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https://github.com/hmedrano/erddap-python

ERDDAP Client for python
https://github.com/hmedrano/erddap-python
erddap library pip
Last synced: 3 months ago
JSON representation
ERDDAP Client for python
Host: GitHub
URL: https://github.com/hmedrano/erddap-python
Owner: hmedrano
License: mit
Created: 2021-01-18T10:42:47.000Z (almost 4 years ago)
Default Branch: main
Last Pushed: 2023-08-26T09:47:10.000Z (about 1 year ago)
Last Synced: 2024-06-11T17:27:27.560Z (5 months ago)
Topics: erddap, library, pip
Language: Jupyter Notebook
Homepage:
Size: 4.37 MB
Stars: 13
Watchers: 1
Forks: 2
Open Issues: 2
Metadata Files:
- Readme: README.md
- Changelog: HISTORY.md
- License: LICENSE.txt
Awesome Lists containing this project

awesome-erddap - erddap-python - Python library for the ERDDAP Restful API, that can obtain status metrics, provides search methods, and it gives tabledap and griddap class objects for metadata and data access. (ERDDAP Clients)
README

        # ERDDAP python library 

[![Anaconda-Server Badge](https://anaconda.org/conda-forge/erddap-python/badges/version.svg)](https://anaconda.org/conda-forge/erddap-python)

[![image](https://img.shields.io/pypi/v/erddap-python.svg)](https://pypi.python.org/pypi/erddap-python)

[![image](https://pepy.tech/badge/erddap-python)](https://pepy.tech/project/erddap-python)

[![Build Status](https://travis-ci.com/hmedrano/erddap-python.svg?branch=main)](https://travis-ci.com/hmedrano/erddap-python)

[![image](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## About

[ERDDAP](https://coastwatch.pfeg.noaa.gov/erddap/information.html) 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. 

erddap-python is a python client for the ERDDAP Restful API, it can obtain server status metrics, provides search methods, gives tabledap and griddap class objects for metadata and data access.

This library was initially built for [CICESE](https://cicese.edu.mx), [CIGOM](https://cigom.org), [OORCO](https://oorco.org), and [CEMIEOceano](https://cemieoceano.mx/) projects for the automation of reports, interactive custom visualizations and data analysis.  Most of the functionality was inspired on the work of [erddapy](https://github.com/ioos/erddapy) library, but designed more for a more flexible backend service construction in mind.

Full API reference can be found [here](https://hmedrano.github.io/erddap-python/).

## Projects using erddap-python

 - [ERDDAP server's status metrics dashboard using Streamlit](https://share.streamlit.io/hmedrano/erddap-status-dashboard/main/dashboard_streamlit_app.py)

 - [Module for Ocean Observatory Data Analysis library](https://github.com/rbardaji/mooda)

## Requirements

 - python 3

 - python libraries numpy, pandas, xarray, netCDF4

## Installation

Using pip:

```

$ pip install erddap-python

```

Also you can use `conda` package manager, from the `conda-forge` channel:

```

$ conda install -c conda-forge erddap-python

```

## Usage

### Explore a ERDDAP Server

Connect to a ERDDAP Server, and get results from a basic search.

```python

>>> from erddapClient import ERDDAP_Server

>>> 

>>> remoteServer = ERDDAP_Server('https://coastwatch.pfeg.noaa.gov/erddap')

>>> remoteServer

Server version:  ERDDAP_version=2.11

```

[search](https://hmedrano.github.io/erddap-python/#ERDDAP_Server.search) and [advancedSerch](https://hmedrano.github.io/erddap-python/#ERDDAP_Server.advancedSearch) methods are available, it builds the search request URL and also can 

make the request to the ERDDAP restful services to obtain results. 

```python

>>> searchRequest = remoteServer.search(searchFor="gliders")

>>> searchRequest

Results:  1

[

  0 -  scrippsGliders , "Gliders, Scripps Institution of Oceanography, 2014-present"

]

```

The methods returns an object with a list of the [ERDDAP_Tabledap](https://hmedrano.github.io/erddap-python/#ERDDAP_Tabledap) or [ERDDAP_Griddap](https://hmedrano.github.io/erddap-python/#ERDDAP_Griddap) objects that matched the search criteria.

### Connect to Tabledap datasets 

Using the [ERDDAP_Tabledap](https://hmedrano.github.io/erddap-python/#ERDDAP_Tabledap) class you can construct ERDDAP data request URL's

```python

>>> from erddapClient import ERDDAP_Tabledap

>>> 

>>> remote = ERDDAP_Tabledap('https://coastwatch.pfeg.noaa.gov/erddap', 'cwwcNDBCMet')

>>> 

>>> remote.setResultVariables(['station','time','atmp'])

>>> print (remote.getURL('htmlTable'))

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

```

The tabledap object internally stores a stack for the result variables, constrainsts and server side operations. You 

can keep adding them and get the different urls.

```python

>>> 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)'

>>> 

```

The class has methods to clear the result variables, the constraints, and the server side operations that are added in the stack: [clearConstraints()](https://hmedrano.github.io/erddap-python/#ERDDAP_Dataset.clearConstraints), [clearResultVariable()](https://hmedrano.github.io/erddap-python/#ERDDAP_Dataset.clearResultVariables), [clearServerSideFunctions()](https://hmedrano.github.io/erddap-python/#ERDDAP_Dataset.clearServerSideFunctions) or [clearQuery()](https://hmedrano.github.io/erddap-python/#ERDDAP_Dataset.clearQuery).

#### Tabledap data subset request

An user can build the data request query by chaining the result variables, constraints and server side adding methods.  And at the end you can make the data request in all the available formats that ERDDAP provides (csv, mat, json, nc, etc).

```python

>>>

>>> 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](https://hmedrano.github.io/erddap-python/#ERDDAP_Griddap) class. 

With this class you can download data subsets in all the available ERDDAP data formats, plus the posibility to request a fully described xarray.DataArrays objects.

This class can parse the griddap query, and detect if the query is malformed before requesting data from the 

ERDDAP server.

Usage sample:

```python

>>> from erddapClient import ERDDAP_Griddap

>>> 

>>> remote = ERDDAP_Griddap('https://coastwatch.pfeg.noaa.gov/erddap', 'hycom_gom310D')

>>> 

>>> print(remote)

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 

```

Right after creating the griddap object you can explore the dimensions information.

```python

>>> print(remote.dimensions)

Dimensions:

 - time (nValues=1977) 1238630400 .. 1409356800

 - depth (nValues=40) 0.0 .. 5500.0

 - latitude (nValues=385) 18.091648 .. 31.960648

 - longitude (nValues=541) -98.0 .. -76.400024

>>> print(remote.dimensions['time'])

Dimension: time

  _nValues : 1977

  _evenlySpaced : True

  _averageSpacing : 1 day

  _dataType : double

  _CoordinateAxisType : Time

  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

```

#### Griddap data request in a xarray.DataArray

Request a data subset and store it in a fully described xarray.DataArray object.

```python

>>> xSubset = ( remote.setResultVariables('temperature')

..:                   .setSubset(time="2012-01-13",

..:                              depth=slice(0,2000),

..:                              latitude=slice(18.09165, 31.96065),

..:                              longitude=slice(-98.0,-76.40002))

..:                   .getxArray() )

>>> xSubset

Dimensions:      (depth: 33, latitude: 385, longitude: 541, time: 1)

Coordinates:

  * time         (time) object 2012-01-13 00:00:00

  * depth        (depth) float64 0.0 5.0 10.0 15.0 ... 1.5e+03 1.75e+03 2e+03

  * latitude     (latitude) float64 18.09 18.13 18.17 ... 31.89 31.93 31.96

  * longitude    (longitude) float64 -98.0 -97.96 -97.92 ... -76.48 -76.44 -76.4

Data variables:

    temperature  (time, depth, latitude, longitude) float32 ...

Attributes: (12/32)

    cdm_data_type:              Grid

    Conventions:                COARDS, CF-1.0, ACDD-1.3

    creator_email:              [email protected]

    creator_name:               Naval Research Laboratory

    creator_type:               institution

    creator_url:                https://www.hycom.org

    ...                         ...

    standard_name_vocabulary:   CF Standard Name Table v70

    summary:                    NRL HYCOM 1/25 deg model output, Gulf of Mexi...

    time_coverage_end:          2014-08-30T00:00:00Z

    time_coverage_start:        2009-04-02T00:00:00Z

    title:                      NRL HYCOM 1/25 deg model output, Gulf of Mexi...

    Westernmost_Easting:        -98.0

```

The above data request can also be done using the ERDDAP opendap extended query format, by example :

```python 

>>> xSubset = ( remote.setResultVariables('temperature[(2012-01-13)][(0):(2000)][(18.09165):(31.96065)][(-98.0):(-76.40002)]')

..:                   .getxArray()            

```

#### Make request for subsets in different formats.

Request a location timeseires and store it in a pandas dataframe, using the [getDataFrame](https://hmedrano.github.io/erddap-python/#ERDDAP_Dataset.getDataFrame) method.

```python

>>> # 

>>>

>>> remote.clearQuery()

>>> dfSubset = ( remote.setResultVariables(['temperature','salinity'])

..:                    .setSubset(time=slice("2009-04-02","2014-8-30"),

..:                               depth=0,

..:                               latitude=22.5,

..:                               longitude=-95.5)

..:                    .getDataFrame(header=0,

..:                                  names=['time','depth','latitude','longitude', 'temperature', 'salinity'],

..:                                  parse_dates=['time'],

..:                                  index_col='time') )

>>> dfSubset

                           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]

>>> 

```

----

## Sample notebooks

Check the demostration [notebooks folder](https://github.com/hmedrano/erddap-python/tree/main/notebooks) for more usage examples of the library classes.