make_swan_input_Lee.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "30ccdffc-7215-4058-8ed2-02ed229898d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "make_swan_input_Lee\n",
    "\n",
    "Code to make SWAN input requesting SPEC2D output for drifters during Hurricane Lee\n",
    "\n",
    "This notebook is based on the really nice example provided by Jacob Davis (davisjr@uw.edu)\n",
    "\n",
    "Example code for loading and working with Spotter data from the NHCI\n",
    "aggregated drifter dataset in Python .pickle format. Since all datasets\n",
    "have the same variable names and structure, this example framework can\n",
    "also be used for DWSD and microSWIFT drifters.\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import pickle\n",
    "from datetime import datetime, timezone\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "DATA_DIRECTORY = '/vortexfs1/home/csherwood/proj/NOPP_Lee/'\n",
    "DRIFTER_DATA_FILENAME = 'hurricane_lee_drifter_data_v1.pickle'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "742435e7-b12b-44a7-9748-f946541fef62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SPOT-31232C\n",
      "SPOT-1207\n",
      "SPOT-1184\n",
      "SPOT-1255\n",
      "SPOT-010349\n",
      "SPOT-010408\n",
      "SPOT-1240\n",
      "SPOT-1188\n",
      "300534061901680\n",
      "300534061901630\n",
      "300534061901660\n",
      "041\n",
      "050\n",
      "059\n",
      "065\n"
     ]
    }
   ],
   "source": [
    "with open(os.path.join(DATA_DIRECTORY, DRIFTER_DATA_FILENAME), 'rb') as handle:\n",
    "    drifters = pickle.load(handle)\n",
    "\n",
    "# `drifters` is a python dictionary keyed by drifter type (spotter,\n",
    "# dwsd, or microswift)\n",
    "# `spotter` is a python dictionary of Pandas DataFrames, keyed by\n",
    "# each drifter ID. The drifter ids can then be accessed as follows:\n",
    "spotter = drifters['spotter']\n",
    "spotter_ids = list(spotter.keys())\n",
    "for id in spotter_ids:\n",
    "    print(id)\n",
    "\n",
    "dwsd = drifters['dwsd']\n",
    "dwsd_ids = list(dwsd.keys())\n",
    "for id in dwsd_ids:\n",
    "    print(id)\n",
    "    \n",
    "mswift = drifters['microswift']\n",
    "mswift_ids = list(mswift.keys())\n",
    "for id in mswift_ids:\n",
    "    print(id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "337b14d4-9ad8-412c-bfcb-51fdd8975526",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SPOT-31232C S232\n",
      "SPOT-1207 S207\n",
      "SPOT-1184 S184\n",
      "SPOT-1255 S255\n",
      "SPOT-010349 S349\n",
      "SPOT-010408 S408\n",
      "SPOT-1240 S240\n",
      "SPOT-1188 S188\n",
      "300534061901680 D680\n",
      "300534061901630 D630\n",
      "300534061901660 D660\n",
      "041 M041\n",
      "050 M050\n",
      "059 M059\n",
      "065 M065\n"
     ]
    }
   ],
   "source": [
    "# Make nicknames for these drifters to conform with the max. 8 char limit in SWAN filesname\n",
    "# after adding three characters for a counter (to differentiate the POINTS).\n",
    "# This is a manual step\n",
    "snames = ['S232','S207','S184','S255','S349','S408','S240','S188']\n",
    "dnames = ['D680','D630','D660']\n",
    "mnames = ['M041','M050','M059','M065']\n",
    "\n",
    "# Check to see if they are correct\n",
    "for i, id in enumerate( spotter_ids) :\n",
    "    print(id,snames[i])\n",
    "for i, id in enumerate( dwsd_ids) :\n",
    "    print(id,dnames[i])\n",
    "for i, id in enumerate( mswift_ids) :\n",
    "    print(id,mnames[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fae83de2-021b-461a-99d7-363bf66ef53a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 221 entries, 2023-09-14 21:25:00+00:00 to 2023-09-17 23:45:00+00:00\n",
      "Data columns (total 18 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   latitude                 221 non-null    float64\n",
      " 1   longitude                221 non-null    float64\n",
      " 2   significant_height       74 non-null     float64\n",
      " 3   peak_period              74 non-null     float64\n",
      " 4   peak_direction           74 non-null     float64\n",
      " 5   peak_directional_spread  74 non-null     float64\n",
      " 6   mean_period              74 non-null     float64\n",
      " 7   mean_direction           74 non-null     float64\n",
      " 8   mean_directional_spread  74 non-null     float64\n",
      " 9   frequency                74 non-null     object \n",
      " 10  energy_density           74 non-null     object \n",
      " 11  a1                       74 non-null     object \n",
      " 12  b1                       74 non-null     object \n",
      " 13  a2                       74 non-null     object \n",
      " 14  b2                       74 non-null     object \n",
      " 15  wave_check_factor        0 non-null      float64\n",
      " 16  sea_surface_temperature  221 non-null    float64\n",
      " 17  sea_level_pressure       221 non-null    float64\n",
      "dtypes: float64(12), object(6)\n",
      "memory usage: 32.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# The data for a single id (e.g. the first) can be accessed as:\n",
    "spotter_id = spotter_ids[0] # = 'SPOT-30068D'\n",
    "spotter[spotter_id].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "04df9e8e-7815-46fd-8778-e1b8f10d1a6a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# example: plot lat, lon for positions with valid wave data\n",
    "import numpy as np\n",
    "fig, ax = plt.subplots(figsize=(6,4))\n",
    "for spotter_id in spotter.keys():\n",
    "    if spotter[spotter_id]['latitude'].max() > 40.:\n",
    "        only_waves = spotter[spotter_id]['energy_density'].notnull()\n",
    "        ax.plot(\n",
    "            spotter[spotter_id][only_waves]['longitude'].loc['2023-09-15':'2023-09-16'],\n",
    "            spotter[spotter_id][only_waves]['latitude'].loc['2023-09-15':'2023-09-16'],'.',\n",
    "            label = spotter_id,\n",
    "        )\n",
    "for mswift_id in mswift.keys():\n",
    "    ax.plot(\n",
    "        mswift[mswift_id]['longitude'].loc['2023-09-15':'2023-09-16'],\n",
    "        mswift[mswift_id]['latitude'].loc['2023-09-15':'2023-09-16'],'o',\n",
    "        label = mswift_id,\n",
    "    )\n",
    "for dwsd_id in dwsd.keys():\n",
    "    ax.plot(\n",
    "        dwsd[dwsd_id]['longitude'].loc['2023-09-15':'2023-09-16'],\n",
    "        dwsd[dwsd_id]['latitude'].loc['2023-09-15':'2023-09-16'],'+',\n",
    "        label = dwsd_id,\n",
    "    )\n",
    "ax.set_ylabel('Latitude')\n",
    "ax.set_xlabel('Longitude')\n",
    "ax.legend()\n",
    "ax.tick_params(axis='x', rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "621c4cce-6446-4a06-9bd1-3ba2081fbd21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open a file text to be cut+pasted into SWAN input file for spotters\n",
    "f = open(DATA_DIRECTORY+'lee_spotter_spec2d.txt', 'w')\n",
    "for nid, spotter_id in enumerate( spotter.keys() ):\n",
    "    # Eliminate southern Spotters\n",
    "    if spotter[spotter_id]['latitude'].max() > 40.:\n",
    "\n",
    "        # Some rows have times with other data but no wave info (Spotters only)\n",
    "        only_waves = spotter[spotter_id]['energy_density'].notnull()\n",
    "\n",
    "        ipt = 0 # index must stay below 100 or file names will be too long\n",
    "        for index, row in spotter[spotter_id][only_waves].loc['2023-09-15':'2023-09-16'].iterrows():\n",
    "            #print(index, row['latitude'])\n",
    "            spectime = index.strftime('%Y%m%d.%H%m')\n",
    "            pname = \"{}{:03d}\".format(snames[nid], ipt)\n",
    "            pline = (\"POINTS '{}' {:.4f} {:.4f}\".format(pname, row['longitude'], row['latitude'] )) \n",
    "            sline = \"SPECOUT '{}' SPEC2D ABS '{}.spc2d' OUTPUT {} 1000 HR\".format( pname, pname, spectime )\n",
    "            # print(pline)\n",
    "            # print(sline)\n",
    "            f.write(pline+'\\n')\n",
    "            f.write(sline+'\\n')\n",
    "            ipt +=1\n",
    "            if(ipt >= 100):\n",
    "                print(\"Warning: SWAN output filename prefix exceeds 8 chars.\")\n",
    "\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e0cc2f62-3049-44ec-869b-86b7ceba5075",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open a file text to be cut+pasted into SWAN input file for microswifts\n",
    "f1 = open(DATA_DIRECTORY+'lee_mswift_spec2d.txt', 'w')\n",
    "for nid, mswift_id in enumerate (mswift.keys() ):\n",
    "    ipt = 0 # index must stay below 100 or file names will be too long\n",
    "    for index, row in mswift[mswift_id].iterrows():\n",
    "        #print(index, row['latitude'])\n",
    "        pname = \"{}{:03d}\".format(mnames[nid], ipt)\n",
    "        starttime = index.strftime('%Y%m%d.%H%m')\n",
    "        pline = (\"POINTS '{}' {:.4f} {:.4f}\".format(pname, row['longitude'], row['latitude'] )) \n",
    "        sline = \"SPECOUT '{}' SPEC2D ABS '{}.spc2d' OUTPUT {} 1000 HR\".format( pname, pname, starttime )\n",
    "        # print(pline)\n",
    "        # print(sline)\n",
    "        f1.write(pline+'\\n')\n",
    "        f1.write(sline+'\\n')\n",
    "        ipt +=1\n",
    "        if(ipt >= 100):\n",
    "            print(\"Warning: SWAN output filename prefix exceeds 8 chars.\")\n",
    "    \n",
    "f1.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6f26faae-ca17-494a-b3c6-94dd4979ccb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open a file text to be cut+pasted into SWAN input file for dwsd drifters\n",
    "# These have ids that are too long, so make a shorter name for SWAN\n",
    "f2 = open(DATA_DIRECTORY+'lee_dwsd_spec2d.txt', 'w')\n",
    "for nid, dwsd_id in enumerate( dwsd.keys() ):\n",
    "    ipt = 0  # index must stay below 100 or file names will be too long\n",
    "    for index, row in dwsd[dwsd_id].iterrows():\n",
    "        pname = \"{}{:03d}\".format(dnames[nid], ipt)\n",
    "        starttime = index.strftime('%Y%m%d.%H%m')\n",
    "        pline = (\"POINTS '{}' {:.4f} {:.4f}\".format(pname, row['longitude'], row['latitude'] )) \n",
    "        sline = \"SPECOUT '{}' SPEC2D ABS '{}.spc2d' OUTPUT {} 1000 HR\".format( pname, pname, starttime )\n",
    "        # print(pline)\n",
    "        # print(sline)\n",
    "        f2.write(pline+'\\n')\n",
    "        f2.write(sline+'\\n')\n",
    "        ipt +=1\n",
    "        if(ipt >= 1000):\n",
    "            print(\"Warning: SWAN output filename prefix exceeds 8 chars.\")\n",
    "    \n",
    "f2.close()"
   ]
  }
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