confirm_NDBC_bulk_stats.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5f40feea",
   "metadata": {
    "tags": []
   },
   "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 roguewave.wavespectra.estimators.estimate import estimate_directional_distribution\n",
    "#%run -i roguewave_estimators.py\n",
    "%run -i wave_stats.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19c4fc9e-157e-41ba-81c6-650948664449",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "roms_file = 'http://icoast.rc.ufl.edu/thredds/dodsC/ian/L1_BEST_AVAILABLE/ocean_his.nc'\n",
    "run_name = 'L1_20230123'\n",
    "\n",
    "ds_mod = xroms.open_netcdf(roms_file)\n",
    "time_ini_model = ds_mod.ocean_time.values[0]\n",
    "time_end_model = ds_mod.ocean_time.values[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f0d63357-99c3-49be-afcb-dd9b1c92eb6f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# These are the GOM buoys with data for Ian\n",
    "buoys = ['42039','42098', '42099']\n",
    "# pick one for testing\n",
    "buoy = buoys[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5b199586-ec1e-4023-92d0-4a2545dc28a9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "MODEL_DIRECTORY = '/vortexfs1/share/usgs-share/Projects/Ian2022/ian10/drifters/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7fa06ee9-96d4-4481-907e-df8081b6035c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-09-26T00:00:00.000000000  to  2022-10-02T00:00:00.000000000\n"
     ]
    }
   ],
   "source": [
    "# Process one buoy as a test\n",
    "fn = \"{}.spc2d\".format(buoy)\n",
    "dsm = read_swan(MODEL_DIRECTORY+fn)\n",
    "# get start/end times\n",
    "stime = dsm.time[0].values\n",
    "etime = dsm.time[-1].values\n",
    "print(stime,' to ',etime)\n",
    "dtmod = np.squeeze( dsm.time.values )\n",
    "hsmod = np.squeeze( dsm.efth.spec.hs().values )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1302c66b-250f-479d-92cb-455fa96eda13",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x2aac19f138b0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dst = dsm.isel(lat=0,lon=0,time=15)\n",
    "dst.spec.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7321b669-3ee7-4a87-b3bb-361301c00f3b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://dods.ndbc.noaa.gov/thredds/dodsC/data/swden/42039/42039w9999.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/mambaforge/envs/NOPP/lib/python3.9/site-packages/dask/core.py:119: RuntimeWarning: invalid value encountered in divide\n",
      "  return func(*(_execute_task(a, cache) for a in args))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dmobs: nan\n",
      "dspr: nan\n",
      "0.2072119026495582\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/mambaforge/envs/NOPP/lib/python3.9/site-packages/dask/core.py:119: RuntimeWarning: invalid value encountered in divide\n",
      "  return func(*(_execute_task(a, cache) for a in args))\n"
     ]
    }
   ],
   "source": [
    "# assume the file has all years, thus w9999 instead of w2022\n",
    "# read the NDBC buoy observations for the same time slice\n",
    "url ='https://dods.ndbc.noaa.gov/thredds/dodsC/data/swden/' + str(buoy) + '/' + str(buoy) + 'w9999' + '.nc'\n",
    "print(url)\n",
    "dso = read_ndbc(url, dd=10.0, chunks={}).sel(time=slice(stime, etime))\n",
    "dtobs = np.squeeze(dso.time)\n",
    "hsobs = np.squeeze(dso.efth.spec.hs().values)\n",
    "dmobs = np.squeeze(dso.efth.spec.dm().values)\n",
    "dsprobs = np.squeeze(dso.efth.spec.dspr().values)\n",
    "sweobs = np.squeeze(dso.efth.spec.swe().values)\n",
    "swobs = np.squeeze(dso.efth.spec.sw().values)\n",
    "print('dmobs:', dmobs[80])\n",
    "print('dspr:', dsprobs[80])\n",
    "\n",
    "# interpolate modeled to times of observation (in case some are missing)\n",
    "dsmi = dsm.interp(time=dso.time, method='nearest')\n",
    "hsmodi = np.squeeze( dsmi.efth.spec.hs().values )\n",
    "rmse = np.sqrt( np.nanmean( (hsobs - hsmodi)**2) )\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ce4ebc44-14f7-49f9-a982-234990ed2614",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.0\n",
      "nan\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x2aac2d902a30>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dsij=dso.isel(time=14,lat=0,lon=0)\n",
    "print(dsij.spec.dpm().values)\n",
    "print(dsij.spec.dspr().values)\n",
    "dsij.spec.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "267e01a6-ab41-4aea-a42f-029dfaa749e5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                (time: 118, frequency: 46, latitude: 1, longitude: 1)\n",
       "Coordinates:\n",
       "  * time                   (time) datetime64[ns] 2022-09-26T00:50:00 ... 2022...\n",
       "  * frequency              (frequency) float32 0.033 0.038 0.043 ... 0.465 0.485\n",
       "  * latitude               (latitude) float32 28.79\n",
       "  * longitude              (longitude) float32 -86.01\n",
       "Data variables:\n",
       "    spectral_wave_density  (time, frequency, latitude, longitude) float32 ...\n",
       "    mean_wave_dir          (time, frequency, latitude, longitude) float64 ...\n",
       "    principal_wave_dir     (time, frequency, latitude, longitude) float64 ...\n",
       "    wave_spectrum_r1       (time, frequency, latitude, longitude) float32 ...\n",
       "    wave_spectrum_r2       (time, frequency, latitude, longitude) float32 ...\n",
       "Attributes:\n",
       "    institution:                     NOAA National Data Buoy Center and Parti...\n",
       "    url:                             http://dods.ndbc.noaa.gov\n",
       "    quality:                         Automated QC checks with daily manual QC\n",
       "    conventions:                     COARDS\n",
       "    station:                         42039\n",
       "    comment:                         PENSACOLA - 115NM SSE of Pensacola, FL\n",
       "    location:                        28.788 N 86.008 W \n",
       "    DODS_EXTRA.Unlimited_Dimension:  time</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1c664e5d-d51e-48fa-aa3c-e8ca504e1c0c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1c664e5d-d51e-48fa-aa3c-e8ca504e1c0c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 118</li><li><span class='xr-has-index'>frequency</span>: 46</li><li><span class='xr-has-index'>latitude</span>: 1</li><li><span class='xr-has-index'>longitude</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9b7e88d4-0825-49cd-a15a-b6b5ea0f3a62' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9b7e88d4-0825-49cd-a15a-b6b5ea0f3a62' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-09-26T00:50:00 ... 2022-09-...</div><input id='attrs-76363288-64cc-453c-8574-dab6efc413ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76363288-64cc-453c-8574-dab6efc413ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ab7c161-cee3-4d0d-b249-714758e3137c' class='xr-var-data-in' type='checkbox'><label for='data-2ab7c161-cee3-4d0d-b249-714758e3137c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Epoch Time</dd><dt><span>short_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-09-26T00:50:00.000000000&#x27;, &#x27;2022-09-26T01:50:00.000000000&#x27;,\n",
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       "       &#x27;2022-09-30T20:50:00.000000000&#x27;, &#x27;2022-09-30T21:50:00.000000000&#x27;,\n",
       "       &#x27;2022-09-30T22:50:00.000000000&#x27;, &#x27;2022-09-30T23:50:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>frequency</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.033 0.038 0.043 ... 0.465 0.485</div><input id='attrs-e0e7c194-d1a9-4966-8e59-441cd95a2335' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0e7c194-d1a9-4966-8e59-441cd95a2335' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8fee15be-5e0e-43ce-bc58-e7854438ea8b' class='xr-var-data-in' type='checkbox'><label for='data-8fee15be-5e0e-43ce-bc58-e7854438ea8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Frequency</dd><dt><span>short_name :</span></dt><dd>frequency</dd><dt><span>standard_name :</span></dt><dd>frequency</dd><dt><span>units :</span></dt><dd>Hz</dd></dl></div><div class='xr-var-data'><pre>array([0.033, 0.038, 0.043, 0.048, 0.053, 0.058, 0.063, 0.068, 0.073, 0.078,\n",
       "       0.083, 0.088, 0.093, 0.1  , 0.11 , 0.12 , 0.13 , 0.14 , 0.15 , 0.16 ,\n",
       "       0.17 , 0.18 , 0.19 , 0.2  , 0.21 , 0.22 , 0.23 , 0.24 , 0.25 , 0.26 ,\n",
       "       0.27 , 0.28 , 0.29 , 0.3  , 0.31 , 0.32 , 0.33 , 0.34 , 0.35 , 0.365,\n",
       "       0.385, 0.405, 0.425, 0.445, 0.465, 0.485], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>28.79</div><input id='attrs-ce247690-e301-45f3-a0e2-0d3500da7726' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce247690-e301-45f3-a0e2-0d3500da7726' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c92dbb5f-52f2-40bd-b058-e24174cfed29' class='xr-var-data-in' type='checkbox'><label for='data-c92dbb5f-52f2-40bd-b058-e24174cfed29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>short_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([28.788], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-86.01</div><input id='attrs-d17c3372-cc20-4d6d-b57f-a5594ca91e2a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d17c3372-cc20-4d6d-b57f-a5594ca91e2a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e28d2813-715b-4510-8824-f67726bc1e2a' class='xr-var-data-in' type='checkbox'><label for='data-e28d2813-715b-4510-8824-f67726bc1e2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>short_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-86.008], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7081a792-0bca-4f85-b392-5da9b55e3329' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7081a792-0bca-4f85-b392-5da9b55e3329' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>spectral_wave_density</span></div><div class='xr-var-dims'>(time, frequency, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-be9a46b0-79b5-44d2-a16e-82ee871938d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be9a46b0-79b5-44d2-a16e-82ee871938d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-729b2e83-3001-40ce-b070-33a542f8fb5f' class='xr-var-data-in' type='checkbox'><label for='data-729b2e83-3001-40ce-b070-33a542f8fb5f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Spectral Wave Density</dd><dt><span>short_name :</span></dt><dd>swden</dd><dt><span>standard_name :</span></dt><dd>spectral_wave_density</dd><dt><span>units :</span></dt><dd>(meter * meter)/Hz</dd></dl></div><div class='xr-var-data'><pre>[5428 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_wave_dir</span></div><div class='xr-var-dims'>(time, frequency, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ab7f81ce-1fc9-4881-aa9f-4d9a97b3cb45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab7f81ce-1fc9-4881-aa9f-4d9a97b3cb45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3487bbd-b8c5-49cc-8caf-8647bcfb93cc' class='xr-var-data-in' type='checkbox'><label for='data-c3487bbd-b8c5-49cc-8caf-8647bcfb93cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean Wave Direction</dd><dt><span>short_name :</span></dt><dd>alpha1</dd><dt><span>standard_name :</span></dt><dd>mean_wave_direction</dd><dt><span>units :</span></dt><dd>degrees_true</dd></dl></div><div class='xr-var-data'><pre>[5428 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>principal_wave_dir</span></div><div class='xr-var-dims'>(time, frequency, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-510ae64d-8336-4f19-8c93-530d9143a89e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-510ae64d-8336-4f19-8c93-530d9143a89e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-684c53ec-c706-4e1b-8049-5c5f5b347c29' class='xr-var-data-in' type='checkbox'><label for='data-684c53ec-c706-4e1b-8049-5c5f5b347c29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Principal Wave Direction</dd><dt><span>short_name :</span></dt><dd>alpha2</dd><dt><span>standard_name :</span></dt><dd>principal_wave_direction</dd><dt><span>units :</span></dt><dd>degrees_true</dd></dl></div><div class='xr-var-data'><pre>[5428 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wave_spectrum_r1</span></div><div class='xr-var-dims'>(time, frequency, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-93200d4a-47cb-41d5-ba94-495d4fdba011' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93200d4a-47cb-41d5-ba94-495d4fdba011' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6aafdf82-fce0-4c7d-a9e7-76ca02b0405e' class='xr-var-data-in' type='checkbox'><label for='data-6aafdf82-fce0-4c7d-a9e7-76ca02b0405e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>First Normalized Polar Coordinate Derived from the Fourier Coefficients</dd><dt><span>short_name :</span></dt><dd>r1</dd><dt><span>standard_name :</span></dt><dd>wave_spectrum_r1</dd></dl></div><div class='xr-var-data'><pre>[5428 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wave_spectrum_r2</span></div><div class='xr-var-dims'>(time, frequency, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3645cfb7-9a64-4e8b-87d4-d75cbbcba241' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3645cfb7-9a64-4e8b-87d4-d75cbbcba241' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d842361-504b-420b-b150-d78b2308701a' class='xr-var-data-in' type='checkbox'><label for='data-5d842361-504b-420b-b150-d78b2308701a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Second Normalized Polar Coordinate Derived from the Fourier Coefficients</dd><dt><span>short_name :</span></dt><dd>r2</dd><dt><span>standard_name :</span></dt><dd>wave_spectrum_r2</dd></dl></div><div class='xr-var-data'><pre>[5428 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b5ab5480-a043-4551-a89d-83d3c48eacf5' class='xr-section-summary-in' type='checkbox'  ><label for='section-b5ab5480-a043-4551-a89d-83d3c48eacf5' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8ae1c4ef-b47c-4c46-9d32-46c4da1c2c97' class='xr-index-data-in' type='checkbox'/><label for='index-8ae1c4ef-b47c-4c46-9d32-46c4da1c2c97' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-09-26 00:50:00&#x27;, &#x27;2022-09-26 01:50:00&#x27;,\n",
       "               &#x27;2022-09-26 02:50:00&#x27;, &#x27;2022-09-26 03:50:00&#x27;,\n",
       "               &#x27;2022-09-26 04:50:00&#x27;, &#x27;2022-09-26 05:50:00&#x27;,\n",
       "               &#x27;2022-09-26 06:50:00&#x27;, &#x27;2022-09-26 07:50:00&#x27;,\n",
       "               &#x27;2022-09-26 08:50:00&#x27;, &#x27;2022-09-26 09:50:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-09-30 13:50:00&#x27;, &#x27;2022-09-30 15:50:00&#x27;,\n",
       "               &#x27;2022-09-30 16:50:00&#x27;, &#x27;2022-09-30 17:50:00&#x27;,\n",
       "               &#x27;2022-09-30 18:50:00&#x27;, &#x27;2022-09-30 19:50:00&#x27;,\n",
       "               &#x27;2022-09-30 20:50:00&#x27;, &#x27;2022-09-30 21:50:00&#x27;,\n",
       "               &#x27;2022-09-30 22:50:00&#x27;, &#x27;2022-09-30 23:50:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=118, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>frequency</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ee16fccb-508e-4633-90b9-d5d3310301ef' class='xr-index-data-in' type='checkbox'/><label for='index-ee16fccb-508e-4633-90b9-d5d3310301ef' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.032999999821186066,  0.03799999877810478,   0.0430000014603138,\n",
       "        0.04800000041723251,  0.05299999937415123, 0.057999998331069946,\n",
       "        0.06300000101327896,  0.06800000369548798,   0.0729999989271164,\n",
       "        0.07800000160932541,  0.08299999684095383,  0.08799999952316284,\n",
       "        0.09300000220537186,  0.10000000149011612,  0.10999999940395355,\n",
       "        0.11999999731779099,  0.12999999523162842,  0.14000000059604645,\n",
       "        0.15000000596046448,   0.1599999964237213,  0.17000000178813934,\n",
       "        0.18000000715255737,   0.1899999976158142,  0.20000000298023224,\n",
       "        0.20999999344348907,   0.2199999988079071,  0.23000000417232513,\n",
       "        0.23999999463558197,                 0.25,  0.25999999046325684,\n",
       "        0.27000001072883606,   0.2800000011920929,  0.28999999165534973,\n",
       "        0.30000001192092896,   0.3100000023841858,   0.3199999928474426,\n",
       "        0.33000001311302185,   0.3400000035762787,   0.3499999940395355,\n",
       "        0.36500000953674316,  0.38499999046325684,   0.4050000011920929,\n",
       "        0.42500001192092896,   0.4449999928474426,   0.4650000035762787,\n",
       "        0.48500001430511475],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;frequency&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8fce3ca6-b7e2-4325-b97d-f003471d803c' class='xr-index-data-in' type='checkbox'/><label for='index-8fce3ca6-b7e2-4325-b97d-f003471d803c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([28.788], dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-78b18b9b-b358-4dcb-b70b-dd631e490434' class='xr-index-data-in' type='checkbox'/><label for='index-78b18b9b-b358-4dcb-b70b-dd631e490434' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-86.008], dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2d425539-410b-4f9e-9ca0-0d8e5b0f4d12' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2d425539-410b-4f9e-9ca0-0d8e5b0f4d12' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>institution :</span></dt><dd>NOAA National Data Buoy Center and Participators in Data Assembly Center</dd><dt><span>url :</span></dt><dd>http://dods.ndbc.noaa.gov</dd><dt><span>quality :</span></dt><dd>Automated QC checks with daily manual QC</dd><dt><span>conventions :</span></dt><dd>COARDS</dd><dt><span>station :</span></dt><dd>42039</dd><dt><span>comment :</span></dt><dd>PENSACOLA - 115NM SSE of Pensacola, FL</dd><dt><span>location :</span></dt><dd>28.788 N 86.008 W </dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                (time: 118, frequency: 46, latitude: 1, longitude: 1)\n",
       "Coordinates:\n",
       "  * time                   (time) datetime64[ns] 2022-09-26T00:50:00 ... 2022...\n",
       "  * frequency              (frequency) float32 0.033 0.038 0.043 ... 0.465 0.485\n",
       "  * latitude               (latitude) float32 28.79\n",
       "  * longitude              (longitude) float32 -86.01\n",
       "Data variables:\n",
       "    spectral_wave_density  (time, frequency, latitude, longitude) float32 ...\n",
       "    mean_wave_dir          (time, frequency, latitude, longitude) float64 ...\n",
       "    principal_wave_dir     (time, frequency, latitude, longitude) float64 ...\n",
       "    wave_spectrum_r1       (time, frequency, latitude, longitude) float32 ...\n",
       "    wave_spectrum_r2       (time, frequency, latitude, longitude) float32 ...\n",
       "Attributes:\n",
       "    institution:                     NOAA National Data Buoy Center and Parti...\n",
       "    url:                             http://dods.ndbc.noaa.gov\n",
       "    quality:                         Automated QC checks with daily manual QC\n",
       "    conventions:                     COARDS\n",
       "    station:                         42039\n",
       "    comment:                         PENSACOLA - 115NM SSE of Pensacola, FL\n",
       "    location:                        28.788 N 86.008 W \n",
       "    DODS_EXTRA.Unlimited_Dimension:  time"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url ='https://dods.ndbc.noaa.gov/thredds/dodsC/data/swden/' + str(buoy) + '/' + str(buoy) + 'w9999' + '.nc'\n",
    "ds = xr.open_dataset(url)\n",
    "time_ini_model = datetime.strptime('2022-09-26', '%Y-%m-%d')\n",
    "time_end_model = datetime.strptime('2022-10-01', '%Y-%m-%d')\n",
    "ds = ds.sel(time=slice(time_ini_model,time_end_model))\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f573b943-29be-4bfb-9ed6-7135d64bb270",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -4.221967654925768\n",
      "83.10877505399026 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -4.0359649172015155\n",
      "87.27106143186796 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -4.072738237199316\n",
      "92.84109046104916 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -4.046901497609779\n",
      "94.77019871992005 nan\n"
     ]
    },
    {
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      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
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      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
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      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
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      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
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      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
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      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
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      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
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     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -4.169776544260986\n",
      "25.762695494123932 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -4.249039880999479\n",
      "30.779369480804803 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -4.0599477504492985\n",
      "24.463026678669507 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -3.713659731684242\n",
      "25.99423925363203 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -3.691323915079887\n",
      "25.607662838025476 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -3.7140319777689097\n",
      "27.92536572730836 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -3.43897880149832\n",
      "28.83818944682929 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -3.3846669881667664\n",
      "19.62226916152241 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -3.7364978627791876\n",
      "16.12792662849165 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -3.7647534891552192\n",
      "12.645062243902373 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -4.043823080842488\n",
      "10.967552673884597 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -4.183724745653001\n",
      "16.112191567909917 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -4.441640581316711\n",
      "13.727488787295954 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -4.370408697321503\n",
      "15.995666450404315 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -4.353301912696994\n",
      "17.67567828418038 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -4.256743393765416\n",
      "25.999009537354766 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -4.160260567553566\n",
      "24.00361791265692 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -4.22502206695508\n",
      "26.7328643701386 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -4.237300584287135\n",
      "24.60959738023496 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -4.121407240354435\n",
      "31.069812404716743 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -4.080455880372061\n",
      "35.21297886894564 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -3.934051547589201\n",
      "35.701086039036966 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -3.8684673954234663\n",
      "38.53220908214769 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -3.5868801530183854\n",
      "47.681465234614905 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -3.7539065315663525\n",
      "52.72397555138184 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -3.697127587592263\n",
      "44.8269522651417 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -3.4834551465364134\n",
      "49.68705060294053 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -3.43511747730441\n",
      "44.76862661055077 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -3.6246304139776395\n",
      "40.24473498434992 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -3.3584835173236405\n",
      "30.91457819049208 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -3.1206551996152267\n",
      "18.615259137904275 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -3.3394228934321752\n",
      "18.979288271569033 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -3.4446336482473052\n",
      "7.729166238008759 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -3.7489456207308836\n",
      "358.22912632379524 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    }
   ],
   "source": [
    "f = np.squeeze( ds.frequency )\n",
    "for i in np.arange(len(ds['time'])):\n",
    "    a1, b1, a2, b2 = to_moment( ds.wave_spectrum_r1[i], ds.wave_spectrum_r1[i], ds.mean_wave_dir[i], ds.principal_wave_dir[i] )\n",
    "    spec1d = np.squeeze( ds.spectral_wave_density[i] )\n",
    "    thetamw = calc_weighted_theta_mean_a1b1( a1, b1, spec1d, f )\n",
    "    sigmatheta = calc_sigma_theta_a1b1( a1, b1, spec1d, f )\n",
    "    print(thetamw, sigmatheta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b033ca5b-9ead-46b1-b408-6ad49191abe4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeff: -2.0670878158110813\n",
      "358.7305956891511 nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vortexfs1/home/csherwood/src/NOPP_Utilities/wave_stats.py:160: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s1 = (180./np.pi) * np.sqrt( 2. * ( 1. - np.sqrt( a1bar**2 + b1bar**2 ) ) )\n"
     ]
    }
   ],
   "source": [
    "#for i in np.arange(len(ds.time)):\n",
    "i=18\n",
    "\n",
    "a1, b1, a2, b2 = to_moment( ds.wave_spectrum_r1[i], ds.wave_spectrum_r1[i], ds.mean_wave_dir[i], ds.principal_wave_dir[i] )\n",
    "spec1d = np.squeeze( ds.spectral_wave_density[i] )\n",
    "thetamw = calc_weighted_theta_mean_a1b1( a1, b1, spec1d, f )\n",
    "sigmatheta = calc_sigma_theta_a1b1( a1, b1, spec1d, f )\n",
    "print(thetamw, sigmatheta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2884c543-06b6-40ad-9f07-ff0a9e489cdc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "plt.plot(f,np.squeeze(spec1d))\n",
    "plt.plot(f,np.squeeze(a1))\n",
    "plt.plot(f,np.squeeze(b1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1fd006c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "time_ini_model = datetime.strptime('2022-09-26', '%Y-%m-%d')\n",
    "time_end_model = datetime.strptime('2022-10-01', '%Y-%m-%d')\n",
    "buoys = \"42097\",\"42099\",\"42098\"\n",
    "\n",
    "for buoy in buoys:\n",
    "     # Open dataset\n",
    "     url ='https://dods.ndbc.noaa.gov/thredds/dodsC/data/stdmet/' + str(buoy) + '/' + str(buoy) + 'h9999.nc'\n",
    "     ds = xr.open_dataset(url)\n",
    "     lat1 = ds.latitude.values\n",
    "     lon1 = ds.longitude.values\n",
    "     print(lon1,lat1)\n",
    "     ds = ds.sel(time=slice(time_ini_model,time_end_model)) ####<<<<<We can us the datetime object to subset. no need to convert to strings. \n",
    "\n",
    "     #HWavel = ds_mod.Hwave.xroms.sel2d(lon1, lat1)\n",
    "\n",
    "     plt.figure()\n",
    "     ds.wave_height.plot()\n",
    "     #HWavel.plot()\n",
    "     plt.title('NDBC buoy observations')\n",
    "     plt.ylabel('Significat Wave height, m')\n",
    "     plt.xlabel('YYYY-MM-DD, UTC')\n",
    "     #plt.gca().legend([buoy, \"L1 COAWST\"])\n",
    "     fig_name = buoy + '_v2_' + run_name + '.png'\n",
    "     #plt.savefig(fig_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "252028bc-06d4-4be3-828b-882f90d12c91",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ds"
   ]
  }
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