{
 "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(2016,1,6) # 1st start date of run\n",
    "#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=20\n",
    "dlist=[t0+dt.timedelta(days=ii*10) for ii in range(0,nlen)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'spring16spun_Z7'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sdir0='/results/SalishSea/nowcast-green/'\n",
    "#sdir1='/results/SalishSea/hindcast/'\n",
    "#sdir1='/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_Z2/'\n",
    "#sdir1='/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_eff0/'\n",
    "#sdir1='/data/eolson/MEOPAR/SS36runs/CedarRuns/spring2015_NoMZME/'\n",
    "#sdir1='/data/eolson/MEOPAR/SS36runs/CedarRuns/spring15spun_Ztest/'\n",
    "sdir1='/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/'\n",
    "tit=[i for i in sdir1.split('/') if len(i)>0][-1]\n",
    "tit"
   ]
  },
  {
   "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/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160106-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160116-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160126-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160205-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160215-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160225-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160306-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160316-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160326-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160405-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160415-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160425-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160505-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160515-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160525-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160604-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160614-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160624-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160704-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160714-*[0-9].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-*[0-9].nc'\n",
    "        fformatP='SalishSea_1h_*_ptrc_T_%Y%m%d-*[0-9].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": [
       "<matplotlib.text.Text at 0x7f3ed077a7b8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Qeh5deWX4NTpgQF7+BMnBkCFhMaVDD40diZQjlRAq0MCBcPvt8OSTISl89114\nfu7c8KV9zDGhzWDSpJYng5RrrgmjmI8/Pvw6lXi+/jp0BujbVyvsScsoIVSo/v1DUhg3Dg44ILQP\nbLNNaHC85JIwpcEqq+R+njZtwmjpNdcMDdr//W/ux5SWGT48JP9jj40diZQrc/fYMWStpqbG6+rq\nYodRVoYODcnBHTp2hHvugT33zP953nwzzLXftSs8+ywss0z+zyGZ1dSE3kWvvNJ4LzGpTmY2wd1r\nmtpPJYQKd+SRcO+9YS2FSZMKkwwANtoonOe110ICWriwMOeR9CZNCgMJjzlGyUBaTgmhChx6aKhO\nWGONwp6nZ88wtcWIEWFRHymeW28Ng9AOPzx2JFLO1MtI8urMM0Mp4U9/Cj2P1Nul8L7/Hu6+O8xp\n1ZyuwyINqYQgeWUW5lfq0SP0aJowIXZEle+BB0IPIzUmS66UECTv2rULA946dgwD5D77LHZElW3I\nEFh3Xdh559iRSLlTQpCCWHXVMChu9uzmz68k2XvnHXjmmTAQUY3JkislBCmY7t1Dt9cXXwwD1yT/\nbr01zELbv3/sSKQSKCFIQR18cJjr6K674O23Y0dTWebNC8tkHnAArL567GikEighSMGdcEK4feih\nuHFUmocfDuteayI7yRclBCm4Ll1g883Doi2SP0OGhLEle+8dOxKpFFETgpmdZWZuZh1ixiGF17s3\nvPCCehzly8cfh/moBg6E1hpNJHkSLSGY2ZrAXoCmQ6sCvXuH+ZQefjh2JJXh9tvD9TzqqNiRSCWJ\nWUK4BjgXKJ/Z9aTFNtkkTHynaqPcLVgQehftuWe4piL5EiUhmFkvYJq7T45xfik+s1BKePJJ+Oab\n2NGUt7FjwzTjakyWfCtYQjCzsWb2epqtF3AhcHGWxznOzOrMrG7WrFmFCleKoHdv+OmnUPctLTdk\nCKy8chgFLpJPBUsI7r6Hu/+y4Qa8D3QFJpvZh0AnYKKZrdbIcQa5e42713Ts2LFQ4UoR9OgBHTqo\n2igXM2eG7rv9+oUpQkTyqej9E9z9NeB/a3UlSaHG3T8vdixSXK1bh0FUI0eGkkLbtrEjKj9Dh4YB\naaoukkLQOAQpqt69w8ycTz8dO5Ly4w6DB4eSVrdusaORShQ9Ibj7WiodVI8994Sll1a1UUs8+2yY\n/kOlAymU6AlBqstSS4WRtQ89pGU2m2vIEFhuOejTJ3YkUqmUEKToeveGadO0eE5zfP013Hcf9O0L\nyy4bOxqPGjPuAAARjklEQVSpVEoIUnT77RembFa1UfaGDw9LZWpVNCkkJQQpupVXhp12UkJojsGD\nYbPNYMstY0cilUwJQaLo3RveeCOs+CWZTZwYtmOO0apoUlhKCBJFapSt1kho2nXXhZ5Zhx8eOxKp\ndEoIEoXWSMjOtGkwbFhYM3mllWJHI5VOCUGi6d0bnn8epk+PHUnp+sc/wuymZ5wROxKpBkoIEo3W\nSMjsm2/g5pvhV7+CtdeOHY1UAyUEiUZrJGR2661h/MFZZ8WORKqFEoJEk1ojYexYrZHQ0Pz5cM01\nsOOOsM02saORaqGEIFFpjYT0HnggLIJz9tmxI5FqooQgUWmNhMW5wxVXwPrrw/77x45GqokSgkSV\nWiPh0UdDSUHC1OATJ4a2g1b6HypFpI+bRKc1En7uyiuhY0c48sjYkUi1UUKQ6LRGwiJvvBFKS6ec\nEqYKFykmJQSJTmskLHL11eF6nHRS7EikGikhSEnQGglhxPbQoTBwYGhoFyk2JQQpCVojAa6/HubN\ngzPPjB2JVCslBCkJ1b5Gwty5cOONcNBBsO66saORaqWEICWjmtdIuO02mD1bA9EkLiUEKRmluEbC\n9Olw+eWhXv/rrwtzjtQ0FT16wHbbFeYcItloHTsAkZT6ayScc068OObPh8cfD5PLPfJImH7aLDR6\nP/ootGmT3/M9+CB88AFcdVV+jyvSXCohSEmJuUbCu+/CBRdA585w4IEhjt/+Ft58M1TpPPEEnHBC\nmFoiX1LTVKy7bjinSExKCFJSir1GwnffwV13wc47h7mDLr88LGT/4IPwySfh8YYbwoABcPHFITH8\n5S/5O/9//gMvvxwSzxJL5O+4Ii1hns+fOwVWU1PjdXV1scOQAnKHddaBjTYK1TOFOkddXagSGj4c\n5swJv9CPOgr694df/KLx9/XvH8YKDB0KRxyReyy9esGzz4aZTZdeOvfjiaRjZhPcvaap/dSGICUl\ntUbCDTeENRKWWy5/x/7iC7j77pAIXnstjAg+5JCwXvFOO4VzNxXbkCGh5HDUUdCpE+yyS8vjeftt\nGDUqlDyUDKQUqMpISk6+10h480047LDwy/+MM6BdO7jpJvjss0XVRU0lg5S2bWHkSFhvvTBm4M03\nWx7X1VeHWE4+ueXHEMknJQQpOflaI8E9lDS22AJGjw4NwpMnhzr7E06AFVZo2XFXXBEeewyWXBJ6\n9mxZA/jMmXDnnaEKapVVWhaHSL4pIUjJyccaCdOnh+kwTjkFdt01/JL/+99h003zE2OXLqHhe9as\nsIjN3LnNe/8NN8CPP4bGZJFSoYQgJSmXNRJGjYJNNoGnngrzAz36KKy2Wv5jrKmBe++FSZOgb98w\nXiEb330XEsKBB8IGG+Q/LpGWUkKQktSSNRLmzoXjjw89dzp1CjOnnnxy9u0DLXHAAXDddaG0cMYZ\n2Y1RuPPO0MCtaSqk1CghSElq7hoJdXWhrWDwYDj3XHjxRejWrfBxQkg6Z50VSiPXXJN53wULQmPy\n1lvDDjsUJz6RbCkhSMnKZo2EBQvCQLHttgtVMU8+CZddFnoDFdPll8OvfhV+9Y8Y0fh+o0bB1Klh\nao5CllxEWkIJQUpWU2skfPBB6DJ60UVhPMGrr4YG5BhatQqD1bbdNgxYe/759PtdeSV07Rq6rIqU\nGiUEKVmNrZHgHr58N9ssDDC7+24YNgxWWilOnClLLRWquNZYIzQYT53689efey5smqZCSpUSgpS0\nhmskzJ4dBpn16wfdu4dxBYcfXjrVLx07hjEKCxfCvvuGxuOUq64KSWvgwHjxiWSihCAlrf4aCePG\nhXEEI0fCX/8aupWutVbU8NJaf/3QVvDf/4b4f/ghlBYefBBOOgmWWSZ2hCLpaS4jKWmpNRIuvTSU\nDtZbL9TP1zQ5TVdc228fpsX49a/DaOT27cM6CqecEjsykcYpIUjJ69MHLrwwTDdx5ZXl8wv70EPh\nww/hvPPC46OPLswAOZF80fTXUvLmz4f33w9VMeXGPYxTuPXWMKK5WGMjROrLdvprtSFIyWvdujyT\nAYTG7htuCDOrKhlIqYuWEMzsVDN7y8ymmNnlseIQKTSz0IYgUuqitCGY2a5AL2Azd//RzDQBsIhI\nZLFKCCcCl7r7jwDuPjNSHCIikoiVENYHdjSzF83saTPbKlIcIiKSKFiVkZmNBdJ1svtdct72wLbA\nVsB9Zra2p+nyZGbHAccBdO7cuVDhiohUvYIlBHffo7HXzOxEYGSSAF4ys4VAB2BWmuMMAgZB6HZa\noHBFRKperCqjfwG7ApjZ+kBb4PNIsYiICPFGKt8G3GZmrwM/Af3TVReJiEjxREkI7v4TcESMc4uI\nSHoaqSwiIoASgoiIJMpqcjszmwV81MK3d6C0G64VX24UX24UX+5KOcYu7t6xqZ3KKiHkwszqspnt\nLxbFlxvFlxvFl7tyiLEpqjISERFACUFERBLVlBAGxQ6gCYovN4ovN4ovd+UQY0ZV04YgIiKZVVMJ\nQUREMqi4hGBm+5jZ22Y21czOT/O6mdl1yeuvmtkWRYxtTTN7yszeSFaKOz3NPruY2ddm9kqyXVys\n+JLzf2hmryXnXmwB68jXb4N61+UVM5tjZmc02Keo18/MbjOzmck0LKnn2pvZE2b2bnK7UiPvzfhZ\nLWB8VySrFb5qZg+a2YqNvDfjZ6GA8V1iZtPq/Rvu28h7Y12/f9aL7UMze6WR9xb8+uWdu1fMBiwB\nvAesTZgwbzLQrcE++wKPA0aYfvvFIsa3OrBFcn854J008e0CPBLxGn4IdMjwerTrl+bfejqhf3W0\n6wfsBGwBvF7vucuB85P75wOXNRJ/xs9qAePbC2id3L8sXXzZfBYKGN8lwNlZ/PtHuX4NXr8KuDjW\n9cv3VmklhK2Bqe7+vof5ku4lLNVZXy/gLg9eAFY0s9WLEZy7f+buE5P73wBvAmsU49x5FO36NbA7\n8J67t3SgYl64+zPAlw2e7gXcmdy/E+id5q3ZfFYLEp+7j3H3+cnDF4BO+T5vthq5ftmIdv1SzMyA\nQ4Hh+T5vLJWWENYAPq73+BMW/8LNZp+CM7O1gM2BF9O83CMpzj9uZhsXNTBwYKyZTUgWJ2qoJK4f\ncBiN/0eMef0AVnX3z5L704FV0+xTKtfxKEKJL52mPguFdGryb3hbI1VupXD9dgRmuPu7jbwe8/q1\nSKUlhLJgZssCI4Az3H1Og5cnAp3dfVPgH4S1I4ppB3fvDvQETjaznYp8/iaZWVvgQOD+NC/Hvn4/\n46HuoCS78pnZ74D5wD2N7BLrs3AToSqoO/AZoVqmFPUlc+mg5P8vNVRpCWEasGa9x52S55q7T8GY\nWRtCMrjH3Uc2fN3d57j7t8n9x4A2ZtahWPG5+7TkdibwIKFoXl/U65foCUx09xkNX4h9/RIzUtVo\nye3MNPvE/hwOAPYHDk+S1mKy+CwUhLvPcPcF7r4QGNzIeWNfv9bAwcA/G9sn1vXLRaUlhJeB9cys\na/Ir8jBgVIN9RgH9kt4y2wJf1yveF1RS53gr8Ka7X93IPqsl+2FmWxP+jb4oUnzLmNlyqfuExsfX\nG+wW7frV0+gvs5jXr55RQP/kfn/goTT7ZPNZLQgz2wc4FzjQ3b9rZJ9sPguFiq9+m9RBjZw32vVL\n7AG85e6fpHsx5vXLSexW7XxvhF4w7xB6IPwuee4E4ITkvgE3JK+/BtQUMbYdCNUHrwKvJNu+DeI7\nBZhC6DXxAtCjiPGtnZx3chJDSV2/5PzLEL7gV6j3XLTrR0hMnwHzCPXYRwMrA08C7wJjgfbJvr8A\nHsv0WS1SfFMJ9e+pz+DNDeNr7LNQpPiGJp+tVwlf8quX0vVLnr8j9Zmrt2/Rr1++N41UFhERoPKq\njEREpIWUEEREBFBCEBGRhBKCiIgASggiIpJQQqhy6WZzTJ7PasbOBu9Zq+FxWhjTL8zsgSz2uzDX\nc2UZz3gza/FauWZWY2bX5TOmBsffxcweydOxTklmD/XGBvSZ2Z7JdAyvJbe71Xtty+T5qRZmxU2N\nCdnJzCaa2XwzO6TB8RbUmz20mGMJpAElBLkD2CfN8+cDT7r7eoQ+9QWZXjgdd//U3Q9pek+anRDM\nbIkWhJQTd69z99OKfd4WepYw6CrTpIGfAwe4+yaEgXdD6712E3AssF6ypT5b/wUGAMPSHO97d++e\nbAfmFr7kQgmhynnjszlmM2Nn6hfhZDObDJxc7/m1zOzfya/CiWbWI3n+LjPrXW+/e8ysV4Nj/q+k\nYWYDzGykmdUmpZXLk+cvBZZKflXekzx3hJm9lDx3S+rL38y+NbOrkhgvMLP7653rf7+uzewmM6uz\nsFbFH9P8rUuY2R1m9nryK/jMNPv0SV6fbGbPpDnHJUmpbLyZvW9mp9V7bz8LE7pNNrOhyXMdzWyE\nmb2cbNun+3eod4ytzex5M5tkZs+Z2QaZrmND7j7J3T/MdI5kn0+Th1MI/w7tkhHGy7v7Cx4GON1F\n8rlx9w/d/VVgYaZjS2SxR8Zpi78Ba9Fgvnfgq3r3rf7jBvu9CuyU3L8idRxgaWDJ5P56QF1yf2fg\nX8n9FYAPSObmTxcP4Vfl+8m+SxJ+ua6ZvPZtvfdsBDwMtEke3wj0S+47cGhyvzXh1+oyyeObgCOS\n+6kRxUsA44FNk8fjgRpgS+CJeudcMc31eA1Yo/7r1FujgTDX/3NAO6ADYdR1G2BjwqjbDg1iGUaY\nJA2gM2Hak4bnrH/85Vm01sEewIimrmMj/64fksVc/sAhwNjkfk3qfvJ4RxqsTUEokR7S4Ln5hEkJ\nXwB6x/7/UM1ba0Sa4O5uZosNabew0taKHkoZEKoOeib32wDXm1l3YAGwfnKsp83sRjPrCPyK8IU1\nn8yedPevk3O+AXTh51MfQ1gfYUvg5aTaeikWTSq3gDChIO4+38xqgQOSdor9CPP6ABxqYZri1oTF\njLoREl7K+8DaZvYP4FFgTJpYnwXuMLP7gMUmL0w86u4/Aj+a2UzC9Ni7Afe7++dJnKlS2x5At+Rv\nAljezJb1ZAK/NFYA7jSz9QiJsE2917K5jlmzMLX4ZYR5enLRxd2nmdnawDgze83d38vxmNICSgjS\nmBlmtrq7f2b1Zuw0s9sJ6zh8Cvwmw/vPBGYAmxGqJn+o99pdwBGECckGZhHLj/XuLyD959aAO939\ngjSv/eDuC+o9vpcw59GXhJLLN2bWFTgb2MrdZ5vZHYRf0v+TPL8ZsDdh/qRDCesJ1N/nBDPbhpBo\nJpjZli38e1JaAdu6+w8Z9qnv/4Cn3P0gC2tujG/heTMys06EGTz71fvynsbPF9vJagZSXzQr6Ptm\nNp7w+VJCiEBtCNKYtDN2uvtAD41/+7r7V8BXZrZDst/h9d6/AvCZhymMjyRUw6TcAZyRHO+NHGKc\nZ2E6cQgN34eY2Srwv15SXRp539OEZRGPJSQHCFUtc4GvzWxVFpV0/sdCr5tW7j4CuCg5RsN91nH3\nF939YmAWP5+iOZNxQB8zWzkVf/L8GODUesfv3sRxVmDRl/CALM/dLEnJ8FHCMqHPpp73MOvtHDPb\n1kKRph/pZ3qtf6yVzKxdcr8DsD2Qy2dCcqCEUOXMbDjwPLCBmX1iZkcnL10K7Glm7xKqLS5t5BAD\ngRssLDRu9Z6/EeifNORuSPiyBcJ894TlQ2/PMfxBwKtmdk+SWC4CxpjZq8AThGqfxSSlhUcIX/qP\nJM9NBiYBbxHq7Z9N89Y1gPHJ33o3kK40ckXS4Pw6oa1gcjZ/iLtPAf4CPJ1cs9T06KcBNUlj8xuE\nkklDrVn06/9y4G9mNokWlADM7DQz+4Tw6/5VMxuSZrdTgHWBi21Rd9FVktdOAoYQZlR9j2Q1NjPb\nKjluH+AWM5uS7L8RUJf8zU8Bl+b4I0FyoNlOpejMbGlC4+sWqTptaTkzO53QkH1ukzuLZKA2BCkq\nM9uDsEjQNUoGuTOzW4FfEtozRHKiEoKIiABqQxARkYQSgoiIAEoIIiKSUEIQERFACUFERBJKCCIi\nAsD/AwQ9d1k3JTi/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3ed07ee780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": 8,
   "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": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160106-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160116-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160126-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160205-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160215-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160225-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160306-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160316-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160326-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160405-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160415-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160425-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160505-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160515-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160525-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160604-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160614-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160624-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160704-*[0-9].nc\n",
      "/data/eolson/MEOPAR/SS36runs/CedarRuns/spring16spun_Z7/SalishSea_1h_*_ptrc_T_20160714-*[0-9].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-*[0-9].nc'\n",
    "        fformatP='SalishSea_1h_*_ptrc_T_%Y%m%d-*[0-9].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": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3ed05f3470>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OoX6VFLUaE4K713f3pkm29dw9K11GZvYXM3Mza5aN40mBq6gIC5D8N+9VTyRX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\nTZTL+vm2J7cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3ed07eebe0>"
      ]
     },
     "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": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3ed05eca58>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XzcXAjyJS1DuRPSEii4Dfsj3dBZgY+H4icEUOby3ItRqS+ERkroik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\nxG6MMTHGErsxxsSY/wdpwrpD2Ph8AAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3ed05ecbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(SiGlobalTot[sdir1]-SiGlobalTot[sdir1][0],'b-')\n",
    "plt.plot(NGlobalTot[sdir1]-NGlobalTot[sdir1][0],'r-')\n",
    "#plt.ylim((-5e9,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(SiGlobalTot[sdir1],open(sdir1+'SiGlobalTot_'+tit+'.pkl','wb'))\n",
    "pickle.dump(NGlobalTot[sdir1],open(sdir1+'NGlobalTot_'+tit+'.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'spring16spun_Z7'"
      ]
     },
     "execution_count": 13,
     "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
}