IOOS_Glider_filipe.ipynb

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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "display_name": "IOOS (Python 2)",
   "language": "python",
   "name": "ioos_python2"
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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Testing Glider DAC access in Python\n",
      "\n",
      "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": [
      "import iris\n",
      "\n",
      "url = 'http://tds.marine.rutgers.edu:8080/thredds/dodsC/cool/glider/all/ru22-20130924T2010.ncCFMA.nc3.nc'\n",
      "\n",
      "cubes = iris.load_raw(url)\n",
      "\n",
      "print(cubes)"
     ],
     "language": "python",
     "metadata": {
      "trusted": true
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0: longitude Variable Quality Flag / (unknown) (-- : 1; -- : 3294; -- : 680)\n",
        "1: CTD Metadata / (1)                  (-- : 1; -- : 3294; -- : 680)\n",
        "2: Profile ID / (unknown)              (-- : 1; -- : 3294)\n",
        "3: longitude / (degrees)               (-- : 1; -- : 3294; -- : 680)\n",
        "4: time / (seconds since 1970-01-01T00:00:00Z) (-- : 1; -- : 3294; -- : 680)\n",
        "5: latitude / (degrees)                (-- : 1; -- : 3294; -- : 680)\n",
        "6: sea_water_electrical_conductivity / (S m-1) (-- : 1; -- : 3294; -- : 680)\n",
        "7: sea_water_temperature / (Celsius)   (-- : 1; -- : 3294; -- : 680)\n",
        "8: sea_water_density / (kg m-3)        (-- : 1; -- : 3294; -- : 680)\n",
        "9: Trajectory Name / (no_unit)         (-- : 1; -- : 64)\n",
        "10: lat_uv Variable Quality Flag / (unknown) (-- : 1; -- : 3294; -- : 680)\n",
        "11: Platform Metadata / (1)             (-- : 1; -- : 3294; -- : 680)\n",
        "12: lon_uv Variable Quality Flag / (unknown) (-- : 1; -- : 3294; -- : 680)\n",
        "13: u Variable Quality Flag / (unknown) (-- : 1; -- : 3294; -- : 680)\n",
        "14: WMO ID / (no_unit)                  (-- : 1; -- : 64)\n",
        "15: sea_water_pressure / (dbar)         (-- : 1; -- : 3294; -- : 680)\n",
        "16: precise_time Variable Quality Flag / (unknown) (-- : 1; -- : 3294; -- : 680)\n",
        "17: precise_lon Variable Quality Flag / (unknown) (-- : 1; -- : 3294; -- : 680)\n",
        "18: sea_water_salinity / (1e-3)         (-- : 1; -- : 3294; -- : 680)\n",
        "19: time_uv Variable Quality Flag / (unknown) (-- : 1; -- : 3294; -- : 680)\n",
        "20: lat Variable Quality Flag / (unknown) (-- : 1; -- : 3294; -- : 680)\n",
        "21: eastward_sea_water_velocity / (m s-1) (-- : 1; -- : 3294)\n",
        "22: v Variable Quality Flag / (unknown) (-- : 1; -- : 3294; -- : 680)\n",
        "23: northward_sea_water_velocity / (m s-1) (-- : 1; -- : 3294)\n",
        "24: latitude Variable Quality Flag / (unknown) (-- : 1; -- : 3294; -- : 680)\n"
       ]
      },
      {
       "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": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cube = cubes.extract('sea_water_temperature')[0]  #<- it always returns a list!\n",
      "print(cube)"
     ],
     "language": "python",
     "metadata": {
      "trusted": true
     },
     "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": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import numpy.ma as ma\n",
      "import seawater as sw\n",
      "import matplotlib.pyplot as plt\n",
      "from scipy.interpolate import interp1d\n",
      "\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def gen_topomask(h, lon, lat, dx=1., kind='linear'):\n",
      "    \"\"\"\n",
      "    generate bottom contour, dx in km\n",
      "    \"\"\"\n",
      "    h, lon, lat = map(np.asanyarray, (h, lon, lat))\n",
      "    x = np.append(0, np.cumsum(sw.dist(lon, lat, units='km')[0]))\n",
      "    Ih = interp1d(x, h, kind=kind, bounds_error=False, fill_value=h[-1])\n",
      "    xm = np.arange(0, x.max() + dx, dx)\n",
      "    hm = Ih(xm)\n",
      "    return xm, hm"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_glider(cube, mask_topo=False, **kw):\n",
      "    \"\"\"Plot glider cube.\"\"\"\n",
      "    cmap = kw.pop('cmap', plt.cm.rainbow)\n",
      "    \n",
      "    lon = cube.coord(axis='X').points.squeeze()\n",
      "    lat = cube.coord(axis='Y').points.squeeze()\n",
      "    z = cube.coord(axis='Z').points.squeeze()\n",
      "    data = cube.data\n",
      "    t = cube.coord(axis='T')\n",
      "    t = t.units.num2date(t.points)\n",
      "    \n",
      "    dist, pha = sw.dist(lat, lon, units='km')\n",
      "    dist = np.r_[0, np.cumsum(dist)]\n",
      "    \n",
      "    dist, z = np.broadcast_arrays(dist[..., None], z)\n",
      "\n",
      "    z_range = cube.coord(axis='Z').attributes['actual_range']\n",
      "    data_range = cube.attributes['actual_range']\n",
      "    \n",
      "    condition = np.logical_and(data >= data_range[0], data <= data_range[1])\n",
      "    data = ma.masked_where(~condition, data)\n",
      "    \n",
      "    condition = np.logical_and(z >= z_range[0], z <= z_range[1])\n",
      "    z = ma.masked_where(~condition, z)\n",
      "    \n",
      "    from matplotlib import rcParams\n",
      "    rcParams['xtick.direction'] = 'in'\n",
      "    rcParams['ytick.direction'] = 'in'\n",
      "    \n",
      "    fig, ax = plt.subplots()\n",
      "    cs = ax.pcolor(dist, z, data, cmap=cmap, **kw)\n",
      "    if mask_topo:\n",
      "        h = z.max(axis=1)\n",
      "        xm, hm = gen_topomask(h, lon, lat, dx=1., kind='linear')\n",
      "        ax.plot(xm, hm, color='black', linewidth='1', zorder=3)\n",
      "        ax.fill_between(xm, hm, y2=hm.max(), color='0.8', zorder=3)\n",
      "    ax.invert_yaxis()\n",
      "    ax.set_title('Glider track from {} to {}'.format(t[0], t[-1]))\n",
      "    ax.set_ylabel('depth (m)')\n",
      "    ax.set_xlabel('alongtrack distance (km)')\n",
      "\n",
      "    return fig, ax, cs"
     ],
     "language": "python",
     "metadata": {
      "trusted": true
     },
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c = cube[0, ::5, ::2]\n",
      "\n",
      "fig, ax, cs = plot_glider(c, mask_topo=False)\n",
      "\n",
      "# FIXME: Masking topo needs some work!\n",
      "#fig, ax, cs = plot_glider(c, mask_topo=True)"
     ],
     "language": "python",
     "metadata": {
      "trusted": true
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VrBQLdDyPWqlAy/eph/pD7CDpCI81P6SpNNGKVG97/vSfvKxb19xY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\nw/KGiAb0x4SVNacgpJ+6sIggcrGx3S75X+RvaXvHA49yP1yo1MHTrby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swhC6HN2Bg4RT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\nGsK/+rX4SJnXVRYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fc5ae31e550>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.shape(cube)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "(1, 3294, 680)"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print cube"
     ],
     "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": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    }
   ],
   "metadata": {}
  }
 ]
}