{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import netCDF4 as nc\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import glob\n",
    "import pickle\n",
    "from salishsea_tools import evaltools as et\n",
    "import datetime as dt\n",
    "import os\n",
    "import re\n",
    "import cmocean\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with nc.Dataset('/ocean/eolson/MEOPAR/NEMO-forcing/grid/mesh_mask201702_noLPE.nc') as mesh:\n",
    "    tmask=mesh.variables['tmask'][0,:,:,:]\n",
    "    e1t=np.expand_dims(mesh.variables['e1t'][:,:,:],1)\n",
    "    e2t=np.expand_dims(mesh.variables['e2t'][:,:,:],1)\n",
    "SOGtmaskPath='/ocean/eolson/MEOPAR/northernNO3PaperCalcs/save/SOGtmask.pkl'\n",
    "(tmaskSOG,_,_,_,_)=pickle.load(open(SOGtmaskPath,'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "idir='/data/eolson/results/MEOPAR/SS36runs/CedarRuns/shortTestTRD/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fP=nc.Dataset(glob.glob(idir+'/SalishSea_1d_*_dian_T*.nc')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['nav_lat', 'nav_lon', 'bounds_nav_lon', 'bounds_nav_lat', 'area', 'deptht', 'deptht_bounds', 'time_centered', 'time_centered_bounds', 'time_counter', 'time_counter_bounds', 'NITR', 'NO3RAD', 'ATF_NO3', 'SMS_NO3', 'RDB_NO3', 'RDN_NO3', 'REM_PON', 'REM_DON', 'REM_NO3', 'REM_bSi'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fP.variables.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'netCDF4._netCDF4.Variable'>\n",
       "float32 REM_DON(time_counter, deptht, y, x)\n",
       "    long_name: ---\n",
       "    units: ---\n",
       "    online_operation: average\n",
       "    interval_operation: 40 s\n",
       "    interval_write: 1 d\n",
       "    cell_methods: time: mean (interval: 40 s)\n",
       "    cell_measures: area: area\n",
       "    _FillValue: 1e+20\n",
       "    missing_value: 1e+20\n",
       "    coordinates: time_centered nav_lat nav_lon\n",
       "unlimited dimensions: time_counter\n",
       "current shape = (5, 40, 898, 398)\n",
       "filling on"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fP.variables['REM_DON']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9.366569, -23.560404)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(np.ma.masked_where(tmask==0,fP.variables['SMS_NO3'][0,:,:,:])),np.min(np.ma.masked_where(tmask==0,fP.variables['SMS_NO3'][0,:,:,:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.4866695, -2.6189249)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(np.ma.masked_where(tmask==0,fP.variables['ATF_NO3'][0,:,:,:])),np.min(np.ma.masked_where(tmask==0,fP.variables['ATF_NO3'][0,:,:,:]))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "np.max(np.ma.masked_where(tmask[0,:,:]==0,fP.variables['ATF_NO3'][0,0,:,:]/fP.variables['AFILTNO3'][0,0,:,:])),np.min(np.ma.masked_where(tmask[0,:,:]==0,fP.variables['ATF_NO3'][0,0,:,:]/fP.variables['AFILTNO3'][0,0,:,:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(np.ma.masked_where(tmask==0,fP.variables['RDB_NO3'][0,:,:,:])),np.min(np.ma.masked_where(tmask==0,fP.variables['RDB_NO3'][0,:,:,:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(np.ma.masked_where(tmask==0,fP.variables['RDN_NO3'][0,:,:,:])),np.min(np.ma.masked_where(tmask==0,fP.variables['RDN_NO3'][0,:,:,:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(np.ma.masked_where(tmask==0,fP.variables['NO3RAD'][0,:,:,:])),np.min(np.ma.masked_where(tmask==0,fP.variables['NO3RAD'][0,:,:,:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.0, -9.764514)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(np.ma.masked_where(tmask==0,fP.variables['REM_DON'][0,:,:,:])),np.min(np.ma.masked_where(tmask==0,fP.variables['REM_DON'][0,:,:,:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.0, -44.278637)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(np.ma.masked_where(tmask==0,fP.variables['REM_bSi'][0,:,:,:])),np.min(np.ma.masked_where(tmask==0,fP.variables['REM_bSi'][0,:,:,:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.0, -5.797069)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(np.ma.masked_where(tmask==0,fP.variables['REM_PON'][0,:,:,:])),np.min(np.ma.masked_where(tmask==0,fP.variables['REM_PON'][0,:,:,:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9.366569, -23.560404)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(np.ma.masked_where(tmask==0,fP.variables['SMS_NO3'][0,:,:,:])),np.min(np.ma.masked_where(tmask==0,fP.variables['SMS_NO3'][0,:,:,:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(fP.variables['NITR'][0,0,:,:]-fP.variables['REM_NO3'][0,0,:,:]),np.min(fP.variables['NITR'][0,0,:,:]-fP.variables['REM_NO3'][0,0,:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f3aad5dc3a0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,3,figsize=(8,3))\n",
    "m0=ax[0].pcolormesh(fP.variables['NITR'][0,0,:,:])\n",
    "plt.colorbar(m0,ax=ax[0])\n",
    "m1=ax[1].pcolormesh(fP.variables['REM_NO3'][0,0,:,:])\n",
    "plt.colorbar(m1,ax=ax[1])\n",
    "m2=ax[2].pcolormesh(fP.variables['NITR'][0,0,:,:]-fP.variables['REM_NO3'][0,0,:,:],cmap=cmocean.cm.balance)\n",
    "plt.colorbar(m2,ax=ax[2])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "fig,ax=plt.subplots(1,3,figsize=(8,3))\n",
    "m0=ax[0].pcolormesh(fP.variables['BIOTRNO3'][0,0,:,:])\n",
    "plt.colorbar(m0,ax=ax[0])\n",
    "m1=ax[1].pcolormesh(fP.variables['SMS_NO3'][0,0,:,:])\n",
    "plt.colorbar(m1,ax=ax[1])\n",
    "m2=ax[2].pcolormesh(fP.variables['BIOTRNO3'][0,0,:,:]-fP.variables['SMS_NO3'][0,0,:,:],cmap=cmocean.cm.balance)\n",
    "plt.colorbar(m2,ax=ax[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['nav_lat', 'nav_lon', 'bounds_nav_lon', 'bounds_nav_lat', 'area', 'depthv', 'depthv_bounds', 'time_centered', 'time_centered_bounds', 'time_counter', 'time_counter_bounds', 'NO3TVDY', 'ATY_NO3', 'ATY_PHY', 'DTY_NO3', 'DTY_PHY', 'LDF_NO3', 'VLDFNO3'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fS=nc.Dataset(glob.glob(idir+'/SalishSea_1d_*_Sutil_V*.nc')[0])\n",
    "fS.variables.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['nav_lat', 'nav_lon', 'bounds_nav_lon', 'bounds_nav_lat', 'area', 'depthv', 'depthv_bounds', 'time_centered', 'time_centered_bounds', 'time_counter', 'time_counter_bounds', 'NO3TVDY', 'ATY_NO3', 'LDF_NO3', 'VLDFNO3'])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fS=nc.Dataset(glob.glob(idir+'/SalishSea_1d_*_Sutil_V*.nc')[0])\n",
    "fS.variables.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11.247603, -16.783497)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(fS.variables['LDF_NO3'][0,:,:,:]),np.min(fS.variables['LDF_NO3'][0,:,:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'netCDF4._netCDF4.Variable'>\n",
       "float32 ATY_NO3(time_counter, depthv, y, x)\n",
       "    long_name: ---\n",
       "    units: ---\n",
       "    online_operation: average\n",
       "    interval_operation: 40 s\n",
       "    interval_write: 1 d\n",
       "    cell_methods: time: mean (interval: 40 s)\n",
       "    cell_measures: area: area\n",
       "    _FillValue: 1e+20\n",
       "    missing_value: 1e+20\n",
       "    coordinates: time_centered nav_lat nav_lon\n",
       "unlimited dimensions: time_counter\n",
       "current shape = (5, 40, 1, 26)\n",
       "filling on"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fS.variables['ATY_NO3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f3aad415670>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,3,figsize=(8,3))\n",
    "m0=ax[0].pcolormesh(fS.variables['NO3TVDY'][0,:,0,:])\n",
    "plt.colorbar(m0,ax=ax[0])\n",
    "m1=ax[1].pcolormesh(fS.variables['ATY_NO3'][0,:,0,:])\n",
    "plt.colorbar(m1,ax=ax[1])\n",
    "m2=ax[2].pcolormesh(fS.variables['NO3TVDY'][0,:,0,:]-fS.variables['ATY_NO3'][0,:,0,:],cmap=cmocean.cm.balance)\n",
    "plt.colorbar(m2,ax=ax[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f3aad415670>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Bds0dpVJzMFi6S0pz0Fu6c7pLcTigVczsbqI/+tKyW0qeG3BNlXbPAn8Sc86u6NAjBjpN4dHbnKRxzTmJI6Gchw12nDKMRLtmHcc156RDG8MBvYZ/u5zGseiuLC7VQ9KtknZLerSkbL6kzZK2h8fRkmM3Stoh6SlJbyspf72kX4ZjXwghOJE0R9K3Q/mDkk7r6Ot3ksc156SEckOxqZ/oq56ABY+Xj42+uKT85U2PlNcf+W350s15Txwsyx8888TyBir3mQ69otxTHD2xfF/31505XpZfOPfFsvxUvty+7++KW3aaDTowPvt14IvAbSVlxVCYayXdEPLXhzCblwGvARYD90o6M6zj/jJRqM0HiMbdVgLfB64C9pnZGZIuA/4X8N52DK5H0pqDwdKda85JBe8JcJxjKQZuafWuLAS82FtRHBcK81Jgg5lNmdmviGbRnhPWx55kZj8Nk2tuq2hTPNd3gIuKd2xOb+Kac9JAyHsCHKca+c6Pz5aFwgxRsiAKoflASb1iWM3p8LyyvNjm+XCuGUkHgFd02mAnWVxzTuJI5IYHoyfAnQCnYcwgX7trdoGkbSX5dSGUaivEhdWsFW6zkXCdTg/hmnNSQUKzsrt0tpP0lBNQuRf7cbvK88OHyiss/D/PleXzvy3f1END5d06uWWLy/PT5eOzQ1Pl1xv6bbmneGCkPEb7wsXlY7E3nvKTsvxornzA+O7fe025fc+Vny996nbB1oqnHccuSYvCHVlpKMy4sJrj4XlleWmbcUnDwMkc2xXcFFnTHAya7gZPc04GGKAlgj4nwGkYI7ori0stEhcKcyNwWZh9fTrRvts/C924L0o6L4y9XlnRpniudwH3hTFcp0dxzTlpoDAxMC71Ez3VE+CkjEXds60i6VvAm4m6cMeBjxETCjOE2bwdeByYAa4Js7QB/opo1vdxRDO0vx/Kvwb8o6QdRHdjl7VurZMJXHNOSgxKT4A7AU7DGO1N0jKzy2MOVQ2FaWafBD5ZpXwbcMwWr2b2Mh7Xva9wzTmpoJzPCSgiaRnRkphXAgWiiTefl/Rx4L8Dvw1V/y7ES+4aw5MVBRVDhTPHl7+c2RVjpcqVNyhMvVyWz/2uPD93T3mM9amTKvZpP6H8fL/bN7cs/4u9S8ry35pzZll+lspjwH/0deVv3yfH/7wsr0LaK4+S29s9K7rLmuZg0HQ3eJpzskE7PQGSVgKfJ9pA6KtmtrbiuMLxVUQbCP2lmf08ToOhzcfpgg4b6QmYAa4LBp4IPCRpczh2k5l9pl0jnN6hkNwfguvOAVxzTgq0ESwo7EJ5M3Ax0cTRrZI2mtnjJdUuIZpzshw4lygY1bnEaLCkbcd12MgughNAcU3ti5Ke4OgaWWeAaGC5Vgev5bpzXHNOOkgiN9zycMA5wI6wIyCSNhAFlSp1Ai4FbguTSB+QNK+4YoXqGnycLtHUtyvExV4BPBiKrpX0SIjPPRrb0OkbzOJTt3DdDTauOSdxJMgNxafaHAkgFSgNLtVwnSoahC7osOGJgZJOAO4APmxmByV9GfgE0dydTwCfBT5Qpd0aopjbDM9rzubKNdqVITgq92LPzy7vNjy8ZF5ZftZI+fjq8L7y9dRMT5dlZ0+Ux3U/cbj8fKi8u8hy5W/njqFXluX/6fC5ZfnjZ5cvAn/h0PFl+RNeXb5Z/aHH55MmhigkdFdWJGndZV5zMFC661XNLVu6tPKw00tI5GpPDKwVpKqRAFI161RqMBQ3pMNmacgJkDQrGPQNM/sugJntKjn+FeB71dqGN2YdwNyly3z9bI+T5AfounOgNzW3YmzMNdfTqN4df60gVXFBpxqqU02D0LgOm6Wuix1mMX4NeMLMPldSvqik2juBRyvbOn2GgRUUmzqJ684BXHNOKkgiN2tWbKrDVmC5pNMlzSaKHbGxos5G4EpFnAccCBEsq2ow2NQVHTbSE3A+cAXwS0kPh7K/Ay6XNEbkqP8a+GAnDHKyjSW0XAvXnRNwzTmJIxoZ+69K2EjqWmAT0RLBW0MgqqvD8VuItqNeRbRT5STw/tC8qgbDUsBPdUOHjawO+DHVxy+6vk42Vz5cigrl+XzFEupDC8s/tMlTytdsW65iDXehfKwzN13egzdUMf6by1ccr4jrPvIf5flZL1bEeP/1qWX5PRV7zefnlp+/cFz5C047spOR3HKttHSXdc3BYOluEDTnZBDl6s0JqEn40767ouyWkucGXFOlXZwGMbMrWjaoBmn/rzi9ROiadZzEcM05qSDU+hLBnsKdAKc5fLqTkzSuOSdppGN2/OxX3AlwmqDzk7EcpzauOSd5JKHh/totMA53ApzGsUQnaTmOa85JDe8JSIHKSVn1qJykVah4NcdsPlaRt4rP2FT7x6buRioVhyt/uyrzlRO8ctPlFWyy3MBCpSbT+G3ssx/kntcc9L/u+kxzTg8gnxPgONXx8VknaVxzTtL4nADHqYIBPj7rJIlrzkmBNjcQ6incCXCawgr16zhOJ3HNOYkjoTbiBPQS2XYCKr789cZfjxlvrXf8mPq1+x0rx2crA8k0tEVELep0e1Zer9L+ROj38dmMay4qqyjod931u+acDCLUYsTAXiPbToCTLazKH5DjdBPXnJMGYe+AQcCdAKcJ5HdlTsK45pzkkfcEOE4MflfmJI1rzkkaqa29A3qJRrYSXibph5KekPSYpA+F8vmSNkvaHh5H27bGKlKuPBVm10mzypMNVyYrT7nyhChPx7wZVpZsqCJVnL9QkY6pn6NmOsaeylT5fiVB5TW7dP3EdNdjmhtI3fWb5pzsI1BuKDbVbS6tlPSUpB2SbqhyXJK+EI4/Iul19dp2S4d1nQBgBrjOzP4IOA+4RtJZwA3AFjNbDmwJeaefsWiiWlzqMK47xzXnpILCnIC4VKftEHAzcAlwFtFW1GdVVLsEWB7SGuDLDbTtig7rOgFmNmFmPw/PXwSeAJYAlwLrQ7X1wDs6YZCTcRK6K3PdOUdwzTlJE7YSjkt1OAfYYWbPmtlhYAORhkq5FLjNIh4A5klaVKdtV3TYSE/AESSdBqwAHgQWmtkERF8e4NSYNmskbZO0LX/oUHvWOqlTpXf6SOraNV13A00vam7Pnj3dM85JBOVysQlYUPysQ1pT0nQJ8HxJfjyU0UCdWm0b0mGzNDwxUNIJwB3Ah83soOrEPC9iZuuAdQBzly6rsyi6PFsvZnm9NdqVvxLHTDJusjex3UnKle1VZ4F35qKlphC9reu663PNVTtHT+muRzW3YmwsU2+j0zz5Qs2P8AUze0PMsWqCaSSahzXYtqM01BMgaRbRl+IbZvbdULwrdF8QHnd3x0QnU7TRNVtvskyV+q47p+3hgGZ055pzIJJW3iw21WEcWFaSXwrsbLBOrbZd0WEjqwMEfA14wsw+V3JoI7A6PF8N3NUJg5xso0J8qtmusckypfVddw7QuuagOd255pwiZsb0TD421WErsFzS6ZJmA5cRaaiUjcCVYZXAecCB0MVfq21XdNjIcMD5wBXALyU9HMr+DlgL3C7pKuA54N2dMMjJOK13TB2Z8AIgqTjh5fGY+q47J6K9ztBmdOeacwAwg3yhtQAVZjYj6VpgEzAE3Gpmj0m6Ohy/BbgbWAXsACaB99dqG07dFR3WdQLM7MfEj2Re1PKVq32xK8dfjxlvrTje6aHCLo/idXwiU8KB1BSWa9VggaRtJfl1YZwUqk94OTfuRInprs81B72tuzY1B03ormuac3oOw5jO173jj29vdjfRH31p2S0lzw24ptG2oXwPXdChRwx0mqJOF2y7k2Uc5xja0By47pwWMDMO1+/27wvcCXCao/Wfz0YmyzjOsbT3l+26c1qizuqAvsGdAKdx2tvR7ciEF+A3RBNe/qJDljn9Svu7CLrunKYxo63hgF4iU05Au2uqm79gZ0/X9bHXLDimLdpQZ8JLavS65mAAdNfG9bOqOyfbGOY9AY5TjXb+cOImvDhOLdp1clx3TrOYweH8YGxf6U6A0xyD4Rw7WcI15ySM0foSwV7DnQCncdofn3Wc5nDNOSlgZkx7T0D2SHz8tg7d3MAki4jB+0HOmuZgsHQ3iJpz0icaDvCJgY5zLAP0B+RkBNeckzBGQ3sE9AXuBDiN412zTtK45pwUMIPDM4MhPHcCnOYYDOfYyRKuOScFBqUnoJFdBG+VtFvSoyVlH5f0G0kPh7SqI9aoPFlFqt/eylLT7Z26tLOjW1PXSUp3rrnM03eaczJPAeNwvhCb2kHSfEmbJW0Pj6Mx9apugS3p05KelPSIpDslzQvlp0n6XYlWb6l23krqOgHA14GVVcpvMrOxkHwN7iBQa1/3zjvNX8d157jmnDSwKGxwXGqTG4AtZrYc2BLyZdTZAnsz8Foz+2PgaeDGkqbPlGj16kaMqesEmNmPgL2NnMzpf5K6K3PdOUVcc07SFKBrPQFEW1mvD8/XA++oUufIFthmdhgoboGNmd1jZjOh3gNE+2G0TCM9AXFcG7ojbo3rznD6j4re77KUEK67AcM15ySORXMC4lKbLDSzCYDweGqVOtW2wF5Spd4HgO+X5E+X9AtJ/yLpjY0Y06oT8GXgD4ExYAL4bFxFSWskbZO0LX/oUIuXczKBEbnIcan7uO4GjR7V3J49exIxzukOBYypfCE2AQuKn3VIa0rbS7pX0qNV0qUNmlB3C2xJHwVmgG+Eogng98xsBfAR4JuSTqp3oZZWB5jZrhJDvgJ8r0bddcA6gLlLlw3GdMs+RaQbqMZ1N3j0quZWjI255nqYKFhQzY/wBTN7Q3x7e0vcMUm7JC0yswlJi4DdVarV3AJb0mrgz4CLzKKuCTObAqbC84ckPQOcCWyr9UJa6gkIhhd5J/BoXF2nv0iza9Z1N5i45pw06OJwwEZgdXi+GrirSp0jW2BLmk20BfZGiFYNANcDbzezyWIDSaeECYVI+gNgOfBsPWPq9gRI+hbwZqLuj3HgY8CbJY0RdU/8GvhgvfM4fUJC8TNcd84RXHNOwhTMurmL4FrgdklXAc8B7waQtBj4qpmtqrMF9heBOcBmSQAPhJUAfwr8vaQZIA9cbWZ1J7rWdQLM7PIqxV+r164rDPq667Rff4KTsTKju7Tf8yyQ5nswiJpzMkG3Agaa2R7goirlO4FVJfmqW2Cb2Rkx570DuKNZezxioNMUHsLVSRrXnJM0hfpzAvoGdwKc5hiM74WTJVxzTgoMSthgdwKcxvHNXJykcc05KVAAprwnoAcYpI3VM4Dv7Y5rLmFcc04aNLBEsG/obSfASZ7B+F44WcI156TAgPgA7gQ4TWCg9jfPcJzGcc05KVAw4/DMYOjOnQCnKbw33Eka15yTBt4TkAIDv/965a9dBt+QfhufzeBbnDwZ112/ac7JPtGcgLStSIZMOQFODzAg3rGTIVxzTsIY0L2AgdnCnQCncXy5lpM0rjknBcxgekDGA9wJcBomWq41GF8MJxu45pw0GKThgJZ2EXQGl27t6Cbp3ZIek1SQ9IaKYzdK2iHpKUlvKyl/vaRfhmNfUNhNQ9IcSd8O5Q/azMyRcx18aCvPffofeO7T/8DBh7a2Z7STCL2ouanDh4+c65sbNrDi7HNYcfY5fHPDhvaMdhKhOBwQl/qJuk6ApFsl7Zb0aEnZfEmbJW0Pj6PdNdPJBAbKx6c2eRT4c+BHUKa77UTbaL4GeC9wZ1F3RHu3ryHaMnM5sDKc6ypgX9ho46aZgwcAyE8eYt+WTSy55kMsuebD7NuyifzkJE6G6VHNTeyMtn7fu28faz/9Gbbcs4n7Nt/D2k9/hn3797dtuNNdouGA+NQOjf5/SloZnNAdkm4oKf+4pN9IejikVSXHqjqvtWikJ+DrHBV6kRuALWa2HNgS8s4gYDVSO6c1e8LMniop+jqR7k4ENpjZFPAXwDhwJfAz4DQz+6mZGXAb8I7Q9lJgfXj+ncLUFGbG5NNPMXLGmQyNHM/QyAgjZ5zJ5NNPtme40316UHMvvvQSZsZ99/2QC970JuaPjjI6bx4XvOlNbNlyX3uGO12nyz0Bdf8/JQ0BNwOXAGcBl0s6q6TKTWY2FtLdoc1ZHHVeVwJfCuepSV0nwMx+BFTuSVwq+PUc/VxaKPoAABADSURBVCI4/UwI3BKXiPZh31aS1rR8qaO6mwU8H4ovBbYBS4ju3uaWNBkP5YTH58N5ZpAoTB4if+AAwycfdbqHTp5HPvQSOBmlRzU3NDTE3r172TkxwdIli480WLJ4MTsnJlo10UkIM5ieiU9t0sj/5znADjN71swOAxtCu3rn3WBmU2b2K2BHOE9NWp0YuNDMJgDMbELSqXEVw5dyDcDwPB816HXqjMO+YGZviDso6V7glVUOfdTM7mrg8guB3xE56nuB2RXHi9YJ+AtJ7wEglwOEVb11zNaaeOdYelFzQ0NDSMKq7EQXphE4GafO4oAFkraV5NeZ2boGT93I/+cRpzIwDpxbkr9W0pVEDup1ZrYvtHmgos0S6tD11QHhjVkHMHfpsuY68Jr8rmQsxknf0e5mLmb2lhaaTQPLSvJLgZ1EAs9VKScc+1cz+5SkYWA6NzLC8MnzePmZHUca5A/sZ9YfnFF+NddcpuhlzY2OjrJk8WL+9Sc/OdLgNzt38sbzz2/BJCdJij0BNWjZ+WzQhGq/LMX/zy8Dnwj5TwCfBT5Qp00sra4O2CVpEUB43N3ieZxewuK7Zbu4jOtF4DJJc4juxF5NNDYLcFjSeWGG9pVA8c5uI7A6PH9Xbs4cJDFy5quY3P40+clJ8pOTTG5/mpEzX9Utu51O0KOaO/GEE5DEhRdewH3338++/fvZt38/991/PxdeeEG37HY6hNHexEAze4uZvbZKuovG/j/Hqe6IYma7zCxvZgXgKxzt8o9tU4tWnYBSwa/m6BfB6Xe6NElL0jsljQP/Cfi/kjaFQ1PA7cDjwDyiCTV5It3dDnyVaOzrGeD7oc3XgFdI2gF8ZPikkwEYGjme0QsvZvzmmxi/+SZGL3orQyPHt2e40316UHOLFi0CYP7oKH973Ue44OKLueDii7n+b65j/qgPi2Yds65ODGzk/3MrsFzS6ZJmE0342whHHIci7yRa5VI872VhuerpRKtXfkYd6g4HSPoW8GaiMZBx4GPAWuB2SVcBzwHvrncepw8wUJeiaJnZncCdxXzQ3U+BBcBfEenun4l0t52gOzOrnLSKmb1MiSZLh6FOOvtcTjr73MomTlbpUc2tGBs7YvQV73sfV7zvfV15DU53aGA4oB2q/n9KWgx81cxWmdmMpGuBTcAQcKuZPRbaf0rSGJEb/Gvgg5HN9pikovM6A1wTnNea1HUCzOzymEMX1WvbcXw7sfRJ6CPIjO5cc+kzaJpzMkG3ogab2R6qaMrMdgKrSvJ3A3dXqXdFjXN/EvhkM/Z42GCnKTyEq5M0rjknaaKegMGY9etOgNMUfmPsJI1rzkka30XQcaog87syJ1lcc04amMl7AnoBX6OdPN2apNUruOaSZ9A15yRPlycGZoqedgKchOnAsizHaQrXnJMShcJgePzuBDhN0NUALY5TBdeckzxmMOM9AY5ThSqx0B2nq7jmnBQw7wlIgU6/55Xn6/BvSeX4cNuzmLM+4NzFwC2p0WOagwHTXT9qzsk+RhRuZwDIlhPgZB//PXaSxjXnpEDOlwg6zrGoMCDfDCczuOacpJHB8HTaViSDOwFOw8jMu2adRHHNOWkgg9kzg6G7tpwASb8m2nYzD8zU2l/52MaN1GnyQ6h3zgTGa/ueDEzS6qruXHPZo9c15/QcMhjqkvMpaT7wbeA0og2A3mNm+6rUWwl8nmgDoa+a2dpQ/m2guAf6PGC/mY1JOg14AngqHHvAzK6uZ08negIuMLMXOnAeJ+sY3dtVo3lcd4OAa85JAZkxu3trBG8g2p56raQbQv76sutLQ8DNwMXAOLBV0kYze9zM3ltS77PAgZKmz5jZWDPG5Fp8Ec6AIrPY5DjdwDXnJI+RKxRiU5tcCqwPz9cD76hS5xxgh5k9a2aHgQ2h3REkCXgP8K12jGnXCTDgHkkPSVpTrYKkNZK2SdqWP3Sozcs56WJQKMSnRA1x3Q0Gvam5PXv2JGmb02FkxvDM4dgELCh+1iFV1UQMC81sAiA8nlqlzhLg+ZL8eCgr5Y3ALjPbXlJ2uqRfSPoXSW9sxJh2hwPON7Odkk4FNkt60sx+VFrBzNYB6wDmLl3Wlut+zHLmdpc3+3htcxiZGJ8lQd255lKmRzW3YmwsE0Y7rWLIag4HvFBrXoike4FXVjn00QYNqPZLU6mpyynvBZgAfs/M9kh6PfDPkl5jZgdrXagtJ8DMdobH3ZLuJOrC+FHtVk4vk4WZ2q67wcI15ySNzMjNTLXc3szeEntuaZekRWY2IWkRsLtKtXFgWUl+KbCz5BzDwJ8Dry+55hQwFZ4/JOkZ4ExgWy1bWx4OkHS8pBOLz4G3Ao+2ej6nByhush2XEsB1N2C45pw0sALKT8WmNtkIrA7PVwN3VamzFVgu6XRJs4HLQrsibwGeNLPxYoGkU8KEQiT9AbAceLaeMe30BCwE7ozmJjAMfNPMftDG+ZzMY1nomnXdDRSuOSclag8HtMNa4HZJVwHPAe8GkLSYaCngKjObkXQtsIloieCtZvZYyTku49gJgX8K/L2kGaKlrFeb2d56xrTsBJjZs8CftNq+H2g7ZnuzF8hCjPeUo7e57gZQd645J3EK0P4df1XMbA9wUZXyncCqkvzdwN0x5/jLKmV3AHc0a49HDHQaxwDf1tVJEteckwZm3ewJyBTuBDhNYFDIp22EM1C45pw0sK71BGQNdwKcxvG7MidpXHNOKhjmPQEpUDEW2fWhyDZ/Wzq+r3sv0G87uvWY5mAAdddvmnMyj5lhA9IT4GGDncYxg3w+PrWBpE9LelLSI5LulDSv5NiNknZIekrS20rKXy/pl+HYF0IYTSTNkfTtUP6glcQAP/jQVp771D/w3Kf+gYMPbW3LZicBelRzU4cPH7nONzdsYMXZ57Di7HP45oYNbdnsJEUBK0zFpn7CnQCnOcziU3tsBl5rZn8MPA3cCCDpLKLlMK8BVgJfKq6FBb4MrCFaD7s8HAe4CthnZmcAN80cjPbXyE8eYt+9m1hy7YdYcu2H2XfvJvKTk+3a7XSbHtTcxM4orsvefftY++nPsOWeTdy3+R7Wfvoz7Nu/v127nW5jYIV8bOon3AlwmsC6FrjFzO6xo4NwDxBFyIJo04wNZjZlZr8CdgDnhEhbJ5nZT83MgNs4uhFH6QYd3ylMTWFmTD79FCNnnMnQyPEMjYwwcsaZTD71ZFt2O92mNzX34ksvYWbcd98PueBNb2L+6Cij8+ZxwZvexJYt97Vlt5MEg9MTkK05AfXIwDL5Utoei017/XWzGJglMj77AaL9tiHaNOOBkmPFjTSmw/PK8mKb5wHMbEbDwxQmD5E/cIDheaNHGgydPI/8wdJdOKuQwY9ooHTXo5qbM2cOe/fuZefEBEuXLD7SYMnixeycmOjOK3A6ill/3fHH0VtOgJM+te++FkgqjVO9LmyqAtTeVMPM7gp1PgrMAN8oNqtS32qUx7QRVnVWXg/9IQ4qPao5SViVIYswjcDJMGYFCgMyMdCdAKdxzOrN1K65s1atTTUAJK0G/gy4yI7+esZtpDHO0e7b0vLSNuOShsnlyI2MMHzyPF5+ZseRBvkD+5n1h39YyyQnbXpUc0NDQ4yOjrJk8WL+9Sc/OdLgNzt38sbzz69lkpMVBqQnwOcEOE1h+XxsagdJK4HrgbebWelsvY3AZWH29elEk7F+FvbhflHSeWGG9pUc3YijdIOOd+XmzEESI2e+isntT5OfnCQ/Ocnk9qcZedWr27Lb6T69qLkTTzgBSVx44QXcd//97Nu/n33793Pf/fdz4YUXtGW3kwChJyAu9RNt9QSEL9HniTY4+KqZre2IVU5E1sZuzbq5c9sXgTlEe7UDPGBmV5vZY5JuBx4n6rK9BrhY0ueBEeCfgUPA90MC+Brwj5J2AHuHTzoZgKGR4xm96GLGv3gTAKMXvZWhkZFuvZ7eJUu661HNLVq0CID5o6P87XUf4YKLLwbg+r+5jvmjR+elONnEMAr5w/UrtoCk+UTzT04Dfg28x8z2Val3K1Ev1W4ze20j7SXdSLRSJQ/8tZltqmdPy05AWDJzM3AxUVfYVkkbzezxVs/p9ABdmqQVllbFHfsk8Ek4orunKdEdcHmp7szsZcLOXABzly47MjB70tnnctLZ55aevVMvwekWPai5FWNjR4R1xfvexxXve1/H7Xe6iFk3JwbeAGwxs7WSbgj566vU+zqRo3pbI+0rlrYuBu6VdKbVeSHtDAecA+wws2fN7DCwgWiZjNOnRFG0utM12wSuuwHCNeekQ9QTEJfapHQ56XqOLjMtt8DsR0C1rYDj2ldd2lrPmHacgCNLYgKly2WcPsUKFpsSwnU3YLjmnDQwy8emNlkY5pcQHk/tUPuWdNrOnIBay2WOVpLWEEXYAph65vqPPNrGNbvNAuCFtI2oQ7dt/P24Ay+yb9O9hdsX1GibxHvnukueJOyrqrte1tzJC05xzbVHar9104cPbpp47p5aupvb6tLU5s1smIZ0Wkk7TkDcMppyC6I3Zh2ApG21lvOkTdbtg3RtNLOV9Wt1HdddwrjmXHNp0Mu6q7U0VdIuSYvMbCJEodzd5Onj2jek00raGQ7YCiyXdLqk2UQTEja2cT7HaQTXnZM0rjmnk5QuJ13N0WWm7bavurS13sladgJCzO1rgU3AE8DtZvZYq+dznEZw3TlJ45pzOsxaoiWn24lWnKwFkLRY0t3FSpK+BfwUeJWkcUlX1WofNFlc2voD4Jp6KwMAVC2sZbeQtKZ03CRrZN0+6A0bs0bW3zO3r//I+nuWdfugN2zsBxJ1AhzHcRzHyQ4eNthxHMdxBpREnABJKyU9JWlHiHCUOpJulbRb0qMlZfMlbZa0PTymFt9T0jJJP5T0hKTHJH0oazZmHdddS/a57trANdeSfa65FOm6E1ASXvgS4Czg8hDeMG2+DlQuAymGY1wObAn5tJgBrjOzPwLOA64J71uWbMwsrruWcd21iGuuZVxzKZJET0AmQ27GhGRsKJxjEpjZhJn9PDx/kWhW8hIyZGPGcd21gOuuLVxzLeCaS5cknIBeCrnZbjjHriDpNGAF8CAZtTGDuO7axHXXNK65NnHNJU8STkBLoQydCEknAHcAHzazg2nb00O47trAddcSrrk2cM2lQxJOQEuhDFNiVwjDSIvhHDuKpFlEX4pvmNl3Q3GmbMwwrrsWcd21jGuuRVxz6ZGEE9BLITfbDefYMSQJ+BrwhJl9ruRQZmzMOK67FnDdtYVrrgVccyljZl1PwCrgaeAZ4KNJXLMBm74FTADTRB78VcAriGahbg+P81O07z8TdSU+Ajwc0qos2Zj15LpryT7XXXvvn2uueftccykmjxjoOI7jOAOKRwx0HMdxnAHFnQDHcRzHGVDcCXAcx3GcAcWdAMdxHMcZUNwJcBzHcZwBxZ0Ax3EcxxlQ3AlwHMdxnAHFnQDHcRzHGVD+P15BYdngjKzSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x216 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,3,figsize=(8,3))\n",
    "m0=ax[0].pcolormesh(fS.variables['NO3TVDY'][0,:,0,:])\n",
    "plt.colorbar(m0,ax=ax[0])\n",
    "m1=ax[1].pcolormesh(fS.variables['ATY_NO3'][0,:,0,:])\n",
    "plt.colorbar(m1,ax=ax[1])\n",
    "m2=ax[2].pcolormesh(fS.variables['NO3TVDY'][0,:,0,:]-fS.variables['ATY_NO3'][0,:,0,:],cmap=cmocean.cm.balance)\n",
    "plt.colorbar(m2,ax=ax[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (py38)",
   "language": "python",
   "name": "py38"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}