{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from salishsea_tools import evaltools as et, viz_tools\n",
    "import cmocean as cmo\n",
    "import datetime as dt\n",
    "import netCDF4 as nc\n",
    "import matplotlib.dates as mdates\n",
    "import matplotlib as mpl\n",
    "import matplotlib.gridspec as gridspec\n",
    "from matplotlib.colors import LogNorm\n",
    "import cmocean\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import os\n",
    "import gsw\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### definitions for matching model output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# path to model files:\n",
    "PATH= '/results/SalishSea/nowcast-green.201812/'\n",
    "\n",
    "# start and end dates for analysis:\n",
    "start_date = dt.datetime(2015,1,1)\n",
    "end_date = dt.datetime(2016,1,1)\n",
    "\n",
    "# number of days per model file:\n",
    "flen=1\n",
    "\n",
    "# dictionary mapping desired model variables to the file types where they are found\n",
    "filemap={'nitrate':'ptrc_T','silicon':'ptrc_T','ammonium':'ptrc_T','diatoms':'ptrc_T','ciliates':'ptrc_T',\n",
    "         'flagellates':'ptrc_T','vosaline':'grid_T','votemper':'grid_T'}\n",
    "\n",
    "# dictionary mapping model file types to their time resolution in hours (1 is hourly files, 24 is daily)\n",
    "fdict={'ptrc_T':1,'grid_T':1}\n",
    "\n",
    "# results format\n",
    "# -- nowcast: files like 01jan15/SalishSea_1h_20150101_20150101_ptrc_T.nc\n",
    "# -- long: files like SalishSea_1h_20150206_20150804_ptrc_T_20150427-20150506.nc, all in one directory\n",
    "namfmt='nowcast'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### load WADE nutrient data and create csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def interpCTDvar(sta,yr,yd,ztarget,ctdvar):\n",
    "    ctdlocs=(dfCTD.Station==sta)&(dfCTD.Year==yr)&(dfCTD.YD==yd)\n",
    "    if np.sum(ctdlocs)==0:\n",
    "        print(f'Warning: Station {sta}, Year {yr}, year day {yd} not found in dfCTD')\n",
    "        return np.nan\n",
    "    else:\n",
    "        val=np.interp(ztarget,dfCTD.loc[ctdlocs,['Z']].values.flatten(),\n",
    "                  dfCTD.loc[ctdlocs,[ctdvar]].values.flatten())\n",
    "        return val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load new datetime info\n",
    "dfTime=pd.read_excel('/ocean/eolson/MEOPAR/obs/WADE/WDE_Data/OlsonSuchyAllen_UBC_PDR_P003790-010721.xlsx',\n",
    "                    engine='openpyxl',sheet_name='EventDateTime')\n",
    "# drop duplicate rows, taking first time\n",
    "dfTime.drop_duplicates(subset=['FlightDate','SiteCode'],keep='first',inplace=True)\n",
    "dfTime['dtPac']=[dt.datetime.combine(idate, itime) for idate, itime \\\n",
    "         in zip(dfTime['FlightDate'],dfTime['TimeDown \\n(Local - PST or PDT)'])]\n",
    "dfTime['dtUTC']=[et.pac_to_utc(ii) for ii in dfTime['dtPac']]\n",
    "# join to date/time\n",
    "dfTime['dtUTC']=[et.pac_to_utc(dt.datetime.combine(idate,itime)) for idate,itime in \\\n",
    "                zip(dfTime['FlightDate'],dfTime['TimeDown \\n(Local - PST or PDT)'])]\n",
    "dfTime2=dfTime.loc[:,['FlightDate','SiteCode','dtUTC']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs=dict()\n",
    "for year in range(2015,2019):\n",
    "    PATH= '/results2/SalishSea/nowcast-green.201905/'\n",
    "    datadir='/ocean/eolson/MEOPAR/obs/WADE/ptools_data/ecology'\n",
    "    dfSta=pickle.load(open(os.path.join(datadir,'sta_df.p'),'rb'))\n",
    "    dfCTD0=pickle.load(open(os.path.join(datadir,f'Casts_{str(year)}.p'),'rb'))\n",
    "    dfs0=pd.merge(left=dfSta,right=dfCTD0,how='right',\n",
    "             left_on='Station',right_on='Station')\n",
    "    dfs0.rename(columns={'Latitude':'Lat','Longitude':'Lon'},inplace=True)\n",
    "    # Calculate Absolute (Reference) Salinity (g/kg) and Conservative Temperature (deg C) from \n",
    "    # Salinity (psu) and Temperature (deg C):\n",
    "    dfs0['Z']=-1*dfs0['Z']\n",
    "    press=gsw.p_from_z(-1*dfs0['Z'],dfs0['Lat'])\n",
    "    dfs0['SA']=gsw.SA_from_SP(dfs0['Salinity'],press,\n",
    "                               dfs0['Lon'],dfs0['Lat'])\n",
    "    dfs0['CT']=gsw.CT_from_t(dfs0['SA'],dfs0['Temperature'],press)\n",
    "    # merge time\n",
    "    dfs[year]=pd.merge(left=dfs0,right=dfTime2,how='left',\n",
    "                      left_on=['Date','Station'],right_on=['FlightDate','SiteCode'])\n",
    "    # if no time available, guess midday\n",
    "    dfs[year]['dtUTC']=[iiD+dt.timedelta(hours=20) if pd.isnull(iiU) else iiU \\\n",
    "                    for iiU,iiD in zip(dfs[year]['dtUTC'],dfs[year]['Date'])]\n",
    "    #dfs[year]['Year']=[ii.year for ii in dfs[year]['dtUTC']]\n",
    "    #dfs[year]['YD']=et.datetimeToYD(dfs[year]['dtUTC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Station</th>\n",
       "      <th>Basin</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Sigma</th>\n",
       "      <th>Chl</th>\n",
       "      <th>Z</th>\n",
       "      <th>SA</th>\n",
       "      <th>CT</th>\n",
       "      <th>SiteCode</th>\n",
       "      <th>dtUTC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ADM001</td>\n",
       "      <td>Admiralty Inlet</td>\n",
       "      <td>48.029813</td>\n",
       "      <td>-122.617933</td>\n",
       "      <td>23.311800</td>\n",
       "      <td>0.809025</td>\n",
       "      <td>118.5</td>\n",
       "      <td>30.307274</td>\n",
       "      <td>9.191911</td>\n",
       "      <td>ADM001</td>\n",
       "      <td>2015-01-20 19:14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ADM001</td>\n",
       "      <td>Admiralty Inlet</td>\n",
       "      <td>48.029813</td>\n",
       "      <td>-122.617933</td>\n",
       "      <td>23.310699</td>\n",
       "      <td>0.829323</td>\n",
       "      <td>118.0</td>\n",
       "      <td>30.305957</td>\n",
       "      <td>9.192287</td>\n",
       "      <td>ADM001</td>\n",
       "      <td>2015-01-20 19:14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ADM001</td>\n",
       "      <td>Admiralty Inlet</td>\n",
       "      <td>48.029813</td>\n",
       "      <td>-122.617933</td>\n",
       "      <td>23.306700</td>\n",
       "      <td>0.791626</td>\n",
       "      <td>117.5</td>\n",
       "      <td>30.301120</td>\n",
       "      <td>9.193727</td>\n",
       "      <td>ADM001</td>\n",
       "      <td>2015-01-20 19:14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ADM001</td>\n",
       "      <td>Admiralty Inlet</td>\n",
       "      <td>48.029813</td>\n",
       "      <td>-122.617933</td>\n",
       "      <td>23.304600</td>\n",
       "      <td>0.791626</td>\n",
       "      <td>117.0</td>\n",
       "      <td>30.298496</td>\n",
       "      <td>9.194526</td>\n",
       "      <td>ADM001</td>\n",
       "      <td>2015-01-20 19:14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ADM001</td>\n",
       "      <td>Admiralty Inlet</td>\n",
       "      <td>48.029813</td>\n",
       "      <td>-122.617933</td>\n",
       "      <td>23.306299</td>\n",
       "      <td>0.817724</td>\n",
       "      <td>116.5</td>\n",
       "      <td>30.300392</td>\n",
       "      <td>9.193442</td>\n",
       "      <td>ADM001</td>\n",
       "      <td>2015-01-20 19:14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55880</th>\n",
       "      <td>WPA113</td>\n",
       "      <td>Willapa Bay</td>\n",
       "      <td>46.644000</td>\n",
       "      <td>-123.993000</td>\n",
       "      <td>22.316300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29.307299</td>\n",
       "      <td>10.628845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-11-19 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55881</th>\n",
       "      <td>WPA113</td>\n",
       "      <td>Willapa Bay</td>\n",
       "      <td>46.644000</td>\n",
       "      <td>-123.993000</td>\n",
       "      <td>22.249900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.5</td>\n",
       "      <td>29.220695</td>\n",
       "      <td>10.627059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-11-19 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55882</th>\n",
       "      <td>WPA113</td>\n",
       "      <td>Willapa Bay</td>\n",
       "      <td>46.644000</td>\n",
       "      <td>-123.993000</td>\n",
       "      <td>22.152100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>29.092593</td>\n",
       "      <td>10.620385</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-11-19 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55883</th>\n",
       "      <td>WPA113</td>\n",
       "      <td>Willapa Bay</td>\n",
       "      <td>46.644000</td>\n",
       "      <td>-123.993000</td>\n",
       "      <td>22.115000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "      <td>29.043971</td>\n",
       "      <td>10.617901</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-11-19 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55884</th>\n",
       "      <td>WPA113</td>\n",
       "      <td>Willapa Bay</td>\n",
       "      <td>46.644000</td>\n",
       "      <td>-123.993000</td>\n",
       "      <td>22.094800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.017655</td>\n",
       "      <td>10.617328</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-11-19 20:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>211283 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Station            Basin        Lat         Lon      Sigma       Chl  \\\n",
       "0      ADM001  Admiralty Inlet  48.029813 -122.617933  23.311800  0.809025   \n",
       "1      ADM001  Admiralty Inlet  48.029813 -122.617933  23.310699  0.829323   \n",
       "2      ADM001  Admiralty Inlet  48.029813 -122.617933  23.306700  0.791626   \n",
       "3      ADM001  Admiralty Inlet  48.029813 -122.617933  23.304600  0.791626   \n",
       "4      ADM001  Admiralty Inlet  48.029813 -122.617933  23.306299  0.817724   \n",
       "...       ...              ...        ...         ...        ...       ...   \n",
       "55880  WPA113      Willapa Bay  46.644000 -123.993000  22.316300       NaN   \n",
       "55881  WPA113      Willapa Bay  46.644000 -123.993000  22.249900       NaN   \n",
       "55882  WPA113      Willapa Bay  46.644000 -123.993000  22.152100       NaN   \n",
       "55883  WPA113      Willapa Bay  46.644000 -123.993000  22.115000       NaN   \n",
       "55884  WPA113      Willapa Bay  46.644000 -123.993000  22.094800       NaN   \n",
       "\n",
       "           Z         SA         CT SiteCode               dtUTC  \n",
       "0      118.5  30.307274   9.191911   ADM001 2015-01-20 19:14:00  \n",
       "1      118.0  30.305957   9.192287   ADM001 2015-01-20 19:14:00  \n",
       "2      117.5  30.301120   9.193727   ADM001 2015-01-20 19:14:00  \n",
       "3      117.0  30.298496   9.194526   ADM001 2015-01-20 19:14:00  \n",
       "4      116.5  30.300392   9.193442   ADM001 2015-01-20 19:14:00  \n",
       "...      ...        ...        ...      ...                 ...  \n",
       "55880    3.0  29.307299  10.628845      NaN 2018-11-19 20:00:00  \n",
       "55881    2.5  29.220695  10.627059      NaN 2018-11-19 20:00:00  \n",
       "55882    2.0  29.092593  10.620385      NaN 2018-11-19 20:00:00  \n",
       "55883    1.5  29.043971  10.617901      NaN 2018-11-19 20:00:00  \n",
       "55884    1.0  29.017655  10.617328      NaN 2018-11-19 20:00:00  \n",
       "\n",
       "[211283 rows x 11 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfAll=pd.concat((dfs[2015],dfs[2016],dfs[2017],dfs[2018]))\n",
    "dfAll.drop(columns=['Desig','Descrip','Date','Max_Depth','Salinity','Temperature','FlightDate','DO','Turb'],inplace=True)\n",
    "dfAll"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Station', 'Basin', 'Lat', 'Lon', 'Sigma', 'Chl', 'Z', 'SA', 'CT',\n",
       "       'SiteCode', 'dtUTC'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfAll.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "dfAll.to_csv('/data/eolson/results/MEOPAR/oldDBs/WADECTD.csv',index=False)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# load PSF bottle data (returns pandas dataframe)\n",
    "# NO23 is nitrate+nitrate, Si is Silicate; Chl is chlorophyll; Z is depth (m); dtUTC is datetime in UTC\n",
    "# SA is reference salinity (g/kg), CT is Conservative Temperature (deg C)\n",
    "df1=et.loadPSF(loadCTD=True)\n",
    "df1.drop(columns=['pLat','pLon','tdiffH'])\n",
    "print(len(df1))\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### load csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "211283\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Station</th>\n",
       "      <th>Basin</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Sigma</th>\n",
       "      <th>Chl</th>\n",
       "      <th>Z</th>\n",
       "      <th>SA</th>\n",
       "      <th>CT</th>\n",
       "      <th>SiteCode</th>\n",
       "      <th>dtUTC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ADM001</td>\n",
       "      <td>Admiralty Inlet</td>\n",
       "      <td>48.029813</td>\n",
       "      <td>-122.617933</td>\n",
       "      <td>23.311800</td>\n",
       "      <td>0.809025</td>\n",
       "      <td>118.5</td>\n",
       "      <td>30.307274</td>\n",
       "      <td>9.191911</td>\n",
       "      <td>ADM001</td>\n",
       "      <td>2015-01-20 19:14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ADM001</td>\n",
       "      <td>Admiralty Inlet</td>\n",
       "      <td>48.029813</td>\n",
       "      <td>-122.617933</td>\n",
       "      <td>23.310699</td>\n",
       "      <td>0.829323</td>\n",
       "      <td>118.0</td>\n",
       "      <td>30.305957</td>\n",
       "      <td>9.192287</td>\n",
       "      <td>ADM001</td>\n",
       "      <td>2015-01-20 19:14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ADM001</td>\n",
       "      <td>Admiralty Inlet</td>\n",
       "      <td>48.029813</td>\n",
       "      <td>-122.617933</td>\n",
       "      <td>23.306700</td>\n",
       "      <td>0.791626</td>\n",
       "      <td>117.5</td>\n",
       "      <td>30.301120</td>\n",
       "      <td>9.193727</td>\n",
       "      <td>ADM001</td>\n",
       "      <td>2015-01-20 19:14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ADM001</td>\n",
       "      <td>Admiralty Inlet</td>\n",
       "      <td>48.029813</td>\n",
       "      <td>-122.617933</td>\n",
       "      <td>23.304600</td>\n",
       "      <td>0.791626</td>\n",
       "      <td>117.0</td>\n",
       "      <td>30.298496</td>\n",
       "      <td>9.194526</td>\n",
       "      <td>ADM001</td>\n",
       "      <td>2015-01-20 19:14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ADM001</td>\n",
       "      <td>Admiralty Inlet</td>\n",
       "      <td>48.029813</td>\n",
       "      <td>-122.617933</td>\n",
       "      <td>23.306299</td>\n",
       "      <td>0.817724</td>\n",
       "      <td>116.5</td>\n",
       "      <td>30.300392</td>\n",
       "      <td>9.193442</td>\n",
       "      <td>ADM001</td>\n",
       "      <td>2015-01-20 19:14:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Station            Basin        Lat         Lon      Sigma       Chl      Z  \\\n",
       "0  ADM001  Admiralty Inlet  48.029813 -122.617933  23.311800  0.809025  118.5   \n",
       "1  ADM001  Admiralty Inlet  48.029813 -122.617933  23.310699  0.829323  118.0   \n",
       "2  ADM001  Admiralty Inlet  48.029813 -122.617933  23.306700  0.791626  117.5   \n",
       "3  ADM001  Admiralty Inlet  48.029813 -122.617933  23.304600  0.791626  117.0   \n",
       "4  ADM001  Admiralty Inlet  48.029813 -122.617933  23.306299  0.817724  116.5   \n",
       "\n",
       "          SA        CT SiteCode               dtUTC  \n",
       "0  30.307274  9.191911   ADM001 2015-01-20 19:14:00  \n",
       "1  30.305957  9.192287   ADM001 2015-01-20 19:14:00  \n",
       "2  30.301120  9.193727   ADM001 2015-01-20 19:14:00  \n",
       "3  30.298496  9.194526   ADM001 2015-01-20 19:14:00  \n",
       "4  30.300392  9.193442   ADM001 2015-01-20 19:14:00  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=pd.read_csv('/data/eolson/results/MEOPAR/oldDBs/WADECTD.csv')\n",
    "# convert dtUTC back to datetime\n",
    "df2['dtUTC']=[dt.datetime.strptime(ii,'%Y-%m-%d %H:%M:%S') for ii in df2['dtUTC']]\n",
    "print(len(df2))\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ferry\n",
      "(Lat,Lon)= 46.453154999999995 -124.00960333333332  not matched to domain\n",
      "(Lat,Lon)= 46.463155 -123.94126833333334  not matched to domain\n",
      "(Lat,Lon)= 46.54537666666667 -123.98016166666666  not matched to domain\n",
      "(Lat,Lon)= 46.644 -123.993  not matched to domain\n",
      "(Lat,Lon)= 46.68676333333333 -123.9735  not matched to domain\n",
      "(Lat,Lon)= 46.70398666666666 -123.83738500000001  not matched to domain\n",
      "(Lat,Lon)= 46.93731333333334 -123.91322333333332  not matched to domain\n",
      "(Lat,Lon)= 46.953421666666664 -124.09295  not matched to domain\n",
      "(Lat,Lon)= 47.21342666666666 -123.07765  not matched to domain\n",
      "progress: 0.0%\n",
      "progress: 9.23395139247987%\n",
      "progress: 18.46790278495974%\n",
      "progress: 27.70185417743961%\n",
      "progress: 36.93580556991948%\n",
      "progress: 46.16975696239935%\n",
      "progress: 55.40370835487922%\n",
      "progress: 64.6376597473591%\n",
      "progress: 73.87161113983896%\n",
      "progress: 83.10556253231883%\n",
      "progress: 92.3395139247987%\n"
     ]
    }
   ],
   "source": [
    "# match model output to observations and return both in a dataframe\n",
    "# the model variables will have their original names prefixed by mod_, eg mod_vosaline\n",
    "# the observation file names are unchanged. \n",
    "data=et.matchData(data=df2,filemap=filemap, fdict=fdict, mod_start=start_date, mod_end=end_date, \n",
    "                  mod_nam_fmt=namfmt, mod_basedir=PATH, mod_flen=flen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Chl:N ratio used later in plots: grab from appropriate namelist\n",
    "mod_chl_N=et.getChlNRatio(basedir=PATH,nam_fmt=namfmt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot matched data locations\n",
    "fig, ax = plt.subplots(figsize = (6,6))\n",
    "viz_tools.set_aspect(ax, coords = 'map')\n",
    "ax.plot(data['Lon'], data['Lat'], 'ro',label='matched obs')\n",
    "grid = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathymetry_201702.nc')\n",
    "viz_tools.plot_coastline(ax, grid, isobath=.001,coords = 'map')\n",
    "ax.set_ylim(47, 49)\n",
    "ax.legend()\n",
    "ax.set_xlim(-124, -122);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dictionary of dataframe views by year\n",
    "datyr=dict()\n",
    "yy=np.array([ii.year for ii in data['dtUTC']])\n",
    "yys=np.unique(yy)\n",
    "for yr in yys:\n",
    "    datyr[yr]=data.loc[yy==yr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f4db15e41f0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArEAAABwCAYAAAAJ8SgVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAOuklEQVR4nO3df2yV133H8c/XGJaQiiRQI36EmB8xcYkbSIkgo8ugVSYyJctK1BSiNjBtKGzasiXpMtpJC3ayqawSlfpjUZmSpoVWCekmobQjZFlVsR+BQK1BS8BQwuqEHyYuNKR0DGzud38818N49vVzr++955573y8JgZ/r5/Ixxz58n3POcx5zdwEAAAAxqQsdAAAAAMgXRSwAAACiQxELAACA6FDEAgAAIDoUsQAAAIgORSwAAACiQxELAACA6FDEAgAAIDoUsQAAAIgORSwAAACiQxELAACA6NSHDgAAAIDL2tvbJ9bX1z8rqUW1MeCYkbS/t7d39fz5899NexJFLAAAQAWpr69/dtKkSR9qaGj4RV1dnYfOU2qZTMa6u7vndHV1PSvpvrTn1UJ1DwAAEJOWhoaG92uhgJWkuro6b2hoOKtk5Dn9eSXKAwAAgMLU1UoB2yf79eZVl1LEAgAA4ApHjhwZvXDhwtkzZ8685aabbrrl6aefnihJp06dGrVo0aKmxsbGlkWLFjV1d3ePkqSurq5RCxcunD127NjbVq5ceWP/91qwYMHN06dPb2lubp7T3Nw85/jx40VZzkoRCwAAgCuMHj1aGzZsOHb06NE39+zZc/C5556b2N7eftW6desmL1my5JednZ37lyxZ8ssnn3xykiSNHTvWn3rqqROtra3HBnu/TZs2He3o6DjQ0dFxYOrUqb3FyMiNXQAAALhCY2NjT2NjY48kXX/99ZlZs2adf/vtt8ds3779uh07dhySpDVr1pxevHjxzZKOjxs3LrN06dJzhw4d+rVyZWQkFgAAIHYnHpqmjvr5OvHQtGK/9aFDh8YcOHBg7OLFi8+dPn26vq+4bWxs7Dlz5kyqAdHVq1dPb25unvPEE09MzmQyRclFEQsAABC791+YKF3K/l48Z8+erbv//vtnrV+//p3x48cXVH1u2bLl6OHDhw/s3Lmz4/XXX//AM888M6EY2ShiAQAAYjfuwXelUdnfi+PChQt2zz33zHrggQfOrFq16j1JmjBhQm9nZ+doSers7Bw9fvz4Yde3zpgx4/+WJSxfvvzM7t27rylGPopYAACA2E3Z/I6ae9s1ZfM7xXi7TCajFStWNM6ePft/WltbT/UdX7p06XsbN26cIEkbN26ccPfdd7+X6316enp08uTJeikpirdt23ZtS0vL+WJk5MYuAAAAXOG11177wNatWyc0NTWdb25uniNJbW1tx9va2k4uW7ZsVmNj4wenTJlycevWrW/1nTN16tQPnzt3blRPT4+9+uqr123btu1wU1PTxbvuuqupp6fHMpmM3Xnnne8//vjj3cXIaO41tZcuAABARdu3b9/P5s6d+/PQOcpt3759H5w7d+70tJ/PcgIAAABEhyIWAAAA0aGIBQAAQHQoYgEAACpLJpPJWOgQ5ZT9evPah5YiFgAAoLLs7+7uvrZWCtlMJmPd3d3XStqfz3lssQUAAFBBent7V3d1dT3b1dXVotoYcMxI2t/b27s6n5PYYgsAAADRqYXqHgAAAFWGIhYAAADRoYgFAABAdChiAQAAEB2KWAAAAESHIhYAAADRoYgFAABAdChiAQAAEB2KWAAAAESHIhYAAADRqQ8d4AoddvkZuM1ueZ/TX9rzS22wfENly+dzBzsn3695qH+7Qt4r7Xvnet9S5hnu7xnJ91spvtfStOvAzylFtoHvWe6fq1z/DkO9ls85I/n5SSttu+T6/h/qnEo11Nc80u/r0P8GI82S9nsz7d9RzJ/PfNtmoNBtk49CftbK0feH7m9zZSjH/zcFYCQWAAAA0aGIBQAAQHTMPfeoOgAAAFBpGIkFAABAdChiAQAAEB2KWAAAAESHIhYAAADRoYgFAABAdChiAQAAEB2KWAAAAESHIhYAAADRoYgFAABAdChiAQAAEB2KWAAAAESHIhYAAADRoYgFAABAdChiAQAAEJ36fE8wMy9FEAAAAKA/d7ehXsu7iJW0xt3/fgR5UGZm9jBtFi/aL260X3WgHeNG+1WnQpYTPFz0FCg12ixutF/caL/qQDvGjfarQqyJBQAAQHQoYgEAABCdQopY1pTEhzaLG+0XN9qvOtCOcaP9qpC5s9kAAAAA4sJyAgAAAESHIhYAAADRoYitMmY2ysy+HToHCmdm40NnAACg0qV62IGZLZI0vf/nu/umEmXCCLj7JTNrMLMx7n4xdB4U5A0z2yvpeUmvOAvXozDcxYe7nylXFoycmc2U9GVJvy4pI2mnpMfc/WjQYEjFzPZJ2iJpi7u/FToPSmPYG7vMbLOkWZL2SrqUPezu/qeljYZCmdlGSR+R9LKkX/Udd/cvBQuF1MzMJN0l6fclLVDSEX/T3Q8HDYaczOy/JLmkwR6R6O4+s8yRMAJmtkvS30l6IXtohaRH3H1huFRIy8waJS3P/soo6Udfcve3gwZDUaUpYg9KmsNoUDzMbN1gx929rdxZMDJm9jFJ35Z0jaR9kj7n7jvDpgKqn5m9MbBgNbNd7n5HqEwojJk1SforSZ9291Gh86B40iwn2C9pkqSTJc6CIqFYjZuZTZD0GUkPSTol6RElo+rzJH1X0oxg4TAsM/vNwY67+7+WOwtG5Idm9jlJLyoZYV8u6Z/6lo2wPKTymdl0SZ9S0naXJP1F0EAoujQjsT9U8p/nbkkX+o67+30lTYaCmVmDkh/WWyRd1Xfc3T8eLBRSM7PDkjZLet7djw14ba27/22YZEjDzL7X78OrlCwJaefnLy7Z5SFDYXlIhTOzNySNVnLhv4W1zNUpTRG7eLDj7r6jJIkwYmb2z0rW//y5pD+UtEpSt7uvDRoMqZiZsXynepjZNElfdPcHQ2cBaoWZNbt7R+gcKC2e2FWFzKzd3eeb2Y/d/dbssR3uPugFCSoLI+nVJXuj3o/d/cOhsyA9Mxsr6XFJN7r7w9l1lTe7+/cDR0MKZnatpHWS+pb37JD0lLufDZcKxTbsmlgzu0PSVyV9SNIYSaMk/crdx5U4GwrXk/39pJndI+mEpBsC5kF+vqNkJP1e9RtJD5oIqZnZV5WsoZSSvbjnKbkpD3F5XlK7pEXZj48pmZqmiI3DN5Tc0/Op7McPKWnT+4MlQtGlWU7wIyVbi3xX0u2SVkpqcve/LH08FMLM7pX0b5KmKbkAGSepzd1fDhoMqTCSHjczW9Xvw15JP3P3/wiVB4Uxsx+5++1m9p/uflv22D53nxs6G4ZnZnvdfd5wxxC3VA87cPcjZjbK3S9Jet7MXi9xLoxAv+mus5I+FjILCsJIesTc/VuhM6AoLprZ1cqOqpvZLPW7uRkV77yZ/Ya7/7skmdlHJZ0PnAlFlqaI/W8zGyNpr5l9UclWW9eUNhYKYWZfyfU6D6iIxl9n13N9VpdH0h8LGwnDMbOf6PIygoEuSHpL0hfcnaUFcVgnabukaWb2HUkflfR7QRMhH38k6VvZvtQknRHtV3XSLCdolPSukq0qHpN0raRn3P1I6eMhH2Z2UckaoJeUjN5d8eQgRoiA0sn2lUOpl9QiqbVvahqVL7tn8x1K+tJd7v7zwJGQJzMbJ0nu/n7oLCg+dieoItkO9wElGzv3Krk56B/d/RdBgyEVRtKrn5m1ufugT9RDZchejLzXdxd79ql5n5DUKelr7n4xYDwMw8xW5nrd3TeVKwtKb8gilqmxuJnZVEkPKtkiZq27bw4cCcNgJB0IL7tJ/jJ3P2Fm8yT9i6QvSLpVUo+7rw6ZD7lldwf5f4cl/Y6kqe6e6l4gxCFXY947zHktkr4piamxCmNmH1FSwP6WpFeUbBODyjdZjKQDoV3t7ieyf/6MpG+4+wYzq5O0N1wspOHuj/T9ObtH86clrZW0S9LfhMqF0hiyiHX3zmHOfStbLKFCmFmbkouPg0qe9/15d+8NmwppuftpSV+X9PV+I+lvZh81y0g6UB79Z0A+LunzkuTumaQmQqUzs3olN3F9VtIbkj7p7oeChkJJsCa2iphZRtJRXd5GpK9xTcmzvm8NEgx5GTCS3i5pg7sfCJsKI2Fmr7j7b4fOgeGZ2ZeVzIqclHSfpNnu3mNmkyV9z91vDxoQOZnZH0v6M0k/kLQ+xYAcIkYRW0WGuTs6zeg6AhpkJH07I+nxyDEzZZK+7+6Ty5kHhclOQS9XUsi+5O7Hs8dvkzTR3V8NmQ+5ZQdz3lXylMP+BQ6DOVWIIhaoEIykx83MLil5Pvtgc853uPvVZY4E1BwGc2pLQXfpMTUGlMSM0AEwIgclrXH3nw58wczeCZAHqDkUqbVlyCJ2mKmxeSVJA9QwOt/otUqqG+K1R4Y4DgAoUK59YpkaAwAAQEXKtZyAqbEqwhIQoPTMrFnS70qaqmRN8wlJL7v7waDBUBT0o0BlyVXEtoqpsaiwBAQIx8zWKtka7UVJu7OHb5D0gpm96O7rg4VDavSj1YuLkOrD7gRVhCUg1YvOt/KZ2WFJt7h7z4DjYyS96e5NYZIhH/SjcWOru9qSc3cCpsaiwxKQiDECFL2MpCmSBt6gNzn7GuJAPxq3PRr6IuS68kZBqeXanYCpsfi0iiUgMaPzjdujkn5gZj+V1Ffs3CjpJkl/EioU8tYq+tGYcRFSQ3LtTsDUGFBGZrZf0rKhOl93nxYgFvJgZnWSFiiZvTJJxyTtcfdLQYMBNcLMPinpJ+5+aJDXPuHuW8ufCqWSazkBU2MRYglI1FrFCFDU3D0jaVfoHBgZ+tF4ufs/5HhtaxmjoAxyjcTeLelrkgadGnP37WVJiNQGLAE5lj18g6QVklgCAgDDoB+NHxchtSPn7gRMjcWFJSDxo/MFwqIfjRsXIbUl5+4ETI1FhyUgEeNmSqAi0I/G7Q80+EXIlyS9KYl+tIrkLGIRnUfF3dExo/MFwntU9KMx4yKkhlDEVhF3325ms8USkFjR+QKB0Y9G71FxEVIzeGIXUCG4mRIARo77eWoHRSxQQeh8AQBIhyIWAAAA0RlqY3UAAACgYlHEAgAAIDoUsQAAAIgORSwAAACiQxELAACA6PwvNT6qcdGK6JUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot matched data sampling times\n",
    "clist=('gold','aqua','plum','c','m','r','g','b','brown','gray')\n",
    "fig,axL=plt.subplots(1,1,figsize=(12,1))\n",
    "for ii, yr in enumerate(yys):\n",
    "    dshift=dt.datetime(yys[0],1,1)-dt.datetime(yr,1,1)\n",
    "    axL.plot(datyr[yr].dtUTC+dshift,np.zeros(np.shape(datyr[yr].dtUTC))+.1*ii,'.',\n",
    "             color=clist[ii],markersize=4,label=str(yr))\n",
    "axL.set_yticks(());\n",
    "yearsFmt = mdates.DateFormatter('%d %b')\n",
    "axL.xaxis.set_major_formatter(yearsFmt)\n",
    "axL.xaxis.set_ticks([dt.datetime(int(yys[0]),1,1), dt.datetime(int(yys[0]),3,1),dt.datetime(int(yys[0]),5,1),dt.datetime(int(yys[0]),7,1),\n",
    "                     dt.datetime(int(yys[0]),9,1),dt.datetime(int(yys[0]),11,1),dt.datetime(int(yys[0])+1,1,1)])\n",
    "for tick in axL.get_xticklabels():\n",
    "        tick.set_rotation(90)\n",
    "        tick.set_horizontalalignment('center')\n",
    "axL.set_ylim(-.1,.1*(len(datyr.keys())+1))\n",
    "axL.set_xlim(dt.datetime(yys[0],1,1),dt.datetime(yys[0],12,31))\n",
    "axL.legend()\n",
    "axL.set_frame_on(False)\n",
    "xmin, xmax = axL.get_xaxis().get_view_interval()\n",
    "ymin, ymax = axL.get_yaxis().get_view_interval()\n",
    "axL.add_artist(mpl.lines.Line2D((xmin, xmax), (ymin, ymin), color='black', linewidth=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SA depth<15:\n",
      "  N: 7910\n",
      "  bias: -0.5188559311472289\n",
      "  RMSE: 1.5541583492257305\n",
      "  WSS: 0.8354347128369622\n",
      "CT depth<15:\n",
      "  N: 7910\n",
      "  bias: 0.03851665034366292\n",
      "  RMSE: 0.8894992822008563\n",
      "  WSS: 0.9629385874655843\n"
     ]
    }
   ],
   "source": [
    "print('SA depth<15:') \n",
    "et.printstats(data.loc[data.Z<15,:],'SA','mod_vosaline')\n",
    "print('CT depth<15:')\n",
    "et.printstats(data.loc[data.Z<15,:],'CT','mod_votemper')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### mod vs. obs plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'SA (g/kg)')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm1=cmocean.cm.thermal\n",
    "bounds = np.array([0,5,10,35,100,200,250,300,450])\n",
    "norm = mpl.colors.BoundaryNorm(boundaries=bounds, ncolors=256)\n",
    "#pcm = ax[1].pcolormesh(X, Y, Z1, norm=norm, cmap='RdBu_r')\n",
    "fig,ax=plt.subplots(1,2,figsize=(8,3))\n",
    "args={'marker':'.','s':8,'norm':norm}\n",
    "ps=et.varvarScatter(ax[0],data,'CT','mod_votemper','Z',cm=cm1,args=args)\n",
    "ps=et.varvarScatter(ax[1],data,'SA','mod_vosaline','Z',cm=cm1,args=args)\n",
    "\n",
    "cb=fig.colorbar(ps,ax=ax[1],boundaries=np.linspace(0,450,46))\n",
    "cb.set_label('Depth (m)')\n",
    "for iax in ax:\n",
    "    iax.set_ylabel('Model')\n",
    "    iax.set_xlabel('Obs')\n",
    "ax[0].set_title('CT (deg C)')\n",
    "ax[1].set_title('SA (g/kg)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (py39)",
   "language": "python",
   "name": "py39"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}