erddap-python 0.0.6b0

Python erddap API client

ERDDAP python library

About

erddap-python is a python API implementation for the ERDDAP server.

ERDDAP is a data server that gives you a simple, consistent way to download subsets of gridded and tabular scientific datasets in common file formats and make graphs and maps.

Full API reference can bue found here.

Requirements

• python 3
• python libraries numpy, pandas, xarray, netCDF4

Installation

Using pip:

$ pip install erddap-python

Usage

Explore a ERDDAP Server

Connect to a ERDDAP Server

>>> from erddapClient import ERDDAP_Server
>>> 
>>> remoteServer = ERDDAP_Server('https://coastwatch.pfeg.noaa.gov/erddap')
>>> remoteServer
<erddapClient.ERDDAP_Server>
Server version:  ERDDAP_version=2.11

search and advancedSerch methods are available, builds the search request URL and also can make the request to the ERDDAP restful services to obtain results. Usage:

>>> searchRequest = remoteServer.advancedSearch(searchFor="gliders")
>>> searchRequest
<erddapClient.ERDDAP_SearchResults>
Results:  1
[
  0 - <erddapClient.ERDDAP_Tabledap> scrippsGliders , "Gliders, Scripps Institution of Oceanography, 2014-present"
]

The methods returns an object with a list of the ERDDAP Tabledap or Griddap objects that matched the search filters.

Tabledap datasets

Using the Tabledap object to build ERDDAP data request URL's

>>> from erddapClient import ERDDAP_Tabledap
>>> 
>>> url = 'https://coastwatch.pfeg.noaa.gov/erddap'
>>> datasetid = 'cwwcNDBCMet'
>>> remote = ERDDAP_Tabledap(url, datasetid)
>>> 
>>> remote.setResultVariables(['station','time','atmp'])
>>> print (remote.getURL('htmlTable'))

'https://coastwatch.pfeg.noaa.gov/erddap/tabledap/cwwcNDBCMet.htmlTable?station%2Ctime%2Catmp'

You can continue adding constraints and server side operations to the request.

>>> import datetime as dt 
>>> 
>>> remote.addConstraint('time>=2020-12-29T00:00:00Z') \
          .addConstraint({ 'time<=' : dt.datetime(2020,12,31) })
>>> remote.getURL()

'https://coastwatch.pfeg.noaa.gov/erddap/tabledap/cwwcNDBCMet.csvp?station%2Ctime%2Catmp&time%3E=2020-12-29T00%3A00%3A00Z&time%3C=2020-12-31T00%3A00%3A00Z'

>>>
>>> remote.orderByClosest(['station','time/1day'])
>>> remote.getURL()

'https://coastwatch.pfeg.noaa.gov/erddap/tabledap/cwwcNDBCMet.csvp?station%2Ctime%2Catmp&time%3E=2020-12-29T00%3A00%3A00Z&time%3C=2020-12-31T00%3A00%3A00Z&orderByClosest(%22station%2Ctime/1day%22)'

>>> 

You can continue adding constraints, server side operations or the distinct operation to the url generation. The class has object has methods to clear the result variables, the constraints, and the server side operations in the stack: clearConstraints(), clearResultVariable(), clearServerSideFunctions or clearQuery()

To request the data:

>>>
>>> remote.clearQuery()
>>>
>>> responseCSV = (
>>>     remote.setResultVariables(['station','time','atmp'])
>>>           .addConstraint('time>=2020-12-29T00:00:00Z')
>>>           .addConstraint('time<=2020-12-31T00:00:00Z')
>>>           .orderByClosest(['station','time/1day'])
>>>           .getData('csvp')
>>> )
>>> 
>>> print(responseCSV)

station,time (UTC),atmp (degree_C)
41001,2020-12-29T00:00:00Z,17.3
41001,2020-12-30T00:00:00Z,13.7
41001,2020-12-31T00:00:00Z,15.9
41004,2020-12-29T00:10:00Z,18.1
41004,2020-12-30T00:00:00Z,17.1
41004,2020-12-31T00:00:00Z,21.2
41008,2020-12-29T00:50:00Z,14.8
...
.

>>>
>>> remote.clearQuery()
>>>
>>> responsePandas = (
>>>     remote.setResultVariables(['station','time','atmp'])
>>>           .addConstraint('time>=2020-12-29T00:00:00Z')
>>>           .addConstraint('time<=2020-12-31T00:00:00Z')
>>>           .orderByClosest(['station','time/1day'])
>>>           .getDataFrame()
>>> )
>>>
>>> responsePandas

     station            time (UTC)  atmp (degree_C)
0      41001  2020-12-29T00:00:00Z             17.3
1      41001  2020-12-30T00:00:00Z             13.7
2      41001  2020-12-31T00:00:00Z             15.9
3      41004  2020-12-29T00:00:00Z             18.2
4      41004  2020-12-30T00:00:00Z             17.1
...      ...                   ...              ...
2006   YKRV2  2020-12-30T00:00:00Z              NaN
2007   YKRV2  2020-12-31T00:00:00Z              8.1
2008   YKTV2  2020-12-29T00:00:00Z             11.3
2009   YKTV2  2020-12-30T00:00:00Z              NaN
2010   YKTV2  2020-12-31T00:00:00Z              7.1

[2011 rows x 3 columns]

Griddap datasets

All the url building functions, and data request functionality is available in the ERDDAP_Griddap class, plus the posibility to get the xarray object from the opendap endpoint provided by ERDDAP.

Usage sample:

>>> from erddapClient import ERDDAP_Griddap
>>> 
>>> url = 'https://coastwatch.pfeg.noaa.gov/erddap'
>>> datasetid = 'hycom_gom310D'
>>> remote = ERDDAP_Griddap(url, datasetid)
>>> 
>>> print(remote)

<erddapClient.ERDDAP_Griddap>
Title:       NRL HYCOM 1/25 deg model output, Gulf of Mexico, 10.04 Expt 31.0, 2009-2014, At Depths
Server URL:  https://coastwatch.pfeg.noaa.gov/erddap
Dataset ID:  hycom_gom310D
Dimensions: 
  time (double) range=(cftime.DatetimeGregorian(2009, 4, 2, 0, 0, 0, 0), cftime.DatetimeGregorian(2014, 8, 30, 0, 0, 0, 0)) 
    Standard name: time 
    Units:         seconds since 1970-01-01T00:00:00Z 
  depth (float) range=(0.0, 5500.0) 
    Standard name: depth 
    Units:         m 
  latitude (float) range=(18.09165, 31.96065) 
    Standard name: latitude 
    Units:         degrees_north 
  longitude (float) range=(-98.0, -76.40002) 
    Standard name: longitude 
    Units:         degrees_east 
Variables: 
  temperature (float) 
    Standard name: sea_water_potential_temperature 
    Units:         degC 
  salinity (float) 
    Standard name: sea_water_practical_salinity 
    Units:         psu 
  u (float) 
    Standard name: eastward_sea_water_velocity 
    Units:         m/s 
  v (float) 
    Standard name: northward_sea_water_velocity 
    Units:         m/s 
  w_velocity (float) 
    Standard name: upward_sea_water_velocity 
    Units:         m/s 

>>> # Get more information about dimensions
>>> from pprint import pprint
>>> pprint(remote.dimensions)

{'depth': {'_CoordinateAxisType': 'Height',
           '_CoordinateZisPositive': 'down',
           '_averageSpacing': 141.02564102564102,
           '_dataType': 'float',
           '_evenlySpaced': False,
           '_nValues': 40,
           'actual_range': (0.0, 5500.0),
           'axis': 'Z',
           'ioos_category': 'Location',
           'long_name': 'Depth',
           'positive': 'down',
           'standard_name': 'depth',
           'units': 'm'},
 'latitude': {'_CoordinateAxisType': 'Lat',
              '_averageSpacing': 0.0361171875,
              '_dataType': 'float',
              '_evenlySpaced': False,
              '_nValues': 385,
              'actual_range': (18.09165, 31.96065),
              'axis': 'Y',
              'ioos_category': 'Location',
              'long_name': 'Latitude',
              'standard_name': 'latitude',
              'units': 'degrees_north'},
 'longitude': {'_CoordinateAxisType': 'Lon',
               '_averageSpacing': 0.039999962962962966,
               '_dataType': 'float',
               '_evenlySpaced': True,
               '_nValues': 541,
               'actual_range': (-98.0, -76.40002),
               'axis': 'X',
               'ioos_category': 'Location',
               'long_name': 'Longitude',
               'standard_name': 'longitude',
               'units': 'degrees_east'},
 'time': {'_CoordinateAxisType': 'Time',
          '_averageSpacing': '1 day',
          '_dataType': 'double',
          '_evenlySpaced': True,
          '_nValues': 1977,
          'actual_range': (cftime.DatetimeGregorian(2009, 4, 2, 0, 0, 0, 0),
                           cftime.DatetimeGregorian(2014, 8, 30, 0, 0, 0, 0)),
          'axis': 'T',
          'calendar': 'standard',
          'ioos_category': 'Time',
          'long_name': 'Time',
          'standard_name': 'time',
          'time_origin': '01-JAN-1970 00:00:00',
          'units': 'seconds since 1970-01-01T00:00:00Z'}}

>>> # Request a subset
>>>
>>> remote.clearQuery()
>>> subset = ( remote.setResultVariables(["temperature[0:last][(0.0)][(22.5)][(-95.5)]",
                                           "salinity[0:last][(0.0)][(22.5)][(-95.5)]"])
                     .getDataFrame(header=0, 
                                   names=["time", "depth", "latitude", "longitude", "temperature", "salinity"], 
                                   parse_dates=["time"],
                                   ndex_col="time")  )
>>>
>>> subset.head()                              

                           depth  latitude  longitude  temperature   salinity
time                                                                         
2009-04-02 00:00:00+00:00    0.0  22.51696  -95.47998    24.801798  36.167076
2009-04-03 00:00:00+00:00    0.0  22.51696  -95.47998    24.605570  36.256450
2009-04-04 00:00:00+00:00    0.0  22.51696  -95.47998    24.477884  36.086346
2009-04-05 00:00:00+00:00    0.0  22.51696  -95.47998    24.552357  36.133224
2009-04-06 00:00:00+00:00    0.0  22.51696  -95.47998    25.761946  36.179676
...                          ...       ...        ...          ...        ...
2014-08-26 00:00:00+00:00    0.0  22.51696  -95.47998    30.277546  36.440037
2014-08-27 00:00:00+00:00    0.0  22.51696  -95.47998    30.258907  36.485844
2014-08-28 00:00:00+00:00    0.0  22.51696  -95.47998    30.298597  36.507530
2014-08-29 00:00:00+00:00    0.0  22.51696  -95.47998    30.246874  36.493400
2014-08-30 00:00:00+00:00    0.0  22.51696  -95.47998    30.387840  36.487934

[1977 rows x 5 columns]

>>> # Get an xarray object
>>> remote.xarray

<xarray.Dataset>
Dimensions:            (latitude: 2759, longitude: 4205, time: 79521)
Coordinates:
  * time               (time) datetime64[ns] 2012-01-01 ... 2021-02-02T07:00:00
  * latitude           (latitude) float32 21.7 21.71 21.72 ... 46.48 46.49 46.49
  * longitude          (longitude) float32 -97.88 -97.87 -97.86 ... -57.2 -57.19
Data variables:
    water_u            (time, latitude, longitude) float32 ...
    water_v            (time, latitude, longitude) float32 ...
    DOPx               (time, latitude, longitude) float32 ...
    DOPy               (time, latitude, longitude) float32 ...
    hdop               (time, latitude, longitude) float32 ...
    number_of_sites    (time, latitude, longitude) float32 ...
    number_of_radials  (time, latitude, longitude) float32 ...
Attributes:
    _CoordSysBuilder:           ucar.nc2.dataset.conv.CF1Convention
    cdm_data_type:              Grid
    Conventions:                COARDS, CF-1.6, ACDD-1.3
    ..
    .

Sample notebooks

Check the demostration notebooks folder for more advanced usage of the library classes.