bulk_param_radial_plot.ipynb

{
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   "id": "5f793040-d8e2-43b3-81d5-c82e65571d4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pickle\n",
    "from datetime import datetime, timezone\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.colors\n",
    "import numpy as np\n",
    "\n",
    "import xarray as xr\n",
    "import xroms\n",
    "import matplotlib.pyplot as plt\n",
    "from wavespectra import read_ww3, read_swan, read_ndbc, read_netcdf\n",
    "from wavespectra.input.swan import read_swans\n",
    "\n",
    "# include my wave stats functions\n",
    "%run -i wave_stats.py\n",
    "\n",
    "# include my radial plotting functions\n",
    "%run -i spec_plot_funcs.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "83eefa4b-c6ec-4079-85c0-301ecd5e5ff8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Read selected drifters\n",
    "# Code from Jake Davis, UW\n",
    "DATA_DIRECTORY = '/vortexfs1/home/csherwood/proj/NOPP_Lee'\n",
    "DATA_FILENAME = 'hurricane_lee_drifter_data_v0.pickle'\n",
    "with open(os.path.join(DATA_DIRECTORY, DATA_FILENAME), 'rb') as handle:\n",
    "    drifters = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3a490e76-fa85-4a70-8df4-103bac2ea7b1",
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    {
     "name": "stdout",
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     "text": [
      "SPOT-31232C\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>significant_height</th>\n",
       "      <th>peak_period</th>\n",
       "      <th>peak_direction</th>\n",
       "      <th>peak_directional_spread</th>\n",
       "      <th>mean_period</th>\n",
       "      <th>mean_direction</th>\n",
       "      <th>mean_directional_spread</th>\n",
       "      <th>frequency</th>\n",
       "      <th>energy_density</th>\n",
       "      <th>a1</th>\n",
       "      <th>b1</th>\n",
       "      <th>a2</th>\n",
       "      <th>b2</th>\n",
       "      <th>wave_check_factor</th>\n",
       "      <th>sea_surface_temperature</th>\n",
       "      <th>sea_level_pressure</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>2023-09-16 11:45:00+00:00</th>\n",
       "      <td>42.86922</td>\n",
       "      <td>-69.75373</td>\n",
       "      <td>4.85</td>\n",
       "      <td>8.533</td>\n",
       "      <td>27.06</td>\n",
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       "      <td>[-0.037146, -0.045943, -0.194526, -0.489736, -...</td>\n",
       "      <td>[-0.376344, -0.466276, -0.13392, 0.182796, 0.2...</td>\n",
       "      <td>[-0.362659, -0.42131, -0.391984, -0.166178, 0....</td>\n",
       "      <td>[0.018573, 0.040078, -0.000978, -0.206256, -0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.74</td>\n",
       "      <td>997.4</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                           latitude  longitude  significant_height  \\\n",
       "time                                                                 \n",
       "2023-09-16 11:45:00+00:00  42.86922  -69.75373                4.85   \n",
       "\n",
       "                           peak_period  peak_direction  \\\n",
       "time                                                     \n",
       "2023-09-16 11:45:00+00:00        8.533           27.06   \n",
       "\n",
       "                           peak_directional_spread  mean_period  \\\n",
       "time                                                              \n",
       "2023-09-16 11:45:00+00:00                 35.31149     7.709045   \n",
       "\n",
       "                           mean_direction  mean_directional_spread  \\\n",
       "time                                                                 \n",
       "2023-09-16 11:45:00+00:00       26.035342                45.375049   \n",
       "\n",
       "                                                                   frequency  \\\n",
       "time                                                                           \n",
       "2023-09-16 11:45:00+00:00  [0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...   \n",
       "\n",
       "                                                              energy_density  \\\n",
       "time                                                                           \n",
       "2023-09-16 11:45:00+00:00  [0.040504319999999996, 0.12828672, 0.742680576...   \n",
       "\n",
       "                                                                          a1  \\\n",
       "time                                                                           \n",
       "2023-09-16 11:45:00+00:00  [-0.037146, -0.045943, -0.194526, -0.489736, -...   \n",
       "\n",
       "                                                                          b1  \\\n",
       "time                                                                           \n",
       "2023-09-16 11:45:00+00:00  [-0.376344, -0.466276, -0.13392, 0.182796, 0.2...   \n",
       "\n",
       "                                                                          a2  \\\n",
       "time                                                                           \n",
       "2023-09-16 11:45:00+00:00  [-0.362659, -0.42131, -0.391984, -0.166178, 0....   \n",
       "\n",
       "                                                                          b2  \\\n",
       "time                                                                           \n",
       "2023-09-16 11:45:00+00:00  [0.018573, 0.040078, -0.000978, -0.206256, -0....   \n",
       "\n",
       "                           wave_check_factor  sea_surface_temperature  \\\n",
       "time                                                                    \n",
       "2023-09-16 11:45:00+00:00                NaN                    18.74   \n",
       "\n",
       "                           sea_level_pressure  \n",
       "time                                           \n",
       "2023-09-16 11:45:00+00:00               997.4  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# `drifters` is a python dictionary keyed by drifter type (spotter,\n",
    "# dwsd, or microswift)\n",
    "spotter = drifters['spotter']\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_ids = list(spotter.keys())\n",
    "\n",
    "# The data for a single id (e.g. the first) can be accessed as:\n",
    "spotter_id = spotter_ids[0]\n",
    "\n",
    "# index zero is 31232C\n",
    "print(spotter_id)\n",
    "# spotter[spotter_id].info()\n",
    "\n",
    "# time intervals are tuned to select only one record near noon on 9/16\n",
    "start_date = datetime(2023, 9, 16, 11, 30, tzinfo=timezone.utc)\n",
    "end_date = datetime(2023, 9, 16, 12, 30, tzinfo=timezone.utc)\n",
    "only_waves = spotter[spotter_id]['energy_density'].notnull()\n",
    "dss = spotter[spotter_id][only_waves][start_date:end_date]\n",
    "dss"
   ]
  },
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      "300534061901630\n"
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       "      <th>time</th>\n",
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       "      <th>2023-09-16 12:00:00+00:00</th>\n",
       "      <td>41.520939</td>\n",
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       "      <td>10</td>\n",
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       "      <td>[0.03125, 0.03515625, 0.0390625, 0.04296875, 0...</td>\n",
       "      <td>[0.3997, 0.3609, 0.27, 0.2753, 0.7186, 0.8472,...</td>\n",
       "      <td>[0.1865, 0.3786, -0.33, -0.1058, -0.458, -0.46...</td>\n",
       "      <td>[0.3009, 0.2181, 0.0486, -0.0402, -0.0062, 0.0...</td>\n",
       "      <td>[-0.0083, 0.7181, -0.014, 0.085, 0.0167, 0.053...</td>\n",
       "      <td>[0.3582, 0.0146, -0.0571, -0.4075, -0.0115, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.11</td>\n",
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       "    </tr>\n",
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       "                            latitude  longitude  significant_height  \\\n",
       "time                                                                  \n",
       "2023-09-16 12:00:00+00:00  41.520939 -69.702394                4.66   \n",
       "\n",
       "                           peak_period  peak_direction  \\\n",
       "time                                                     \n",
       "2023-09-16 12:00:00+00:00           10            31.8   \n",
       "\n",
       "                           peak_directional_spread  mean_period  \\\n",
       "time                                                              \n",
       "2023-09-16 12:00:00+00:00                19.015568     7.443817   \n",
       "\n",
       "                           mean_direction  mean_directional_spread  \\\n",
       "time                                                                 \n",
       "2023-09-16 12:00:00+00:00       23.655941                46.325156   \n",
       "\n",
       "                                                                   frequency  \\\n",
       "time                                                                           \n",
       "2023-09-16 12:00:00+00:00  [0.03125, 0.03515625, 0.0390625, 0.04296875, 0...   \n",
       "\n",
       "                                                              energy_density  \\\n",
       "time                                                                           \n",
       "2023-09-16 12:00:00+00:00  [0.3997, 0.3609, 0.27, 0.2753, 0.7186, 0.8472,...   \n",
       "\n",
       "                                                                          a1  \\\n",
       "time                                                                           \n",
       "2023-09-16 12:00:00+00:00  [0.1865, 0.3786, -0.33, -0.1058, -0.458, -0.46...   \n",
       "\n",
       "                                                                          b1  \\\n",
       "time                                                                           \n",
       "2023-09-16 12:00:00+00:00  [0.3009, 0.2181, 0.0486, -0.0402, -0.0062, 0.0...   \n",
       "\n",
       "                                                                          a2  \\\n",
       "time                                                                           \n",
       "2023-09-16 12:00:00+00:00  [-0.0083, 0.7181, -0.014, 0.085, 0.0167, 0.053...   \n",
       "\n",
       "                                                                          b2  \\\n",
       "time                                                                           \n",
       "2023-09-16 12:00:00+00:00  [0.3582, 0.0146, -0.0571, -0.4075, -0.0115, 0....   \n",
       "\n",
       "                           wave_check_factor  sea_surface_temperature  \\\n",
       "time                                                                    \n",
       "2023-09-16 12:00:00+00:00                NaN                    17.11   \n",
       "\n",
       "                           sea_level_pressure  \n",
       "time                                           \n",
       "2023-09-16 12:00:00+00:00                 NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dwsd = drifters['dwsd']\n",
    "dwsd_ids = list(dwsd.keys())\n",
    "start_date = datetime(2023, 9, 16, 11, 50, tzinfo=timezone.utc)\n",
    "end_date = datetime(2023, 9, 16, 12, 10, tzinfo=timezone.utc)\n",
    "dwsd_id = dwsd_ids[1]\n",
    "dsd = dwsd[dwsd_id][start_date:end_date]\n",
    "print(dwsd_id)\n",
    "dsd"
   ]
  },
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   "id": "9bffe7c7-8b97-47af-8ec1-7e4830f79780",
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     "text": [
      "['041', '050', '059', '065']\n",
      "065\n"
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       "      <th>2023-09-16 12:35:12+00:00</th>\n",
       "      <td>42.320221</td>\n",
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       "      <td>[-0.07, -0.19, -0.21, -0.37, -0.2, 0.03, -0.07...</td>\n",
       "      <td>[0.11, -0.32, -0.23, -0.33, -0.23, 0.44, 0.68,...</td>\n",
       "      <td>[-0.18, -0.18, -0.23, -0.01, -0.1, -0.22, 0.06...</td>\n",
       "      <td>[0.1, 0.1, 0.1, 0.1, 0.3, 0.8, 1.2, 1.1, 1.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "                            latitude  longitude  significant_height  \\\n",
       "time                                                                  \n",
       "2023-09-16 12:35:12+00:00  42.320221  -70.02636            3.964844   \n",
       "\n",
       "                           peak_period  peak_direction  \\\n",
       "time                                                     \n",
       "2023-09-16 12:35:12+00:00     9.664062       46.735705   \n",
       "\n",
       "                           peak_directional_spread  mean_period  \\\n",
       "time                                                              \n",
       "2023-09-16 12:35:12+00:00                20.846626     7.969647   \n",
       "\n",
       "                           mean_direction  mean_directional_spread  \\\n",
       "time                                                                 \n",
       "2023-09-16 12:35:12+00:00       22.777554                46.929629   \n",
       "\n",
       "                                                                   frequency  \\\n",
       "time                                                                           \n",
       "2023-09-16 12:35:12+00:00  [0.009765625, 0.021484375, 0.033203125, 0.0449...   \n",
       "\n",
       "                                                              energy_density  \\\n",
       "time                                                                           \n",
       "2023-09-16 12:35:12+00:00  [0.74560546875, 0.96923828125, 1.3427734375, 1...   \n",
       "\n",
       "                                                                          a1  \\\n",
       "time                                                                           \n",
       "2023-09-16 12:35:12+00:00  [-0.17, 0.15, 0.08, -0.01, -0.37, -0.73, -0.87...   \n",
       "\n",
       "                                                                          b1  \\\n",
       "time                                                                           \n",
       "2023-09-16 12:35:12+00:00  [-0.07, -0.19, -0.21, -0.37, -0.2, 0.03, -0.07...   \n",
       "\n",
       "                                                                          a2  \\\n",
       "time                                                                           \n",
       "2023-09-16 12:35:12+00:00  [0.11, -0.32, -0.23, -0.33, -0.23, 0.44, 0.68,...   \n",
       "\n",
       "                                                                          b2  \\\n",
       "time                                                                           \n",
       "2023-09-16 12:35:12+00:00  [-0.18, -0.18, -0.23, -0.01, -0.1, -0.22, 0.06...   \n",
       "\n",
       "                                                           wave_check_factor  \\\n",
       "time                                                                           \n",
       "2023-09-16 12:35:12+00:00  [0.1, 0.1, 0.1, 0.1, 0.3, 0.8, 1.2, 1.1, 1.0, ...   \n",
       "\n",
       "                           sea_surface_temperature  sea_level_pressure  \n",
       "time                                                                    \n",
       "2023-09-16 12:35:12+00:00                      NaN                 NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dwms = drifters['microswift']\n",
    "dwms_ids = list(dwms.keys())\n",
    "# we are looking for microswift 065\n",
    "print(dwms_ids)\n",
    "print(dwms_ids[3])\n",
    "start_date = datetime(2023, 9, 16, 11, 50, tzinfo=timezone.utc)\n",
    "end_date = datetime(2023, 9, 16, 13, tzinfo=timezone.utc)\n",
    "dwms_id = dwms_ids[3]\n",
    "dsm = dwms[dwms_id][start_date:end_date]\n",
    "dsm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "521ee9ce-e94d-4e8a-9b8e-f44215be1aa4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "MODEL_DIRECTORY = '/proj/usgs-share/Projects/NOPP_forecast/Lee/'\n",
    "\n",
    "swan_list = ['/proj/usgs-share/Projects/NOPP_forecast/Lee/dwsd630_2Dspec.txt',\n",
    "'/proj/usgs-share/Projects/NOPP_forecast/Lee/n44098_2Dspec.txt',\n",
    "'/proj/usgs-share/Projects/NOPP_forecast/Lee/s31232_2Dspec.txt',\n",
    "'/proj/usgs-share/Projects/NOPP_forecast/Lee/sw065_2Dspec.txt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5b7c2855-9da7-49e9-9cea-364b3d865fac",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def swan_stats( fn, tid = 24 ):\n",
    "    # Read ASCII SWAN file and return statistics for time index = tid\n",
    "    # uses wavespectra\n",
    "    df = read_swan( fn ).isel(time=tid)\n",
    "    f = df['freq']\n",
    "    dirs = df['dir']\n",
    "    efth = np.squeeze( df.efth )\n",
    "    stats = df.efth.spec.stats([\"hs\", \"tp\", \"tm01\", \"tm02\", \"dpm\", \"dm\", \"dspr\", \"swe\"])\n",
    "    Hs = np.array( np.squeeze( stats['hs'] ) )\n",
    "    Tp = np.array( np.squeeze( stats['tp'] ) )\n",
    "    Dp = np.array( np.squeeze( stats['dpm'] ) )\n",
    "    Dsprd = np.array( np.squeeze( stats[ 'dspr' ]) )\n",
    "    S = integrate_in_direction( efth, 10., daxis=1)\n",
    "    hs2 = calc_Hs_1d( S,f )\n",
    "    sigf = calc_sigmaf_1d( S, f )\n",
    "    print('Hs = {:.2f}\\nHs2 = {:.2f}\\nTp = {:.2f}\\nsigf = {:.2f}\\nDp = {:.2f}\\nDsprd = {:.2f}\\n'.\n",
    "          format(Hs, hs2, Tp, sigf, Dp, Dsprd))\n",
    "    return Hs, Tp, sigf, Dp, Dsprd \n",
    "\n",
    "\n",
    "def drifter_stats( dss ):\n",
    "    # Read pickled drifter file and return statistics for time index = tid\n",
    "    f = np.array( dss['frequency'][0] )\n",
    "    S = np.array( dss['energy_density'][0] )\n",
    "    Hs = np.array( dss['significant_height'][0] )\n",
    "    Tp = np.array( dss['peak_period'][0])\n",
    "    Dp = np.array( dss['peak_direction'][0] )\n",
    "    Dsprd =  np.array( dss['peak_directional_spread'][0] )\n",
    "    hs2 = calc_Hs_1d( S,f )\n",
    "    sigf = calc_sigmaf_1d( S, f )\n",
    "    print('Hs = {:.2f}\\nHs2 = {:.2f}\\nTp = {:.2f}\\nsigf = {:.2f}\\nDp = {:.2f}\\nDsprd = {:.2f}\\n'.\n",
    "          format(Hs, hs2, Tp, sigf, Dp, Dsprd))\n",
    "    return Hs, Tp, sigf, Dp, Dsprd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "676af1f1-6b61-4032-9dbf-92f55169c1fa",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hs = 4.85\n",
      "Hs2 = 4.85\n",
      "Tp = 8.53\n",
      "sigf = 0.09\n",
      "Dp = 27.06\n",
      "Dsprd = 35.31\n",
      "\n",
      "Hs = 5.80\n",
      "Hs2 = 5.80\n",
      "Tp = 8.79\n",
      "sigf = 0.14\n",
      "Dp = 28.89\n",
      "Dsprd = 50.30\n",
      "\n",
      "[20.         10.          5.          3.33333333  2.5       ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Statistics for Spotter 31232 data\n",
    "Hss, Tps, sigfs, Dps, Dsprds = drifter_stats( dss )\n",
    "# Statistics for SWAN output\n",
    "Hs, Tp, sigf, Dp, Dsprd = swan_stats( swan_list[2] )\n",
    "\n",
    "# try plots for log radius rings\n",
    "radii_f=[0.05, 0.1, 0.2, 0.3, 0.4] \n",
    "print( 1./ np.array( radii_f ) )\n",
    "\n",
    "x0 = 0.\n",
    "y0 = 0.\n",
    "    \n",
    "fig = plt.figure( )\n",
    "ax = fig.add_subplot()\n",
    "ax.set_aspect('equal', adjustable='box')\n",
    "\n",
    "# frequency rings\n",
    "for f in np.array( radii_f ):\n",
    "    r = logr( f )\n",
    "    circle(ax, x0, y0, r, zorder=0)\n",
    "\n",
    "# label rings\n",
    "for i in np.array(radii_f):\n",
    "    ptext(ax, x0, y0, logr(i), 45, \"{}\".format(i) )\n",
    "ax.axis('off')\n",
    "\n",
    "plt_rdata(ax, x0, y0, Hs, Tp, sigf, Dp, Dsprd, ec='cyan')\n",
    "plt_rdata(ax, x0, y0, Hss, Tps, sigfs, Dps, Dsprds, ec='magenta')\n",
    "plt.savefig('spotter31232.png', dpi=200, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a81e59fa-40ec-48ec-b197-6b07aa0a1366",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hs = 4.66\n",
      "Hs2 = 4.66\n",
      "Tp = 10.00\n",
      "sigf = 0.12\n",
      "Dp = 31.80\n",
      "Dsprd = 19.02\n",
      "\n",
      "Hs = 7.69\n",
      "Hs2 = 7.69\n",
      "Tp = 13.92\n",
      "sigf = 0.07\n",
      "Dp = 57.56\n",
      "Dsprd = 30.24\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Statistics for DWSD 630 data\n",
    "Hsd, Tpd, sigfd, Dpd, Dsprdd = drifter_stats( dsd )\n",
    "# Statistics for SWAN output\n",
    "Hs, Tp, sigf, Dp, Dsprd = swan_stats( swan_list[0] )\n",
    "\n",
    "fig = plt.figure( )\n",
    "ax = fig.add_subplot()\n",
    "ax.set_aspect('equal', adjustable='box')\n",
    "\n",
    "# frequency rings\n",
    "for f in np.array( radii_f ):\n",
    "    r = logr( f )\n",
    "    circle(ax, x0, y0, r, zorder=0)\n",
    "\n",
    "# label rings\n",
    "for i in np.array(radii_f):\n",
    "    ptext(ax, x0, y0, logr(i), 90, \"{}\".format(i) )\n",
    "ax.axis('off')\n",
    "\n",
    "plt_rdata(ax, x0, y0, Hs, Tp, sigf, Dp, Dsprd, ec='cyan')\n",
    "plt_rdata(ax, x0, y0, Hsd, Tpd, sigfd, Dpd, Dsprdd, ec='magenta')\n",
    "plt.savefig('dwsd630.png', dpi=200, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4f1ddd7f-2d50-46ef-b92d-1079132c90d0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hs = 3.96\n",
      "Hs2 = 4.06\n",
      "Tp = 9.66\n",
      "sigf = 0.12\n",
      "Dp = 46.74\n",
      "Dsprd = 20.85\n",
      "\n",
      "Hs = 5.75\n",
      "Hs2 = 5.75\n",
      "Tp = 9.81\n",
      "sigf = 0.13\n",
      "Dp = 43.43\n",
      "Dsprd = 42.72\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Statistics for microswift 63\n",
    "Hsm, Tpm, sigfm, Dpm, Dsprdm = drifter_stats( dsm )\n",
    "# Statistics for SWAN output\n",
    "Hs, Tp, sigf, Dp, Dsprd = swan_stats( swan_list[3] )\n",
    "\n",
    "fig = plt.figure( )\n",
    "ax = fig.add_subplot()\n",
    "ax.set_aspect('equal', adjustable='box')\n",
    "\n",
    "# frequency rings\n",
    "for f in np.array( radii_f ):\n",
    "    r = logr( f )\n",
    "    circle(ax, x0, y0, r, zorder=0)\n",
    "\n",
    "# label rings\n",
    "for i in np.array(radii_f):\n",
    "    ptext(ax, x0, y0, logr(i), 45, \"{}\".format(i) )\n",
    "ax.axis('off')\n",
    "\n",
    "plt_rdata(ax, x0, y0, Hs, Tp, sigf, Dp, Dsprd, ec='cyan')\n",
    "plt_rdata(ax, x0, y0, Hsm, Tpm, sigfm, Dpm, Dsprdm, ec='magenta')\n",
    "plt.savefig('microswift63.png', dpi=200, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f700b4f-9249-4803-981a-75182acab740",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}