Glider_DAC_test.ipynb

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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Testing Glider DAC access in Python"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "import netCDF4\n",
      "import numpy.ma as ma\n",
      "import seawater\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is a url from Kerfooot's TDS server, using the multidimensional NetCDF datasets created by a private ERDDAP instance.  These multidimensonal datasets are also available from ERDDAP, along with a flattened NetCDF representation and a ragged NetCDF representation.\n",
      "\n",
      "The glider ERDDAP is here:\n",
      "http://erddap.marine.rutgers.edu/erddap\n",
      "\n",
      "The glider TDS is here: http://tds.marine.rutgers.edu:8080/thredds/catalog/cool/glider/all/catalog.html"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url = 'http://tds.marine.rutgers.edu:8080/thredds/dodsC/cool/glider/all/ru22-20130924T2010.ncCFMA.nc3.nc'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nc = netCDF4.Dataset(url)\n",
      "ncv = nc.variables"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ncv.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "[u'trajectory',\n",
        " u'wmo_id',\n",
        " u'profile_id',\n",
        " u'time',\n",
        " u'latitude',\n",
        " u'longitude',\n",
        " u'time_uv',\n",
        " u'lat_uv',\n",
        " u'lon_uv',\n",
        " u'u',\n",
        " u'v',\n",
        " u'precise_time',\n",
        " u'depth',\n",
        " u'pressure',\n",
        " u'temperature',\n",
        " u'conductivity',\n",
        " u'salinity',\n",
        " u'density',\n",
        " u'precise_lat',\n",
        " u'precise_lon',\n",
        " u'platform_meta',\n",
        " u'instrument_ctd',\n",
        " u'precise_time_qc',\n",
        " u'depth_qc',\n",
        " u'pressure_qc',\n",
        " u'temperature_qc',\n",
        " u'conductivity_qc',\n",
        " u'salinity_qc',\n",
        " u'density_qc',\n",
        " u'precise_lat_qc',\n",
        " u'precise_lon_qc',\n",
        " u'time_qc',\n",
        " u'latitude_qc',\n",
        " u'longitude_qc',\n",
        " u'time_uv_qc',\n",
        " u'lat_uv_qc',\n",
        " u'lon_uv_qc',\n",
        " u'u_qc',\n",
        " u'v_qc']"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lon = ncv['longitude'][:]\n",
      "lat = ncv['latitude'][:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import iris"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t = iris.load_cube(url,'sea_water_temperature')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/home/usgs/anaconda/lib/python2.7/site-packages/iris/fileformats/cf.py:269: UserWarning: Missing CF-netCDF ancillary data variable u'lat_qc', referenced by netCDF variable u'precise_lat'\n",
        "  warnings.warn(message % (name, nc_var_name))\n",
        "/home/usgs/anaconda/lib/python2.7/site-packages/iris/fileformats/cf.py:269: UserWarning: Missing CF-netCDF ancillary data variable u'lon_qc', referenced by netCDF variable u'precise_lon'\n",
        "  warnings.warn(message % (name, nc_var_name))\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print t"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "sea_water_temperature / (Celsius)   (-- : 1; -- : 3294; -- : 680)\n",
        "     Auxiliary coordinates:\n",
        "          latitude                      x       x          -\n",
        "          longitude                     x       x          -\n",
        "          time                          x       x          -\n",
        "          depth                         x       x          x\n",
        "     Attributes:\n",
        "          Conventions: Unidata Dataset Discovery v1.0, COARDS, CF-1.6\n",
        "          DODS.dimName: wmo_id_strlen\n",
        "          DODS.strlen: 7\n",
        "          Easternmost_Easting: -74.2846128922\n",
        "          Metadata_Conventions: Unidata Dataset Discovery v1.0, COARDS, CF-1.6\n",
        "          Northernmost_Northing: 37.7880349112\n",
        "          Southernmost_Northing: 37.0371716345\n",
        "          Westernmost_Easting: -75.5659652921\n",
        "          _ChunkSize: 1\n",
        "          acknowledgment: This deployment supported by NOAA U.S. IOOS\n",
        "          actual_range: [  9.8394  23.0101]\n",
        "          cdm_data_type: TrajectoryProfile\n",
        "          cdm_profile_variables: time_uv,lat_uv,lon_uv,u,v,profile_id,time,latitude,longitude\n",
        "          cdm_trajectory_variables: trajectory,wmo_id\n",
        "          colorBarMaximum: 32.0\n",
        "          colorBarMinimum: 0.0\n",
        "          comment: Glider operatored by the Rutgers University Coastal Ocean Observation Lab,...\n",
        "          contributor_name: Scott Glenn, Oscar Schofield, Josh Kohut, David Aragon, Tina Haskins, Chip...\n",
        "          contributor_role: Principal Investigator, Principal Investigator, Principal Investigator,...\n",
        "          creator_email: kerfoot@marine.rutgers.edu\n",
        "          creator_name: John Kerfoot\n",
        "          creator_url: http://rucool.marine.rutgers.edu\n",
        "          date_created: 2014-10-03T15:53:28Z\n",
        "          date_issued: 2014-10-03T15:53:28Z\n",
        "          defaultDataQuery: &trajectory=ru22-20130924T2010\n",
        "          defaultGraphQuery: longitude,latitude,time&.draw=linesAndMarkers&.marker=2|5&.color=0xFFF...\n",
        "          featureType: TrajectoryProfile\n",
        "          format_version: IOOS_Glider_NetCDF_v2.0.nc\n",
        "          geospatial_lat_max: 37.7880349112\n",
        "          geospatial_lat_min: 37.0371716345\n",
        "          geospatial_lat_units: degrees_north\n",
        "          geospatial_lon_max: -74.2846128922\n",
        "          geospatial_lon_min: -75.5659652921\n",
        "          geospatial_lon_units: degrees_east\n",
        "          geospatial_vertical_max: 91.53\n",
        "          geospatial_vertical_min: 1.0\n",
        "          geospatial_vertical_positive: down\n",
        "          geospatial_vertical_units: m\n",
        "          history: 2014-10-03T15:53:28Z /home/kerfoot/slocum/matlab/spt/export/nc/IOOS/DAC/writeIoosGliderFlatNc.m\n",
        "2014-10-11T14:34:46Z...\n",
        "          id: ru22-20130924T2010_f070_8f49_1646\n",
        "          infoUrl: http://rucool.marine.rutgers.edu\n",
        "          institution: Rutgers University\n",
        "          instrument: instrument_ctd\n",
        "          ioos_category: Temperature\n",
        "          keywords: AUVS > Autonomous Underwater Vehicles, Oceans > Ocean Pressure > Water...\n",
        "          keywords_vocabulary: GCMD Science Keywords\n",
        "          license: This data may be redistributed and used without restriction.  Data provided...\n",
        "          naming_authority: edu.rutgers.marine\n",
        "          observation_type: measured\n",
        "          platform: platform\n",
        "          platform_type: Slocum Glider\n",
        "          processing_level: Timestamp and gps positions checked for validity.\n",
        "          project: MARACOOS\n",
        "          publisher_email: kerfoot@marine.rutgers.edu\n",
        "          publisher_name: John Kerfoot\n",
        "          publisher_url: http://rucool.marine.rutgers.edu\n",
        "          sea_name: Mid-Atlantic Bight\n",
        "          source: Observational data from a profiling glider\n",
        "          sourceUrl: (local files)\n",
        "          standard_name_vocabulary: CF-v25\n",
        "          subsetVariables: trajectory,wmo_id,time_uv,lat_uv,lon_uv,u,v,profile_id,time,latitude,l...\n",
        "          summary: U.S. IOOS Mid-Atlantic Regional Consortium of Ocean Observing Systems (MARACOOS)...\n",
        "          time_coverage_end: 2013-10-17T00:19:33Z\n",
        "          time_coverage_start: 2013-09-24T20:18:47Z\n",
        "          title: ru22-20130924T2010\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lon=t.coord(axis='X')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lat=t.coord(axis='Y')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z = t.coord(axis='Z')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tvar = t.coord(axis='T')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "shape(t)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "(1, 3294, 680)"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tvals= t[0,::5,::2].data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tvals=ma.masked_where(tvals==-999.,tvals)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pcolormesh(flipud(tvals.T));colorbar()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "<matplotlib.colorbar.Colorbar instance at 0x7f2548545e60>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Wy4C7YP058nx3sAIcXr2GI5zK5BqnDRetn3G+ACZpygwA7p87gzuOvQym4KPX\nv5kD/7kl3c934A82/x78M3AAmKDyAt8EB19oVVM7IPhbRz/C13g5/H9GOCf21iMA03yen2XfW88z\nKUlsuDd/Bt8676L0mJ1XwxoSbZP1wEPWHAIcu7A3PRcuH0UJjl3Wmwrlg6bK45ybtv1akuAN+oGz\n4VQO86VjrzI2+S2mX8fXncK3Nl8EQ/AoF/AIF7CT7UzPDSQPsnFv4LS03gh+XxADSZh68lsstgbq\nP8CbdXHLsjEvBc1ovVmuoQC/3oRgLdC95gqIJotlwcnOItmvHkXjB+vbi50w8L87QV0OHpeurFwq\nUi4VoASf43WVnhQnjFcBMxihFw7mXGn8coHE13fu4Gr2jm/mW6ddxBHWc99+69Nsr/9HOB/uwQhL\nN7C2Hu7kZdXBLkPGjW+CtUbAjpOYQ44U1qfnx/MCmCisTZP3W++LEoU00MWZgFwQyBqzfmb0WeYB\nsG4DrDFBNju5BPrNoJ8TvDNTg0nEY5ki0wxSoMTk6oGMH8k/302qK80K7qL33x/My6sX7qvgLXcq\n79L0/7syBOqbtDs13maJAnkZcL6rfkJ0h715nD1zorA2idBzYdJQabaASqFQpmC0t1KBuRN9MAk7\npy+Bb5LelJNWUM5iZhR02L782xmX8gTnmC9vw2ieh+Hk40OM8mLj5fC5Pj76hjdjXCHgwNFNRtM+\nCHwC+Dljlrj+Pz+QHuNVJNFvkwzzFa5BN5v6n+DXYLM9Fm/C2FnrxvYQF1Ny9uIx08aMM1m4vm/E\n2Jm3wuHrV5n1B+HYdb3sZ5PJmFcYMA8jG/QxRy8bGWfuRF8ijF35nHUtdL7f7jy7h2HJnuvw92s7\nvv24Hu6VPk9AhyyloMvTgheCf3xvWCECO5oslpEsTwbrU+u8CPazib1sTgRxmQLPYqLKZOHjXqPL\npYIrYGrfqZXBH7Pw+Pe3GQ3ZJSX1Hg5f5DVmQGsL6IXwrTMuMu5sB41gfJCL4X4Xon0MKDH32GrT\nls5zz2nbefi15/JB3gHfJdVef9oe9zXwFGu5ixEm1g3AQzN85tgvwEttm17E3VODa6AEt/FKdq0+\nH92A0cTXYB4abiZuN7tKP7AdPsTbzXk7Ab9V+Ig5sD5jS/8e22A9HGE90wzSyxyMGT9vSINu7ucy\nphmoHLizOLfDJKrPnUN7znNpRbspBv8bXefzZu2M1/pGNPa86cZer+l/37Wt24Vy1JCXgdeai2Y2\n9LJwJ7pHQ4IoAAAgAElEQVTPaWAFJlmVhEr79suQcKAvqVcqGE3zYEVlI4inbOa6eVveT+JZsZfN\nZtCuHx5Z9yPcxxWmnQljP546OQxTmEE8xxGsVjvJBGuT/MyUSHx7//SF/82YM37CupnNbbZC/Qgz\nU4NwmW3TC9c+xEboM//H2cD+deuN4N9svTSuIM2H4aXD/AS/lmi2fz/+C4kJaJoBnmALbDJ+x+Ns\nMO53u80D0NVzNntjPupLBlYLWdLWCWJngqqlwSyH6WC5buB69t8i2SaKRnh9lwvfLKJAXiaKUCqc\nQqJ4FTHanc3k5DRil3j+KdYya+3GpUAw+76xjoljNp1mqWAG7Q6TCl5T2QjpWdLZBC+Ce/7S2HHv\n5oXcyxWwDj7G20xujBlgDA594WyOHDwVDsBXuCZt+C5MHR5kkmE+xluN7/MERotdBdc/+AHYAred\ndxWPcD5P79to9sMhGOvjnjO2cz+XppF2z1/FS07+X/gJuPvkT3IvVxjPkSmSgJnjG05h9uWkLm0b\nTYTdgedtMQ+V3XDy/wwlbx0TrOUQG2ADfOXoNcwwSB9zMApHTq6nRIHpaSPIJxnmEZtdz9jmi5UD\nedikRI1qp4tN+Ma1EOrMSlGTIkZj/XVNhfHbNfW/h3w7sc87Nf08k4jZ3pYJ39PCcaUaAb3KCNdZ\nq6G5qZpqmSkcTlDPTKVZ4ZglNUtAqqFNeR9IU1AWYHx8A1+bfjlsMCaKCdYaITlh65/ohTGYOOrl\nUXYJ4BlnmgGeYi373n+eeRi4i2gPsN5o4HP0QUnS7HAlmGQVY06d3mI02CO3ngnXwZFvnsleNhsz\nRQm4yL4NFIsm+KOI8Z/Gmj0mrFvcD4HRNAPeHH3mIbYBSvMFDo+vZ4BpmIDJCROKPncifSNJbPLe\ng7CcaMul9He0HjJLQi0tO2t+uHqXzps0NWUs1Jzxem1Oa12IZuwL+pVI1JCXiZ9Vhv7xJE+et77S\nSF+AUh8MM2kHmExazTmbPAjIidgzv9A0g2Z5qo/SvG10hsrZsZz3wgRGWJqp93jyf6w3bm1FOPnw\nEFPPORUuNkJxP5uMRl2y20wIHJ43/sOOCbezUjIBKx/BDMC9HOOtsQ/0bcYssYdz4bD1zmAeThhh\nuZPt5nxcBW/hRviZeb7w/FfAh03I9AyDUIIvb34pMwxSLhSMsN0MBy9aww/O22C07r3WLa4HuNXY\nrE303SCHjm5gdj2cPDLEyUeGjIY8YQR0mQJzB1dX5JqeYZA9nJsIYv+cFymnA7WLfaPUav+Faj55\nvE3NJ9K5RIG8vDzK+ZWRN/0kblbO9SoMiw6Fcc11JUmTjYcRW3uo0LT+nReY0Oc+jBDdi8nFTMEI\nyRmMjXje/rd210qM+SLxVrD1Z19k2mIKvrjuFcbLAfPdCDYT3jdLL+PTxi6956qz+N5nLgHuM9Mr\nHYdvfuWnjP/zlNF4nXCcZBXHt5/CHH0UKHMOTwDWHv1d04c5+pKou7mpQSYHV1Xmr7YPpgJl2F2Z\nb3qCtcmEspm4N4BGb5R225GvXGJBG4bBv7aB/WeZH55pJol6tOBlISI3ici4iDzklT1fRP5DRB4U\nkdtEZDhrtyIyZus8ICL31evmyhTIRbiNV6XRYwDnKV/ktRVC2LcdFylTosBaJvgH3ljVpMuRvP68\nJ820SlApdJwduYQRup5d+Uu8kq/NvdzYZt8McAzOhkNfOZtDD55tNOCHMDbG3bax73o7LyV/TFAL\nq+DADMzCbYOv5NjNvfAB+Cd+liOcanyBDxpPB5iBEzDGFqZ2nwpnw1/z6yZwhX3GZLIb+BdrejgA\nd/AyO6+gyQ29t28zg0zzOOdyHo8DZpCOMaCcZoibZhAOFI0WPQVM2HqzUOwpM8Y58E3Yw3nWdlzg\nCbYkkXlOM64YYHXRf42ylBpOmc7wqojUpzUN+Wbg6qDsE8C7VPV5wBeB383ZswIjqnqJql6eUydh\nZQrk1ysf4D3GH9ZLlL6T7Ul+41TzTV2yIPWq+Dy/WBk9Nme2KTmXt1AQO06QJq63fqt/+3/fwtMH\nT+Uf/88rca5s9zx/O7wHI9TGgftnjE3W+S7vcTuZtxrmNFBkD+ca7wj2wZQ5pisL34TDo4BNdfng\nFjjdDUpeCDOYAbv7gd+Av5n+Dficaf8b/KTp74dnjHA+PMYjnE8fswzPTlKmyFe4lgJl1nPEno8Z\nI4Qnjb3mEBts8vkBeBIjeL8LHDaeI8wYk8WRo+uTsOrDdiJZl9bUvbGE3i6lrSxepjSoLcDracft\n0saXe8DymUALAllV7waeCoq32nIw00j8TI29581IXcXKFMiW2664Cq5Ibyo3ip/6HpuzHwaDOAaZ\nrvK8qCKc589pzb4Q+TxwuGgEl72LJ1hrvDRKpIN/R0gHCWfCHU0m25kBv6NwwuSHGJveAoxTsnZx\nDmLd2jYAA3AcHuASmILjzzmF6ckBGwU4z6NHL7APlXEbiHGMSYYpUGbw+EnAZmYD+pi1Qn7GmnBK\noMZ8UeqzD4CD1od5DE9LN5O1luZNdOMB6wJn/JCHk3Psv72432a275TmB/Q6OXIOqgfpmpmeLLIw\n2h8Y8rCIvNou/xzkzq+mwB0icr+IvKVeozUFsohsFpG7RORhEfmuiLzdlu8QkX3+lNjeNteLyGMi\nsltErmrs2BaHbwTz2X2UdyZTNG3gENMMVEaDBThB7XxuZ+d6KZc8cwWkwhSMYBuj+ga7FXg33Myv\n4FTqxzkXDs+Yrw959dwctk9628+C0axhbHqLCRRhDE7A//rP32Hq707FTSk9zoZkoPHxrz8XZAC+\nC9+77RLj7dA3zMm/SV1Q5navtkECx6wt95DxhQbkuBHGD3FxMhDnBgqN8DR9mmAte1efYR50D1j/\n5n3AHqzrHXCwj5NHhmCCipm9jxxdb8/xQPWsLJhw7Ar8B93/qKHBhjbUdgk85y3RLo+EMJl7Izbj\nSPPUyO42+n3Y8c/pp0F+FXiriNyP8eGay6l3papeArwCeJuI1Jxks97L0jzwDlXdKSKrgG+LyO0Y\nqf9+VX2/X1lEtgGvA7YBZ2KeDOer6sk6+1kUsgaL/FDdxtzd0vwVgM1h4T1Gs2700IwxBYynUYLg\nAj/mgYG0DcUGmpQqBT3gVOapg+utAJ1M7dU7077OMJhq2fdgLro9mORBTuveCU6Y+t4gSfhyqUC5\n12izRziVOfo4xAZrYjgVmEmEJ5A81PayGb5tz/Fhs+8xFyZug1/MYKOXYW/e5Q3J/i3KhULnvtKv\nZDexlUaNa2jkeebjuOGf6jenqo9gfJwQkfOBa3PqHbD/fygiXwQuB+7Oqgt1NGRVPaiqO+3yFPA9\njKCFbLvIq4HPquq8qo5hREFdQ/Zi4c8k7XCDSenEpqsS32SozvYGNpTXCuHZGatRO+HmC2TndRGG\npdow6sdve679Ms+/84J0G7+iu8cnIJHsyaBeCe6RdN2ErbfPNVXg0P6NRgh/F2PZGsLk2thp6s7R\nZwcOZ4ABu+293vkqMvmUmdiVorH3PsTFPME5zNFr+z1vzRjGg2OSYXqZM+tsCDg/NN19fNdzTed2\nA191fejlu/ufR5kiJ8vFxFwxyaps89FiOuv7Gqozb82y9N4VUHktvFLNJ9Ie2myyEJHT7P9TgP8O\nfDyjzqDzvhCRIUzGmYfCej4N25BF5BzgEozeBfDbIvIdEfmkiDjVbxOVnrn7SAV4RzDJMOfYeZXq\nulzhvDH6koCQk7N9bkVtQrPFDOmZA+6899q0XoVKPVPxj2S1rTNG6oExRRKcAdbb4USvMTc7zbcH\nMxxxGDhuteATkGS7L4EzdxgNucTJ8aEkSm6OPvYf25Scp0e4AICJubVJn6ZODjPDAI+Mnw/zVrAf\nt227GVNOkAS4zNIHE31JHosw9Wk4KQBQmVy/3VwRCL6litZaiSHKnUoLg3oi8lng34ELRGSviPwq\ncJ2IPIJRUvep6v+2dTeJyJftpqcDd4vITozW88+q+vV63ayLNVd8HvgdVZ0SkY8D77Wr/wgz/8Sb\nczbPvOp27NiRLI+MjDAyMtJIV5oiazDuvfwxN/GLiS25l9mkXiGwP3yFn+Yy/s0MYB3uY7pQghNF\n6LcmhYuAxzJ2fCKjbBYTzOEYtf/HXIETylZQBgmLEh4gjdybBP6VxNvBuKIJPIJ50o+BGY8bg++e\ng5lVqoBxJbapL3cDdvZoM1h4DO6Abz/vMp46Y4D9bGLm8FrKq02uiZ3HtgOHefr+0+3G8xzZt5EH\nz77YJOUvw779m815GQK+4/V1L1CyppsJE75OqVAxqBe+oeQNuOZyvcKf1xjU3o8JN/58Tp3F1oyz\nJgAN78JnuGY8OjrK6OhoexttweylqtflrPpQRt39WPOFqn6fJlWJut0UkR7gn4C/U9Vb7Y4Oees/\nAXzJfn2SytHGs6gcnkrwBfJicTO/kbvOH9UfZJqP8k4Afo2PVtWbNVmJjHbsZLabuTlLUw4Fsqvj\nz6/nzornY1zJ0bSCry1/22unhH0fMRWmGTAmjAJptOBmu34vts4gxs/ORgJOpfufO9EHzMO8MTVM\nbzN5i12ypAJlZu55lml4t9fvA0Vmzh40bRWAqT5zzMcxNnF3/Pb/3MnexHRBKRXCeUI58swiVNBu\nuOGG1htd5rSajVLzyhcRAT4J7FLVD3rlZzhjNWY+CWcXuQ34jIi8H2Oq2ArUjU5ZasoUeBuf4G3A\nL3Mjw1Uzkaa4vBesIsnwptuKyH9QPRuIW87SkIFUyGJedCBj8M5pytNpkZ9X+Ymg+kOpuWNibq0x\nZ5xGMNv1UePR0TPAXYzYBn/UrPI18d32Xf1JYNLMnH2IjUna0gKl1CA1CubymYEne4wm694W9mDe\niya8fsySnJ9SqWA15LUwJsw928xp6M61P/gJpOHT71L4kwZcOv2k7D4f8rYNNdVLl1ErzdKaI+1l\nmefKa5R6qsiVmMncHxSRB2zZ72HsJ9sxt90T2Ok2VXWXiNwC7MLcfm9V1Y6+2tyAVB5JyPQUsErS\nLHLjNOdKFdqKH7aFOxvZeN7b2Xiw7l+BHzF9PZH6ICf5L0oD6XZT5/AoF2Ak5+WmzTGv/Z1en15j\nfJwnGYaictjNG+i8QEbBqOoluB/KP11MhfsdXvfcw8Q7V5NPGbe6rx19OYwZjxM3z56Z6imYbfrZ\nNU5NM0Ejb+/oSzGymKwEDVlVv0n2wN9Xa2zzPuB9LfZrUXkLn06Wa+VCrsCXiVCZ5S0kV1DPVy8e\nBPMzhL5yeQ3OUPmzHUvWJ4nzJ7F25nmYdwL5KMycYzOzOTeQeU9Dn0+13wPAHii/pGgH98qJN0ry\nYPGP32nGrtm9QXfdIdjAQxd6PjcxDKXK2UL8SWfBDvRt8Nq7vkEtORLx6RIr2IqO1GsUXyMzhowM\n3NRHzhzh/mcJ36yw6gp5O58KzB9m7SwUzse85TEqjcpHk/pzJ/qSHBLGCnPUGxB8DEqYyVkhbWOv\n186YXdzzGBw2Lm8mlWchnVl7rz1AX5An5gdb5AYdT5Aq9H6ypFLBaNNTYlODDidRhokvtaVAKftm\nul4r/0PX3HSRZaBLsr094y/h2/i5hW34a4o+W5BrgvJ5jKNEKFMrBHcPqK1w3CurUsPDdQ43Ihjs\n5ERvELY94zVnFmZ2Pst+d/bssbTuCdfmJBw2Lm9HTq6HKUkfWrP+fu3/sjXtTJD6YrtdlkjTkvrd\nnUjXu4E8lxv5CusXHYm0jS6RdFFDbpAzXvCEEShWqOizF/LaHJoe3L9QWoVMe+tnqLQDeNtNSRos\n4uomyvS8+T7m779EOrrozBA9wGMwZjTXI2Ob4J+9KaUOQ6W7yAxMWt/iMa/p8HB9ThQr3jDG2Zjk\npy6TTuXkgnW0YD6RyIJpfy6LRSEK5CbQK8wnl5qDfKH9Iqzc00TDVZmH0mI3z1+Fjdvtb5/XlC/g\nLScg8ZwoWa13n8CJNMFQEqSi7ovZvkwh1Xo9E3Xuc2YiXecGTl06VDDZ6QqUogtcpD10ickiCuQ6\nzNLH9NwA03MD2RWcwCmT/pi+21vuwH4tjTjkKJV2gFAg23UuW9xuVzRPOug3D9wXDMfOVPYjieAz\nNuI9nGcG+mZMUqPqwU+7/bjNY+Hkszv+MtleEFNUnCMTMdlrc2ZsTDTkMkUz0UAk0ipdMqdeVD/q\nUKbA07tPN1+el1PJ2UnNBg3ijM3+95AsrdlXO4NtDmMEsvptP0k6ynbUc7NzQjrUkHuAIkzawc4p\noGw8OCYZts0GztcTNomQO/bQVzrECeRZ04XDc+vp7Z2j5OVFTlsv4OYDSC7W6zOecv055ZEIdI2k\n65JuLh9lCs2nbmzaDpU3EhgK7TqEgSqU8N3iYL7xB0aPzY0xBUwagTxHb+Xksa7NPhsBqOSn4vb7\nNVVZNneij3JvgWkGmLMPD9/1rRBzBUdapUskXZd0c5lpxrqQS55UydF2gVQY+25vWfWslptE3ZVI\nBbz7WJeGyYztkhA6gGGcRj15bDhpc+7gau5dd0XG86EEZXhkLse0kHXYd1Sum5kaZHL1MJPTw/QN\nzlXlqJZ1UfONtEiXSLpoQ24nTvgsaMqhJjThqh16aTqrNOSg0FkvKq5Qfzntx8zUoGm6DBzMSmdq\nhf5T8PQDp9fuqv8cqZjQFZvPosBlg/czzaBJOhSJtBHnqdPIZzmJArkO5ZOF/MGpECfXMpXhvEd0\nVuVaTsxVUjf9fjDcfh1VaJaHhq8hD5AEftzfZ0zQJdO2y9KWtv+LJCr3v2U06+OfP5uwPwmrPlxk\n4ujaJOvbXpufqkyBn8rP5R2JNEy52PhnOYkCuQ6HTzkzWwZmUbNO3spiA3XqYQWk50pmbLlOIOd4\niFQw47m9WXZiIgmLwAnrnpasDtz4/PwVWfieJ85RxEtsNGfzTc+d7E3mPGyKOKAXqUG3COQusaws\nL/rCBiv6Mqphs0U7RqxCDdm1eSYm8GMdxnXO+hhXuM55+5+AJIKviAmTnsBkjyvCLrZZt6D5tE7J\nLvvTEjh8GRmaLNx/l8Zjokj57CKXnvJt9nMGANfWlfKRSGPM9uXPnVlNfrKxxSZqyO2mpjZdY0Cu\nJWyqzjDtpzh78Hn2vxvoc0IZKp7J6vXnMJWJ8Us2Ab6fGbPsrfRThGbhP6B8l2rrVscUzDDAHL08\nxI/xl/z3Og16RO04UodyodDwJ0REbhKRcRF5yCu7XETus5M8f0tEfixrvyJytZ3w+TEReXe9fkaB\n3G7a8s5Rb4Avx8bsnCvcepcq1CWjr8gql/Vw8FzkMrLZzc71Vgpp9fo6c6x6g1qcYXd1Iv2UKHAX\nVzfXTiTSAOnkB/U/GdwMVRfmnwP/w84o/Qf2ewUiUsDME3Q1ZuLn60Tkwlr9jAK5XbikPr52nGm2\naIsPXYA1M/iBdwom/UTJLsxjBPNRqLDRzgTL0+liH2l+ihOYAJlEUDuzh4sGzJwYJiXrrcGFeVuB\nPJMxKW0k0g781K71PiGqejdmZkqfA5ipE8C8N2bdAJcDe1R1TFXngc9hJoLOJdqQ20EfFTNiJBQy\nyhbk3lYPG0ASPgDcrly2NYoYr4ijmNm1wFOrSf2VvcvCDbxNYJP0E9R1y01qyG7XnolnQYN5kUgD\nLEJOlPcA3xSRv8Qotv8lo86ZVGYH3wfUyoYTNeS2EgrfBfkjN0Pg6RDakIu2/Ay/cIwkPLqqDZdJ\nbqAySdAJUntvRd0xb7mJB43bTQkj4O33yWNRIEcWhxZNFll8Eni7qp4NvAO4KaNO04MbUUNeSbgg\nDiARrBDkRJ4nKMxoJPhessK2av6/BQ5Iuj66VKavM/8L472puTsSaSO1BO09o7PcM9q0Z8Xlqupm\ne/g88ImMOuGkz5vJ9kdKiAJ5JRGaTdyvW6XZhkK56K0rgbPlDtk6pZ4kOKTaBD5A9bRSUDMPR86z\noLyxKlFGJNIWwnB8n0tGerlkJP3+oRuqNI8s9ojIi1T1X4GXAI9m1Lkf2Coi5wD7gdcB19VqNArk\nFYPzdoA0f8WAV+YWcqL8Kiray2KtbcvNy5fp2mbTdVZpy54wdnbiPu87VHpsRCKLSCs2ZBH5LPAi\n4FQR2YvxqvivwEdFpA9z8f9XW3cTcKOqXquqJRH5LeBrmBGlT6rq9zJ3YokCuZ2cSp0XkqWilPO1\nVsZ4SIWq9VceCDTcg2H9eVJXugV4j8QsbpElognbcBWqmqfVVg3Qqep+4Frv+1epMSl0SBTILaK3\np8vyQtBjIA15by2Ct8UUpN4OPRlpQLPsGXnrS7A26GM/Xs5kMAkpzsnYFhKTxQyVwSRhlUhkCWhF\nIC8lUSCvZDJlvnNry4jUA1KtdwaK1lThBuEmw3p+yF0k0rlk+Rd3IjXd3kRks4jcJSIPi8h3ReTt\ntnydiNwuIo+KyNdFZK23zfU2THC3iFy12AfQSeg3MgpzH3ntUA8Du20SOZeV6N7/nyVAQ+k9b/tu\nZgRhhowE+P52Ud2NdC5lig1/lpN6fsjzwDtU9bnAjwNvs6F/7wFuV9XzgTvtd0RkG2YkcRsmXPBj\nIvLM9XXur7WyHSaLLCHolSXXViOZ5mrgpluq8qse8NqIWnKkc1kEP+RFoaawVNWDqrrTLk9hUoed\nCbwK+JSt9ingNXb51cBnVXVeVccw4/KXL0K/VwDt0CizhGAJY1uY9+RtVhRdHWHua99uoLIicZJb\naCS1Z87uIpElwkyi29hnOWlYP7e+dJcA9wIbVXXcrhrHZk0ANgH3eJvtwwjwZzaLlozMl3Lup5zG\nCNNjmCiLHsxPFOI0dN/sEcQyu0i/VXZ5H/Acf9smtfwyFQON/oBoJLKYdIsNuSGBLCKrgH8CfkdV\nJ0XSmSxVVUWklsjJXLdjx45keWRkhJGRkUa60j2EYcyLwmROeTCbdGZEXUY+5LCNMHd+Eujnr2hH\n+tBIJGV0dJTR0dG2trnctuFGqdtLEenBCOO/VdVbbfG4iJyuqgdF5AzgkC0PQwXPIicNmC+QVxzF\nZtzfWiUM7PA03EJYp5bbmz/wZ70w/ETy52A05YehMmvRJDXtEYuezyOy0ggVtBtuuKHlNpfbNtwo\n9bwsBJNEY5eqftBbdRvwRrv8RuBWr/z1ItIrIluArcB97e1y56MLSHxmaPYpnuVN4ZWVm20zaM/N\nn+cngDsQblNHQ+6O+yCywumWQb16d+uVwBuAB0XkAVt2PfCnwC0i8mZMyq+fB1DVXSJyC7ALcxu/\nVVXjdA6LRlYYtLMrHIITW8m38/qDcc5VLhDILlR6BpNEcB5Se/Qxu90RYH1+F0MNuVyjS5HIIrEi\nbMiq+k3yteiXZRWq6vuA97XYr5VBP4vsXeBrp77/cVZuiVq4ufYCnIm6Qog6we1WLsDdLXpcRJaY\nuSSRSmfTHZbuLkWPetPaAe1XDae95XlM4vlxuzydMQAX9sUlBlqPEbC+rXk+1W4V83CpSAY0g5l5\nZIw4sBfpdJbbFNEoUSCvGHqomBOPUo1sagMYb0T/5w89J3qqld+K6ZtKwAYS4R+nX4p0MCvCZBHp\ndPJSaYZubyEuJ3KTGnvF7hZ46ZSIA32RJWfFuL1FFoNg3rq2MYMZZLOSsyrPth/mHA7E+dJ2DHiO\n1Yg9oZ34Vs8D6zCadgNC3eVCjtHVkWUimiwiAOg8eHE0i8wx0vntrLtaxSSm7jvUT79Z/bUyxOdC\nu794CUU6n24RyM/cxD9LSGOOf626HhQxfmrevHkzyR/LOq/uAGmOixyh6ooFb7ZpK9wHXFu1cllE\nd4pIZ7BS/JAjXYUvAEt21mi/bDVpUKXzsMjDW9ePuVKEVEuOV06ki5htwe1NRG7CzAJySFUv9sp/\nG3grxh/py6r67oxtxzCaTxmYV9WaydbibbUisVpy1bjeAHAxxkbcYDNgZvw4NWinD5hsPG+UPgby\nPLv8rYY3i0TaQoua783Ah4FPuwIReTEm6+XzVHVeRE7L2VaBEVU92siOokBe0YQmg6zk9SFewnmX\n7Q1S74iSrTPg141EOpsW59S722a79PlN4E9Udd7W+WGNJhoeRYo25CWlljBsVbhlzdwRJv4ZJg0I\nycI9n+30TKu8VQfwwqCH4QLsZVarPYPur93zSGSxKVFo+NMgW4EXisg9IjIqIpfl1FPgDhG5X0Te\nUq/RqCEvKe3WKOtpu9PB9x7MT571s/szSNv//ZBkfTvut9Fjgk7WAk+tw0QIhvSgCs/g+WIiHUQt\nP+QnRn/A2OgPmm2yCDxLVX9cRH4MuAV4dka9K1X1gDVp3C4iu1X17lqNRlYEDbixNev1UACYSZtR\nt5+i8XFeDzzlzCDRfBHpXGqZLM4e2cLZI1uS7/96w7810uQ+4AsAqvotETkpIutV9YhfSVUP2P8/\nFJEvYmZQyhXIUX/pGFp1EfNn8XBthaN6JYzx90lrbsh7Hvu+y/MZzhg9JmhkLRgzSCTS2SyC29ut\nwEsAROR8oDcUxiIyKCLDdnkIuAp4qFajUUPuGFqN3vOTxjtBHAr5IvmabF75TLppwnAQb9KYdqwP\nNlQtEmk7sy3MlScinwVeBKwXkb3AHwA3ATeJyEPAHPDLtu4m4EZVvRY4HfiCnWGpCPy9qn691r6i\nQO4Y2vFTON9iZwt2Za7tSdLgkHC/A1QK8PnKxYruDXrPj4Fgm55Kf+VIpANoJZeFql6Xs+qXMuru\nx/gso6rfB7Y3s68okJ8ROA32GEYgl7zyBqdfCt/kEiHd4KzTkcgystwReI0SbcjLiOrG+pVqkmfm\n6PHW49WZSb83osHusW0kCerD6aHATJuYj55sYD+RyCITQ6cjbaKVwb55TLh02FbJmhWcacOZNYLQ\n68OQCO+KK+UYlNbZQb1IpPOJ+ZAjHUTGoFsBKPVgtOacy8BPv9nvr8jyvKgkzqQY6SRiPuRIQ6iu\nQ2TB01R7OO02nB8v5ydOcgvViLSb8fq1Cqqk8KRt3i/WKIwjncdymyIaJQrkZUD1HET2LXc3PEKz\niEdbpGkAABfTSURBVJvI1PPY6Pf9nC2zwGmYsOpIpIOZa8HtbSmJArlDUF3doqbsu7lNeuVOgA4E\n/8NtsmwQfoCJM28MUJWaM0bpRTqcaEOOLDFOSOa5ofmBIzTwy3uJ7il5SnS8ZCLdR7fYkOu6vYnI\nTSIybiNSXNkOEdknIg/Yzyu8ddeLyGMisltErlqsjnc7qmd5y6tr1GwUZzfOEsh+ZEc9bTZcb78/\n7dohbStOWBrpErrF7a0RP+SbgauDMgXer6qX2M9XAURkG/A6YJvd5mMiMd9XykZUz8ld25pgdsLS\nCeRwHr31VJslXN3Q5c2VefksDkI65ZPlBNFaEekKVoxAtqninspYlZV0+dXAZ1V1XlXHMKEFNacs\nieTR7BTNfv12z2XnBHKpogiIFoxIV7AI+ZAXhVa0198Wke+IyCdFxIUIbMKkpXPsAxqf52cFozqA\n6mKGGWdpuA4XMj0f1HORgqHrm5/s3rrR7czYZcl+olSOdDhlig1/lpOF7v3jwHvt8h8BfwW8Oadu\nplfqjh07kuWRkRFGRkYW2JWVykJ/mkRKBrg0mX72trwHRMa+55M/kciiMjo6yujoaFvbXNFub6rq\npi5GRD4BfMl+fRLY7FU9y5ZV4QvkSIoJFBmneZNFPZzw9Wf32IhJz1rEhFgfC+q6gcKeykRD7rvv\niBGJtIlQQbvhhhtabnO5TRGNsiCThYic4X19LWnS5duA14tIr4hswcw7dV9rXYzUxzc3VOXKzKYA\nRmuulZzIo5T8qaRcXRSJdBorxmThJWc+1SZn/kNgRES2Y8wRTwC/DqCqu0TkFmAX5u59q2oMpG0f\njaijecElvnki62f3y/yAkax9NpgtLhLpEJbbe6JR6grknOTMN9Wo/z7gfa10KpKH01zDqZl8fC32\nKMYUUQIGc+pPUhU0UvG9xkMgc1WcXy/SebQikEXkJkzS+UOqerEt2wH8GvBDW+16Vf2XjG2vBj6I\neSf9hKr+Wa19RR/hriL0kqiHny/Zf/bWE5h5s4oEVEVc/wuqURhHOo8W/ZAbicXIEsYF4CN2223A\ndSJyYa1+RoHcZai+qU4NX2BPN1CnVgRf6A6X8TBo99hjJLIIzNLX8CekyVgMn8uBPao6pqrzwOcw\nsRq5RIHc0Sx0gCHLpziPBqMDKwSv1+Y81DahRCLLzyJF6mXFYvicCez1vteNy4gC+RlFxVTRltA8\nAZWZ4cJkRMH2s23qWiSyiCyCQP44sAUziekBTCxGSNND3zHEqmMJZ4FulHo2hPAnX0f14N0AqeDN\n8rSIduJId1HLD/nE6H2cGG3OO7dGLIZPGJexmcpI5iqiQO5QVDe2YSYRN1deWBZ+z5s1JCtREWmb\nDbo8RyLLTS3/4p6RF9Az8oLk+9M3fLRueyJyhqq6qRn8WAyf+4GtInIOsB+TeC3Lay0h3k4rgjxN\nuocKs0MmR8gPoXZt1FgVB/UiXUCLbm8Nx2KIyCbgRlW9VlVLIvJbwNcwbm+fVNXv1dpXFMgrnlCg\nWj/hcta6LLzcx1ltTrXQtUhkiWhFIDcTi6Gq+zE+y+77V4GvNrqvKJA7ENWN9v9WRB5scus8bTnL\nE2KGVNJmbefsx3lJ76MtOdIdzM6t4ORCkW4jQ3CegPww64BMDwvrsbGKyin8IpEOpFzqDlHXHb2M\nkO910YwRt5jWTxTmPE233qVhJzyNV1CkCyiXVkgui8hKpMEMbz51PfDmUf3pBfYnEllcokCOLBqq\nv4nIx+230Ic4T2MOBa6zH9dLauxvl3G5ZAWURiIdRmk+CuTIkhAKYN8EkffzusG6cL2fjCgvt0VI\nDJuOdD4ny90h6rqjlxFgdZtNAk6QhwI1x3RRkYjev2yiQI50AdFkEWkHqs9D5I4mt/IG7zKZB8br\ntJGTmN5fLrp6Pai+sqkeRiJLyonuEHXd0ctIC+T9xHl25/m0TMhJjxJD9CJdRpdcrlEgr1iKVApO\nlzBo3isPB+yKXtmMCfZ0m1e98bmAkY3t7ngk0n6iQI50D1mDewFVwXrjNJxLORJZbqJAjiwvToJm\npc4skgR2VFGsrFqqLjbMeBd5vIwiHc5CMtkuA/FOWlFkzZuXpxr4OSpyZhjxmyuE9SKRLqJcv0on\nEAVyF6D6sgZqhT7EYcBIlt+xP1tIKGx/xPwTPEU62D7J9BYFdaTDiSaLyPIyaP+7marDwb285PWu\nbDf0bTWb9WE1DGszdl51yebD7e58JNJeTix3Bxqj7px6InKTiIyLyENe2ToRuV1EHhWRr/sT/InI\n9SLymIjsFpGrFqvjkXqEc+X5md2yTBWhcJ6vLO73tqtSiKOGHOlwSk18lpFGJjm9Gbg6KHsPcLuq\nng/cab8jItsw05Rss9t8TETiRKrLwgb73wlfF1E3TDqLiPOScO5uvmCdr1y1FvIFb60ZRyKRDmCl\nCGRVvZvqFDKvAj5llz8FvMYuvxr4rKrOq+oYsAe4vD1djdTHF5gzQfmMt+zqhYLU15J/1FQr5KyG\nDN/kSKRDaUEg51gJ/khEviMiO0XkThHZXL0liMiYiDwoIg+ISN2ZVBeqvW5UVRd7O04aHbCJyllV\n9wFnLnAfkRqo/mbOGmcHDs0SR7060znb+rNN32fNFMAQGROHRK040kXMN/GpJstK8Oeq+nxV3Q7c\niplnLwsFRlT1ElWtq5y2PKinqioimQG2Xoeq2LFjR7I8MjLCyMhIq115hlNvdun5YF3WTNIZJgtI\nZW/F7CDRbhxZHEZHRxkdHW1voy24vanq3XbmaL/MnydnFXC4RhPS6L4WKpDHReR0VT0oImcAh2z5\nk4Cvup9ly6rwBXKk3fjeEqFvchgQ4q8fJrUrl4xJogfjZVE1/udvF7XlSPsIFbQbbrih9UYXwTYs\nIn8M/BLmlfPHc6opcIeIlIG/UdUba7W5UIF8G/BG4M/s/1u98s+IyPsxpoqtQF27SWSx8bXjGeA8\nYDJICucEdYYgz9Qu/Cs8ek9GOpxabm+PjMKjo003qaq/D/y+iLwH+ADwKxnVrlTVAyJyGnC7iOy2\n43KZ1L2TROSzwIuAU0VkL/AHwJ8Ct4jIm4Ex4OdtB3eJyC3ALswd+1ZVrWXOiLSA6q8h8mH7zU8W\nFJon3How7m8bgBkjfyf99RmmjCFvOfdqGcxbEYl0BrU05HNHzMfxz01r5J8BvpK1QlUP2P8/FJEv\nYpwcFi6QVfW6nFWZ4WOq+j7gffXajSwWTu31A0H8dZMYoZs3QWkwY8hppPK9L2+fMTAk0uG02WQh\nIltV9TH79dXAAxl1BoGCqk6KyBBwFVBT2sd3zRWJk6C+xpuRZKifNhEH+CIdTgsCOcNK8IfANSJy\nAcag9zjwm7buJuBGVb0WOB34goiAkbV/r6pfr7WvKJC7Hj8sGtKcxy53hZ/1rQfjpWjXVf36rqAE\nXGgW1wI/9FYJ8aqJdB8tZHvLsRLclFN3P3CtXf4+sL2ZfcUouhVNrYxvPTUEaxF4LHXWyMpn3y35\nDCMRMHpso59lJOo6K4J6c+iFzACrg0g7/1IoVRe7Uepkmzi5aaSL6JLkQlEgrxjqJJtP1Ftnxuix\nm+TNFrK60gPuRFg1+h5HuoiYfjPSEWROVDpI/Z8+mCtvKlx/CONmHol0AV1iYYsCueupSEzsLWeZ\nFJyWbFNzVrmxOaOxbbNIaqJYhdGSo0NFpBuJM4ZEOosMSZopXAdIElbUyvQWiXQT0WQRWV6yQpu9\nBEJFAj9kPyXnJHCWifcYxnjK+RRcvUikS4gCObK81DCaCZ527A/2+djouwKVThxJtXjpRLqIaEOO\nLA/OadgtO4L0mmuxmq4fTu3qzKd1XL0qgRyNyZEuYna5O9AYUSCvSDwTRQEoOSFty/uxUzJBktci\n2cZPUu99dRe0S8kZL51INxFNFpGlJ7TrDngKc2A/rtB0G7gMgpxD0NM1I9eRSDRZRJaBYSpVgQGj\nCc9AKniLgUB265ykXU0ivH1Thfvfj416ipdOpIvoEuUh3lUritWYfMfuZx3MELylGiHTznzRCNGG\nHOkioskisvRsxAhkz4UtCf5wgraYri5DtWC1RuNnBcXugp7ABIlA1+QHiESiQI4sAxm5K5J8FUEC\nogKeP/FM5TZufZ5HnKvbJXa5SKRbrtUokLueYYxAdfmOoUJDTliHyT/hCeUet72nPbv/a71NwxDr\nom27S+xykUi3uL3FfMgriqyZpqFy0O68aruy+PX8/95XL6+Ffh+6RuWIRCCd1ayRT4CI3CQi4yLy\nkFf2FyLyPRH5joh8QUTWZO1WRK4Wkd0i8piIvLteN6NAXpFYgSpZ6zyt2QnmQlY9j8z3qC4xykUi\nkE6i08inmpuBq4OyrwPPVdXnA48C14cbiUgB+IjddhtwnYhcWKubUSCvKFbb/6Ffm685bwwEbLHS\nzixQNWmp31zBL8TUzxT8kUgH0cKMIap6N/BUUHa7qp60X+8FzsrY6+XAHlUdU9V54HOYCVFziQJ5\nReHsyKHrml8WelX0WK8JW17IqpNDPc06EukUWjBZNMCvAl/JKD8T2Ot932fLcomDeisK5y2x0SyH\n2qwTyn6OY9alg3Qhvlbse10AyZVblfw+EulAFsnCJiK/D8yp6mcyVjd9d7QkkEVkDOP4WgbmVfVy\nEVkH/APwI8AY8POqOtHKfiKNknfV+QmHvJ98DGAg9StOCC4Lf0wwEcrRyyLSRdQag54ehZnRppsU\nkTcB1wAvzanyJLDZ+74ZoyXn0qqGrMCIqh71yt4D3K6qf25HFd9jP5FFZ5hsc4MLh7ZacOhf7OdF\nHgAmvTamqLxKkrqeDTkS6XRqub0VRmDVSPr9qRvqNiciVwO/C7xIVfNCpO4HtorIOcB+4HXAdbXa\nbYcNObwlXwV8yi5/CnhNG/YRaYjBnPIiRih7yYX8nEK+3fgsuzwQbN5v/69vZ38jkSWiNbe3zwL/\nDlwgIntF5FeBD2PeLW8XkQdE5GO27iYR+TKAqpaA3wK+BuwC/kFVv1erm+3QkO8QkTLwN6p6I7BR\nVd0cE+NUzZYZWTKqktDX4TIvg5t/ZSRRfVQK6jioF+kWWnCbV9UsrfamnLr7gWu9718FvtrovloV\nyFeq6gEROQ3zpNgddE5FJNOwvWPHjmR5ZGSEkZGRFrsSMTgftVDFhQoNGaqTDN0/DpdtTNf5m7mU\nyS44RHuQ08I2IpHWGR0dZXR0tL2Ndsl4h6i2Z5hcRP4QY3F8C8aufFBEzgDuUtXnBHW1XfuNgMj7\nUX0nIkcxLpEXmxUDZ8F5QBJfdBQowiWrjQliCLgDuAy4f96sv2wj3H8ULltnmlllPwcxg4AF0Nvt\nfteRzIeqMXAvskiICKq64NEKEVGGm5A3k63trxUWbEMWkUERGbbLQ8BVmFv/NuCNttobgVtb7WSk\nNqrvDEqsW4T//pNcXlZzHsJoDRJscxpAsSo2hNMxQtyzflQM5UYinczi+iG3jVZMFhuBL4qIa+fv\nVfXrInI/cIuIvBnr9tZyLyNNkmEz7idNVO98imcJZp5232vYnB9rSwcjkaWlS97gFiyQVfUJYHtG\n+VHgZa10KtImwixtvuZbtOvDMOpVwC8NwIHK4or/kUi30SWpV+IttiKxP6tvXpgGEVIh3Uc672k4\nOTVkD4JkXdRx4pBIpG1EgbziqDMFk58q2XeWH8YM0N08jupG5AVUe1DEGUIikUUlJhdakXwB1dVV\nk4QkA31lKjO4PQacatc7gR6FbySy5EQNeUVSw46Q9Yv3UD2416jN7QQkSQgjkY6lO0b1okBeyYS/\nrgxUmiHcsguLZgB34eqDIG8xq/W9XhNbgzZjYEikK+iOUb0okFcUwc8ZJngL/IiBVKBaE4bOryYS\nWXlEDTnSKVhXN50GeZ4tW4uxEzsBnafpzuSURyJdRXdcyFEgrzh+FJPTyTKVEVEXCl+nSYfa851u\nQtNIpNuJGnJkyRlHdavxN87D/eIlKn59/VaGfXhtxvbR7zjSlUQbcmTJaVBa+gnq+8nXkGPu48iK\nIWrIkWWjjmC2v7reBPLs9DtDDTTdHdd1JBIQNeTIklP5c+oPGqqW1v9WUBBmfItEupbu0CSiQH6m\n0ojCkOV5ESjfMQ9ypDtYuJeFiNyEmQXkkKpebMsamsw5ayLoWvuKodMrCNWz7FK+yUL/0y64uV38\n6ZnCuv/Ypo5FIstOSwmRbwauDsrcZM7nA3eSP5Gzmwj6knrCGKJAjti89JHIyma+iU8lqno38FRQ\n3Mxkzg3PPhIF8jMRXyv2kwxFIiuWtk8Z0uhkzm4i6PtFXDKCfOKtuCKpk4LTH6xr8grQXU13JhLp\nAGoNdjxoPwuj1mTOZEwEbTXuTKJAXpHUdo/QLyxRNyKRjqGW5rvNfhyfaaTBcRE53ZvM+VBWJVU9\nYP//UES+CFz+/7d3PrFVVFEc/n5QiCKGprIgQJN2gaYYI2AiqCBBETH+W0oXLHCrwcQES1kYd6Ib\nXRg3BI0hBhdIDI0JohESNwJKK38LNkJSVIhuiHGjpsfFvU+m5T0o9s3c23K+ZMK8y2vO15n3zkzP\n3JkDNEzIXrKYgpgtT63gOJnx/2vIDbhhM+frNIJuiJ8h3+L8N+vCcaY0E5r2thtYDcyVNAy8Dmyn\nTjNnSfOBHWb2NKFX+96xjaCvG8usUemjPCRZiriO40w+JGFm456pUOfnLcxcGy+bJhRvIvgZsuM4\ntwCT49bpUmrIktZLGpT0o6SeMmI4juOMn6bXkEuh6QlZ0nTgPcKdLYuBbkldzY5TBocOHUqtUJdc\nvSBfN/e6OXL1ah5Nn4dcCmWcIT8IDJnZBTP7G/gEeL6EOE0n1w9lrl6Qr5t73Ry5ejWPyXGGXEYN\neQEwXHh9EfB5WI7jJGRy1JDLSMg+fcJxnMyYHD31mj7tTdIK4A0zWx9f9wIjZvZW4T2etB3HGTcT\nn/ZWXbyJUEZCbgHOAo8DvwBHgG4zO9PUQI7jOFOMppcszOwfSS8DXxCeKbbTk7HjOM6NSXKnnuM4\njnMtlT5cKOUNI5I+kHRZ0onCWJukLyWdk3RAUmvh/3qj56CkdSW7tUs6KOmUpJOSNufgJ+k2SYcl\nDUg6LenNHLwKsaZL6pfUl4uXpAuSjkevI7l4xVitkvZIOhP35/LUbpLuiduqtlyRtDm1VzLMrJKF\nUL4YAjoIPSoGgK4K468ClgInCmNvA6/F9R5ge1xfHP1mRN8hYFqJbvOAJXF9NqEG35WDHzAr/tsC\nfAuszMErxnsV+BjYl8v+BM4DbWPGknvFeB8BLxb255xc3GL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       "text": [
        "<matplotlib.figure.Figure at 0x7f254866f990>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dist, pha = seawater.extras.dist(lat.points[0],lon.points[0],units='km')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = np.cumsum(dist)\n",
      "d = np.insert(d,0,0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print shape(d)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(3294,)\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = (d*np.ones([680,1])).T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "zval = z.points"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "shape(zval)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "(1, 3294, 680)"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print shape(x[::5,::2])\n",
      "print shape(zval[0,::5,::2])\n",
      "print shape(tvals)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(659, 340)\n",
        "(659, 340)\n",
        "(659, 340)\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "shape(x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "(3294, 680)"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pcolormesh(x[::5,::2],zval[0,::5,::2],tvals)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "<matplotlib.collections.QuadMesh at 0x7f253f9db690>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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XI2J1RARwab0OSVKXtfW9/Hrg+ojYAvwc+BcAmbk1Im4FtgL7gLX1GU8Aa4EbgEXAnZ7x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\nWfBJSTReAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f2548571650>"
       ]
      }
     ],
     "prompt_number": 25
    }
   ],
   "metadata": {}
  }
 ]
}