{
 "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",
    "    e3t0=mesh.variables['e3t_0'][:,:,:,:]\n",
    "SOGtmaskPath='/ocean/eolson/MEOPAR/northernNO3PaperCalcs/save/SOGtmask.pkl'\n",
    "(tmaskSOG,_,_,_,_)=pickle.load(open(SOGtmaskPath,'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f7b0a658d30>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmask2=np.copy(tmaskSOG)\n",
    "tmask2[0,:,:,:250]=2\n",
    "tmask2[0,:,:,300:]=2\n",
    "tmask2[0,:,:410,:]=2\n",
    "tmask2[0,:,500:,:]=2\n",
    "plt.pcolormesh(tmask2[0,0,:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "idir='/data/eolson/results/MEOPAR/SS36runs/CedarRuns/shortTestnoSMS/'\n",
    "idir2='/data/eolson/results/MEOPAR/SS36runs/CedarRuns/shortTestTRD/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "fP=nc.Dataset(glob.glob(idir+'/SalishSea_1d_*_dian_T*.nc')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "fP2=nc.Dataset(glob.glob(idir2+'/SalishSea_1d_*_dian_T*.nc')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig,ax=plt.subplots(1,1,figsize=(3,6))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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', 'time_instant', 'time_instant_bounds', 'RIVNO3', 'PHYSTRNO3', 'ALLTRNO3', 'NO3_E3TSNAP', 'ATF_NO3', 'RDB_NO3', 'RDN_NO3'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fP.variables.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(545.99677, -1.8279446)"
      ]
     },
     "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": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.4866695, -2.6189249)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(np.ma.masked_where(tmask==0,fP2.variables['ATF_NO3'][0,:,:,:])),np.min(np.ma.masked_where(tmask==0,fP2.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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.13636784, 0.0)"
      ]
     },
     "execution_count": 10,
     "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": 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,fP2.variables['RDB_NO3'][0,:,:,:])),np.min(np.ma.masked_where(tmask==0,fP2.variables['RDB_NO3'][0,:,:,:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.3685015, 0.0)"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(np.ma.masked_where(tmask==0,fP2.variables['RDN_NO3'][0,:,:,:])),np.min(np.ma.masked_where(tmask==0,fP2.variables['RDN_NO3'][0,:,:,:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fe9dfd41ca0>"
      ]
     },
     "execution_count": 14,
     "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",
    "fig.subplots_adjust(wspace=.5)\n",
    "m0=ax[0].pcolormesh(np.ma.masked_where(tmask[0,:,:]==0,fP.variables['ATF_NO3'][0,0,:,:]),vmin=-.05,vmax=.05)\n",
    "plt.colorbar(m0,ax=ax[0])\n",
    "m1=ax[1].pcolormesh(np.ma.masked_where(tmask[0,:,:]==0,fP2.variables['ATF_NO3'][0,0,:,:]),vmin=-.05,vmax=.05)\n",
    "plt.colorbar(m1,ax=ax[1])\n",
    "m2=ax[2].pcolormesh(np.ma.masked_where(tmask[0,:,:]==0,\n",
    "                    fP.variables['ATF_NO3'][0,0,:,:]-fP2.variables['ATF_NO3'][0,0,:,:]),\n",
    "                    cmap=cmocean.cm.balance,vmin=-.05,vmax=.05)\n",
    "plt.colorbar(m2,ax=ax[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 40, 898, 398)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(tmaskSOG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#look at Asselin filter distribution in original TRD run:\n",
    "fig,ax=plt.subplots(1,1,figsize=(3,6))\n",
    "for ii in range(0,398):\n",
    "    for jj in range(0,898):\n",
    "        if tmaskSOG[0,0,jj,ii]==1:\n",
    "            ax.plot(np.ma.masked_where(tmask[:,jj,ii]==0,fP2.variables['ATF_NO3'][-1,:,jj,ii]/e3t0[0,:,jj,ii]),'k-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "physSum=np.sum(np.sum(np.sum(tmaskSOG*fP.variables['PHYSTRNO3'][:,:,:,:],3),2),1)\n",
    "allSum=np.sum(np.sum(np.sum(tmaskSOG*fP.variables['ALLTRNO3'][:,:,:,:],3),2),1)\n",
    "rivSum=np.sum(np.sum(np.sum(tmaskSOG*fP.variables['RIVNO3'][:,:,:,:],3),2),1)\n",
    "afiltSum=np.sum(np.sum(np.sum(tmaskSOG*fP.variables['ATF_NO3'][:,:,:,:],3),2),1)\n",
    "afiltSum_0=np.sum(np.sum(np.sum(tmaskSOG*fP2.variables['ATF_NO3'][:,:,:,:],3),2),1)\n",
    "afiltSum2=np.sum(np.sum(np.sum(tmask2*fP.variables['ATF_NO3'][:,:,:,:],3),2),1)\n",
    "afiltSum2_0=np.sum(np.sum(np.sum(tmask2*fP2.variables['ATF_NO3'][:,:,:,:],3),2),1)\n",
    "no3Sum=np.sum(np.sum(np.sum(tmaskSOG*e1t*e2t*fP.variables['NO3_E3TSNAP'][:,:,:,:],3),2),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data=[2669.855224609375, 2661.239990234375,\n",
       "                   2685.151123046875, 2701.3095703125, 2676.051025390625],\n",
       "             mask=[False, False, False, False, False],\n",
       "       fill_value=1e+20,\n",
       "            dtype=float32)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "afiltSum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data=[-72447.953125, -110218.71875, -121727.296875,\n",
       "                   -108173.546875, -77526.484375],\n",
       "             mask=[False, False, False, False, False],\n",
       "       fill_value=1e+20,\n",
       "            dtype=float32)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "afiltSum_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data=[7121.599609375, 6762.38037109375, 6752.140625,\n",
       "                   6814.40185546875, 6800.83447265625],\n",
       "             mask=[False, False, False, False, False],\n",
       "       fill_value=1e+20,\n",
       "            dtype=float32)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "afiltSum2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data=[-393241.25, -517312.5625, -573720.3125, -529414.25,\n",
       "                   -389891.09375],\n",
       "             mask=[False, False, False, False, False],\n",
       "       fill_value=1e+20,\n",
       "            dtype=float32)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "afiltSum2_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "no3diff=(no3Sum[1:]-no3Sum[:-1])/(24*3600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data=[2669.855224609375, 2661.239990234375,\n",
       "                   2685.151123046875, 2701.3095703125, 2676.051025390625],\n",
       "             mask=[False, False, False, False, False],\n",
       "       fill_value=1e+20,\n",
       "            dtype=float32)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "afiltSum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data=[1449680.75, 138001.09375, -99406.421875, -130194.1875,\n",
       "                   -377263.625],\n",
       "             mask=[False, False, False, False, False],\n",
       "       fill_value=1e+20,\n",
       "            dtype=float32)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allSum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f64cea42a30>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,3,figsize=(16,5))\n",
    "ax[0].plot(range(1,5),no3diff,'k-',label='no3diff')\n",
    "ax[0].plot(range(0,5),allSum,'g--',label='all')\n",
    "ax[0].plot(range(0,5),afiltSum,'g--',label='afilt')\n",
    "#ax[0].plot(range(0,5),afiltSum,'b--',label='afilt')\n",
    "ax[0].legend()\n",
    "ax[1].plot(range(0,5),allSum,'g-',label='all')\n",
    "ax[1].plot(range(0,5),physSum+afiltSum,'r--',label='phys+afilt')\n",
    "ax[1].legend()"
   ]
  },
  {
   "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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\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
}