 0.001, 9.8e3, {trg_inputs['nper']}, {trg_inputs['t']}\n",
    "        zmax, depth, rizero, Min_Slope_Angle (degrees), Max_Slope_Angle (degrees)\n",
    "        {trg_inputs['zmax']}, {trg_inputs['depth']}, {trg_inputs['rizero']}, 0., 90.0\n",
    "        \"\"\"\n",
    "        )\n",
    "        + geoparams[:-1]\n",
    "        + textwrap.dedent(\n",
    "            f\"\"\"\n",
    "        cri(1), cri(2), ..., cri(nper)\n",
    "        {', '.join(map(str, trg_inputs['cri']))}\n",
    "        capt(1), capt(2), ..., capt(n), capt(n+1)\n",
    "        {', '.join(map(str, trg_inputs['capt']))}\n",
    "        File name of slope angle grid (slofil)\n",
    "        TRIGRS_2/inputs/slope.asc\n",
    "        File name of digital elevation grid (elevfil)\n",
    "        TRIGRS_2/inputs/dem.asc\n",
    "        File name of property zone grid (zonfil)\n",
    "        TRIGRS_2/inputs/zones.asc\n",
    "        File name of depth grid (zfil)\n",
    "        TRIGRS_2/inputs/zmax.asc\n",
    "        File name of initial depth of water table grid   (depfil)\n",
    "        TRIGRS_2/inputs/depthwt.asc\n",
    "        File name of initial infiltration rate grid   (rizerofil)\n",
    "        TRIGRS_2/inputs/rizero.asc\n",
    "        List of file name(s) of rainfall intensity for each period, (rifil())\n",
    "        \"\"\"\n",
    "        )\n",
    "        + trg_inputs[\"rifil\"][:-2]\n",
    "        + textwrap.dedent(\n",
    "            f\"\"\"\n",
    "        File name of grid of D8 runoff receptor cell numbers (nxtfil)\n",
    "        TRIGRS_2/inputs/TIdscelGrid_{trg_inputs['proj_name']}.txt\n",
    "        File name of list of defining runoff computation order (ndxfil)\n",
    "        TRIGRS_2/inputs/TIcelindxList_{trg_inputs['proj_name']}.txt\n",
    "        File name of list of all runoff receptor cells  (dscfil)\n",
    "        TRIGRS_2/inputs/TIdscelList_{trg_inputs['proj_name']}.txt\n",
    "        File name of list of runoff weighting factors  (wffil)\n",
    "        TRIGRS_2/inputs/TIwfactorList_{trg_inputs['proj_name']}.txt\n",
    "        Folder where output grid files will be stored  (folder)\n",
    "        TRIGRS_2/outputs/\n",
    "        Identification code to be added to names of output files (suffix)\n",
    "        {trg_inputs['proj_name']}\n",
    "        Save grid files of runoff? Enter T (.true.) or F (.false.)\n",
    "        F\n",
    "        Save grid of minimum factor of safety? Enter T (.true.) or F (.false.)\n",
    "        T\n",
    "        Save grid of depth of minimum factor of safety? Enter T (.true.) or F (.false.)\n",
    "        T\n",
    "        Save grid of pressure head at depth of minimum factor of safety? Enter T (.true.) or F (.false.)\n",
    "        T\n",
    "        Save grid of computed water table depth or elevation? Enter T (.true.) or F (.false.) followed by 'depth,' or 'eleva'\n",
    "        T, depth\n",
    "        Save grid files of actual infiltration rate? Enter T (.true.) or F (.false.)\n",
    "        F\n",
    "        Save grid files of unsaturated zone basal flux? Enter T (.true.) or F (.false.)\n",
    "        F\n",
    "        Save listing of pressure head and factor of safety (\"flag\")? (-9 sparse xmdv , -8 down-sampled xmdv, -7 full xmdv, -6 sparse ijz, -5 down-sampled ijz, -4 full ijz, -3 Z-P-Fs-saturation list -2 detailed Z-P-Fs, -1 Z-P-Fs list, 0 none). Enter flag value followed by down-sampling interval (integer).\n",
    "        {trg_inputs['ψ_flag']}, {trg_inputs['nzs']}\n",
    "        Number of times to save output grids and (or) ijz/xmdv files\n",
    "        {trg_inputs['n_outputs']}\n",
    "        Times of output grids and (or) ijz/xmdv files\n",
    "        {', '.join(map(str, trg_inputs['t_n_outputs']))}\n",
    "        Skip other timesteps? Enter T (.true.) or F (.false.)\n",
    "        F\n",
    "        Use analytic solution for fillable porosity?  Enter T (.true.) or F (.false.)\n",
    "        T\n",
    "        Estimate positive pressure head in rising water table zone (i.e. in lower part of unsat zone)?  Enter T (.true.) or F (.false.)\n",
    "        T\n",
    "        Use psi0=-1/alpha? Enter T (.true.) or F (.false.) (False selects the default value, psi0=0)\n",
    "        F\n",
    "        Log mass balance results?   Enter T (.true.) or F (.false.)\n",
    "        T\n",
    "        Flow direction (Enter \"gener\", \"slope\", or \"hydro\")\n",
    "        {trg_inputs['flowdir']}\n",
    "        Add steady background flux to transient infiltration rate to prevent drying beyond the initial conditions during periods of zero infiltration?\n",
    "        T\n",
    "        Specify file extension for output grids. Enter T (.true.) for \".asc\" or F for \".txt\"\n",
    "        T\n",
    "        Ignore negative pressure head in computing factor of safety (saturated infiltration only)?   Enter T (.true.) or F (.false.)\n",
    "        T\n",
    "        Ignore height of capillary fringe in computing pressure head for unsaturated infiltration option?   Enter T (.true.) or F (.false.)\n",
    "        T\n",
    "        Parameters for deep pore-pressure estimate in SCOOPS ijz output: Depth below ground surface (positive, use negative value to cancel this option), pressure option (enter 'zero' , 'flow' , 'hydr' , or 'relh')\n",
    "        -50.0,flow\n",
    "        \"\"\"\n",
    "        )\n",
    "    )\n",
    "    # Create txt file\n",
    "    with open(\"tr_in.txt\", \"w\") as file:\n",
    "        file.write(trg_in_template)\n",
    "    return\n",
    "\n",
    "\n",
    "def run_trigrs(trg_inputs):\n",
    "    # Correcting sign for `Alpha` parameters for saturated/unsaturated models\n",
    "    for z in trg_inputs[\"geoparams\"].keys():\n",
    "        zone = trg_inputs[\"geoparams\"][z]\n",
    "        if \"S\" in trg_inputs[\"model\"]:\n",
    "            zone[\"α\"] = -1 * abs(zone[\"α\"])\n",
    "        else:\n",
    "            zone[\"α\"] = abs(zone[\"α\"])\n",
    "\n",
    "    # Correcting sign for `mmax` parameters for finite/infinite models\n",
    "    if \"I\" in trg_inputs[\"model\"]:\n",
    "        trg_inputs[\"mmax\"] = -1 * abs(trg_inputs[\"mmax\"])\n",
    "    else:\n",
    "        trg_inputs[\"mmax\"] = abs(trg_inputs[\"mmax\"])\n",
    "\n",
    "    # Generating initializing file and running TRIGRS executable\n",
    "    tr_in_maker(trg_inputs)  # Initializing files from template\n",
    "    if trg_inputs[\"parallel\"]:\n",
    "        exe = \"./TRIGRS_2/Exe_TRIGRS_par\"\n",
    "        subprocess.call([f\"chmod +x {exe}\"], shell=True)  # Change permission\n",
    "        subprocess.call(\n",
    "            [f'mpirun -np {trg_inputs[\"NP\"]} {exe}'], shell=True\n",
    "        )  # Running TRIGRS executable\n",
    "    else:\n",
    "        exe = \"./TRIGRS_2/Exe_TRIGRS_ser\"\n",
    "        subprocess.call([f\"chmod +x {exe}\"], shell=True)  # Change permission\n",
    "        subprocess.call([exe], shell=True)  # Running TRIGRS executable\n",
    "\n",
    "    # Moving files to output folders and renaming it\n",
    "    proj_name, model = trg_inputs[\"proj_name\"], trg_inputs[\"model\"]\n",
    "    os.rename(os.path.join(workdir, \"TrigrsLog.txt\"), os.path.join(outputs_dir, f\"TrigrsLog_{proj_name}_{model}.txt\"))\n",
    "    os.rename(os.path.join(workdir, \"tr_in.txt\"), os.path.join(exec_dir, f\"tr_in_{proj_name}_{model}.txt\"))\n",
    "    groups = (\n",
    "        \"TRfs_min\",\n",
    "        \"TRp_at_fs_min\",\n",
    "        \"TRwater_depth\",\n",
    "        \"TRz_at_fs_min\",\n",
    "        \"TR_ijz_p_th\",\n",
    "        # \"TRrunoffPer\",\n",
    "    )\n",
    "    [os.makedirs(f\"TRIGRS_2/outputs/{grp}\", exist_ok=True) for grp in groups]\n",
    "    idx = np.arange(1, trg_inputs[\"n_outputs\"] + 1, 1)\n",
    "    for grp, i in itertools.product(groups, idx):\n",
    "        if grp == \"TRrunoffPer\":\n",
    "            os.rename(\n",
    "                f\"TRIGRS_2/outputs/{grp}{i}{proj_name}.asc\",\n",
    "                f\"TRIGRS_2/outputs/{grp}/{grp}_{proj_name}_{model}_{i}.asc\",\n",
    "            )\n",
    "        elif grp == \"TR_ijz_p_th\":\n",
    "            os.rename(\n",
    "                f\"TRIGRS_2/outputs/{grp}_{proj_name}_{i}.txt\",\n",
    "                f\"TRIGRS_2/outputs/{grp}/{grp}_{proj_name}_{model}_{i}.txt\",\n",
    "            )\n",
    "        else:\n",
    "            os.rename(\n",
    "                f\"TRIGRS_2/outputs/{grp}_{proj_name}_{i}.asc\",\n",
    "                f\"TRIGRS_2/outputs/{grp}/{grp}_{proj_name}_{model}_{i}.asc\",\n",
    "            )\n",
    "    return\n",
    "\n",
    "\n",
    "def get_param(zone, param):\n",
    "    return trg_inputs['geoparams'][zone.iloc[0]][param]\n",
    "\n",
    "def get_df(file):\n",
    "    γ_w = 9.8e3  # [N/m3] unit weight of water\n",
    "    # Reading file\n",
    "    df = pd.read_csv(file,\n",
    "                     names=['i_tr', 'j_tr', 'elev', 'ψ', 'θ'],\n",
    "                     skiprows=7, sep='\\s+')#delim_whitespace=True)\n",
    "    # Correcting indices to match numpy notation\n",
    "    df['i'] = df['j_tr'].max() - df['j_tr']\n",
    "    df['j'] = df['i_tr'] - 1\n",
    "    df.drop(columns=['i_tr', 'j_tr'], inplace=True)\n",
    "    # Calculating depths from elevations\n",
    "    elev_max = df.groupby(['i', 'j'])['elev'].transform('max')\n",
    "    df['z'] = elev_max - df['elev'] + 0.001\n",
    "    df = df[['i', 'j', 'elev', 'z', 'ψ', 'θ']]\n",
    "    # Assigning zones and geotechnical parameters\n",
    "    df['zone'] = zones[df['i'], df['j']]\n",
    "    for param_name in trg_inputs['geoparams'][1].keys():\n",
    "        df[param_name] = df.groupby('zone')['zone'].transform(\n",
    "            get_param, param=param_name)\n",
    "    # Correting rows with ψ==0 (watertable) due to numerical errors\n",
    "    mask = (df['ψ'] == 0) & (df['z'] > 0.001) & (df['θ'] == df['θ_sat'])\n",
    "    # df.loc[mask, 'z'] = df.loc[mask, 'z'] + 1/df.loc[mask, 'α']\n",
    "    # df.loc[mask, 'elev'] = df.loc[mask, 'elev'] - 1/df.loc[mask, 'α']\n",
    "    # df.sort_values(['i', 'j', 'z'], inplace=True)\n",
    "    df.drop(df[mask].index, inplace=True)  # This eliminates the wt rows\n",
    "    # Assigning slope\n",
    "    df['δ'] = slope[df['i'], df['j']]\n",
    "    δrad = np.deg2rad(df['δ'])\n",
    "    # Calculating effective stress parameter, χ\n",
    "    df['χ'] = (df['θ'] - df['θ_res']) / (df['θ_sat'] - df['θ_res'])\n",
    "    # Specific gravity\n",
    "    df['Gs'] = (df['γ_sat']/γ_w - df['θ_sat']) / (1 - df['θ_sat'])\n",
    "    # unit weight at unsat and sat zones\n",
    "    df['γ_nat'] = (df['Gs'] * (1 - df['θ_sat']) + df['θ']) * γ_w\n",
    "    # average unit weight\n",
    "    γ_cumul = df.groupby(['i', 'j'])['γ_nat'].transform('cumsum')\n",
    "    idx = df.groupby(['i', 'j'])['γ_nat'].cumcount() + 1\n",
    "    df['γ_avg'] = γ_cumul / idx\n",
    "    # Factor of safety\n",
    "    fs1 = np.tan(np.deg2rad(df['φ'])) / np.tan(δrad)\n",
    "    fs2_num = df['c'] - df['ψ'] * γ_w * df['χ'] * np.tan(np.deg2rad(df['φ']))\n",
    "    fs2_den = df['γ_avg'] * df['z'] * np.sin(δrad) * np.cos(δrad)\n",
    "    fs = fs1 + fs2_num/fs2_den\n",
    "    fs[fs > 3] = 3\n",
    "    df['fs'] = fs\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading executables and input files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def download_file_from_github(file, url_path, output_path):\n",
    "    url = f\"{url_path}/{file}\"\n",
    "    response = requests.get(url)  # Send a GET request to the URL\n",
    "    if response.status_code == 200:  # Check if the request was successful\n",
    "        output_path = f\"{output_path}/{file}\"\n",
    "        with open(output_path, 'wb') as file:  # Write the content to a file\n",
    "            file.write(response.content)\n",
    "        print(f\"File downloaded successfully and saved to {output_path}\")\n",
    "    else:\n",
    "        print(f\"Failed to download file. HTTP status code: {response.status_code}\")\n",
    "\n",
    "\n",
    "url_path = \"https://raw.githubusercontent.com/eamontoyaa/data4testing/main/trigrs/\"  # URL of the raw file on GitHub\n",
    "output_path = \"./TRIGRS_2/\"  # Path where you want to save the downloaded file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TopoIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File downloaded successfully and saved to ./TRIGRS_2//Exe_TopoIndex\n"
     ]
    }
   ],
   "source": [
    "file = \"Exe_TopoIndex\"  # Name of the file to download\n",
    "download_file_from_github(file, url_path, output_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if 'google.colab' in str(get_ipython()):\n",
    "#     topoidx_file = files.upload()\n",
    "#     # Move the file to the executables folder\n",
    "#     os.rename(list(topoidx_file.keys())[0], os.path.join(exec_dir, list(topoidx_file.keys())[0]))\n",
    "# else:\n",
    "#     tk.Tk().withdraw()\n",
    "#     topoidx_file = askopenfilename()\n",
    "#     # Move the file to the executables folder\n",
    "#     os.rename(topoidx_file, os.path.join(exec_dir, os.path.basename(topoidx_file))) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TRIGRS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File downloaded successfully and saved to ./TRIGRS_2//Exe_TRIGRS_ser\n"
     ]
    }
   ],
   "source": [
    "file = \"Exe_TRIGRS_ser\"  # Name of the file to download\n",
    "download_file_from_github(file, url_path, output_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if 'google.colab' in str(get_ipython()):\n",
    "#     trigrs_file = files.upload()\n",
    "#     # Move the file to the executables folder\n",
    "#     os.rename(list(trigrs_file.keys())[0], os.path.join(exec_dir, list(trigrs_file.keys())[0]))\n",
    "# else:\n",
    "#     tk.Tk().withdraw()\n",
    "#     trigrs_file = askopenfilename()\n",
    "#     # Move the file to the executables folder\n",
    "#     os.rename(trigrs_file, os.path.join(exec_dir, os.path.basename(trigrs_file))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Exe_TopoIndex',\n",
       " 'Exe_TRIGRS_ser',\n",
       " 'inputs',\n",
       " 'outputs',\n",
       " 'tpx_in_SPATIAL.txt',\n",
       " 'tr_in_SPATIAL_SI.txt',\n",
       " 'tr_in_SPATIAL_UF.txt']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the files in the executables folder\n",
    "os.listdir(exec_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running the TRIGRS model for a one-cell spatial domain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### General inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "proj_name = \"SPATIAL\"\n",
    "model = \"SI\"  # Choose among UF, UI, SF, SI → | U: unsaturated | S: saturated | F: finite | I: infinite |\n",
    "rainfall_int =  np.array([3.e-7, 9.e-5])  # [m/s]\n",
    "# rainfall_int =  np.array([0.5e-4, 0.5e-4])  # [m/s]\n",
    "cumul_duration = np.array([0, 172800, 216000]) # [s]  Always starts at 0 and contains n+1 elements, where n is the number of rainfall intensities values\n",
    "output_times = np.array([0.01, 43200, 86400, 100800, 183600, 216000]) # [s]  It does not have to include the same values as cumul_duration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating `.asc` files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File downloaded successfully and saved to ./TRIGRS_2/\n",
      "File downloaded successfully and saved to ./TRIGRS_2/\n",
      "File downloaded successfully and saved to ./TRIGRS_2/\n",
      "File downloaded successfully and saved to ./TRIGRS_2/\n",
      "File downloaded successfully and saved to ./TRIGRS_2/\n",
      "File downloaded successfully and saved to ./TRIGRS_2/\n",
      "File downloaded successfully and saved to ./TRIGRS_2/\n"
     ]
    }
   ],
   "source": [
    "def load_ascii_raster(path, dtype=None):\n",
    "    raster = np.loadtxt(path, skiprows=6)\n",
    "    header = np.loadtxt(path, max_rows=6, dtype=object)\n",
    "    header = {\n",
    "        \"ncols\": int(header[0, 1]),\n",
    "        \"nrows\": int(header[1, 1]),\n",
    "        \"xllcorner\": float(header[2, 1]),\n",
    "        \"yllcorner\": float(header[3, 1]),\n",
    "        \"cellsize\": float(header[4, 1]),\n",
    "        \"nodata_value\": eval(header[5, 1]),\n",
    "    }\n",
    "    raster[np.where(raster == header[\"nodata_value\"])] = np.nan\n",
    "    raster = raster.astype(dtype) if dtype is not None else raster\n",
    "    return (raster, header)\n",
    "\n",
    "def get_asc_from_url(file):\n",
    "    base_url = \"https://raw.githubusercontent.com/eamontoyaa/data4testing/main/trigrs/tutorial_data/\"\n",
    "    data_url = f\"{base_url}/{file}.asc\"\n",
    "    response = requests.get(data_url)  # Send a GET request to the URL\n",
    "    if response.status_code == 200:  # Check if the request was successful\n",
    "        with open(f\"{inputs_dir}/{file}.asc\", 'wb') as file:  # Write the content to a file\n",
    "            file.write(response.content)\n",
    "        print(f\"File downloaded successfully and saved to {output_path}\")\n",
    "    else:\n",
    "        print(f\"Failed to download file. HTTP status code: {response.status_code}\")\n",
    "\n",
    "for file in ['dem', 'zmax', 'depthwt', 'slope', 'directions', 'rizero', 'zones']:\n",
    "    get_asc_from_url(file)\n",
    "\n",
    "dem, header = load_ascii_raster(f\"{inputs_dir}/dem.asc\")\n",
    "slope, header = load_ascii_raster(f\"{inputs_dir}/slope.asc\")\n",
    "directions, header = load_ascii_raster(f\"{inputs_dir}/directions.asc\")\n",
    "zones, header = load_ascii_raster(f\"{inputs_dir}/zones.asc\")\n",
    "depth, header = load_ascii_raster(f\"{inputs_dir}/zmax.asc\")\n",
    "depth, header = load_ascii_raster(f\"{inputs_dir}/rizero.asc\")\n",
    "depth_w, header = load_ascii_raster(f\"{inputs_dir}/depthwt.asc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:**\n",
    "\n",
    "If you are loading locally your own spatial input data, uncomment the following cell and rerun the notebook from this point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dem, header = load_ascii_raster(f\"{inputs_dir}/dem.asc\")\n",
    "# slope, header = load_ascii_raster(f\"{inputs_dir}/slope.asc\")\n",
    "# directions, header = load_ascii_raster(f\"{inputs_dir}/directions.asc\")\n",
    "# zones, header = load_ascii_raster(f\"{inputs_dir}/zones.asc\")\n",
    "# depth, header = load_ascii_raster(f\"{inputs_dir}/zmax.asc\")\n",
    "# depth_w, header = load_ascii_raster(f\"{inputs_dir}/depthwt.asc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   TopoIndex: Topographic Indexing and\n",
      "  flow distribution factors for routing\n",
      "  runoff through Digital Elevation Models\n",
      "             By Rex L. Baum\n",
      "        U.S. Geological Survey\n",
      "      Version 1.0.14, 11May2015\n",
      " -----------------------------------------\n",
      " \n",
      " TopoIndex analysis - Project: SPATIAL\n",
      "         100  = number of data cells\n",
      "         100  = total number of cells\n",
      " Reading elevation grid data\n",
      " Initial elevation indexing completed\n",
      " Finding D8 neighbor cells\n",
      " nxtcel() nodata (integer,floating)=        -9999  -9999.00000    \n",
      " Identifying downslope cells and grid mismatches\n",
      " No grid mismatch found!\n",
      " Correcting cell index numbers\n",
      " Computing weighting factors\n",
      " Saving results to disk\n"
     ]
    }
   ],
   "source": [
    "# Running TopoIndex\n",
    "tpx_inputs = {\n",
    "    \"flow_dir_scheme\": 2,\n",
    "    \"exponent_weight_fact\": 25,\n",
    "    \"iterarions\": 10,\n",
    "    \"proj_name\": proj_name,\n",
    "}\n",
    "run_topoidx(tpx_inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      " TRIGRS: Transient Rainfall Infiltration\n",
      " and Grid-based Regional Slope-Stability\n",
      "                Analysis\n",
      "        Version 2.1.00c, 02 Feb 2022\n",
      "   By Rex L. Baum and William Z. Savage\n",
      "        U.S. Geological Survey\n",
      " -----------------------------------------\n",
      " \n",
      " Opening default initialization file\n",
      " TRIGRS, version 2.1.00c, (meter-kilogram-second-degrees units)                                                                                                                                                                                                 \n",
      " Negative or zero value of Alpha for property zone           1\n",
      " Saturated infiltration model will be used for cells in zone           1 .\n",
      " Negative or zero value of Alpha for property zone           2\n",
      " Saturated infiltration model will be used for cells in zone           2 .\n",
      " ******** Zone            1  *********\n",
      " Using saturated infiltration model to avoid\n",
      " early-time errors in unsaturated infiltration model.\n",
      " ******** Zone            2  *********\n",
      " Using saturated infiltration model to avoid\n",
      " early-time errors in unsaturated infiltration model.\n",
      " ********  ********  ********  *********\n",
      " TRIGRS_2/inputs/TIgrid_size.txt T\n",
      " Reading input grids\n",
      " Testing and adjusting steady infiltration rates\n",
      " Adjusted steady infiltration rate at            0  cells\n",
      " Starting runoff-routing computations\n",
      " Initial size of time-steps    5400.0000000000000     \n",
      " ******** Output times ********\n",
      " number, timestep #,  time\n",
      " One or more specified output times unavailable, \n",
      " Nearest available time substituted.\n",
      "           1           1   0.00000000    \n",
      " One or more specified output times unavailable, \n",
      " Nearest available time substituted.\n",
      "           2           6   43200.0000    \n",
      " One or more specified output times unavailable, \n",
      " Nearest available time substituted.\n",
      "           3          11   86400.0000    \n",
      " One or more specified output times unavailable, \n",
      " Nearest available time substituted.\n",
      "           4          12   95040.0000    \n",
      " One or more specified output times unavailable, \n",
      " Nearest available time substituted.\n",
      "           5          26   183600.000    \n",
      " One or more specified output times unavailable, \n",
      " Nearest available time substituted.\n",
      "           6          41   216000.000    \n",
      " Starting computations of pressure head and factor of safety\n",
      " Ignoring unsaturated zone\n",
      " Calling multistep saturated infinite-depth model\n",
      " Starting computations\n",
      " for infinite-depth saturated zone\n",
      " Cells completed: \n",
      "           0  \n",
      "         100  cells completed\n",
      " Saving results\n",
      " TRIGRS finished!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "STOP 0\n"
     ]
    }
   ],
   "source": [
    "trg_inputs = {\n",
    "    \"proj_name\": proj_name,  # Name of the project\n",
    "    \"model\": model,  # S-saturated or U-unsaturated + F-finite or I-infinite\n",
    "    \"tx\": 20, # [] tx*nper determines how many time steps are used in the computations\n",
    "    \"nmax\": 30,  # [] Ma number of roots 𝚲n and terms in series solutions for UNS.zone\n",
    "    \"mmax\": 100,  # [] Max number of terms in series solution for FIN depth. If <0: INF depth\n",
    "    \"zones\": 2,  # [] Number of zones\n",
    "    \"nzs\": 20,  # [] Vertical increments until zmax\n",
    "    \"nper\": len(rainfall_int),  # [-] Periods of rainfall (N)\n",
    "    \"t\": cumul_duration[-1],  # [s] Elapsed time since the start of the storm (t)\n",
    "    \"zmax\": -3.001,  # [m] Max. depth to compute Fs and Ψ (if <0 read from grid file)\n",
    "    \"depth\": -2.4,  # [m] Water table depth (d) (if <0 read from grid file)\n",
    "    \"rizero\": -1e-9,  # [m/s] Steady pre-storm and long-term infilt. (Izlt) (if <0 read from grid file)\n",
    "    \"geoparams\":{\n",
    "        1: {\n",
    "            \"c\": 3.5e3,  # [Pa] soil cohesion for effective stress (c')\n",
    "            \"φ\": 35,  # [deg] soil friction angle for effective stress (φ')\n",
    "            \"γ_sat\": 22.0e3,  # [N/m3]  unit weight of soil (γs)\n",
    "            \"D_sat\": 6.0e-6,  # [m2/s] Saturated hydraulic diffusivity (D0)\n",
    "            \"K_sat\": 1.0e-7,  # [m/s] Saturated hydraulic conductivity (Ks)\n",
    "            \"θ_sat\": 0.45,  # [-] Saturated water content (θs)\n",
    "            \"θ_res\": 0.05,  # [-] Residual water content (θr)\n",
    "            \"α\": 0.5,  # [1/m] Fitting parameter. If <0: saturated infiltration\n",
    "        },\n",
    "        2: {\n",
    "            \"c\": 8e3,  # [Pa] soil cohesion for effective stress (c')\n",
    "            \"φ\": 31,  # [deg] soil friction angle for effective stress (φ')\n",
    "            \"γ_sat\": 22.0e3,  # [N/m3]  unit weight of soil (γs)\n",
    "            \"D_sat\": 8.0e-4,  # [m2/s] Saturated hydraulic diffusivity (D0)\n",
    "            \"K_sat\": 1.0e-4,  # [m/s] Saturated hydraulic conductivity (Ks)\n",
    "            \"θ_sat\": 0.45,  # [-] Saturated water content (θs)\n",
    "            \"θ_res\": 0.06,  # [-] Residual water content (θr)\n",
    "            \"α\": 8,  # [1/m] Fitting parameter. If <0: saturated infiltration\n",
    "        }\n",
    "    },\n",
    "    \"cri\": tuple(rainfall_int),  # [m/s] Rainfall intensities  (Inz)\n",
    "    \"capt\": tuple(cumul_duration),  # [s] Cumulative duration of rainfall cri[i]\n",
    "    \"flowdir\": \"gener\",  # Method for maximum allowed Ψ (\"gener\", \"slope\", or \"hydro\"))\n",
    "    \"n_outputs\": len(output_times),  # [-] No of raster outputs at different t\n",
    "    \"t_n_outputs\": tuple(output_times),#t,  # [s] Time at which to save raster files\n",
    "    \"ψ_flag\": -4,\n",
    "    \"parallel\": False,  # [-] Run in parallel\n",
    "    \"NP\" : 4, # Number of processors\n",
    "    # \"inz_n_outputs\": inz_n_outputs #inz,  # [s] Rainfall intensities at which to save raster files\n",
    "}\n",
    "\n",
    "run_trigrs(trg_inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing spatial inputs (conditioning factors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7f1810093a8c407abfde81c2bada1f90",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=1000.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Bac…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hsd = get_hillshade(dem, sun_azimuth=000, sun_altitude=30, cellsize=header['cellsize'])\n",
    "\n",
    "fig, axs = plt.subplot_mosaic([['a', 'b', 'c'], ['d', 'e', 'f'], ['.', 'g', '.']],\n",
    "                              figsize=(10, 3*3), sharex=True, sharey=True)\n",
    "(ax0, ax1, ax2, ax3, ax4, ax5, ax6) = axs.values()\n",
    "for ax in [ax0, ax1, ax2, ax3, ax4, ax5, ax6]:\n",
    "    im_hsd = ax.matshow(hsd, cmap=\"gray\", interpolation=\"none\")\n",
    "\n",
    "# DEM\n",
    "im_dem = ax0.matshow(dem, cmap=\"terrain\", alpha=0.7, interpolation=\"none\")\n",
    "ax0.set_title(\"DEM\", fontsize='x-large')\n",
    "fig.colorbar(im_dem, ax=ax0, orientation='vertical', shrink=0.75, pad=0.01, label='Elevation [m]')\n",
    "# Slope\n",
    "im_slope = ax1.matshow(slope, cmap=\"RdYlGn_r\", alpha=0.7, interpolation=\"none\")\n",
    "ax1.set_title(\"Slope\", fontsize='x-large')\n",
    "fig.colorbar(im_slope, ax=ax1, orientation='vertical', shrink=0.75, pad=0.01, label='Slope [°]')\n",
    "# Directions\n",
    "im_dirflow = ax2.matshow(directions, cmap=\"twilight\", alpha=0.7, interpolation=\"none\")\n",
    "ax2.set_title(\"Flow direction\", fontsize='x-large')\n",
    "fig.colorbar(im_dirflow, ax=ax2, orientation='vertical', shrink=0.75, pad=0.01, label='Flow dir index (ESRI notation)')\n",
    "# Zmax\n",
    "im_zmax = ax3.matshow(depth, cmap='viridis', alpha=0.7, interpolation=\"none\")\n",
    "ax3.set_title(\"$Z_\\\\mathrm{max}$\", fontsize='x-large')\n",
    "fig.colorbar(im_zmax, ax=ax3, orientation='vertical', shrink=0.75, pad=0.01, label='Depth [m]')\n",
    "# Water table depth\n",
    "im_depth = ax4.matshow(depth_w, cmap='Blues', alpha=0.7, interpolation=\"none\")\n",
    "ax4.set_title(\"Water table\", fontsize='x-large')\n",
    "fig.colorbar(im_depth, ax=ax4, orientation='vertical', shrink=0.75, pad=0.01, label='Depth [m]')\n",
    "# Zones\n",
    "im_zones = ax5.matshow(zones, cmap=plt.colormaps.get_cmap(\"Dark2\").resampled(len(np.unique(zones))),\n",
    "                       alpha=0.7, interpolation=\"none\")\n",
    "ax5.set_title(\"Zones\", fontsize='x-large')\n",
    "fig.colorbar(im_zones, ax=ax5, orientation='vertical', shrink=0.75, pad=0.01, label='Zones', ticks=np.unique(zones))\n",
    "# Factor of safety\n",
    "fs_output_file = f\"{outputs_dir}/TRfs_min/TRfs_min_{proj_name}_{model}_1.asc\"\n",
    "fs, header = load_ascii_raster(fs_output_file)\n",
    "im_fs = ax6.matshow(fs, cmap=\"RdYlGn\", alpha=0.7, interpolation=\"none\", vmin=1, vmax=2)\n",
    "ax6.set_title(\"FS at $t=0$\", fontsize='x-large')\n",
    "fig.colorbar(im_fs, ax=ax6, orientation='vertical', shrink=0.75, pad=0.01, label='FS')\n",
    "\n",
    "for ax in [ax0, ax1, ax2, ax3, ax4, ax5, ax6]:\n",
    "    # if ax.get_subplotspec().is_last_col():\n",
    "    if ax == ax6:\n",
    "        ax.xaxis.set_ticks_position('bottom')\n",
    "        ax.yaxis.set_ticks_position('left')\n",
    "    else:\n",
    "        ax.xaxis.set_ticks_position('none')\n",
    "        \n",
    "fig.canvas.header_visible = False\n",
    "fig.canvas.toolbar_position = 'bottom'\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing spatial outputs of *FS*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0.0, 43200.0, 86400.0, 95040.0, 183600.0, 216000.0],\n",
       " [0.01, 43200.0, 86400.0, 100800.0, 183600.0, 216000.0])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def read_time(file):\n",
    "    with open(file, 'r') as file:\n",
    "        lines = file.readlines()\n",
    "        time = float(lines[4].split()[-1])\n",
    "    return time\n",
    "\n",
    "actual_output_times = []\n",
    "for i in range(1, trg_inputs[\"n_outputs\"]+1):\n",
    "    time = read_time(f'{outputs_dir}/TR_ijz_p_th/TR_ijz_p_th_{proj_name}_{model}_{i}.txt')\n",
    "    actual_output_times.append(time)\n",
    "\n",
    "actual_output_times, list(output_times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "911607c50871494b84135dd67cacfcab",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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ICtHL+F44YNGzRu3Dht2rRJnTt31v/+7/9q+PDh+uyzz7hFPwAAAACg0XB8Bn3+/PkaMWKEOnfuLEkaO3asjh07piVLljibGAAAAAAANnK8QV+1apV69eoV+drv96tnz55auXKlg1kBAAAAAGAvR5e4f/PNNyouLlarVq2qvX7WWWdp48aNUY8LhUIKhUKRr4uLiy3LEQCsRD0D4BXUMwCoP0dn0MvKyiRJgUCg2uuBQCDyXm1ycnIUDAYjW2ZmpqV5AoBVqGcAvIJ6BgD152iDnpqaKknVPm2t+rrqvdpMnz5dRUVFka2goMDSPAHAKtQzAF5BPQOA+nN0ifsZZ5yhYDCowsLCaq/v379fHTt2jHpcIBCoMesOAA0R9QyAV1DPAKD+HL9J3JAhQ7Rp06bI14ZhKDc3Vz/60Y8czAoAAAAAAHs53qDfc889euONN7Rjxw5J0tNPP62EhASNGzfO4cwAAAAAALCPo0vcJalPnz5asmSJxowZoyZNmsjv92v58uVKS0tzOjUAAAAAAGzjeIMuSSNHjtTIkSOdTgMAAAAAAMc4vsQdAAAAAADQoAMAAAAA4Ao06AAAAAAAuAANOgAAAAAALkCDDgAAAACAC7jiLu5m8X23WeXYsWMWjv5v3c8/0/IYF5wZsDyGJOXuPWxLHKt1OiPVljiXt+9ieYzEXfmWx5CkUaclWTp+6ZEKXW1pBGdRz+KIYVM9+9Aj9ewHZza1Jc5lmedZHsO/43PLY0jSVWnWzWeUJh71dC2TvFPP+lzYyvIY3VraU8827Su1JY7VOp/RxJY4l7W9wPog27daH0PSVU0trGcJ3q9ndmAGHQAAAAAAF6BBBwAAAADABWjQAQAAAABwARp0AAAAAABcgAYdAAAAAAAXoEEHAAAAAMAFaNABAAAAAHABGnQAAAAAAFyABh0AAAAAABegQQcAAAAAwAVo0AEAAAAAcAEadAAAAAAAXIAGHQAAAAAAF6BBBwAAAADABWjQAQAAAABwARp0AAAAAABcgAYdAAAAAAAXoEEHAAAAAMAFaNABAAAAAHABGnQAAAAAAFyABh0AAAAAABegQQcAAAAAwAVo0AEAAAAAcAEadAAAAAAAXIAGHQAAAAAAF0h0OoGG5LwfnGZLnP9INyyPMfCM/7A8hiR9XbTZ8hhjUqz/vhw98K3lMSTJKP7E8hjllZWWx5Ckyspjlo5/9OhRS8f3Orvq2blB6+vZgDPOtTyGJB0+stnyGJeHm1oe4+i+IstjSFLlN5ssj1FxzK56Zl2coxXUsvrqcf4ZtsQ5J836etanuU31rOwjy2MMP2pDPdtzyPIYknT0Xxstj1FpVz07Zt3vZ9QzczCDDgAAAACAC9CgAwAAAADgAjToAAAAAAC4AA06AAAAAAAuQIMOAAAAAIAL0KADAAAAAOACNOgAAAAAALiAK56D/txzz2nx4sWqrKxUcXGxsrKy9MADDygrK8vp1AAAAAAADVhpaamWLFmilStXKj8/X2VlZUpNTVXHjh11ySWX6IYbblDTpk2dTlOSS2bQx44dqylTpmjVqlVav369mjRpoksvvVShUMjp1AAAAAAADdTWrVt17rnn6r777tPRo0fVq1cvXXLJJerVq5cqKio0d+5c/eAHP9Dnn3/udKqSXDKDftVVV2n48OGSJL/fr9tvv129e/dWbm6u+vfv73B2AAAAAICG6JZbbtG8efM0duxY+Xy+WvdZunSpbr75Zq1evdrm7GpyxQz6888/X+3rlJQUSWIGHQAAAABQZ+FwWNdff33U5lw6vqI7HA7bmFV0rphB/74PPvhAGRkZGjhwYK3vh0Khas17cXGxXakBgKmoZwC8gnoGwI2OHDmivLw8dezYMeo+eXl5KisrszGr6FzXoIdCIT3wwAN67LHHlJSUVOs+OTk5mjNnjs2ZAYD5qGcAvIJ6BsCNpk+frgsvvFCXX365evXqpVatWikQCCgUCqmwsFCbNm3SG2+8oSVLljidqiQXNugTJ07U6NGjNXLkyKj7TJ8+XXfeeWfk6+LiYmVmZtqRHgCYinoGwCuoZwDcaOTIkWrTpo0WLFigOXPmVFvdEwwGNWTIEK1YsUK9e/d2MMt/c1WDfs899yg1NVVz58496X6BQECBQMCmrADAOtQzAF5BPQPgVn369NELL7wgSfr2228jj1k7/fTTHc6sJtc06PPnz1dBQYGeeuopSdKmTZskST179nQyLQAAAACAScrLy1VRUWHqmIZh1LgJXLQPDU8//XRXNuZVXNGgL1q0SEuXLtXixYuVm5srSXr99deVlZVFgw4AAAAAHlBeXq7mrVooVFxq6rjNmjXT4cOHq702a9YszZ4929Q4dnC8QS8pKdEtt9yicDhc45nnTzzxhENZAQAAAADMVFFRoVBxqYbed5MSU5JNGfNYeYVWzfizCgoKlJ6eHnk9nktusrOzVV5ervXr15uSU3043qCnpaWpsrLS6TQAAAAAADZIahJQUhNz7llRtbQ9PT29WoMejwEDBpi+7L6uHG/QAQAAAABwSk5OjtMpRNCgAwAAAADs4/cd38wa6xSOHj2q3NxchUIhDRw4UAkJCZH3cnNz9c9//lOSdMMNN5iTUz34nU4AAAAAANB4+Hw+U7eT2bVrly644AL1799fgwcPVlZWll599dVq++Tn5+vnP/+5laccMxp0AAAAAIAn3X333erTp482btyoTz75RL/5zW909913R+7w3qNHD40YMcLZJE9Agw4AAAAAsI3PZ+52Mjt37tSTTz6pnj17qmvXrrrpppv08ccfa9euXbr11lu/y8ek5fYm8NQ16Od3baGklCTLxm/bLOHUO5lgaJsLLI/x/PZNlseQpLFNW1geo/xgkeUxKsLHLI8hSTpUZnkIo3svy2NI0m8+WGvp+JUhm74nDrG6nnVubk89G5LR3fIYr+RttDyGJP0k+XTLY5QfOmR5jIpjRy2PIUkqs76eqUcf62NImrPeunrm9VomST0vOFNJTayrZx3T7JlvGnRWd8tjvJq/wfIYkjQqyYZ6VnTI8hgVlTb9+zlofT3z9epneQxJmr3ufcvGjquexdJZxzPWSQSDwRqvBQIBPfHEE5o+fbquv/563XbbbebkYgJm0AEAAAAAnuT3+7Vy5cpa38vJydF5552nCRMm2JxVdDToAAAAAADb2HmTuAkTJmjEiBEaPXp0re9PmzZNt9xyi2uWudOgAwAAAAA86dprr1UoFNKzzz4bdZ9JkybpkA2XmcXCU9egAwAAAADczec/vpk1lhmaNWtmzkD1RIMOAAAAALBNLEvT4xnLS1jiDgAAAACACzCDDgAAAACwj993fDNrLA+hQQcAAAAA2MbGx6A3OCxxBwAAAADABZhBBwAAAADYhpvERccMOgAAAAAALsAMOgAAAADAPj4TbxLnsRl0GnQAAAAAgG1Y4h4dS9wBAAAAAHABZtABAAAAALbhMWvR0aADAAAAAOxDhx4VS9wBAAAAAHABZtABAAAAALbx+X3ymXQXd7PGcQtm0AEAAAAAcAFm0AEAAAAAtuES9Oho0AEAAAAAtmGJe3QscQcAAAAAwAWYQQcAAAAA2McnE9e4mzOMW9CgAwAAAABswzXo0bHEHQAAAAAAF/DUDHqbpglKbpJg2fgjO3SzbOwTHa08anmM0ZUplseQpNI9+6wPEki2PERCgnV/r04UOr+75TGSbTqX/bvLLB0/XHHM0vGd1jmYoICF9Wx45oWWjX2iY+EKy2P8Z3mS5TEkqfTAfuuDJFt/LnbVs4puF1geI2DTuXy1s9Sysb1eyySpU9CvQBPr5oSGZthUzwzrfz+78ohN9azQI7+f+e2ZazzavbflMZIT7GnL3FLPuElcdJ5q0AEAAAAAbueTeRePe6tBZ4k7AAAAAAAuwAw6AAAAAMA+Ji5xl8eWuDODDgAAAACACzCDDgAAAACwDY9Zi44GHQAAAABgH7/PvKXpLHEHAAAAAABmc1WD/thjj8nn8+ndd991OhUAAAAAgAX8+vcker03p0/GZK5Z4r5371498MADTqcBAAAAALCQ3+eT36SLx80axy1c84HDbbfdpl//+tdOpwEAAAAAgCNcMYP+2muvKSkpScOHD3c6FQAAAACAhbhHXHSON+ilpaW69957tXz5coVCoZiOCYVC1fYtLi62Kj0AsBT1DIBXUM8AoP4cX+I+c+ZMTZo0Sa1bt475mJycHAWDwciWmZlpYYYAYB3qGQCvoJ4BiFXVNehmbV7iaIOem5ur9evXa9KkSXEdN336dBUVFUW2goICizIEAGtRzwB4BfUMQKxMu4O7iUvl3cLRJe5vvPGGjhw5oiFDhkiSysvLJUmTJ09W8+bNtXjxYnXq1KnGcYFAQIFAwNZcAcAK1DMAXkE9A4D6c7RBnzlzpmbOnBn5eufOnerQoYMeeughDR482LnEAAAAAACW4DFr0Tl+kzgAAAAAQOPBXdyjc/wmcVUmT56sMWPG1PhvAAAAAAAaA9fMoD/00ENOpwAAAAAAsJjf51MCS9xr5ZoZdAAAAAAAGjPXzKADAAAAALyPa9Cjo0EHAAAAANiGu7hHxxJ3AAAAAABcgBl0AAAAAIBtmEGPzlMN+uXtz1PTpimWje/f/aVlY58o8V8HLY9RkWjT4onT0q2PUVZueYhff3PI8hiS9NWHb1seo0lKE8tjSFJGRoal41eGjuobSyM4a2ibCyytZ778bZaNfSL/t4csj1Fh18VnzdOsj2FDPftNcYnlMSRp57PLLY/hhXrm9VomSdlnWVvPlP+5dWOfwPeNDb+f2VXP7Pj9rPSI5SF+U3LY8hiStPPZtyyP0djqGdegR8cSdwAAAAAAXMBTM+gAAAAAAHdjBj06ZtABAAAAAHABZtABAAAAALbhJnHR0aADAAAAAGxDgx4dS9wBAAAAAHABZtABAAAAALbhJnHR0aADAAAAAGzjM7FB99gKd5a4AwAAAADgBsygAwAAAABsk+DzKcGkqW+zxnELZtABAAAAAHABZtABAAAAALbxy8THrIkZdAAAAAAA6qTqLu5mbafyySef6OGHH9YzzzyjcDgsSZo3b5569uypiy66SI899pjFZxw7ZtABAAAAAJ708ssva8yYMQoGg/rXv/6ll19+WX379tU777yjHj16aN++fZoyZYoqKyv1q1/9yul0mUEHAAAAANjHzhn0BQsWaN26dSosLNSbb76pF154QT/60Y+0cuVK/fnPf9brr7+uDz74QEuWLLHl3E+FGXQAAAAAgG38PhOvQT/FOCkpKerevbskafjw4fqP//gPnX/++dX26dGjh5o1a2ZKPvXFDDoAAAAAwJMOHjyow4cPS5IKCgq0d+9e7dy5s9o+hw4d0pEjRxzIriZm0AEAAAAAtrFzBv3iiy9Wt27d1Lt3b61Zs0bz5s3T4MGDNWnSJLVv31779u3T4sWLdeWVV5qST33RoAMAAAAAbGNng75gwQI1a9ZMW7Zs0bx58zRu3DglJSVp2rRpOnjwoAKBgMaNG6fZs2ebkk990aADAAAAADwpOTlZc+fOrfbaL37xC/3iF7/QgQMH1KJFC/lM+rDADDToAAAAAADb+P3HN7PGkqTi4uJqrwcCAQUCgZMe27JlS3OSMBE3iQMAAAAANGiZmZkKBoORLScnJ+Zjs7Oz1bdvXwuzix0z6AAAAAAA2/hk3kxx1eL0goICpaenR14/1ez5iQYMGKCKigqTMqofGnQAAAAAgG38vuObWWNJUnp6erUGPR7xzLZbzVMNuv+rfPmbJFs2fmVJqWVjn6jsnB9YHiMhwZ5v/Wu7P7E8xvPrdlke47TmzS2PIUmtW2dYHiMYDFoeQ5KSk5IsHf9YuTs+5bSKb9d2+SytZ4ctG/tER849z/IYiQkJlseQpNcLPrY8xjMbqWfx8EI983otkyTt+kKysp4Vl1g29onKf3CB5TESEu35/ez13Zstj/HMh7stj0E9ix/1zP24Bh0AAAAAYJvjM+g+kzZzcho9erQ5A9WTp2bQAQAAAADu5pd5M8WnGmf37thWcxQUFNQ/GRPQoAMAAAAAPOnss89WOBw+6T6GYbjmWeg06AAAAAAA2/h8xzezxjqZKVOmqLCwUOPHj4+6j2EYuvPOO81JqJ5o0AEAAAAAnjR37lxlZ2crMzNTHTt2jLqfXTfqOxUadAAAAACAbax4zFo0SUlJWrZsmUpLT/5ErhUrVpiTUD3RoAMAAAAAbFN1B3azxjqV9u3bn3KfJIsfERwrVzxmLS8vT6NGjdLFF1+srl27ql+/fvrwww+dTgsAAAAAANs43qAfOHBAQ4cO1a9+9SutXr1aW7ZsUWpqqnbs2OF0agAAAAAAk1UtcTdr8xLHl7gvWLBA/fv316BBgyRJiYmJevzxx5WamupwZgAAAAAAs9l5F/eGxvEZ9BdffDHSnFfp1KmTMjIyHMoIAAAAAAD7OTqDXlpaqvz8fFVWVuq6667Tzp071axZM02ePFmXXXZZ1ONCoZBCoVDk6+LiYjvSBQDTUc8AeAX1DECs/DJvptjxGWeTOXo+hw4dkiTNnDlTU6dO1dq1azV16lRdccUVevvtt6Mel5OTo2AwGNkyMzNtyhgAzEU9A+AV1DMAqD9HG/SEhARJ0hVXXKFu3bpJkoYOHaohQ4bo4Ycfjnrc9OnTVVRUFNkKCgpsyRcAzEY9A+AV1DMAsfKZeIM4r12D7ugS95YtWyoQCKhNmzbVXm/fvr3+8Y9/RD0uEAgoEAhYnR4AWI56BsArqGcAYmXm3de9dhd3x2fQBw4cqH379lV7vbCwUO3atXMoKwAAAAAA7Of4NfXTpk3TK6+8ot27d0uStm7dqhUrVuiWW25xODMAAAAAgNl8Jm9e4vhz0IcNG6ZHHnlEV111lZo1a6Zjx47pySef1OWXX+50agAAAAAAk/l9PvlNunjcrHHcwvEGXZLGjh2rsWPHOp0GAAAAAACOcUWDDgAAAABoHPwy8SZx5gzjGl47HwAAAAAAGiRm0AEAAAAAtvHLvJlir80406ADAAAAAGzj8x/fzBrLSzx2OgAAAAAANEzMoAMAAAAAbMNj1qLzVINe0SpTiU1TLBv/3i/fs2zsE321Ps+WOHZITEyyPEbrs86yPEaztDTLY0hSenq65TGSk6z/nkhSOBy2dHxfpWHp+E47lpGloxbWs5n/sKee7Xx2uS1x7EA9iw/1LDZer2WSdKxNR2vr2dp3LRv7RDuffcuWOHagnsWHehabeOoZ16BH57XzAQAAAACgQfLUDDoAAAAAwN38PhOfg+6tFe7MoAMAAAAA4AbMoAMAAAAAbOPzHd/MGstLaNABAAAAALbxybyl3B7rz1niDgAAAACAGzCDDgAAAACwDTeJi44GHQAAAABgGxr06FjiDgAAAACACzCDDgAAAACwje+7/5k1lpfQoAMAAAAAbMMS9+hY4g4AAAAAgAswgw4AAAAAsI3f55PfZ87Ut1njuAUz6AAAAAAAuAAz6AAAAAAA2/h8PvlMmvk2axy3oEEHAAAAANjGL/OWcnttSbjXzgcAAAAAgAaJGXQAAAAAgG18Pr98PnPmis0axy1o0AEAAAAAtvH5jm9mjeUl3vq4AQAAAACABooZdAAAAACAbfzyyS+TnoNu0jhuwQw6AAAAAAAuwAw6AAAAAMA+Jj4H3WsXodOgAwAAAABswxL36DzVoE969i35kqw7pcTEJMvGPlFKShPLYzRJSbE8hiSlNLH+XJoHg5bHSE5OtjyGJCUlWv9P8icXZ1keQ5JeWbvX0vGPGt4qxt/3X8/+jXoWI+pZfLxUz8YMzbI8hiS9uMa6eub1WiZJ//XMG9SzGFHP4kM9ix/1zP081aADAAAAANzNZ+ISd9OWyrsEDToAAAAAwDa+7/5n1lhewl3cAQAAAABwAWbQAQAAAAC28ft88pu0NN2scdyCGXQAAAAAAFyAGXQAAAAAgG24SVx0NOgAAAAAANv4fcc3s8byEseXuIdCId1xxx3q1q2bsrOz1bdvX7300ktOpwUAAAAAgK0cn0G/77779PLLL2vz5s0KBoP66KOP1K9fP23YsEHdunVzOj0AAAAAgIl4zFp0js+gb968Wb1791YwGJQkXXjhhQoGg3rnnXcczgwAAAAAYDaffJHr0Ou90aCba9SoUVqzZo12794tSVq+fLkOHDigVq1aOZwZAAAAAAD2cXyJ+/jx41VWVqYLLrhArVu31hdffKFrrrlG1157bdRjQqGQQqFQ5Ovi4mI7UgUA01HPAHgF9QxArPyS/CbNfDs+42wyx89n8eLFmj9/vjZt2qTPPvtMubm56tevn/z+6Knl5OQoGAxGtszMTBszBgDzUM8AeAX1DADqz9EG3TAMTZ06VRMnTtTZZ58tSerWrZvefPNNzZs3L+px06dPV1FRUWQrKCiwK2UAMBX1DIBXUM8AxMq0689NfJ66Wzi6xP3AgQM6ePCgsrKyqr3eoUMHvfDCC5oxY0atxwUCAQUCARsyBABrUc8AeAX1DECs/PKZuMTdWw26ozPoLVq0UCAQ0L59+6q9vm/fPqWmpjqUFQAAAADAC3Jzc/Xcc89p//79kddWrVqlK6+8Uueff75GjBihv/3tbw5mWJ2jDbrf79e4ceO0ePFiHTx4UNLxP8C33377pDeJAwAAAAA0THYucb/lllv0+OOPq6ysTJL01ltv6dJLL1UoFFL//v2VmJioUaNG6a9//asdp35Kjt/F/fe//71mz56toUOHKjU1VSUlJZo/f75uv/12p1MDAAAAAJjNzGvHTzFOSUmJ1q5dG7kJ+cMPP6zly5dryJAhkX22bt2qm266Sddcc405OdWD4w16amqqFi5c6HQaAAAAAACPadWqVbUnhJWUlFRrziWpS5cuSkx0vDWW5ILHrAEAAAAAGo+qm8SZtZ3MaaedprVr10a+zsrK0j//+c9q+3z11VcqKSmx5Fzj5Y6PCQAAAAAAMNnChQv1ox/9SAMGDNCgQYM0YMAADR48WFdeeaUyMjK0b98+vfLKK3rkkUecTlUSDToAAAAAwEZmPr/8VON07NhRGzZs0IIFC7RgwQLl5+dLkpYsWaJAIKCBAwdq2bJluuSSS0zJp75o0AEAAAAAtvHLvGutYxmnRYsWeuCBB/TAAw+orKxMhw4dUkJCglq2bFnt+nQ3oEEHAAAAADQKqampSk1NdTqNqGjQAQAAAAC2sWKJe3FxcbXXA4GAAoGAKTHs5KkGPZCcIn+yhadk1rP6ThXGhhgpTZrYEEVqHgxaHiM5OdnyGElJSZbHkKSfXJxleYxwOGx5DEl6buJPLB2/uLhUZ939B0tjOIl6FjvqWXzsqmdjhmZZHsOwqZ79deK1lo1dXFyqMz1cyyTqWTyoZ/GhnsXvr5PGWDZ2cfHhmOuZz+eXz2fO0vKqcTIzM6u9PmvWLM2ePTumMbKzs1VeXq7169ebklN9eKpBBwAAAAA0PgUFBUpPT498Hc/s+YABA1RRUWFFWnGjQQcAAAAA2MaKm8Slp6dXa9DjkZOTY1I29UeDDgAAAACwjRVL3L3CW2cDAAAAAECcRo8e7XQKkphBBwAAAADYyPfd/8wa62R2794d0zgFBQVmpFNvNOgAAAAAAE86++yzT/kUI8MwTHvsW33RoAMAAAAAbGPFc9CjmTJligoLCzV+/Pio+xiGoTvvvNOUfOqLBh0AAAAAYBuf/PKZdDu0U40zd+5cZWdnKzMzUx07doy6XzAYNCWf+uImcQAAAAAAT0pKStKyZctUXl5+0v1WrFhhU0Ynxww6AAAAAMA2dt4kTpLat29/yn2SkpLMSKfeaNABAAAAALbhOejReetsAAAAAABooJhBBwAAAADYxqfYlqbHOpaXMIMOAAAAAIALMIMOAAAAALCNnc9Bb2ho0AEAAAAANjLvOeheWxTurbMBAAAAAKCBYgYdAAAAAGAbu5+D3pDQoAMAAAAAbMNz0KPz1tkAAAAAANBAMYMOAAAAALANd3GPjhl0AAAAAABcgBl0AAAAAIBtfCY+Zs28x7W5gycadMMwJEnho8esDWTT8gk7olSGjtoQRTpWXmF5DH/Y8hDyVRrWB5F05HC55TGq/r1Yrbi41NLxS74b367zsQv1LH7Us/h4qZ7JA/XMq7VMop7VBfUsPtSz+BUXH7Zs7Hjqmd/nl9+km7uZNY5beKJBLykpkSQdeG69w5k0HPudTgC1Wu10AiaabFOckpISBYNBm6JZj3oWP+qZO3mpnt1qQwyv1TKJelYX1DN3op7Fx4v1zE6eaNAzMjJUUFCgtLS0mG8SUFxcrMzMTBUUFCg9Pd3iDK3jlfOQOBe3cuu5GIahkpISZWRkOJ2KqahnDf88JM7Fjdx6Hl6tZVL89cyt36O64FzcxyvnIbn3XOKrZz6Zty7FWzeJ80SD7vf71bZt2zodm56e7qq/2HXllfOQOBe3cuO5ePHTWeqZd85D4lzcyI3n4cVaJtW9nrnxe1RXnIv7eOU8JHeeS6z1zP/dVehmMGsct/DWgn0AAAAAABooT8ygAwAAAAAaCJ//+GbWWB7irbOJQyAQ0KxZsxQIBJxOpV68ch4S5+JWXjoXr/LK98gr5yFxLm7klfPwMi99jzgX9/HKeUjeOhfU5DO8+FwPAAAAAICrFBcXKxgMqvCblUpPb2rSmKVqdcaPVFRU5Lpr8uuCJe4AAAAAAPv4fCYucffWTeJo0AEAAAAAMElpaam2bdumQCCgLl26xPzoXIkGHQAAAABgo4b8mLWvvvpK//M//yNJatasme68885q799///26//77FQqFJEkZGRlatGiRRowYEdP4NOgAAAAAANv4vvufWWPZ6Y033tDs2bOVkpJSo+lesmSJZs6cKel4Y96qVStt3bpVV199tXJzc9W1a9dTjt9o7+IOAAAAAEA83n//fXXp0kXbtm3T888/X+29++67Tz6fT5MmTdLu3bu1adMmbd++XZ06ddLDDz8c0/g06AAAAAAA2/h8flM3O33yySfKyclRZmZmtdfXr1+vvLw8NW3aVAsWLJDffzyvtm3bau7cuXrvvfdiGp8GvYHIysqSz+eLedu5c2fk2Ndee00jRoxQq1atlJycrDPPPFPdunXT2LFjtWjRIhUUFFiW95IlSzR79mzLxm9s1q1bV+37/O677zqdEhAXahkkahm8gXoGiXrWGO3fv199+/at8frrr78uSbr88suVlpZW7b2BAwfG/O+aBr2B2Llzp058ZL1hGLVu+fn51Y6bOXOmrrzySnXs2FFr1qxRcXGxPv74Y/32t7/VRx99pJtvvll/+ctfLMt7yZIlmjNnjmXjW2HDhg0aNWqULrvsMp133nmaMGGC9uzZU+u+X3zxhX7605/qxhtv1C9/+UvdddddKikpiTvmbbfdpmXLlp1yv379+skwDM2aNSvuGIAbUMvsc8899+iDDz7QwYMHVVZWpk8//VQ5OTn66quvat2/rKxMF1xwQVwxtm3bpquvvlp33HGH7rzzTt1www0qLCw85XHUMngB9cxesfzOVdeaVJ9jqWd14zP5f3Y6dOiQmjdvXuP1119/XT6fT1deeWWN90477TSFw+HYAhhoUCQZJ/u25efnG5KM/Px8Iy8vz/D7/cawYcNq3XfXrl1GSkqKMWvWLIuyNYzs7OyT5nsqO3bsMFq3bm3s2rXLxKyiy83NNS6//HKjuLjYMAzDKCoqMgYOHGi0bNnSyM/Pr7bvp59+amRmZhpr1qwxDMMw9u7da2RmZsb957lx40bD7/cbTzzxRMzHzJo1y5BkrF69Oq5YgFtQy6xX9Wdctfn9fuO3v/1trftu2LDB6NWrV1znWFRUZLRt29Z46qmnIq/df//9xnnnnWeEQqGYxqCWwQuoZ9aL5Xeu+tQk6pl9ioqKDEnGwUPvG5XhzaZsBw+9b0gyioqKbDmHVq1aGdu3b6/22meffWb4fD4jEAgYhw4dqnFMXl6ekZWVFdP4zKB7TPv27VVSUqL27dtr48aNCofDUWdE2rVrpx//+Mdq1qyZzVnG7rXXXtO3336rM88805Z4c+bM0Z/+9KfIspT09HQ98sgjOnDggO65557IfseOHdPIkSM1ZcoU/fCHP4y8duTIEZ1++ukxxwuHw5ozZ07sn6gBjQS1rP7atWunCRMm6PLLL9fkyZOVm5sbubNslc8++0wjRozQH/7wByUkJMQ1/sKFCxUKhTRmzJjIa5MmTdLnn38eefwMAOpZfcX6O1d9ahL1DPHo1auXHnvssWqvVa1KGT58uILBYI1jXn31VbVp0ya2AKZ8jADbKMqntCd+OlvlzTffNCQZAwcONCV2aWmp8d///d/GJZdcYrRp08ZISkoyMjMzjYkTJxqFhYXV9n3iiSdqzN5UbfHMFF999dVGdna2KfnHolmzZkaXLl2M8vLyaq83b97cOOOMMyJf/+UvfzGSkpKMgwcP1iveY489Zjz99NNx/7lUfUr7zjvvGP/v//0/o0OHDkZycrJxzjnnGP/3f/9Xr5wAO1DLrBdvvHHjxsU1q9a5c2fj8ssvr/H6eeedZwwZMiSmMahl8ALqmbVi/Z2rPjWJemafqhn0Q0VrjbCxxZTtUNFaW2fQX331VcPn8xmXXXaZMXXqVOOiiy4yfD6fkZCQYHzwwQfV9q2srDRefPFF4/TTTzemTp0a0/jMoHtYz549lZqaqrVr12rMmDH67LPP6jXe1q1bdfPNN6tr167asGGDDh06pGXLlmndunUaMGCAiouLI/uOHz9ehmEoOztbUvXrssaPHx9zzPfff1+DBw+uV97xaNu2rfbs2aOjR49Wez0QCKi8vDzy9bJly9SpU6darz+J1f79+5WXl6cBAwbUeYz58+eroqJCGzZs0LZt29SmTRtdd9112rhxY53HBNyGWuY+JSUl2r59u9q1a1fjvYyMDG3atCmu8ahlaCyoZ/GL5Xeu+tQk6pkzfIa5m52uuOIK3XvvvVqxYoUeeOABvf/++0pNTdXDDz+sfv36RfZbt26dmjRpolGjRungwYMaNmxYTOPToDdQ378zaIcOHWrsc+aZZ2rBggXy+Xx69tln1aVLF3Xv3l0zZszQunXrqt3YJBapqam67LLL9Pvf/14ZGRlKTU3VwIED9eSTT+rLL7/Un//8Z1PO7dlnn9Wll16qvn376uuvv9bKlSt16aWX6o9//KMp45/Mhg0btH379mpLy/bt26fCwsLIPzjDMPT++++rZcuWWr16te69915NnjxZo0aN0pYtW2KOdf/99+vXv/51vfINBAKaPn26WrRooaysLC1YsECGYejpp5+u17iAXahl1qmoqND8+fM1ZcoU3X333brmmmu0Y8cOU8betWuXpOOXAX1f06ZNVVRUpIqKipjHo5bBC6hn5ov1d6761CTqGepi7ty52rFjh5599lm9+eabys/P1y233FJtnx/84AdasWKFVq9erdWrV0c+HDuVRCsShvW+X8B37txZ6w+CW2+9VT169FBOTo6WL1+uLVu2aMuWLbr//vvVsWNHzZgxQz//+c9jitmlSxe9+eabNV4///zzJUlr167VlClT6nA21Y0ePVqjR4/Wn/70J23evFlvv/22mjRpUuu+kyZN0ubNm+Maf+HChRo0aFCt76WlpdV4LMKjjz6qhIQEzZs3T5L0zTffqLy8XF9//bW2bdum+++/X5K0evVqXXTRRVq3bp26dOly0hxWrVql7t2764wzzqjTXd+rXHXVVdW+PueccyRJ27dvr/OYgJ2oZceZXcsk6euvv9Z1110XeU7r0qVLddFFF2nz5s1q1apVXLG+r2pWLjk5ucZ7TZs2lXT8LrexXqNKLYMXUM+OM7Oexfo7V31qEvXMIYZxfDNrLBvt3r1bkuT3+/WTn/wk6n7BYDDmpvxENOiNwIABA/Taa6/p4MGD+tvf/qZXX31Vr7/+uvLy8nTjjTfqq6++qnHjoGjef/99LVy4UFu2bNGePXtUWVkZee/gwYOm5r169Wr17t076g8ASVq0aJGpMb8vPz9fjz76qGbPnq0+ffpIUuSc8/Pzqy0Ju/jii5Wenq6pU6dGnoNYm4qKCj399NOmPEIlIyOj2tdVM/9lZWX1HhtwG2pZfL744gv5/f9eKPfTn/5U//Vf/6V58+bp4YcfrtfYVTeU8/lqPtqm6hKhY8eOxTwetQyNDfUsNrH+zlWfmkQ9c4gRPr6ZNZaNsrKyJB3/O1NSUqLU1FRTx2eJu0dkZWXJMIzIX5janHbaafrZz36mZ555Rnv27NHEiRMlHV9qHcss7tNPP61Bgwbp66+/1osvvqjS0tLItUtSzU+O6+vdd9+t06dOZqmoqNB1112nX/3qV5oxY0bk9ao7hnbq1EkpKSnVjmnbtq1WrFihUCgUddwHH3xQkydPrvUHQby+/wOyakyzvxeAXahl5jmxOZeO/xLasmVLvfLKK/Ueu2XLllHfKy0tlaS47kJNLYMXUc/qL9bfuepTk6hnqIv8/Hzl5eUpNTVVu3fvrrHVBw26hx06dEgffPBBre8Fg0H94Q9/UEZGhkKhkLZt23bK8ebMmSPDMPT444+rZ8+eCgQCZqcc8emnn6qwsNDRBn3ixIm6+uqrdd9991V7PSkpSS1btqz1cWqBQEBHjx7Vt99+W+uYeXl5Onz4cNTHqwCoiVoWvx/+8Ie66KKLarweDoe1f//+eo/fqlUr+Xy+WmfnSktL1bx581qv5wQaO+pZfGL9nas+NYl65pSoDxWo42Yfn8+n9u3bq3379pKOfxjXoUMHdejQIfLf9cESdw/bvHmzhg0bprKyMiUm1vxWJyQkqFWrVtq7d29MBX3nzp2SpM6dO1d7/ciRI1GPqess8erVq5WYmBi5w/nBgwd1+PDhyLWUVW666SZ99NFHcY394IMPnvS6TUm677771KNHD912222R15566ildf/31kqR+/frVei1RKBRSIBCI+mnsW2+9pQ8//FD/+Z//GXmtqKhIkvTII4/o5Zdf1l133RV5zicAalk0J6tlW7Zs0dlnn13j9QMHDkR+oaiPpk2b6sILL1RBQUGN93bs2KHu3bvXOwbgRdSz2p2snsXyO1diYmKdaxL1DPWVn58vwzDUsWNH5efn13s8GnSPO3r0qF555RWNGjWqxntfffWVtm7dqtatW5/ypmaS1K5dO3355Zf6+OOP1bdv38jr77//ftRjTjvtNElSeXm5UlJS9OCDD2rt2rV64YUXThprzZo16t69e2RJ0cMPPxxZ9nUis+5OeqJnnnlGwWCwWnMuHV/WVdWg//SnP9WECRMi5yUdX7q0bds2XXnllZEfutu3b1fbtm0jy51++ctf6pe//GWNcd99913dfvvtcT3mBGhMqGXxGT58eI3rQDdv3qxQKKSxY8fGPd73a5kkjRgxQn/5y19kGEbkF/4vv/xSBQUFmjZtWv1OAPAw6ll8Yv2dK9aaRD1ziQZ8Dfr3VX3wXTWzXl8scW8EfvGLX+ihhx5SXl6eQqGQ9u/frxdffFHDhg1TZWWlFi1aFLlBxsnccccdko5/MrphwwaVlZXp73//uyZNmhT1mF69ekmSVq5cqX/961968skna9wlvTaVlZWRa7Y2btyo1NRUtW7dOoazrZ+1a9dq2rRpWr9+vcaOHRvZxowZE7kOSTp+N9Nu3brpkUceibz2/PPPy+/3a8GCBZKk9957T+ecc0612fLaVN2A5PvPXgdQHbUsdtOmTdMdd9wRuR+GYRh68MEH1b9/f02dOrXWY8rLyyXVvJFRtFp28803q7S0VEuXLo289uijj6pLly666aabTDwbwHuoZ7GL5XcuKbaaRD1zkaq7uJu1NQDvvfdebDsaaBDat29f6wUXq1evjnpMKBQyVqxYYdx1111G//79jXbt2hmBQMBISUkxOnfubNx4443Gxx9/HFcey5YtM3r16mU0a9bMSEtLMy6++GJjxYoV1XJ64oknIvsXFxcbP/vZz4wzzjjDSE9PN0aMGGHs2bPnlHE2b95sDBgwwJgyZYqxcOFCo7KyMq4866pFixZRL26ZMWNGtX2/+eYb44YbbjBGjRpljBkzxvjJT35ifPHFF5H3P//8c+PMM880br755lpjffnll8agQYOM008/3ZBkpKWlGQMHDjT+8Y9/RM0vPz+/Rl7t27c3DMMwxo0bV+O9E78XgBtQy+ypZYZhGO+9955xzTXXGNdff71x1VVXGTNmzDCOHDlSbZ/CwkJj2LBhRteuXSPn3aJFC2Pw4MHG0qVLDcM4eS376KOPjB//+MfG5MmTjQkTJhgjR440du/efcrcqGXwAuqZffXsVL9zVTlVTaKeOa+oqMiQZBT96x3DqNhgylb0r3eOj1lUZMs5+P3+uF6P9f0qPsNoIB85AAAAAAAarOLiYgWDQRUdWKX09Njvjn/yMQ8r2HKoioqKbLmhX0JCQrXHGZ7q9Sp+v1/h8KmX43MNOgAAAADAPoZh4jXo9s43G4YR9RKUk12aEusNGmnQAQAAAACIUV0WodOgAwAAAADcx8ybu9k8g+7z+U66lD2aWG78KHEXdwAAAAAAYtKuXTtLj2MGHQAAAABgnwb8HPT8/HxLj6NBBwAAAADYpwEvcbcaS9wBAAAAAHABT8ygh8Nh7d27V2lpaTHfHQ9Aw2YYhkpKSpSRkSG/3zufNVLPgMbFq7VMop4BjU189cz4bjMlsknjuIMnGvS9e/cqMzPT6TQAOKCgoEBt27Z1Og3TUM+AxslrtUyingGNVUz1rAFfg241TzToaWlpkqSW1/aTP9m6U2qSkmLZ2F51pLzc6RTMYde1LTbMMFzR90zLY0jSJweOWDr+sdBRbVqwMvLv3yuoZ+5lRz1LSLJhBtVD9Wxxh9MtjyFJOquFZUOXHanQNb/6X8/VMol65mZ21LNA02TLY3jJojbp9gSinrmeJxr0qmVT/uRES38AWDm2V/nDHvkz89AvtMlNkiyPIUmJKcdsieO1ZZPUM/eyo575adDj0jTFnnqmJtY3Gl6rZRL1zM1sqWcBvi/xaGz1zDDCMkya+TZrHLfw1sVOAAAAAAA0UHy0BQAAAACwD49Zi4oGHQAAAABgH24SFxVL3AEAAAAAcAFXNOgvvfSSevfurYsuukjZ2dn69NNPnU4JAAAAAGCFqiXuZm11lJeXZ+JJmcPxBn3Dhg0aN26c/u///k9r1qzRhAkTNHz4cJWUlDidGgAAAADAdGGTt7r5xS9+Efnvn/3sZ3Uex0yOX4M+f/58jRgxQp07d5YkjR07VlOnTtWSJUt02223OZwdAAAAAKCheu+996K+d+jQIa1Zs0aGYejzzz+3MavoHG/QV61apd/85jeRr/1+v3r27KmVK1fSoAMAAACA19h4F/fBgwdHns1u1LJvdna2pNie324HR5e4f/PNNyouLlarVq2qvX7WWWcpPz8/6nGhUEjFxcXVNgBoiKhnALyCegbAjR555BFlZmZq0aJFys/Pj2x5eXnq27dv5L/79OnjdKqSHG7Qy8rKJEmBQKDa64FAIPJebXJychQMBiNbZmampXkCgFWoZwC8gnoGIGaGTLxJ3MlD3Xrrrfrggw+0fPlyTZ8+XU2aNFH79u2VlZVV7b/79etny6mfiqMNempqqqTjn7ieKBQKRd6rzfTp01VUVBTZCgoKLM0TAKxCPQPgFdQzADGreg66WdsptG7dWn/961/105/+VJdccon+9Kc/1djn97//vRVnGjdHr0E/44wzFAwGVVhYWO31/fv3q2PHjlGPCwQCNWbdAaAhop4B8ArqGQC3u+KKKzR06FDNnDlTP/zhD1VeXu50SjU4fpO4IUOGaNOmTZGvDcNQbm6u7r33XgezAgAAAABYw8SbxJ1qjfv3pKam6ne/+51yc3P15ptvmpSDeRxv0O+55x5dcskl2rFjhzp16qSnn35aCQkJGjdunNOpAQAAAABMV7/nl9ccK349evRQjx49TMrBPI436H369NGSJUs0ZswYNWnSRH6/X8uXL1daWprTqQEAAAAAGoDvPzmioV5243iDLkkjR47UyJEjnU4DAAAAAGC1sHF8M2ssqcaTI2bNmqXZs2fHNER2drbKy8u1fv16c3KqB1c06AAAAAAA1FVBQYHS09MjX8czez5gwABVVFRYkVbcaNABAAAAAPYxTLxJ3HfjpKenV2vQ45GTk2NOLiagQQcAAAAA2Mj5m8S5ld/pBAAAAAAAcNLo0aOdTkESM+gAAAAAADtZcJO4aHbv3h3TMAUFBWZkU2+eatB9321WOXbsmIWj/1u/HmdZHqNby2TLY0jSpr1ltsSx2tlnpNgS57K2F1gfZPtW62NIuirV2gU6pf6jutrSCM7ySj27+qK2lsf4welNLY8hSZ98XWRLHKtdX2Ll36x/O/C3HZbHWPfyJ5bHkKTDJdb9ewkZ3lqaWRuv1LOxQzJPvVM9nXNa0PIYkvTpgW9siWO1Ud/aE8eWevZsbE1kfbmmnllwDXo0Z599tsLhk+dmGIZ8Pnt+Pp6Kpxp0AAAAAACqTJkyRYWFhRo/fnzUfQzD0J133mlfUidBgw4AAAAAsI+NM+hz585Vdna2MjMz1bFjx6j7BYP2rGA5FRp0AAAAAIB9wuHjm1ljnURSUpKWLVum0tLSk+63YsUKc/KpJxp0AAAAAIBntW/f/pT7JCUl2ZDJqdGgAwAAAADsY3y3mTWWh/AcdAAAAAAAXIAZdAAAAACAfWy8Br2hoUEHAAAAANjHxru4NzQscQcAAAAAwAWYQQcAAAAA2IcZ9Kho0AEAAAAA9glLCpvUWHvrEnSWuAMAAAAA4AbMoAMAAAAA7MMS96iYQQcAAAAAwAWYQQcAAAAA2McwzLsG3WMz6DToAAAAAAD7GOHjm1ljeQhL3AEAAAAAcAFm0AEAAAAAtuEecdHRoAMAAAAA7MMS96hY4g4AAAAAgAswgw4AAAAAsE/YxLu4mzWOSzCDDgAAAACACzCDDgAAAACwD3eJi4oGHQAAAABgHxr0qFjiDgAAAACACzCDHode57ewJc45adY/KqBX8FzLY0jS4bLNlse4pCLV8hhHCw5aHkOSjv5ro+UxKo9VWh7jeJxjlo5/tOKopeN73ZX9z7IlTofUBMtjNJ//vuUxJOknV/yH5THW/e4Ty2O8cdjaf5tVwjY89aay0p5zqay0rm6GPPZ4ICeMGdTaljjtmwQsj5Fy32rLY0jSCOpZXKhnsYmrnoXD5v3B2vENshENOgAAAADAPsZ3m1ljeQhL3AEAAAAAcAFm0AEAAAAA9mGJe1TMoAMAAAAA4ALMoAMAAAAA7MNj1qKiQQcAAAAA2IcGPSqWuAMAAAAA4AKumEF/7rnntHjxYlVWVqq4uFhZWVl64IEHlJWV5XRqAAAAAAAzhY3jm1ljeYgrZtDHjh2rKVOmaNWqVVq/fr2aNGmiSy+9VKFQyOnUAAAAAACmMiQjbM7msQehu6JBv+qqqzR8+HBJkt/v1+23365t27YpNzfX4cwAAAAAALCHK5a4P//889W+TklJkSRm0AEAAADAa8LfbWaN5SGuaNC/74MPPlBGRoYGDhxY6/uhUKha815cXGxXagBgKuoZAK+gngFA/bliifuJQqGQHnjgAT322GNKSkqqdZ+cnBwFg8HIlpmZaXOWAGAO6hkAr6CeAYiZWdefR65D9w7XNegTJ07U6NGjNXLkyKj7TJ8+XUVFRZGtoKDAxgwBwDzUMwBeQT0DECsjbJi6eYmrlrjfc889Sk1N1dy5c0+6XyAQUCAQsCkrALAO9QyAV1DPAKD+XNOgz58/XwUFBXrqqackSZs2bZIk9ezZ08m0AAAAAABmMmTe09G8NYHujgZ90aJFWrp0qRYvXhx5tNrrr7+urKwsGnQAAAAA8JJK4/hm1lge4niDXlJSoltuuUXhcFj9+/ev9t4TTzzhUFYAAAAAANjL8QY9LS1NlZWVTqcBAAAAALCDYRzfzBrLQxxv0AEAAAAAjUj4u82ssTzEdY9ZAwAAAACgMWIGHQAAAABgn3D4+GbWWB7CDDoAAAAAAC7ADDoAAAAAwDZG+Phm1lhe4qkGvfcFrZTUJMmy8bPSfJaNfaIftupueYzX8zdYHkOSRiY0tzxGeVGx5TEqDJueNPDtIctD+Hr3P/VOJvjt+vctHb8ydMzS8Z125YAMJTdJtmz8a/8VsmzsE+37n08sj9Hyxx0tjyFJ635n/bkcOlhueYxQRYXlMSTJb8MavQt6plkfRFKr/xpo2dilRyr08O1LLRvfDUYPaqNAqnX17PJ91v+7kaR9f/rY8hjUs/hQz+LnmnpmGFKYu7jXhiXuAAAAAAC4gKdm0AEAAAAALsdz0KOiQQcAAAAA2MYIGzJMWuJu1jhuwRJ3AAAAAABcgBl0AAAAAIB9wibeJI4ZdAAAAAAAYDZm0AEAAAAA9mEGPSoadAAAAACAfYzvNrPG8hCWuAMAAAAA4ALMoAMAAAAA7MMS96ho0AEAAAAA9qFBj4ol7gAAAAAAuAAz6AAAAAAA2xjh45tZY3kJDToAAAAAwLOOHj2q3NxchUIhDRw4UAkJCZH3cnNz9c9//lOSdMMNNziVYgRL3AEAAAAA9gmHzd1OYteuXbrgggvUv39/DR48WFlZWXr11Ver7ZOfn6+f//znVp5xzGjQAQAAAAC2MQzD1O1k7r77bvXp00cbN27UJ598ot/85je6++67NXv2bElSjx49NGLECBvOOjY06AAAAAAAT9q5c6eefPJJ9ezZU127dtVNN92kjz/+WLt27dKtt94qSfL5fA5n+W806AAAAAAA+xiSwiZtp3jKWjAYrPFaIBDQE088obS0NF1//fWqrKw046xMQYMOAAAAALBPWP9+Fnq9t5OH8vv9WrlyZa3v5eTk6LzzztOECRPMP8c6okEHAAAAAHjShAkTNGLECI0ePbrW96dNm6ZbbrnFNcvcadABAAAAALYxwoap28lce+21CoVCevbZZ6PuM2nSJB06dMjks6wbnoMOAAAAAGjQiouLq30dCAQUCARiPr5Zs2Zmp1QnzKADAAAAAOxjmLxJyszMVDAYjGw5OTkxp5Odna0+ffqYcWb1xgw6AAAAAMA+4fDxzayxJBUUFCg9PT3ycjyz5wMGDFBFRYU5+dQTDToAAAAAoEFLT0+v1qDHI57Zdqt5qkHvGPQp0MS6u+8NzbjQsrFPdMw4anmMy48kWR5DkkqL9lsfJCX2T8fqKsFvz9UgRy+0fmlNcoI9/+x355daOn644pil4ztt5DcVappyigd71sO+V7+wbOwTJSRZ/2/nnd9+YnkMSTp6LGR5DLMmE04mMSHB+iCSzu1mQ21OtKeevei37ntf4bf+Z77TLi0MqWmKdX+5qWfxo57Fh3oWm7jqWdUzzM1gw981O3ENOgAAAADAPqY9A/27zQTRHsNmN0/NoAMAAAAAUGX37t0x7VdQUGBxJrGhQQcAAAAA2MYwDBmGOTPfpxrn7LPPVvgU11wYhiGfz7pLpeNBgw4AAAAA8KQpU6aosLBQ48ePj7qPYRi688477UvqJGjQAQAAAAC2McKGDJOuHT/VOHPnzlV2drYyMzPVsWPHqPsFg0FT8qkvGnQAAAAAgH1svIt7UlKSli1bptLSkz9haMWKFSYlVD806AAAAAAAz2rfvv0p90lKsucx1KfiqsesPfbYY/L5fHr33XedTgUAAAAAYIGwYSgcNmkz6WZzbuGaGfS9e/fqgQcecDoNAAAAAICVDOP4ZtZYHuKaGfTbbrtNv/71r51OAwAAAAAAR7hiBv21115TUlKShg8f7nQqAAAAAAAL2XkX94bG8Qa9tLRU9957r5YvX65QKBTTMaFQqNq+xcXFVqUHAJaingHwCuoZgFjRoEfn+BL3mTNnatKkSWrdunXMx+Tk5CgYDEa2zMxMCzMEAOtQzwB4BfUMAOrP0QY9NzdX69ev16RJk+I6bvr06SoqKopsBQUFFmUIANaingHwCuoZgFhV3SPOrM1LHF3i/sYbb+jIkSMaMmSIJKm8vFySNHnyZDVv3lyLFy9Wp06dahwXCAQUCARszRUArEA9A+AV1DMAqD9HG/SZM2dq5syZka937typDh066KGHHtLgwYOdSwwAAAAAYI2wcXwzaywPcfwmcQAAAACAxoObxEXn+E3iqkyePFljxoyp8d8AAAAAADQGrplBf+ihh5xOAQAAAABgMcMwZJh0dzezxnEL1zToAAAAAADvM8LHN7PG8hLXLHEHAAAAAKAxYwYdAAAAAGAfE5e4e+1B6MygAwAAAADgAsygAwAAAADsw3PQo/JUg5591gVq2jTFugB5W60b+wS+fx2yPEZFok2LJ5qnWx/jSLnlIWYfPmx5DEnKe/Yty2M0SWlieQxJysjIsHT8ytBRfWNpBGftf3OHUhMTLBt/x6ello19orKySstjHD16zPIYkmTHP53yI9bHGLroEuuDSHoubP3fsb+tL7Q8hiS1/KLCsrErQ0ctG9stqGexo57Fh3oWP7fUM8Mw8SZx3urPWeIOAAAAAIAbeGoGHQAAAADgboYRlmHSFLpZ47gFDToAAAAAwDY8Bz06lrgDAAAAAOACzKADAAAAAOwTlol3cTdnGLdgBh0AAAAAABdgBh0AAAAAYJuwYShs0vPRzBrHLWjQAQAAAAD2CRsmLnH3VoPOEncAAAAAAFyAGXQAAAAAgG14zFp0NOgAAAAAANsYYUOGSUvTzRrHLVjiDgAAAACACzCDDgAAAACwj2Ec38way0OYQQcAAAAAwAWYQQcAAAAA2IabxEVHgw4AAAAAsA03iYuOJe4AAAAAALgAM+gAAAAAANsYMmSYdHM3Q96aQadBBwAAAADYJhw+vpk1lpewxB0AAAAAABdgBh0AAAAAYBsegx4dM+gAAAAAALgAM+gAAAAAANswgx4dDToAAAAAwDZh4/hm1lhe4q0GfefnUpNky4avLCm1bOwTlXftZnmMhER7vvWv795seYxnPtxteYzTmje3PIYktW6dYXmMYDBoeQxJSk5KsnT8Y+UVlo7vtLzPy5Tis+4qpMOlxywb+0Sdu1pfaxITA5bHkKQ2/3mu5TH+N5hgeYybNlpfMyWpWVqa5THaZ2VZHkOSRl/S2bKxy0vLdc9DL1s2vhtQz2JHPYsP9Sx+1DP381aDDgAAAABwNZa4R0eDDgAAAACwjWHic9ANnoMOAAAAAADMxgw6AAAAAMA2LHGPjgYdAAAAAGAbI2ze0nSWuAMAAAAAANMxgw4AAAAAsA3PQY/OFTPoeXl5GjVqlC6++GJ17dpV/fr104cffuh0WgAAAAAA2MbxBv3AgQMaOnSofvWrX2n16tXasmWLUlNTtWPHDqdTAwAAAACYrOomcWZtXuL4EvcFCxaof//+GjRokCQpMTFRjz/+uFJTUx3ODAAAAABgtrCJz0E3axy3cHwG/cUXX4w051U6deqkjIwMhzICAAAAAMB+js6gl5aWKj8/X5WVlbruuuu0c+dONWvWTJMnT9Zll10W9bhQKKRQKBT5uri42I50AcB01DMAXkE9AxArnoMenaMz6IcOHZIkzZw5U1OnTtXatWs1depUXXHFFXr77bejHpeTk6NgMBjZMjMzbcoYAMxFPQPgFdQzALHiGvToHG3QExISJElXXHGFunXrJkkaOnSohgwZoocffjjqcdOnT1dRUVFkKygosCVfADAb9QyAV1DPAKD+HF3i3rJlSwUCAbVp06ba6+3bt9c//vGPqMcFAgEFAgGr0wMAy1HPAHgF9QxArAyZuMTdnGFcw/EZ9IEDB2rfvn3VXi8sLFS7du0cygoAAAAAAPs5/pi1adOmacyYMdq9e7fatWunrVu3asWKFfrrX//qdGoAAAAAAJPZ/Zi10tJSLVmyRCtXrlR+fr7KysqUmpqqjh076pJLLtENN9ygpk2bmpNQPTneoA8bNkyPPPKIrrrqKjVr1kzHjh3Tk08+qcsvv9zp1AAAAAAAJrPzLu5bt27V8OHDdezYMfXs2VO9evVSIBBQKBTS/v37NXfuXOXk5GjFihU699xzzUmqHhxv0CVp7NixGjt2rNNpAAAAAAA85JZbbtG8efM0duxY+Xy+WvdZunSpbr75Zq1evdrm7Gpy9Bp0AAAAAEDjYoTN3U4mHA7r+uuvj9qcS8cnjMNmrbmvJxp0AAAAAIBtwoa528kcOXJEeXl5J90nLy9PZWVlJp5h3bliiTsAAAAAAGabPn26LrzwQl1++eXq1auXWrVqFbkGvbCwUJs2bdIbb7yhJUuWOJ2qJBp0AAAAAICdTLxJ3KkehD5y5Ei1adNGCxYs0Jw5c1RcXBx5LxgMasiQIVqxYoV69+5tUkL1Q4MOAAAAAPCsPn366IUXXpAkffvtt5HHrJ1++ukOZ1YTDToAAAAAwDZ2Pwf9RKeffrorG/MqnmrQj7XtpKNNUywbf9Y/3rVs7BPlPfuWLXHskJiYZHmM1medZXmMZmlplseQpPT0dMtjJCdZ/z2RZPmdMH2VZq2Lcqd2nZKVmphg2fgtbhxk2dgn8nX6geUxnv3iQ8tjSFJiovU/Mj/YUmR5jBYtW1oeQ7Knno2+pLPlMSTp2k4XWjZ2cXGp7rFsdHegnsWOehYf6ln83FLPDJ1yZXrMqsY5cem6JAUCAQUCAZOi2Ie7uAMAAAAAGrTMzEwFg8HIlpOTE/Ox2dnZ6tu3r4XZxc5TM+gAAAAAAHeL5fnl8YwlSQUFBdVWO8Qzez5gwABVVFSYk1A90aADAAAAAGwTy/PL4xlLOn4pQl0vR4hntt1qLHEHAAAAAMAFaNABAAAAALapWuJu1maG0aNHmzNQPbHEHQAAAADgSbt3745pv4KCAosziQ0NOgAAAADANoePVph2DXrZsZPf3O3ss88+5eN/DcOQz+czJ6F6okEHAAAAAFguOTlZZ511lq5d9bSp45511llKTk6u9b0pU6aosLBQ48ePj3q8YRi68847Tc2prmjQAQAAAACWS0lJUX5+vumPNEtOTlZKSkqt782dO1fZ2dnKzMxUx44do44RDAZNzamuaNABAAAAALZISUmJ2kxbISkpScuWLVNpaelJ91uxYoVNGZ0cDToAAAAAwLPat29/yn2SkpJsyOTUeMwaAAAAAAAuQIMOAAAAAIAL0KADAAAAAOACNOgAAAAAALgADToAAAAAAC5Agw4AAAAAgAvQoAMAAAAA4AI06AAAAAAAuAANOgAAAAAALkCDDgAAAACAC9CgAwAAAADgAjToAAAAAAC4AA06AAAAAAAukOh0Amb6r2fekC/JulNKTEyybOwTpaQ0sTxGk5QUy2NIUkoT68+leTBoeYzk5GTLY0hSUqL1/yTHDM2yPIYkvbhmr6XjHzV8lo7vtBazrlPTptb9O30hb7NlY58ocfc/LY+xekuR5TEke+pZixYtLI9hVz0b/aNOlscYVxS2PIYkGRb+XK6osOccnEQ9ix31LD7Us/hRz9yPGXQAAAAAAFyABh0AAAAAABegQQcAAAAAwAVo0AEAAAAAcAEadAAAAAAAXIAGHQAAAAAAF6BBBwAAAADABRxv0EOhkO644w5169ZN2dnZ6tu3r1566SWn0wIAAAAAwFaJTidw33336eWXX9bmzZsVDAb10UcfqV+/ftqwYYO6devmdHoAAAAAANjC8Rn0zZs3q3fv3goGg5KkCy+8UMFgUO+8847DmQEAAAAAYB/HG/RRo0ZpzZo12r17tyRp+fLlOnDggFq1auVwZgAAAAAA2MfxJe7jx49XWVmZLrjgArVu3VpffPGFrrnmGl177bVRjwmFQgqFQpGvi4uL7UgVAExHPQPgFdQzAKg/x2fQFy9erPnz52vTpk367LPPlJubq379+snvj55aTk6OgsFgZMvMzLQxYwAwD/UMgFdQzwCg/hxt0A3D0NSpUzVx4kSdffbZkqRu3brpzTff1Lx586IeN336dBUVFUW2goICu1IGAFNRzwB4BfUMAOrP0SXuBw4c0MGDB5WVlVXt9Q4dOuiFF17QjBkzaj0uEAgoEAjYkCEAWIt6BsArqGcAUH+OzqC3aNFCgUBA+/btq/b6vn37lJqa6lBWAAAAAADYz9EG3e/3a9y4cVq8eLEOHjwoScrNzdXbb7990pvEAQAAAADgNY7fxf33v/+9Zs+eraFDhyo1NVUlJSWaP3++br/9dqdTAwAAAADANo436KmpqVq4cKHTaQAAAAAA4CjHH7MGAAAAAABo0AEAAAAAcAUadAAAAAAAXIAGHQAAAAAAF6BBBwAAAADABWjQAQAAAABwAccfs2amQHKK/MkWnpLPZ93YJ4axIUZKkyY2RJGaB4OWx0hOTrY8RlJSkuUxJGnM0CzLYxjhsOUxJOmvk8ZYOn5x8WGdefcfLI3hpEnL3qKexaj5aafZEMU79ezFW35qeQxJCmzcYHmMsGFYHkOSSn/7jHVjh0KWje0W1LPYUc/iQz2LH/XM/ZhBBwAAAADABWjQAQAAAABwARp0AAAAAABcgAYdAAAAAAAXoEEHAAAAAMAFaNABAAAAAHABGnQAAAAAAFyABh0AAAAAABegQQcAAAAAwAVo0AEAAAAAcAEadAAAAAAAXIAGHQAAAAAAF6BBBwAAAADABWjQAQAAAABwARp0AAAAAABcgAYdAAAAAAAXoEEHAAAAAMAFaNABAAAAAHABGnQAAAAAAFyABh0AAAAAABegQQcAAAAAwAVo0AEAAAAAcAEadAAAAAAAXCDR6QTMYBiGJCl89Ji1gXw+a8evCmNDjMrQURuiSMfKKyyP4Q9bHkK+SsP6IJKOHC63Pohhz7kUFx+2dPyS4lJJ//737xXUs/hRz+JT/N2/HasFSq2vZ3b9+y8LhSwbuyR0/O+V12qZRD2rC+pZfKhn8aOeuZ/P8MCf4FdffaXMzEyn0wDggIKCArVt29bpNExDPQMaJ6/VMol6BjRWXqxndvJEgx4Oh7V3716lpaXJF+OnqMXFxcrMzFRBQYHS09MtztA6XjkPiXNxK7eei2EYKikpUUZGhvx+71ytQz1r+OchcS5u5Nbz8Gotk+KvZ279HtUF5+I+XjkPyb3n4uV6ZidPLHH3+/11/pQmPT3dVX+x68or5yFxLm7lxnMJBoNOp2A66pl3zkPiXNzIjefhxVom1b2eufF7VFeci/t45Twkd56LV+uZnfhoAwAAAAAAF6BBBwAAAADABRptgx4IBDRr1iwFAgGnU6kXr5yHxLm4lZfOxau88j3yynlInIsbeeU8vMxL3yPOxX28ch6St84FNXniJnEAAAAAADR0jXYGHQAAAAAAN6FBBwAAAADABWjQAQAAAABwARp0AAAAAABcoFE26C+99JJ69+6tiy66SNnZ2fr000+dTqlOnnvuOQ0bNkxDhw5V79699ZOf/EQ7d+50Oq16eeyxx+Tz+fTuu+86nUqd5eXladSoUbr44ovVtWtX9evXTx9++KHTacUtFArpjjvuULdu3ZSdna2+ffvqpZdecjotfA/1zL2oZ+5ALWsYqGXu1tDrmRdqmUQ9azSMRmb9+vVGWlqa8cUXXxiGYRhPPvmk0aZNG6O4uNjhzOKXlJRkvPXWW4ZhGEZlZaVx/fXXG+ecc45RXl7ucGZ1s2fPHqNdu3aGJGP16tVOp1MnX3/9tZGVlWX8/e9/NwzDMI4ePWpcfPHFxrJlyxzOLH4zZswwsrKyjEOHDhmGYRi5ublGcnKysXnzZoczQxXqmXtRz9yDWuZ+1DJ3a+j1zCu1zDCoZ41Fo5tBnz9/vkaMGKHOnTtLksaOHatjx45pyZIlziZWB1dddZWGDx8uSfL7/br99tu1bds25ebmOpxZ3dx222369a9/7XQa9bJgwQL1799fgwYNkiQlJibq8ccfj3zdkGzevFm9e/dWMBiUJF144YUKBoN65513HM4MVahn7kU9cw9qmftRy9ytodczr9QyiXrWWDS6Bn3VqlXq1atX5Gu/36+ePXtq5cqVDmZVN88//3y1r1NSUiQdX/7S0Lz22mtKSkqK/FBrqF588cUaBb9Tp07KyMhwKKO6GzVqlNasWaPdu3dLkpYvX64DBw6oVatWDmeGKtQzd6KeuQu1zP2oZe7lhXrmlVomUc8ai0SnE7DTN998o+Li4hp/ic866yxt3LjRoazM88EHHygjI0MDBw50OpW4lJaW6t5779Xy5csb7A8w6fh55Ofnq7KyUtddd5127typZs2aafLkybrsssucTi9u48ePV1lZmS644AK1bt1aX3zxha655hpde+21TqcGUc/cinrmPtQyd6OWuZcX6pmXaplEPWssGlWDXlZWJkkKBALVXg8EApH3GqpQKKQHHnhAjz32mJKSkpxOJy4zZ87UpEmT1Lp16wZ9I5VDhw5JOn4+q1evVrdu3bRq1SoNHz5cf/vb33TJJZc4m2CcFi9erPnz52vTpk06++yztWXLFq1cuVJ+f6NbeONK1DN3op65D7XM3ahl7uWFeualWiZRzxqLRvXdTE1NlVRzmVEoFIq811BNnDhRo0eP1siRI51OJS65ublav369Jk2a5HQq9ZaQkCBJuuKKK9StWzdJ0tChQzVkyBA9/PDDTqYWN8MwNHXqVE2cOFFnn322JKlbt2568803NW/ePIezg0Q9cyPqmftQy9yPWuZOXqlnXqllEvWsMWlUDfoZZ5yhYDCowsLCaq/v379fHTt2dCir+rvnnnuUmpqquXPnOp1K3N544w0dOXJEQ4YM0eDBgzVmzBhJ0uTJkzV48GDt2LHD4Qxj17JlSwUCAbVp06ba6+3bt1d+fr5DWdXNgQMHdPDgQWVlZVV7vUOHDnrhhRecSQrVUM/ch3rmPtQy96OWuZNX6plXaplEPWtMGtUSd0kaMmSINm3aFPnaMAzl5ubq3nvvdTCrups/f74KCgr01FNPSVLk3Hr27OlkWjGbOXOmZs6cGfl6586d6tChgx566CENHjzYucTqICEhQQMHDtS+ffuqvV5YWKh27do5lFXdtGjRQoFAoMa57Nu3r8HPaHgJ9cxdqGfuQy1rGKhl7uOVeuaVWiZRzxqTRjWDLh3/RPONN96IfPL39NNPKyEhQePGjXM4s/gtWrRIS5cu1W233abc3Fx9+OGHeu211/TJJ584nVqjNW3aNL3yyiuRu2tu3bpVK1as0C233OJwZvHx+/0aN26cFi9erIMHD0o6vtzt7bff5kYkLkI9g5W8UM+oZQ0DtQxW8kItk6hnjYnPMAzD6STs9tJLL+n+++9XkyZN5Pf79cc//lFdu3Z1Oq24lJSUqHnz5gqHwzXee+KJJzR+/Hj7k6qnyZMna926dVq/fr26deumc889V88884zTacVt6dKl+t3vfqdmzZrp2LFjmjx5skaPHu10WnErKyvT7NmztXLlSqWmpqqkpETjxo3THXfcIZ/P53R6+A71zJ2oZ+5BLWsYqGXu5YV65oVaJlHPGotG2aADAAAAAOA2jW6JOwAAAAAAbkSDDgAAAACAC9CgAwAAAADgAjToAAAAAAC4AA06AAAAAAAuQIMOAAAAAIAL/H+kNQWwYDX9FAAAAABJRU5ErkJggg==' width=1000.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Bac…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_subplots = len(actual_output_times)\n",
    "nrows=np.ceil(n_subplots/3).astype(int)\n",
    "\n",
    "fig, axs = plt.subplots(ncols=3, nrows=nrows, figsize=(10, 3*nrows), layout='constrained', sharex=True, sharey=True)\n",
    "all_fs = []\n",
    "for i, t in enumerate(actual_output_times):\n",
    "    fs_output_file = f\"{outputs_dir}/TRfs_min/TRfs_min_{proj_name}_{model}_{i+1}.asc\"\n",
    "    fs, header = load_ascii_raster(fs_output_file)\n",
    "    all_fs.append(fs)\n",
    "    ax = axs[i//3, i%3]\n",
    "    im_hsd = ax.matshow(hsd, cmap=\"gray\", interpolation=\"none\")    \n",
    "    im_fs = ax.matshow(fs, cmap='RdYlGn', alpha=0.7)#, vmin=1.0, vmax=2, interpolation=\"none\")\n",
    "\n",
    "    ax.set_title(f\"FS at $t={t/3600:.1f}$ h\", fontsize='x-large')\n",
    "    # ax.set_xticks([])\n",
    "    # ax.set_yticks([])\n",
    "\n",
    "min_fs, max_fs = np.nanmin(all_fs), np.nanmax(all_fs)  # change vmin and vmax to each subplot\n",
    "max_fs = min(max_fs, 2)\n",
    "for ax in axs.flatten():\n",
    "    ax.images[1].set_clim(vmin=min_fs, vmax=max_fs)\n",
    "    ax.xaxis.set_ticks_position('bottom') if ax.get_subplotspec().is_last_row() else None\n",
    "\n",
    "cbar = fig.colorbar(im_fs, ax=axs, orientation='vertical', shrink=0.75, pad=0.01)\n",
    "cbar.ax.tick_params(axis=\"y\", labelrotation=90, pad=1.5)\n",
    "cbar.set_label('FS', rotation=90, size=\"x-large\")\n",
    "[label.set_va(\"center\") for label in cbar.ax.get_yticklabels()]\n",
    "fig.canvas.header_visible = False\n",
    "fig.canvas.toolbar_position = 'bottom'\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing spatial outputs of $\\psi$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "86b9f8a147e64391a7b7e1a7ffa0b04b",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=1000.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Bac…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_subplots = len(actual_output_times)\n",
    "nrows=np.ceil(n_subplots/3).astype(int)\n",
    "\n",
    "fig, axs = plt.subplots(ncols=3, nrows=nrows, figsize=(10, 3*nrows), layout='constrained', sharex=True, sharey=True)\n",
    "all_psi = []\n",
    "for i, t in enumerate(actual_output_times):\n",
    "    psi_output_file = f\"{outputs_dir}/TRp_at_fs_min/TRp_at_fs_min_{proj_name}_{model}_{i+1}.asc\"\n",
    "    psi, header = load_ascii_raster(psi_output_file)\n",
    "    all_psi.append(psi)\n",
    "    ax = axs[i//3, i%3]\n",
    "    im_hsd = ax.matshow(hsd, cmap=\"gray\", interpolation=\"none\")\n",
    "    im_psi = ax.matshow(psi, cmap='ocean_r', alpha=0.7, interpolation=\"none\")#, vmin=0, vmax=np.nanmax(depth))\n",
    "\n",
    "    ax.set_title(f\"$\\psi$ at $t={t/3600:.1f}$ h\", fontsize='x-large')\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "\n",
    "min_psi, max_psi = np.nanmin(all_psi), np.nanmax(all_psi)  # change vmin and vmax to each subplot\n",
    "for ax in axs.flatten():\n",
    "    ax.images[1].set_clim(vmin=min_psi, vmax=max_psi)\n",
    "    ax.xaxis.set_ticks_position('bottom') if ax.get_subplotspec().is_last_row() else None\n",
    "\n",
    "cbar = fig.colorbar(im_psi, ax=axs, orientation='vertical', shrink=0.75, pad=0.01)\n",
    "cbar.ax.tick_params(axis=\"y\", labelrotation=90, pad=1.5)\n",
    "cbar.set_label('Pressure head, $\\psi$  [m]', rotation=90, size=\"x-large\")\n",
    "[label.set_va(\"center\") for label in cbar.ax.get_yticklabels()]\n",
    "fig.canvas.header_visible = False\n",
    "fig.canvas.toolbar_position = 'bottom'\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Output to dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_param(zone, param):\n",
    "    return trg_inputs['geoparams'][zone.iloc[0]][param]\n",
    "\n",
    "def get_df(file):\n",
    "    γ_w = 9.8e3  # [N/m3] unit weight of water\n",
    "    # Reading file\n",
    "    df = pd.read_csv(file, names=['i_tr', 'j_tr', 'elev', 'ψ', 'θ'], skiprows=7, sep='\\s+')\n",
    "    # Correcting indices to match numpy notation\n",
    "    df['i'] = df['j_tr'].max() - df['j_tr']\n",
    "    df['j'] = df['i_tr'] - 1\n",
    "    df.drop(columns=['i_tr', 'j_tr'], inplace=True)\n",
    "    # Calculating depths from elevations\n",
    "    elev_max = df.groupby(['i', 'j'])['elev'].transform('max')\n",
    "    df['z'] = elev_max - df['elev'] + 0.001\n",
    "    df = df[['i', 'j', 'elev', 'z', 'ψ', 'θ']]\n",
    "    # Assigning zones and geotechnical parameters\n",
    "    df['zone'] = zones[df['i'], df['j']]\n",
    "    for param_name in trg_inputs['geoparams'][1].keys():\n",
    "        df[param_name] = df.groupby('zone')['zone'].transform(get_param, param=param_name)\n",
    "    # Correting rows with ψ==0 (watertable) due to numerical errors\n",
    "    mask = (df['ψ'] == 0) & (df['z'] > 0.001) & (df['θ'] == df['θ_sat'])\n",
    "    # df.loc[mask, 'z'] = df.loc[mask, 'z'] + 1/df.loc[mask, 'α']\n",
    "    # df.loc[mask, 'elev'] = df.loc[mask, 'elev'] - 1/df.loc[mask, 'α']\n",
    "    # df.sort_values(['i', 'j', 'z'], inplace=True)\n",
    "    df.drop(df[mask].index, inplace=True)  # This eliminates the wt rows\n",
    "    # Assigning slope\n",
    "    df['δ'] = slope[df['i'], df['j']]\n",
    "    δrad = np.deg2rad(df['δ'])\n",
    "    # Calculating effective stress parameter, χ\n",
    "    df['χ'] = (df['θ'] - df['θ_res']) / (df['θ_sat'] - df['θ_res'])\n",
    "    # Specific gravity\n",
    "    df['Gs'] = (df['γ_sat']/γ_w - df['θ_sat']) / (1 - df['θ_sat'])\n",
    "    # unit weight at unsat and sat zones\n",
    "    df['γ_nat'] = (df['Gs'] * (1 - df['θ_sat']) + df['θ']) * γ_w\n",
    "    # average unit weight\n",
    "    γ_cumul = df.groupby(['i', 'j'])['γ_nat'].transform('cumsum')\n",
    "    idx = df.groupby(['i', 'j'])['γ_nat'].cumcount() + 1\n",
    "    df['γ_avg'] = γ_cumul / idx\n",
    "    # Factor of safety\n",
    "    fs1 = np.tan(np.deg2rad(df['φ'])) / np.tan(δrad)\n",
    "    fs2_num = df['c'] - df['ψ'] * γ_w * df['χ'] * np.tan(np.deg2rad(df['φ']))\n",
    "    fs2_den = df['γ_avg'] * df['z'] * np.sin(δrad) * np.cos(δrad)\n",
    "    fs = fs1 + fs2_num/fs2_den\n",
    "    fs[fs > 3] = 3\n",
    "    df['fs'] = fs\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Verification of FS and head pressure at a point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>i</th>\n",
       "      <th>j</th>\n",
       "      <th>elev</th>\n",
       "      <th>z</th>\n",
       "      <th>ψ</th>\n",
       "      <th>θ</th>\n",
       "      <th>zone</th>\n",
       "      <th>c</th>\n",
       "      <th>φ</th>\n",
       "      <th>γ_sat</th>\n",
       "      <th>...</th>\n",
       "      <th>K_sat</th>\n",
       "      <th>θ_sat</th>\n",
       "      <th>θ_res</th>\n",
       "      <th>α</th>\n",
       "      <th>δ</th>\n",
       "      <th>χ</th>\n",
       "      <th>Gs</th>\n",
       "      <th>γ_nat</th>\n",
       "      <th>γ_avg</th>\n",
       "      <th>fs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-0.04532</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>35</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e-07</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.263451</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>95.9</td>\n",
       "      <td>0.101</td>\n",
       "      <td>-0.07715</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>35</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e-07</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.263451</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>95.8</td>\n",
       "      <td>0.201</td>\n",
       "      <td>-0.10210</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>35</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e-07</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.263451</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>95.7</td>\n",
       "      <td>0.301</td>\n",
       "      <td>-0.12030</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>35</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e-07</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.263451</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>95.6</td>\n",
       "      <td>0.401</td>\n",
       "      <td>-0.13190</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>35</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e-07</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.263451</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     i  j  elev      z        ψ     θ  zone       c   φ    γ_sat  ...  \\\n",
       "484  2  2  96.0  0.001 -0.04532  0.45   1.0  3500.0  35  22000.0  ...   \n",
       "485  2  2  95.9  0.101 -0.07715  0.45   1.0  3500.0  35  22000.0  ...   \n",
       "486  2  2  95.8  0.201 -0.10210  0.45   1.0  3500.0  35  22000.0  ...   \n",
       "487  2  2  95.7  0.301 -0.12030  0.45   1.0  3500.0  35  22000.0  ...   \n",
       "488  2  2  95.6  0.401 -0.13190  0.45   1.0  3500.0  35  22000.0  ...   \n",
       "\n",
       "            K_sat  θ_sat  θ_res    α     δ    χ        Gs    γ_nat    γ_avg  \\\n",
       "484  1.000000e-07   0.45   0.05 -0.5  23.0  1.0  3.263451  22000.0  22000.0   \n",
       "485  1.000000e-07   0.45   0.05 -0.5  23.0  1.0  3.263451  22000.0  22000.0   \n",
       "486  1.000000e-07   0.45   0.05 -0.5  23.0  1.0  3.263451  22000.0  22000.0   \n",
       "487  1.000000e-07   0.45   0.05 -0.5  23.0  1.0  3.263451  22000.0  22000.0   \n",
       "488  1.000000e-07   0.45   0.05 -0.5  23.0  1.0  3.263451  22000.0  22000.0   \n",
       "\n",
       "      fs  \n",
       "484  3.0  \n",
       "485  3.0  \n",
       "486  3.0  \n",
       "487  3.0  \n",
       "488  3.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell = (2, 2)  # (i, j) indices of the unique cell in the domain (i=y, j=x) in numpy notation\n",
    "\n",
    "file = f'{outputs_dir}/TR_ijz_p_th/TR_ijz_p_th_{proj_name}_{model}_4.txt'\n",
    "df = get_df(file)\n",
    "cell_df = df[(df['i']==cell[0]) & (df['j']==cell[1])]\n",
    "# cell_df.drop(cell.index[cell['ψ'] == 0], inplace=True)\n",
    "cell_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting results at a point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "clrs = ['#6F4C9B', '#6059A9', '#5568B8', '#4E79C5', '#4D8AC6', '#4E96BC',\n",
    "        '#549EB3', '#59A5A9', '#60AB9E', '#69B190', '#77B77D', '#8CBC68',\n",
    "        '#A6BE54', '#BEBC48', '#D1B541', '#DDAA3C', '#E49C39', '#E78C35', \n",
    "        '#E67932', '#E4632D', '#DF4828', '#DA2222']\n",
    "cmap = mpl.colors.LinearSegmentedColormap.from_list('cmap_name', clrs)\n",
    "# cmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "32a3515d01134bb3b0ab85b81f9b549e",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=700.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Bac…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(7, 4), sharey=True, layout='constrained')\n",
    "\n",
    "palette = mpl.colormaps['gnuplot_r']\n",
    "palette = cmap\n",
    "for ax in axs:\n",
    "    ax.grid(True, color='0.3', ls='--', lw=0.7)\n",
    "    ax.spines[\"top\"].set_linewidth(1.5)\n",
    "    ax.spines[\"left\"].set_linewidth(1.5)\n",
    "    ax.xaxis.set_ticks_position('top')\n",
    "    ax.xaxis.set_tick_params(top=True, bottom=False)\n",
    "    ax.xaxis.set_label_position('top')\n",
    "    ax.set_prop_cycle('color', palette(np.linspace(0, 1, len(output_times))))\n",
    "\n",
    "axs[0].axvline(0, color='k', lw=1.5, ls='--')\n",
    "axs[1].axvline(1, color='k', lw=1.5, ls='--')\n",
    "\n",
    "for n in np.arange(len(output_times)) + 1:\n",
    "    file = f'TRIGRS_2/outputs/TR_ijz_p_th/TR_ijz_p_th_{proj_name}_{model}_{n}.txt'\n",
    "    t = read_time(file)\n",
    "    df = get_df(file)\n",
    "    df_cell = df[(df['i']==cell[0]) & (df['j']==cell[1])]\n",
    "    axs[0].plot(df_cell['ψ'], df_cell['z'], label=f'$t={t/3600:.0f}$ h')\n",
    "    axs[1].plot(df_cell['fs'], df_cell['z'], label=f'$t={t/3600:.0f}$ h')\n",
    "    axs[0].set(xlabel='Pressure head, $\\\\psi$ [m]', ylabel='$Z$ [m]')\n",
    "    axs[1].set(xlabel='FS')\n",
    "    \n",
    "axs[1].invert_yaxis()\n",
    "handles, labels = axs[0].get_legend_handles_labels()\n",
    "fig.legend(handles, labels, loc='outside right center')\n",
    "fig.canvas.header_visible = False\n",
    "fig.canvas.toolbar_position = 'bottom'\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "EAFIT-2-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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}