{
 "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_NoMZME/'\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_NoMZME/SalishSea_1h_*_ptrc_T_20150206-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150216-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150226-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150308-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150318-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150328-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150407-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150417-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150427-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150507-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150517-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150527-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150606-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150616-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150626-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150706-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150716-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150726-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150805-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150815-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150825-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150904-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150914-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/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_NoMZME/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 0x7fea75d06208>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0b3gd+BXgjrL7j1Smpb4E2JpGsHQB2yLiq5K+BWyTdBHwIPBWgIjYLWkbcCcw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea75e39be0>"
      ]
     },
     "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_NoMZME/SalishSea_1h_*_ptrc_T_20150206-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150216-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150226-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150308-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150318-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150328-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150407-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150417-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150427-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150507-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150517-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150527-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150606-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150616-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150626-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150706-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150716-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150726-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150805-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150815-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150825-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150904-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/SalishSea_1h_*_ptrc_T_20150914-*.nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/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 0x7fea5e056f98>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KtNSvpri/3jUj0j8ZER+rTZC0hOKO2GcDpwM3SDqrnTef7sn8iZWTPXd2wdy6tFLuLI05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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea75c7a940>"
      ]
     },
     "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
}