{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DFO Si Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import pandas as pd\n",
    "import netCDF4 as nc\n",
    "import datetime as dt\n",
    "from salishsea_tools import evaltools as et, viz_tools\n",
    "import gsw\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib as mpl\n",
    "import matplotlib.dates as mdates\n",
    "import cmocean as cmo\n",
    "import scipy.interpolate as sinterp\n",
    "import pickle\n",
    "import cmocean\n",
    "\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "mpl.rc('legend', fontsize=12)\n",
    "mpl.rc('axes', titlesize=12)\n",
    "mpl.rc('axes', labelsize=12)\n",
    "mpl.rc('figure', titlesize=12)\n",
    "mpl.rc('font', size=12)\n",
    "mpl.rc('text', usetex=True)\n",
    "mpl.rc('text.latex', preamble = ','.join(r'''\n",
    " \\usepackage{txfonts}\n",
    " \\usepackage{lmodern}\n",
    " '''.split()))\n",
    "mpl.rc('font', family='sans-serif', weight='normal', style='normal')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Ammonium</th>\n",
       "      <th>Ammonium_units</th>\n",
       "      <th>Chlorophyll_Extracted</th>\n",
       "      <th>Chlorophyll_Extracted_units</th>\n",
       "      <th>N</th>\n",
       "      <th>Si</th>\n",
       "      <th>Silicate_units</th>\n",
       "      <th>AbsSal</th>\n",
       "      <th>ConsT</th>\n",
       "      <th>Z</th>\n",
       "      <th>dtUTC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1982.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.35</td>\n",
       "      <td>49.25</td>\n",
       "      <td>-123.943</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>5.28</td>\n",
       "      <td>mg/m^3</td>\n",
       "      <td>6.6</td>\n",
       "      <td>13.6</td>\n",
       "      <td>umol/L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1982-07-16 01:21:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1982.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.35</td>\n",
       "      <td>49.25</td>\n",
       "      <td>-123.943</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0.61</td>\n",
       "      <td>mg/m^3</td>\n",
       "      <td>21.2</td>\n",
       "      <td>45.0</td>\n",
       "      <td>umol/L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.5</td>\n",
       "      <td>1982-07-16 01:21:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.35</td>\n",
       "      <td>49.25</td>\n",
       "      <td>-123.943</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mg/m^3</td>\n",
       "      <td>23.5</td>\n",
       "      <td>47.4</td>\n",
       "      <td>umol/L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.5</td>\n",
       "      <td>1982-07-16 01:21:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1982.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.35</td>\n",
       "      <td>49.25</td>\n",
       "      <td>-123.943</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mg/m^3</td>\n",
       "      <td>28.0</td>\n",
       "      <td>50.2</td>\n",
       "      <td>umol/L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.3</td>\n",
       "      <td>1982-07-16 01:21:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1982.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.35</td>\n",
       "      <td>49.25</td>\n",
       "      <td>-123.943</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mg/m^3</td>\n",
       "      <td>26.5</td>\n",
       "      <td>49.1</td>\n",
       "      <td>umol/L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.4</td>\n",
       "      <td>1982-07-16 01:21:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  Month   Day  Hour    Lat      Lon  Pressure  Depth  Ammonium  \\\n",
       "0  1982.0    7.0  16.0  1.35  49.25 -123.943       NaN   12.0       NaN   \n",
       "1  1982.0    7.0  16.0  1.35  49.25 -123.943       NaN   21.5       NaN   \n",
       "2  1982.0    7.0  16.0  1.35  49.25 -123.943       NaN   30.5       NaN   \n",
       "3  1982.0    7.0  16.0  1.35  49.25 -123.943       NaN   52.3       NaN   \n",
       "4  1982.0    7.0  16.0  1.35  49.25 -123.943       NaN   75.4       NaN   \n",
       "\n",
       "  Ammonium_units  Chlorophyll_Extracted Chlorophyll_Extracted_units     N  \\\n",
       "0           None                   5.28                      mg/m^3   6.6   \n",
       "1           None                   0.61                      mg/m^3  21.2   \n",
       "2           None                    NaN                      mg/m^3  23.5   \n",
       "3           None                    NaN                      mg/m^3  28.0   \n",
       "4           None                    NaN                      mg/m^3  26.5   \n",
       "\n",
       "     Si Silicate_units  AbsSal  ConsT     Z               dtUTC  \n",
       "0  13.6         umol/L     NaN    NaN  12.0 1982-07-16 01:21:00  \n",
       "1  45.0         umol/L     NaN    NaN  21.5 1982-07-16 01:21:00  \n",
       "2  47.4         umol/L     NaN    NaN  30.5 1982-07-16 01:21:00  \n",
       "3  50.2         umol/L     NaN    NaN  52.3 1982-07-16 01:21:00  \n",
       "4  49.1         umol/L     NaN    NaN  75.4 1982-07-16 01:21:00  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=et.loadDFO()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with nc.Dataset('/ocean/eolson/MEOPAR/NEMO-forcing/grid/mesh_mask201702_noLPE.nc') as mesh:\n",
    "    navlon=mesh.variables['nav_lon'][:,:]\n",
    "    navlat=mesh.variables['nav_lat'][:,:]\n",
    "    tmask=mesh.variables['tmask'][:,:,:,:]\n",
    "    gdept=mesh.variables['gdept_1d'][0,:]\n",
    "    e3t0=mesh.variables['e3t_0'][0,:,:,:]\n",
    "bathy=np.sum(e3t0,0)\n",
    "with nc.Dataset('/results/SalishSea/hindcast.201812/01jan16/SalishSea_1h_20160101_20160101_ptrc_T.nc') as ftemp:\n",
    "        bounds=np.copy(ftemp.variables['deptht_bounds'][:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "135"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "355-220"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# drop Saanich Inlet\n",
    "tmask2=np.copy(tmask[0,0,:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CtbCtYnV6v73PP3+uvYuPtDTtl3PfgPy2zs2izZXBfgfSYnabDuz7+nCOqgI+Defocubz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcb1a4c550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(2,3,figsize=(16,24))\n",
    "#kk=30 #36,34,32,30\n",
    "simin=40\n",
    "simax=80\n",
    "for mon in (1,):\n",
    "    iax=ax[mon-1,0]\n",
    "    kk=0\n",
    "    iax.contourf(tmask[0,kk,:,:],levels=(0,.5,1.5),lw=.5,colors=('darkgray','k','k'))\n",
    "    viz_tools.set_aspect(iax,coords='grid')\n",
    "    iax.set_title('Depth='+str(gdept[kk]))\n",
    "    iax.set_ylabel('Month='+str(mon))\n",
    "    iax.plot((186,186),(220,320),'r-')\n",
    "    iax.plot((220,355),(355,355),'c-')\n",
    "    iax.plot((130,255),(585,585),'g-')\n",
    "    \n",
    "    iax=ax[mon-1,1]\n",
    "    kk=27\n",
    "    iax.contourf(tmask[0,kk,:,:],levels=(0,.5,1.5),lw=.5,colors=('darkgray','k','k'))\n",
    "    viz_tools.set_aspect(iax,coords='grid')\n",
    "    iax.set_title('Depth='+str(gdept[kk]))\n",
    "    iax.plot((186,186),(220,320),'r-')\n",
    "    iax.plot((220,355),(355,355),'c-')\n",
    "    iax.plot((130,255),(585,585),'g-')\n",
    "    \n",
    "    iax=ax[mon-1,2]\n",
    "    kk=28#33\n",
    "    iax.contourf(tmask[0,kk,:,:],levels=(0,.5,1.5),lw=.5,colors=('darkgray','k','k'))\n",
    "    viz_tools.set_aspect(iax,coords='grid')\n",
    "    iax.set_title('Depth='+str(gdept[kk]))\n",
    "    iax.plot((186,186),(220,320),'r-')\n",
    "    iax.plot((220,355),(355,355),'c-')\n",
    "    iax.plot((130,255),(585,585),'g-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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