ooi_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import xray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "url = 'http://opendap-devel.ooi.rutgers.edu:8090/thredds/dodsC/eov-1/Coastal_Pioneer/CP05MOAS/02-FLORTM000/recovered_host/CP05MOAS-GL388-02-FLORTM000-flort_m_glider_recovered-recovered_host/CP05MOAS-GL388-02-FLORTM000-recovered_host-flort_m_glider_recovered-20141207T213258-20141208T190652.nc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ds = xray.open_dataset(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xray.Dataset>\n",
       "Dimensions:                          (computed_provenance_dim: 1, instrument_provenance_dim: 1, l0_provenance: 41, obs: 34612, query_parameter_provenance_dim: 1)\n",
       "Coordinates:\n",
       "    time                             (obs) datetime64[ns] ...\n",
       "    int_ctd_pressure                 (obs) float64 ...\n",
       "    lon                              (obs) float64 ...\n",
       "    lat                              (obs) float64 ...\n",
       "  * computed_provenance_dim          (computed_provenance_dim) int64 0\n",
       "  * instrument_provenance_dim        (instrument_provenance_dim) int64 0\n",
       "  * l0_provenance                    (l0_provenance) int64 0 1 2 3 4 5 6 7 8 ...\n",
       "  * obs                              (obs) int64 0 1 2 3 4 5 6 7 8 9 10 11 ...\n",
       "  * query_parameter_provenance_dim   (query_parameter_provenance_dim) int64 0\n",
       "Data variables:\n",
       "    query_parameter_provenance       (query_parameter_provenance_dim) |S64 ...\n",
       "    computed_provenance              (computed_provenance_dim) |S64 ...\n",
       "    l0_provenance_keys               (l0_provenance) |S64 ...\n",
       "    l0_provenance_data               (l0_provenance) |S64 ...\n",
       "    instrument_provenance            (instrument_provenance_dim) |S64 ...\n",
       "    port_timestamp                   (obs) datetime64[ns] ...\n",
       "    driver_timestamp                 (obs) datetime64[ns] ...\n",
       "    internal_timestamp               (obs) datetime64[ns] ...\n",
       "    preferred_timestamp              (obs) object ...\n",
       "    deployment                       (obs) int32 ...\n",
       "    flort_m_bback_total              (obs) float32 ...\n",
       "    flort_m_scat_seawater            (obs) float32 ...\n",
       "    provenance                       (obs) |S64 ...\n",
       "    ingestion_timestamp              (obs) datetime64[ns] ...\n",
       "    m_present_secs_into_mission      (obs) float64 ...\n",
       "    m_present_time                   (obs) datetime64[ns] ...\n",
       "    id                               (obs) |S64 ...\n",
       "    sci_flbbcd_bb_ref                (obs) float64 ...\n",
       "    sci_flbbcd_bb_sig                (obs) float64 ...\n",
       "    sci_flbbcd_bb_units              (obs) float64 ...\n",
       "    sci_flbbcd_cdom_ref              (obs) float64 ...\n",
       "    sci_flbbcd_cdom_sig              (obs) float64 ...\n",
       "    sci_flbbcd_cdom_units            (obs) float64 ...\n",
       "    sci_flbbcd_chlor_ref             (obs) float64 ...\n",
       "    sci_flbbcd_chlor_sig             (obs) float64 ...\n",
       "    sci_flbbcd_chlor_units           (obs) float64 ...\n",
       "    sci_flbbcd_therm                 (obs) float64 ...\n",
       "    sci_flbbcd_timestamp             (obs) datetime64[ns] ...\n",
       "    sci_m_present_secs_into_mission  (obs) float64 ...\n",
       "    sci_m_present_time               (obs) datetime64[ns] ...\n",
       "    l0_provenance_information        (obs) |S64 ...\n",
       "Attributes:\n",
       "    node: GL388\n",
       "    comment: \n",
       "    publisher_email: \n",
       "    sourceUrl: http://oceanobservatories.org/\n",
       "    collection_method: recovered_host\n",
       "    stream: flort_m_glider_recovered\n",
       "    featureType: point\n",
       "    creator_email: \n",
       "    publisher_name: Ocean Observatories Initiative\n",
       "    date_modified: 2015-12-28T20:04:06.185702\n",
       "    date_created: 2015-12-28T20:04:06.185694\n",
       "    keywords: \n",
       "    cdm_data_type: Point\n",
       "    references: More information can be found at http://oceanobservatories.org/\n",
       "    Metadata_Conventions: Unidata Dataset Discovery v1.0\n",
       "    deployment: 1\n",
       "    id: CP05MOAS-GL388-02-FLORTM000-recovered_host-flort_m_glider_recovered\n",
       "    requestUUID: 3b34d6d1-3d1d-4f60-955d-8579d147392c\n",
       "    contributor_role: \n",
       "    summary: Dataset Generated by Stream Engine from Ocean Observatories Initiative\n",
       "    keywords_vocabulary: \n",
       "    institution: Ocean Observatories Initiative\n",
       "    naming_authority: org.oceanobservatories\n",
       "    feature_Type: point\n",
       "    infoUrl: http://oceanobservatories.org/\n",
       "    license: \n",
       "    contributor_name: \n",
       "    uuid: 3b34d6d1-3d1d-4f60-955d-8579d147392c\n",
       "    creator_name: Ocean Observatories Initiative\n",
       "    title: Data produced by Stream Engine version 0.8.4 for CP05MOAS-GL388-02-FLORTM000-recovered_host-flort_m_glider_recovered\n",
       "    sensor: 02-FLORTM000\n",
       "    standard_name_vocabulary: NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table 29\n",
       "    acknowledgement: \n",
       "    Conventions: CF-1.6\n",
       "    project: Ocean Observatories Initiative\n",
       "    source: CP05MOAS-GL388-02-FLORTM000-recovered_host-flort_m_glider_recovered\n",
       "    publisher_url: http://oceanobservatories.org/\n",
       "    creator_url: http://oceanobservatories.org/\n",
       "    nodc_template_version: NODC_NetCDF_TimeSeries_Orthogonal_Template_v1.1\n",
       "    subsite: CP05MOAS\n",
       "    processing_level: L2\n",
       "    history: 2015-12-28T20:04:06.185586 generated from Stream Engine\n",
       "    time_coverage_start: 2014-12-07T21:32:58.344210\n",
       "    time_coverage_end: 2014-12-08T19:06:52.651550\n",
       "    time_coverage_resolution: P2.24S\n",
       "    location_name: None\n",
       "    geospatial_lat_min: -88.0465462022\n",
       "    geospatial_lat_max: 39.8952888751\n",
       "    geospatial_lon_min: -198.801452897\n",
       "    geospatial_lon_max: -70.8965500002\n",
       "    geospatial_lat_units: degrees_north\n",
       "    geospatial_lat_resolution: 0.1\n",
       "    geospatial_lon_units: degrees_east\n",
       "    geospatial_lon_resolution: 0.1\n",
       "    geospatial_vertical_units: m\n",
       "    geospatial_vertical_resolution: 0.1\n",
       "    geospatial_vertical_positive: down\n",
       "    DODS.strlen: 151\n",
       "    DODS.dimName: string151"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Create a Pandas time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vts = pd.Series(ds['flort_m_bback_total'].values,index=ds['time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9dea84bd90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aJWnGYA/IyTI/ap4XM/pxDOAApCIwI1XZwDxT0vp+1zcUtw22zaz+G5jZHEnzJN0+2AMS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\nABH/Dc5kMQn8NBuYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9deb16e750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vts_1h = vts.resample('1min', how='mean')\n",
    "vts_1h.plot(figsize=(12,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}