{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import matplotlib as mpl\n",
    "import datetime as dt\n",
    "from salishsea_tools import evaltools as et, places, viz_tools, visualisations\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import os\n",
    "import bloomdrivers\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#start=dt.datetime(2015,1,1)\n",
    "#end=dt.datetime(2015,1,5)\n",
    "dateslist=[[dt.datetime(2015,1,1),dt.datetime(2015,1,5)],\n",
    "           [dt.datetime(2015,1,5),dt.datetime(2015,1,10)],\n",
    "           [dt.datetime(2015,1,10),dt.datetime(2015,1,15)]]\n",
    "year=str(dateslist[0][0].year)\n",
    "modver='201812'\n",
    "loc='S3'\n",
    "\n",
    "#savedir='/ocean/aisabell/MEOPAR/extracted_files'\n",
    "savedir='/ocean/eolson/MEOPAR/'\n",
    "\n",
    "fname=f'testJanToMarch_TimeSeries_{year}_{loc}_{modver}.pkl'\n",
    "fname2=f'testJanToMarch_TimeSeries_{year}_{modver}.pkl' # for non-location specific variables\n",
    "savepath=os.path.join(savedir,fname)\n",
    "savepath2=os.path.join(savedir,fname2)\n",
    "recalc=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[datetime.datetime(2015, 1, 1, 0, 0), datetime.datetime(2015, 1, 5, 0, 0)],\n",
       " [datetime.datetime(2015, 1, 5, 0, 0), datetime.datetime(2015, 1, 10, 0, 0)],\n",
       " [datetime.datetime(2015, 1, 10, 0, 0), datetime.datetime(2015, 1, 15, 0, 0)]]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dateslist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[datetime.datetime(2015, 1, 1, 0, 0), datetime.datetime(2015, 1, 5, 0, 0)]\n",
      "[datetime.datetime(2015, 1, 5, 0, 0), datetime.datetime(2015, 1, 10, 0, 0)]\n",
      "[datetime.datetime(2015, 1, 10, 0, 0), datetime.datetime(2015, 1, 15, 0, 0)]\n"
     ]
    }
   ],
   "source": [
    "for el in dateslist:\n",
    "    print(el)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1363636363636362"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loc='S3'\n",
    "\n",
    "# lat and lon informatin for place:\n",
    "lon,lat=places.PLACES['S3']['lon lat']\n",
    "# get place information on SalishSeaCast grid:\n",
    "ij,ii=places.PLACES['S3']['NEMO grid ji']\n",
    "# GEM2.5 grid ji is atm forcing grid for ops files\n",
    "jw,iw=places.PLACES['S3']['GEM2.5 grid ji']\n",
    "\n",
    "fig, ax = plt.subplots(1,1,figsize = (6,6))\n",
    "with xr.open_dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/mesh_mask201702.nc') as mesh:\n",
    "    ax.contour(mesh.nav_lon,mesh.nav_lat,mesh.tmask.isel(t=0,z=0),[0.1,],colors='k')\n",
    "    tmask=np.array(mesh.tmask)\n",
    "    gdept_1d=np.array(mesh.gdept_1d)\n",
    "    e3t_0=np.array(mesh.e3t_0)\n",
    "ax.plot(lon, lat, '.', markersize=14, color='red')\n",
    "ax.set_ylim(48,50)\n",
    "ax.set_xlim(-125,-122)\n",
    "ax.set_title('Location of Station S3')\n",
    "ax.set_xlabel('Longitude')\n",
    "ax.set_ylabel('Latitude')\n",
    "viz_tools.set_aspect(ax,coords='map')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "if recalc==True or not os.path.isfile(savepath):\n",
    "    basedir='/results/SalishSea/nowcast-green.201812/'\n",
    "    nam_fmt='nowcast'\n",
    "    flen=1 # files contain 1 day of data each\n",
    "    ftype= 'ptrc_T' # load bio files\n",
    "    tres=24 # 1: hourly resolution; 24: daily resolution  \n",
    "    bio_time=list()\n",
    "    diat_alld=list()\n",
    "    no3_alld=list()\n",
    "    flag_alld=list()\n",
    "    cili_alld=list()\n",
    "    microzoo_alld=list()\n",
    "    mesozoo_alld=list()\n",
    "    intdiat=list()\n",
    "    intphyto=list()\n",
    "    spar=list()\n",
    "    intmesoz=list()\n",
    "    intmicroz=list()\n",
    "    grid_time=list()\n",
    "    temp=list()\n",
    "    salinity=list()\n",
    "    u_wind=list()\n",
    "    v_wind=list()\n",
    "    twind=list()\n",
    "    solar=list()\n",
    "    ik=0\n",
    "    for ind, datepair in enumerate(dateslist):\n",
    "        start=datepair[0]\n",
    "        end=datepair[1]\n",
    "        flist=et.index_model_files(start,end,basedir,nam_fmt,flen,ftype,tres)\n",
    "        flist3 = et.index_model_files(start,end,basedir,nam_fmt,flen,\"grid_T\",tres)\n",
    "        fliste3t = et.index_model_files(start,end,basedir,nam_fmt,flen,\"carp_T\",tres)\n",
    "        with xr.open_mfdataset(flist['paths']) as bio:\n",
    "            bio_time.append(np.array(bio.time_centered[:]))\n",
    "            no3_alld.append(np.array(bio.nitrate.isel(y=ij,x=ii)) )\n",
    "            diat_alld.append(np.array(bio.diatoms.isel(y=ij,x=ii)))\n",
    "            flag_alld.append(np.array(bio.flagellates.isel(y=ij,x=ii)))\n",
    "            cili_alld.append(np.array(bio.ciliates.isel(y=ij,x=ii)))\n",
    "            microzoo_alld.append(np.array(bio.microzooplankton.isel(y=ij,x=ii)))\n",
    "            mesozoo_alld.append(np.array(bio.mesozooplankton.isel(y=ij,x=ii)))\n",
    "\n",
    "            with xr.open_mfdataset(fliste3t['paths']) as carp:\n",
    "                intdiat.append(np.array(np.sum(bio.diatoms.isel(y=ij,x=ii)*carp.e3t.isel(y=ij,x=ii),1))) # depth integrated diatom\n",
    "                intphyto.append(np.array(np.sum((bio.diatoms.isel(y=ij,x=ii)+bio.flagellates.isel(y=ij,x=ii)\\\n",
    "                                +bio.ciliates.isel(y=ij,x=ii))*carp.e3t.isel(y=ij,x=ii),1)))\n",
    "                spar.append(np.array(carp.PAR.isel(deptht=ik,y=ij,x=ii)))\n",
    "                intmesoz.append(np.array(np.sum(bio.mesozooplankton.isel(y=ij,x=ii)*carp.e3t.isel(y=ij,x=ii),1)))\n",
    "                intmicroz.append(np.array(np.sum(bio.microzooplankton.isel(y=ij,x=ii)*carp.e3t.isel(y=ij,x=ii),1)))\n",
    "\n",
    "        with xr.open_mfdataset(flist3['paths']) as grid:\n",
    "            grid_time.append(np.array(grid.time_centered[:]))\n",
    "            temp.append(np.array(grid.votemper.isel(deptht=ik,y=ij,x=ii)) )#surface temperature\n",
    "            salinity.append(np.array(grid.vosaline.isel(deptht=ik,y=ij,x=ii))) #surface salinity\n",
    "\n",
    "        opsdir='/results/forcing/atmospheric/GEM2.5/operational'\n",
    "\n",
    "        flist2=et.index_model_files(start,end,opsdir,nam_fmt='ops',flen=1,ftype='None',tres=24)\n",
    "        with xr.open_mfdataset(flist2['paths']) as winds:\n",
    "            u_wind.append(np.array(winds.u_wind.isel(y=jw,x=iw)))\n",
    "            v_wind.append(np.array(winds.v_wind.isel(y=jw,x=iw)))\n",
    "            twind.append(np.array(winds.time_counter))\n",
    "            solar.append(np.array(winds.solar.isel(y=jw,x=iw)))\n",
    "    \n",
    "    bio_time=np.concatenate(bio_time,axis=0)\n",
    "    diat_alld=np.concatenate(diat_alld,axis=0)\n",
    "    no3_alld=np.concatenate(no3_alld,axis=0)\n",
    "    flag_alld=np.concatenate(flag_alld,axis=0)\n",
    "    cili_alld=np.concatenate(cili_alld,axis=0)\n",
    "    microzoo_alld=np.concatenate(microzoo_alld,axis=0)\n",
    "    mesozoo_alld=np.concatenate(mesozoo_alld,axis=0)\n",
    "    intdiat=np.concatenate(intdiat,axis=0)\n",
    "    intphyto=np.concatenate(intphyto,axis=0)\n",
    "    spar=np.concatenate(spar,axis=0)\n",
    "    intmesoz=np.concatenate(intmesoz,axis=0)\n",
    "    intmicroz=np.concatenate(intmicroz,axis=0)\n",
    "    grid_time=np.concatenate(grid_time,axis=0)\n",
    "    temp=np.concatenate(temp,axis=0)\n",
    "    salinity=np.concatenate(salinity,axis=0)\n",
    "    u_wind=np.concatenate(u_wind,axis=0)\n",
    "    v_wind=np.concatenate(v_wind,axis=0)\n",
    "    twind=np.concatenate(twind,axis=0)\n",
    "    solar=np.concatenate(solar,axis=0)\n",
    "    allvars=(bio_time,diat_alld,no3_alld,flag_alld,cili_alld,microzoo_alld,mesozoo_alld,\n",
    "              intdiat,intphyto,spar,intmesoz,intmicroz,\n",
    "            grid_time,temp,salinity,u_wind,v_wind,twind,solar)\n",
    "    pickle.dump(allvars,open(savepath,'wb'))\n",
    "else:\n",
    "    pvars=pickle.load(open(savepath,'rb'))\n",
    "    (bio_time,diat_alld,no3_alld,flag_alld,cili_alld,microzoo_alld,mesozoo_alld,\n",
    "              intdiat,intphyto,spar,intmesoz,intmicroz,\n",
    "            grid_time,temp,salinity,u_wind,v_wind,twind,solar)=pvars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "basedir='/results/SalishSea/nowcast-green.201812/'\n",
    "nam_fmt='nowcast'\n",
    "flen=1 # files contain 1 day of data each\n",
    "ftype= 'ptrc_T' # load bio files\n",
    "tres=24 # 1: hourly resolution; 24: daily resolution  \n",
    "flist=et.index_model_files(dt.datetime(2015,1,1),dt.datetime(2015,1,5),basedir,nam_fmt,flen,ftype,tres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = xr.open_mfdataset(flist['paths'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "v1=np.array(test.nitrate.isel(y=ij,x=ii)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "v2=np.array(test.time_centered[:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "test2=dict()\n",
    "test2[0]=v1\n",
    "test2[1]=v1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "v1b=np.concatenate((v1,v1),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 40)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(v1b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8,)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(np.concatenate((v2,v2),axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "### calculations based on saved values\n",
    "no3_30to90m=np.sum(no3_alld[:,22:26]*e3t_0[:,22:26,ij,ii],1)/np.sum(e3t_0[:,22:26,ij,ii]) # average, considering cell thickness\n",
    "sno3=no3_alld[:,0]\n",
    "sdiat=diat_alld[:,0]\n",
    "sflag=flag_alld[:,0]\n",
    "scili=cili_alld[:,0]\n",
    "intzoop=intmesoz+intmicroz\n",
    "fracdiat=intdiat/intphyto # depth integrated fraction of diatoms\n",
    "zoop_alld=microzoo_alld+mesozoo_alld\n",
    "sphyto=sdiat+sflag+scili\n",
    "phyto_alld=diat_alld+flag_alld+cili_alld\n",
    "percdiat=sdiat/sphyto # fraction surface diatoms\n",
    "\n",
    "# wind speed:\n",
    "wspeed=np.sqrt(u_wind**2 + v_wind**2)\n",
    "# wind direction in degrees from east\n",
    "d = np.arctan2(v_wind, u_wind)\n",
    "winddirec=np.rad2deg(d + (d < 0)*2*np.pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x7f730c345eb0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define sog region:\n",
    "fig, ax = plt.subplots(1,2,figsize = (6,6))\n",
    "with xr.open_dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathymetry_201702.nc') as bathy:\n",
    "    bath=np.array(bathy.Bathymetry)\n",
    "ax[0].contourf(bath,np.arange(0,250,10))\n",
    "viz_tools.set_aspect(ax[0],coords='grid')\n",
    "sogmask=np.copy(tmask[:,:,:,:])\n",
    "sogmask[:,:,740:,:]=0\n",
    "sogmask[:,:,700:,170:]=0\n",
    "sogmask[:,:,550:,250:]=0\n",
    "sogmask[:,:,:,302:]=0\n",
    "sogmask[:,:,:400,:]=0\n",
    "sogmask[:,:,:,:100]=0\n",
    "#sogmask250[bath<250]=0\n",
    "ax[1].contourf(np.ma.masked_where(sogmask[0,0,:,:]==0,bathy.Bathymetry),[0,100,250,550])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.5000002726327963\n",
      "1 1.5000031443492432\n",
      "2 2.5000115035531536\n",
      "3 3.500030550916236\n",
      "4 4.500070415944791\n",
      "5 5.500150828149344\n",
      "6 6.500310215091616\n",
      "7 7.500623422387349\n",
      "8 8.501236225390784\n",
      "9 9.502432540550814\n",
      "10 10.504765304051489\n",
      "11 11.509311270243131\n",
      "12 12.518166842463359\n",
      "13 13.535412118866162\n",
      "14 14.568982157803276\n",
      "15 15.634287368141315\n",
      "16 16.761173418470406\n",
      "17 18.00713455587004\n",
      "18 19.48178513731196\n",
      "19 21.38997868353431\n",
      "20 24.10025665445164\n",
      "21 28.229915135962585\n",
      "22 34.68575798315476\n",
      "23 44.51772486309295\n",
      "24 58.4843336842095\n",
      "25 76.58558444650436\n",
      "26 98.06295924154529\n",
      "27 121.86651840226745\n",
      "28 147.08945807358322\n",
      "29 173.11448216959397\n",
      "30 199.57304923038515\n",
      "31 226.26030573521862\n",
      "32 253.06663732712263\n",
      "33 279.9345497590177\n",
      "34 306.8341973623137\n",
      "35 333.750169728144\n",
      "36 360.67453179815686\n",
      "37 387.60320347419827\n",
      "38 414.5340883529307\n",
      "39 441.4661096800038\n"
     ]
    }
   ],
   "source": [
    "for ind,z in enumerate(gdept_1d[0]):\n",
    "    print(ind,z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "### loops that are not location specific (do not need to be redone for each location):\n",
    "k250=32 # approximate index for 250 m\n",
    "if recalc==True or not os.path.isfile(savepath2):\n",
    "\n",
    "    basedir='/results/SalishSea/nowcast-green.201812/'\n",
    "    nam_fmt='nowcast'\n",
    "    flen=1 # files contain 1 day of data each\n",
    "    ftype= 'ptrc_T' # load bio files\n",
    "    tres=24 # 1: hourly resolution; 24: daily resolution  \n",
    "    flist=et.index_model_files(start,end,basedir,nam_fmt,flen,ftype,tres)\n",
    "    flist3 = et.index_model_files(start,end,basedir,nam_fmt,flen,\"grid_T\",tres)\n",
    "    fliste3t = et.index_model_files(start,end,basedir,nam_fmt,flen,\"carp_T\",tres)\n",
    "\n",
    "    ik=0\n",
    "    with xr.open_mfdataset(flist['paths']) as bio:\n",
    "        no3_past250m=np.array(np.sum(np.sum(np.sum(bio.nitrate.isel(deptht=slice(32,40))*sogmask[:,32:,:,:]*e3t_0[:,32:,:,:],3),2),1)\\\n",
    "                /np.sum(sogmask[0,32:,:,:]*e3t_0[0,32:,:,:]))\n",
    "        \n",
    "    # reading Fraser river flow files\n",
    "    dfFra=pd.read_csv('/ocean/eolson/MEOPAR/obs/ECRivers/Flow/FraserHopeDaily__Feb-8-2021_06_29_29AM.csv',\n",
    "                      skiprows=1)\n",
    "    # the original file contains both flow and water level information in the same field (Value)\n",
    "    # keep only the flow data, where PARAM=1 (drop PARAM=2 values, water level data)\n",
    "    # flow units are m3/s\n",
    "    # DD is YD, year day (ie. 1 is jan 1)\n",
    "    dfFra.drop(dfFra.loc[dfFra.PARAM==2].index,inplace=True)  \n",
    "\n",
    "    # rename 'Value' column to 'Flow' now that we have removed all the water level rows\n",
    "    dfFra.rename(columns={'Value':'Flow'}, inplace=True) \n",
    "        # inplace=True does this function on the orginal dataframe\n",
    "\n",
    "    # no time information so use dt.date\n",
    "    dfFra['Date']=[dt.date(iyr,1,1)+dt.timedelta(days=idd-1) for iyr, idd in zip(dfFra['YEAR'],dfFra['DD'])]\n",
    "    # taking the value from the yr column, jan1st date, and making jan1 column to be 1 not 0\n",
    "    dfFra.head(2)\n",
    "\n",
    "    # select portion of dataframe in desired date range\n",
    "    dfFra2=dfFra.loc[(dfFra.Date>=start.date())&(dfFra.Date<=end.date())]\n",
    "    riv_time=dfFra2['Date'].values\n",
    "    rivFlow=dfFra2['Flow'].values\n",
    "    # could also write dfFra['Date'], sometimes this is required\n",
    "    # newstart is a datetime object, so we convert it to just a date with .date\n",
    "    \n",
    "    allvars=(no3_past250m,riv_time,rivFlow)\n",
    "    pickle.dump(allvars,open(savepath2,'wb'))\n",
    "else:\n",
    "    pvars=pickle.load(open(savepath2,'rb'))\n",
    "    (no3_past250m,riv_time,rivFlow)=pvars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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