What’s New

Contents

What’s New#

release date PyPI Conda

pypi dwn conda dwn

Coming up next (unreleased)#

Features and front-end API#

Internals#

Documentation#

  • Define a policy regarding generative AI usage in argopy contributions #637. (#639) by K. Balem.

v1.4.0 (5 Jan. 2026)#

Features and front-end API#

  • Predict nutrients and carbonates in the global ocean with their uncertainties with the two new classes Dataset.argo.canyon_b and Dataset.argo.content. The first class allows users to make predictions of the water-column nutrient concentrations (NO3, PO4, SiOH4) and carbonate system variables (AT, DIC, pHT, pCO2) using the CANYON-B model (see doc.) while the second class provides improved predictions of carbonate system variables using the CONTENT (see doc.) model. (#535 and #542) by F. Ricour. It goes as simply as:

from argopy import DataFetcher
ArgoSet = DataFetcher(ds='bgc', params='DOXY', measured='DOXY').float(1902605)
ds = ArgoSet.to_xarray()
ds.argo.canyon_b.predict()
# or
ds.argo.content.predict()

Internals#

  • Add llms.txt generation, a file to provide information to help LLMs use Argopy documentation. See https://llmstxt.org for more.

  • Fix bug whereby an error was raise when interpolating on SDL a dataset loaded from an ArgoFloat instance. (#564) by G. Maze.

  • Fix bug whereby a scatter_plot would fail for a parameter with only N_PROF as dimensions. (#564) by G. Maze.

  • Fix bug whereby missing values in <PARAM>_QC variables of xarray dataset were casted as 0 instead of 9. (@d7bbc23) by G. Maze.

  • Fix upstream compatibility and imposes xarray >=2025.7 and <=2025.9.0. (#539).

  • Removed python 3.10 environments from ci/env_managers CLI utility. (#557) by G. Maze.

Breaking changes#

  • In the pandas.DataFrame output of the ArgoIndex.to_dataframe() method (#528) by G. Maze:

    • column institution_code is renamed institution, to preserve the original Argo index file column name,

    • column institution is renamed by institution_name, to make explicit this is an Argopy addition.

  • Deprecation warning for plot.scatter_plot() arguments this_x, this_y and param that are replaced by x, y and param. (#557) by G. Maze.

  • Internal refactoring, former argopy.utils.transform now argopy.utils.transformers, former argopy.utils.compute now argopy.utils.computers. (#557) by G. Maze.

Energy#

https://img.shields.io/badge/Total%20carbon%20emitted%20by%20release%20v1.4.0%20%5BgCO2eq%5D-1666-black?style=plastic&labelColor=grey

v1.3.1 (22 Oct. 2025)#

Features and front-end API#

Internals#

  • For mainteners, add a public CLI script cli/update_json_assets to update static assets. (#540) by G. Maze.

  • Update list of Reference tables with R14 on “Argo technical parameter names” and R31 on “Argo float ending cause”. The ArgoNVSReferenceTables internal list of valid reference tables is now taken from a static asset file generated with cli/update_json_assets on each new release. (#540) by G. Maze.

  • Fix bug whereby no static asset subfolders were included in the pypi and conda distribution, thus making the Dataset.argo.canyon_med predictor to raise an error when predictions were triggered #530. (#531) by G. Maze.

  • Fix bug whereby profiles with Real time only data mode data failed to pass Dataset.argo.datamode.merge() without raising an error, #517, #519. (#518) by G. Maze.

  • Updated User-Agent header field for HTTP requests. The default aiottp header field User-Agent is now complemented with Argopy information in order to ease server side log analysis. The User-Agent is now somethink like: "User-Agent": Python/3.11 aiohttp/3.12.14 Argopy/1.3.0 (+https://github.com/euroargodev/argopy), #533. (#534) by G. Maze.

  • New post method for the stores.httpstore allowing for HTTP POST methods to web-API, by G. Maze.

  • Fix upstream compatibility whereby xarray >= 2025.8 deprecation cycle for changing default keyword arguments in xarray.merge() and xarray.concat() would make Argopy to fail with internal data processing, #521. (#504) by G. Maze.

Energy#

https://img.shields.io/badge/Total%20carbon%20emitted%20by%20release%20v1.3.1%20%5BgCO2eq%5D-1186-black?style=plastic&labelColor=grey

v1.3.0 (22 Aug. 2025)#

Added in version v1.3.0: This new argopy version requires Python 3.11 !

Features and front-end API#

  • ArgoIndex plotting features: you can now use the new ArgoIndex.plot accessor to call on visualizations tool for trajectories or parameters bar plots. See all details on the documentation section about data visualization From ArgoIndex instance. (#504) by G. Maze.

  • ArgoFloat plotting features: you can now use the new ArgoFloat.plot accessor to call on visualizations tool for trajectories and parameters. See all details on the documentation section about data visualization From ArgoFloat instance. (#501) by G. Maze.

Internals#

  • Fix bug whereby depending on the new longitude convention option value, some floats trajectory would not show up on a map correctly. (#504) by G. Maze.

  • Refactoring of ArgoIndex and ArgoFloat to follow the same specs/facade/implementations/extensions design. (#503)by G. Maze.

  • Fix bug whereby the ArgoIndex could not select profiles through the dateline #494. (#495) by G. Maze. Modified by #506.

  • Fix bug whereby a gdacfs could not return one path info under Windows platform. #499. (#451) by G. Maze.

  • Identified incompatible versions of xarray. Because of this issue argopy is not compatible with xarray versions from 2024.3.0 to 2025.6.1, included. (#451) by G. Maze.

Breaking changes#

Energy#

https://img.shields.io/badge/Total%20carbon%20emitted%20by%20release%20v1.3.0%20%5BgCO2eq%5D-1258-black?style=plastic&labelColor=grey

v1.2.0 (9 June 2025)#

Features and front-end API#

  • Optical modeling diagnostics for BGC data. We introduce a preliminary implementation of standard diagnostics from optical modeling. These are available through the new Dataset.argo.optic extension. All details are in the dedicated Optical modeling documentation page and on the API reference. (#463) by G. Maze.

  • Optimized diagnostic per profile. If you want to execute your own diagnostic method on a collection of Argo profiles, argopy now provides an efficient method to do so: Dataset.argo.reduce_profile(). Typical use case would include computation of the mixed layer depth or euphotic layer depth. All details are in the dedicated Per profile custom diagnostic documentation page and on the API reference of the method. (#463) by G. Maze.

  • ArgoIndex now support composition of several search criteria. Thanks to a re-design of the Argo index search engine, it is now easy to use multiple search criteria to query an Argo files index. Checkout the dedicated Argo Index store documentation page. (#470) by G. Maze.

  • Fetch Argo data as netCDF4.Dataset. For the sake of compatibility with legacy codes and to encourage argopy adoption for all loading/reading operations, we now support data output as a netCDF4 Dataset object. This new feature is available at high level with the DataFetcher and lower-level with the ArgoFloat and gdacfs classes. (#484) by G. Maze.

Internals#

  • Open netcdf files lazily from ftp server. Adding to s3 and http, we now support laziness with ftp using kerchunk. Checkout the dedicated Lazy dataset access section of the documentation. (#460) by G. Maze.

Energy#

https://img.shields.io/badge/Total%20carbon%20emitted%20by%20release%20v1.2.0%20%5BgCO2eq%5D-0.27-black?style=plastic&labelColor=grey

v1.1.0 (18 March 2025)#

Added in version v1.1.0: Most new features in this version can be considered advanced tools, since they require a more intimate knowledge of the Argo dataset. Therefore, we re-organised and completed the argopy documentation to give them more visibility.

Features and front-end API#

  • New ArgoFloat store for Argo netcdf file load/read operations. Whatever the Argo netcdf file location, local or remote, you can now delegate to argopy the burden of transfer protocol and GDAC paths handling. This store is primarily intended to be used by third party libraries or in workflow by operators and experts. Checkout the dedicated Argo Float store documentation page. (#429) by G. Maze.

  • New generic file system for any GDAC host gdacfs. This class allows to easily creates a file system for any of the possible GDAC paths. This class returns one of the argopy file systems (file, http, ftp or s3) with a prefixed directory, so that you don’t have to include the GDAC path in resources to open. The goal of this class is to separate the data source from the data processing in your workflow. Checkout the dedicated Store for GDAC files documentation page. (#385, #440) by G. Maze.

  • Experimental new data source: AWS S3 netcdf files. This support is primarily made available for benchmarking as part of the ADMT working group on Argo cloud format activities. (#385) by G. Maze. In order to use the experimental S3 GDAC, you can point the gdac option to the appropriate s3 bucket:

Listing 3 AWS S3 example#
with argopy.set_options(gdac='s3://argo-gdac-sandbox/pub'):
    ds = DataFetcher(src='gdac').float(6903091).to_xarray()

  • ArgoIndex support meta index file. We now offer support for the index of meta dataset files. This support brings two new methods to search the profiler type index column: one method based on the profiler type number and another method based on a string match in the profile type label, as described in the Argo Reference table 8. Checkout the dedicated Argo Index store documentation page.

Listing 4 ArgoIndex support for the meta file index#
from argopy import ArgoIndex

idx = ArgoIndex(index_file='meta').load()
idx.search_profiler_type([838, 878])
idx.search_profiler_label('ARVOR')

  • New class utils.GreenCoding to compute argopy carbon footprint. This class makes it easy to use the Green-Coding Solutions API to retrieve argopy energy consumption data. This class is primarily used for reporting. Checkout the dedicated Energy documentation page. (#437) by G. Maze.

Listing 5 Argopy carbon footprint metrics#
from argopy.utils import GreenCoding

GreenCoding().measurements(branch='master', start_date='2025-01-01')
GreenCoding().total_measurements(branches=['385/merge', '437/merge'])

GreenCoding().footprint_since_last_release()
GreenCoding().footprint_for_release('v1.0.0')
GreenCoding().footprint_all_releases()
GreenCoding().footprint_baseline()

Internals#

  • argopy file systems refactoring. Long due internal refactoring of File systems. The submodule now adopt a more readable specification vs implementation design. This should not break or change high level APIs.(#425) by G. Maze.

  • Support Argo dataset export to zarr. Provide preliminary support to export Argo datasets to zarr files (local or remote). (#423) by G. Maze.

Listing 6 Export to zarr#
from argopy import DataFetcher
ds = DataFetcher(src='gdac').float(6903091).to_xarray()
# then:
ds.argo.to_zarr("6903091_prof.zarr")
# or:
ds.argo.to_zarr("s3://argopy/sample-data/6903091_prof.zarr")

  • Open netcdf files lazily. We provide an experimental low-level support for opening a netcdf Argo dataset lazily using kerchunk. Checkout the dedicated Lazy dataset access section of the documentation. (#385) by G. Maze.

  • Fix bug raised when the Argo reference table 8 return by the NVS server has a missing altLabel. ID of platform types are now extracted from the NVS url ID property. #420, (#421) by G. Maze.

  • When argopy is sending a http request to a data server, add a custom http header Argopy-Version to ease server log analysis. (#407) by G. Maze.

  • New ArgoIndex.copy() method (#418) by G. Maze. This copy allows for a:

    • deep copy, i.e. a new instance with same parameters (e.g. index_file) and cleared search,

    • shallow copy, i.e. a new instance with same parameters and search results if any.

  • Fix bug raising an error for STATION_PARAMETERS with a blank entry, with bgc dataset and gdac data source (well spotted K. Balem). (#418) by G. Maze.

  • Fix bug raising an error when exporting a dataset to netcdf after erddap fetch, #412. (#413) by G. Maze.

  • Make the Dataset.argo accessor and its extensions able to work with dataset from a DataFetcher and from a ArgoFloat. This was necessary because the time variable does not have the same name in these dataset (TIME vs JULD). But this point should be addressed later. (#450) by G. Maze.

Energy#

https://img.shields.io/badge/Total%20carbon%20emitted%20by%20release%20v1.1.0%20%5BgCO2eq%5D-4302.71-black?style=plastic&labelColor=grey

v1.0.0 (16 Oct. 2024)#

Added in version v1.0.0: The team proudly assumes that argopy is all grown up !

This version comes with improved performances and support for the BGC-Argo dataset. But since this is a major, we also introduces breaking changes and significant internal refactoring possibly with un-expected side effects ! So don’t hesitate to report issues on the source code repository.

Features and front-end API#

  • Improved support for BGC

    • argopy now support `standard` and `research` user modes with the bgc dataset. These new user modes follows the last available ADMT recommendations to bring users a finely tuned set of BGC parameters. Details of the BGC data processing chain for each user modes can be found in the Definitions section.

    • Predict nutrients and carbonates in the Mediterranean Sea with the new BGC method Dataset.argo.canyon_med. The new method allows to make predictions of the water-Column nutrient concentrations and carbonate system variables in the Mediterranean Sea with the CANYON-MED model. This model can be used to predict PO4, NO3, DIC, SiOH4, AT and pHT. (#364) by G. Maze.

    from argopy import DataFetcher
ArgoSet = DataFetcher(ds='bgc', mode='standard', params='DOXY', measured='DOXY').float(1902605)
ds = ArgoSet.to_xarray()

ds.argo.canyon_med.predict()
ds.argo.canyon_med.predict('PO4')

    • More BGC expert features with support for the auxiliary index file with argopy.ArgoIndex. Simply use the keyword aux. (#356) by G. Maze.

    from argopy import ArgoIndex
ArgoIndex(index_file="aux").load()

  • More scalable data fetching using multi-processing or a Dask Cluster.

It is now possible to use multi-processing with all data fetchers and even possibly a Dask client object. This is set with the parallel option. In doing so, the Argo data pre-processing steps (download and conformation to internal conventions) will be distributed to all available resources, significantly improving performances for fetching large selection of Argo data. (#392) by G. Maze.

Check the documentation on Parallelization methods for all the details.

from dask.distributed import Client
client = Client(processes=True)

from argopy import DataFetcher
DataFetcher(parallel=client, src='argovis').region([-75, -65, 25, 40, 0, 250, '2020-01-01', '2021-01-01']).to_xarray()

  • Xarray argo accessor extensions mechanism.

This should allows users to easily develop their own Argo dataset methods. This is possible thanks to a new class decorator argopy.extensions.register_argo_accessor that allows to register a class as a property to the Dataset.argo accessor. (#364) by G. Maze.

Example:

@register_argo_accessor('floats')
class WorkWithWMO(ArgoAccessorExtension):
    """Example of a new Argo dataset feature"""

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self._uid = argopy.utils.to_list(np.unique(self._obj["PLATFORM_NUMBER"].values))

    @property
    def wmo(self):
        return self._uid

    @property
    def N(self):
         return len(self.wmo)

This makes syntax like this possible:

ds.argo.floats.N
ds.argo.floats.wmo

Breaking changes#

Energy#

https://img.shields.io/badge/Total%20carbon%20emitted%20by%20release%20v1.0.0%20%5BgCO2eq%5D-1328.99-black?style=plastic&labelColor=grey

v0.1.17 (20 Sep. 2024)#

This is the last minor version release

We’re very happy to announce that this v0.1.17 is the last of the minor version releases. Its main role is to introduce deprecation warnings before things break in the major version release.

Comping up soon by the end of October the first major argopy release: v1.0.0

Important

List of deprecations before the upcoming major release v1.0.0. (#389) by G. Maze.

  • Change of signature and action with xarray Argo accessor Dataset.argo.filter_data_mode()

Internals#

  • Refactor Argovis CI tests to use mocked http server (#383) by G. Maze

  • Improve error and warning messages from mocked http server to address #381 (#382) by G. Maze

Energy#

https://img.shields.io/badge/Total%20carbon%20emitted%20by%20release%20v0.1.17%20%5BgCO2eq%5D-234.30-black?style=plastic&labelColor=grey

v0.1.16 (27 Aug. 2024)#

Features and front-end API#

  • Support for AWS S3 index files. This support is experimental and is primarily made available for benchmarking as part of the ADMT working group on Argo cloud format activities. The ADMT working group discussion items are listed here. Both CORE and BGC index files are supported. The new ArgoIndex not only support access to the AWS S3 index files but also implement improved performances for search methods on WMO and cycle numbers, using boto3.client.select_object_content SQL queries. Indeed, the https and ftp default GDAC server index files are downloaded and loaded in memory before being searched. But with s3, index files can directly be queried on the server using SQL syntax; the full index is not necessarily downloaded. (#326) by G. Maze

from argopy import ArgoIndex
idx = ArgoIndex(host='s3')  # you can also use 'aws' as a keyword for 's3://argo-gdac-sandbox/pub/idx'

# Optimised perf with boto3.client.select_object_content queries for WMO and cycle numbers:
idx.search_wmo(6903091)
idx.search_cyc(12)
idx.search_wmo_cyc(6903091, 23)

# Other search methods will trigger download of the index file, eg:
idx.search_tim([-60, -55, 40., 45., '2007-08-01', '2007-09-01'])

  • argovis data source now support the new API server. This upgrade comes with a new option to define the optional API KEY to use. You can get a free key here. (#371) by Bill Katie-Anne Mills.

  • argopy is concerned about its environmental impact and we’d like to understand and optimize the carbon emissions of our digital activities. Starting June 1st 2024, we use Green Coding tools to assess energy consumption and CO2eq emissions from our activities on Github infrastructure. All results and data are available on the new dedicated web page: Carbon emissions. (#354) by G. Maze.

Internals#

  • Drop support for Python 3.8, add support for Python 3.10. (#379) by G. Maze

  • Update argopy.ArgoNVSReferenceTables to handle new NVS server output format. (#378) by G. Maze.

  • Update Ifremer erddap server information. The Argo reference for DMQC (returned by the DataFetcher fetcher with ds='ref' argument ) and Argo CTD-reference for DQMC (returned by the CTDRefDataFetcher fetcher) now indicate the dataset version used. (#344) by G. Maze.

  • Pin upper bound on xarray < 2024.3 to fix failing upstream tests because of AttributeError: 'ScipyArrayWrapper' object has no attribute 'oindex', reported here. (#326) by G. Maze

  • Fix argopy.ArgoDocs that was not working with new Archimer webpage design, #351. (#352) by G. Maze.

  • Fix bug with ArgoIndex cache, #345. (#346) by G. Maze.

  • Keep dependencies up to date. (#333, #337) by G. Maze.

  • Update argopy.ArgoDocs with last BGC cookbooks on pH. (#321) by G. Maze.

  • Fix for fsspec > 2023.10.0. (#318) by G. Maze.

Breaking changes#

Energy#

https://img.shields.io/badge/Total%20carbon%20emitted%20by%20release%20v0.1.16%20%5BgCO2eq%5D-561.76-black?style=plastic&labelColor=grey

v0.1.15 (12 Dec. 2023)#

Internals#

v0.1.14 (29 Sep. 2023)#

Added in version v0.1.14: This new release brings to pip and conda default install of argopy all new features introduced in the release candidate v0.1.14rc2 and v0.1.14rc1. For simplicity we merged all novelties to this v0.1.14 changelog.

Features and front-end API#

  • argopy now support BGC dataset in `expert` user mode for the `erddap` data source. The BGC-Argo content of synthetic multi-profile files is now available from the Ifremer erddap. Like for the core dataset, you can fetch data for a region, float(s) or profile(s). One novelty with regard to core, is that you can restrict data fetching to some parameters and furthermore impose no-NaNs on some of these parameters. Check out the new documentation page for Dataset. (#278) by G. Maze

import argopy
from argopy import DataFetcher

argopy.set_options(src='erddap', mode='expert')

DataFetcher(ds='bgc')  # All variables found in the access point will be returned
DataFetcher(ds='bgc', params='all')  # Default: All variables found in the access point will be returned
DataFetcher(ds='bgc', params='DOXY') # Only the DOXY variable will be returned
DataFetcher(ds='bgc', params=['DOXY', 'BBP700']) # Only DOXY and BBP700 will be returned

DataFetcher(ds='bgc', measured=None)  # Default: all params are allowed to have NaNs
DataFetcher(ds='bgc', measured='all')  # All params found in the access point cannot be NaNs
DataFetcher(ds='bgc', measured='DOXY') # Only DOXY cannot be NaNs
DataFetcher(ds='bgc', measured=['DOXY', 'BBP700']) # Only DOXY and BBP700 cannot be NaNs

DataFetcher(ds='bgc', params='all', measured=None)  # Return the largest possible dataset
DataFetcher(ds='bgc', params='all', measured='all')  # Return the smallest possible dataset
DataFetcher(ds='bgc', params='all', measured=['DOXY', 'BBP700'])  # Return all possible params for points where DOXY and BBP700 are not NaN

  • New methods in the ArgoIndex for BGC. The ArgoIndex has now full support for the BGC profile index files, both bio and synthetic index. In particular it is possible to search for profiles with specific data modes on parameters. (#278) by G. Maze

from argopy import ArgoIndex

idx = ArgoIndex(index_file="bgc-b")  # Use keywords instead of exact file names: `core`, `bgc-b`, `bgc-s`
idx.search_params(['C1PHASE_DOXY', 'DOWNWELLING_PAR'])  # Search for profiles with parameters
idx.search_parameter_data_mode({'TEMP': 'D'})  # Search for profiles with specific data modes
idx.search_parameter_data_mode({'BBP700': 'D'})
idx.search_parameter_data_mode({'DOXY': ['R', 'A']})
idx.search_parameter_data_mode({'DOXY': 'D', 'CDOM': 'D'}, logical='or')

  • New xarray argo accessor features. Easily retrieve an Argo sample index and domain extent with the index and domain properties. Get a list with all possible (PLATFORM_NUMBER, CYCLE_NUMBER) with the list_WMO_CYC method. (#278) by G. Maze

  • New search methods for Argo reference tables. It is now possible to search for a string in tables title and/or description using the related.ArgoNVSReferenceTables.search() method.

from argopy import ArgoNVSReferenceTables

id_list = ArgoNVSReferenceTables().search('sensor')

  • Updated documentation. In order to better introduce new features, we updated the documentation structure and content.

  • argopy cheatsheet ! Get most of the argopy API in a 2 pages pdf !

_images/argopy-cheatsheet.png

  • Our internal Argo index store is promoted as a frontend feature. The IndexFetcher is a user-friendly fetcher built on top of our internal Argo index file store. But if you are familiar with Argo index files and/or cares about performances, you may be interested in using directly the Argo index store. We thus decided to promote this internal feature as a frontend class ArgoIndex. See Argo Index store. (#270) by G. Maze

  • Easy access to all Argo manuals from the ADMT. More than 20 pdf manuals have been produced by the Argo Data Management Team. Using the new ArgoDocs class, it’s now easier to navigate this great database for Argo experts. All details in ADMT Documentation. (#268) by G. Maze

from argopy import ArgoDocs

ArgoDocs().list

ArgoDocs(35385)
ArgoDocs(35385).ris
ArgoDocs(35385).abstract
ArgoDocs(35385).show()
ArgoDocs(35385).open_pdf()
ArgoDocs(35385).open_pdf(page=12)

ArgoDocs().search("CDOM")

  • New ‘research’ user mode. This new feature implements automatic filtering of Argo data following international recommendations for research/climate studies. With this user mode, only Delayed Mode with good QC data are returned. Check out the Definitions section for all the details. (#265) by G. Maze

  • argopy now provides a specific xarray engine to properly read Argo netcdf files. Using engine='argo' in xarray.open_dataset(), all variables will properly be casted, i.e. returned with their expected data types, which is not the case otherwise. This works with ALL Argo netcdf file types (as listed in the Reference table R01). Some details in here: argopy.xarray.ArgoEngine (#208) by G. Maze

import xarray as xr
ds = xr.open_dataset("dac/aoml/1901393/1901393_prof.nc", engine='argo')

from argopy import CTDRefDataFetcher

with argopy.set_options(user="john_doe", password="***"):
    f = CTDRefDataFetcher(box=[15, 30, -70, -60, 0, 5000.0])
    ds = f.to_xarray()

Warning

argopy is ready but the Argo CTD reference database for DMQC is not fully published on the Ifremer ERDDAP yet. This new feature will thus be fully operational soon, and while it’s not, argopy should raise an ErddapHTTPNotFound error when using the new fetcher.

  • New option to control the expiration time of cache file: cache_expiration.

Internals#

  • Utilities refactoring. All classes and functions have been refactored to more appropriate locations like argopy.utils or argopy.related. A deprecation warning message should be displayed every time utilities are being used from the deprecated locations. (#290) by G. Maze

  • Fix bugs due to fsspec new internal cache handling and Windows specifics. (#293) by G. Maze

  • New utility class utils.MonitoredThreadPoolExecutor to handle parallelization with a multi-threading Pool that provide a notebook or terminal computation progress dashboard. This class is used by the httpstore open_mfdataset method for erddap requests.

  • New utilities to handle a collection of datasets: utils.drop_variables_not_in_all_datasets() will drop variables that are not in all datasets (the lowest common denominator) and utils.fill_variables_not_in_all_datasets() will add empty variables to dataset so that all the collection have the same data_vars and coords. These functions are used by stores to concat/merge a collection of datasets (chunks).

  • related.load_dict() now relies on ArgoNVSReferenceTables instead of static pickle files.

  • argopy.ArgoColors colormap for Argo Data-Mode has now a fourth value to account for a white space FillValue.

  • New quick and dirty plot method plot.scatter_plot()

  • Refactor pickle files in argopy/assets as json files in argopy/static/assets

  • Refactor list of variables by data types used in related.cast_Argo_variable_type() into assets json files in argopy/static/assets

  • Change of behaviour: when setting the cachedir option, path it’s not tested for existence but for being writable, and is created if doesn’t exists (but seems to break CI upstream in Windows)

  • And misc. bug and warning fixes all over the code.

  • Update new argovis dashboard links for floats and profiles. (#271) by G. Maze

  • Index store can now export search results to standard Argo index file format. See all details in Argo Index store. (#260) by G. Maze

from argopy import ArgoIndex as indexstore
# or:
# from argopy.stores import indexstore_pd as indexstore
# or:
# from argopy.stores import indexstore_pa as indexstore

idx = indexstore().search_wmo(3902131)  # Perform any search
idx.to_indexfile('short_index.txt')  # export search results as standard Argo index csv file

  • Index store can now load/search the Argo Bio and Synthetic profile index files. Simply gives the name of the Bio or Synthetic Profile index file and retrieve the full index. This store also comes with a new search criteria for BGC: by parameters. See all details in Argo Index store. (#261) by G. Maze

from argopy import ArgoIndex as indexstore
# or:
# from argopy.stores import indexstore_pd as indexstore
# or:
# from argopy.stores import indexstore_pa as indexstore

idx = indexstore(index_file="argo_bio-profile_index.txt").load()
idx.search_params(['C1PHASE_DOXY', 'DOWNWELLING_PAR'])

  • Use a mocked server for all http and GDAC ftp requests in CI tests (#249, #252, #255) by G. Maze

  • Removed support for minimal dependency requirements and for python 3.7. (#252) by G. Maze

  • Changed License from Apache to EUPL 1.2

Breaking changes#

  • Some documentation pages may have moved to new urls.

  • The legacy index store is deprecated, now available in argopy.stores.argo_index_deprec.py only (#270) by G. Maze

v0.1.14rc2 (27 Jul. 2023)#

Features and front-end API#

  • argopy now support BGC dataset in `expert` user mode for the `erddap` data source. The BGC-Argo content of synthetic multi-profile files is now available from the Ifremer erddap. Like for the core dataset, you can fetch data for a region, float(s) or profile(s). One novelty with regard to core, is that you can restrict data fetching to some parameters and furthermore impose no-NaNs on some of these parameters. Check out the new documentation page for Dataset. (#278) by G. Maze

import argopy
from argopy import DataFetcher

argopy.set_options(src='erddap', mode='expert')

DataFetcher(ds='bgc')  # All variables found in the access point will be returned
DataFetcher(ds='bgc', params='all')  # Default: All variables found in the access point will be returned
DataFetcher(ds='bgc', params='DOXY') # Only the DOXY variable will be returned
DataFetcher(ds='bgc', params=['DOXY', 'BBP700']) # Only DOXY and BBP700 will be returned

DataFetcher(ds='bgc', measured=None)  # Default: all params are allowed to have NaNs
DataFetcher(ds='bgc', measured='all')  # All params found in the access point cannot be NaNs
DataFetcher(ds='bgc', measured='DOXY') # Only DOXY cannot be NaNs
DataFetcher(ds='bgc', measured=['DOXY', 'BBP700']) # Only DOXY and BBP700 cannot be NaNs

DataFetcher(ds='bgc', params='all', measured=None)  # Return the largest possible dataset
DataFetcher(ds='bgc', params='all', measured='all')  # Return the smallest possible dataset
DataFetcher(ds='bgc', params='all', measured=['DOXY', 'BBP700'])  # Return all possible params for points where DOXY and BBP700 are not NaN

  • New methods in the ArgoIndex for BGC. The ArgoIndex has now full support for the BGC profile index files, both bio and synthetic index. In particular it is possible to search for profiles with specific data modes on parameters. (#278) by G. Maze

from argopy import ArgoIndex

idx = ArgoIndex(index_file="bgc-b")  # Use keywords instead of exact file names: `core`, `bgc-b`, `bgc-s`
idx.search_params(['C1PHASE_DOXY', 'DOWNWELLING_PAR'])  # Search for profiles with parameters
idx.search_parameter_data_mode({'TEMP': 'D'})  # Search for profiles with specific data modes
idx.search_parameter_data_mode({'BBP700': 'D'})
idx.search_parameter_data_mode({'DOXY': ['R', 'A']})
idx.search_parameter_data_mode({'DOXY': 'D', 'CDOM': 'D'}, logical='or')

  • New xarray argo accessor features. Easily retrieve an Argo sample index and domain extent with the index and domain properties. Get a list with all possible (PLATFORM_NUMBER, CYCLE_NUMBER) with the list_WMO_CYC method. (#278) by G. Maze

  • New search methods for Argo reference tables. It is now possible to search for a string in tables title and/or description using the related.ArgoNVSReferenceTables.search() method.

from argopy import ArgoNVSReferenceTables

id_list = ArgoNVSReferenceTables().search('sensor')

  • Updated documentation. In order to better introduce new features, we updated the documentation structure and content.

Internals#

  • New utility class utils.MonitoredThreadPoolExecutor to handle parallelization with a multi-threading Pool that provide a notebook or terminal computation progress dashboard. This class is used by the httpstore open_mfdataset method for erddap requests.

  • New utilities to handle a collection of datasets: utils.drop_variables_not_in_all_datasets() will drop variables that are not in all datasets (the lowest common denominator) and utils.fill_variables_not_in_all_datasets() will add empty variables to dataset so that all the collection have the same data_vars and coords. These functions are used by stores to concat/merge a collection of datasets (chunks).

  • related.load_dict() now relies on ArgoNVSReferenceTables instead of static pickle files.

  • argopy.ArgoColors colormap for Argo Data-Mode has now a fourth value to account for a white space FillValue.

  • New quick and dirty plot method plot.scatter_plot()

  • Refactor pickle files in argopy/assets as json files in argopy/static/assets

  • Refactor list of variables by data types used in related.cast_Argo_variable_type() into assets json files in argopy/static/assets

  • Change of behaviour: when setting the cachedir option, path it’s not tested for existence but for being writable, and is created if doesn’t exists (but seems to break CI upstream in Windows)

  • And misc. bug and warning fixes all over the code.

Breaking changes#

  • Some documentation pages may have moved to new urls.

v0.1.14rc1 (31 May 2023)#

Features and front-end API#

  • argopy cheatsheet ! Get most of the argopy API in a 2 pages pdf !

_images/argopy-cheatsheet.png

  • Our internal Argo index store is promoted as a frontend feature. The IndexFetcher is a user-friendly fetcher built on top of our internal Argo index file store. But if you are familiar with Argo index files and/or cares about performances, you may be interested in using directly the Argo index store. We thus decided to promote this internal feature as a frontend class ArgoIndex. See Argo Index store. (#270) by G. Maze

  • Easy access to all Argo manuals from the ADMT. More than 20 pdf manuals have been produced by the Argo Data Management Team. Using the new ArgoDocs class, it’s now easier to navigate this great database for Argo experts. All details in ADMT Documentation. (#268) by G. Maze

from argopy import ArgoDocs

ArgoDocs().list

ArgoDocs(35385)
ArgoDocs(35385).ris
ArgoDocs(35385).abstract
ArgoDocs(35385).show()
ArgoDocs(35385).open_pdf()
ArgoDocs(35385).open_pdf(page=12)

ArgoDocs().search("CDOM")

  • New ‘research’ user mode. This new feature implements automatic filtering of Argo data following international recommendations for research/climate studies. With this user mode, only Delayed Mode with good QC data are returned. Check out the Definitions section for all the details. (#265) by G. Maze

  • argopy now provides a specific xarray engine to properly read Argo netcdf files. Using engine='argo' in xarray.open_dataset(), all variables will properly be casted, i.e. returned with their expected data types, which is not the case otherwise. This works with ALL Argo netcdf file types (as listed in the Reference table R01). Some details in here: argopy.xarray.ArgoEngine (#208) by G. Maze

import xarray as xr
ds = xr.open_dataset("dac/aoml/1901393/1901393_prof.nc", engine='argo')

from argopy import CTDRefDataFetcher

with argopy.set_options(user="john_doe", password="***"):
    f = CTDRefDataFetcher(box=[15, 30, -70, -60, 0, 5000.0])
    ds = f.to_xarray()

Warning

argopy is ready but the Argo CTD reference database for DMQC is not fully published on the Ifremer ERDDAP yet. This new feature will thus be fully operational soon, and while it’s not, argopy should raise an ErddapHTTPNotFound error when using the new fetcher.

  • New option to control the expiration time of cache file: cache_expiration.

Internals#

  • Update new argovis dashboard links for floats and profiles. (#271) by G. Maze

  • Index store can now export search results to standard Argo index file format. See all details in Argo Index store. (#260) by G. Maze

from argopy import ArgoIndex as indexstore
# or:
# from argopy.stores import indexstore_pd as indexstore
# or:
# from argopy.stores import indexstore_pa as indexstore

idx = indexstore().search_wmo(3902131)  # Perform any search
idx.to_indexfile('short_index.txt')  # export search results as standard Argo index csv file

  • Index store can now load/search the Argo Bio and Synthetic profile index files. Simply gives the name of the Bio or Synthetic Profile index file and retrieve the full index. This store also comes with a new search criteria for BGC: by parameters. See all details in Argo Index store. (#261) by G. Maze

from argopy import ArgoIndex as indexstore
# or:
# from argopy.stores import indexstore_pd as indexstore
# or:
# from argopy.stores import indexstore_pa as indexstore

idx = indexstore(index_file="argo_bio-profile_index.txt").load()
idx.search_params(['C1PHASE_DOXY', 'DOWNWELLING_PAR'])

  • Use a mocked server for all http and GDAC ftp requests in CI tests (#249, #252, #255) by G. Maze

  • Removed support for minimal dependency requirements and for python 3.7. (#252) by G. Maze

  • Changed License from Apache to EUPL 1.2

Breaking changes#

  • The legacy index store is deprecated, now available in argopy.stores.argo_index_deprec.py only (#270) by G. Maze

v0.1.13 (28 Mar. 2023)#

Features and front-end API#

from argopy import OceanOPSDeployments

deployment = OceanOPSDeployments()
deployment = OceanOPSDeployments([-90,0,0,90])
deployment = OceanOPSDeployments([-90,0,0,90], deployed_only=True) # Remove planification

df = deployment.to_dataframe()
deployment.status_code
fig, ax = deployment.plot_status()
_images/scatter_map_deployment_status.png

  • New scatter map utility for easy Argo-related variables plotting. The new argopy.plot.scatter_map() utility function is dedicated to making maps with Argo profiles positions coloured according to specific variables: a scatter map. Profiles colouring is finely tuned for some variables: QC flags, Data Mode and Deployment Status. By default, floats trajectories are always shown, but this can be changed. See the new documentation section on Scatter Maps for more. (#245) by G. Maze

from argopy.plot import scatter_map

fig, ax = scatter_map(ds_or_df,
                      x='LONGITUDE', y='LATITUDE', hue='PSAL_QC',
                      traj_axis='PLATFORM_NUMBER')
_images/scatter_map_qcflag.png

  • New Argo colors utility to manage segmented colormaps and pre-defined Argo colors set. The new argopy.plot.ArgoColors utility class aims to easily provide colors for Argo-related variables plot. See the new documentation section on Argo colors for more (#245) by G. Maze

from argopy.plot import ArgoColors

ArgoColors().list_valid_known_colormaps
ArgoColors().known_colormaps.keys()

ArgoColors('data_mode')
ArgoColors('data_mode').cmap
ArgoColors('data_mode').definition

ArgoColors('Set2').cmap
ArgoColors('Spectral', N=25).cmap

Internals#

  • Because of the new argopy.plot.ArgoColors, the argopy.plot.discrete_coloring utility is deprecated in 0.1.13. Calling it will raise an error after argopy 0.1.14. (#245) by G. Maze

  • New method to check status of web API: now allows for a keyword check rather than a simple url ping. This comes with 2 new utilities functions utilities.urlhaskeyword() and utilities.isalive(). (#247) by G. Maze.

  • Removed dependency to Scikit-learn LabelEncoder (#239) by G. Maze

Breaking changes#

  • Data source localftp is deprecated and removed from argopy. It’s been replaced by the gdac data source with the appropriate ftp option. See Data sources. (#240) by G. Maze

  • argopy.utilities.ArgoNVSReferenceTables methods all_tbl and all_tbl_name are now properties, not methods.

v0.1.12 (16 May 2022)#

Internals#

v0.1.11 (13 Apr. 2022)#

Features and front-end API#

  • New data source ``gdac`` to retrieve data from a GDAC compliant source, for DataFetcher and IndexFetcher. You can specify the FTP source with the ftp fetcher option or with the argopy global option ftp. The FTP source support http, ftp or local files protocols. This fetcher is optimised if pyarrow is available, otherwise pandas dataframe are used. See update on Data sources. (#157) by G. Maze

from argopy import IndexFetcher
from argopy import DataFetcher
argo = IndexFetcher(src='gdac')
argo = DataFetcher(src='gdac')
argo = DataFetcher(src='gdac', ftp="https://data-argo.ifremer.fr")  # Default and fastest !
argo = DataFetcher(src='gdac', ftp="ftp://ftp.ifremer.fr/ifremer/argo")
with argopy.set_options(src='gdac', ftp='ftp://usgodae.org/pub/outgoing/argo'):
    argo = DataFetcher()

Note

The new gdac fetcher uses Argo index to determine which profile files to load. Hence, this fetcher may show poor performances when used with a region access point. Don’t hesitate to check Performances to try to improve performances, otherwise, we recommend to use a webAPI access point (erddap or argovis).

Warning

Since the new gdac fetcher can use a local copy of the GDAC ftp server, the legacy localftp fetcher is now deprecated. Using it will raise a error up to v0.1.12. It will then be removed in v0.1.13.

  • New dashboard for profiles and new 3rd party dashboards. Calling on the data fetcher dashboard method will return the Euro-Argo profile page for a single profile. Very useful to look at the data before load. This comes with 2 new utilities functions to get Coriolis ID of profiles (utilities.get_coriolis_profile_id()) and to return the list of profile webpages (utilities.get_ea_profile_page()). (#198) by G. Maze.

from argopy import DataFetcher as ArgoDataFetcher
ArgoDataFetcher().profile(5904797, 11).dashboard()

from argopy.utilities import get_coriolis_profile_id, get_ea_profile_page
get_coriolis_profile_id([6902755, 6902756], [11, 12])
get_ea_profile_page([6902755, 6902756], [11, 12])

The new profile dashboard can also be accessed with:

import argopy
argopy.dashboard(5904797, 11)

We added the Ocean-OPS (former JCOMMOPS) dashboard for all floats and the BGC-Argo dashboard for BGC floats:

import argopy
argopy.dashboard(5904797, type='ocean-ops')
# or
argopy.dashboard(5904797, 12, type='bgc')

  • New utility :class:`argopy.utilities.ArgoNVSReferenceTables` to retrieve Argo Reference Tables. (@cc8fdbe) by G. Maze.

from argopy.utilities import ArgoNVSReferenceTables
R = ArgoNVSReferenceTables()
R.all_tbl_name()
R.tbl(3)
R.tbl('R09')

Internals#

  • gdac and localftp data fetchers can return an index without loading the data. (#157) by G. Maze

from argopy import DataFetcher
argo = DataFetcher(src='gdac').float(6903076)
argo.index

  • New index store design. A new index store is used by data and index gdac fetchers to handle access and search in Argo index csv files. It uses pyarrow table if available or pandas dataframe otherwise. More details at Argo Index store. Directly using this index store is not recommended but provides better performances for expert users interested in Argo sampling analysis.

from argopy.stores.argo_index_pa import indexstore_pyarrow as indexstore
idx = indexstore(host="https://data-argo.ifremer.fr", index_file="ar_index_global_prof.txt")  # Default
idx.load()
idx.search_lat_lon_tim([-60, -55, 40., 45., '2007-08-01', '2007-09-01'])
idx.N_MATCH  # Return number of search results
idx.to_dataframe()  # Convert search results to a dataframe

  • Refactoring of CI tests to use more fixtures and pytest parametrize. (#157) by G. Maze

  • Fix bug in erddap fata fetcher that was causing a profile request to do not account for cycle numbers. (@301e557) by G. Maze.

Breaking changes#

  • Index fetcher for local FTP no longer support the option index_file. The name of the file index is internally determined using the dataset requested: ar_index_global_prof.txt for ds='phy' and argo_synthetic-profile_index.txt for ds='bgc'. Using this option will raise a deprecation warning up to v0.1.12 and will then raise an error. (#157) by G. Maze

  • Complete refactoring of the argopy.plotters module into argopy.plot. (#198) by G. Maze.

  • Remove deprecation warnings for: ‘plotters.plot_dac’, ‘plotters.plot_profilerType’. These now raise an error.

v0.1.10 (4 Mar. 2022)#

Internals#

  • Update and clean up requirements. Remove upper bound on all dependencies (#182) by R. Abernathey.

v0.1.9 (19 Jan. 2022)#

Features and front-end API#

  • New method to preprocess data for OWC software. This method can preprocessed Argo data and possibly create float_source/<WMO>.mat files to be used as inputs for OWC implementations in Matlab and Python. See the Salinity calibration documentation page for more. (#142) by G. Maze.

from argopy import DataFetcher as ArgoDataFetcher
ds = ArgoDataFetcher(mode='expert').float(6902766).load().data
ds.argo.create_float_source("float_source")
ds.argo.create_float_source("float_source", force='raw')
ds_source = ds.argo.create_float_source()

This new method comes with others methods and improvements:

  • New dataset properties accessible from the argo xarray accessor: N_POINTS, N_LEVELS, N_PROF. Note that depending on the format of the dataset (a collection of points or of profiles) these values do or do not take into account NaN. These information are also visible by a simple print of the accessor. (#142) by G. Maze.

from argopy import DataFetcher as ArgoDataFetcher
ds = ArgoDataFetcher(mode='expert').float(6902766).load().data
ds.argo.N_POINTS
ds.argo.N_LEVELS
ds.argo.N_PROF
ds.argo

  • New plotter function argopy.plotters.open_sat_altim_report() to insert the CLS Satellite Altimeter Report figure in a notebook cell. (#159) by G. Maze.

from argopy.plotters import open_sat_altim_report
open_sat_altim_report(6902766)
open_sat_altim_report([6902766, 6902772, 6902914])
open_sat_altim_report([6902766, 6902772, 6902914], embed='dropdown')  # Default
open_sat_altim_report([6902766, 6902772, 6902914], embed='slide')
open_sat_altim_report([6902766, 6902772, 6902914], embed='list')
open_sat_altim_report([6902766, 6902772, 6902914], embed=None)

from argopy import DataFetcher
from argopy import IndexFetcher
DataFetcher().float([6902745, 6902746]).plot('qc_altimetry')
IndexFetcher().float([6902745, 6902746]).plot('qc_altimetry')

from argopy import TopoFetcher
box = [-75, -45, 20, 30]
ds = TopoFetcher(box).to_xarray()
ds = TopoFetcher(box, ds='gebco', stride=[10, 10], cache=True).to_xarray()

For convenience we also added a new property to the data fetcher that return the domain covered by the dataset.

loader = ArgoDataFetcher().float(2901623)
loader.domain  # Returns [89.093, 96.036, -0.278, 4.16, 15.0, 2026.0, numpy.datetime64('2010-05-14T03:35:00.000000000'),  numpy.datetime64('2013-01-01T01:45:00.000000000')]

Internals#

v0.1.8 (2 Nov. 2021)#

Features and front-end API#

  • Improve plotting functions. All functions are now available for both the index and data fetchers. See the Data visualisation page for more details. Reduced plotting dependencies to Matplotlib only. Argopy will use Seaborn and/or Cartopy if available. (#56) by G. Maze.

from argopy import IndexFetcher as ArgoIndexFetcher
from argopy import DataFetcher as ArgoDataFetcher
obj = ArgoIndexFetcher().float([6902766, 6902772, 6902914, 6902746])
# OR
obj = ArgoDataFetcher().float([6902766, 6902772, 6902914, 6902746])

fig, ax = obj.plot()
fig, ax = obj.plot('trajectory')
fig, ax = obj.plot('trajectory', style='white', palette='Set1', figsize=(10,6))
fig, ax = obj.plot('dac')
fig, ax = obj.plot('institution')
fig, ax = obj.plot('profiler')

from argopy import DataFetcher as ArgoDataFetcher
loader = ArgoDataFetcher().float([6902766, 6902772, 6902914, 6902746])
loader.load()
loader.data
loader.index
loader.to_index()

from argopy import IndexFetcher as ArgoIndexFetcher
indexer = ArgoIndexFetcher().float([6902766, 6902772])
indexer.load()
indexer.index

  • Add optional speed of sound computation to xarray accessor teos10 method. (#90) by G. Maze.

  • Code spell fixes (#89) by K. Schwehr.

Internals#

  • Check validity of access points options (WMO and box) in the facade, no checks at the fetcher level. (#92) by G. Maze.

  • More general options. Fix #91. (#102) by G. Maze.

    • trust_env to allow for local environment variables to be used by fsspec to connect to the internet. Useful for those using a proxy.

  • Documentation on Read The Docs now uses a pip environment and get rid of memory eager conda. (#103) by G. Maze.

  • xarray.Dataset argopy accessor argo has a clean documentation.

Breaking changes#

  • Drop support for python 3.6 and older. Lock range of dependencies version support.

  • In the plotters module, the plot_dac and plot_profilerType functions have been replaced by bar_plot. (#56) by G. Maze.

Internals#

  • Internal logging available and upgrade dependencies version support (#56) by G. Maze. To see internal logs, you can set-up your application like this:

import logging
DEBUGFORMATTER = '%(asctime)s [%(levelname)s] [%(name)s] %(filename)s:%(lineno)d: %(message)s'
logging.basicConfig(
    level=logging.DEBUG,
    format=DEBUGFORMATTER,
    datefmt='%m/%d/%Y %I:%M:%S %p',
    handlers=[logging.FileHandler("argopy.log", mode='w')]
)

v0.1.7 (4 Jan. 2021)#

Long due release !

Features and front-end API#

import argopy
argopy.status()
# or
argopy.status(refresh=15)
_images/status_monitor.png

  • Optimise large data fetching with parallelization, for all data fetchers (erddap, localftp and argovis). See documentation page on Parallel data fetching. Two parallel methods are available: multi-threading or multi-processing. (#28) by G. Maze.

from argopy import DataFetcher as ArgoDataFetcher
loader = ArgoDataFetcher(parallel=True)
loader.float([6902766, 6902772, 6902914, 6902746]).to_xarray()
loader.region([-85,-45,10.,20.,0,1000.,'2012-01','2012-02']).to_xarray()

Breaking changes#

  • In the teos10 xarray accessor, the standard_name attribute will now be populated using values from the CF Standard Name table if one exists. The previous values of standard_name have been moved to the long_name attribute. (#74) by A. Barna.

  • The unique resource identifier property is now named uri for all data fetchers, it is always a list of strings.

Internals#

  • New open_mfdataset and open_mfjson methods in Argo stores. These can be used to open, pre-process and concatenate a collection of paths both in sequential or parallel order. (#28) by G. Maze.

  • Unit testing is now done on a controlled conda environment. This allows to more easily identify errors coming from development vs errors due to dependencies update. (#65) by G. Maze.

v0.1.6 (31 Aug. 2020)#

  • JOSS paper published. You can now cite argopy with a clean reference. (#30) by G. Maze and K. Balem.

Maze G. and Balem K. (2020). argopy: A Python library for Argo ocean data analysis. Journal of Open Source Software, 5(52), 2425 doi: 10.21105/joss.02425.

v0.1.5 (10 July 2020)#

Features and front-end API#

  • A new data source with the argovis data fetcher, all access points available (#24). By T. Tucker and G. Maze.

from argopy import DataFetcher as ArgoDataFetcher
loader = ArgoDataFetcher(src='argovis')
loader.float(6902746).to_xarray()
loader.profile(6902746, 12).to_xarray()
loader.region([-85,-45,10.,20.,0,1000.,'2012-01','2012-02']).to_xarray()

  • Easily compute TEOS-10 variables with new argo accessor function teos10. This needs gsw to be installed. (#37) By G. Maze.

from argopy import DataFetcher as ArgoDataFetcher
ds = ArgoDataFetcher().region([-85,-45,10.,20.,0,1000.,'2012-01','2012-02']).to_xarray()
ds = ds.argo.teos10()
ds = ds.argo.teos10(['PV'])
ds_teos10 = ds.argo.teos10(['SA', 'CT'], inplace=False)

conda install -c conda-forge argopy

Breaking changes#

  • The local_ftp option of the localftp data source must now points to the folder where the dac directory is found. This breaks compatibility with rsynced local FTP copy because rsync does not give a dac folder (e.g. #33). An instructive error message is raised to notify users if any of the DAC name is found at the n-1 path level. (#34).

Internals#

  • Implement a webAPI availability check in unit testing. This allows for more robust erddap and argovis tests that are not only based on internet connectivity only. (@5a46a39).

v0.1.4 (24 June 2020)#

Features and front-end API#

  • Standard levels interpolation method available in standard user mode (#23). By K. Balem.

ds = ArgoDataFetcher().region([-85,-45,10.,20.,0,1000.,'2012-01','2012-12']).to_xarray()
ds = ds.argo.point2profile()
ds_interp = ds.argo.interp_std_levels(np.arange(0,900,50))

import argopy
argopy.dashboard()
# or
argopy.dashboard(wmo=6902746)

  • The localftp index and data fetcher now have the region and profile access points available (#25). By G. Maze.

Breaking changes#

[None]

Internals#

  • Now uses fsspec as file system for caching as well as accessing local and remote files (#19). This closes issues #12, #15 and #17. argopy fetchers must now use (or implement if necessary) one of the internal file systems available in the new module argopy.stores. By G. Maze.

  • Erddap fetcher now uses netcdf format to retrieve data (#19).

v0.1.3 (15 May 2020)#

Features and front-end API#

  • New index fetcher to explore and work with meta-data (#6). By K. Balem.

from argopy import IndexFetcher as ArgoIndexFetcher
idx = ArgoIndexFetcher().float(6902746)
idx.to_dataframe()
idx.plot('trajectory')

The index fetcher can manage caching and works with both Erddap and localftp data sources. It is basically the same as the data fetcher, but do not load measurements, only meta-data. This can be very useful when looking for regional sampling or trajectories.

Tip

Performance: we recommend to use the localftp data source when working this index fetcher because the erddap data source currently suffers from poor performances. This is linked to #16 and is being addressed by Ifremer.

The index fetcher comes with basic plotting functionalities with the argopy.IndexFetcher.plot() method to rapidly visualise measurement distributions by DAC, latitude/longitude and floats type.

Warning

The design of plotting and visualisation features in argopy is constantly evolving, so this may change in future releases.

Breaking changes#

  • The backend option in data fetchers and the global option datasrc have been renamed to src. This makes the code more coherent (@ec6b32e).

Internals#

v0.1.2 (15 May 2020)#

We didn’t like this one this morning, so we move one to the next one !

v0.1.1 (3 Apr. 2020)#

Features and front-end API#

  • Added new data fetcher backend localftp in DataFetcher (@c5f7cb6):

from argopy import DataFetcher as ArgoDataFetcher
argo_loader = ArgoDataFetcher(backend='localftp', path_ftp='/data/Argo/ftp_copy')
argo_loader.float(6902746).to_xarray()

  • Introduced global OPTIONS to set values for: cache folder, dataset (eg:phy or bgc), local ftp path, data fetcher (erddap or localftp) and user level (standard or expert). Can be used in context with (@83ccfb5):

with argopy.set_options(mode='expert', datasrc='erddap'):
    ds = argopy.DataFetcher().float(3901530).to_xarray()

  • Added a argopy.tutorial module to be able to load sample data for documentation and unit testing (@4af09b5):

ftproot, flist = argopy.tutorial.open_dataset('localftp')
txtfile = argopy.tutorial.open_dataset('weekly_index_prof')

  • Improved xarray argo accessor. Added methods for casting data types, to filter variables according to data mode, to filter variables according to quality flags. Useful methods to transform collection of points into collection of profiles, and vice versa (@14cda55):

ds = argopy.DataFetcher().float(3901530).to_xarray() # get a collection of points
dsprof = ds.argo.point2profile() # transform to profiles
ds = dsprof.argo.profile2point() # transform to points

  • Changed License from MIT to Apache (@25f90c9)

Internal machinery

  • Add __all__ to control from argopy import * (@83ccfb5)

  • All data fetchers inherit from class ArgoDataFetcherProto in proto.py (@44f45a5)

  • Data fetchers use default options from global OPTIONS

  • In Erddap fetcher: methods to cast data type, to filter by data mode and by QC flags are now delegated to the xarray argo accessor methods.

  • Data fetchers methods to filter variables according to user mode are using variable lists defined in utilities.

  • argopy.utilities augmented with listing functions of: backends, standard variables and multiprofile files variables.

  • Introduce custom errors in errors.py (@2563c9f)

  • Front-end API ArgoDataFetcher uses a more general way of auto-discovering fetcher backend and their access points. Turned of the deployments access point, waiting for the index fetcher to do that.

  • Improved xarray argo accessor. More reliable point2profile and data type casting with cast_type

Internals#

  • Add CI with github actions (@ecbf9ba)

  • Contribution guideline for data fetchers (@b332495)

  • Improve unit testing (all along commits)

  • Introduce code coverage (@b490ab5)

  • Added explicit support for python 3.6 , 3.7 and 3.8 (@58f60fe)

v0.1.0 (17 Mar. 2020)#

  • Initial release.

  • Erddap data fetcher

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