INP_TO_TXT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Node           X          Y\n",
      "0     1  1650094.63  4944639.0\n",
      "1     2  1650348.88  4944585.0\n",
      "2     3  1650592.63  4945068.0\n",
      "3     4  1650829.50  4945554.0\n",
      "4     5  1651029.88  4945905.5\n",
      "Node,X,Y\n",
      "J-1,1650094.63,4944639.0\n",
      "J-2,1650348.88,4944585.0\n",
      "J-3,1650592.63,4945068.0\n",
      "J-4,1650829.5,4945554.0\n",
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      "J-6,1651232.38,4946354.5\n",
      "J-7,1651227.63,4946378.0\n",
      "J-8,1650986.0,4946490.5\n",
      "J-9,1650757.0,4946024.5\n",
      "J-10,1650577.5,4945659.5\n",
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      "J-241,1655230.88,4944258.0\n",
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      "J-243,1654925.75,4944801.0\n",
      "J-244,1654858.25,4944795.0\n",
      "J-245,1655479.0,4944922.5\n",
      "J-246,1655494.88,4944873.0\n",
      "J-247,1654742.88,4946936.5\n",
      "J-248,1653702.88,4947431.0\n",
      "J-249,1652127.0,4945847.0\n",
      "J-250,1652323.25,4945747.5\n",
      "J-251,1651497.63,4944781.5\n",
      "J-252,1650826.88,4944939.5\n",
      "J-253,1650026.0,4946453.5\n",
      "J-254,1653025.75,4943589.5\n",
      "J-255,1653405.25,4943607.0\n",
      "J-256,1654542.0,4946550.0\n",
      "J-257,1651141.13,4944445.0\n",
      "J-258,1650052.63,4944638.0\n",
      "J-259,1650012.63,4944551.5\n",
      "J-260,1649989.63,4944541.5\n",
      "J-261,1650020.38,4944365.0\n",
      "J-262,1650106.88,4944325.5\n",
      "J-263,1650209.75,4944195.5\n",
      "J-264,1650017.88,4946758.0\n",
      "J-265,1652260.88,4945816.5\n",
      "J-266,1652433.0,4943954.0\n",
      "J-267,1652708.25,4944155.0\n",
      "J-268,1653541.75,4944409.5\n",
      "T-269,1654257.63,4946332.5\n",
      "T-270,1650149.75,4946528.5\n",
      "T-271,1650191.75,4944416.5\n",
      "T-272,1654428.63,4942467.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from io import StringIO\n",
    "import os\n",
    "\n",
    "# Create the directory if it does not exist\n",
    "os.makedirs('Data/WDN_Data', exist_ok=True)\n",
    "\n",
    "# Your data as a multi-line string\n",
    "raw_data = \"\"\"\n",
    " Node            \tX         \tY\n",
    " 1               \t1650094.63      \t4944639.00      \n",
    " 2               \t1650348.88      \t4944585.00      \n",
    " 3               \t1650592.63      \t4945068.00      \n",
    " 4               \t1650829.50      \t4945554.00      \n",
    " 5               \t1651029.88      \t4945905.50      \n",
    " 6               \t1651232.38      \t4946354.50      \n",
    " 7               \t1651227.63      \t4946378.00      \n",
    " 8               \t1650986.00      \t4946490.50      \n",
    " 9               \t1650757.00      \t4946024.50      \n",
    " 10              \t1650577.50      \t4945659.50      \n",
    " 11              \t1650064.50      \t4945875.00      \n",
    " 12              \t1649881.13      \t4945376.00      \n",
    " 13              \t1650029.25      \t4945313.50      \n",
    " 14              \t1649770.50      \t4945419.00      \n",
    " 15              \t1649922.38      \t4944957.00      \n",
    " 16              \t1650100.25      \t4944685.50      \n",
    " 17              \t1650073.75      \t4944698.00      \n",
    " 18              \t1649896.88      \t4946121.00      \n",
    " 19              \t1650065.00      \t4946470.50      \n",
    " 20              \t1649998.63      \t4946733.50      \n",
    " 21              \t1650051.00      \t4946979.00      \n",
    " 22              \t1650379.13      \t4946826.00      \n",
    " 23              \t1650670.25      \t4946655.00      \n",
    " 24              \t1651438.88      \t4946731.50      \n",
    " 25              \t1651852.38      \t4946763.00      \n",
    " 26              \t1652162.25      \t4946635.00      \n",
    " 27              \t1652182.25      \t4946677.00      \n",
    " 28              \t1651699.38      \t4946109.00      \n",
    " 29              \t1652135.63      \t4945859.50      \n",
    " 30              \t1652188.25      \t4945828.00      \n",
    " 31              \t1652250.75      \t4945788.50      \n",
    " 32              \t1652137.88      \t4946048.50      \n",
    " 33              \t1652747.88      \t4946233.50      \n",
    " 34              \t1653164.75      \t4946233.50      \n",
    " 35              \t1653238.38      \t4946135.50      \n",
    " 36              \t1652471.50      \t4947147.50      \n",
    " 37              \t1652974.13      \t4946893.00      \n",
    " 38              \t1653000.50      \t4947593.00      \n",
    " 39              \t1652997.88      \t4947950.00      \n",
    " 40              \t1653465.13      \t4947628.00      \n",
    " 41              \t1653953.13      \t4947276.50      \n",
    " 42              \t1653669.25      \t4946887.50      \n",
    " 43              \t1653412.88      \t4946500.50      \n",
    " 44              \t1653325.38      \t4946374.50      \n",
    " 45              \t1653405.75      \t4946332.50      \n",
    " 46              \t1653576.75      \t4946261.50      \n",
    " 47              \t1653305.50      \t4946224.00      \n",
    " 48              \t1653327.63      \t4946206.00      \n",
    " 49              \t1654320.88      \t4947050.50      \n",
    " 50              \t1653865.63      \t4946305.00      \n",
    " 51              \t1654128.63      \t4946356.50      \n",
    " 52              \t1654151.00      \t4946388.00      \n",
    " 53              \t1651249.88      \t4944650.00      \n",
    " 54              \t1651009.13      \t4945449.00      \n",
    " 55              \t1651234.38      \t4945272.50      \n",
    " 56              \t1651454.00      \t4945036.00      \n",
    " 57              \t1651631.75      \t4944839.00      \n",
    " 58              \t1651706.13      \t4944872.00      \n",
    " 59              \t1652209.75      \t4945096.00      \n",
    " 60              \t1651938.88      \t4944437.00      \n",
    " 61              \t1652230.25      \t4944648.50      \n",
    " 62              \t1652461.25      \t4944548.00      \n",
    " 63              \t1651242.38      \t4944380.50      \n",
    " 64              \t1651105.25      \t4944112.50      \n",
    " 65              \t1651206.00      \t4944094.00      \n",
    " 66              \t1651172.75      \t4943897.00      \n",
    " 67              \t1651089.50      \t4943759.50      \n",
    " 68              \t1651047.88      \t4943688.50      \n",
    " 69              \t1650871.63      \t4943730.00      \n",
    " 70              \t1650699.50      \t4943831.00      \n",
    " 71              \t1650643.50      \t4943741.50      \n",
    " 72              \t1650545.13      \t4943797.50      \n",
    " 73              \t1650520.88      \t4943731.00      \n",
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    " 102             \t1653007.75      \t4945362.00      \n",
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    " 111             \t1654144.25      \t4945621.50      \n",
    " 112             \t1653650.88      \t4945006.00      \n",
    " 113             \t1653850.25      \t4944888.50      \n",
    " 114             \t1653644.13      \t4944567.00      \n",
    " 115             \t1654038.25      \t4945213.00      \n",
    " 116             \t1654814.75      \t4946886.50      \n",
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    " 120             \t1654764.13      \t4946651.50      \n",
    " 121             \t1655205.88      \t4946327.00      \n",
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    " 127             \t1654739.50      \t4944761.00      \n",
    " 128             \t1655066.00      \t4944789.00      \n",
    " 129             \t1655412.50      \t4944625.00      \n",
    " 130             \t1655182.00      \t4944156.00      \n",
    " 131             \t1655000.88      \t4943750.00      \n",
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    " 135             \t1654630.13      \t4943284.00      \n",
    " 136             \t1654391.75      \t4942756.00      \n",
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    " 146             \t1651251.13      \t4944414.50      \n",
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    " 150             \t1651645.38      \t4944302.00      \n",
    " 151             \t1652019.13      \t4944321.50      \n",
    " 152             \t1652208.25      \t4944271.00      \n",
    " 153             \t1652519.13      \t4944189.50      \n",
    " 154             \t1651329.50      \t4943687.50      \n",
    " 155             \t1652392.63      \t4943383.50      \n",
    " 156             \t1652393.38      \t4943359.50      \n",
    " 157             \t1653184.25      \t4943033.00      \n",
    " 158             \t1654010.50      \t4942993.00      \n",
    " 159             \t1654455.63      \t4942851.00      \n",
    " 160             \t1654626.38      \t4942950.50      \n",
    " 161             \t1654827.25      \t4943438.00      \n",
    " 162             \t1654111.88      \t4943677.50      \n",
    " 163             \t1654341.00      \t4943549.50      \n",
    " 164             \t1653947.88      \t4943634.00      \n",
    " 165             \t1653878.00      \t4943632.00      \n",
    " 166             \t1655342.50      \t4944429.00      \n",
    " 167             \t1655290.50      \t4944410.00      \n",
    " 168             \t1655221.38      \t4944282.00      \n",
    " 169             \t1654399.38      \t4944572.00      \n",
    " 170             \t1653136.13      \t4944368.00      \n",
    " 171             \t1653035.25      \t4944362.50      \n",
    " 172             \t1653147.88      \t4944201.00      \n",
    " 173             \t1653102.13      \t4944214.00      \n",
    " 174             \t1655032.00      \t4945217.50      \n",
    " 175             \t1655018.25      \t4945301.50      \n",
    " 176             \t1655514.13      \t4945400.50      \n",
    " 177             \t1655702.38      \t4945732.00      \n",
    " 178             \t1655636.25      \t4945947.50      \n",
    " 179             \t1655473.38      \t4946058.00      \n",
    " 180             \t1655702.00      \t4945843.50      \n",
    " 181             \t1655004.88      \t4946866.00      \n",
    " 182             \t1654848.63      \t4946920.00      \n",
    " 183             \t1654504.88      \t4947010.00      \n",
    " 184             \t1654147.63      \t4947127.50      \n",
    " 185             \t1654320.63      \t4946836.00      \n",
    " 186             \t1654196.13      \t4946476.50      \n",
    " 187             \t1653374.00      \t4946313.50      \n",
    " 188             \t1654034.00      \t4946220.50      \n",
    " 189             \t1653117.88      \t4945984.50      \n",
    " 190             \t1652987.50      \t4945869.00      \n",
    " 191             \t1652919.50      \t4945750.50      \n",
    " 192             \t1652879.75      \t4945759.50      \n",
    " 193             \t1652625.50      \t4947099.50      \n",
    " 194             \t1652295.38      \t4946618.50      \n",
    " 195             \t1652739.13      \t4946283.50      \n",
    " 196             \t1652044.50      \t4946708.50      \n",
    " 197             \t1652433.00      \t4945703.00      \n",
    " 198             \t1652664.75      \t4945450.00      \n",
    " 199             \t1652626.63      \t4945439.50      \n",
    " 200             \t1652531.63      \t4945339.50      \n",
    " 201             \t1652665.13      \t4945336.00      \n",
    " 202             \t1652217.38      \t4945696.50      \n",
    " 203             \t1652211.63      \t4945412.00      \n",
    " 204             \t1652469.38      \t4945212.50      \n",
    " 205             \t1652212.00      \t4945062.50      \n",
    " 206             \t1651102.75      \t4946039.00      \n",
    " 207             \t1650501.50      \t4946742.50      \n",
    " 208             \t1650496.13      \t4946770.00      \n",
    " 209             \t1650076.63      \t4946503.50      \n",
    " 210             \t1649932.25      \t4946560.50      \n",
    " 211             \t1649851.13      \t4946001.00      \n",
    " 212             \t1649984.88      \t4945956.00      \n",
    " 213             \t1649627.88      \t4945098.00      \n",
    " 214             \t1650151.88      \t4944850.00      \n",
    " 215             \t1649929.63      \t4944395.00      \n",
    " 216             \t1650210.88      \t4944252.00      \n",
    " 217             \t1650325.75      \t4943930.50      \n",
    " 218             \t1652407.25      \t4944555.50      \n",
    " 219             \t1652869.63      \t4945446.50      \n",
    " 220             \t1654186.38      \t4945631.50      \n",
    " 221             \t1652715.75      \t4945564.50      \n",
    " 222             \t1654757.00      \t4943407.50      \n",
    " 223             \t1655204.38      \t4945090.00      \n",
    " 224             \t1655307.38      \t4945032.50      \n",
    " 225             \t1655520.50      \t4945027.50      \n",
    " 226             \t1655510.63      \t4945305.50      \n",
    " 227             \t1655546.63      \t4945480.00      \n",
    " 228             \t1655316.13      \t4946179.00      \n",
    " 229             \t1655280.25      \t4946245.50      \n",
    " 230             \t1651133.00      \t4944753.00      \n",
    " 231             \t1651333.63      \t4946515.00      \n",
    " 232             \t1651621.00      \t4946776.50      \n",
    " 233             \t1652620.25      \t4945096.50      \n",
    " 234             \t1653535.38      \t4945415.00      \n",
    " 235             \t1653638.50      \t4945351.50      \n",
    " 236             \t1654106.25      \t4945524.00      \n",
    " 237             \t1653382.38      \t4945500.00      \n",
    " 238             \t1653496.88      \t4944266.00      \n",
    " 239             \t1653901.25      \t4943949.00      \n",
    " 240             \t1654462.38      \t4943456.50      \n",
    " 241             \t1655230.88      \t4944258.00      \n",
    " 242             \t1655162.75      \t4944163.00      \n",
    " 243             \t1654925.75      \t4944801.00      \n",
    " 244             \t1654858.25      \t4944795.00      \n",
    " 245             \t1655479.00      \t4944922.50      \n",
    " 246             \t1655494.88      \t4944873.00      \n",
    " 247             \t1654742.88      \t4946936.50      \n",
    " 248             \t1653702.88      \t4947431.00      \n",
    " 249             \t1652127.00      \t4945847.00      \n",
    " 250             \t1652323.25      \t4945747.50      \n",
    " 251             \t1651497.63      \t4944781.50      \n",
    " 252             \t1650826.88      \t4944939.50      \n",
    " 253             \t1650026.00      \t4946453.50      \n",
    " 254             \t1653025.75      \t4943589.50      \n",
    " 255             \t1653405.25      \t4943607.00      \n",
    " 256             \t1654542.00      \t4946550.00      \n",
    " 257             \t1651141.13      \t4944445.00      \n",
    " 258             \t1650052.63      \t4944638.00      \n",
    " 259             \t1650012.63      \t4944551.50      \n",
    " 260             \t1649989.63      \t4944541.50      \n",
    " 261             \t1650020.38      \t4944365.00      \n",
    " 262             \t1650106.88      \t4944325.50      \n",
    " 263             \t1650209.75      \t4944195.50      \n",
    " 264             \t1650017.88      \t4946758.00      \n",
    " 265             \t1652260.88      \t4945816.50      \n",
    " 266             \t1652433.00      \t4943954.00      \n",
    " 267             \t1652708.25      \t4944155.00      \n",
    " 268             \t1653541.75      \t4944409.50  \n",
    " 269             \t1654257.63      \t4946332.50      \n",
    " 270             \t1650149.75      \t4946528.50     \n",
    " 271           \t    1650191.75      \t4944416.50      \n",
    " 272           \t    1654428.63      \t4942467.00           \n",
    "\"\"\"\n",
    "\n",
    "# Read the raw data into a pandas DataFrame\n",
    "data = StringIO(raw_data)\n",
    "df = pd.read_csv(data, delim_whitespace=True)\n",
    "\n",
    "df.to_csv(index=False, sep='\\t')\n",
    "print(df.head())\n",
    "\n",
    "# Extract only required columns\n",
    "# df_nodes = df[['ID', 'Node1']]\n",
    "df_nodes = df[['Node', 'X', 'Y']]\n",
    "\n",
    "# Apply 'T-' to reservoir nodes and 'J-' to all other nodes\n",
    "# df_nodes['ID'] = df_nodes['ID'].apply(lambda x: 'T-' + str(x) if x == 269 or x == 270 or x == 271 or x == 272 else 'J-' + str(x))\n",
    "df_nodes['Node'] = df_nodes['Node'].apply(lambda x: 'T-' + str(x) if x == 269 or x == 270 or x == 271 or x == 272 else 'J-' + str(x))\n",
    "# df_nodes['Node2'] = df_nodes['Node2'].apply(lambda x: 'T-' + str(x) if x == 269 or x == 270 or x == 271 or x == 272 else 'J-' + str(x))\n",
    "\n",
    "# Convert the DataFrame to a CSV-formatted string\n",
    "csv_string = df_nodes.to_csv(index=False, header=True, sep=',')\n",
    "\n",
    "# Print the CSV string\n",
    "print(csv_string)\n"
   ]
  }
 ],
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