Glider_CMRE_test.ipynb

{
 "metadata": {
  "gist_id": "b7cc96e08cad001a7e9c",
  "kernelspec": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "display_name": "IOOS (Python 2)",
   "language": "python",
   "name": "ioos_python2"
  },
  "name": "",
  "signature": "sha256:115e94774d65365a1710a0c7d06358eb82d7d395edb73b54165a269eabd58842"
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 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Testing Glider CMRE access in Python\n",
      "\n",
      "This is a url from NATO CMRE \"grid\" format, where glider data are stored as profiles. These follow the GROOM convention, and in contrast the the IOOS Glide DAC 2.0 format, instead of one combined profile for the down and up paths,  down and up are split into separate profiles.   \n",
      "\n",
      "In addition to the \"grid\" files, there are also \"raw\" and \"processed\" glider netcdf files:\n",
      "\n",
      "http://comt.sura.org/thredds/catalog/comt_2_full/testing/glider_cmre/catalog.html"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import iris\n",
      "\n",
      "url = 'http://comt.sura.org/thredds/dodsC/comt_2_full/testing/glider_cmre/GL-20140621-elettra-MEDREP14depl005-grid-R.nc'\n",
      "# adding coordinates attribute so that Iris can find the coordinates\n",
      "url = 'http://comt.sura.org/thredds/dodsC/comt_2_full/testing/glider_cmre/foo2.ncml'\n",
      "\n",
      "cubes = iris.load_raw(url)\n",
      "\n",
      "print(cubes)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0: sea_water_potential_temperature / (Celsius) (-- : 36; -- : 170)\n",
        "1: sea_water_temperature / (Celsius)   (-- : 36; -- : 170)\n",
        "2: sea_water_salinity / (1e-3)         (-- : 36; -- : 170)\n",
        "3: sound velocity / (m s-1)            (-- : 36; -- : 170)\n",
        "4: sea_water_potential_density / (unknown) (-- : 36; -- : 170)\n",
        "5: distance flown since mission start / (km) (-- : 36)\n",
        "6: Profile ID / (unknown)              (-- : 36)\n",
        "7: sea_water_potential_temperature / (Celsius) (-- : 36; -- : 170)\n",
        "8: sound velocity / (m s-1)            (-- : 36; -- : 170)\n",
        "9: sea_water_electrical_conductivity / (S m-1) (-- : 36; -- : 170)\n",
        "10: sea_water_electrical_conductivity / (S m-1) (-- : 36; -- : 170)\n",
        "11: sea_water_potential_density / (unknown) (-- : 36; -- : 170)\n",
        "12: water pressure / (bar)              (-- : 36; -- : 170)\n",
        "13: profile direction / (no_unit)       (-- : 64)\n",
        "14: water pressure / (bar)              (-- : 36; -- : 170)\n",
        "15: sea_water_temperature / (Celsius)   (-- : 36; -- : 170)\n",
        "16: sea_water_salinity / (1e-3)         (-- : 36; -- : 170)\n",
        "17: profile direction / (no_unit)       (-- : 64)\n",
        "18: distance flown since mission start / (km) (-- : 36)\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cube = cubes.extract('sea_water_salinity')[0]  #<- it always returns a list!\n",
      "print(cube)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "sea_water_salinity / (1e-3)         (-- : 36; -- : 170)\n",
        "     Auxiliary coordinates:\n",
        "          latitude                      x        -\n",
        "          longitude                     x        -\n",
        "          time                          x        -\n",
        "          depth                         -        x\n",
        "     Attributes:\n",
        "          Conventions: CF-1.6\n",
        "          DODS.dimName: ctdProfile\n",
        "          DODS.strlen: 36\n",
        "          QC_indicator: 0.0\n",
        "          anomaly: none\n",
        "          cdm_data_type: TrajectoryProfile\n",
        "          contact: http://www.cmre.nato.int/\n",
        "          cruise: MEDREP14\n",
        "          data_classification: NATO Unclassified\n",
        "          data_mode: R\n",
        "          data_type: Glider profiles data\n",
        "          date_update: 2014-06-23T16:45:18Z\n",
        "          disclaimer: The processing software that generated this dataset is under development....\n",
        "          distribution_statement: NATO UNCLASSIFIED releasable to project partner only\n",
        "          featureType: TrajectoryProfile\n",
        "          format_version: 0.99\n",
        "          geospatial_lat_max: 39.9588181424\n",
        "          geospatial_lat_min: 39.8547639286\n",
        "          geospatial_lon_max: 7.44207619048\n",
        "          geospatial_lon_min: 7.30733570387\n",
        "          geospatial_vertical_max: 173.0\n",
        "          geospatial_vertical_min: 0.0\n",
        "          history: 2014-06-23 16:45:18: File created\n",
        "          institution: STO-CMRE\n",
        "          institution_reference: http://www.cmre.nato.int/\n",
        "          launch_date: 09-Jun-2014 10:00:00\n",
        "          launch_latitude: 39.9537735847\n",
        "          launch_longitude: 7.34393181681\n",
        "          launch_qc: 0.0\n",
        "          mission: depl005\n",
        "          netcdf_version: 3.6\n",
        "          pi_name: R. Onken\n",
        "          platform_code: elettra\n",
        "          platform_maker: Teledyne Webb Research Corporation\n",
        "          platform_model: WEBB SLOCUM\n",
        "          positioning_system: GPS\n",
        "          processing: real time\n",
        "          product_description: This file contains a glider data product derived from processed data. Trajectory...\n",
        "          project_name: MEDREP14\n",
        "          provider: STO-CMRE (NATO Science & Technology Organization - Centre for Maritime...\n",
        "          qcCodes: 0: no quality control performed on that value; 1: good value (GPS fix);...\n",
        "          qc_manual: http://www.groom.org/data/quality_control_manual.pdf\n",
        "          quality_index: 0.0\n",
        "          references: http://geos3.cmre.nato.int/REP14\n",
        "          sensors_ctd_manufacturer: Seabird\n",
        "          sensors_ctd_model: Slocum Glider CTD\n",
        "          sensors_ctd_name: pumpedCTD\n",
        "          sensors_ctd_serial_number: 48.0\n",
        "          source: Glider observation\n",
        "          summary: MEDREP14 glider data\n",
        "          time_coverage_end: 2014-06-23T09:55:13Z\n",
        "          time_coverage_start: 2014-06-21T09:28:56Z\n",
        "          title: MEDREP14\n",
        "          trans_system: IRIDIUM\n",
        "          type: Glider data file\n",
        "          update_interval: hourly\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "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": []
    },
    {
     "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",
      "    data = ma.masked_invalid(data,copy=True)\n",
      "    z = ma.masked_invalid(z,copy=True)\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",
      "    fig, ax = plt.subplots(figsize=(9, 3.75))\n",
      "    cs = ax.pcolor(dist, z, data, cmap=cmap, snap=True, **kw)\n",
      "    plt.colorbar(cs)\n",
      "    if mask_topo:\n",
      "        h = z.max(axis=1)\n",
      "        x = dist[:, 0]\n",
      "        ax.plot(x, h, color='black', linewidth='0.5', zorder=3)\n",
      "        ax.fill_between(x, h, y2=h.max(), color='0.9', zorder=3)\n",
      "    ax.invert_yaxis()\n",
      "    ax.set_title('Glider track from {} to {}'.format(t[0], t[-1]))\n",
      "    fig.tight_layout()\n",
      "    return fig, ax, cs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c = cube[:,:]\n",
      "fig, ax, cs = plot_glider(c, mask_topo=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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iKVQM58qYE3kz4vAeqz/WKF678bG4BsoFKMdxYnQSx89PKa2Qj3Rue8pc2P7Q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\nEEIIISry/ybSXoQjzHfrAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0c53cbbf90>"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}