{
 "metadata": {
  "name": ""
 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook looks for large wind events in Sandheads historical wind data. We are looking for a wind storm that did not result in a surge. We would like to ensure that the model does not make a surge if one did not actually occur. \n",
      "\n",
      "This codes uses a combination of Doug's class for reading Sandheads data and Kate's methodology for finding ssh anamolies."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import division, print_function\n",
      "\n",
      "import arrow\n",
      "from dateutil import tz\n",
      "import numpy as np\n",
      "%matplotlib inline\n",
      "from matplotlib import pylab\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class SOGWindTimeseries(object):\n",
      "    \"\"\"Time series object that knows how to read SOG-forcing wind data files.\n",
      "    \"\"\"\n",
      "    def __init__(self, datafile):\n",
      "        self.datafile = datafile\n",
      "        \n",
      "    def read_data(self):\n",
      "        \"\"\"Read time stamps, cross- and along-strait wind components\n",
      "        from data file, and calculate the wind speeds\n",
      "        \"\"\"\n",
      "        self.timestamps, self.cross_winds, self.along_winds = [], [], []\n",
      "        with open(self.datafile, 'rt') as data:\n",
      "            for timestamp, cross_wind, along_wind in self._interesting(data):\n",
      "                self.timestamps.append(timestamp)\n",
      "                self.cross_winds.append(cross_wind)\n",
      "                self.along_winds.append(along_wind)\n",
      "        self.timestamps = np.array(self.timestamps)\n",
      "        self.cross_strait = np.array(self.cross_winds)\n",
      "        self.along_strait = np.array(self.along_winds)\n",
      "        self.speeds = np.sqrt(self.cross_strait**2 + self.along_strait**2)\n",
      "        \n",
      "    def _interesting(self, data):\n",
      "        \"\"\"Generator to extract interesting values from the data stream.\n",
      "        \"\"\"\n",
      "        tzinfo = tz.gettz('Canada/Pacific')\n",
      "        for row in data:\n",
      "            day, month, year, hour, cross_wind, along_wind = row.split()\n",
      "            day, month, year = map(int, (day, month, year))\n",
      "            hour = float(hour)\n",
      "            timestamp = arrow.get(year, month, day, int(hour), tzinfo=tzinfo)\n",
      "            cross_wind, along_wind = map(float, (cross_wind, along_wind))\n",
      "            yield timestamp, cross_wind, along_wind"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wind = SOGWindTimeseries('/ocean/nsoontie/SOG-projects/SOG-forcing/wind/SH_pp_08Feb2014.dat')\n",
      "wind.read_data()\n",
      "\n",
      "wind.timestamps[0], wind.timestamps[-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "(<Arrow [1967-01-01T00:00:00-08:00]>, <Arrow [2014-02-08T23:00:00-08:00]>)"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wind.timestamps.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "(412944,)"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imax = np.argmax(wind.speeds)\n",
      "imax, wind.timestamps[imax], wind.speeds[imax]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "(72246, <Arrow [1975-03-30T06:00:00-08:00]>, 27.199829429796065)"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First, we should think about what kinds of wind speeds are significant. The December 15, 2006 great wind event has speeds up to 25 m/s. This event did see a large sea surface height anamoly (greater than 40 cm) but this did not occur at high tide and hance had no significant flooding. \n",
      "\n",
      "Some other surge events in the SFU review saw average wind speeds between 6m/s and 18m/s.\n",
      "\n",
      "Let's use 20 m/s as a threshold value. This is equivalent to 72 km/hr so is quite windy. I will idenitfy winds events greater than this value and then cross reference them with the large ssh anomalies that Kate found. I will select dates with large wind events but not large ssh anomaly.\n",
      "\n",
      "Something to consider:\n",
      "The duration of the wind event may matter. So I may have to search for the threshold and duration conditions. \n",
      "\n",
      "Looking at dates after 2001 because of CGRF forcing runs from 2002-2010. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wind_thres = 20 #threshold for wind speed\n",
      "yr_thres = 2001 #data must be later than 2000\n",
      "\n",
      "ind_thres = np.where(wind.speeds > wind_thres)\n",
      "winds=wind.speeds[ind_thres]\n",
      "dates_thres=wind.timestamps[ind_thres]\n",
      "\n",
      "ds={}\n",
      "for i in range(0,dates_thres.shape[0]):\n",
      "    if dates_thres[i].year > yr_thres:\n",
      "        ds[i]=dates_thres[i]\n",
      "       \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ds"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "{125: <Arrow [2002-05-19T22:00:00-07:00]>,\n",
        " 126: <Arrow [2003-01-03T01:00:00-08:00]>,\n",
        " 127: <Arrow [2003-10-28T18:00:00-08:00]>,\n",
        " 128: <Arrow [2006-11-15T09:00:00-08:00]>,\n",
        " 129: <Arrow [2006-12-11T14:00:00-08:00]>,\n",
        " 130: <Arrow [2006-12-15T03:00:00-08:00]>,\n",
        " 131: <Arrow [2007-01-05T23:00:00-08:00]>,\n",
        " 132: <Arrow [2007-01-09T15:00:00-08:00]>,\n",
        " 133: <Arrow [2007-11-12T06:00:00-08:00]>,\n",
        " 134: <Arrow [2007-11-12T07:00:00-08:00]>,\n",
        " 135: <Arrow [2007-11-12T08:00:00-08:00]>,\n",
        " 136: <Arrow [2008-01-14T22:00:00-08:00]>,\n",
        " 137: <Arrow [2009-11-18T18:00:00-08:00]>,\n",
        " 138: <Arrow [2009-11-18T19:00:00-08:00]>,\n",
        " 139: <Arrow [2010-01-18T03:00:00-08:00]>,\n",
        " 140: <Arrow [2010-01-18T04:00:00-08:00]>,\n",
        " 141: <Arrow [2010-01-18T05:00:00-08:00]>,\n",
        " 142: <Arrow [2010-01-18T06:00:00-08:00]>,\n",
        " 143: <Arrow [2010-03-29T01:00:00-07:00]>,\n",
        " 144: <Arrow [2010-04-02T12:00:00-07:00]>,\n",
        " 145: <Arrow [2010-04-02T13:00:00-07:00]>,\n",
        " 146: <Arrow [2010-04-02T14:00:00-07:00]>,\n",
        " 147: <Arrow [2010-04-02T17:00:00-07:00]>,\n",
        " 148: <Arrow [2010-04-02T18:00:00-07:00]>,\n",
        " 149: <Arrow [2012-03-12T03:00:00-07:00]>,\n",
        " 150: <Arrow [2012-03-12T04:00:00-07:00]>,\n",
        " 151: <Arrow [2012-03-12T06:00:00-07:00]>,\n",
        " 152: <Arrow [2012-03-12T07:00:00-07:00]>,\n",
        " 153: <Arrow [2012-03-12T09:00:00-07:00]>,\n",
        " 154: <Arrow [2012-11-18T21:00:00-08:00]>}"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Some possible days with more than one hour of high wind:\n",
      "\n",
      "Nov 12, 2007 - flagged by Kate\n",
      "\n",
      "Nov 18, 2009 \n",
      "\n",
      "Jan 18, 2010 - flagged by Kate\n",
      "\n",
      "Apr 2, 2010\n",
      "\n",
      "Mar 12, 2010 - flagged by Kate and out of range anyways\n",
      "\n",
      "\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This leaves Nov 18, 2009 and Apr 2, 2010 as potential dates.  I may want to check on the CGRF data to compare with observations. \n",
      "\n",
      "My preference would be to examine the Nov 18, 2009 case. We know that storm surges are more likely to occur in the winter so this would be a good test that our model does not predict a storm when one did not occur."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's check the wind at Sandheads during that week to confirm this is a good case. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pytz \n",
      "times=[]\n",
      "for i in range(0,wind.timestamps.shape[0]):\n",
      "    t=wind.timestamps[i]\n",
      "    dt=t.datetime\n",
      "    tim=dt.astimezone(pytz.timezone('UTC'))\n",
      "    times.append(tim)\n",
      "\n",
      "\n",
      "times[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "datetime.datetime(1967, 1, 1, 9, 0, tzinfo=<UTC>)"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import datetime\n",
      "fig, ax = plt.subplots(1)\n",
      "fig.autofmt_xdate()\n",
      "\n",
      "# use a more precise date string for the x axis locations in the\n",
      "# toolbar\n",
      "import matplotlib.dates as mdates\n",
      "ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n",
      "\n",
      "pylab.plot(times,wind.speeds)\n",
      "ax.set_xlim([datetime.datetime(2009,11,15), datetime.datetime(2009,11,20)])\n",
      "pylab.xlabel('Time (UTC)')\n",
      "pylab.ylabel('Wind speed (m/s)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<matplotlib.text.Text at 0x106d1dd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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XumvWrMGLL76IxMREXLhwAfPmzcOaNWuMXax6SWtPqWrodsLaMtRVtKnr3Fn7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\nGMYYY3rBCYYxxphe/D8kitoxWtWphwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xe11d210>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's check on water level at Point Atkinson during this event. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "start='15-Nov-2009'\n",
      "end='20-Nov-2009'\n",
      "emin=-3\n",
      "emax=3\n",
      "plt.figure(figsize=(18,12))\n",
      "\n",
      "obs = {'PointAtkinson': 7795, 'Victoria': 7120, 'PatriciaBay': 7277, 'CampbellRiver': 8074}\n",
      "datums = {'PointAtkinson': 3.09, 'Victoria': 1.881, 'PatriciaBay': 2.256, 'CampbellRiver': 2.916}\n",
      "wlev_pred_xtide={}\n",
      "wlev_meas={}\n",
      "\n",
      "        \n",
      "import datetime\n",
      "import pandas as pd\n",
      "import pytz\n",
      "from salishsea_tools import tidetools\n",
      "key='PointAtkinson'\n",
      "statID_PA =obs[key]\n",
      "filename = 'wlev_' +str(statID_PA) + '_' + start +'_' +end +'.csv'\n",
      "tidetools.get_dfo_wlev(statID_PA,start,end)\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[key] = pd.read_csv(filename,skiprows=7,parse_dates=[0],date_parser=dateParserMeasured)\n",
      "wlev_meas[key] = wlev_meas[key].rename(columns={'Obs_date': 'time', 'SLEV(metres)': 'slev'})\n",
      "pylab.plot(wlev_meas[key].time, wlev_meas[key].slev-datums[key],'k*',label='observed') #subtracted off the datum from t_xtide\n",
      "i=i+1\n",
      "    \n",
      "#plotting t_xtide predictions\n",
      "filename = key+ ' (2)_xtide_prediction_' + start+'_'+end+'.csv'\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",
      "wlev_pred_xtide[key] = pd.read_csv(filename,delimiter='\\t',parse_dates=[0],date_parser=dateParserMeasured2)\n",
      "wlev_pred_xtide[key] = wlev_pred_xtide[key].rename(columns={'Time_Local ': 'time', ' wlev_pred ': 'slev'})\n",
      "pylab.plot(wlev_pred_xtide[key].time, wlev_pred_xtide[key].slev-datums[key], label='t_xtide')\n",
      "pylab.grid()\n",
      "\n",
      "pylab.legend(loc=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<matplotlib.legend.Legend at 0x10d9ed10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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sDado0uOPP261b9/eys7Ottq3b2/NnTvXsizLuuMOyzrnHOfHWbrU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\n4M1v9vcYxVq9Wl1LjI3pXomdbE8WOI5qRXjggfmPeXnxf99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7+1Vbg+nGx1X8Hzsmq7LI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\nAESJzyfl5ESvFaGkhJ0FcK9YF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       "text": [
        "<matplotlib.figure.Figure at 0xe1d1050>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This confirms that the high wind event does not result in a large ssh anomaly. Although the anomaly on Nov 17 is pretty large but doesn't last more than 12 hours. This date does not come up in the lit searches. "
     ]
    }
   ],
   "metadata": {}
  }
 ]
}