{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import netCDF4 as nc\n",
    "import numpy as np\n",
    "import os\n",
    "import glob\n",
    "import datetime as dt\n",
    "import subprocess\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### some useful sites:\n",
    "* http://research.jisao.washington.edu/data_sets/nco/\n",
    "* http://nco.sourceforge.net/nco.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Start from Jan 1, 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#for yr in ('15','16','17'): # years\n",
    "yr='17'\n",
    "fjan=glob.glob('/results/SalishSea/nowcast-green/*jan'+yr+'/SalishSea_1d*_grid_T.nc')\n",
    "ffeb=glob.glob('/results/SalishSea/nowcast-green/*feb'+yr+'/SalishSea_1d*_grid_T.nc')\n",
    "fmar=glob.glob('/results/SalishSea/nowcast-green/*mar'+yr+'/SalishSea_1d*_grid_T.nc')\n",
    "fapr=glob.glob('/results/SalishSea/nowcast-green/*apr'+yr+'/SalishSea_1d*_grid_T.nc')\n",
    "fmay=glob.glob('/results/SalishSea/nowcast-green/*may'+yr+'/SalishSea_1d*_grid_T.nc')\n",
    "fjun=glob.glob('/results/SalishSea/nowcast-green/*jun'+yr+'/SalishSea_1d*_grid_T.nc')\n",
    "fjul=glob.glob('/results/SalishSea/nowcast-green/*jul'+yr+'/SalishSea_1d*_grid_T.nc')\n",
    "faug=glob.glob('/results/SalishSea/nowcast-green/*aug'+yr+'/SalishSea_1d*_grid_T.nc')\n",
    "fsep=glob.glob('/results/SalishSea/nowcast-green/*sep'+yr+'/SalishSea_1d*_grid_T.nc')\n",
    "foct=glob.glob('/results/SalishSea/nowcast-green/*oct'+yr+'/SalishSea_1d*_grid_T.nc')\n",
    "fnov=glob.glob('/results/SalishSea/nowcast-green/*nov'+yr+'/SalishSea_1d*_grid_T.nc')\n",
    "fdec=glob.glob('/results/SalishSea/nowcast-green/*dec'+yr+'/SalishSea_1d*_grid_T.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fJFM=fjan+ffeb+fmar\n",
    "fAMJ=fapr+fmay+fjun\n",
    "fJAS=fjul+faug+fsep\n",
    "fOND=foct+fnov+fdec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 8 ms, total: 8 ms\n",
      "Wall time: 1min 39s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "b''"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# index 21 is 28.229916 m\n",
    "foutJFM='/data/vdo/MEOPAR/for-devin/meanJFM_0-30m_20'+yr+'.nc'\n",
    "%time subprocess.check_output('ncra -v votemper,vosaline -d deptht,,21,1 '+' '.join(fJFM)+' '+foutJFM,shell=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 12 ms, sys: 16 ms, total: 28 ms\n",
      "Wall time: 9min 53s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "b''"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# index 21 is 28.229916 m\n",
    "foutAMJ='/data/vdo/MEOPAR/for-devin/meanAMJ_0-30m_20'+yr+'.nc'\n",
    "%time subprocess.check_output('ncra -v votemper,vosaline -d deptht,,21,1 '+' '.join(fAMJ)+' '+foutAMJ,shell=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 32 ms, sys: 8 ms, total: 40 ms\n",
      "Wall time: 14min 23s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "b''"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# index 21 is 28.229916 m\n",
    "foutJAS='/data/vdo/MEOPAR/for-devin/meanJAS_0-30m_20'+yr+'.nc'\n",
    "%time subprocess.check_output('ncra -v votemper,vosaline -d deptht,,21,1 '+' '.join(fJAS)+' '+foutJAS,shell=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 28 ms, sys: 8 ms, total: 36 ms\n",
      "Wall time: 13min 14s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "b''"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# index 21 is 28.229916 m\n",
    "foutOND='/data/vdo/MEOPAR/for-devin/meanOND_0-30m_20'+yr+'.nc'\n",
    "%time subprocess.check_output('ncra -v votemper,vosaline -d deptht,,21,1 '+' '.join(fOND)+' '+foutOND,shell=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now combine seasonal averages in 1 file\n",
    "* there maybe be a way to create the averages and combine them all in one step, maybe using hyperslabs and/or subcycle features, but this may require even day divisions (months do not have the same number of days\n",
    "* you can only concatenate along the \"record dimension\", typically unlimited dimension. If you download from ERDDAP, you have to make time a record dimesion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 8 ms, total: 8 ms\n",
      "Wall time: 8.53 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "b''"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fout='/data/vdo/MEOPAR/for-devin/seasonal-30_20'+yr+'.nc'\n",
    "flist=foutJFM+' '+foutAMJ+' '+foutJAS+' '+foutOND\n",
    "%time subprocess.check_output('ncrcat '+flist+' '+fout,shell=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f=nc.Dataset(fout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "odict_keys(['bounds_lat', 'bounds_lon', 'deptht', 'deptht_bounds', 'nav_lat', 'nav_lon', 'time_centered', 'time_centered_bounds', 'time_counter', 'time_counter_bounds', 'vosaline', 'votemper'])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.variables.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.229916"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.variables['deptht'][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[datetime.datetime(2017, 2, 15, 0, 0),\n",
       " datetime.datetime(2017, 5, 16, 12, 0),\n",
       " datetime.datetime(2017, 8, 16, 0, 0),\n",
       " datetime.datetime(2017, 11, 16, 0, 0)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[dt.datetime(1900,1,1)+dt.timedelta(seconds=ii) for ii in f.variables['time_centered']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### strange that 3rd quarter ends on 9/5?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f0cb0baa048>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Q3p0Zl49cxG/2raAMl4f2PU89Ph8bsbBVrxEyhvZkCCFqP3QuYhSvwsWt9Sk7XI9p97to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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0c4485d780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,4,figsize=(8,3))\n",
    "ax[0].pcolormesh(f.variables['vosaline'][0,0,:,:])\n",
    "ax[0].set_title('JFM Salinity')\n",
    "ax[1].pcolormesh(f.variables['vosaline'][1,0,:,:])\n",
    "ax[1].set_title('AMJ Salinity')\n",
    "ax[2].pcolormesh(f.variables['vosaline'][2,0,:,:])\n",
    "ax[2].set_title('JAS Salinity')\n",
    "ax[3].pcolormesh(f.variables['vosaline'][3,0,:,:])\n",
    "ax[3].set_title('OND Salinity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f.close()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# necessary to successfully rerun code:\n",
    "os.remove(fout)\n",
    "os.remove(fJFM)\n",
    "os.remove(fAMJ)\n",
    "os.remove(fJAS)\n",
    "os.remove(fOND)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### to calculate for deepest cell:\n",
    "* use bathy file to create mask that selects out the deepest ocean bin at each depth\n",
    "* concatenate masks together to appropriate length to for each season\n",
    "* concatenate season files together\n",
    "* add mask file to concatenated file using ncks -A\n",
    "* multiply T and S by mask using NCAP2\n",
    "* use ncra with appropriate flags to sum over depth\n",
    "* use ncra to average in time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}