{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the 2016 renewal in nowcast-green, nowcast and obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import netCDF4 as nc\n",
    "import datetime\n",
    "from dateutil import tz\n",
    "import os\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "\n",
    "from salishsea_tools import (\n",
    "    geo_tools,\n",
    "    places,\n",
    "    psu_tools,\n",
    "    teos_tools,\n",
    "    data_tools,\n",
    "    tidetools,\n",
    ")\n",
    "\n",
    "from nowcast import analyze\n",
    "from nowcast.figures import shared\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set_context('talk')\n",
    "sns.set_style('darkgrid')\n",
    "sns.set_color_codes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "runs={}\n",
    "t_o=datetime.datetime(2016,1,1); t_f = datetime.datetime(2016,10,10)\n",
    "fnames = analyze.get_filenames(t_o, t_f, '1d', 'grid_T', '/results/SalishSea/nowcast-green/')\n",
    "grid_B=nc.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/bathy_downonegrid2.nc')\n",
    "mesh_mask = nc.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/mesh_mask_downbyone2.nc')\n",
    "\n",
    "runs = {'nowcast-green': {'grid': grid_B,\n",
    "                          'mesh': mesh_mask,\n",
    "                          'fnames': fnames,\n",
    "                          'nemo36': True}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fnames = analyze.get_filenames(t_o, t_f, '1d', 'grid_T', '/results/SalishSea/nowcast/')\n",
    "grid_B=nc.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/bathy_meter_SalishSea2.nc')\n",
    "mesh_mask = nc.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/mesh_mask_SalishSea2.nc')\n",
    "\n",
    "runs['nowcast']= {'grid': grid_B,\n",
    "                  'mesh': mesh_mask,\n",
    "                  'fnames': fnames,\n",
    "                  'nemo36': False}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_onc_TS_time_series(station, t_o, t_f):\n",
    "    \"\"\"Grab the ONC temperature and salinity time series for a station between dates\n",
    "    t_o and t_f. Return results as separate temperature and salinty data frames.\"\"\"\n",
    "    numdays = (t_f-t_o).days\n",
    "    dates = [t_o + datetime.timedelta(days=num)\n",
    "             for num in range(0, numdays+1)]\n",
    "    sal_pd = pd.DataFrame({'time':[],\n",
    "                           'data': []})\n",
    "    temp_pd = pd.DataFrame({'time': [],\n",
    "                            'data': []})\n",
    "    station_code = places.PLACES[station]['ONC stationCode']\n",
    "    for date in dates:\n",
    "        onc_data = data_tools.get_onc_data(\n",
    "            'scalardata', 'getByStation', os.environ['ONC_USER_TOKEN'],\n",
    "            station=station_code,\n",
    "            deviceCategory='CTD', sensors='salinity,temperature',\n",
    "            dateFrom=data_tools.onc_datetime(date, 'utc'))\n",
    "        try:\n",
    "            ctd_data=data_tools.onc_json_to_dataset(onc_data, teos=False) #keep in PSU!\n",
    "            #quality control\n",
    "            qc_sal = np.array(ctd_data.salinity.qaqcFlag)\n",
    "            qc_temp = np.array(ctd_data.temperature.qaqcFlag)\n",
    "            #average \n",
    "            sal_pd = sal_pd.append({'time': ctd_data.salinity.sampleTime.values[0],\n",
    "                                    'data': ctd_data.salinity.values[qc_sal==1].mean()}, \n",
    "                                    ignore_index=True)\n",
    "            temp_pd = temp_pd.append({'time': ctd_data.temperature.sampleTime.values[0],\n",
    "                                      'data': ctd_data.temperature.values[qc_temp==1].mean()}, \n",
    "                                      ignore_index=True)\n",
    "        except TypeError:\n",
    "            print('No data for {} at {}'.format(date, station))\n",
    "    return sal_pd, temp_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_model_time_series(station, fnames, grid_B, mesh_mask, nemo_36=True):\n",
    "    \"\"\"Retrieve the density, salinity and temperature time series at a station. \n",
    "    Time series is created from files listed in fnames\"\"\"\n",
    "    if nemo_36:\n",
    "        depth_var='gdept_0'\n",
    "        depth_var_w = 'gdepw_0'\n",
    "    else:\n",
    "        depth_var='gdept'\n",
    "        depth_var_w = 'gdepw'\n",
    "    \n",
    "    #station info\n",
    "    lon = places.PLACES[station]['lon lat'][0]\n",
    "    lat = places.PLACES[station]['lon lat'][1]\n",
    "    depth = places.PLACES[station]['depth']\n",
    "    \n",
    "    # model corresponding locations and variables\n",
    "    bathy, X, Y = tidetools.get_bathy_data(grid_B)\n",
    "    j, i = geo_tools.find_closest_model_point(lon,lat,X,Y, land_mask=bathy.mask)\n",
    "    model_depths = mesh_mask.variables[depth_var][0,:,j,i]\n",
    "    tmask = mesh_mask.variables['tmask'][0,:,j,i]\n",
    "    wdeps = mesh_mask.variables[depth_var_w][0,:,j,i]\n",
    "    sal, time = analyze.combine_files(fnames,'vosaline','None',j,i)\n",
    "    temp, time = analyze.combine_files(fnames,'votemper','None',j,i)\n",
    "    \n",
    "    # interpolate:\n",
    "    sal_interp=np.array(\n",
    "        [shared.interpolate_tracer_to_depths(\n",
    "            sal[i,:],model_depths,depth,tmask,wdeps)\n",
    "        for i in range(sal.shape[0])])\n",
    "    temp_interp=np.array(\n",
    "        [shared.interpolate_tracer_to_depths(\n",
    "            temp[i,:],model_depths,depth,tmask,wdeps)\n",
    "        for i in range(temp.shape[0])])\n",
    "    \n",
    "    # convert to psu for using density function\n",
    "    if nemo_36:\n",
    "        sal_interp = teos_tools.teos_psu(sal_interp)\n",
    "    density = psu_tools.calculate_density(temp_interp, sal_interp)\n",
    "    \n",
    "    return density, sal_interp, temp_interp, time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/numpy/core/_methods.py:59: RuntimeWarning: Mean of empty slice.\n",
      "  warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for 2016-05-02 00:00:00 at Central node\n",
      "No data for 2016-08-15 00:00:00 at Central node\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nsoontie/anaconda3/envs/analysis/lib/python3.5/site-packages/ipykernel/__main__.py:28: VisibleDeprecationWarning: boolean index did not match indexed array along dimension 0; dimension is 86398 but corresponding boolean dimension is 86397\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data for 2016-10-08 00:00:00 at Central node\n",
      "No data for 2016-10-09 00:00:00 at Central node\n",
      "No data for 2016-10-10 00:00:00 at Central node\n"
     ]
    }
   ],
   "source": [
    "obs_sal={}\n",
    "obs_temp={}\n",
    "for station in ['Central node']:\n",
    "    obs_sal[station], obs_temp[station] = get_onc_TS_time_series(station, t_o, t_f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nowcast\n",
      "Central node\n",
      "nowcast-green\n",
      "Central node\n"
     ]
    }
   ],
   "source": [
    "rhos = {'nowcast': {}, 'nowcast-green': {}}\n",
    "times = {'nowcast': {}, 'nowcast-green': {}}\n",
    "sals={'nowcast': {}, 'nowcast-green': {}}\n",
    "temps={'nowcast': {}, 'nowcast-green': {}}\n",
    "stations = ['Central node',]\n",
    "for sim in ['nowcast', 'nowcast-green']:\n",
    "    print(sim)\n",
    "    for station in stations:\n",
    "        print(station)\n",
    "        rhos[sim][station], sals[sim][station], temps[sim][station], times[sim][station] = \\\n",
    "            get_model_time_series(station, runs[sim]['fnames'], runs[sim]['grid'],\n",
    "                                  runs[sim]['mesh'], nemo_36=runs[sim]['nemo36'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['nowcast-green'])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runs.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(736008.0, 736232.0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Kzs7OLFy4kN27d7NhwwbmzZsHwJUrVxg9ejTPnz/HyMiIfv36UatWLZ1jCA19mVWP91HR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yOYponTacfvDxGJRAmEIgRCUYKhSPx+LM02GI7isMVTauOjnk0ptG6XDZfDht1qxIJpmwWXw4o7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa51f75d940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,1,figsize=(15,5), sharex=True)\n",
    "names = [ 'Salinty [g/kg]',]\n",
    "titles =  ['salinity',]\n",
    "ticks = [[29.6, 32],]\n",
    "cols = ['r','b']\n",
    "labels={'nowcast-green': 'Version 2', 'nowcast': 'Version 1'}\n",
    "#obs=['',]\n",
    "obs = [obs_sal,]\n",
    "for i, station in enumerate(stations):\n",
    "    axc = [axs,]\n",
    "    for sim, col in zip(['nowcast-green','nowcast'], cols):\n",
    "        variables = [sals[sim], ]\n",
    "        t = times[sim]\n",
    "        for var, name, title, ax, ob, tick in zip(variables, names, titles, axc, obs,ticks):\n",
    "            if sim == 'nowcast-green': #only plot obs once\n",
    "                ax.plot(ob[station].time, teos_tools.psu_teos(ob[station].data), 'k', label='Observations', lw=2)\n",
    "            ax.plot(np.array(t[station]), teos_tools.psu_teos(np.array(var[station])), c=col, \n",
    "                    label=labels[sim], lw=2)\n",
    "            ax.set_ylabel('Daily averaged {}'.format(name))\n",
    "            ax.set_title('{} - {} m'.format(station, places.PLACES[station]['depth']))\n",
    "            ax.set_ylim(tick)\n",
    "for ax in axc:\n",
    "    ax.get_yaxis().get_major_formatter().set_useOffset(False)\n",
    "    ax.legend(loc=0)\n",
    "fig.autofmt_xdate()\n",
    "ax.set_xlim([datetime.datetime(2016,2,14), datetime.datetime(2016,9,25)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig.savefig('/home/nsoontie/Desktop/Central_2016.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}