{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "from sqlalchemy import create_engine, Column, String, Integer, Float, Date, MetaData, Table, type_coerce\n",
    "from sqlalchemy.orm import mapper, create_session\n",
    "import csv\n",
    "from sqlalchemy import case\n",
    "import numpy as np\n",
    "from sqlalchemy.ext.automap import automap_base\n",
    "import matplotlib.pyplot as plt\n",
    "import sqlalchemy.types as types\n",
    "import numbers\n",
    "from sqlalchemy.sql import and_, or_, not_, func\n",
    "from sqlalchemy.sql import select\n",
    "import datetime as dt\n",
    "import os\n",
    "import re\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "import netCDF4 as nc\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# First, calculate river concentrations from EC data at Hope and Gravesend\n",
    "# NOTE: am assuming all forms of N are reported in grams of N, so that mole conversion is \n",
    "#  by division by 14.006720 (mol. weight N), rather than weights of full molecules. This \n",
    "#  is consistent with NO23 values in Susan's notebook.\n",
    "#  This is consistent with report in 'precdetectlimits.pdf', although it is rather old.\n",
    "#  However, assume Si in grams of SiO2 (mo. weight = 60.08). This is consistent with \n",
    "#  Si data in Susan's notebook (http://nbviewer.jupyter.org/url/bitbucket.org/salishsea/tools/\n",
    "#     raw/tip/I_ForcingFiles/Rivers/FraserRiverNutrients.ipynb?at=default&fileviewer=file-\n",
    "#     view-default)\n",
    "# Also appears to result in rough consistency with Debby's river Si and NO3 values\n",
    "mwN=14.006720\n",
    "#mwSiO3=76.083820\n",
    "mwSiO2=60.08\n",
    "mwAmm=17.031 ## ammonia NH3 g/mol\n",
    "#\n",
    "#  nh4 muM  (N) = nh4_cst from mean of data at Hope/Gravesend\n",
    "#  don muM  (N) = don_cst from mean of data at Hope/Gravesend\n",
    "#     - only 1 data point available. Should use it or set to zero? Sutton (2013) suggests DON may be unreactive on timescales\n",
    "#       of exchange of water in SoG, but Clair (2013) reports that organic nitrogen dominated total nitrogen in \n",
    "#       Canadian rivers.\n",
    "#       -> for now, using available data point\n",
    "#  no3 muM  (N) -> daily from smoothed data\n",
    "#  sil muM (Si) -> daily from smoothed data\n",
    "#  dia muM  (N) = 0.001\n",
    "dia = 0.001\n",
    "#  phy muM  (N) = 0.001\n",
    "phy = 0.001\n",
    "#  mes muM  (N) = 0.001\n",
    "mes = 0.001\n",
    "#  zoo muM  (N) = 0.001\n",
    "zoo = 0.001\n",
    "#  pon muM  (N) = 0.0\n",
    "pon = 0.0\n",
    "#  bsi muM (Si) = 0.0\n",
    "don=0.0\n",
    "bsi = 0.0\n",
    "#  oxy ? empty\n",
    "oxy = 160\n",
    "# other rivers const vals:\n",
    "const_si=59.57 ## updated June 2019\n",
    "const_no=6.18 ## updated June 2019\n",
    "const_nh=4.30 ## updated June 2019\n",
    "const_tur=1.0 # default river turbidity (NTU)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load EC Rivers database\n",
    "dbpath='/ocean/eolson/MEOPAR/obs/ECRivers/ECRiversDB'\n",
    "engine = create_engine('sqlite:///'+dbpath+'.sqlite')\n",
    "Base = automap_base()\n",
    "Base.prepare(engine, reflect=True)\n",
    "Profs=Base.classes.profiles\n",
    "session = create_session(bind = engine, autocommit = False, autoflush = True)\n",
    "# relevant data types: \n",
    "#('NITROGEN DISSOLVED NITRATE',)\n",
    "#('NITROGEN DISSOLVED NO3 & NO2',)\n",
    "#('NITROGEN DISSOLVED ORGANIC (CALCD.)',)\n",
    "#('NITROGEN NITRITE',)\n",
    "#('NITROGEN TOTAL',)\n",
    "#('NITROGEN TOTAL DISSOLVED',)\n",
    "#('NITROGEN TOTAL ORGANIC (CALCD.)',)\n",
    "#('SALINITY',)\n",
    "#('SILICA DISSOLVED',)\n",
    "#('SILICA EXTRACTABLE',)\n",
    "#('SILICA REACTIVE',)\n",
    "#('SILICON DISSOLVED',)\n",
    "#('SILICON EXTRACTABLE',)\n",
    "#('SILICON TOTAL',)\n",
    "#('TURBIDITY',)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define function to plot results of query as repeating fxn of year-day if output rows contain Year, Month, Day, value:\n",
    "def plotYMDV(qout,pcolor,ptitle):\n",
    "    Y=[]\n",
    "    M=[]\n",
    "    D=[]\n",
    "    Amm=[]\n",
    "    date=[]\n",
    "    for Y0, M0, D0, Amm0 in qout:\n",
    "        Y.append(Y0)\n",
    "        M.append(M0)\n",
    "        D.append(D0)\n",
    "        Amm.append(Amm0)\n",
    "        date.append(dt.date(Y0,M0,D0))\n",
    "    Y=np.array(Y)\n",
    "    M=np.array(M)\n",
    "    D=np.array(D)\n",
    "    Amm=np.array(Amm)\n",
    "    date=np.array(date)\n",
    "    YD=0.0*Y\n",
    "    for i in range(0,len(Y)):\n",
    "        YD[i]=date[i].timetuple().tm_yday\n",
    "    plt.plot(YD,Amm,'.',color=pcolor)\n",
    "    plt.plot(YD+365.0,Amm,'.',color=pcolor)\n",
    "    plt.title(ptitle)\n",
    "    plt.plot((366,366),(0,np.max(Amm)),'k--')\n",
    "    return(YD,Amm)\n",
    "    \n",
    "# definte function to smooth data with gaussian filter, plot result, and return dict of days, values\n",
    "def gsmooth(YD,Amm,L):\n",
    "    allt=np.arange(1,367)\n",
    "    fil=np.empty(np.size(allt))\n",
    "    s=L/2.355\n",
    "    sdict={}\n",
    "    for t in allt:\n",
    "        diff=[min(abs(x-t),abs(x-t+365), abs(x-t-365)) for x in YD]\n",
    "        weight=[np.exp(-.5*x**2/s**2) if x <= 3*L else 0.0 for x in diff]\n",
    "        #weight=[np.exp(-.5*x**2/s**2) for x in diff]\n",
    "        fil[t-1]=np.sum(weight*Amm)/np.sum(weight)\n",
    "        sdict[t]=fil[t-1]\n",
    "    plt.plot(allt,fil,'k-')\n",
    "    plt.plot(allt+365,fil,'k-')\n",
    "    diff_ex=[min(abs(x-t),abs(x-t+365), abs(x-t-365)) for x in allt]\n",
    "    weight_ex=[np.exp(-.5*x**2/s**2) if x <= 3*L else 0.0 for x in diff_ex]\n",
    "    plt.plot(allt,weight_ex/np.sum(weight_ex)*np.max(2.0*Amm),'k:')\n",
    "    plt.plot(allt+365,weight_ex/np.sum(weight_ex)*np.max(2.0*Amm),'k:')\n",
    "    return(sdict)\n",
    "    #plt.plot(YD,weight,'*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "units: ('MG/L',)\n",
      "don_cst = 17.1346325192 uM N ....DON\"T USE\n"
     ]
    },
    {
     "data": {
      "image/png": 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cKWm/hmL/BD4KfKPJJjYAPRFxQERMaDNe2wx47CCz7tHKlcAEYFFELImIdcAsYGqxQETc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\nqliT4q62AfwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e4d072cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate mean DON from data at Hope and Gravesend:\n",
    "\n",
    "qdataG=session.query(Profs.Year, Profs.Month, Profs.Day, Profs.value).\\\n",
    "            filter(and_(\n",
    "                Profs.variable_name=='NITROGEN DISSOLVED ORGANIC (CALCD.)',\n",
    "                Profs.station_name=='Fraser River (Main Arm) at Gravesend Reach - Buoy'))\n",
    "if qdataG.count()>0:\n",
    "    YDG, valG = plotYMDV(qdataG.all(),'r','DON, red=Gravesend, blue=Hope')\n",
    "    \n",
    "qdataH=session.query(Profs.Year, Profs.Month, Profs.Day, Profs.value).\\\n",
    "            filter(and_(\n",
    "                Profs.variable_name=='NITROGEN DISSOLVED ORGANIC (CALCD.)',\n",
    "                Profs.station_name.like('%Hope%')))\n",
    "if qdataH.count()>0:\n",
    "    YDH, valH = plotYMDV(qdataH.all(),'b',)\n",
    "else:\n",
    "    YDH=[]\n",
    "    valH=[]\n",
    "\n",
    "units=session.query(Profs.unit_code).\\\n",
    "            filter(and_(\n",
    "                Profs.variable_name=='NITROGEN DISSOLVED ORGANIC (CALCD.)',\n",
    "                or_(Profs.station_name=='Fraser River (Main Arm) at Gravesend Reach - Buoy',\n",
    "                    Profs.station_name.like('%Hope%')))).group_by(Profs.unit_code).all()\n",
    "for row in units:\n",
    "    print('units:',row)\n",
    "val=np.concatenate((valG,valH))\n",
    "don_cst=np.mean(val)\n",
    "print('don_cst =', don_cst/mwN*1000, 'uM N ....DON\"T USE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def returnDF(fname):\n",
    "    obsdir='/ocean/eolson/MEOPAR/obs/ECRivers/SOGRivers'\n",
    "    df=pd.read_csv(os.path.join(obsdir,fname))\n",
    "    temp=[dt.datetime.strptime(s, \"%Y-%m-%d %H:%M:%S\") for s in df['Sample time']]\n",
    "    df['TS']=temp\n",
    "    df['YD']=[(t-dt.datetime(t.year-1,12,31)).days for t in temp]\n",
    "    return df\n",
    "df0=dict()\n",
    "df0['Fraser-Hope']=returnDF('BC08MF0001_FraserHope_19790717_to_20170125.csv')\n",
    "df0['Fraser-Gravesend']=returnDF('BC08MH0453_FraserGravesend_20080902_to_20161104.csv')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "# calculate mean NH4 from data at Hope and Gravesend\n",
    "\n",
    "dataG=session.query(Profs.Year, Profs.Month, Profs.Day, Profs.value).\\\n",
    "            filter(and_(\n",
    "                Profs.variable_name=='AMMONIA DISSOLVED',\n",
    "                Profs.station_name=='Fraser River (Main Arm) at Gravesend Reach - Buoy')).all()\n",
    "YDG, valG = plotYMDV(dataG,'r','temp')\n",
    "    \n",
    "dataH=session.query(Profs.Year, Profs.Month, Profs.Day, Profs.value).\\\n",
    "            filter(and_(\n",
    "                Profs.variable_name=='AMMONIA DISSOLVED',\n",
    "                Profs.station_name.like('%Hope%'))).all()\n",
    "YDH, valH = plotYMDV(dataH,'b','NH4, red=Gravesend, blue=Hope')\n",
    "\n",
    "units=session.query(Profs.unit_code).\\\n",
    "            filter(and_(\n",
    "                Profs.variable_name=='AMMONIA DISSOLVED',\n",
    "                or_(Profs.station_name=='Fraser River (Main Arm) at Gravesend Reach - Buoy',\n",
    "                    Profs.station_name.like('%Hope%')))).group_by(Profs.unit_code).all()\n",
    "for row in units:\n",
    "    print('units:',row)\n",
    "val=np.concatenate((valG,valH))\n",
    "nh4_cst=np.mean(val)\n",
    "print('nh4_cst =', nh4_cst/mwN*1000, 'uM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('Fraser-Gravesend', 'Ammonia Dissolved [Lab: -54] [VMV: 1109]'), ('Fraser-Hope', 'Ammonia Dissolved [Lab: 1] [VMV: 7557]')]\n",
      "nh4_cst = 4.23255330777 uM\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEACAYAAABVtcpZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncfhw72l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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e49cdfa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate mean NH4 from data at Hope and Gravesend\n",
    "\n",
    "llNH=list()\n",
    "for ff in df0.keys():\n",
    "    llNH=llNH+[(ff,i) for i in df0[ff].keys() if ((re.search('[Aa]mm',i) is not None) \\\n",
    "                                                  and (re.search('[Dd]is',i) is not None))]\n",
    "print(llNH)\n",
    "plt.plot(df0['Fraser-Hope']['YD'],\n",
    "         df0['Fraser-Hope']['Ammonia Dissolved [Lab: 1] [VMV: 7557]'],'r.')\n",
    "plt.plot(df0['Fraser-Gravesend']['YD'],\n",
    "         df0['Fraser-Gravesend']['Ammonia Dissolved [Lab: -54] [VMV: 1109]'],'b.')\n",
    "val=np.concatenate((df0['Fraser-Hope']['Ammonia Dissolved [Lab: 1] [VMV: 7557]'],\n",
    "                    df0['Fraser-Gravesend']['Ammonia Dissolved [Lab: -54] [VMV: 1109]']))\n",
    "\n",
    "nh4_cst=np.nanmean(val)\n",
    "print('nh4_cst =', nh4_cst/mwAmm*1000, 'uM')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# NO2+NO3 at Hope and Gravesend\n",
    "dataG=session.query(Profs.Year, Profs.Month, Profs.Day, Profs.value).\\\n",
    "            filter(and_(\n",
    "                Profs.value<2.5, # remove single high outlier\n",
    "                Profs.variable_name=='NITROGEN DISSOLVED NO3 & NO2',\n",
    "                Profs.station_name.like('%Gravesend%'))).all()\n",
    "YDG, valG = plotYMDV(dataG,'r','temp')\n",
    "\n",
    "dataH=session.query(Profs.Year, Profs.Month, Profs.Day, Profs.value).\\\n",
    "            filter(and_(\n",
    "                Profs.variable_name=='NITROGEN DISSOLVED NO3 & NO2',\n",
    "                Profs.station_name.like('%Hope%'))).all()\n",
    "YDH, valH = plotYMDV(dataH,'b','NO3, red=Gravesend, blue=Hope')\n",
    "\n",
    "units=session.query(Profs.unit_code).\\\n",
    "            filter(and_(\n",
    "                Profs.variable_name=='NITROGEN DISSOLVED NO3 & NO2',\n",
    "                or_(Profs.station_name.like('%Gravesend%'),\n",
    "                    Profs.station_name.like('%Hope%')))).group_by(Profs.unit_code).all()\n",
    "for row in units:\n",
    "    print('units:',row)\n",
    "    \n",
    "YD=np.concatenate((YDG,YDH))\n",
    "val=np.concatenate((valG, valH))\n",
    "no3dict=gsmooth(YD,val,60)\n",
    "#print(i,np.max([v for v in no3dict.values()]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NO3\n",
      "Fraser-Gravesend Nitrogen Dissolved Nitrate [Lab: -54] [VMV: 1115] 2009-05-13 20:20:00 2015-04-30 20:45:00\n",
      "Fraser-Hope Nitrogen Dissolved Nitrate [Lab: 1] [VMV: 7315] 1994-04-26 17:00:00 1994-09-27 21:15:00\n",
      "Fraser-Hope Nitrogen Dissolved Nitrate [Lab: 36] [VMV: 7315] 1994-10-11 21:35:00 1998-12-08 22:10:00\n",
      "Fraser-Hope Nitrogen Dissolved Nitrate [Lab: -54] [VMV: 1115] 2006-09-26 21:45:00 2015-05-25 18:11:00\n"
     ]
    },
    {
     "data": {
      "image/png": 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k5Tm+gUTDDgjiRgsSEtopy3kn441IkHU5gIFNbOKZvGf41/OPcZF42H0EzWor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\nzp3LOaP681T0h07zJWboOcqIuW+rco1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dwQ033MCIESOgKJ3VT/6RC9/4BiMArWT1smxm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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e49b8f3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NO2+NO3 at Hope and Gravesend\n",
    "llNO3=list()\n",
    "for ff in df0.keys():\n",
    "    llNO3=llNO3+[(ff,i) for i in df0[ff].keys() if ((re.search('[Nn]itrate',i) is not None and re.search('[Nn]itrite',i) is None and re.search('[Dd]is',i) is not None))]\n",
    "print('NO3')\n",
    "for (ff,i) in llNO3:\n",
    "    print(ff,i,np.min(df0[ff]['TS'][df0[ff][i]>=0]),np.max(df0[ff]['TS'][df0[ff][i]>=0]))\n",
    "\n",
    "plt.plot(df0['Fraser-Hope']['YD'],\n",
    "         df0['Fraser-Hope']['Nitrogen Dissolved Nitrate [Lab: 1] [VMV: 7315]'],'r.')\n",
    "plt.plot(df0['Fraser-Hope']['YD'],\n",
    "         df0['Fraser-Hope']['Nitrogen Dissolved Nitrate [Lab: 36] [VMV: 7315]'],'m.')\n",
    "plt.plot(df0['Fraser-Hope']['YD'],\n",
    "         df0['Fraser-Hope']['Nitrogen Dissolved Nitrate [Lab: -54] [VMV: 1115]'],'.',color='darkorange')\n",
    "plt.plot(df0['Fraser-Gravesend']['YD'],\n",
    "         df0['Fraser-Gravesend']['Nitrogen Dissolved Nitrate [Lab: -54] [VMV: 1115]'],'b.')\n",
    "plt.plot(df0['Fraser-Hope']['YD']+365,\n",
    "         df0['Fraser-Hope']['Nitrogen Dissolved Nitrate [Lab: 1] [VMV: 7315]'],'r.')\n",
    "plt.plot(df0['Fraser-Hope']['YD']+365,\n",
    "         df0['Fraser-Hope']['Nitrogen Dissolved Nitrate [Lab: 36] [VMV: 7315]'],'m.')\n",
    "plt.plot(df0['Fraser-Hope']['YD']+365,\n",
    "         df0['Fraser-Hope']['Nitrogen Dissolved Nitrate [Lab: -54] [VMV: 1115]'],'.',color='darkorange')\n",
    "plt.plot(df0['Fraser-Gravesend']['YD']+365,\n",
    "         df0['Fraser-Gravesend']['Nitrogen Dissolved Nitrate [Lab: -54] [VMV: 1115]'],'b.')\n",
    "val=np.concatenate((df0['Fraser-Hope']['Nitrogen Dissolved Nitrate [Lab: 1] [VMV: 7315]'],\n",
    "                    df0['Fraser-Hope']['Nitrogen Dissolved Nitrate [Lab: 36] [VMV: 7315]'],\n",
    "                    df0['Fraser-Hope']['Nitrogen Dissolved Nitrate [Lab: -54] [VMV: 1115]'],\n",
    "                    df0['Fraser-Gravesend']['Nitrogen Dissolved Nitrate [Lab: -54] [VMV: 1115]']))\n",
    "YD=np.concatenate((df0['Fraser-Hope']['YD'],\n",
    "                    df0['Fraser-Hope']['YD'],\n",
    "                    df0['Fraser-Hope']['YD'],\n",
    "                    df0['Fraser-Gravesend']['YD']))\n",
    "ii=~np.isnan(val)\n",
    "no3dict=gsmooth(YD[ii],val[ii],60)\n",
    "#print(i,np.max([v for v in no3dict.values()]))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Si at Hope and Gravesend\n",
    "dataG=session.query(Profs.Year, Profs.Month, Profs.Day, Profs.value).\\\n",
    "            filter(and_(\n",
    "                Profs.value<2.5, # remove single high outlier\n",
    "                or_(Profs.variable_name=='SILICA DISSOLVED',\n",
    "                      Profs.variable_name=='SILICON DISSOLVED'),\n",
    "                Profs.station_name.like('%Gravesend%'))).all()\n",
    "YDG, valG = plotYMDV(dataG,'r','temp')\n",
    "\n",
    "dataH=session.query(Profs.Year, Profs.Month, Profs.Day, Profs.value).\\\n",
    "            filter(and_(\n",
    "                or_(Profs.variable_name=='SILICA DISSOLVED',\n",
    "                      Profs.variable_name=='SILICON DISSOLVED'),\n",
    "                Profs.station_name.like('%Hope%'))).all()\n",
    "YDH, valH = plotYMDV(dataH,'b','Si, red=Gravesend, blue=Hope')\n",
    "\n",
    "units=session.query(Profs.unit_code).\\\n",
    "            filter(and_(\n",
    "                or_(Profs.variable_name=='SILICA DISSOLVED',\n",
    "                      Profs.variable_name=='SILICON DISSOLVED'),\n",
    "                or_(Profs.station_name.like('%Gravesend%'),\n",
    "                    Profs.station_name.like('%Hope%')))).group_by(Profs.unit_code).all()\n",
    "for row in units:\n",
    "    print('units:',row)\n",
    "    \n",
    "YD=np.concatenate((YDG,YDH))\n",
    "val=np.concatenate((valG, valH))\n",
    "sidict=gsmooth(YD,val,60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Si\n",
      "Fraser-Hope Silica Dissolved [Lab: 36] [VMV: 103469] 1991-03-08 18:10:00 1999-12-21 22:10:00\n"
     ]
    },
    {
     "data": {
      "image/png": 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PBwLcZ06P2E0iRlrPbxEKOZsJT19XNBaTEydOcPr06WzXrh2fffZZ5x19HtFY\nOBzmokWLWFpaym4dOvAFQCt1BrTWr66XLDtvnUw9Y7MpgE8BrAGwAcB0m/Wsb6JkJok3d/hlIyKK\nRSrRqy66ZJ7fGOHAgQO89dZb2bVrVy5ZsiT+F5yU0Nldv1jHYj4H5jykYZ+f/O53vLq0lB0KCvjr\nX/+aVYsX14/A1NMjxhs/1rZFY7G/54LGNm7cyMGDB3PYsGHaKNh4ZFljNTk5/O9f/pKDevViSXEx\nX3/9dZ58//36/QKnlhp6YJqBjEXiAJpH/gcBfAJgiMU61jeR0yjHLkryGkk0U+swC33KlOSrHkIh\nvvfUUzyrUyfefvvtsR+eESvHePbZ1iMY4+zb8hyY0hLh7t25NBjkJaefzs65ufxdIMCjem2ysfMt\nJ4dctcr5tkVj9riosZpDhzjj3nvZrm1bzpgxI3YFS5Y0drx7d84JBtmrSRNecNppnB8IsHbAgHqN\nNW1av8/Nm2NvOwtk4xmbzQH8A0CxxWexJ9dJZJJ4jzR1TiFeMzXer7tR6FZPHXIaHRhEeLikhBPG\nj2enTp347rvvnrqesZbbLkespyOcphPsHGrESdYGApzXuTPPGziQfbp04e8ffJAnzOfMqqNSt9du\n27EctWis/nOXNbalTx8OHTKEgwcP5saNG+1tyaDGjgYCnHnGGTzrzDN5+fnn88MnnmA4VY1lgUxG\n4oFIOiUE4P/ZrOOt5qjbxGumxvp1NztTwzwgSdU2W4jwgw8+YLdu3ThmzBju3r078Wgj1g1o16Q1\n3ZAnDhzgq4WFLAb4nWbNOPe117ScqtX6oVD06Ez9kXaxnLXXUh5u42GN1X78MZ999lm2a9eOjz76\nKI99/XXi0awLGjuwbRt/FSlFvbZlS/6jvNx+fbPG9HJVpy23DJCNSLxlJJ0yzOIzTp06te6vXD+5\nDQWz45w9O/rix4sgjWK3ElEi0YHx+4aqnyNHjnDSpEls164dn7jnHh5PsvP0lP3YOY3IDVFdXc2n\nn36aZ3XowEuU4l8BhvUbRseqU1Kfw8Kqo7EhO2s7fKCxbdu28dprr2W3M8/kX5zOXRMLhxrbuXMn\nJ06cyDaRUtQNVvu10tjmzdoAMeDUx9plQWPl5eVRfjIr1SkAfgngAYv3034CsoqT5rzxc32+GLvR\njGYROYkOjBFLjKqfzZs388rLL2evpk25IBhkWM8RJordoIuIDbt37+YjjzzC9u3bc9SoUVpUFCsv\nanWj2o2yWk51AAATZElEQVSsc9rsb0hkW2Pmcx5DY4v/8hf2bdqUVyjFdU4fd2dFHI2tW7eO4yLT\nWd9///3caRy960RjhqkcaA4sPKCxTFWn5ANoFXl9GoBlAC61WC8Tx5xdjDeFXRPQPF9MSYnzXKDx\n+/GqMxxMWbrwz39mSVERzx00iAsWLEh8XggLpxEeMIAfBgK8vlUrtm7VihOuuYab9U5J8znSsXPU\nVvswRpEe6HjKONnSmNU5jxO5nzhwgE/fdx87FBTwuuuu49q1a5M7XpPG/t2/P18LBDikeXMW5ufz\nV7/4RfRDrRPRmN0Pl0c0likn3h/A6khO/DMAP7dZLxPH7A3iCcBqWLrTplusiMLpUHcDtbW1fPvt\ntzlw4EB2796dv/nNb1iZSLVCKMTw8uVc+be/cfLYsewGsBjgM4EAqzp3rncidscWCsWvCDHflB7q\neMoamdZYokPdDRw5coRPPvkkzzjjDA4ZMoSvvvqq8zEMEXtOfPQRF82dy/Hf/z7bArwM4DsATyoV\nf+6aeBqzcvoe0Zj3Bvs0FpzOZ5xox0koRM6c6TyicJLXi9yg4cOH+emnn3L8+PFs3bo1zznnHD74\n4IN8/fXXuWbNGu7YsYOHDh3inj17uHnzZi5atIhP//rXHPXd77JDQQG7d+/Ohx54gGu6d9fyoOY/\nu7ldYk0LGstmj3Q8ZY10aEx31vPnO3fY8TRmaC2cOHGC8+bN48iRI9miRQtedtllfOyxxzh//nxu\n2LCBe/bs4aFDh1hZWcl169bxnddf52N33MHLv/td5uXl8cILL+SM6dO5o1evU/VlHEcQ61z5TGPi\nxLOF0/x1Ih0n5mgC0Dr94k1VYLetGKMmT5w4wWXLlvGxxx7jqFGj2L9/fxYWFrJly5bs0LYtu3fr\nxsu++13e2a4d/xAIcJtxbnNjs9X4p88DY9X810dTJlKXnej5a2i4rTF9e8Fgfe20WWOJbi/GY+5C\noRDnzZvHyZMnc8SIEezduzcLCgrYsmVLnpGfz749e/IHLVvyQaU4v2tXHjAO87fSWDCoacxqxKeP\nNeZvJ+6BToWUcFsAxg4Y/c845WoidumOW++USqQaIV7O3ehcmjbVRl02bVpfFmdVl1xSkp2nqIjG\n6rF7ulGqGkt2AI9pRK9tP4k+9bDxh8cqXZItjaWIf524RzoVPIVVJK43D/WHIDjB3Azv0cNZkzHR\n6WX1zjHjlK3mmm+r7WYq9ygai8Z4Ppo2jQ4YUtGYfr2dnGNz2iOWNs0aM9obCESnTDyS304G/zpx\nH5/0tBIKaZP0dO1aL1g9yo11kxgjTovefscpmGRz7sbI3KrmOxu5R9HYqYRCmvPr27c+rRIMOptj\nxU5jxllLnew/WW2aAxxjxO2R/HYy+NeJ+/ikxyXVJrzR+QSD8dMhVhFnAp1Rp7xOZvpP/Xvz59t3\nymZjSlfR2KlYza0S78cuUY1Z6cMNjc2aFbtU1Yd9KP514qRvT3pM3GjCm6Mcc/2vWdyJRpzGTqDi\nYmdTuBqJNbWw3ei4bCEas/++MRq2aoElqzFz3nvz5sTsDYXsO0yNE1o1bZp9fbmAv514Q8TNR7gZ\nB3wYX1tFRIlEnEuWRDdLY0X6VtGQ1VwUVp/ZPDtUSBE3NGb+cXNTY+Z8eadO8UslzWkauw5Ts3a9\n9MjCJBEn7jXS3YSPNXeG04hz/vzoG6FrV/vOy1gDQsy1u7E+E9zD6xqrrKxvjemdkHYdmGaNGSui\nrDpMxYlb/gUguEdeHvDRR8CyZdr/vLz6z6qrgRUrtP9OsFq/pATo1w/IyQG6dAE6d67/TPshjU/z\n5tHLs2bV2wvU73P9emDDBqCmBti4EXjzTW2f/foBublAjx7A3/5Wf4y6bVafCe7hlsbs1i0pAfr0\nAYJBoHdv7ZrqONHY9u1AbW39clGRpgUnGtu0Sdt3bq5mx1//Gn2MF1ygvZ+To/0///z49jQGUv0V\n0P8gkbg9sfKYVp1UsdbXc9L6ZzYDemJu22pghHmfxlypsf47VkVBQ8xB+wU7zSSqLyt9JKJfY0vB\n6rm68TQWr9qlgWkMkk7xCYlMIRprfavPjHXaxiHUVp1D+g1n5YjfeKP+cWnG7VjNS50sqVRVCLGJ\nNfdJKvoyP1zBicaWLDnVEYdC5OOPp1djPtSXOHG/YJfHTOYpNvGqC4yRubFzSB+8YxVNbd4c/bxL\n49SiemSW6mg4Nyp3BHusNOOGvqw6NhPVWCik1aYb89nGqRzMrctktOFTfYkT9xNWzcB4N5OTtIU5\n8rEbTWfsNMrJ0Sba0rc9aVL0DTZxYvS+kpmXwowMvkk/VlUnbujL3HpLVGNWU0rMmhVto16SmGzZ\noE/1JU68IZBKji9eOZgxv6g7Y6tRb8ZIXKn6B8qSsaO5RJqu6a6qEKxxW1/G951qrLIyOhI31ng3\ncn2JE2/sJFoOZp4dzljLvXmzNnrP6MD1bdk1rxNtujawTqkGT6zoNlGNhULalBL6bJfG7TRifYkT\nb+zESnXYVSXEe0CD3X7koQ2Nj1iVJvrnbmisEevLDScet05cKdVJKfWhUmqDUuqfSql7Xa5yFFKF\npvrd6mqgtBQYOlT7r9cC5+UBy5cDS5dqf8uXx6/l1ut5+/WzrgkvLo6uJRYaDnl5wHvvaeMDvvoK\nGDmyXktuagyI1rDoKzHieXkAHQEMjLxuAWAzgD4W62Xih0swYhexuDn8P1Z9sM+arkISZEtjjURf\nyEQkTnIvybWR10cAbAJQmJ6fFCEh7CIWtyIZ84i6DRvqP8vLAwYPllGZDZ1saUz05RhFc1M81spK\ndQVQAaAk4tCNnzGRbQkuUV2tCd+Y7oj1fqLbLi3Vbq7i4lOHeQuNA9FY2lBKgaRKaRtOHa9SqgU0\nB/4rkvMtPufUqVPrlsvKylBWVpaKbUKm0PPeJSWn3kBu3KiCIBoDAFRUVKCioqJu+dFHH82ME1dK\n5QB4F8AikjNt1pFI3I/okZB+EzWySEjIAKIxW9yIxJ3OYvhfADbaOXDBx8TKewuCG4jG0oqTEsMh\nAG4GcIlSao1SarVSakT6TRMygpRzCelGNJZWEurYjLkhSaf4l0aUkxSyhGjMkox2bDowRpy4IAhC\nAmQyJy4IgiB4EHHigiAIPkacuCAIgo8RJy4IguBjxIkLgiD4GHHigiAIPkacuCAIgo8RJy4IguBj\nxIkLgiD4GHHigiAIPkacuCAIgo8RJy4IguBjxIkLgiD4GHHigiAIPkacuCAIgo9x8mSfOUqpfUqp\ndZkwSBAEQXCOk0j8FQBXpNsQQRAEIXHiOnGSfwdwKAO2CIIgCAkiOXFBEAQfk+PmxqZNm1b3uqys\nDGVlZW5uXhAEwddUVFSgoqLC1W06elCyUqoLgIUkB8RYRx6ULAiCkACZfFCyivwJgiAIHsJJieGf\nACwH0EsptUMpNS79ZgmCIAhOcJROcbQhSacIgiAkRCbTKYIgCIIHEScuCILgY8SJC4Ig+Bhx4oIg\nCD5GnLggCIKPEScuCILgY8SJC4Ig+Bhx4oIgCD5GnLggCIKPEScuCILgY8SJC4Ig+Bhx4oIgCD7G\nVScuE2AJbkMS4XAYx48fR1VVVbbNERog4XDY174rLZF4TU0NJk6ciHA4nI7NCw2Y/fv34+uvv65b\nvuWWW/D2229j5cqVGDeufhbk9evX4/nnn8+GiYLP2bJlC06ePFm33KVLF+zbty+LFqWGq05cKW1G\nxZMnT6K4uBiBgLb5gwcP4o033nBzV0IDwhgFPffcc1i6dGnd8iuvvILRo0djyJAhmDdvXt37eXl5\nKCwsrFtevXo1VqxYkRmDBd9h1NiDDz6IrVu31i1/+eWX6NixYzbMcgWnj2cbAeC30Jz+HJKPW6xj\nO5/41q1bMW/ePPz85z8HAFRVVaFJkyZo3rx5KrYLDYAFCxZg4cKFmD17dkrbWbx4Mb799ltcc801\nAIC9e/eiffv2CAaDbpgp+JgpU6agd+/eUS05r+DGfOIgGfMPmuP+AkAXALkA1gLoY7EenfLKK69w\nypQpjtd3i/Ly8ozvMxkasp27du3i5MmT65arq6tZXV3tolUat912G999912SDft8ZgOv21leXs6X\nX365zs5vvvmGJ0+ezK5RNkT8Zlw/HOvPSTrlfABbSG4neRLAmwCuTuWH48c//jGmT59et3zfffdh\n0aJFqWzSEW4/ZTpdNCQ7a2pq8NRTT9U1Z/Pz8zFw4MC6z1u0aIEWLVq4btvLL7+MkSNHAgDKy8sx\ndOhQz+c9G9J1zyS7du3Ca6+9VrdcWFiIPn361NmZn5+PnJycLFmXfpw48UIAOw3LuyLvpYSePweA\nhx56CBdeeGHd8lVXXYVVq1bVLX/yySeorq5OdZdCmti7d29UR9F9991XV0mSk5OD6upqHDt2DADQ\ntGlT3HjjjRmxy6ix2bNno6CgAABw/Phx9OzZEzU1NQC06oQPPvggIzYJiRMOh7F79+665X379mHS\npEl1y0opHDhwoG65Z8+eGDJkSEZtzCaeqBPv2LEjWrduXbc8Z84c9OvXL2r5m2++qVs+77zz8Pnn\nn9ctjxo1Kqqj4rbbbsP27dvrlu+4446o5QkTJmDHjh11yz/96U+jlu+8886Yn0+YMCFqez/5yU+i\nlm+//faoZbM948ePx7Zt2+qWx40bF7U8b968qOUf/ehHUctjx46NWr755pvx1Vdf1S2PGTMmavmm\nm26KWr7hhhuilkePHo0vv/yybvn666+PWh41alTU8iWXXIJ//etfUd83np+hQ4fWdWoDwLRp07La\n/6GUQu/eveucepMmTVBeXl4XnVVXV+OZZ56pW3///v3o2rVr3fKhQ4dw6aWX1i1XVVVh1KhRUcuj\nR4+OWjb+UB0+fBhjxoyJ+vzmm2+OWjZ/bvx+VVUVbrjhhqjl66+/PqY9P/zhD6OWr7322qjj0fsO\n9OWrr746avkHP/hB1PL3vve9uuVjx47VtXL0z0eMGBG1fMUVV9QtHzx4EMOHD49avvzyy6OWjed3\n//79GDRoUNSycf8tW7bEOeecU7dcWFiI++67D42VuB2bSqnBAKaRHBFZngItj/O4aT3/FloKgiBk\nCabYsenEiQcBbAZwKYA9AP4B4CaSm1LZsSAIgpA6cbP9JGuVUvcAWIz6EkNx4IIgCB7AUZ24IAiC\n4E1S7thUSo1QSn2ulPqXUmqyG0alYMscpdQ+pdQ6w3ttlFKLlVKblVJ/VUq1Mnz2kFJqi1Jqk1Jq\nuPVWXbexk1LqQ6XUBqXUP5VS93rUzqZKqU+VUmsitk73op2GfQeUUquVUgu8aqdSaptS6rPIOf2H\nh+1spZR6K7LfDUqpC7xmp1KqV+Q8ro78P6yUuteDdj4UOYfrlFKvK6WauG5jKkXmcDgQKFN/AC4G\nMBDAOsN7jwOYFHk9GcBvIq+LAayBllLqGjkOlQEbOwIYGHndAlp/Qx+v2RnZd/PI/yCATwAM8aKd\nkf1PBPAagAVevO6RfX8JoI3pPS/a+XsA4yKvcwC08qKdBnsDAHYDOMtLdkLzi18CaBJZ/jOAW922\nMVUjBwNYZFieAmByJi+gzYkzOvHPAXSIvO4I4HMrWwEsAnBBFuz9C4DLvGwngObQOrSLvWgngE4A\nlgAoQ70T96KdXwFoZ3rPU3YCaAlgq8X7nrLTZNtwAB95zU4AbSL2tIk45gXpuNdTTaekZSCQyxSQ\n3AcAJPcCKIi8b7a9Ehm2XSnVFVrL4RNoF9VTdkZSFGsA7AVQQXKjF+0E8DSABwEYO3i8aCcBLFFK\n/a9S6naP2tkNwH6l1CuRVMVLSqnmHrTTyA0A/hR57Rk7SR4C8BSAHZH9HSa51G0bPTHYJ8N4oidX\nKdUCwNsA7iN5BKfalXU7SYZJDoIW6ZYqpcrgMTuVUt8DsI/kWgCx6m2zfj4BDCF5DoCRAO5WSpXC\nY+cTWsR4DoBnI7YehRYhes1OAIBSKhfAVQDeirzlGTuVUkXQ0nxdAJwJ4HSl1M0WNqVkY6pOvBJA\nZ8Nyp8h7XmKfUqoDACilOgLQJ6uuhJZD08mY7UqpHGgO/I8k53vVTh2SIQDvATjXg3YOAXCVUupL\nAG8AuEQp9UcAez1mJ0juifz/Bloa7Xx473zuArCT5MrI8lxoTt1rdupcCWAVyf2RZS/ZeS6Aj0ke\nJFkLYB6Ai9y2MVUn/r8AeiiluiilmgC4EVreJ5soREdkCwD8OPL6VgDzDe/fGOkt7gagB7S8byb4\nLwAbSc70qp1KqXy911wpdRqAy6F1unjKTpIPk+xMsgia/j4keQuAhV6yUynVPNL6glLqdGh53H/C\ne+dzH4CdSqlekbcuBbDBa3YauAnaj7eOl+zcDGCwUqqZUkpBO5cbXbfRheT9iIixWwBMyWSHhoUt\nf4LWS30cWh5qHLROhaURGxcDaG1Y/yFoPcCbAAzPkI1DANRCq+RZA2B15By29Zid/SO2rQHwGYCf\nR973lJ0mm4ehvmPTU3ZCyzXr1/yf+r3iNTsj+z0bWoC2FsA70KpTvGhncwDfAMgzvOcpO6H11WwA\nsA7Aq9Cq+Fy1UQb7CIIg+JjG2LEpCILQYBAnLgiC4GPEiQuCIPgYceKCIAg+Rpy4IAiCjxEnLgiC\n4GPEiQuCIPgYceKCIAg+5v8Db49M/qXFUbMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e49cdf5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Si at Hope and Gravesend\n",
    "ll=list()\n",
    "for ff in df0.keys():\n",
    "    ll=ll+[(ff,i) for i in df0[ff].keys() if ((re.search('[Ss]ilica',i) is not None) and (re.search('[Dd]is',i) is not None)) ]\n",
    "print('Si')\n",
    "for (ff,i) in ll:\n",
    "    print(ff,i,np.min(df0[ff]['TS'][df0[ff][i]>=0]),np.max(df0[ff]['TS'][df0[ff][i]>=0]))\n",
    "    \n",
    "plt.plot(df0['Fraser-Hope']['YD'],\n",
    "         df0['Fraser-Hope']['Silica Dissolved [Lab: 36] [VMV: 103469]'],'r.')\n",
    "plt.plot(df0['Fraser-Hope']['YD']+365,\n",
    "         df0['Fraser-Hope']['Silica Dissolved [Lab: 36] [VMV: 103469]'],'r.')\n",
    "  \n",
    "YD=df0['Fraser-Hope']['YD']\n",
    "val=df0['Fraser-Hope']['Silica Dissolved [Lab: 36] [VMV: 103469]']\n",
    "ii=~np.isnan(val)\n",
    "sidict=gsmooth(YD[ii],val[ii],60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f2e49a88438>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9D7hpxFs3AFePKLsQWBoRwxGxCdgIzCzSjplZmVpdWnuMWDhK0ZHrniB6YE0jFwLPRMQT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e4d0d9a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find fraser location\n",
    "mesh=nc.Dataset('/ocean/eolson/MEOPAR/NEMO-forcing/grid/mesh_mask201702.nc')\n",
    "tmask=mesh.variables['tmask'][0,0,:,:]\n",
    "f=nc.Dataset('/results/forcing/rivers/R201702DFraCElse_y2016m01d23.nc') # example for dims\n",
    "plt.pcolormesh(np.ma.masked_where(tmask==0,f.variables['rorunoff'][0,:,:]))\n",
    "plt.colorbar()\n",
    "plt.xlim(350,399)\n",
    "plt.ylim(360,540)\n",
    "plt.plot((380,398,398,380,380),(400,400,520,520,400),'k-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data = [--],\n",
       "             mask = [ True],\n",
       "       fill_value = 9.96921e+36)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.variables['time_counter'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create daily files, use data to initialize fields\n",
    "year=2016\n",
    "fname='/data/eolson/MEOPAR/NEMO-forcing-new/rivers-climatology/bio/R201906_bioClim_'\n",
    "for yearday in range(1,367):\n",
    "    t2=dt.date(year, 1, 1) + dt.timedelta(days = yearday - 1)\n",
    "    #datestr='y'+t2.strftime('%Y')+'m'+t2.strftime('%m')+'d'+t2.strftime('%d')\n",
    "    datestr='m'+t2.strftime('%m')+'d'+t2.strftime('%d')\n",
    "    new=nc.Dataset(fname+datestr+'.nc','w')\n",
    "    #Copy dimensions\n",
    "    for dname, the_dim in f.dimensions.items():\n",
    "        #print (dname, len(the_dim) if not the_dim.isunlimited() else None)\n",
    "        new.createDimension(dname, len(the_dim) if not the_dim.isunlimited() else None)\n",
    "    #new_tc=new.createVariable('time_counter',float,('time_counter'),zlib=True)\n",
    "    #new_tc[:]=f.variables['time_counter'][:]\n",
    "    \n",
    "    new_run=new.createVariable('no3',float,('time_counter', 'y', 'x'),zlib=True)\n",
    "    new_run[:,:,:]=const_no # other river value\n",
    "    new_run[:,400:520,380:]=no3dict[yearday]/mwN*1000.0 # convert to muM; Fraser value\n",
    "\n",
    "    new_run=new.createVariable('sil',float,('time_counter', 'y', 'x'),zlib=True)\n",
    "    new_run[:,:,:]=const_si # other river value\n",
    "    new_run[:,400:520,380:]=sidict[yearday]/mwSiO2*1000.0 # convert to muM; Fraser value\n",
    "    \n",
    "    new.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create constants file\n",
    "f=nc.Dataset('/results/forcing/rivers/R201702DFraCElse_y2016m01d23.nc') # example for dims\n",
    "year=2016\n",
    "fnameC='/data/eolson/MEOPAR/NEMO-forcing-new/rivers-climatology/bio/R201906_bioConst'\n",
    "new=nc.Dataset(fnameC+'.nc','w')\n",
    "#Copy dimensions\n",
    "for dname, the_dim in f.dimensions.items():\n",
    "    #print (dname, len(the_dim) if not the_dim.isunlimited() else None)\n",
    "    new.createDimension(dname, len(the_dim) if not the_dim.isunlimited() else None)\n",
    "\n",
    "#new_tc=new.createVariable('time_counter',float,('time_counter'),zlib=True)\n",
    "#new_tc[:]=f.variables['time_counter'][:]\n",
    "\n",
    "new_run=new.createVariable('no3',float,('time_counter', 'y', 'x'),zlib=True)\n",
    "new_run[:,:,:]=const_no # other rivers\n",
    "new_run[:,400:520,380:]=np.mean([no3dict[yearday]/mwN*1000.0 for yearday in range(1,367)])\n",
    "\n",
    "new_run=new.createVariable('nh4',float,('time_counter', 'y', 'x'),zlib=True)\n",
    "new_run[:,:,:]=const_nh # other river value\n",
    "new_run[:,400:520,380:]=nh4_cst/mwAmm*1000.0 # convert to muM; Fraser value\n",
    "\n",
    "new_run=new.createVariable('sil',float,('time_counter', 'y', 'x'),zlib=True)\n",
    "new_run[:]=const_si # other river value\n",
    "new_run[:,400:520,380:]=np.mean([sidict[yearday]/mwSiO2*1000.0 for yearday in range(1,367)])\n",
    "\n",
    "new_run=new.createVariable('dia',float,('time_counter', 'y', 'x'),zlib=True)\n",
    "new_run[:]=dia\n",
    "\n",
    "new_run=new.createVariable('phy',float,('time_counter', 'y', 'x'),zlib=True)\n",
    "new_run[:]=phy\n",
    "\n",
    "new_run=new.createVariable('mes',float,('time_counter', 'y', 'x'),zlib=True)\n",
    "new_run[:]=mes\n",
    "\n",
    "new_run=new.createVariable('zoo',float,('time_counter', 'y', 'x'),zlib=True)\n",
    "new_run[:]=zoo\n",
    "\n",
    "new_run=new.createVariable('don',float,('time_counter', 'y', 'x'),zlib=True)\n",
    "new_run[:]=don\n",
    "\n",
    "new_run=new.createVariable('pon',float,('time_counter', 'y', 'x'),zlib=True)\n",
    "new_run[:]=pon\n",
    "\n",
    "new_run=new.createVariable('bsi',float,('time_counter', 'y', 'x'),zlib=True)\n",
    "new_run[:]=bsi\n",
    "\n",
    "new_run=new.createVariable('tur',float,('time_counter', 'y', 'x'),zlib=True)\n",
    "new_run[:]=const_tur\n",
    "\n",
    "new.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "session.close()\n",
    "engine.dispose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f.close()\n",
    "mesh.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 [('no3', 6.1799999999999997, 8.2761044385521991), ('sil', 57.08212707723338, 59.57)]\n",
      "2 [('no3', 6.1799999999999997, 8.2882785962522743), ('sil', 57.206163580199082, 59.57)]\n",
      "3 [('no3', 6.1799999999999997, 8.2994811508050681), ('sil', 57.329740172292432, 59.57)]\n",
      "4 [('no3', 6.1799999999999997, 8.3097422957544573), ('sil', 57.452843088808478, 59.57)]\n",
      "5 [('no3', 6.1799999999999997, 8.3190924830764814), ('sil', 57.575459917952642, 59.57)]\n",
      "6 [('no3', 6.1799999999999997, 8.3275622467559174), ('sil', 57.697579976153257, 59.57)]\n",
      "7 [('no3', 6.1799999999999997, 8.335182031345342), ('sil', 57.819194697302521, 59.57)]\n",
      "8 [('no3', 6.1799999999999997, 8.3419820273049403), ('sil', 57.940298032288815, 59.57)]\n",
      "9 [('no3', 6.1799999999999997, 8.347992014782081), ('sil', 58.06088685553464, 59.57)]\n",
      "10 [('no3', 6.1799999999999997, 8.3532412174565813), ('sil', 58.180961373846529, 59.57)]\n",
      "11 [('no3', 6.1799999999999997, 8.3577581677750405), ('sil', 58.300525533589997, 59.57)]\n",
      "12 [('no3', 6.1799999999999997, 8.3615705848992636), ('sil', 58.419587421660282, 59.57)]\n",
      "13 [('no3', 6.1799999999999997, 8.3647052663268013), ('sil', 58.538159655556683, 59.57)]\n",
      "14 [('no3', 6.1799999999999997, 8.3671879941058815), ('sil', 58.656259757986291, 59.57)]\n",
      "15 [('no3', 6.1799999999999997, 8.3690434562051923), ('sil', 58.773910511271517, 59.57)]\n",
      "16 [('no3', 6.1799999999999997, 8.3702951835098478), ('sil', 58.891140287107483, 59.57)]\n",
      "17 [('no3', 6.1799999999999997, 8.3709655026844718), ('sil', 59.007983347211464, 59.57)]\n",
      "18 [('no3', 6.1799999999999997, 8.3710755048617571), ('sil', 59.124480110786024, 59.57)]\n",
      "19 [('no3', 6.1799999999999997, 8.3706450300838178), ('sil', 59.240677384888244, 59.57)]\n",
      "20 [('no3', 6.1799999999999997, 8.3696926671118295), ('sil', 59.356628554282757, 59.57)]\n",
      "21 [('no3', 6.1799999999999997, 8.3682357681479882), ('sil', 59.472393727748241, 59.57)]\n",
      "22 [('no3', 6.1799999999999997, 8.3662904778163565), ('sil', 59.57, 59.588039838143551)]\n",
      "23 [('no3', 6.1799999999999997, 8.3638717756908445), ('sil', 59.57, 59.703640694292837)]\n",
      "24 [('no3', 6.1799999999999997, 8.3609935314903332), ('sil', 59.57, 59.819276983026114)]\n",
      "25 [('no3', 6.1799999999999997, 8.3576685719585395), ('sil', 59.57, 59.935036220408811)]\n",
      "26 [('no3', 6.1799999999999997, 8.3539087584889025), ('sil', 59.57, 60.051012651672657)]\n",
      "27 [('no3', 6.1799999999999997, 8.349725074277508), ('sil', 59.57, 60.167307099898551)]\n",
      "28 [('no3', 6.1799999999999997, 8.3451277199737248), ('sil', 59.57, 60.284026764046693)]\n",
      "29 [('no3', 6.1799999999999997, 8.3401262165584242), ('sil', 59.57, 60.401284967266172)]\n",
      "30 [('no3', 6.1799999999999997, 8.3347295142408147), ('sil', 59.57, 60.51920085704112)]\n",
      "31 [('no3', 6.1799999999999997, 8.3289461061946852), ('sil', 59.57, 60.63789905892326)]\n",
      "32 [('no3', 6.1799999999999997, 8.3227841457506564), ('sil', 59.57, 60.757509286051636)]\n",
      "33 [('no3', 6.1799999999999997, 8.316251565877506), ('sil', 59.57, 60.878165906826055)]\n",
      "34 [('no3', 6.1799999999999997, 8.3093561996377794), ('sil', 59.57, 61.000007473369472)]\n",
      "35 [('no3', 6.1799999999999997, 8.3021059003083071), ('sil', 59.57, 61.123176213543715)]\n",
      "36 [('no3', 6.1799999999999997, 8.2945086599383036), ('sil', 59.57, 61.2478174893145)]\n",
      "37 [('no3', 6.1799999999999997, 8.2865727250959633), ('sil', 59.57, 61.374079224321768)]\n",
      "38 [('no3', 6.1799999999999997, 8.2783067084946946), ('sil', 59.57, 61.502111303569059)]\n",
      "39 [('no3', 6.1799999999999997, 8.2697196953832606), ('sil', 59.57, 61.632064947924647)]\n",
      "40 [('no3', 6.1799999999999997, 8.2608213434550279), ('sil', 59.57, 61.764092066132378)]\n",
      "41 [('no3', 6.1799999999999997, 8.2516219751300834), ('sil', 59.57, 61.898344587015082)]\n",
      "42 [('no3', 6.1799999999999997, 8.2421326612054777), ('sil', 59.57, 62.034973774167938)]\n",
      "43 [('no3', 6.1799999999999997, 8.2323652947107373), ('sil', 59.57, 62.174129525537943)]\n",
      "44 [('no3', 6.1799999999999997, 8.2223326541131634), ('sil', 59.57, 62.315959660014812)]\n",
      "45 [('no3', 6.1799999999999997, 8.2120484549737061), ('sil', 59.57, 62.460609193147583)]\n",
      "46 [('no3', 6.1799999999999997, 8.2015273891641609), ('sil', 59.57, 62.608219603705258)]\n",
      "47 [('no3', 6.1799999999999997, 8.1907851510825349), ('sil', 59.57, 62.758928093172891)]\n",
      "48 [('no3', 6.1799999999999997, 8.1798384501945627), ('sil', 59.57, 62.912866839730022)]\n",
      "49 [('no3', 6.1799999999999997, 8.1687050094360369), ('sil', 59.57, 63.070162248489993)]\n",
      "50 [('no3', 6.1799999999999997, 8.1574035492017583), ('sil', 59.57, 63.230934199710809)]\n",
      "51 [('no3', 6.1799999999999997, 8.1459537566704796), ('sil', 59.57, 63.395295296636689)]\n",
      "52 [('no3', 6.1799999999999997, 8.1343762404249915), ('sil', 59.57, 63.563350114774003)]\n",
      "53 [('no3', 6.1799999999999997, 8.1226924705313195), ('sil', 59.57, 63.735194454340935)]\n",
      "54 [('no3', 6.1799999999999997, 8.1109247042585313), ('sil', 59.57, 63.910914597933811)]\n",
      "55 [('no3', 6.1799999999999997, 8.0990958979525978), ('sil', 59.57, 64.090586575338406)]\n",
      "56 [('no3', 6.1799999999999997, 8.087229605647094), ('sil', 59.57, 64.274275437728335)]\n",
      "57 [('no3', 6.1799999999999997, 8.0753498650929441), ('sil', 59.57, 64.462034543555149)]\n",
      "58 [('no3', 6.1799999999999997, 8.0634810722731025), ('sil', 59.57, 64.653904858672874)]\n",
      "59 [('no3', 6.1799999999999997, 8.0516478453546654), ('sil', 59.57, 64.849914273379142)]\n",
      "60 [('no3', 6.1799999999999997, 8.0398748793365087), ('sil', 59.57, 65.05007693912853)]\n",
      "61 [('no3', 6.1799999999999997, 8.0281867927940294), ('sil', 59.57, 65.254392628124208)]\n",
      "62 [('no3', 6.1799999999999997, 8.0166079681165083), ('sil', 59.57, 65.462846118831322)]\n",
      "63 [('no3', 6.1799999999999997, 8.0051623868725859), ('sil', 59.57, 65.675406610836873)]\n",
      "64 [('no3', 6.1799999999999997, 7.9938734619150642), ('sil', 59.57, 65.892027172655105)]\n",
      "65 [('no3', 6.1799999999999997, 7.9827638679891617), ('sil', 59.57, 66.112644225955108)]\n",
      "66 [('no3', 6.1799999999999997, 7.9718553725403316), ('sil', 59.57, 66.337177070159072)]\n",
      "67 [('no3', 6.1799999999999997, 7.9611686685211636), ('sil', 59.57, 66.565527451173793)]\n",
      "68 [('no3', 6.1799999999999997, 7.9507232109363706), ('sil', 59.57, 66.797579178254693)]\n",
      "69 [('no3', 6.1799999999999997, 7.9405370588180002), ('sil', 59.57, 67.033197793018928)]\n",
      "70 [('no3', 6.1799999999999997, 7.9306267243038748), ('sil', 59.57, 67.272230294676135)]\n",
      "71 [('no3', 6.1799999999999997, 7.9210070303016522), ('sil', 59.57, 67.514504925479613)]\n",
      "72 [('no3', 6.1799999999999997, 7.9116909782355105), ('sil', 59.57, 67.75983102045042)]\n",
      "73 [('no3', 6.1799999999999997, 7.9026896271237392), ('sil', 59.57, 68.0079989255252)]\n",
      "74 [('no3', 6.1799999999999997, 7.8940119850893602), ('sil', 59.57, 68.258779987732595)]\n",
      "75 [('no3', 6.1799999999999997, 7.885664914328629), ('sil', 59.57, 68.51192662153781)]\n",
      "76 [('no3', 6.1799999999999997, 7.8776530502533069), ('sil', 59.57, 68.767172454871357)]\n",
      "77 [('no3', 6.1799999999999997, 7.8699787353512676), ('sil', 59.57, 69.024232558296191)]\n",
      "78 [('no3', 6.1799999999999997, 7.8626419682162707), ('sil', 59.57, 69.282803760643873)]\n",
      "79 [('no3', 6.1799999999999997, 7.8556403678305786), ('sil', 59.57, 69.542565054068447)]\n",
      "80 [('no3', 6.1799999999999997, 7.848969153077312), ('sil', 59.57, 69.803178091223231)]\n",
      "81 [('no3', 6.1799999999999997, 7.8426211373441301), ('sil', 59.57, 70.064287776742532)]\n",
      "82 [('no3', 6.1799999999999997, 7.8365867376743452), ('sil', 59.57, 70.325522955103992)]\n",
      "83 [('no3', 6.1799999999999997, 7.8308539980217091), ('sil', 59.57, 70.586497196023672)]\n",
      "84 [('no3', 6.1799999999999997, 7.8254086258462987), ('sil', 59.57, 70.846809678334964)]\n",
      "85 [('no3', 6.1799999999999997, 7.8202340411854516), ('sil', 59.57, 71.106046172568611)]\n",
      "86 [('no3', 6.1799999999999997, 7.8153114373008901), ('sil', 59.57, 71.363780121597102)]\n",
      "87 [('no3', 6.1799999999999997, 7.8106198518680543), ('sil', 59.57, 71.619573818405925)]\n",
      "88 [('no3', 6.1799999999999997, 7.8061362475925318), ('sil', 59.57, 71.872979678683492)]\n",
      "89 [('no3', 6.1799999999999997, 7.8018356011454744), ('sil', 59.57, 72.123541605494708)]\n",
      "90 [('no3', 6.1799999999999997, 7.7976909993248018), ('sil', 59.57, 72.370796442254615)]\n",
      "91 [('no3', 6.1799999999999997, 7.7936737412241079), ('sil', 59.57, 72.614275509030747)]\n",
      "92 [('no3', 6.1799999999999997, 7.7897534453922184), ('sil', 59.57, 72.853506216686512)]\n",
      "93 [('no3', 6.1799999999999997, 7.7858981608810449), ('sil', 59.57, 73.088013751734593)]\n",
      "94 [('no3', 6.1799999999999997, 7.7820744811712315), ('sil', 59.57, 73.31732282432263)]\n",
      "95 [('no3', 6.1799999999999997, 7.7782476600703712), ('sil', 59.57, 73.540959470360789)]\n",
      "96 [('no3', 6.1799999999999997, 7.7743817286960857), ('sil', 59.57, 73.758452897859115)]\n",
      "97 [('no3', 6.1799999999999997, 7.7704396127850428), ('sil', 59.57, 73.969337366734138)]\n",
      "98 [('no3', 6.1799999999999997, 7.7663832496462977), ('sil', 59.57, 74.173154090260311)]\n",
      "99 [('no3', 6.1799999999999997, 7.7621737041448231), ('sil', 59.57, 74.36945314551663)]\n",
      "100 [('no3', 6.1799999999999997, 7.7577712832260266), ('sil', 59.57, 74.557795379692621)]\n",
      "101 [('no3', 6.1799999999999997, 7.7531356485622869), ('sil', 59.57, 74.737754298121189)]\n",
      "102 [('no3', 6.1799999999999997, 7.7482259269662039), ('sil', 59.57, 74.908917919798455)]\n",
      "103 [('no3', 6.1799999999999997, 7.7430008183270393), ('sil', 59.57, 75.070890585869321)]\n",
      "104 [('no3', 6.1799999999999997, 7.737418700890494), ('sil', 59.57, 75.223294705856247)]\n",
      "105 [('no3', 6.1799999999999997, 7.7314377337259943), ('sil', 59.57, 75.365772427463654)]\n",
      "106 [('no3', 6.1799999999999997, 7.7250159563570504), ('sil', 59.57, 75.497987215217563)]\n",
      "107 [('no3', 6.1799999999999997, 7.7181113855165133), ('sil', 59.57, 75.619625323903094)]\n",
      "108 [('no3', 6.1799999999999997, 7.7106821090491264), ('sil', 59.57, 75.73039715391289)]\n",
      "109 [('no3', 6.1799999999999997, 7.7026863770631593), ('sil', 59.57, 75.830038475825262)]\n",
      "110 [('no3', 6.1799999999999997, 7.694082690374799), ('sil', 59.57, 75.918311513104172)]\n",
      "111 [('no3', 6.1799999999999997, 7.6848298864258107), ('sil', 59.57, 75.995005872982873)]\n",
      "112 [('no3', 6.1799999999999997, 7.6748872227848457), ('sil', 59.57, 76.059939317130898)]\n",
      "113 [('no3', 6.1799999999999997, 7.6642144583828813), ('sil', 59.57, 76.112958365070483)]\n",
      "114 [('no3', 6.1799999999999997, 7.6527719327130468), ('sil', 59.57, 76.153938725514095)]\n",
      "115 [('no3', 6.1799999999999997, 7.6405206431142458), ('sil', 59.57, 76.182785552347866)]\n",
      "116 [('no3', 6.1799999999999997, 7.6274223203873541), ('sil', 59.57, 76.199433523855319)]\n",
      "117 [('no3', 6.1799999999999997, 7.6134395029387774), ('sil', 59.57, 76.203846746310631)]\n",
      "118 [('no3', 6.1799999999999997, 7.5985356096518304), ('sil', 59.57, 76.19601848424054)]\n",
      "119 [('no3', 6.1799999999999997, 7.5826750117332731), ('sil', 59.57, 76.175970722374259)]\n",
      "120 [('no3', 6.1799999999999997, 7.5658231037503967), ('sil', 59.57, 76.143753565861587)]\n",
      "121 [('no3', 6.1799999999999997, 7.5479463741110511), ('sil', 59.57, 76.099444486991715)]\n",
      "122 [('no3', 6.1799999999999997, 7.529012475225108), ('sil', 59.57, 76.043147428822778)]\n",
      "123 [('no3', 6.1799999999999997, 7.5089902936094441), ('sil', 59.57, 75.97499177716692)]\n",
      "124 [('no3', 6.1799999999999997, 7.4878500201974125), ('sil', 59.57, 75.895131213958109)]\n",
      "125 [('no3', 6.1799999999999997, 7.4655632211001137), ('sil', 59.57, 75.803742466127332)]\n",
      "126 [('no3', 6.1799999999999997, 7.4421029091217727), ('sil', 59.57, 75.701023965124776)]\n",
      "127 [('no3', 6.1799999999999997, 7.4174436162450439), ('sil', 59.57, 75.587194432537203)]\n",
      "128 [('no3', 6.1799999999999997, 7.391561467376877), ('sil', 59.57, 75.462491408158101)]\n",
      "129 [('no3', 6.1799999999999997, 7.3644342555950626), ('sil', 59.57, 75.327169736783802)]\n",
      "130 [('no3', 6.1799999999999997, 7.3360415190829498), ('sil', 59.57, 75.181500029716645)]\n",
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      "304 [('no3', 5.4786165423106059, 6.1799999999999997), ('sil', 48.913443027917772, 59.57)]\n",
      "305 [('no3', 5.5410417564812979, 6.1799999999999997), ('sil', 49.039587264065879, 59.57)]\n",
      "306 [('no3', 5.6036115253388914, 6.1799999999999997), ('sil', 49.167127185110949, 59.57)]\n",
      "307 [('no3', 5.6662945476002458, 6.1799999999999997), ('sil', 49.295936826058039, 59.57)]\n",
      "308 [('no3', 5.729058624128526, 6.1799999999999997), ('sil', 49.425895544086352, 59.57)]\n",
      "309 [('no3', 5.7918706224484486, 6.1799999999999997), ('sil', 49.556888090844673, 59.57)]\n",
      "310 [('no3', 5.8546964512223347, 6.1799999999999997), ('sil', 49.68880465774776, 59.57)]\n",
      "311 [('no3', 5.9175010455686881, 6.1799999999999997), ('sil', 49.821540895981713, 59.57)]\n",
      "312 [('no3', 5.980248363952156, 6.1799999999999997), ('sil', 49.954997913506361, 59.57)]\n",
      "313 [('no3', 6.0429013971455721, 6.1799999999999997), ('sil', 50.089082250621829, 59.57)]\n",
      "314 [('no3', 6.1054221896618204, 6.1799999999999997), ('sil', 50.223705835916192, 59.57)]\n",
      "315 [('no3', 6.1677718738058855, 6.1799999999999997), ('sil', 50.358785924645325, 59.57)]\n",
      "316 [('no3', 6.1799999999999997, 6.2299107163160174), ('sil', 50.494245020799916, 59.57)]\n",
      "317 [('no3', 6.1799999999999997, 6.2917981774517067), ('sil', 50.63001078459169, 59.57)]\n",
      "318 [('no3', 6.1799999999999997, 6.3533929821159489), ('sil', 50.76601592690708, 59.57)]\n",
      "319 [('no3', 6.1799999999999997, 6.4146532024972931), ('sil', 50.902198091475128, 59.57)]\n",
      "320 [('no3', 6.1799999999999997, 6.4755363515708426), ('sil', 51.038499726572411, 59.57)]\n",
      "321 [('no3', 6.1799999999999997, 6.535999486622516), ('sil', 51.174867946831732, 59.57)]\n",
      "322 [('no3', 6.1799999999999997, 6.5959993218767785), ('sil', 51.311254385988917, 59.57)]\n",
      "323 [('no3', 6.1799999999999997, 6.6554923492084397), ('sil', 51.447615041818487, 59.57)]\n",
      "324 [('no3', 6.1799999999999997, 6.7144349658377021), ('sil', 51.583910113364389, 59.57)]\n",
      "325 [('no3', 6.1799999999999997, 6.7727836078476562), ('sil', 51.720103831300385, 59.57)]\n",
      "326 [('no3', 6.1799999999999997, 6.8304948883460224), ('sil', 51.856164281984384, 59.57)]\n",
      "327 [('no3', 6.1799999999999997, 6.8875257390682281), ('sil', 51.992063225528923, 59.57)]\n",
      "328 [('no3', 6.1799999999999997, 6.9438335542407446), ('sil', 52.127775908133842, 59.57)]\n",
      "329 [('no3', 6.1799999999999997, 6.9993763355260654), ('sil', 52.263280869420264, 59.57)]\n",
      "330 [('no3', 6.1799999999999997, 7.0541128369523234), ('sil', 52.39855974479908, 59.57)]\n",
      "331 [('no3', 6.1799999999999997, 7.1080027087445155), ('sil', 52.533597063219879, 59.57)]\n",
      "332 [('no3', 6.1799999999999997, 7.1610066390764757), ('sil', 52.66838004101718, 59.57)]\n",
      "333 [('no3', 6.1799999999999997, 7.2130864928240657), ('sil', 52.802898371856813, 59.57)]\n",
      "334 [('no3', 6.1799999999999997, 7.2642054464799539), ('sil', 52.937144013465286, 59.57)]\n",
      "335 [('no3', 6.1799999999999997, 7.3143281184806836), ('sil', 53.071110971741184, 59.57)]\n",
      "336 [('no3', 6.1799999999999997, 7.3634206942730271), ('sil', 53.204795082638206, 59.57)]\n",
      "337 [('no3', 6.1799999999999997, 7.4114510455308915), ('sil', 53.33819379261783, 59.57)]\n",
      "338 [('no3', 6.1799999999999997, 7.4583888429873708), ('sil', 53.471305938545918, 59.57)]\n",
      "339 [('no3', 6.1799999999999997, 7.5042056624305058), ('sil', 53.604131527619131, 59.57)]\n",
      "340 [('no3', 6.1799999999999997, 7.5488750834526019), ('sil', 53.736671518524894, 59.57)]\n",
      "341 [('no3', 6.1799999999999997, 7.592372780558164), ('sil', 53.868927604836152, 59.57)]\n",
      "342 [('no3', 6.1799999999999997, 7.6346766063094238), ('sil', 54.000902001519179, 59.57)]\n",
      "343 [('no3', 6.1799999999999997, 7.6757666661535309), ('sil', 54.132597236164095, 59.57)]\n",
      "344 [('no3', 6.1799999999999997, 7.7156253845973382), ('sil', 54.264015945991751, 59.57)]\n",
      "345 [('no3', 6.1799999999999997, 7.754237562399954), ('sil', 54.395160681989339, 59.57)]\n",
      "346 [('no3', 6.1799999999999997, 7.7915904244102148), ('sil', 54.526033721852585, 59.57)]\n",
      "347 [('no3', 6.1799999999999997, 7.8276736576749286), ('sil', 54.656636892999479, 59.57)]\n",
      "348 [('no3', 6.1799999999999997, 7.8624794394268411), ('sil', 54.786971407309224, 59.57)]\n",
      "349 [('no3', 6.1799999999999997, 7.8960024545129697), ('sil', 54.917037709221923, 59.57)]\n",
      "350 [('no3', 6.1799999999999997, 7.9282399018424172), ('sil', 55.046835338766492, 59.57)]\n",
      "351 [('no3', 6.1799999999999997, 7.9591914894412179), ('sil', 55.176362811078555, 59.57)]\n",
      "352 [('no3', 6.1799999999999997, 7.9888594176324315), ('sil', 55.305617514166428, 59.57)]\n",
      "353 [('no3', 6.1799999999999997, 8.0172483500209957), ('sil', 55.434595626367347, 59.57)]\n",
      "354 [('no3', 6.1799999999999997, 8.0443653718908052), ('sil', 55.56329205510351, 59.57)]\n",
      "355 [('no3', 6.1799999999999997, 8.0702199357420792), ('sil', 55.691700398422753, 59.57)]\n",
      "356 [('no3', 6.1799999999999997, 8.0948237938433092), ('sil', 55.819812930648453, 59.57)]\n",
      "357 [('no3', 6.1799999999999997, 8.1181909176580547), ('sil', 55.947620613450894, 59.57)]\n",
      "358 [('no3', 6.1799999999999997, 8.1403374043036578), ('sil', 56.075113133478176, 59.57)]\n",
      "359 [('no3', 6.1799999999999997, 8.161281370233592), ('sil', 56.202278967449601, 59.57)]\n",
      "360 [('no3', 6.1799999999999997, 8.1810428325623885), ('sil', 56.329105475543237, 59.57)]\n",
      "361 [('no3', 6.1799999999999997, 8.1996435786654853), ('sil', 56.455579023648681, 59.57)]\n",
      "362 [('no3', 6.1799999999999997, 8.2171070248225977), ('sil', 56.581685134675631, 59.57)]\n",
      "363 [('no3', 6.1799999999999997, 8.2334580648882838), ('sil', 56.707408669124732, 59.57)]\n",
      "364 [('no3', 6.1799999999999997, 8.2487229102184187), ('sil', 56.832734034515347, 59.57)]\n",
      "365 [('no3', 6.1799999999999997, 8.2629289221420184), ('sil', 56.957645423143013, 59.57)]\n",
      "366 [('no3', 6.1799999999999997, 8.2761044385521991), ('sil', 57.08212707723338, 59.57)]\n"
     ]
    }
   ],
   "source": [
    "year=2016\n",
    "for yearday in range(1,367):\n",
    "    t2=dt.date(year, 1, 1) + dt.timedelta(days = yearday - 1)\n",
    "    #datestr='y'+t2.strftime('%Y')+'m'+t2.strftime('%m')+'d'+t2.strftime('%d')\n",
    "    datestr='m'+t2.strftime('%m')+'d'+t2.strftime('%d')\n",
    "    new=nc.Dataset(fname+datestr+'.nc')\n",
    "    print(yearday,[(ikey, np.min(new.variables[ikey]), np.max(new.variables[ikey])) for ikey in new.variables.keys()])\n",
    "    new.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "366 [('no3', 6.1235179805710107, 6.1799999999999997), ('nh4', 4.2325533077690913, 4.2999999999999998), ('sil', 59.554968806333505, 59.57), ('dia', 0.001, 0.001), ('phy', 0.001, 0.001), ('mes', 0.001, 0.001), ('zoo', 0.001, 0.001), ('don', 0.0, 0.0), ('pon', 0.0, 0.0), ('bsi', 0.0, 0.0), ('tur', 1.0, 1.0)]\n"
     ]
    }
   ],
   "source": [
    "new=nc.Dataset(fnameC+'.nc')\n",
    "print(yearday,[(ikey, np.min(new.variables[ikey]), np.max(new.variables[ikey])) for ikey in new.variables.keys()])\n",
    "new.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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