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 "metadata": {
  "name": "",
  "signature": "sha256:9dcea9a13e4db564bf455614b219e03a8654068200938932e924c529f22cb3b7"
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook creates monthly forcing files for the sea surface height (hourly frequency) at Port Hardy, in the northern part of the domain. We want sea surface height forcing separated from the tides. We will take the observed sea surface height and remove the tidal predictions."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I will begin by looking at Jan 1, 2006 through to to Dec. 31, 2006. There was another great storm surge in Dec. 2006. Since NEMO is in UTC but the observations come in PST I will download data rom Dec 31, 2005 to Jan 2, 2007.\n",
      "\n",
      "Storm surge dates: Nov 16, 2006, Dec 15, 2006\n",
      "\n",
      "Observations: DFO\n",
      "\n",
      "Tidal predictions: t_ttide"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "%matplotlib inline\n",
      "from matplotlib import pylab\n",
      "import matplotlib.pyplot as plt\n",
      "import netCDF4 as NC\n",
      "import numpy as np\n",
      "import datetime\n",
      "import requests\n",
      "\n",
      "from salishsea_tools import nc_tools\n",
      "from salishsea_tools import tidetools\n",
      "import os\n",
      "import csv"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Port Hardy mmsl over 1982-2001 (in metres)\n",
      "msl = 2.896"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "DFO observed water level\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Begin by collecting water level data from the DFO.\n",
      "\n",
      "In order to generate forcing files for a different set of dates, modify \"start\" and \"end\".  The time period needs to be at least one month long. Due to local time vs UTC, \"start\" needs to be one day before the month of interest and \"end\" needs to be two days after the desired end date. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "yr=2009\n",
      "\n",
      "statID = '8408'#Port Hardystation ID number\n",
      "start = '31-Dec-' + str(yr-1) #start date \n",
      "end = '02-Jan-' + str(yr+1) #end date\n",
      "\n",
      "#tidetools.get_dfo_wlev(statID,start,end)\n",
      "# now there is a csv file in this directory with the sear surface height information. Let's take a look at the plots.\n",
      "filename = '/data/nsoontie/MEOPAR/storm-surge/Revisions/tides/forcing/wlev_' +str(statID) + '_' + start +'_' +end +'.csv'\n",
      "\n",
      "import datetime\n",
      "import pandas as pd\n",
      "import pytz\n",
      "\n",
      "def dateParserMeasured(s):\n",
      "    #convert the string to a datetime object\n",
      "    unaware = datetime.datetime.strptime(s, \"%Y/%m/%d %H:%M\")\n",
      "    #add in the local time zone (Canada/Pacific)\n",
      "    aware = unaware.replace(tzinfo=pytz.timezone('Canada/Pacific'))\n",
      "    #convert to UTC\n",
      "    return aware.astimezone(pytz.timezone('UTC'))\n",
      "    \n",
      "wlev_meas = pd.read_csv(filename,skiprows=7,parse_dates=[0],date_parser=dateParserMeasured)\n",
      "wlev_meas = wlev_meas.rename(columns={'Obs_date': 'time', 'SLEV(metres)': 'slev'})\n",
      "\n",
      "print(wlev_meas.time[0].tzinfo)\n",
      "\n",
      "#the average sea surface height over this time. \n",
      "sshavg = wlev_meas.slev.mean()\n",
      "print sshavg\n",
      "\n",
      "plt.figure(figsize=(14,6))\n",
      "pylab.subplot(1,2,1)\n",
      "plt.plot(wlev_meas.time,wlev_meas.slev)\n",
      "plt.title('Port Hardy Observations' + start+' to ' + end)\n",
      "plt.plot(wlev_meas.time,sshavg*np.ones(wlev_meas.time.size),'--k')\n",
      "plt.xlabel('Time (UTC)')\n",
      "plt.ylabel('Water Level Height (m)')\n",
      "\n",
      "pylab.subplot(1,2,2)\n",
      "plt.plot(wlev_meas.time,wlev_meas.slev-sshavg)\n",
      "plt.title('Port Hardy Observations, mean removed: ' + start+' to ' + end)\n",
      "plt.xlabel('Time (UTC)')\n",
      "plt.ylabel('Water Level Anomoly (m)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "UTC\n",
        "2.86029745686\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<matplotlib.text.Text at 0x7ff18ee55390>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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CarmqvuA1a1Y2mUH37sCFF7oPl/KDN9zJW3tt4PDD3YX388/AI4+4C6+arLNO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\nU+0qxmz2sVTBJGmVDM8ePG9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0VA2gyxv38ePT2W7YPnTtGj3HSVAWhaDXXzeDdQDAv/+d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\nIiIiIiIi0uz/ARf5T0ky9c6ZAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff18ef3e450>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Additionally, download measurements from the year prior. These measurements will be used to calculate harmonics and tidal predictions for the year of interest."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Predictions from t_tide"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Run t_tide on the time series to get the tidal predictions. \n",
      "\n",
      "This is a little bit clunky, but I have modified Kate's get_tidal_anomaly.m matlab script to use t_tide. This script saves the predcitions in location_t_tide_compare8_dates_snr2_filter.csv file. It also saves the harmoncis in location_harmonics_dates_filter.csv. Before this part of the notebook is executed, we must go to matlab to generate these files:\n",
      "\n",
      "eg.: get_ttide_8_filter('wlev_8408_31-Dec-2006_02-Jan-2007.csv', 'Port Hardy', '31-Dec-2005', '02-Jan-2007');\n",
      "\n",
      "The first string is a filename that stores the water level measurements. A harmonic analysis is performed on this time series. The second string indicates the location and the last two string indicate the beginning and end dates of the tidal predications. \n",
      "\n",
      "The matlab script get_ttide_8_filter.m is stored in the storm-surge/Revisions/tides/ repository. It is recommended to add this directory to your path and run the script in the directory where you would like the predictions to be stored. You also need t_tide added you your path. \n",
      "\n",
      "This has already been done for 2006, 2009 and 2012. The results are stored in storm-surge/Revisions/tides/forcing/\n",
      "\n",
      "For now, load the data produced by the matlab script."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "filename = '/data/nsoontie/MEOPAR/storm-surge/Revisions/tides/forcing/PortHardy_t_tide_compare8_' + start+'_'+end+'_snr2_filter.csv'\n",
      "\n",
      "\n",
      "import datetime\n",
      "import pandas as pd\n",
      "import pytz\n",
      "\n",
      "def dateParserMeasured2(s):\n",
      "    #convert the string to a datetime object\n",
      "    unaware = datetime.datetime.strptime(s, \"%d-%b-%Y %H:%M:%S \")\n",
      "    #add in the local time zone (Canada/Pacific)\n",
      "    aware = unaware.replace(tzinfo=pytz.timezone('Canada/Pacific'))\n",
      "    #convert to UTC\n",
      "    return aware.astimezone(pytz.timezone('UTC'))\n",
      "\n",
      "#read msl\n",
      "line_number = 1     \n",
      "with open(filename, 'rb') as f:\n",
      "    mycsv = csv.reader(f); mycsv = list(mycsv)\n",
      "    \n",
      "ttide = pd.read_csv(filename,skiprows=3,parse_dates=[0],date_parser=dateParserMeasured2)\n",
      "ttide = ttide.rename(columns={'Time_Local ': 'time', ' pred_8 ': 'pred_8', ' pred_all ': 'pred_all'})\n",
      "\n",
      "ssanomaly = np.zeros(len(wlev_meas.time))\n",
      "\n",
      "for i in np.arange(0,len(wlev_meas.time)):\n",
      "    #check that there is a corresponding time \n",
      "    #if any(wlev_pred.time == wlev_meas.time[i]):\n",
      "    ssanomaly[i] =(wlev_meas.slev[i] - (ttide.pred_all[ttide.time==wlev_meas.time[i]]+msl))\n",
      "    if not(ssanomaly[i]):\n",
      "        ssanomaly[i]=float('Nan')\n",
      "        \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "    print 'Comparison between ttide predictions and measurements'\n",
      "    \n",
      "    plt.figure(figsize=(13,6))\n",
      "    plt.subplot(2,1,1)\n",
      "    plt.plot(wlev_meas.time,wlev_meas.slev,'b',label='measured')\n",
      "    plt.plot(ttide.time,ttide.pred_all+msl,'m',label='predicted')\n",
      "    #plt.xlim(datetime.datetime(req_year,1,1,0,0,0),datetime.datetime(req_year,12,31,23,59,59))\n",
      "    plt.legend(bbox_to_anchor=(1.05, 1), loc=2)\n",
      "    plt.xlabel('Time (UTC)')\n",
      "    plt.ylabel('Elevation (m)')\n",
      "    \n",
      "    plt.subplot(2,1,2)\n",
      "    plt.plot(wlev_meas.time,ssanomaly,'g',label='anomaly (meas-pred)')\n",
      "    plt.ylim(-1,1)\n",
      "    plt.legend(bbox_to_anchor=(1.05, 1), loc=2)\n",
      "    plt.xlabel('Time (UTC)')\n",
      "    plt.ylabel('Elevation (m)')\n",
      "    plt.plot(wlev_meas.time,0*np.ones(wlev_meas.time.size),'--r')\n",
      "    \n",
      "    ssarray = np.array(ssanomaly)\n",
      "    mean_anom= ssarray.mean()\n",
      "    print 'Mean anomaly: ' + str(mean_anom)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Comparison between ttide predictions and measurements\n",
        "Mean anomaly: -0.0356977391008"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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FUc+zwure9y4+uLDsBlo/a0VnVnjKF6coQaW7h7X70uV/L7usHuQuytW07rGS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sTM12SbVyhPRRy3oDXj7/2yDHPx2gy+v0yKegZTPfPa2vx7sqjfSyUuKbelrLa0PFqe1ThWlNTi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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff18eb1b610>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Save as a NetCDF"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Follow Susan's SSH notebook for this. Make a new netcdf file for each month. I will save the ssanonmaly at each point along the western boundary."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Get some preliminary data first: bathymetry, indices of the edges. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "starti = 32\n",
      "endi = 62\n",
      "lengthi = endi-starti\n",
      "r = 1\n",
      "\n",
      "fB = NC.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/bathy_meter_SalishSea2.nc','r')\n",
      "lat = fB.variables['nav_lat'][:]\n",
      "lon = fB.variables['nav_lon'][:]\n",
      "fB.close()\n",
      "print lat.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(898, 398)\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A function for saving the netcdf file."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def prepare_netcdf(ssh_file, count, ssh,month,year):\n",
      "    # dataset attributes\n",
      "    nc_tools.init_dataset_attrs(\n",
      "    ssh_file, \n",
      "    title='Port Hardy SSH hourly values', \n",
      "    notebook_name='SSH_PortHardy', \n",
      "    nc_filepath='/data/nsoontie/MEOPAR/NEMO-forcing/open_boundaries/north/ssh/' + filename,\n",
      "    comment='SSH from Port Hardy: measured - tides (from t_tide)')\n",
      "\n",
      "    \n",
      "    #dimensions\n",
      "    ssh_file.createDimension('xbT', lengthi*r)\n",
      "    ssh_file.createDimension('yb', 1)\n",
      "    ssh_file.createDimension('time_counter', None)\n",
      "    # variables\n",
      "    # time_counter\n",
      "    time_counter = ssh_file.createVariable('time_counter', 'float32', ('time_counter'))\n",
      "    time_counter.long_name = 'Time axis'\n",
      "    time_counter.axis = 'T'\n",
      "    time_counter.units = 'hour since 00:00:00 on 01/' + str(month) +'/'+ str(year)\n",
      "    # nav_lat and nav_lon\n",
      "    nav_lat = ssh_file.createVariable('nav_lat','float32',('yb','xbT'))\n",
      "    nav_lat.long_name = 'Latitude'\n",
      "    nav_lat.units = 'degrees_north'\n",
      "    nav_lon = ssh_file.createVariable('nav_lon','float32',('yb','xbT'))\n",
      "    nav_lon.long_name = 'Longitude'\n",
      "    nav_lon.units = 'degrees_east'\n",
      "    # ssh\n",
      "    sossheig = ssh_file.createVariable('sossheig', 'float32', \n",
      "                               ('time_counter','yb','xbT'), zlib=True)\n",
      "    sossheig.units = 'm'\n",
      "    sossheig.long_name = 'Sea surface height'   \n",
      "    sossheig.coordinates = 'nav_lon nav_lat time_counter'\n",
      "    sossheig.grid = 'SalishSea2'\n",
      "    # vobtcrtx, vobtcrty\n",
      "    vobtcrtx = ssh_file.createVariable('vobtcrtx', 'float32',\n",
      "                                   ('time_counter','yb','xbT'), zlib=True)\n",
      "    vobtcrtx.units = 'm/s'\n",
      "    vobtcrtx.long_name = 'Barotropic U Velocity- ZEROD'   \n",
      "    vobtcrtx.grid = 'SalishSea2'\n",
      "    vobtcrty = ssh_file.createVariable('vobtcrty', 'float32',\n",
      "                                   ('time_counter','yb','xbT'), zlib=True)\n",
      "    vobtcrty.units = 'm/s'\n",
      "    vobtcrty.long_name = 'Barotropic V Velocity- ZEROD'   \n",
      "    vobtcrty.grid = 'SalishSea2'\n",
      "    # nbidta, ndjdta, ndrdta\n",
      "    nbidta = ssh_file.createVariable('nbidta', 'int32' , ('yb','xbT'), zlib=True)\n",
      "    nbidta.long_name = 'i grid position'\n",
      "    nbidta.units = 1\n",
      "    nbjdta = ssh_file.createVariable('nbjdta', 'int32' , ('yb','xbT'), zlib=True)\n",
      "    nbjdta.long_name = 'j grid position'\n",
      "    nbjdta.units = 1\n",
      "    nbrdta = ssh_file.createVariable('nbrdta', 'int32' , ('yb','xbT'), zlib=True)\n",
      "    nbrdta.long_name = 'position from boundary'\n",
      "    nbrdta.units = 1\n",
      "    \n",
      "    for ir in range(0,r):\n",
      "        j = 897-ir\n",
      "        nav_lat[0,ir*lengthi:(ir+1)*lengthi] = lat[j,starti:endi]\n",
      "        nav_lon[0,ir*lengthi:(ir+1)*lengthi] = lon[j,starti:endi]\n",
      "        nbidta[0,ir*lengthi:(ir+1)*lengthi] =  range(starti,endi)\n",
      "        nbjdta[0,ir*lengthi:(ir+1)*lengthi] = j\n",
      "        nbrdta[0,ir*lengthi:(ir+1)*lengthi] = ir\n",
      "        \n",
      "    for ib in range(0,lengthi*r):\n",
      "        sossheig[0:count,0,ib] = ssh[0:count]\n",
      "        time_counter[0:count] = range(1,count+1)\n",
      "        vobtcrtx[0:count,0,ib] = 0*np.ones(count)\n",
      "        vobtcrty[0:count,0,ib] = 0*np.ones(count)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Loop through the ssanomaly and time arrays to create the netcdf files.  Save them month by month.\n",
      "\n",
      "This is really clunky for now. I need the netcdf files in UTC, but all of the data files are in local time. So I will just ignore/delete the first and last month's files since these are incomplete. There is definitely room for improvement with this part of the code."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#setting up the loop through data\n",
      "\n",
      "#length of data:\n",
      "l=len(ssanomaly)\n",
      "hour_start = wlev_meas.time[0].hour;  yr_start= wlev_meas.time[0].year; month_start =wlev_meas.time[0].month; \n",
      "yr_end= wlev_meas.time[l-1].year;  month_end =wlev_meas.time[l-1].month; \n",
      "print 'Date range: ' +str(wlev_meas.time[0]) + ' to ' + str(wlev_meas.time[l-1]) + ' UTC'\n",
      "\n",
      "#files that will be deleted if hour does not begin at zero. \n",
      "if month_start<10:\n",
      "    filename = 'sshNorth_y' +str(yr_start)+'m0'+str(month_start)+'.nc'\n",
      "else:\n",
      "    filename = 'sshNorth_y' +str(yr_start)+'m'+str(month_start)+'.nc'\n",
      "file1=filename\n",
      "if month_end<10:\n",
      "    file2 = 'sshNorth_y' +str(yr_end)+'m0'+str(month_end)+'.nc'\n",
      "else:\n",
      "    file2 = 'sshNorth_y' +str(yr_end)+'m'+str(month_end)+'.nc'\n",
      "\n",
      "m=month_start; yr=yr_start; count =0; ssh=[];\n",
      "\n",
      "for ii in range(0, l):\n",
      "    yr=wlev_meas.time[ii].year\n",
      "    month = wlev_meas.time[ii].month\n",
      "    #If we entered a new month, save all the data.\n",
      "    if month != m:\n",
      "        #save the month's data:\n",
      "        ssh_file = NC.Dataset(filename, 'w', zlib=True)\n",
      "        print filename\n",
      "        print count       \n",
      "        #save data in netcdf    \n",
      "        prepare_netcdf(ssh_file,count,ssh, m, yr)\n",
      "        #Set up the next month's file\n",
      "        m=month;\n",
      "        if m<10:\n",
      "            filename = 'sshNorth_y' +str(yr)+'m0'+str(m)+'.nc'\n",
      "        else:\n",
      "            filename = 'sshNorth_y' +str(yr)+'m'+str(m)+'.nc'\n",
      "        #clear the ssh and count\n",
      "        ssh = []; count=0\n",
      "        ssh_file.close()\n",
      " \n",
      "    ssh.append(ssanomaly[ii])\n",
      "    count=count+1\n",
      "    \n",
      "    #If we are at the last item, save all the data.\n",
      "    if ii == l-1:\n",
      "        ssh_file = NC.Dataset(filename, 'w', zlib=True)\n",
      "        print filename\n",
      "        print count       \n",
      "        prepare_netcdf(ssh_file,count,ssh,month,yr)\n",
      "        #clear the ssh and count\n",
      "        ssh = []; count=0\n",
      "        ssh_file.close()\n",
      "    \n",
      "#If the hour is not zero then we have incomplete monthly data. Delete the incomplete files. \n",
      "#Note: this is not a proper check for incomplete data. For example, if the day is not the first day of the month...\n",
      "if hour_start != 0:\n",
      "    print '**** Removing ' + file1 + ' and ' + file2\n",
      "    os.remove(file1); os.remove(file2)\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Date range: 2008-12-31 08:12:00+00:00 to 2010-01-02 08:12:00+00:00 UTC\n",
        "sshNorth_y2008m12.nc\n",
        "16\n",
        "file format: NETCDF4"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Conventions: CF-1.6\n",
        "title: Port Hardy SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_PortHardy.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 16:58:52] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Port Hardy: measured - tides (from t_tide)\n",
        "sshNorth_y2009m01.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "744\n",
        "file format: NETCDF4\n",
        "Conventions: CF-1.6\n",
        "title: Port Hardy SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_PortHardy.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 16:58:52] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Port Hardy: measured - tides (from t_tide)\n",
        "sshNorth_y2009m02.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "671\n",
        "file format: NETCDF4\n",
        "Conventions: CF-1.6\n",
        "title: Port Hardy SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_PortHardy.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 16:58:53] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Port Hardy: measured - tides (from t_tide)\n",
        "sshNorth_y2009m03.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "744\n",
        "file format: NETCDF4"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Conventions: CF-1.6\n",
        "title: Port Hardy SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_PortHardy.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 16:58:53] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Port Hardy: measured - tides (from t_tide)\n",
        "sshNorth_y2009m04.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "720\n",
        "file format: NETCDF4\n",
        "Conventions: CF-1.6\n",
        "title: Port Hardy SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_PortHardy.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 16:58:54] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Port Hardy: measured - tides (from t_tide)\n",
        "sshNorth_y2009m05.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "744\n",
        "file format: NETCDF4\n",
        "Conventions: CF-1.6\n",
        "title: Port Hardy SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_PortHardy.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 16:58:54] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Port Hardy: measured - tides (from t_tide)\n",
        "sshNorth_y2009m06.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "720\n",
        "file format: NETCDF4\n",
        "Conventions: CF-1.6\n",
        "title: Port Hardy SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_PortHardy.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 16:58:55] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Port Hardy: measured - tides (from t_tide)\n",
        "sshNorth_y2009m07.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "744\n",
        "file format: NETCDF4\n",
        "Conventions: CF-1.6\n",
        "title: Port Hardy SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_PortHardy.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 16:58:55] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Port Hardy: measured - tides (from t_tide)\n",
        "sshNorth_y2009m08.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "744\n",
        "file format: NETCDF4"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Conventions: CF-1.6\n",
        "title: Port Hardy SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_PortHardy.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 16:58:56] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Port Hardy: measured - tides (from t_tide)\n",
        "sshNorth_y2009m09.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "720\n",
        "file format: NETCDF4"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Conventions: CF-1.6\n",
        "title: Port Hardy SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_PortHardy.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 16:58:56] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Port Hardy: measured - tides (from t_tide)\n",
        "sshNorth_y2009m10.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "744\n",
        "file format: NETCDF4\n",
        "Conventions: CF-1.6\n",
        "title: Port Hardy SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_PortHardy.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 16:58:56] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Port Hardy: measured - tides (from t_tide)\n",
        "sshNorth_y2009m11.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "720\n",
        "file format: NETCDF4"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Conventions: CF-1.6\n",
        "title: Port Hardy SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_PortHardy.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 16:58:57] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Port Hardy: measured - tides (from t_tide)\n",
        "sshNorth_y2009m12.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "744\n",
        "file format: NETCDF4\n",
        "Conventions: CF-1.6\n",
        "title: Port Hardy SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_PortHardy.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 16:58:57] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Port Hardy: measured - tides (from t_tide)\n",
        "sshNorth_y2010m01.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "33\n",
        "file format: NETCDF4"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Conventions: CF-1.6\n",
        "title: Port Hardy SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_PortHardy.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 16:58:58] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Port Hardy: measured - tides (from t_tide)\n",
        "**** Removing sshNorth_y2008m12.nc and sshNorth_y2010m01.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Check the NetCDF files\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A quick check that the data is saved in the netcdf file as expected. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "filename = 'sshNorth_y2009m12.nc'\n",
      "\n",
      "f=  NC.Dataset(filename,'r');\n",
      "\n",
      "for dim in f.dimensions.values():\n",
      "    print dim\n",
      "    \n",
      "ssh=f.variables['sossheig'];\n",
      "us=f.variables['vobtcrtx'];\n",
      "vs=f.variables['vobtcrty'];\n",
      "print us.shape\n",
      "print us[:,0,:]\n",
      "print vs[:,0,:]\n",
      "    \n",
      "mn = ssh[:].min(); print mn\n",
      "mx = ssh[:].max(); print mx\n",
      "\n",
      "fig, ((ax_net,ax_data)) = plt.subplots(1, 2, figsize=(14,4))\n",
      "ax_net.plot(ssh[:,0,1])\n",
      "ax_net.set_xlabel(filename)\n",
      "ax_net.axis([10*24, 17*24, mn,mx])\n",
      "\n",
      "ax_data.plot(wlev_meas.time,ssanomaly)\n",
      "ax_data.set_xlim(['Dec 11 2009', 'Dec 18 2009'])\n",
      "ax_data.set_ylim([mn,mx])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<type 'netCDF4.Dimension'>: name = 'xbT', size = 30\n",
        "\n",
        "<type 'netCDF4.Dimension'>: name = 'yb', size = 1\n",
        "\n",
        "<type 'netCDF4.Dimension'> (unlimited): name = 'time_counter', size = 744\n",
        "\n",
        "(744, 1, 30)\n",
        "[[ 0.  0.  0. ...,  0.  0.  0.]\n",
        " [ 0.  0.  0. ...,  0.  0.  0.]\n",
        " [ 0.  0.  0. ...,  0.  0.  0.]\n",
        " ..., \n",
        " [ 0.  0.  0. ...,  0.  0.  0.]\n",
        " [ 0.  0.  0. ...,  0.  0.  0.]\n",
        " [ 0.  0.  0. ...,  0.  0.  0.]]\n",
        "[[ 0.  0.  0. ...,  0.  0.  0.]\n",
        " [ 0.  0.  0. ...,  0.  0.  0.]\n",
        " [ 0.  0.  0. ...,  0.  0.  0.]\n",
        " ..., \n",
        " [ 0.  0.  0. ...,  0.  0.  0.]\n",
        " [ 0.  0.  0. ...,  0.  0.  0.]\n",
        " [ 0.  0.  0. ...,  0.  0.  0.]]\n",
        "-0.270444\n",
        "0.407423\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "(-0.27044401, 0.40742299)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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uAgYOlN8LNmyQFa4RI6R83SwmVUTkSosWARdfbP55BQoATZtmL19joDIuJUUa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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff18e8c7f10>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This looks good so let's move the files out of my working directory and into the forcing directory. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "srcdir='.'\n",
      "dstdir='/data/nsoontie/MEOPAR/NEMO-forcing/open_boundaries/north/ssh/'\n",
      "\n",
      "import shutil\n",
      "\n",
      "for basename in os.listdir(srcdir):\n",
      "    if basename.endswith('.nc'):\n",
      "        pathname = os.path.join(srcdir, basename)\n",
      "        if os.path.isfile(pathname):\n",
      "            shutil.copy2(pathname, dstdir)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}