modify_ssm_inputs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Eli Schwat\n",
    "\n",
    "elilouis@uw.edu\n",
    "\n",
    "Created for Professor Michael Brett's CEWA547 Course, Winter 2021\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modify Salish Sea Model Point Source Inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook walks you through the process of modifying point source effluent concentration data. \n",
    "\n",
    "User input is required when you see this...\n",
    "\n",
    "**<span style=\"color:red\">USER INPUT REQUIRED</span>**\n",
    "\n",
    "Good luck."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Input Variables:\n",
    "\n",
    "**<span style=\"color:red\">USER INPUT REQUIRED</span>**\n",
    "\n",
    "Put in the path to the file `ssm_pnt_wq.dat`, which should come with your packaging of the SSM model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_file = \"/Users/elischwat/Google Drive/UW/Classes Winter 2021/Watershed MGMT/salish sea model/SSM_WQM_model_inputs/inputs/ssm_pnt_wq.dat\"\n",
    "# input_file = \"/Users/elischwat/Google Drive/UW/Classes Winter 2021/Watershed MGMT/salish sea model/SSM_WQM_model_inputs/inputs/ssm_pnt_wqSHORT.dat\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<span style=\"color:red\">USER INPUT REQUIRED</span>**\n",
    "\n",
    "Put in a path to where you will write the modified file when you are done."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_file = \"/Users/elischwat/Google Drive/UW/Classes Winter 2021/Watershed MGMT/salish sea model/SSM_WQM_model_inputs/inputs/ssm_pnt_wqMODIFIED.dat\"\n",
    "# output_file = \"/Users/elischwat/Google Drive/UW/Classes Winter 2021/Watershed MGMT/salish sea model/SSM_WQM_model_inputs/inputs/ssm_pnt_wqSHORTMODIFIED.dat\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "variable_name_dict = {\n",
    "    0: \"Flow (OFF, b/c from FVCOM)\",\n",
    "    1: \"Temperature (OFF, b/c from FVCOM)\",\n",
    "    2: \"Salinity (OFF, b/c from FVCOM)\",\n",
    "    3: \"TSS\",\n",
    "    4: \"Algal 1 (Algal group 1)\",\n",
    "    5: \"Algal 2 (Algal group 2)\",\n",
    "    6: \"Algal 3 (Algal group 3) (unused)\",\n",
    "    7: \"Zooplankton 1 (Zooplankton – species 1)\",\n",
    "    8: \"Zooplankton 2 (Zooplankton species 2)\",\n",
    "    9: \"Labile DOC (Labile dissolved organic carbon)\",\n",
    "    10: \"Refractory DOC (Refractory dissolved organic carbon)\",\n",
    "    11: \"Labile POC (Labile particulate organic carbon)\",\n",
    "    12: \"Refractory POC (Refractory particulate organic carbon)\",\n",
    "    13: \"Ammonium (NH4)\",\n",
    "    14: \"Nitrate + Nitrite (NO3+NO2)\",\n",
    "    15: \"Urea\",\n",
    "    16: \"Labile DON (Labile dissolved organic nitrogen)\",\n",
    "    17: \"Refractory DON (Refractory dissolved organic nitrogen)\",\n",
    "    18: \"Labile PON (Labile particular organic nitrogen)\",\n",
    "    19: \"Refractory PON (Refractory particulate organic nitrogen)\",\n",
    "    20: \"Total PO4 (Total phosphate)\",\n",
    "    21: \"Labile DOP (Labile dissolved organic phosphate)\",\n",
    "    22: \"Refractory DOP (Refractory dissolved organic phosphate)\",\n",
    "    23: \"Labile POP (Labile particulate organic phosphate)\",\n",
    "    24: \"Refractory POP (Refractory particulate organic phosphate)\",\n",
    "    25: \"Particulate inorganic P (Particulate inorganic phosphate)\",\n",
    "    26: \"COD (Chemical oxygen demand)\",\n",
    "    27: \"DO (Dissolved Oxygen)\",\n",
    "    28: \"Particulate Silica\",\n",
    "    29: \"Dissolved Silica\",\n",
    "    30: \"internal P group for Alga 1, Droop model (currently off)\",\n",
    "    31: \"internal P group for Alga 2, Droop model (currently off)\",\n",
    "    32: \"internal P group for Alga 3, Droop model (currently off)\",\n",
    "    33: \"DIC\",\n",
    "    34: \"Alkalinity\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data(input_file, num_params):\n",
    "    \"\"\"\n",
    "    Params:\n",
    "    input_file (str): path to input file\n",
    "    num_params (int): number of parameters contained in the file. Usually 35.\n",
    "    \n",
    "    Returns:\n",
    "    (df, header_lines): df is a dataframe containing the data separated by parameter, point source, \n",
    "        and date. header_lines is a list of strings containing all the header data that must be \n",
    "        written to the new file.\n",
    "    \"\"\"\n",
    "    with open(input_file) as f:\n",
    "        lines = [line.rstrip() for line in f]\n",
    "    num_point_sources = int(lines[1])\n",
    "    print(f\"Found {num_point_sources} point sources\")\n",
    "    header_lines = lines[:num_point_sources*2+3]\n",
    "    data_lines =  lines[num_point_sources*2+3:]\n",
    "    num_daily_data = int(header_lines[-1])\n",
    "    print(f\"Found {num_daily_data} days of data\")\n",
    "    df_list = []\n",
    "    for n_day in range(0, num_daily_data):\n",
    "        day_num = data_lines[n_day*(num_params+1)]\n",
    "        lines = data_lines[n_day*(num_params+1) + 1: n_day*(num_params+1) + 1 + num_params]\n",
    "        df_list.append(__extract_daily_data(lines, day_num))\n",
    "    df = pd.concat(df_list)\n",
    "    df = df.reset_index(drop=True)\n",
    "    df['hour'] = df['hour'].astype('float')\n",
    "    return df, header_lines\n",
    "          \n",
    "def write_data(output_file, df, header_lines):\n",
    "    \"\"\"\n",
    "    Params:\n",
    "    output_file (str): path to output file.\n",
    "    df (pandas.DataFrame): a dataframe containing data. such as is returned by the read_data function\n",
    "                defined above.\n",
    "    header_lines: list of strings, such as is returned by the read_data function defined above.\n",
    "    \"\"\"\n",
    "    writer = open(output_file, \"w\")\n",
    "    writer.write(\"\\n\".join(header_lines))\n",
    "    for day, day_df in df.groupby('hour'):\n",
    "        #generate a days worth of data which is composed of:\n",
    "        #1. a first single line with the julian day\n",
    "        writer.write(\"\\n\")\n",
    "        day_line_string = \"     {:.2f}\".format(day)\n",
    "        writer.write(day_line_string)\n",
    "        #2. num_params lines of data, each line is a series of single-space-separated floats (formatted in sci notation),\n",
    "        #    each line is num_point_sources floats long. make sure lines are written in the order of the parameter number.    \n",
    "        for index, row in day_df.iloc[:,2:].iterrows():\n",
    "            line_string = ' ' + ' '.join([ #add a space here because that's how the original file is\n",
    "                '{:.3E}'.format(single_param_vals) for single_param_vals in row\n",
    "            ])\n",
    "            writer.write(\"\\n\")\n",
    "            writer.write(line_string)\n",
    "    writer.write(\"\\n\") #to put an empty line at the beginning, as the original files have\n",
    "    writer.close()\n",
    "          \n",
    "def __extract_daily_data(lines, day_num):\n",
    "    assert len(lines)==num_params, f\"Expecting {num_params} lines of data\"\n",
    "    arr_list = []\n",
    "    for i in range(0, len(lines)):\n",
    "        line = lines[i]\n",
    "        param_index = i\n",
    "        arr_list.append(\n",
    "            [float(x) for x in line.strip().split(' ')]\n",
    "        )\n",
    "    df = pd.DataFrame(arr_list)\n",
    "    df.insert(0, 'hour', day_num)\n",
    "    df.insert(0, 'param', df.index)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_params = 35"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 193 point sources\n",
      "Found 365 days of data\n"
     ]
    }
   ],
   "source": [
    "df, header_lines = read_data(input_file, num_params)\n",
    "source_lines = header_lines[2:195]\n",
    "source_names_series = pd.Series(source_lines).apply(lambda x: x.split(',')[1].split('---')[0].strip())\n",
    "point_source_types = source_names_series.apply(lambda x: x.split(' - ')[1].split(' (')[0].strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(193, 193)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(source_names_series), len(point_source_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: Fraser - River (ECY ID: 258)\n",
      "1: Fraser - River (ECY ID: 258)\n",
      "2: Nooksack - River (ECY ID: 238)\n",
      "3: Nooksack - River (ECY ID: 238)\n",
      "4: Samish_Bell south - River (ECY ID: 246)\n",
      "5: Samish_Bell south - River (ECY ID: 246)\n",
      "6: Skagit - River (ECY ID: 249)\n",
      "7: Skagit - River (ECY ID: 249)\n",
      "8: Stillaguamish - River (ECY ID: 253)\n",
      "9: Stillaguamish - River (ECY ID: 253)\n",
      "10: Snohomish - River (ECY ID: 251)\n",
      "11: Snohomish - River (ECY ID: 251)\n",
      "12: Lake Washington - River (ECY ID: 223)\n",
      "13: Lake Washington - River (ECY ID: 223)\n",
      "14: Green_Duwamish - River (ECY ID: 222)\n",
      "15: Green_Duwamish - River (ECY ID: 222)\n",
      "16: Puyallup - River (ECY ID: 201)\n",
      "17: Puyallup - River (ECY ID: 201)\n",
      "18: Nisqually - River (ECY ID: 207)\n",
      "19: Nisqually - River (ECY ID: 207)\n",
      "20: Budd_Deschutes - River (ECY ID: 209)\n",
      "21: Budd_Deschutes - River (ECY ID: 209)\n",
      "22: Tahuya - River (ECY ID: 254)\n",
      "23: Tahuya - River (ECY ID: 254)\n",
      "24: Skokomish - River (ECY ID: 250)\n",
      "25: Skokomish - River (ECY ID: 250)\n",
      "26: Hamma Hamma - River (ECY ID: 233)\n",
      "27: Hamma Hamma - River (ECY ID: 233)\n",
      "28: Duckabush - River (ECY ID: 230)\n",
      "29: Duckabush - River (ECY ID: 230)\n",
      "30: Dosewallips - River (ECY ID: 229)\n",
      "31: Dosewallips - River (ECY ID: 229)\n",
      "32: Quilcene - River (ECY ID: 245)\n",
      "33: Quilcene - River (ECY ID: 245)\n",
      "34: Dungeness - River (ECY ID: 231)\n",
      "35: Dungeness - River (ECY ID: 231)\n",
      "36: Elwha - River (ECY ID: 232)\n",
      "37: Elwha - River (ECY ID: 232)\n",
      "38: Howe Sound - River (ECY ID: 259)\n",
      "39: Sunshine Coast - River (ECY ID: 260)\n",
      "40: Vancouver Isl C - River (ECY ID: 261)\n",
      "41: Vancouver Isl N - River (ECY ID: 262)\n",
      "42: Vancouver Isl S - River (ECY ID: 263)\n",
      "43: Victoria_SJdF - River (ECY ID: 264)\n",
      "44: South King - River (ECY ID: 202)\n",
      "45: Colvos Passage - River (ECY ID: 203)\n",
      "46: Tacoma Narrows - River (ECY ID: 204)\n",
      "47: Quartermaster - River (ECY ID: 205)\n",
      "48: Chambers - River (ECY ID: 206)\n",
      "49: McAllister - River (ECY ID: 208)\n",
      "50: McAllister - River (ECY ID: 208)\n",
      "51: Henderson - River (ECY ID: 210)\n",
      "52: Henderson - River (ECY ID: 210)\n",
      "53: Eld - River (ECY ID: 211)\n",
      "54: Eld - River (ECY ID: 211)\n",
      "55: Totten - River (ECY ID: 212)\n",
      "56: Totten - River (ECY ID: 212)\n",
      "57: Little Skookum - River (ECY ID: 213)\n",
      "58: Little Skookum - River (ECY ID: 213)\n",
      "59: Goldsborough - River (ECY ID: 214)\n",
      "60: Oakland Bay - River (ECY ID: 215)\n",
      "61: Oakland Bay - River (ECY ID: 215)\n",
      "62: Case north - River (ECY ID: 216)\n",
      "63: Case north - River (ECY ID: 216)\n",
      "64: Pickering - River (ECY ID: 217)\n",
      "65: Pickering - River (ECY ID: 217)\n",
      "66: Dana Passage - River (ECY ID: 218)\n",
      "67: Case south - River (ECY ID: 219)\n",
      "68: Case south - River (ECY ID: 219)\n",
      "69: Carr south - River (ECY ID: 220)\n",
      "70: Carr north - River (ECY ID: 221)\n",
      "71: Carr north - River (ECY ID: 221)\n",
      "72: Sinclair Dyes - River (ECY ID: 224)\n",
      "73: Birch Bay - River (ECY ID: 225)\n",
      "74: Clallam Bay - River (ECY ID: 226)\n",
      "75: Dabob Bay - River (ECY ID: 227)\n",
      "76: Discovery Bay - River (ECY ID: 228)\n",
      "77: Kitsap NE - River (ECY ID: 234)\n",
      "78: Kitsap_Hood - River (ECY ID: 235)\n",
      "79: Lopez Island - River (ECY ID: 236)\n",
      "80: Lunch Cove - River (ECY ID: 237)\n",
      "81: Lynch Cove - River (ECY ID: 237)\n",
      "82: North Olympic - River (ECY ID: 239)\n",
      "83: NW Hood - River (ECY ID: 240)\n",
      "84: Orcas Island - River (ECY ID: 241)\n",
      "85: Port Angeles - River (ECY ID: 242)\n",
      "86: Port Gamble - River (ECY ID: 243)\n",
      "87: Port Townsend - River (ECY ID: 244)\n",
      "88: San Juan Island - River (ECY ID: 247)\n",
      "89: Sequim Bay - River (ECY ID: 248)\n",
      "90: South Snohomish - River (ECY ID: 252)\n",
      "91: Whatcom_Bell north - River (ECY ID: 255)\n",
      "92: Whidbey east - River (ECY ID: 256)\n",
      "93: Whidbey west - River (ECY ID: 257)\n",
      "94: Bainbridge Island City - Point Source (ECY ID: 201)\n",
      "95: Bainbridge Kitsap Co 7 - Point Source (ECY ID: 202)\n",
      "96: Boston Harbor - Point Source (ECY ID: 203)\n",
      "97: Bremerton - Point Source (ECY ID: 204)\n",
      "98: Carlyon - Point Source (ECY ID: 205)\n",
      "99: Central Kitsap - Point Source (ECY ID: 206)\n",
      "100: Chambers Creek - Point Source (ECY ID: 207)\n",
      "101: Fort Lewis - Point Source (ECY ID: 208)\n",
      "102: Gig Harbor - Point Source (ECY ID: 209)\n",
      "103: Hartstene - Point Source (ECY ID: 210)\n",
      "104: Kitsap Co Kingston - Point Source (ECY ID: 211)\n",
      "105: Suquamish - Point Source (ECY ID: 212)\n",
      "106: Lakota - Point Source (ECY ID: 213)\n",
      "107: LOTT - Point Source (ECY ID: 214)\n",
      "108: Manchester - Point Source (ECY ID: 215)\n",
      "109: McNeil Is - Point Source (ECY ID: 216)\n",
      "110: Midway - Point Source (ECY ID: 217)\n",
      "111: Miller Creek - Point Source (ECY ID: 218)\n",
      "112: Port Orchard - Point Source (ECY ID: 219)\n",
      "113: Redondo - Point Source (ECY ID: 220)\n",
      "114: Rustlewood - Point Source (ECY ID: 221)\n",
      "115: Salmon Creek - Point Source (ECY ID: 222)\n",
      "116: Seashore Villa - Point Source (ECY ID: 223)\n",
      "117: Shelton - Point Source (ECY ID: 224)\n",
      "118: West Rock - Point Source (ECY ID: 225)\n",
      "119: South King - Point Source (ECY ID: 226)\n",
      "120: Tacoma Central - Point Source (ECY ID: 227)\n",
      "121: Tacoma North - Point Source (ECY ID: 228)\n",
      "122: Tamoshan - Point Source (ECY ID: 229)\n",
      "123: Taylor Bay - Point Source (ECY ID: 230)\n",
      "124: US Oil & Refining - Point Source (ECY ID: 231)\n",
      "125: Vashon - Point Source (ECY ID: 232)\n",
      "126: West Point - Point Source (ECY ID: 233)\n",
      "127: Alderbrook - Point Source (ECY ID: 234)\n",
      "128: Alderwood - Point Source (ECY ID: 235)\n",
      "129: Anacortes - Point Source (ECY ID: 236)\n",
      "130: Bellingham - Point Source (ECY ID: 237)\n",
      "131: Birch Bay - Point Source (ECY ID: 238)\n",
      "132: Blaine - Point Source (ECY ID: 239)\n",
      "133: BP Cherry Point - Point Source (ECY ID: 240)\n",
      "134: Clallam Bay POTW - Point Source (ECY ID: 241)\n",
      "135: Conoco Phillips - Point Source (ECY ID: 242)\n",
      "136: Coupeville - Point Source (ECY ID: 243)\n",
      "137: Eastsound Orcas Village - Point Source (ECY ID: 244)\n",
      "138: Eastsound Water District - Point Source (ECY ID: 245)\n",
      "139: Edmonds - Point Source (ECY ID: 246)\n",
      "140: Everett Snohomish - Point Source (ECY ID: 247)\n",
      "141: Fisherman Bay - Point Source (ECY ID: 248)\n",
      "142: Friday Harbor - Point Source (ECY ID: 249)\n",
      "143: Intalco - Point Source (ECY ID: 251)\n",
      "144: Kimberly-Clark Snohomish - Point Source (ECY ID: 252)\n",
      "145: La Conner - Point Source (ECY ID: 253)\n",
      "146: Lake Stevens 001 - Point Source (ECY ID: 254)\n",
      "147: Langley - Point Source (ECY ID: 255)\n",
      "148: Lummi Goose Pt - Point Source (ECY ID: 256)\n",
      "149: Lummi Sandy Pt - Point Source (ECY ID: 257)\n",
      "150: Lynnwood - Point Source (ECY ID: 258)\n",
      "151: Makah - Point Source (ECY ID: 259)\n",
      "152: Marysville Snohomish - Point Source (ECY ID: 260)\n",
      "153: Mt Vernon - Point Source (ECY ID: 261)\n",
      "154: Mukilteo - Point Source (ECY ID: 262)\n",
      "155: Nippon Paper - Point Source (ECY ID: 263)\n",
      "156: Oak Harbor Lagoon - Point Source (ECY ID: 264)\n",
      "157: Oak Harbor RBC - Point Source (ECY ID: 265)\n",
      "158: OF100 - Point Source (ECY ID: 266)\n",
      "159: Port Ludlow - Point Source (ECY ID: 267)\n",
      "160: Penn Cove - Point Source (ECY ID: 268)\n",
      "161: Port Gamble - Point Source (ECY ID: 269)\n",
      "162: Port Angeles - Point Source (ECY ID: 270)\n",
      "163: Port Townsend Paper - Point Source (ECY ID: 271)\n",
      "164: Port Townsend - Point Source (ECY ID: 273)\n",
      "165: Roche Harbor - Point Source (ECY ID: 274)\n",
      "166: Rosario Utilities - Point Source (ECY ID: 275)\n",
      "167: Sekiu - Point Source (ECY ID: 276)\n",
      "168: Sequim - Point Source (ECY ID: 277)\n",
      "169: Shell Oil - Point Source (ECY ID: 278)\n",
      "170: Skagit County 2 Big Lake - Point Source (ECY ID: 279)\n",
      "171: Snohomish - Point Source (ECY ID: 280)\n",
      "172: Stanwood - Point Source (ECY ID: 281)\n",
      "173: Swinomish - Point Source (ECY ID: 282)\n",
      "174: Tesoro - Point Source (ECY ID: 284)\n",
      "175: Tulalip - Point Source (ECY ID: 285)\n",
      "176: Annacis - Point Source (ECY ID: 286)\n",
      "177: Iona - Point Source (ECY ID: 287)\n",
      "178: Lions Gate - Point Source (ECY ID: 288)\n",
      "179: Lulu - Point Source (ECY ID: 289)\n",
      "180: NW Langley - Point Source (ECY ID: 290)\n",
      "181: Clover Point - Point Source (ECY ID: 291)\n",
      "182: Gulf Islands - Point Source (ECY ID: 292)\n",
      "183: Macaulay - Point Source (ECY ID: 293)\n",
      "184: Saanich - Point Source (ECY ID: 294)\n",
      "185: Clallam DOC - Point Source (ECY ID: 295)\n",
      "186: Larrabee State Park - Point Source (ECY ID: 296)\n",
      "187: Warm Beach Campground - Point Source (ECY ID: 297)\n",
      "188: Whidbey Naval Station - Point Source (ECY ID: 298)\n",
      "189: Puyallup - Point Source (ECY ID: 299)\n",
      "190: Brightwater - Point Source (ECY ID: 300)\n",
      "191: Lake Stevens 002 - Point Source (ECY ID: 301)\n",
      "192: Messenger House - Point Source (ECY ID: 302)\n"
     ]
    }
   ],
   "source": [
    "for key,src in source_names_series.to_dict().items():\n",
    "    print(str(key) + ': ' + src)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "126    West Point - Point Source (ECY ID: 233)\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source_names_series[source_names_series.str.contains('West Point')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>param</th>\n",
       "      <th>hour</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>...</th>\n",
       "      <th>183</th>\n",
       "      <th>184</th>\n",
       "      <th>185</th>\n",
       "      <th>186</th>\n",
       "      <th>187</th>\n",
       "      <th>188</th>\n",
       "      <th>189</th>\n",
       "      <th>190</th>\n",
       "      <th>191</th>\n",
       "      <th>192</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>640.00</td>\n",
       "      <td>640.00</td>\n",
       "      <td>31.070</td>\n",
       "      <td>31.070</td>\n",
       "      <td>5.635</td>\n",
       "      <td>5.635</td>\n",
       "      <td>158.700</td>\n",
       "      <td>158.700</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5063</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.006036</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>0.000985</td>\n",
       "      <td>0.01353</td>\n",
       "      <td>0.1927</td>\n",
       "      <td>0.7577</td>\n",
       "      <td>0.1218</td>\n",
       "      <td>0.00019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.73</td>\n",
       "      <td>3.73</td>\n",
       "      <td>5.939</td>\n",
       "      <td>5.939</td>\n",
       "      <td>5.939</td>\n",
       "      <td>5.939</td>\n",
       "      <td>5.939</td>\n",
       "      <td>5.939</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0600</td>\n",
       "      <td>15.060</td>\n",
       "      <td>15.060000</td>\n",
       "      <td>15.060000</td>\n",
       "      <td>15.060000</td>\n",
       "      <td>15.06000</td>\n",
       "      <td>15.0600</td>\n",
       "      <td>15.0600</td>\n",
       "      <td>15.0600</td>\n",
       "      <td>15.06000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 195 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   param  hour       0       1       2       3      4      5        6  \\\n",
       "0      0   0.0  640.00  640.00  31.070  31.070  5.635  5.635  158.700   \n",
       "1      1   0.0    3.73    3.73   5.939   5.939  5.939  5.939    5.939   \n",
       "2      2   0.0    0.00    0.00   0.000   0.000  0.000  0.000    0.000   \n",
       "3      3   0.0    0.00    0.00   0.000   0.000  0.000  0.000    0.000   \n",
       "4      4   0.0    0.00    0.00   0.000   0.000  0.000  0.000    0.000   \n",
       "\n",
       "         7  ...      183     184        185        186        187       188  \\\n",
       "0  158.700  ...   0.5063   0.113   0.006036   0.000063   0.000985   0.01353   \n",
       "1    5.939  ...  15.0600  15.060  15.060000  15.060000  15.060000  15.06000   \n",
       "2    0.000  ...   0.0000   0.000   0.000000   0.000000   0.000000   0.00000   \n",
       "3    0.000  ...   0.0000   0.000   0.000000   0.000000   0.000000   0.00000   \n",
       "4    0.000  ...   0.0000   0.000   0.000000   0.000000   0.000000   0.00000   \n",
       "\n",
       "       189      190      191       192  \n",
       "0   0.1927   0.7577   0.1218   0.00019  \n",
       "1  15.0600  15.0600  15.0600  15.06000  \n",
       "2   0.0000   0.0000   0.0000   0.00000  \n",
       "3   0.0000   0.0000   0.0000   0.00000  \n",
       "4   0.0000   0.0000   0.0000   0.00000  \n",
       "\n",
       "[5 rows x 195 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<span style=\"color:red\">USER INPUT REQUIRED</span>**\n",
    "\n",
    "Choose sources and parameters to modify.\n",
    "1. Look at the printed list of sources above and write the numerical index (on the left side) of the sources you want to modify in the `sources` list below.\n",
    "2. Look at where the `variable_name_dict` variable is created above. Right the numerical index of the variables you want to modify, along with the constant value you want to set, in the `params_values_dict` dictionary below\n",
    "\n",
    "For example, if I want to modify the West Point (126) and South King (119) WWTPs, setting \n",
    "\n",
    "| index        | Nitrogen           | mg/L  |\n",
    "| ------------- |:-------------:| -----:|\n",
    "| 13 | Ammonium          | 25 |\n",
    "| 14 | Nitrate + Nitrite | 4 |\n",
    "| 16 | Labile DON        | 2 |\n",
    "| 18 | Labile PON        | 1 |\n",
    "\n",
    "I enter the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sources = [126, 119]\n",
    "params_values_dict = {\n",
    "    13: 25.0, \n",
    "    14: 4.0,\n",
    "    16: 2.0, \n",
    "    18: 1.0\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modify Specified Parameters and Point Sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modifying sources [126, 119]\n",
      "\tWest Point - Point Source (ECY ID: 233)\n",
      "\tSouth King - Point Source (ECY ID: 226)\n",
      "\n",
      "\tSetting param 13 Ammonium (NH4) to 25.0\n",
      "\tSetting param 14 Nitrate + Nitrite (NO3+NO2) to 4.0\n",
      "\tSetting param 16 Labile DON (Labile dissolved organic nitrogen) to 2.0\n",
      "\tSetting param 18 Labile PON (Labile particular organic nitrogen) to 1.0\n"
     ]
    }
   ],
   "source": [
    "print(f'Modifying sources {sources}')\n",
    "for src in sources:\n",
    "    print(f'\\t{source_names_series.to_dict()[src]}')\n",
    "print()\n",
    "for param_index, param_value in params_values_dict.items():\n",
    "    print(f\"\\tSetting param {param_index} {variable_name_dict[param_index]} to {param_value}\")\n",
    "    mod_df.loc[mod_df.param==param_index, sources] = param_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows\n",
    "pd.options.display.max_columns=500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>126</th>\n",
       "      <th>119</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.96600</td>\n",
       "      <td>3.1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.06000</td>\n",
       "      <td>15.0600</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.89300</td>\n",
       "      <td>4.0830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.89300</td>\n",
       "      <td>4.0830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.54800</td>\n",
       "      <td>9.0090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11.10000</td>\n",
       "      <td>18.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12.98000</td>\n",
       "      <td>29.2400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6.38500</td>\n",
       "      <td>1.7360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>8.0280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.77000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.99000</td>\n",
       "      <td>4.1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.32650</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.05634</td>\n",
       "      <td>0.2208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>10.07000</td>\n",
       "      <td>10.0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>3840.00000</td>\n",
       "      <td>3840.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3840.00000</td>\n",
       "      <td>3840.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>3.96600</td>\n",
       "      <td>3.1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>15.06000</td>\n",
       "      <td>15.0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>3.89300</td>\n",
       "      <td>4.0830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>3.89300</td>\n",
       "      <td>4.0830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5.54800</td>\n",
       "      <td>9.0090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>11.10000</td>\n",
       "      <td>18.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>12.98000</td>\n",
       "      <td>29.2400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>6.38500</td>\n",
       "      <td>1.7360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           126        119\n",
       "0      3.96600     3.1400\n",
       "1     15.06000    15.0600\n",
       "2      0.00000     0.0000\n",
       "3      0.00000     0.0000\n",
       "4      0.00000     0.0000\n",
       "5      0.00000     0.0000\n",
       "6      0.00000     0.0000\n",
       "7      0.00000     0.0000\n",
       "8      0.00000     0.0000\n",
       "9      3.89300     4.0830\n",
       "10     3.89300     4.0830\n",
       "11     5.54800     9.0090\n",
       "12    11.10000    18.0200\n",
       "13    12.98000    29.2400\n",
       "14     6.38500     1.7360\n",
       "15     0.00000     0.0000\n",
       "16     0.00000     8.0280\n",
       "17     0.00000     0.0000\n",
       "18     1.77000     0.0000\n",
       "19     0.00000     0.0000\n",
       "20     1.99000     4.1800\n",
       "21     0.32650     0.0000\n",
       "22     0.00000     0.0000\n",
       "23     0.05634     0.2208\n",
       "24     0.00000     0.0000\n",
       "25     0.00000     0.0000\n",
       "26     0.00000     0.0000\n",
       "27    10.07000    10.0700\n",
       "28     0.00000     0.0000\n",
       "29     0.00000     0.0000\n",
       "30     0.00000     0.0000\n",
       "31     0.00000     0.0000\n",
       "32     0.00000     0.0000\n",
       "33  3840.00000  3840.0000\n",
       "34  3840.00000  3840.0000\n",
       "35     3.96600     3.1400\n",
       "36    15.06000    15.0600\n",
       "37     0.00000     0.0000\n",
       "38     0.00000     0.0000\n",
       "39     0.00000     0.0000\n",
       "40     0.00000     0.0000\n",
       "41     0.00000     0.0000\n",
       "42     0.00000     0.0000\n",
       "43     0.00000     0.0000\n",
       "44     3.89300     4.0830\n",
       "45     3.89300     4.0830\n",
       "46     5.54800     9.0090\n",
       "47    11.10000    18.0200\n",
       "48    12.98000    29.2400\n",
       "49     6.38500     1.7360"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[126,119]].head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>126</th>\n",
       "      <th>119</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.96600</td>\n",
       "      <td>3.1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.06000</td>\n",
       "      <td>15.0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.89300</td>\n",
       "      <td>4.0830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.89300</td>\n",
       "      <td>4.0830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.54800</td>\n",
       "      <td>9.0090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11.10000</td>\n",
       "      <td>18.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>25.00000</td>\n",
       "      <td>25.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.00000</td>\n",
       "      <td>4.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.00000</td>\n",
       "      <td>2.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.99000</td>\n",
       "      <td>4.1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.32650</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.05634</td>\n",
       "      <td>0.2208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>10.07000</td>\n",
       "      <td>10.0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>3840.00000</td>\n",
       "      <td>3840.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3840.00000</td>\n",
       "      <td>3840.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>3.96600</td>\n",
       "      <td>3.1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>15.06000</td>\n",
       "      <td>15.0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>3.89300</td>\n",
       "      <td>4.0830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>3.89300</td>\n",
       "      <td>4.0830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5.54800</td>\n",
       "      <td>9.0090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>11.10000</td>\n",
       "      <td>18.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>25.00000</td>\n",
       "      <td>25.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>4.00000</td>\n",
       "      <td>4.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           126        119\n",
       "0      3.96600     3.1400\n",
       "1     15.06000    15.0600\n",
       "2      0.00000     0.0000\n",
       "3      0.00000     0.0000\n",
       "4      0.00000     0.0000\n",
       "5      0.00000     0.0000\n",
       "6      0.00000     0.0000\n",
       "7      0.00000     0.0000\n",
       "8      0.00000     0.0000\n",
       "9      3.89300     4.0830\n",
       "10     3.89300     4.0830\n",
       "11     5.54800     9.0090\n",
       "12    11.10000    18.0200\n",
       "13    25.00000    25.0000\n",
       "14     4.00000     4.0000\n",
       "15     0.00000     0.0000\n",
       "16     2.00000     2.0000\n",
       "17     0.00000     0.0000\n",
       "18     1.00000     1.0000\n",
       "19     0.00000     0.0000\n",
       "20     1.99000     4.1800\n",
       "21     0.32650     0.0000\n",
       "22     0.00000     0.0000\n",
       "23     0.05634     0.2208\n",
       "24     0.00000     0.0000\n",
       "25     0.00000     0.0000\n",
       "26     0.00000     0.0000\n",
       "27    10.07000    10.0700\n",
       "28     0.00000     0.0000\n",
       "29     0.00000     0.0000\n",
       "30     0.00000     0.0000\n",
       "31     0.00000     0.0000\n",
       "32     0.00000     0.0000\n",
       "33  3840.00000  3840.0000\n",
       "34  3840.00000  3840.0000\n",
       "35     3.96600     3.1400\n",
       "36    15.06000    15.0600\n",
       "37     0.00000     0.0000\n",
       "38     0.00000     0.0000\n",
       "39     0.00000     0.0000\n",
       "40     0.00000     0.0000\n",
       "41     0.00000     0.0000\n",
       "42     0.00000     0.0000\n",
       "43     0.00000     0.0000\n",
       "44     3.89300     4.0830\n",
       "45     3.89300     4.0830\n",
       "46     5.54800     9.0090\n",
       "47    11.10000    18.0200\n",
       "48    25.00000    25.0000\n",
       "49     4.00000     4.0000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_df[[126,119]].head(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Write Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "write_data(output_file, mod_df, header_lines)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check that there is no difference or there is a difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "infile = input_file.replace(\" \", \"\\ \")\n",
    "outfile = output_file.replace(\" \", \"\\ \")\n",
    "!cmp --silent {infile} {outfile} && echo '### SUCESS: Files are the same!'|| echo '### WARNING: Files Are Different! ###'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "infile, outfile"
   ]
  }
 ],
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