{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DFO Si Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import pandas as pd\n",
    "import netCDF4 as nc\n",
    "import datetime as dt\n",
    "from salishsea_tools import evaltools as et, viz_tools\n",
    "from salishsea_tools import geo_tools\n",
    "import gsw\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib as mpl\n",
    "import matplotlib.dates as mdates\n",
    "import cmocean as cmo\n",
    "import scipy.interpolate as sinterp\n",
    "import pickle\n",
    "import cmocean\n",
    "\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "mpl.rc('legend', fontsize=12)\n",
    "mpl.rc('axes', titlesize=12)\n",
    "mpl.rc('axes', labelsize=12)\n",
    "mpl.rc('figure', titlesize=12)\n",
    "mpl.rc('font', size=12)\n",
    "mpl.rc('text', usetex=True)\n",
    "mpl.rc('text.latex', preamble = ','.join(r'''\n",
    " \\usepackage{txfonts}\n",
    " \\usepackage{lmodern}\n",
    " '''.split()))\n",
    "mpl.rc('font', family='sans-serif', weight='normal', style='normal')\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 mesh:\n",
    "    navlon=mesh.variables['nav_lon'][:,:]\n",
    "    navlat=mesh.variables['nav_lat'][:,:]\n",
    "    tmask=mesh.variables['tmask'][:,:,:,:]\n",
    "    gdept=mesh.variables['gdept_1d'][0,:]\n",
    "    e3t0=mesh.variables['e3t_0'][0,:,:,:]\n",
    "bathy=np.sum(e3t0,0)\n",
    "with nc.Dataset('/results/SalishSea/hindcast.201812/01jan16/SalishSea_1h_20160101_20160101_ptrc_T.nc') as ftemp:\n",
    "        bounds=np.copy(ftemp.variables['deptht_bounds'][:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#d1=et._gridHoriz(df.loc[df.Z>150].copy(deep=True),tmask,navlon,navlat,wrapSearch=False,resetIndex=True);\n",
    "d1=pickle.load(open('/data/eolson/MEOPAR/SS36runs/calcFiles/evalMatches/dataDFO_k.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD9CAYAAABZVQdHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nD7TG6PavadENAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7b0f959400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# drop Saanich Inlet\n",
    "tmask2=np.copy(tmask[0,0,:,:])\n",
    "tmask2[328:371,180:217]=tmask[0,0,328:371,180:217]+2\n",
    "plt.pcolormesh(tmask2)\n",
    "plt.xlim(150,250)\n",
    "plt.ylim(310,400)\n",
    "d1.drop(d1.loc[(d1.i>=180)&(d1.i<217)&(d1.j>=328)&(d1.j<371)].index.values,inplace=True)\n",
    "# also drop east side Vancouver Island points:\n",
    "d1.drop(d1.loc[(d1.i<10)&(d1.j>=460)&(d1.j<480)].index.values,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d1['YD']=[(dt.datetime(int(yy),int(mm),int(dd))-dt.datetime(int(yy),1,1)).total_seconds()/(24*3600) for yy,mm,dd in zip(d1['Year'].values,d1['Month'].values,d1['Day'].values)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "times=np.array([dt.datetime(int(yy),int(mm),int(dd)) for yy,mm,dd in zip(d1.Year,d1.Month,d1.Day)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dfVic=d1.loc[(d1.Lat>48.15)&(d1.Lat<48.4)&(d1.Lon>-123.3)&(d1.Lon<-123.1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dfW=d1.loc[(d1.Lat>48.15)&(d1.Lat<49)&(d1.Lon>-125)&(d1.Lon<-124.5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7b0f2ec198>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LhVAoBEopstns3IWLKIqQJAmSJKFYLILneZycnCCRSIBSikuXLiGbzZotauYN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\nEMQa/D8z6vIp9Fm+IAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7b0f444ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,2,figsize=(10,4))\n",
    "#kk=30 #36,34,32,30\n",
    "simin=40\n",
    "simax=80\n",
    "for mon in range(1,2):\n",
    "    iax=ax[0];#ax[mon-1,1]\n",
    "    kk=34\n",
    "    iax.contour(navlon,navlat,tmask[0,0,:,:],levels=(.5,),linewidths=(.5,),colors=('w','w','w'))\n",
    "    iax.contourf(navlon,navlat,tmask[0,kk,:,:],levels=(0,.5,1.5),colors=('lightgray','darkgray','k'))\n",
    "    viz_tools.set_aspect(iax,coords='map')\n",
    "    d1bath=np.array([bathy[j,i] for i,j in zip(d1['i'],d1['j'])])\n",
    "    df0=d1.loc[(d1.Lon<-123.3)&(d1.Lon>-123.6)&(d1.Lat<49.2)&(d1.Lat>48.85)&(d1.Si>=0)&(d1bath>400)]\n",
    "    #lo,la,mm=binmean(df0,latEdges=np.linspace(47,51,31),lonEdges=np.linspace(-126,-122,41))\n",
    "    #iax.pcolormesh(lo,la,np.ma.masked_where(np.isnan(mm),mm))\n",
    "    #mesh=iax.scatter(lo,la,c=mm,s=6,vmin=simin,vmax=simax,zorder=2)\n",
    "\n",
    "ax[1].pcolormesh(tmask[0,0,:,:])\n",
    "ax[1].plot((260,280,280,260),(420,420,460,460),'r-')\n",
    "ax[1].plot((199),(262),'c+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7b0f1e1780>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7b0f95c908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r=np.array(df0[['j','i']].drop_duplicates())\n",
    "plt.plot(r[:,0],r[:,1],'o')\n",
    "plt.plot(423,264,'rx')\n",
    "plt.plot(423,275,'rx')\n",
    "plt.plot(455,284,'rx')\n",
    "plt.plot(456,264,'rx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7b0f654d30>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD8CAYAAACSCdTiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIQ8AgRHyABAYIQ8AgRHyABAYIQ8Agf0/F31ZlxQ5sCcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7b0f9590f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r=np.array(dfVic[['j','i']].drop_duplicates())\n",
    "plt.plot(r[:,0],r[:,1],'o')\n",
    "#plt.plot(423,264,'rx')\n",
    "#plt.plot(423,275,'rx')\n",
    "#plt.plot(455,284,'rx')\n",
    "#plt.plot(456,264,'rx')\n",
    "# use 262,199"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[413,   7],\n",
       "       [394,  31]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAD8CAYAAACFK0QrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCHMACOD/VXEmIgT7X/EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7b0f0b83c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r=np.array(dfW[['j','i']].drop_duplicates())\n",
    "plt.plot(r[:,0],r[:,1],'o')\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(424, 266)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j,i=geo_tools.find_closest_model_point(-123.426075,49.040138,navlon,navlat)\n",
    "j,i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Po7IQQhAZGYnCwkLk5uaitrYW06ZNw9SpUxutf+rUKUybNk2oPsWK2kFoyOTXuXNnWFpa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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7b0a2a6a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#map\n",
    "fig,ax=plt.subplots(1,1,figsize=(3,4))\n",
    "ax.contour(tmask[0,0,:,:],levels=(.5,),linewidths=(.5,),colors=('k',))\n",
    "ax.plot(20,412,'.',color='navy',ms=25,alpha=.5)\n",
    "ax.plot(199,262,'.',color='darkviolet',ms=25,alpha=.5)\n",
    "ax.plot(273,440,'.',color='deeppink',ms=25,alpha=.5)\n",
    "ax.plot(i,j,'.',color='teal',ms=30,alpha=.5)\n",
    "ax.plot(i,j,'.',color='darkorange',ms=12,alpha=1)\n",
    "ax.set_xlim(0,350)\n",
    "ax.set_ylim(150,600)\n",
    "viz_tools.set_aspect(ax)\n",
    "ax.xaxis.set_visible(None)\n",
    "ax.yaxis.set_visible(None)\n",
    "fig.savefig('/home/eolson/pyCode/notebooks/figs/smallMapIUGG.png',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mod_basedir= '/data/eolson/results/MEOPAR/SS36runs/linkHC201812/'\n",
    "start_date = dt.datetime(2015,1,1)\n",
    "end_date = dt.datetime(2018,1,1)\n",
    "flen=1\n",
    "namfmt='nowcast'\n",
    "#varmap={'N':'nitrate','Si':'silicon','Ammonium':'ammonium'}\n",
    "filemap={'nitrate':'ptrc_T','silicon':'ptrc_T','ammonium':'ptrc_T','diatoms':'ptrc_T','ciliates':'ptrc_T','flagellates':'ptrc_T','vosaline':'grid_T','votemper':'grid_T'}\n",
    "#gridmap={'nitrate':'tmask','silicon':'tmask','ammonium':'tmask'}\n",
    "fdict={'ptrc_T':24,'grid_T':24}\n",
    "flist=dict()\n",
    "for ift in fdict.keys():\n",
    "    flist[ift]=et.index_model_files(start_date,end_date,mod_basedir,namfmt,flen,ift,fdict[ift])\n",
    "    #SalishSea_1d_20150601_20150601_ptrc_T.nc"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "f=nc.Dataset('/data/eolson/results/MEOPAR/SS36runs/linkHC201812/01jan15/SalishSea_1d_20150101_20150101_grid_T.nc')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "jV,iV=geo_tools.find_closest_model_point(-123.426075,49.040138,navlon,navlat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "CT=list()\n",
    "SA=list()\n",
    "Chl=list()\n",
    "dSi=list()\n",
    "dSiVic=list()\n",
    "dSiW=list()\n",
    "bSi=list()\n",
    "diat=list()\n",
    "tt=list()\n",
    "for ifile in flist['grid_T']['paths']:\n",
    "    #Venus Central Node\n",
    "    #LATITUDE:     49.040138                         / Latitude North\n",
    "    #LONGITUDE:  -123.426075                         / Longitude East\n",
    "    #DEPTH:       300.0  \n",
    "    with nc.Dataset(ifile) as f:\n",
    "        CT.append(f.variables['votemper'][0,34,jV,iV])\n",
    "        SA.append(f.variables['vosaline'][0,34,jV,iV])\n",
    "        tt.append(dt.datetime(1900,1,1)+dt.timedelta(seconds=f.variables['time_centered'][0]))\n",
    "for ifile in flist['ptrc_T']['paths']:\n",
    "    with nc.Dataset(ifile) as f:\n",
    "        Chl.append(2*np.mean(np.mean(np.mean(f.variables['diatoms'][0,:3,420:460,260:280]+\\\n",
    "                                      f.variables['ciliates'][0,:3,420:460,260:280]+\\\n",
    "                                      f.variables['flagellates'][0,:3,420:460,260:280],2),1),0))\n",
    "        dSi.append(f.variables['silicon'][0,:,440,273])\n",
    "        dSiVic.append(f.variables['silicon'][0,:,262,199])\n",
    "        dSiW.append(f.variables['silicon'][0,:,412,20])\n",
    "        bSi.append(f.variables['biogenic_silicon'][0,:,440,273])\n",
    "        diat.append(f.variables['diatoms'][0,:,440,273])\n",
    "CT=np.array(CT)\n",
    "SA=np.array(SA)\n",
    "tt=np.array(tt)\n",
    "Chl=np.array(Chl)\n",
    "dSi=np.array(dSi)\n",
    "dSiVic=np.array(dSiVic)\n",
    "dSiW=np.array(dSiW)\n",
    "bSi=np.array(bSi)\n",
    "diat=np.array(diat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1065"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "355*3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load ONC VENUS data\n",
    "dfV=pd.read_csv('/ocean/eolson/MEOPAR/obs/ONC/VENUS/search9384608/'+\\\n",
    "                'Central_StraitofGeorgiaVENUSInstrumentPlatform_CTD_20080924T200000Z_20190702T000000Z-clean_avg1hour.csv',\n",
    "                skiprows=174,header=None,names=(\"TimeUTC\", \"ConductivitySm\", \"ConductivityQCFlag\", \"ConductivityCount\", \n",
    "                                                \"Density_kgm3\",\"DensityQCFlag\", \"DensityCount\", \"Depth_m\", \"DepthQCFlag\", \"DepthCount\", \n",
    "                                                \"PracticalSalinity_psu\", \"PracticalSalinityQCFlag\", \"PracticalSalinityCount\", \n",
    "                                                \"Pressure_dbar\", \"PressureQCFlag\", \"PressureCount\", \"Sigmat_kgm3\", \"SigmatQCFlag\", \"SigmatCount\", \n",
    "                                                \"Sigmatheta_0dbar_kgm3\", \"SigmathetaQCFlag\", \"SigmathetaCount\", \n",
    "                                                \"SoundSpeed_ms\", \"SoundSpeedQCFlag\", \"SoundSpeedCount\", \n",
    "                                                \"Temperature_C\", \"TemperatureQCFlag\" , \"TemperatureCount\"),na_values=' NaN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "timesV=np.array([dt.datetime.strptime(ii[0:19],'%Y-%m-%dT%H:%M:%S') for ii in dfV['TimeUTC']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "z=300.0\n",
    "lon=-123.426075\n",
    "lat=49.040138\n",
    "p=gsw.p_from_z(-1*z,lat)\n",
    "dfV['SA']=gsw.SA_from_SP(dfV['PracticalSalinity_psu'],p,lon,lat)\n",
    "dfV['CT']=gsw.CT_from_t(dfV['SA'],dfV['Temperature_C'],p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# load daily ferry Chl\n",
    "dfCh=pd.read_csv('/ocean/eolson/MEOPAR/obs/ONC/ferryChl_1d/search9397468/'+\\\n",
    "                'BritishColumbiaFerries_Tsawwassen-DukePoint_Turbidity-ChlorophyllandFluorescence_20120509T000000Z_20190703T000000Z-clean_avg1day.csv',\n",
    "                skiprows=102,header=None,names=(\"TimeUTC\", \"CDOMFluor_ppb\", \"CDOMFluorQCFlag\", \"CDOMFluorCount\", \n",
    "                     \"Chl_ugl\", \"ChlQCFlag\", \"ChlCount\", \"Turbidity_NTU\", \"TurbidityQCFlag\",\"TurbidityCount\", \n",
    "                     \"Latitude_deg\", \"LatitudeQCFlag\", \"LatitudeCount\", \"Longitude_deg\", \"LongitudeQCFlag\", \"LongitudeCount\",\n",
    "                     \"Pitch_deg\", \"PitchQCFlag\" , \"PitchCount\", \"Roll_deg\", \"RollQCFlag\" , \"RollCount\", \n",
    "                     \"TrueHeading_deg\", \"TrueHeadingQCFlag\" , \"TrueHeadingCount\"),na_values=' NaN')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "timesCh=np.array([dt.datetime.strptime(ii[0:19],'%Y-%m-%dT%H:%M:%S') for ii in dfCh['TimeUTC']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df0=d1.loc[(d1.Lon<-123.3)&(d1.Lon>-123.6)&(d1.Lat<49.2)&(d1.Lat>48.85)&(d1.Si>=0)&(d1bath>400)]\n",
    "times0=times[(d1.Lon<-123.3)&(d1.Lon>-123.6)&(d1.Lat<49.2)&(d1.Lat>48.85)&(d1.Si>=0)&(d1bath>400)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(735964.0, 736695.0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+O03q2JSnC2NiWLR3LzuNptV9f+RIHr/ySocdc//Fi/h4eBDhW3t5hXb153It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4WlEARisPIP0iIiw7FMY0JT0AZIitMuBwiNhHuehKr1ISUePLwABQ79A216q3XuHavWAcjOhJQE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\n20REREREfvL/h0lG5KNG6M4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd392ffdb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs=plt.subplots(4,1,figsize=(12,7))\n",
    "ik=36\n",
    "ax=axs[0]\n",
    "ax.plot(timesV,20*(dfV['PracticalSalinity_psu']-np.nanmean(dfV['PracticalSalinity_psu'])),'-',color='teal')\n",
    "ax.plot(timesV,10*(dfV['Temperature_C']-np.nanmean(dfV['Temperature_C'])),'-',color='coral')\n",
    "ax.plot(times0[df0.k==ik],df0.loc[df0.k==ik,['Si']]-np.nanmean(df0.loc[df0.k==ik,['Si']]),'o',color='deeppink')\n",
    "ax.set_xlim((dt.datetime(2009,1,1),dt.datetime(2011,1,1)))\n",
    "ax=axs[1]\n",
    "ax.plot(timesV,20*(dfV['PracticalSalinity_psu']-np.nanmean(dfV['PracticalSalinity_psu'])),'-',color='teal')\n",
    "ax.plot(timesV,10*(dfV['Temperature_C']-np.nanmean(dfV['Temperature_C'])),'-',color='coral')\n",
    "ax.plot(times0[df0.k==ik],df0.loc[df0.k==ik,['Si']]-np.nanmean(df0.loc[df0.k==ik,['Si']]),'o',color='deeppink')\n",
    "ax.set_xlim((dt.datetime(2011,1,1),dt.datetime(2013,1,1)))\n",
    "ax=axs[2]\n",
    "ax.plot(timesV,20*(dfV['PracticalSalinity_psu']-np.nanmean(dfV['PracticalSalinity_psu'])),'-',color='teal')\n",
    "ax.plot(timesV,10*(dfV['Temperature_C']-np.nanmean(dfV['Temperature_C'])),'-',color='coral')\n",
    "ax.plot(times0[df0.k==ik],df0.loc[df0.k==ik,['Si']]-np.nanmean(df0.loc[df0.k==ik,['Si']]),'o',color='deeppink')\n",
    "ax.set_xlim((dt.datetime(2013,1,1),dt.datetime(2015,1,1)))\n",
    "ax=axs[3]\n",
    "ax.plot(timesV,20*(dfV['PracticalSalinity_psu']-np.nanmean(dfV['PracticalSalinity_psu'])),'-',color='teal')\n",
    "ax.plot(timesV,10*(dfV['Temperature_C']-np.nanmean(dfV['Temperature_C'])),'-',color='coral')\n",
    "ax.plot(times0[df0.k==ik],df0.loc[df0.k==ik,['Si']]-np.nanmean(df0.loc[df0.k==ik,['Si']]),'o',color='deeppink')\n",
    "ax.set_xlim((dt.datetime(2016,1,1),dt.datetime(2018,1,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "timesVic=np.array([dt.datetime(int(yy),int(mm),int(dd)) for yy,mm,dd in zip(dfVic.Year,dfVic.Month,dfVic.Day)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "timesW=np.array([dt.datetime(int(yy),int(mm),int(dd)) for yy,mm,dd in zip(dfW.Year,dfW.Month,dfW.Day)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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c7hLCiQR/ffAgNYWF+Fwu3r96NdYZxkOlBrpo6u2hves1TPW3cXexC/xNJMNBAuEQ7lQc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\nPWMqwj995gyvt7SwJUupqi0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RGzeDzKi0VfLlZV+elZiqjlgHP+r5EXZhJyqjfLr003pR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\nhBBCCCFUgf8BzP+4huNmJcwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3602b3ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Tcol='orangered'\n",
    "Scol='teal'\n",
    "Sicol='deeppink'\n",
    "Sicol2='fuchsia'\n",
    "Sicol3='darkviolet'\n",
    "Sicol4='navy'\n",
    "Chcol='limegreen'\n",
    "fig=plt.figure(figsize=(12,6))\n",
    "h=.2\n",
    "w=.32\n",
    "axPos=((.1,.75,w,h),(.1,.46,w,h),(.1,.17,w,h))\n",
    "axPos2=((.56,.75,w,h),(.56,.46,w,h),(.56,.17,w,h))\n",
    "axSi=dict(); axS=dict(); axT=dict(); axCh=dict()\n",
    "for i in range(0,3):\n",
    "    axS[i]=fig.add_axes(axPos[i])\n",
    "    axT[i]=axS[i].twinx()\n",
    "    axT[i].yaxis.tick_left()\n",
    "    axT[i].spines['left'].set_position(('outward', 40))\n",
    "    axT[i].spines['left'].set_color(Tcol)\n",
    "    axCh[i]=axS[i].twinx()\n",
    "    axCh[i].spines['right'].set_position(('outward', 25))\n",
    "    axCh[i].spines['right'].set_color(Chcol)\n",
    "    axSi[i]=axS[i].twinx()\n",
    "    axSi[i].spines['right'].set_color(Sicol)\n",
    "    axS[i].spines['left'].set_color(Scol)\n",
    "    pSi2,=axSi[i].plot(times0[df0.k==(ik-2)],df0.loc[df0.k==(ik-2),['Si']],'o',color=Sicol2,alpha=.5)\n",
    "    pSi,=axSi[i].plot(times0[df0.k==ik],df0.loc[df0.k==ik,['Si']],'o',color=Sicol,alpha=.5)\n",
    "    pSiV,=axSi[i].plot(timesVic[dfVic.k==27],dfVic.loc[dfVic.k==27,['Si']],'o',color=Sicol3,alpha=.5)\n",
    "    pSiW,=axSi[i].plot(timesW[dfW.k==27],dfW.loc[dfW.k==27,['Si']],'o',color=Sicol4,alpha=.5)\n",
    "    pS,=axS[i].plot(timesV,dfV['SA'],'-',color=Scol,alpha=.5)\n",
    "    pT,=axT[i].plot(timesV,dfV['CT'],'-',color=Tcol,alpha=.5)\n",
    "    pCh,=axCh[i].plot(timesCh,dfCh['Chl_ugl'],'.',color=Chcol,alpha=.5)\n",
    "    axSi[i].set_ylim((30,85))\n",
    "    axCh[i].set_ylim((0,25))\n",
    "    axSi[i].spines['left'].set_visible(False)\n",
    "    axCh[i].spines['left'].set_visible(False)\n",
    "    axS[i].spines['right'].set_visible(False)\n",
    "    axT[i].spines['right'].set_visible(False)\n",
    "    axSi[i].set_ylim((30,86))\n",
    "    axCh[i].set_ylim((0,25))\n",
    "    axS[i].set_ylim((30,32.2))\n",
    "    axT[i].set_ylim((7.5,11))\n",
    "axS[0].set_xlim((dt.datetime(2015,1,1),dt.datetime(2016,1,1)))\n",
    "axS[1].set_xlim((dt.datetime(2016,1,1),dt.datetime(2017,1,1)))\n",
    "axS[2].set_xlim((dt.datetime(2017,1,1),dt.datetime(2018,1,1)))\n",
    "axT[0].yaxis.set_label_position(\"left\")\n",
    "axT[0].set_ylabel('CT ($^{\\circ}$C)',color=Tcol)\n",
    "axT[1].spines['left'].set_position(('outward', 55))\n",
    "axS[1].set_ylabel('SA (g kg$^{-1}$)',color=Scol)\n",
    "#axSi[2].set_ylabel('dSi ($\\muup$M)',color=Sicol)\n",
    "#axCh[2].spines['right'].set_position(('outward', 55))\n",
    "#axCh[2].set_ylabel('Chl ($\\muup$g L$^{-1}$)',color=Chcol)\n",
    "axT[0].set_title('Observed')\n",
    "plt.legend((pT,pS,pSi2,pSi,pSiV,pSiW,pCh),('Conservative Temperature (Central Node)','Absolute Salinity (Central Node)', \n",
    "                str(int(bounds[ik-2,0]))+'--'+str(int(bounds[ik-2,1]))+' m SOG Silicate (DFO)',\n",
    "                str(int(bounds[ik,0]))+'--'+str(int(bounds[ik,1]))+' m SOG Silicate (DFO)',\n",
    "                                 str(int(bounds[ik,0]))+'--'+str(int(bounds[27,1]))+' m Victoria (DFO)',\n",
    "                                 str(int(bounds[ik,0]))+'--'+str(int(bounds[27,1]))+' m West SJDF (DFO)',\n",
    "                                 'Chlorophyll (ferry)'),loc=8,ncol=4,bbox_to_anchor=[1.25,-.84,0,0])\n",
    "for i in range(0,3):\n",
    "    axS[i]=fig.add_axes(axPos2[i])\n",
    "    axT[i]=axS[i].twinx()\n",
    "    axT[i].yaxis.tick_left()\n",
    "    axT[i].spines['left'].set_position(('outward', 25))\n",
    "    axT[i].spines['left'].set_color(Tcol)\n",
    "    axCh[i]=axS[i].twinx()\n",
    "    axCh[i].spines['right'].set_position(('outward', 40))\n",
    "    axCh[i].spines['right'].set_color(Chcol)\n",
    "    axSi[i]=axS[i].twinx()\n",
    "    axSi[i].spines['right'].set_color(Sicol)\n",
    "    axS[i].spines['left'].set_color(Scol)\n",
    "    pSi2,=axSi[i].plot(times0[df0.k==(ik-2)],df0.loc[df0.k==(ik-2),['mod_silicon']],'o',color=Sicol2,alpha=.5)\n",
    "    pSi,=axSi[i].plot(times0[df0.k==ik],df0.loc[df0.k==ik,['mod_silicon']],'o',color=Sicol,alpha=.5)\n",
    "    pSiV,=axSi[i].plot(timesVic[dfVic.k==27],dfVic.loc[dfVic.k==27,['mod_silicon']],'o',color=Sicol3,alpha=.5)\n",
    "    pSiW,=axSi[i].plot(timesW[dfW.k==27],dfW.loc[dfW.k==27,['mod_silicon']],'o',color=Sicol4,alpha=.5)\n",
    "    axSi[i].plot(tt,dSi[:,ik-3],'--',color=Sicol2,alpha=.5)\n",
    "    axSi[i].plot(tt,dSiVic[:,27],'--',color=Sicol3,alpha=.5)\n",
    "    pS,=axS[i].plot(tt,SA,'-',color=Scol,alpha=.5)\n",
    "    pT,=axT[i].plot(tt,CT,'-',color=Tcol,alpha=.5)\n",
    "    pCh,=axCh[i].plot(tt,Chl*1.7/2,'.',color=Chcol,alpha=.5)\n",
    "    axSi[i].set_ylim((30,86))\n",
    "    axCh[i].set_ylim((0,25))\n",
    "    axS[i].set_ylim((30,32.2))\n",
    "    axT[i].set_ylim((7.5,11))\n",
    "    axSi[i].spines['left'].set_visible(False)\n",
    "    axCh[i].spines['left'].set_visible(False)\n",
    "    axS[i].spines['right'].set_visible(False)\n",
    "    axT[i].spines['right'].set_visible(False)\n",
    "axS[0].set_xlim((dt.datetime(2015,1,1),dt.datetime(2016,1,1)))\n",
    "axS[1].set_xlim((dt.datetime(2016,1,1),dt.datetime(2017,1,1)))\n",
    "axS[2].set_xlim((dt.datetime(2017,1,1),dt.datetime(2018,1,1)))\n",
    "axT[0].set_title('Modeled')\n",
    "\n",
    "#axT[0].yaxis.set_label_position(\"left\")\n",
    "#axT[0].set_ylabel('CT ($^{\\circ}$C)',color=Tcol)\n",
    "#axT[1].spines['left'].set_position(('outward', 55))\n",
    "#axS[1].set_ylabel('SA (g kg$^{-1}$)',color=Scol)\n",
    "axSi[2].set_ylabel('dSi ($\\muup$M)',color=Sicol)\n",
    "axCh[2].spines['right'].set_position(('outward', 55))\n",
    "axCh[2].set_ylabel('Chl ($\\muup$g L$^{-1}$)',color=Chcol)\n",
    "fig.savefig('/data/eolson/results/MEOPAR/biomodelevalpaper/figsEval/dfoDWRMod.png',dpi=300,transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dSi[:,ik-3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig,ax=plt.subplots(1,1,figsize=(12,3))\n",
    "simin=20; simax=60\n",
    "m1=ax.scatter(timesVic,dfVic.k,c=dfVic.Si,s=6,vmin=simin,vmax=simax)\n",
    "fig.colorbar(m1)\n",
    "ax.set_xlim((dt.datetime(1999,1,1),dt.datetime(2019,1,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig,ax=plt.subplots(1,1,figsize=(12,3))\n",
    "ii=dfVic.k==27\n",
    "ax.plot(timesVic[ii],dfVic.Si[ii],'k.')\n",
    "ax.set_xlim((dt.datetime(1999,1,1),dt.datetime(2019,1,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig,ax=plt.subplots(1,1,figsize=(12,3))\n",
    "simin=20; simax=60\n",
    "m1=ax.scatter(timesW,dfW.k,c=dfW.Si,s=6,vmin=simin,vmax=simax)\n",
    "fig.colorbar(m1)\n",
    "ax.set_xlim((dt.datetime(1999,1,1),dt.datetime(2019,1,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig,ax=plt.subplots(1,1,figsize=(12,3))\n",
    "ii=dfW.k==27\n",
    "ax.plot(timesW[ii],dfW.Si[ii],'k.')\n",
    "ax.set_xlim((dt.datetime(1999,1,1),dt.datetime(2019,1,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ik=36\n",
    "ax=axs[0]\n",
    "ax.plot(timesV,20*(dfV['PracticalSalinity_psu']-np.nanmean(dfV['PracticalSalinity_psu'])),'-',color='teal')\n",
    "ax.plot(timesV,10*(dfV['Temperature_C']-np.nanmean(dfV['Temperature_C'])),'-',color='coral')\n",
    "ax.plot(times[df0.k==ik],df0.loc[df0.k==ik,['Si']]-np.nanmean(df0.loc[df0.k==ik,['Si']]),'o',color='deeppink')\n",
    "ax.set_xlim((dt.datetime(2009,1,1),dt.datetime(2011,1,1)))\n",
    "ax=axs[1]\n",
    "ax.plot(timesV,20*(dfV['PracticalSalinity_psu']-np.nanmean(dfV['PracticalSalinity_psu'])),'-',color='teal')\n",
    "ax.plot(timesV,10*(dfV['Temperature_C']-np.nanmean(dfV['Temperature_C'])),'-',color='coral')\n",
    "ax.plot(times[df0.k==ik],df0.loc[df0.k==ik,['Si']]-np.nanmean(df0.loc[df0.k==ik,['Si']]),'o',color='deeppink')\n",
    "ax.set_xlim((dt.datetime(2011,1,1),dt.datetime(2013,1,1)))\n",
    "ax=axs[2]\n",
    "ax.plot(timesV,20*(dfV['PracticalSalinity_psu']-np.nanmean(dfV['PracticalSalinity_psu'])),'-',color='teal')\n",
    "ax.plot(timesV,10*(dfV['Temperature_C']-np.nanmean(dfV['Temperature_C'])),'-',color='coral')\n",
    "ax.plot(times[df0.k==ik],df0.loc[df0.k==ik,['Si']]-np.nanmean(df0.loc[df0.k==ik,['Si']]),'o',color='deeppink')\n",
    "ax.set_xlim((dt.datetime(2013,1,1),dt.datetime(2015,1,1)))\n",
    "ax=axs[3]\n",
    "ax.plot(timesV,20*(dfV['PracticalSalinity_psu']-np.nanmean(dfV['PracticalSalinity_psu'])),'-',color='teal')\n",
    "ax.plot(timesV,10*(dfV['Temperature_C']-np.nanmean(dfV['Temperature_C'])),'-',color='coral')\n",
    "ax.plot(times[df0.k==ik],df0.loc[df0.k==ik,['Si']]-np.nanmean(df0.loc[df0.k==ik,['Si']]),'o',color='deeppink')\n",
    "ax.set_xlim((dt.datetime(2015,1,1),dt.datetime(2017,1,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig,ax=plt.subplots(1,3,figsize=(16,4))\n",
    "fig.subplots_adjust(wspace=.5)\n",
    "col=('midnightblue','steelblue','darkturquoise','lime',\n",
    "     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',\n",
    "     'lightcoral','fuchsia','mediumorchid','blueviolet')\n",
    "ps=dict()\n",
    "for valm, groupedm in df0.groupby(['Month']):\n",
    "    ik=37\n",
    "    ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)\n",
    "    ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)\n",
    "    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})\n",
    "    stds=groupedm.groupby(['k']).agg({'Si':np.std})\n",
    "    ns=groupedm.groupby(['k'])['Si'].count()\n",
    "    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],\n",
    "               label=str(int(valm)))\n",
    "    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])\n",
    "ax[0].legend(handles=[ps[1],ps[2],ps[3],ps[4],ps[5],ps[6],ps[7],ps[8],ps[9],ps[10],ps[11],ps[12]],\n",
    "         bbox_to_anchor=(1.3, 1.0))\n",
    "ax[0].set_ylim(400,50)\n",
    "ax[0].set_xlim(30,100)\n",
    "ax[0].set_ylabel('Depth (m)')\n",
    "ax[0].set_xlabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_ylabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_title('Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')\n",
    "ax[1].set_ylabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_title('Si vs CT, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig,ax=plt.subplots(1,4,figsize=(20,4))\n",
    "fig.subplots_adjust(wspace=.5)\n",
    "col=('midnightblue','steelblue','darkturquoise','lime',\n",
    "     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',\n",
    "     'lightcoral','fuchsia','mediumorchid','blueviolet')\n",
    "ps=dict()\n",
    "for valm, groupedm in df0.groupby(['Month']):\n",
    "    ik=36\n",
    "    ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)\n",
    "    ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)\n",
    "    ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)\n",
    "    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})\n",
    "    stds=groupedm.groupby(['k']).agg({'Si':np.std})\n",
    "    ns=groupedm.groupby(['k'])['Si'].count()\n",
    "    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],\n",
    "               label=str(int(valm)))\n",
    "    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])\n",
    "ax[0].legend(handles=[ps[1],ps[2],ps[3],ps[4],ps[5],ps[6],ps[7],ps[8],ps[9],ps[10],ps[11],ps[12]],\n",
    "         bbox_to_anchor=(1.3, 1.0))\n",
    "ax[0].set_ylim(400,50)\n",
    "ax[0].set_xlim(30,100)\n",
    "ax[0].set_ylabel('Depth (m)')\n",
    "ax[0].set_xlabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_ylabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_title('Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig,ax=plt.subplots(1,4,figsize=(20,4))\n",
    "fig.subplots_adjust(wspace=.5)\n",
    "col=('midnightblue','steelblue','darkturquoise','lime',\n",
    "     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',\n",
    "     'lightcoral','fuchsia','mediumorchid','blueviolet')\n",
    "ps=dict()\n",
    "for valm, groupedm in df0.groupby(['Month']):\n",
    "    ik=34\n",
    "    ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)\n",
    "    ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)\n",
    "    ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)\n",
    "    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})\n",
    "    stds=groupedm.groupby(['k']).agg({'Si':np.std})\n",
    "    ns=groupedm.groupby(['k'])['Si'].count()\n",
    "    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],\n",
    "               label=str(int(valm)))\n",
    "    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])\n",
    "ax[0].legend(handles=[ps[1],ps[2],ps[3],ps[4],ps[5],ps[6],ps[7],ps[8],ps[9],ps[10],ps[11],ps[12]],\n",
    "         bbox_to_anchor=(1.3, 1.0))\n",
    "ax[0].set_ylim(400,50)\n",
    "ax[0].set_xlim(30,100)\n",
    "ax[0].set_ylabel('Depth (m)')\n",
    "ax[0].set_xlabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_ylabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_title('Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig,ax=plt.subplots(1,4,figsize=(20,4))\n",
    "fig.subplots_adjust(wspace=.5)\n",
    "col=('midnightblue','steelblue','darkturquoise','lime',\n",
    "     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',\n",
    "     'lightcoral','fuchsia','mediumorchid','blueviolet')\n",
    "ps=dict()\n",
    "for valm, groupedm in df0.groupby(['Month']):\n",
    "    ik=32\n",
    "    ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)\n",
    "    ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)\n",
    "    ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)\n",
    "    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})\n",
    "    stds=groupedm.groupby(['k']).agg({'Si':np.std})\n",
    "    ns=groupedm.groupby(['k'])['Si'].count()\n",
    "    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],\n",
    "               label=str(int(valm)))\n",
    "    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])\n",
    "ax[0].legend(handles=[ps[1],ps[2],ps[3],ps[4],ps[5],ps[6],ps[7],ps[8],ps[9],ps[10],ps[11],ps[12]],\n",
    "         bbox_to_anchor=(1.3, 1.0))\n",
    "ax[0].set_ylim(400,50)\n",
    "ax[0].set_xlim(30,100)\n",
    "ax[0].set_ylabel('Depth (m)')\n",
    "ax[0].set_xlabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_ylabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_title('Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig,ax=plt.subplots(1,4,figsize=(20,4))\n",
    "fig.subplots_adjust(wspace=.5)\n",
    "col=('midnightblue','steelblue','darkturquoise','lime',\n",
    "     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',\n",
    "     'lightcoral','fuchsia','mediumorchid','blueviolet')\n",
    "ps=dict()\n",
    "for valm, groupedm in df0.groupby(['Month']):\n",
    "    ik=30\n",
    "    ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)\n",
    "    ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)\n",
    "    ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)\n",
    "    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})\n",
    "    stds=groupedm.groupby(['k']).agg({'Si':np.std})\n",
    "    ns=groupedm.groupby(['k'])['Si'].count()\n",
    "    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],\n",
    "               label=str(int(valm)))\n",
    "    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])\n",
    "ax[0].legend(handles=[ps[1],ps[2],ps[3],ps[4],ps[5],ps[6],ps[7],ps[8],ps[9],ps[10],ps[11],ps[12]],\n",
    "         bbox_to_anchor=(1.3, 1.0))\n",
    "ax[0].set_ylim(400,50)\n",
    "ax[0].set_xlim(30,100)\n",
    "ax[0].set_ylabel('Depth (m)')\n",
    "ax[0].set_xlabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_ylabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_title('Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig,ax=plt.subplots(1,4,figsize=(20,4))\n",
    "fig.subplots_adjust(wspace=.5)\n",
    "col=('midnightblue','steelblue','darkturquoise','lime',\n",
    "     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',\n",
    "     'lightcoral','fuchsia','mediumorchid','blueviolet')\n",
    "ps=dict()\n",
    "for valm, groupedm in df0.groupby(['Month']):\n",
    "    ik=29\n",
    "    ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)\n",
    "    ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)\n",
    "    ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)\n",
    "    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})\n",
    "    stds=groupedm.groupby(['k']).agg({'Si':np.std})\n",
    "    ns=groupedm.groupby(['k'])['Si'].count()\n",
    "    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],\n",
    "               label=str(int(valm)))\n",
    "    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])\n",
    "ax[0].legend(handles=[ps[1],ps[2],ps[3],ps[4],ps[5],ps[6],ps[7],ps[8],ps[9],ps[10],ps[11],ps[12]],\n",
    "         bbox_to_anchor=(1.3, 1.0))\n",
    "ax[0].set_ylim(400,50)\n",
    "ax[0].set_xlim(30,100)\n",
    "ax[0].set_ylabel('Depth (m)')\n",
    "ax[0].set_xlabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_ylabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_title('Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig,ax=plt.subplots(1,4,figsize=(20,4))\n",
    "fig.subplots_adjust(wspace=.5)\n",
    "col=('midnightblue','steelblue','darkturquoise','lime',\n",
    "     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',\n",
    "     'lightcoral','fuchsia','mediumorchid','blueviolet')\n",
    "ps=dict()\n",
    "for valm, groupedm in df0.groupby(['Month']):\n",
    "    ik=28\n",
    "    ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)\n",
    "    ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)\n",
    "    ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)\n",
    "    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})\n",
    "    stds=groupedm.groupby(['k']).agg({'Si':np.std})\n",
    "    ns=groupedm.groupby(['k'])['Si'].count()\n",
    "    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],\n",
    "               label=str(int(valm)))\n",
    "    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])\n",
    "ax[0].legend(handles=[ps[1],ps[2],ps[3],ps[4],ps[5],ps[6],ps[7],ps[8],ps[9],ps[10],ps[11],ps[12]],\n",
    "         bbox_to_anchor=(1.3, 1.0))\n",
    "ax[0].set_ylim(400,50)\n",
    "ax[0].set_xlim(30,100)\n",
    "ax[0].set_ylabel('Depth (m)')\n",
    "ax[0].set_xlabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_ylabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_title('Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig,ax=plt.subplots(1,4,figsize=(20,4))\n",
    "fig.subplots_adjust(wspace=.5)\n",
    "col=('midnightblue','steelblue','darkturquoise','lime',\n",
    "     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',\n",
    "     'lightcoral','fuchsia','mediumorchid','blueviolet')\n",
    "ps=dict()\n",
    "for valm, groupedm in df0.groupby(['Month']):\n",
    "    ik=34\n",
    "    ax[1].plot(groupedm.loc[(groupedm.k==ik)&(groupedm.N>=0),['AbsSal']],\n",
    "               groupedm.loc[(groupedm.k==ik)&(groupedm.N>=0),['Si']].values/groupedm.loc[(groupedm.k==ik)&(groupedm.N>=0),['N']].values,\n",
    "               'o',color=col[int(valm)],alpha=1)\n",
    "    ax[2].plot(groupedm.loc[(groupedm.k==ik)&(groupedm.N>=0),['ConsT']],\n",
    "               groupedm.loc[(groupedm.k==ik)&(groupedm.N>=0),['Si']].values/groupedm.loc[(groupedm.k==ik)&(groupedm.N>=0),['N']].values,\n",
    "               'o',color=col[int(valm)],alpha=1)\n",
    "    ax[3].plot(groupedm.loc[(groupedm.k==ik)&(groupedm.N>=0),['AbsSal']],\n",
    "               groupedm.loc[(groupedm.k==ik)&(groupedm.N>=0),['ConsT']].values,\n",
    "               'o',color=col[int(valm)],alpha=1)\n",
    "    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})\n",
    "    stds=groupedm.groupby(['k']).agg({'Si':np.std})\n",
    "    ns=groupedm.groupby(['k'])['Si'].count()\n",
    "    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],\n",
    "               label=str(int(valm)))\n",
    "    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])\n",
    "ax[0].legend(handles=[ps[1],ps[2],ps[3],ps[4],ps[5],ps[6],ps[7],ps[8],ps[9],ps[10],ps[11],ps[12]],\n",
    "         bbox_to_anchor=(1.3, 1.0))\n",
    "ax[0].set_ylim(400,50)\n",
    "ax[0].set_xlim(30,100)\n",
    "ax[0].set_ylabel('Depth (m)')\n",
    "ax[0].set_xlabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_ylabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_title('Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df0.loc[(df0.k==34)].groupby(['Year'])['Si'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df0.loc[(df0.k==35)].groupby(['Year'])['Si'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df0.loc[(df0.k==36)].groupby(['Year'])['Si'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig,ax=plt.subplots(1,4,figsize=(20,4))\n",
    "fig.subplots_adjust(wspace=.5)\n",
    "col=('midnightblue','steelblue','darkturquoise','lime',\n",
    "     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',\n",
    "     'lightcoral','fuchsia','mediumorchid','blueviolet')\n",
    "ps=dict()\n",
    "df1=df0.loc[df0.Year==2010]\n",
    "for valm, groupedm in df1.groupby(['Month']):\n",
    "    ik=34\n",
    "    ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)\n",
    "    ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)\n",
    "    ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)\n",
    "    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})\n",
    "    stds=groupedm.groupby(['k']).agg({'Si':np.std})\n",
    "    ns=groupedm.groupby(['k'])['Si'].count()\n",
    "    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],\n",
    "               label=str(int(valm)))\n",
    "    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])\n",
    "ax[0].legend(handles=[ps[ii] for ii in range(1,13) if ii in ps.keys()],\n",
    "         bbox_to_anchor=(1.3, 1.0))\n",
    "ax[0].set_ylim(400,50)\n",
    "ax[0].set_xlim(30,100)\n",
    "ax[0].set_ylabel('Depth (m)')\n",
    "ax[0].set_xlabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_ylabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_title('2010 Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig,ax=plt.subplots(1,4,figsize=(20,4))\n",
    "fig.subplots_adjust(wspace=.5)\n",
    "col=('midnightblue','steelblue','darkturquoise','lime',\n",
    "     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',\n",
    "     'lightcoral','fuchsia','mediumorchid','blueviolet')\n",
    "ps=dict()\n",
    "df1=df0.loc[df0.Year==2010]\n",
    "for valm, groupedm in df1.groupby(['Month']):\n",
    "    ik=35\n",
    "    ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)\n",
    "    ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)\n",
    "    ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)\n",
    "    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})\n",
    "    stds=groupedm.groupby(['k']).agg({'Si':np.std})\n",
    "    ns=groupedm.groupby(['k'])['Si'].count()\n",
    "    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],\n",
    "               label=str(int(valm)))\n",
    "    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])\n",
    "ax[0].legend(handles=[ps[ii] for ii in range(1,13) if ii in ps.keys()],\n",
    "         bbox_to_anchor=(1.3, 1.0))\n",
    "ax[0].set_ylim(400,50)\n",
    "ax[0].set_xlim(30,100)\n",
    "ax[0].set_ylabel('Depth (m)')\n",
    "ax[0].set_xlabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_ylabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_title('2010 Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig,ax=plt.subplots(1,4,figsize=(20,4))\n",
    "fig.subplots_adjust(wspace=.5)\n",
    "col=('midnightblue','steelblue','darkturquoise','lime',\n",
    "     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',\n",
    "     'lightcoral','fuchsia','mediumorchid','blueviolet')\n",
    "ps=dict()\n",
    "df1=df0.loc[df0.Year==2002]\n",
    "for valm, groupedm in df1.groupby(['Month']):\n",
    "    ik=34\n",
    "    ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)\n",
    "    ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)\n",
    "    ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)\n",
    "    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})\n",
    "    stds=groupedm.groupby(['k']).agg({'Si':np.std})\n",
    "    ns=groupedm.groupby(['k'])['Si'].count()\n",
    "    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],\n",
    "               label=str(int(valm)))\n",
    "    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])\n",
    "ax[0].legend(handles=[ps[ii] for ii in range(1,13) if ii in ps.keys()],\n",
    "         bbox_to_anchor=(1.3, 1.0))\n",
    "ax[0].set_ylim(400,50)\n",
    "ax[0].set_xlim(30,100)\n",
    "ax[0].set_ylabel('Depth (m)')\n",
    "ax[0].set_xlabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_ylabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_title(' 2002 Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig,ax=plt.subplots(1,4,figsize=(20,4))\n",
    "fig.subplots_adjust(wspace=.5)\n",
    "col=('midnightblue','steelblue','darkturquoise','lime',\n",
    "     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',\n",
    "     'lightcoral','fuchsia','mediumorchid','blueviolet')\n",
    "ps=dict()\n",
    "df1=df0.loc[df0.Year==2003]\n",
    "for valm, groupedm in df1.groupby(['Month']):\n",
    "    ik=36\n",
    "    ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)\n",
    "    ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)\n",
    "    ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)\n",
    "    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})\n",
    "    stds=groupedm.groupby(['k']).agg({'Si':np.std})\n",
    "    ns=groupedm.groupby(['k'])['Si'].count()\n",
    "    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],\n",
    "               label=str(int(valm)))\n",
    "    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])\n",
    "ax[0].legend(handles=[ps[ii] for ii in range(1,13) if ii in ps.keys()],\n",
    "         bbox_to_anchor=(1.3, 1.0))\n",
    "ax[0].set_ylim(400,50)\n",
    "ax[0].set_xlim(30,100)\n",
    "ax[0].set_ylabel('Depth (m)')\n",
    "ax[0].set_xlabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_ylabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_title(' 2003 Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig,ax=plt.subplots(1,4,figsize=(20,4))\n",
    "fig.subplots_adjust(wspace=.5)\n",
    "col=('midnightblue','steelblue','darkturquoise','lime',\n",
    "     'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',\n",
    "     'lightcoral','fuchsia','mediumorchid','blueviolet')\n",
    "ps=dict()\n",
    "df1=df0.loc[df0.Year==2003]\n",
    "for valm, groupedm in df1.groupby(['Month']):\n",
    "    ik=36\n",
    "    cmin=0\n",
    "    cmax=365\n",
    "    if np.sum(groupedm.k==ik)>0:\n",
    "        ax[1].scatter(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],c=groupedm.loc[groupedm.k==ik,['YD']],\n",
    "                      vmin=cmin,vmax=cmax,cmap=plt.get_cmap('hsv'))\n",
    "        ax[2].scatter(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],c=groupedm.loc[groupedm.k==ik,['YD']],\n",
    "                      vmin=cmin,vmax=cmax,cmap=plt.get_cmap('hsv'))\n",
    "        m=ax[3].scatter(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],c=groupedm.loc[groupedm.k==ik,['YD']],\n",
    "                        vmin=cmin,vmax=cmax,cmap=plt.get_cmap('hsv'))\n",
    "    means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})\n",
    "    stds=groupedm.groupby(['k']).agg({'Si':np.std})\n",
    "    ns=groupedm.groupby(['k'])['Si'].count()\n",
    "    ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],\n",
    "               label=str(int(valm)))\n",
    "    ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])\n",
    "ax[0].legend(handles=[ps[ii] for ii in range(1,13) if ii in ps.keys()],\n",
    "         bbox_to_anchor=(1.3, 1.0))\n",
    "ax[0].set_ylim(400,50)\n",
    "ax[0].set_xlim(30,100)\n",
    "ax[0].set_ylabel('Depth (m)')\n",
    "ax[0].set_xlabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_ylabel('dSi ($\\muup$M Si)')\n",
    "ax[1].set_title(' 2003 Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')\n",
    "fig.colorbar(m,ax=ax[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1.loc[df1.k==ik,['Year','Month','Day','Lat','Lon','Pressure','Si','AbsSal','ConsT']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.scatter(df1.loc[df1.k==ik,['Lon']],df1.loc[df1.k==ik,['Lat']],c=df1.loc[df1.k==ik,['Si']])\n",
    "plt.xlim(-123.58,-123.53)\n",
    "plt.ylim(49.16,49.17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.scatter(xx,xx,c=xx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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