{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import netCDF4 as nc\n",
    "import datetime as dt\n",
    "import subprocess\n",
    "import requests\n",
    "import matplotlib.pyplot as plt\n",
    "import cmocean\n",
    "import numpy as np\n",
    "import os\n",
    "import glob\n",
    "import dateutil as dutil\n",
    "from salishsea_tools import viz_tools\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with nc.Dataset('/ocean/eolson/MEOPAR/NEMO-forcing/grid/mesh_mask201702_noLPE.nc') as fm:\n",
    "    tmask=np.copy(fm.variables['tmask'])\n",
    "    umask=np.copy(fm.variables['umask'])\n",
    "    vmask=np.copy(fm.variables['vmask'])\n",
    "    navlon=np.copy(fm.variables['nav_lon'])\n",
    "    navlat=np.copy(fm.variables['nav_lat'])\n",
    "    dept=np.copy(fm.variables['gdept_1d'])\n",
    "    e3t_0=np.copy(fm.variables['e3t_0'])\n",
    "    e3u_0=np.copy(fm.variables['e3u_0'])\n",
    "    e3v_0=np.copy(fm.variables['e3v_0'])\n",
    "    e1t=np.copy(fm.variables['e1t'])\n",
    "    e2t=np.copy(fm.variables['e2t'])\n",
    "    e1v=np.copy(fm.variables['e1v'])\n",
    "    e2u=np.copy(fm.variables['e2u'])\n",
    "    A=fm.variables['e1t'][0,:,:]*fm.variables['e2t'][0,:,:]*tmask[0,0,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "t0=dt.datetime(2015,2,6) # 1st start date of run\n",
    "#te=dt.datetime(2016,12,1)# last start date of runfnum=18\n",
    "stm=np.shape(tmask)\n",
    "SiN=2.0\n",
    "#nlen=36*2\n",
    "nlen=24\n",
    "dlist=[t0+dt.timedelta(days=ii*10) for ii in range(0,nlen)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#sdir0='/results/SalishSea/nowcast-green/'\n",
    "#sdir1='/results/SalishSea/hindcast/'\n",
    "#sdir3='/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_HCMZ/'\n",
    "sdir1='/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tmaskC=np.copy(tmask)\n",
    "tmaskC[:,:,370:490,:12]=0\n",
    "tmaskC[:,:,887:,30:70]=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150206-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150216-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150226-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150308-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150318-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150328-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150407-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150417-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150427-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150507-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150517-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150527-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150606-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150616-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150626-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150706-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150716-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150726-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150805-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150815-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150825-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150904-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150914-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150924-*.nc\n"
     ]
    }
   ],
   "source": [
    "tlist=dlist\n",
    "SiGlobalTot=dict()\n",
    "SiTot=dict()\n",
    "BSiTot=dict()\n",
    "DiatTot=dict()\n",
    "changeSiGlobalTot=dict()\n",
    "for idir in (sdir1,):\n",
    "    fformat1='%d%b%y/'\n",
    "    if idir.startswith('/data/eolson/MEOPAR/SS36runs/CedarRuns/'):\n",
    "        fformatT='SalishSea_1h_*_ptrc_T_%Y%m%d-*.nc'\n",
    "        fformatP='SalishSea_1h_*_ptrc_T_%Y%m%d-*.nc'\n",
    "    #elif idir==sdir0:\n",
    "    #    fformatT='SalishSea_1h_%Y%m%d_%Y%m%d_ptrc_T.nc'\n",
    "    #    fformatP='SalishSea_1h_%Y%m%d_%Y%m%d_grid_T.nc'\n",
    "    elif idir==sdir1:\n",
    "        fformatT='SalishSea_1h_%Y%m%d_%Y%m%d_ptrc_T.nc'\n",
    "        fformatP='SalishSea_1h_%Y%m%d_%Y%m%d_carp_T.nc'\n",
    "    sumSi=np.zeros((len(tlist),stm[1]))\n",
    "    sumBSi=np.zeros((len(tlist),stm[1]))\n",
    "    sumDiat=np.zeros((len(tlist),stm[1]))\n",
    "    ind=-1\n",
    "    for idt0 in tlist:\n",
    "        ind=ind+1\n",
    "        cdir=idt0.strftime(fformat1).lower()\n",
    "        iffT=idt0.strftime(fformatT)\n",
    "        iffP=idt0.strftime(fformatP)\n",
    "        if idir.startswith('/data/eolson/MEOPAR/SS36runs/CedarRuns/'):\n",
    "            sffT=idir+iffT\n",
    "            sffP=idir+iffP\n",
    "        elif idir.startswith('/results/'):\n",
    "            sffT=idir+cdir+iffT\n",
    "            sffP=idir+cdir+iffP\n",
    "        f=nc.Dataset(glob.glob(sffT)[0])\n",
    "        print(sffT)\n",
    "        fP=nc.Dataset(glob.glob(sffP)[0])\n",
    "        #if idir==sdir0:\n",
    "        #    e3t=np.expand_dims((1+fP.variables['sossheig'][0,:,:]/np.sum(e3t_0*tmask,1)),0)*e3t_0\n",
    "        if idir==sdir1:\n",
    "            e3t=fP.variables['e3t'][:2,:,:,:]\n",
    "        Vol=A*np.ones(np.shape(e3t))\n",
    "        sumSi[ind,:]=1e-3*np.sum(np.sum(tmaskC[0,:,:,:]*Vol[0,:,:,:]*f.variables['silicon'][0,:,:,:],2),1) #mmol/m3*m3*10^-3=mol\n",
    "        sumBSi[ind,:]=1e-3*np.sum(np.sum(tmaskC[0,:,:,:]*Vol[0,:,:,:]*f.variables['biogenic_silicon'][0,:,:,:],2),1) #mmol/m3*m3*10^-3=mol\n",
    "        sumDiat[ind,:]=SiN*1e-3*np.sum(np.sum(tmaskC[0,:,:,:]*Vol[0,:,:,:]*f.variables['diatoms'][0,:,:,:],2),1) #mmol/m3*m3*10^-3=mol\n",
    "        f.close()\n",
    "        fP.close()\n",
    "    SiGlobalTot[idir]=sumSi+sumBSi+sumDiat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150924-*.nc'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sffT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "siplot=SiGlobalTot[idir]-SiGlobalTot[idir][0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fa459c10d30>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0AlemOHhx2n4F8GvA70saAJ4G1kZEAKPu22idxg3qEXFhnU3n1cl/GXBZI5UyM2spKXsY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa459d2ee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm1=cmocean.cm.balance\n",
    "#plt.pcolormesh([t0+dt.timedelta(10*ii) for ii in range(0,30)],dept[0,:],np.transpose(siplot[:30,:]),cmap=cm1,vmin=-1e9,vmax=1e9)\n",
    "plt.pcolormesh([t0+dt.timedelta(10*ii) for ii in range(0,nlen)],dept[0,:],np.transpose(siplot),cmap=cm1,vmin=-1e8,vmax=1e8)\n",
    "plt.ylim(400,0)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## repeat for N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150206-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150216-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150226-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150308-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150318-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150328-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150407-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150417-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150427-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150507-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150517-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150527-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150606-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150616-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150626-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150706-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150716-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150726-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150805-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150815-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150825-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150904-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150914-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/SalishSea_1h_*_ptrc_T_20150924-*.nc\n"
     ]
    }
   ],
   "source": [
    "tlist=dlist\n",
    "NGlobalTot=dict()\n",
    "VolTot=dict()\n",
    "NO3Tot=dict()\n",
    "NH4Tot=dict()\n",
    "PONTot=dict()\n",
    "DONTot=dict()\n",
    "DiatTot=dict()\n",
    "MyriTot=dict()\n",
    "NanoTot=dict()\n",
    "MiZoTot=dict()\n",
    "changeNGlobalTot=dict()\n",
    "for idir in (sdir1,):\n",
    "    fformat1='%d%b%y/'\n",
    "    if idir.startswith('/data/eolson/MEOPAR/SS36runs/CedarRuns/'):\n",
    "        fformatT='SalishSea_1h_*_ptrc_T_%Y%m%d-*.nc'\n",
    "        fformatP='SalishSea_1h_*_ptrc_T_%Y%m%d-*.nc'\n",
    "    #elif idir==sdir0:\n",
    "    #    fformatT='SalishSea_1h_%Y%m%d_%Y%m%d_ptrc_T.nc'\n",
    "    #    fformatP='SalishSea_1h_%Y%m%d_%Y%m%d_grid_T.nc'\n",
    "    elif idir==sdir1:\n",
    "        fformatT='SalishSea_1h_%Y%m%d_%Y%m%d_ptrc_T.nc'\n",
    "        fformatP='SalishSea_1h_%Y%m%d_%Y%m%d_carp_T.nc'\n",
    "    sumNO3=np.zeros((len(tlist),stm[1]))\n",
    "    sumVol=np.zeros((len(tlist),stm[1]))\n",
    "    sumNH4=np.zeros((len(tlist),stm[1]))\n",
    "    sumPON=np.zeros((len(tlist),stm[1]))\n",
    "    sumDON=np.zeros((len(tlist),stm[1]))\n",
    "    sumDiat=np.zeros((len(tlist),stm[1]))\n",
    "    sumMyri=np.zeros((len(tlist),stm[1]))\n",
    "    sumNano=np.zeros((len(tlist),stm[1]))\n",
    "    sumMiZo=np.zeros((len(tlist),stm[1]))\n",
    "    ind=-1\n",
    "    for idt0 in tlist:\n",
    "        ind=ind+1\n",
    "        cdir=idt0.strftime(fformat1).lower()\n",
    "        iffT=idt0.strftime(fformatT)\n",
    "        iffP=idt0.strftime(fformatP)\n",
    "        if idir.startswith('/data/eolson/MEOPAR/SS36runs/CedarRuns/'):\n",
    "            sffT=idir+iffT\n",
    "            sffP=idir+iffP\n",
    "        elif idir.startswith('/results/'):\n",
    "            sffT=idir+cdir+iffT\n",
    "            sffP=idir+cdir+iffP\n",
    "        f=nc.Dataset(glob.glob(sffT)[0])\n",
    "        print(sffT)\n",
    "        fP=nc.Dataset(glob.glob(sffP)[0])\n",
    "        #if idir==sdir0:\n",
    "        #    e3t=np.expand_dims((1+fP.variables['sossheig'][0,:,:]/np.sum(e3t_0*tmask,1)),0)*e3t_0\n",
    "        if idir==sdir1:\n",
    "            e3t=fP.variables['e3t'][:2,:,:,:]\n",
    "        Vol=A*np.ones(np.shape(e3t))\n",
    "        sumVol[ind,:]=1e-3*np.sum(np.sum(tmaskC[0,:,:,:]*Vol[0,:,:,:],2),1) #mmol/m3*m3*10^-3=mol\n",
    "        sumNO3[ind,:]=1e-3*np.sum(np.sum(tmaskC[0,:,:,:]*Vol[0,:,:,:]*f.variables['nitrate'][0,:,:,:],2),1) #mmol/m3*m3*10^-3=mol\n",
    "        sumNH4[ind,:]=1e-3*np.sum(np.sum(tmaskC[0,:,:,:]*Vol[0,:,:,:]*f.variables['ammonium'][0,:,:,:],2),1) #mmol/m3*m3*10^-3=mol\n",
    "        sumPON[ind,:]=1e-3*np.sum(np.sum(tmaskC[0,:,:,:]*Vol[0,:,:,:]*f.variables['particulate_organic_nitrogen'][0,:,:,:],2),1) #mmol/m3*m3*10^-3=mol\n",
    "        sumDON[ind,:]=1e-3*np.sum(np.sum(tmaskC[0,:,:,:]*Vol[0,:,:,:]*f.variables['dissolved_organic_nitrogen'][0,:,:,:],2),1) #mmol/m3*m3*10^-3=mol\n",
    "        sumDiat[ind,:]=1e-3*np.sum(np.sum(tmaskC[0,:,:,:]*Vol[0,:,:,:]*f.variables['diatoms'][0,:,:,:],2),1) #mmol/m3*m3*10^-3=mol\n",
    "        sumMyri[ind,:]=1e-3*np.sum(np.sum(tmaskC[0,:,:,:]*Vol[0,:,:,:]*f.variables['ciliates'][0,:,:,:],2),1) #mmol/m3*m3*10^-3=mol\n",
    "        sumMiZo[ind,:]=1e-3*np.sum(np.sum(tmaskC[0,:,:,:]*Vol[0,:,:,:]*f.variables['microzooplankton'][0,:,:,:],2),1) #mmol/m3*m3*10^-3=mol\n",
    "        f.close()\n",
    "        fP.close()\n",
    "    NGlobalTot[idir]=sumNO3+sumNH4+sumPON+sumDON+sumDiat+sumMyri+sumNano+sumMiZo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Nplot=NGlobalTot[idir]-NGlobalTot[idir][0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fa441f69b38>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qEXFhnU3n1Ml/OXB5I5UyM2spKadPfVypS+X6UWlX1qx/hmJYeKl9G+U7Ss2s6xSzNFbz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa459b910b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#plt.pcolormesh([t0+dt.timedelta(10*ii) for ii in range(0,30)],dept[0,:],np.transpose(siplot[:30,:]),cmap=cm1,vmin=-1e9,vmax=1e9)\n",
    "plt.pcolormesh([t0+dt.timedelta(10*ii) for ii in range(0,nlen)],dept[0,:],np.transpose(Nplot),cmap=cm1,vmin=-1e8,vmax=1e8)\n",
    "plt.ylim(400,0)\n",
    "plt.colorbar()"
   ]
  },
  {
   "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 [conda env:python36]",
   "language": "python",
   "name": "conda-env-python36-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}