In [1]:
%matplotlib inline

In [2]:
import pandas as pd

In [3]:
import xray

In [4]:
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'

In [5]:
ds = xray.open_dataset(url)

In [6]:
ds


Out[6]:
<xray.Dataset>
Dimensions:                          (computed_provenance_dim: 1, instrument_provenance_dim: 1, l0_provenance: 41, obs: 34612, query_parameter_provenance_dim: 1)
Coordinates:
    time                             (obs) datetime64[ns] ...
    int_ctd_pressure                 (obs) float64 ...
    lon                              (obs) float64 ...
    lat                              (obs) float64 ...
  * computed_provenance_dim          (computed_provenance_dim) int64 0
  * instrument_provenance_dim        (instrument_provenance_dim) int64 0
  * l0_provenance                    (l0_provenance) int64 0 1 2 3 4 5 6 7 8 ...
  * obs                              (obs) int64 0 1 2 3 4 5 6 7 8 9 10 11 ...
  * query_parameter_provenance_dim   (query_parameter_provenance_dim) int64 0
Data variables:
    query_parameter_provenance       (query_parameter_provenance_dim) |S64 ...
    computed_provenance              (computed_provenance_dim) |S64 ...
    l0_provenance_keys               (l0_provenance) |S64 ...
    l0_provenance_data               (l0_provenance) |S64 ...
    instrument_provenance            (instrument_provenance_dim) |S64 ...
    port_timestamp                   (obs) datetime64[ns] ...
    driver_timestamp                 (obs) datetime64[ns] ...
    internal_timestamp               (obs) datetime64[ns] ...
    preferred_timestamp              (obs) object ...
    deployment                       (obs) int32 ...
    flort_m_bback_total              (obs) float32 ...
    flort_m_scat_seawater            (obs) float32 ...
    provenance                       (obs) |S64 ...
    ingestion_timestamp              (obs) datetime64[ns] ...
    m_present_secs_into_mission      (obs) float64 ...
    m_present_time                   (obs) datetime64[ns] ...
    id                               (obs) |S64 ...
    sci_flbbcd_bb_ref                (obs) float64 ...
    sci_flbbcd_bb_sig                (obs) float64 ...
    sci_flbbcd_bb_units              (obs) float64 ...
    sci_flbbcd_cdom_ref              (obs) float64 ...
    sci_flbbcd_cdom_sig              (obs) float64 ...
    sci_flbbcd_cdom_units            (obs) float64 ...
    sci_flbbcd_chlor_ref             (obs) float64 ...
    sci_flbbcd_chlor_sig             (obs) float64 ...
    sci_flbbcd_chlor_units           (obs) float64 ...
    sci_flbbcd_therm                 (obs) float64 ...
    sci_flbbcd_timestamp             (obs) datetime64[ns] ...
    sci_m_present_secs_into_mission  (obs) float64 ...
    sci_m_present_time               (obs) datetime64[ns] ...
    l0_provenance_information        (obs) |S64 ...
Attributes:
    node: GL388
    comment: 
    publisher_email: 
    sourceUrl: http://oceanobservatories.org/
    collection_method: recovered_host
    stream: flort_m_glider_recovered
    featureType: point
    creator_email: 
    publisher_name: Ocean Observatories Initiative
    date_modified: 2015-12-28T20:04:06.185702
    date_created: 2015-12-28T20:04:06.185694
    keywords: 
    cdm_data_type: Point
    references: More information can be found at http://oceanobservatories.org/
    Metadata_Conventions: Unidata Dataset Discovery v1.0
    deployment: 1
    id: CP05MOAS-GL388-02-FLORTM000-recovered_host-flort_m_glider_recovered
    requestUUID: 3b34d6d1-3d1d-4f60-955d-8579d147392c
    contributor_role: 
    summary: Dataset Generated by Stream Engine from Ocean Observatories Initiative
    keywords_vocabulary: 
    institution: Ocean Observatories Initiative
    naming_authority: org.oceanobservatories
    feature_Type: point
    infoUrl: http://oceanobservatories.org/
    license: 
    contributor_name: 
    uuid: 3b34d6d1-3d1d-4f60-955d-8579d147392c
    creator_name: Ocean Observatories Initiative
    title: Data produced by Stream Engine version 0.8.4 for CP05MOAS-GL388-02-FLORTM000-recovered_host-flort_m_glider_recovered
    sensor: 02-FLORTM000
    standard_name_vocabulary: NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table 29
    acknowledgement: 
    Conventions: CF-1.6
    project: Ocean Observatories Initiative
    source: CP05MOAS-GL388-02-FLORTM000-recovered_host-flort_m_glider_recovered
    publisher_url: http://oceanobservatories.org/
    creator_url: http://oceanobservatories.org/
    nodc_template_version: NODC_NetCDF_TimeSeries_Orthogonal_Template_v1.1
    subsite: CP05MOAS
    processing_level: L2
    history: 2015-12-28T20:04:06.185586 generated from Stream Engine
    time_coverage_start: 2014-12-07T21:32:58.344210
    time_coverage_end: 2014-12-08T19:06:52.651550
    time_coverage_resolution: P2.24S
    location_name: None
    geospatial_lat_min: -88.0465462022
    geospatial_lat_max: 39.8952888751
    geospatial_lon_min: -198.801452897
    geospatial_lon_max: -70.8965500002
    geospatial_lat_units: degrees_north
    geospatial_lat_resolution: 0.1
    geospatial_lon_units: degrees_east
    geospatial_lon_resolution: 0.1
    geospatial_vertical_units: m
    geospatial_vertical_resolution: 0.1
    geospatial_vertical_positive: down
    DODS.strlen: 151
    DODS.dimName: string151

In [7]:
#Create a Pandas time series

In [8]:
vts = pd.Series(ds['flort_m_bback_total'].values,index=ds['time'])

In [9]:
vts_1h = vts.resample('1min', how='mean')
vts_1h.plot(figsize=(12,4))


Out[9]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f9dea84bd90>

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