{
 "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",
    "    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(2015,1,11) # 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\n",
    "nlen=6\n",
    "dlist=[t0+dt.timedelta(days=ii*10) for ii in range(0,nlen)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/data/eolson/MEOPAR/SS36runs/CedarRuns/spring15spun_Ztest/SalishSea_1h_20150111_20150311_ptrc_T_20150302-20150311.nc'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ftest=nc.Dataset(glob.glob('/data/eolson/MEOPAR/SS36runs/CedarRuns/spring15spun_Ztest/SalishSea_1h_*_ptrc_T_20150302-*.nc')[0])\n",
    "fZ5=nc.Dataset(glob.glob('/data/eolson/MEOPAR/SS36runs/CedarRuns/spring15spun_Z5/SalishSea_1h_*_ptrc_T_20150302-*.nc')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring15spun_Ztest/SalishSea_1h_*_ptrc_T_20150111-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring15spun_Ztest/SalishSea_1h_*_ptrc_T_20150121-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring15spun_Ztest/SalishSea_1h_*_ptrc_T_20150131-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring15spun_Ztest/SalishSea_1h_*_ptrc_T_20150210-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring15spun_Ztest/SalishSea_1h_*_ptrc_T_20150220-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring15spun_Ztest/SalishSea_1h_*_ptrc_T_20150302-*.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/'):\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[2],stm[3]))\n",
    "    sumBSi=np.zeros((len(tlist),stm[2],stm[3]))\n",
    "    sumDiat=np.zeros((len(tlist),stm[2],stm[3]))\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/'):\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*e3t\n",
    "        sumSi[ind,:,:]=1e-3*np.sum(tmaskC[0,:,:,:]*Vol[0,:,:,:]*f.variables['silicon'][0,:,:,:],0) #mmol/m3*m3*10^-3=mol\n",
    "        sumBSi[ind,:,:]=1e-3*np.sum(tmaskC[0,:,:,:]*Vol[0,:,:,:]*f.variables['biogenic_silicon'][0,:,:,:],0) #mmol/m3*m3*10^-3=mol\n",
    "        sumDiat[ind,:,:]=SiN*1e-3*np.sum(tmaskC[0,:,:,:]*Vol[0,:,:,:]*f.variables['diatoms'][0,:,:,:],0) #mmol/m3*m3*10^-3=mol\n",
    "        f.close()\n",
    "        fP.close()\n",
    "    SiGlobalTot[idir]=np.sum(np.sum(sumSi+sumBSi+sumDiat,2),1)\n",
    "    SiTot[idir]=np.sum(np.sum(sumSi,2),1)\n",
    "    BSiTot[idir]=np.sum(np.sum(sumBSi,2),1)\n",
    "    DiatTot[idir]=np.sum(np.sum(sumDiat,2),1)\n",
    "    changeSiGlobalTot[idir]=[SiGlobalTot[idir][ii+1]-SiGlobalTot[idir][ii] for ii in range(0,len(tlist)-1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/data/eolson/MEOPAR/SS36runs/CedarRuns/spring15spun_Ztest/SalishSea_1h_*_ptrc_T_20150302-*.nc'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sffT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f183967e4a8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lduJW+Zw4zFqx885w8slw+eUpiVjnGzsW/vWvVBAv13BS6xgnDrM2/PznqYvu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\ntYsTh5mZtYsTh5mZtYsTh5mZtcv/A4UD95ynAhXRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18397098d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig,ax=plt.subplots(1,1,figsize=(6,5))\n",
    "ax.plot(SiGlobalTot[sdir1]-SiGlobalTot[sdir1][0],'b-')\n",
    "ax.set_xlabel('10-day intervals since Jan 1 2015')\n",
    "ax.set_ylabel('Difference in Total Si')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# copy restart and add 7 to Si old and new"
   ]
  },
  {
   "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/spring15spun_Ztest/SalishSea_1h_*_ptrc_T_20150111-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring15spun_Ztest/SalishSea_1h_*_ptrc_T_20150121-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring15spun_Ztest/SalishSea_1h_*_ptrc_T_20150131-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring15spun_Ztest/SalishSea_1h_*_ptrc_T_20150210-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring15spun_Ztest/SalishSea_1h_*_ptrc_T_20150220-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring15spun_Ztest/SalishSea_1h_*_ptrc_T_20150302-*.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[2],stm[3]))\n",
    "    sumVol=np.zeros((len(tlist),stm[2],stm[3]))\n",
    "    sumNH4=np.zeros((len(tlist),stm[2],stm[3]))\n",
    "    sumPON=np.zeros((len(tlist),stm[2],stm[3]))\n",
    "    sumDON=np.zeros((len(tlist),stm[2],stm[3]))\n",
    "    sumDiat=np.zeros((len(tlist),stm[2],stm[3]))\n",
    "    sumMyri=np.zeros((len(tlist),stm[2],stm[3]))\n",
    "    sumNano=np.zeros((len(tlist),stm[2],stm[3]))\n",
    "    sumMiZo=np.zeros((len(tlist),stm[2],stm[3]))\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*e3t\n",
    "        sumVol[ind,:,:]=1e-3*np.sum(tmaskC[0,:,:,:]*Vol[0,:,:,:],0) #mmol/m3*m3*10^-3=mol\n",
    "        sumNO3[ind,:,:]=1e-3*np.sum(tmaskC[0,:,:,:]*Vol[0,:,:,:]*f.variables['nitrate'][0,:,:,:],0) #mmol/m3*m3*10^-3=mol\n",
    "        sumNH4[ind,:,:]=1e-3*np.sum(tmaskC[0,:,:,:]*Vol[0,:,:,:]*f.variables['ammonium'][0,:,:,:],0) #mmol/m3*m3*10^-3=mol\n",
    "        sumPON[ind,:,:]=1e-3*np.sum(tmaskC[0,:,:,:]*Vol[0,:,:,:]*f.variables['particulate_organic_nitrogen'][0,:,:,:],0) #mmol/m3*m3*10^-3=mol\n",
    "        sumDON[ind,:,:]=1e-3*np.sum(tmaskC[0,:,:,:]*Vol[0,:,:,:]*f.variables['dissolved_organic_nitrogen'][0,:,:,:],0) #mmol/m3*m3*10^-3=mol\n",
    "        sumDiat[ind,:,:]=1e-3*np.sum(tmaskC[0,:,:,:]*Vol[0,:,:,:]*f.variables['diatoms'][0,:,:,:],0) #mmol/m3*m3*10^-3=mol\n",
    "        sumMyri[ind,:,:]=1e-3*np.sum(tmaskC[0,:,:,:]*Vol[0,:,:,:]*f.variables['ciliates'][0,:,:,:],0) #mmol/m3*m3*10^-3=mol\n",
    "        sumMiZo[ind,:,:]=1e-3*np.sum(tmaskC[0,:,:,:]*Vol[0,:,:,:]*f.variables['microzooplankton'][0,:,:,:],0) #mmol/m3*m3*10^-3=mol\n",
    "        f.close()\n",
    "        fP.close()\n",
    "    NGlobalTot[idir]=np.sum(np.sum(sumNO3+sumNH4+sumPON+sumDON+sumDiat+sumMyri+sumNano+sumMiZo,2),1)\n",
    "    VolTot[idir]=np.sum(np.sum(sumVol,2),1)\n",
    "    NO3Tot[idir]=np.sum(np.sum(sumNO3,2),1)\n",
    "    NH4Tot[idir]=np.sum(np.sum(sumNH4,2),1)\n",
    "    PONTot[idir]=np.sum(np.sum(sumPON,2),1)\n",
    "    DONTot[idir]=np.sum(np.sum(sumDON,2),1)\n",
    "    DiatTot[idir]=np.sum(np.sum(sumDiat,2),1)\n",
    "    MyriTot[idir]=np.sum(np.sum(sumMyri,2),1)\n",
    "    NanoTot[idir]=np.sum(np.sum(sumNano,2),1)\n",
    "    MiZoTot[idir]=np.sum(np.sum(sumMiZo,2),1)\n",
    "    changeNGlobalTot[idir]=[NGlobalTot[idir][ii+1]-NGlobalTot[idir][ii] for ii in range(0,len(tlist)-1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f1839617d30>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HWS3q3DmNsOrSBU49FT74ADbZpPR/vJt7ny++KN1n7dAhfc7OndN9w+MZM1K/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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1839670128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.plot(SiGlobalTot[sdir0]-SiGlobalTot[sdir0][0],'r-')\n",
    "#plt.plot(SiGlobalTot[sdir3]-SiGlobalTot[sdir3][0],'g-')\n",
    "#plt.plot(40,test-SiGlobalTot[sdir1][0],'r*')\n",
    "#plt.plot(40,test2-SiGlobalTot[sdir1][0],'k*')\n",
    "fig,ax=plt.subplots(1,1,figsize=(6,5))\n",
    "ax.plot(NGlobalTot[sdir1]-NGlobalTot[sdir1][0],'r-')\n",
    "ax.set_xlabel('10-day intervals since Jan 1 2015')\n",
    "ax.set_ylabel('Difference in Total N')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1822cd2748>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PMe1ZSOJmLHl5eSE/Pz/uMDLbU0/5vMypp8LIkX51jsgvv0C3br4/YOpU6NgxsqElZitX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1822cd26a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(SiGlobalTot[sdir1]-SiGlobalTot[sdir1][0],'b-')\n",
    "plt.plot(NGlobalTot[sdir1]-NGlobalTot[sdir1][0],'r-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(SiGlobalTot[sdir1],open('SiGlobalTot_'+tit+'.pkl','wb'))\n",
    "pickle.dump(NGlobalTot[sdir1],open('NGlobalTot_'+tit+'.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'spring15spun_Ztest'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tit"
   ]
  },
  {
   "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
}