Quick introduction

gliderpy can be installed with conda

conda install --channel conda-forge gliderpy

or pip

pip install gliderpy

gliderpy aims to make querying and downloading glider data easier. Here is how one would build a query using erddapy:

[1]:

from erddapy import ERDDAP

e = ERDDAP(
    server="https://gliders.ioos.us/erddap",
    protocol="tabledap",
    response="csv",
)
e.dataset_id = "whoi_406-20160902T1700"

e.variables = [
    "depth",
    "latitude",
    "longitude",
    "salinity",
    "temperature",
    "profile_id",
    "time",
]

df = e.to_pandas(
    index_col="time (UTC)",
    parse_dates=True,
)
df.head()

[1]:
depth (m) latitude (degrees_north) longitude (degrees_east) salinity (1) temperature (Celsius) profile_id
time (UTC)
2016-09-02 17:05:38+00:00 6.43 41.120018 -71.017118 32.079258 20.5407 1
2016-09-02 17:05:38+00:00 7.82 41.120018 -71.017118 32.095406 20.5071 1
2016-09-02 17:05:38+00:00 8.69 41.120018 -71.017118 32.111920 20.4904 1
2016-09-02 17:05:38+00:00 9.54 41.120018 -71.017118 32.126587 20.4897 1
2016-09-02 17:05:38+00:00 10.32 41.120018 -71.017118 32.140717 20.4909 1

And here is how to use gliderpy to obtain the same results but with fewer lines and a cleaner code:

[2]:

from gliderpy.fetchers import GliderDataFetcher

glider_grab = GliderDataFetcher()

glider_grab.fetcher.dataset_id = "whoi_406-20160902T1700"
df = glider_grab.to_pandas()
df.head()

[2]:
latitude longitude pressure profile_id salinity temperature dataset_url
time (utc)
2016-09-02 17:05:38 41.120018 -71.017118 6.43 1 32.079258 20.5407 https://gliders.ioos.us/erddap/tabledap/whoi_4...
2016-09-02 17:05:38 41.120018 -71.017118 7.82 1 32.095406 20.5071 https://gliders.ioos.us/erddap/tabledap/whoi_4...
2016-09-02 17:05:38 41.120018 -71.017118 8.69 1 32.111920 20.4904 https://gliders.ioos.us/erddap/tabledap/whoi_4...
2016-09-02 17:05:38 41.120018 -71.017118 9.54 1 32.126587 20.4897 https://gliders.ioos.us/erddap/tabledap/whoi_4...
2016-09-02 17:05:38 41.120018 -71.017118 10.32 1 32.140717 20.4909 https://gliders.ioos.us/erddap/tabledap/whoi_4...

Much easier, right? The variable names are standardized by gliderpy, making it easier to fetch from different data sources and comparing the results.

The gliderpy library can subset the data on the server side by passing a geographic bounding box and time interval.

Querying multiple datasets

The most common use is to search all datasets for data that falls within the certain space-time bounds.

[3]:

glider_grab = GliderDataFetcher()

df = glider_grab.query(
    min_lat=10,
    max_lat=40,
    min_lon=-90,
    max_lon=8,
    min_time="2010-01-01",
    max_time="2013-06-02",
)
df

[3]:
Title Institution Dataset ID info_url
0 ru23-20130305T2004 Rutgers University ru23-20130305T2004 https://gliders.ioos.us/erddap/info/ru23-20130...
1 ru23-20121025T1944 Rutgers University ru23-20121025T1944 https://gliders.ioos.us/erddap/info/ru23-20121... 
[4]:

datasets = glider_grab.to_pandas()
datasets.keys()

[4]:

dict_keys(['ru23-20130305T2004', 'ru23-20121025T1944'])

[5]:

datasets["ru23-20121025T1944"].head()

[5]:
latitude longitude pressure profile_id salinity (1e-3) temperature dataset_url
time (utc)
2012-10-27 18:20:25 39.999821 -73.510566 1.67 289 31.973030 16.9787 https://gliders.ioos.us/erddap/tabledap/ru23-2...
2012-10-27 18:20:25 39.999821 -73.510566 1.68 289 31.973806 16.9775 https://gliders.ioos.us/erddap/tabledap/ru23-2...
2012-10-27 18:20:25 39.999821 -73.510566 NaN 289 NaN NaN https://gliders.ioos.us/erddap/tabledap/ru23-2...
2012-10-27 18:20:25 39.999821 -73.510566 1.82 289 31.973087 16.9778 https://gliders.ioos.us/erddap/tabledap/ru23-2...
2012-10-27 18:20:25 39.999821 -73.510566 NaN 289 NaN NaN https://gliders.ioos.us/erddap/tabledap/ru23-2...