{
 "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 Dec 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#for yr in ('15','16','17'): # years\n",
    "yr='15'\n",
    "fdec=glob.glob('/results/SalishSea/nowcast-green/*dec'+str(int(yr))-1+'/SalishSea_1d*_grid_T.nc')\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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fDJF=fdec+fjan+ffeb\n",
    "fMAM=fmar+fapr+fmay\n",
    "fJJA=fjun+fjul+faug\n",
    "fSON=fsep+foct+fnov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4 ms, sys: 4 ms, total: 8 ms\n",
      "Wall time: 56.5 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "b''"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# index 21 is 28.229916 m\n",
    "foutDJF='/data/vdo/MEOPAR/for-devin/meanDJF_0-30m_20'+yr+'.nc'\n",
    "%time subprocess.check_output('ncra -v votemper,vosaline -d deptht,,21,1 '+' '.join(fDJF)+' '+foutDJF,shell=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4 ms, sys: 4 ms, total: 8 ms\n",
      "Wall time: 52.2 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "b''"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# index 21 is 28.229916 m\n",
    "foutMAM='/data/vdo/MEOPAR/for-devin/meanMAM_0-30m_20'+yr+'.nc'\n",
    "%time subprocess.check_output('ncra -v votemper,vosaline -d deptht,,21,1 '+' '.join(fMAM)+' '+foutMAM,shell=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4 ms, sys: 4 ms, total: 8 ms\n",
      "Wall time: 57.4 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "b''"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# index 21 is 28.229916 m\n",
    "foutJJA='/data/vdo/MEOPAR/for-devin/meanJJA_0-30m_20'+yr+'.nc'\n",
    "%time subprocess.check_output('ncra -v votemper,vosaline -d deptht,,21,1 '+' '.join(fJJA)+' '+foutJJA,shell=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4 ms, sys: 4 ms, total: 8 ms\n",
      "Wall time: 1min 45s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "b''"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# index 21 is 28.229916 m\n",
    "foutSON='/data/vdo/MEOPAR/for-devin/meanSON_0-30m_20'+yr+'.nc'\n",
    "%time subprocess.check_output('ncra -v votemper,vosaline -d deptht,,21,1 '+' '.join(fSON)+' '+foutSON,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: 4 ms, total: 4 ms\n",
      "Wall time: 6.8 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=foutDJF+' '+foutMAM+' '+foutJJA+' '+foutSON\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(2015, 1, 15, 0, 0),\n",
       " datetime.datetime(2015, 4, 16, 0, 0),\n",
       " datetime.datetime(2015, 7, 17, 0, 0),\n",
       " datetime.datetime(2015, 10, 16, 12, 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": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7faec587f710>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l0i+iSHlsPNuOUXotH4inVdZ+13tmFMYIGkEbqX8OnhsB2JoUiUWHgiVQaXCO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fae6be93748>"
      ]
     },
     "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('DJF Salinity')\n",
    "ax[1].pcolormesh(f.variables['vosaline'][1,0,:,:])\n",
    "ax[1].set_title('MAM Salinity')\n",
    "ax[2].pcolormesh(f.variables['vosaline'][2,0,:,:])\n",
    "ax[2].set_title('JJA Salinity')\n",
    "ax[3].pcolormesh(f.variables['vosaline'][3,0,:,:])\n",
    "ax[3].set_title('SON Salinity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = nc.Dataset('/data/vdo/MEOPAR/for-devin/seasonal-30_2015.nc', 'a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mesh = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/mesh_mask201702.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mask = mesh.variables['e3t_0'][:,:22,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mask = np.ma.masked_array(mask, mask = 1- mesh.variables['tmask'][:,:22,:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "averaged = np.sum((f.variables['votemper'][:] * mask), axis=1) / np.sum(mask, axis = 1)\n",
    "averaged.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "temps = f.createVariable('votemperoverdepth', 'f4', ('time_counter', 'y', 'x'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "temps[:] = averaged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sals = f.createVariable('vosalineoverdepth', 'f4', ('time_counter', 'y', 'x'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "averaged2 = np.sum((f.variables['vosaline'][:] * mask), axis=1) / np.sum(mask, axis = 1)\n",
    "averaged2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sals[:] = averaged2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.variables['vosalineoverdepth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.variables['votemperoverdepth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.close()"
   ]
  }
 ],
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