{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Once deleted, variables cannot be recovered. Proceed (y/[n])? y\n"
     ]
    }
   ],
   "source": [
    "%reset\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import xarray as xr\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['image.cmap'] = 'jet'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nowcast_tracer_path = \"/results/SalishSea/nowcast-green/12apr16/SalishSea_1h_20160412_20160412_ptrc_T.nc\"\n",
    "diat_iopt_84_tracer_path = \"/data/jpetrie/MEOPAR/SalishSea/results/apr12_double_iopt_diat/SalishSea_1h_20160412_20160412_ptrc_T.nc\"\n",
    "diat_iopt_55_tracer_path = \"/data/jpetrie/MEOPAR/SalishSea/results/april12_55_iopt_diat/SalishSea_1h_20160412_20160412_ptrc_T.nc\"\n",
    "\n",
    "n_grid_t = xr.open_dataset(nowcast_tracer_path)\n",
    "#m_grid_t = xr.open_dataset(diat_iopt_84_tracer_path)\n",
    "m_grid_t = xr.open_dataset(diat_iopt_55_tracer_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'votemper' (time_counter: 24, deptht: 40, y: 898, x: 398)>\n",
       "[343107840 values with dtype=float64]\n",
       "Coordinates:\n",
       "  * deptht         (deptht) float32 0.5 1.5 2.50001 3.50003 4.50007 5.50015 ...\n",
       "    nav_lat        (y, x) float32 46.8597 46.8615 46.8634 46.8653 46.8672 ...\n",
       "    nav_lon        (y, x) float32 -123.429 -123.424 -123.419 -123.413 ...\n",
       "    time_centered  (time_counter) datetime64[ns] 2016-04-12T00:30:00 ...\n",
       "  * time_counter   (time_counter) datetime64[ns] 2016-04-12T00:30:00 ...\n",
       "  * x              (x) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 ...\n",
       "  * y              (y) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 ...\n",
       "Attributes:\n",
       "    standard_name: sea_water_potential_temperature\n",
       "    long_name: temperature\n",
       "    units: degC\n",
       "    online_operation: average\n",
       "    interval_operation: 40 s\n",
       "    interval_write: 1 h\n",
       "    cell_methods: time: mean (interval: 40 s)\n",
       "    cell_measures: area: area"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_grid_t.votemper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "m_grid_t.PHY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "NO3_subset_20m = ((m_grid_t[\"NO3\"] - n_grid_t[\"NO3\"]).where(m_grid_t[\"O2\"] != 0).isel(deptht = 18, time_counter = 23))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize = (15,15))\n",
    "NO3_subset_20m.plot(ax = ax, cmap = \"jet\", robust = True)\n",
    "plt.title(\"NO3(Iopt_diat=55) - NO3(Iopt_diat=42) After 1 Day at Depth=20m \", size = 35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "NO3_subset_0m = ((m_grid_t[\"NO3\"] - n_grid_t[\"NO3\"]).where(m_grid_t[\"O2\"] != 0).isel(deptht = 0, time_counter = 23))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize = (15,15))\n",
    "NO3_subset_0m.plot(ax = ax, cmap = \"jet\", robust = True)\n",
    "plt.title(\"NO3(Iopt_diat=55) - NO3(Iopt_diat=42) After 1 Day at Depth=0m \", size = 35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "PHY2_subset_20m = ((m_grid_t[\"PHY2\"] - n_grid_t[\"PHY2\"]).where(m_grid_t[\"O2\"] != 0).isel(deptht = 19, time_counter = 23))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize = (15,15))\n",
    "PHY2_subset_20m.plot(ax = ax, cmap = \"jet\", robust = True)\n",
    "plt.title(\"PHY2(Iopt_diat=55) - PHY2(Iopt_diat=42) After 1 Day at Depth=20m \", size = 35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "PHY2_subset_0m = ((m_grid_t[\"PHY2\"] - n_grid_t[\"PHY2\"]).where(m_grid_t[\"O2\"] != 0).isel(deptht = 0, time_counter = 23))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize = (15,15))\n",
    "PHY2_subset_0m.plot(ax = ax, cmap = \"jet\", robust = True)\n",
    "plt.title(\"PHY2(Iopt_diat=55) - PHY2(Iopt_diat=42) After 1 Day at Depth=0m \", size = 35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Have to keep this and the next cell apart or it fails with memory error. Something to do with \n",
    "# Jupyter notebook caching\n",
    "tracer = \"PHY2\"\n",
    "mod_tracer_depth_time =  m_grid_t[tracer].where(m_grid_t[tracer] != 0).mean(dim = [\"x\",\"y\"]).to_dataframe(name = \"MEAN_\" + tracer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "now_tracer_depth_time =  n_grid_t[tracer].where(n_grid_t[tracer] != 0).mean(dim = [\"x\",\"y\"]).to_dataframe(name = \"MEAN_\" + tracer)\n",
    "var_name = \"MEAN_\" + tracer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mod_tracer_depth_time[\"DATA_SOURCE\"] = \"IOPT DIAT = 55\"\n",
    "now_tracer_depth_time[\"DATA_SOURCE\"] = \"IOPT DIAT = 42\"\n",
    "combined = pd.concat([mod_tracer_depth_time, now_tracer_depth_time])\n",
    "combined.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "RRuntimeError",
     "evalue": "Error in .External2(C_X11, paste(\"png::\", filename, sep = \"\"), g$width,  : \n  unable to start device PNG\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRRuntimeError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-2765bc9a58c7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'R'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'require(ggplot2)\\nrequire(data.table)\\nlibrary(repr)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m/home/jpetrie/anaconda3/lib/python3.5/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[1;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[0;32m   2118\u001b[0m             \u001b[0mmagic_arg_s\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvar_expand\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstack_depth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2119\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2120\u001b[1;33m                 \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2121\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2122\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<decorator-gen-128>\u001b[0m in \u001b[0;36mR\u001b[1;34m(self, line, cell, local_ns)\u001b[0m\n",
      "\u001b[1;32m/home/jpetrie/anaconda3/lib/python3.5/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(f, *a, **k)\u001b[0m\n\u001b[0;32m    191\u001b[0m     \u001b[1;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    192\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 193\u001b[1;33m         \u001b[0mcall\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    194\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    195\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/jpetrie/anaconda3/lib/python3.5/site-packages/rpy2/ipython/rmagic.py\u001b[0m in \u001b[0;36mR\u001b[1;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[0;32m    663\u001b[0m                     \u001b[0mro\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0massign\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlocalconverter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpy2ri\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    664\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 665\u001b[1;33m         \u001b[0mtmpd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetup_graphics\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    666\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    667\u001b[0m         \u001b[0mtext_output\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/jpetrie/anaconda3/lib/python3.5/site-packages/rpy2/ipython/rmagic.py\u001b[0m in \u001b[0;36msetup_graphics\u001b[1;34m(self, args)\u001b[0m\n\u001b[0;32m    413\u001b[0m                 \u001b[1;31m# Note: that %% is to pass into R for interpolation there\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    414\u001b[0m                 ro.r.png(\"%s/Rplots%%03d.png\" % tmpd_fix_slashes,\n\u001b[1;32m--> 415\u001b[1;33m                         **argdict)\n\u001b[0m\u001b[0;32m    416\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdevice\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    417\u001b[0m                 self.cairo.CairoSVG(\"%s/Rplot.svg\" % tmpd_fix_slashes,\n",
      "\u001b[1;32m/home/jpetrie/anaconda3/lib/python3.5/site-packages/rpy2/robjects/functions.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    176\u001b[0m                 \u001b[0mv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    177\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mr_k\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 178\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSignatureTranslatedFunction\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    179\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    180\u001b[0m \u001b[0mpattern_link\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mre\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mr'\\\\link\\{(.+?)\\}'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/jpetrie/anaconda3/lib/python3.5/site-packages/rpy2/robjects/functions.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    104\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    105\u001b[0m             \u001b[0mnew_kwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconversion\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpy2ri\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 106\u001b[1;33m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFunction\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mnew_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mnew_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    107\u001b[0m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconversion\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mri2ro\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mres\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    108\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRRuntimeError\u001b[0m: Error in .External2(C_X11, paste(\"png::\", filename, sep = \"\"), g$width,  : \n  unable to start device PNG\n"
     ]
    }
   ],
   "source": [
    "%%R \n",
    "require(ggplot2)\n",
    "require(data.table)\n",
    "library(repr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3ZmKnClYCuTsiVmYPCYigjwQpu+8i/O0eO1WwEsjdEbEye0hABH0kSN1230cgdxgG5O6I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\nXgAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i combined,var_name -h 1200 -w 1500 -r 400\n",
    "combined = data.table(combined)\n",
    "ggplot(combined[deptht < 25 & time_counter == max(time_counter) ], aes_string(y = \"deptht\", x = var_name, colour = \"DATA_SOURCE\")) + \n",
    "    geom_path() + ylim(25,0) +\n",
    "    facet_wrap(~time_counter) +\n",
    "    ylab(\"Depth (m)\") + \n",
    "    xlab(\"Phytoplankton (mmolN/m^3)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Have to keep this and the next cell apart or it fails with memory error. Something to do with \n",
    "# Jupyter notebook caching\n",
    "tracer = \"NO3\"\n",
    "mod_tracer_depth_time =  m_grid_t[tracer].where(m_grid_t[\"O2\"] != 0).mean(dim = [\"x\",\"y\"]).to_dataframe(name = \"MEAN_\" + tracer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "now_tracer_depth_time =  n_grid_t[tracer].where(m_grid_t[\"O2\"] != 0).mean(dim = [\"x\",\"y\"]).to_dataframe(name = \"MEAN_\" + tracer)\n",
    "var_name = \"MEAN_\" + tracer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mod_tracer_depth_time[\"DATA_SOURCE\"] = \"IOPT_DIAT_55\"\n",
    "now_tracer_depth_time[\"DATA_SOURCE\"] = \"IOPT_DIAT_42\"\n",
    "combined = pd.concat([mod_tracer_depth_time, now_tracer_depth_time])\n",
    "combined.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f24d81a0048>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EFQwWrwvctLXy3BaWpPPxBzDs1D7k/XRcUtq7bcpdrMlbG/tCw6hAKlXCKsnA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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f24f8057cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fg = sns.FacetGrid(data=combined, hue=\"DATA_SOURCE\")\n",
    "fg.map(plt.plot,\"deptht\", var_name).add_legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "RRuntimeError",
     "evalue": "Error in SYS[[length(SYS) - 3L]][[1L]] == \"knit_print.default\" : \n  comparison (1) is possible only for atomic and list types\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRRuntimeError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-29-f8881ecb569a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'R'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'wide_combined[, DIF := IOPT_DIAT_55 - IOPT_DIAT_42]'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m/home/jpetrie/anaconda3/lib/python3.5/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[1;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[0;32m   2118\u001b[0m             \u001b[0mmagic_arg_s\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvar_expand\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstack_depth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2119\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2120\u001b[1;33m                 \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2121\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2122\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<decorator-gen-128>\u001b[0m in \u001b[0;36mR\u001b[1;34m(self, line, cell, local_ns)\u001b[0m\n",
      "\u001b[1;32m/home/jpetrie/anaconda3/lib/python3.5/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(f, *a, **k)\u001b[0m\n\u001b[0;32m    191\u001b[0m     \u001b[1;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    192\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 193\u001b[1;33m         \u001b[0mcall\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    194\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    195\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/jpetrie/anaconda3/lib/python3.5/site-packages/rpy2/ipython/rmagic.py\u001b[0m in \u001b[0;36mR\u001b[1;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[0;32m    680\u001b[0m                     \u001b[0mold_writeconsole_regular\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mri\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_writeconsole_regular\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    681\u001b[0m                     \u001b[0mri\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_writeconsole_regular\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite_console_regular\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 682\u001b[1;33m                     \u001b[0mro\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    683\u001b[0m                     \u001b[0mtext_output\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    684\u001b[0m                     \u001b[0mri\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_writeconsole_regular\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mold_writeconsole_regular\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/jpetrie/anaconda3/lib/python3.5/site-packages/rpy2/robjects/functions.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    176\u001b[0m                 \u001b[0mv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    177\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mr_k\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 178\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSignatureTranslatedFunction\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    179\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    180\u001b[0m \u001b[0mpattern_link\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mre\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mr'\\\\link\\{(.+?)\\}'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/jpetrie/anaconda3/lib/python3.5/site-packages/rpy2/robjects/functions.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    104\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    105\u001b[0m             \u001b[0mnew_kwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconversion\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpy2ri\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 106\u001b[1;33m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFunction\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mnew_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mnew_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    107\u001b[0m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconversion\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mri2ro\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mres\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    108\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRRuntimeError\u001b[0m: Error in SYS[[length(SYS) - 3L]][[1L]] == \"knit_print.default\" : \n  comparison (1) is possible only for atomic and list types\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "ggplot(combined, aes(x = ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: Cell magic `%%R` not found.\n"
     ]
    }
   ],
   "source": [
    "%%R -i combined,var_name -h 4200 -w 2400 -r 400\n",
    "combined = data.table(combined)\n",
    "ggplot(combined[deptht < 25,], aes_string(y = \"deptht\", x = var_name)) + \n",
    "    geom_path() + ylim(25,0) +\n",
    "    facet_wrap(~time_counter, ncol = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_counter</th>\n",
       "      <th>deptht</th>\n",
       "      <th>time_centered</th>\n",
       "      <th>MEAN_NO3</th>\n",
       "      <th>DATA_SOURCE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
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       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>5.214778</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>1.500003</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>5.452885</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
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       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>2.500011</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>5.486822</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>3.500031</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>5.594474</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>4.500071</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>5.920553</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>5.500151</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>6.151240</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>6.500310</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>6.447138</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>7.500623</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>6.792811</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>8.501236</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>7.200050</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>9.502433</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>7.727039</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>10.504766</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>8.421840</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>11.509312</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>9.241469</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>12.518167</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>10.177139</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>13.535412</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>11.197317</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>14.568982</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>12.282061</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>15.634288</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>13.473866</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>16.761173</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>14.781768</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>18.007135</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>16.144154</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>19.481785</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>17.475997</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>21.389978</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>18.891814</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>24.100256</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>20.516083</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>28.229916</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>22.333914</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>34.685757</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>24.324998</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>44.517723</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>26.184282</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>58.484333</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>27.685490</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>76.585587</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>28.915714</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>98.062958</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>29.956416</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>121.866516</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>30.934335</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>147.089462</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>31.828866</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>173.114487</td>\n",
       "      <td>2016-04-12 00:30:00</td>\n",
       "      <td>32.697370</td>\n",
       "      <td>IOPT_DIAT_55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>10.504766</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>8.792402</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>11.509312</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>9.443269</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>12.518167</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>10.178115</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>13.535412</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>10.986300</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1894</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>14.568982</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>11.851626</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1895</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>15.634288</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>12.771132</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1896</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>16.761173</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>13.818082</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1897</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>18.007135</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>15.014769</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1898</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>19.481785</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>16.283267</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1899</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>21.389978</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>17.844842</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>24.100256</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>19.783106</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>28.229916</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>21.978578</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1902</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>34.685757</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>24.191177</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1903</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>44.517723</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>26.089551</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1904</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>58.484333</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>27.568494</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1905</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>76.585587</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>28.797396</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1906</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>98.062958</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>29.944437</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1907</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>121.866516</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>31.001501</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1908</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>147.089462</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>31.909937</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1909</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>173.114487</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>32.749400</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1910</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>199.573044</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>33.508556</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1911</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>226.260300</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>34.428200</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1912</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>253.066635</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>35.240501</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1913</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>279.934540</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>36.002680</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1914</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>306.834198</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>36.545321</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1915</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>333.750183</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>36.801954</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>360.674530</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>37.012192</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1917</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>387.603210</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>37.074555</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1918</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>414.534088</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>37.063028</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1919</th>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>441.466095</td>\n",
       "      <td>2016-04-12 23:30:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IOPT_DIAT_42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1920 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            time_counter      deptht       time_centered   MEAN_NO3  \\\n",
       "0    2016-04-12 00:30:00    0.500000 2016-04-12 00:30:00   5.214778   \n",
       "1    2016-04-12 00:30:00    1.500003 2016-04-12 00:30:00   5.452885   \n",
       "2    2016-04-12 00:30:00    2.500011 2016-04-12 00:30:00   5.486822   \n",
       "3    2016-04-12 00:30:00    3.500031 2016-04-12 00:30:00   5.594474   \n",
       "4    2016-04-12 00:30:00    4.500071 2016-04-12 00:30:00   5.920553   \n",
       "5    2016-04-12 00:30:00    5.500151 2016-04-12 00:30:00   6.151240   \n",
       "6    2016-04-12 00:30:00    6.500310 2016-04-12 00:30:00   6.447138   \n",
       "7    2016-04-12 00:30:00    7.500623 2016-04-12 00:30:00   6.792811   \n",
       "8    2016-04-12 00:30:00    8.501236 2016-04-12 00:30:00   7.200050   \n",
       "9    2016-04-12 00:30:00    9.502433 2016-04-12 00:30:00   7.727039   \n",
       "10   2016-04-12 00:30:00   10.504766 2016-04-12 00:30:00   8.421840   \n",
       "11   2016-04-12 00:30:00   11.509312 2016-04-12 00:30:00   9.241469   \n",
       "12   2016-04-12 00:30:00   12.518167 2016-04-12 00:30:00  10.177139   \n",
       "13   2016-04-12 00:30:00   13.535412 2016-04-12 00:30:00  11.197317   \n",
       "14   2016-04-12 00:30:00   14.568982 2016-04-12 00:30:00  12.282061   \n",
       "15   2016-04-12 00:30:00   15.634288 2016-04-12 00:30:00  13.473866   \n",
       "16   2016-04-12 00:30:00   16.761173 2016-04-12 00:30:00  14.781768   \n",
       "17   2016-04-12 00:30:00   18.007135 2016-04-12 00:30:00  16.144154   \n",
       "18   2016-04-12 00:30:00   19.481785 2016-04-12 00:30:00  17.475997   \n",
       "19   2016-04-12 00:30:00   21.389978 2016-04-12 00:30:00  18.891814   \n",
       "20   2016-04-12 00:30:00   24.100256 2016-04-12 00:30:00  20.516083   \n",
       "21   2016-04-12 00:30:00   28.229916 2016-04-12 00:30:00  22.333914   \n",
       "22   2016-04-12 00:30:00   34.685757 2016-04-12 00:30:00  24.324998   \n",
       "23   2016-04-12 00:30:00   44.517723 2016-04-12 00:30:00  26.184282   \n",
       "24   2016-04-12 00:30:00   58.484333 2016-04-12 00:30:00  27.685490   \n",
       "25   2016-04-12 00:30:00   76.585587 2016-04-12 00:30:00  28.915714   \n",
       "26   2016-04-12 00:30:00   98.062958 2016-04-12 00:30:00  29.956416   \n",
       "27   2016-04-12 00:30:00  121.866516 2016-04-12 00:30:00  30.934335   \n",
       "28   2016-04-12 00:30:00  147.089462 2016-04-12 00:30:00  31.828866   \n",
       "29   2016-04-12 00:30:00  173.114487 2016-04-12 00:30:00  32.697370   \n",
       "...                  ...         ...                 ...        ...   \n",
       "1890 2016-04-12 23:30:00   10.504766 2016-04-12 23:30:00   8.792402   \n",
       "1891 2016-04-12 23:30:00   11.509312 2016-04-12 23:30:00   9.443269   \n",
       "1892 2016-04-12 23:30:00   12.518167 2016-04-12 23:30:00  10.178115   \n",
       "1893 2016-04-12 23:30:00   13.535412 2016-04-12 23:30:00  10.986300   \n",
       "1894 2016-04-12 23:30:00   14.568982 2016-04-12 23:30:00  11.851626   \n",
       "1895 2016-04-12 23:30:00   15.634288 2016-04-12 23:30:00  12.771132   \n",
       "1896 2016-04-12 23:30:00   16.761173 2016-04-12 23:30:00  13.818082   \n",
       "1897 2016-04-12 23:30:00   18.007135 2016-04-12 23:30:00  15.014769   \n",
       "1898 2016-04-12 23:30:00   19.481785 2016-04-12 23:30:00  16.283267   \n",
       "1899 2016-04-12 23:30:00   21.389978 2016-04-12 23:30:00  17.844842   \n",
       "1900 2016-04-12 23:30:00   24.100256 2016-04-12 23:30:00  19.783106   \n",
       "1901 2016-04-12 23:30:00   28.229916 2016-04-12 23:30:00  21.978578   \n",
       "1902 2016-04-12 23:30:00   34.685757 2016-04-12 23:30:00  24.191177   \n",
       "1903 2016-04-12 23:30:00   44.517723 2016-04-12 23:30:00  26.089551   \n",
       "1904 2016-04-12 23:30:00   58.484333 2016-04-12 23:30:00  27.568494   \n",
       "1905 2016-04-12 23:30:00   76.585587 2016-04-12 23:30:00  28.797396   \n",
       "1906 2016-04-12 23:30:00   98.062958 2016-04-12 23:30:00  29.944437   \n",
       "1907 2016-04-12 23:30:00  121.866516 2016-04-12 23:30:00  31.001501   \n",
       "1908 2016-04-12 23:30:00  147.089462 2016-04-12 23:30:00  31.909937   \n",
       "1909 2016-04-12 23:30:00  173.114487 2016-04-12 23:30:00  32.749400   \n",
       "1910 2016-04-12 23:30:00  199.573044 2016-04-12 23:30:00  33.508556   \n",
       "1911 2016-04-12 23:30:00  226.260300 2016-04-12 23:30:00  34.428200   \n",
       "1912 2016-04-12 23:30:00  253.066635 2016-04-12 23:30:00  35.240501   \n",
       "1913 2016-04-12 23:30:00  279.934540 2016-04-12 23:30:00  36.002680   \n",
       "1914 2016-04-12 23:30:00  306.834198 2016-04-12 23:30:00  36.545321   \n",
       "1915 2016-04-12 23:30:00  333.750183 2016-04-12 23:30:00  36.801954   \n",
       "1916 2016-04-12 23:30:00  360.674530 2016-04-12 23:30:00  37.012192   \n",
       "1917 2016-04-12 23:30:00  387.603210 2016-04-12 23:30:00  37.074555   \n",
       "1918 2016-04-12 23:30:00  414.534088 2016-04-12 23:30:00  37.063028   \n",
       "1919 2016-04-12 23:30:00  441.466095 2016-04-12 23:30:00        NaN   \n",
       "\n",
       "       DATA_SOURCE  \n",
       "0     IOPT_DIAT_55  \n",
       "1     IOPT_DIAT_55  \n",
       "2     IOPT_DIAT_55  \n",
       "3     IOPT_DIAT_55  \n",
       "4     IOPT_DIAT_55  \n",
       "5     IOPT_DIAT_55  \n",
       "6     IOPT_DIAT_55  \n",
       "7     IOPT_DIAT_55  \n",
       "8     IOPT_DIAT_55  \n",
       "9     IOPT_DIAT_55  \n",
       "10    IOPT_DIAT_55  \n",
       "11    IOPT_DIAT_55  \n",
       "12    IOPT_DIAT_55  \n",
       "13    IOPT_DIAT_55  \n",
       "14    IOPT_DIAT_55  \n",
       "15    IOPT_DIAT_55  \n",
       "16    IOPT_DIAT_55  \n",
       "17    IOPT_DIAT_55  \n",
       "18    IOPT_DIAT_55  \n",
       "19    IOPT_DIAT_55  \n",
       "20    IOPT_DIAT_55  \n",
       "21    IOPT_DIAT_55  \n",
       "22    IOPT_DIAT_55  \n",
       "23    IOPT_DIAT_55  \n",
       "24    IOPT_DIAT_55  \n",
       "25    IOPT_DIAT_55  \n",
       "26    IOPT_DIAT_55  \n",
       "27    IOPT_DIAT_55  \n",
       "28    IOPT_DIAT_55  \n",
       "29    IOPT_DIAT_55  \n",
       "...            ...  \n",
       "1890  IOPT_DIAT_42  \n",
       "1891  IOPT_DIAT_42  \n",
       "1892  IOPT_DIAT_42  \n",
       "1893  IOPT_DIAT_42  \n",
       "1894  IOPT_DIAT_42  \n",
       "1895  IOPT_DIAT_42  \n",
       "1896  IOPT_DIAT_42  \n",
       "1897  IOPT_DIAT_42  \n",
       "1898  IOPT_DIAT_42  \n",
       "1899  IOPT_DIAT_42  \n",
       "1900  IOPT_DIAT_42  \n",
       "1901  IOPT_DIAT_42  \n",
       "1902  IOPT_DIAT_42  \n",
       "1903  IOPT_DIAT_42  \n",
       "1904  IOPT_DIAT_42  \n",
       "1905  IOPT_DIAT_42  \n",
       "1906  IOPT_DIAT_42  \n",
       "1907  IOPT_DIAT_42  \n",
       "1908  IOPT_DIAT_42  \n",
       "1909  IOPT_DIAT_42  \n",
       "1910  IOPT_DIAT_42  \n",
       "1911  IOPT_DIAT_42  \n",
       "1912  IOPT_DIAT_42  \n",
       "1913  IOPT_DIAT_42  \n",
       "1914  IOPT_DIAT_42  \n",
       "1915  IOPT_DIAT_42  \n",
       "1916  IOPT_DIAT_42  \n",
       "1917  IOPT_DIAT_42  \n",
       "1918  IOPT_DIAT_42  \n",
       "1919  IOPT_DIAT_42  \n",
       "\n",
       "[1920 rows x 5 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  }
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