API reference

This page provides an auto-generated summary of argopy’s API. For more details and examples, refer to the relevant chapters in the main part of the documentation.

Argo Data Fetchers

DataFetcher

alias of ArgoDataFetcher

Data selection methods

DataFetcher.region()	Space/time domain data fetcher
DataFetcher.float()	Float data fetcher
DataFetcher.profile()	Profile data fetcher

Data access methods

DataFetcher.load([force])	Fetch data (and compute a profile index) if not already in memory
DataFetcher.to_xarray(**kwargs)	Fetch and return data as xarray.DataSet
DataFetcher.to_dataframe(**kwargs)	Fetch and return data as pandas.DataFrame
DataFetcher.to_index([full, coriolis_id])	Return a profile index of Argo data, fetch data if necessary
DataFetcher.to_dataset(**kwargs)	Fetch and return data as netCDF4.Dataset

Data visualisation methods

DataFetcher.plot([ptype])	Create custom plots from this fetcher data or index.
DataFetcher.dashboard(**kw)	Open access point dashboard.
IndexFetcher.plot([ptype])	Create custom plots from this fetcher index.

Properties

DataFetcher.data	Data structure
DataFetcher.index	Index structure, as returned by the to_index method
DataFetcher.domain	Space/time domain of the dataset
DataFetcher.uri	List of resources to load for a request

Dataset.argo (xarray accessor to work with Argo dataset)

Dataset.argo

Class registered under scope argo to access a xarray.Dataset object.

This accessor extends xarray.Dataset. Proper use of this accessor should be like:

>>> import xarray as xr         # first import xarray
>>> import argopy               # import argopy (the dataset 'argo' accessor is then registered)
>>> from argopy import DataFetcher
>>> ds = DataFetcher().float([6902766, 6902772, 6902914, 6902746]).load().data
>>> ds.argo
>>> ds.argo.filter_qc()

Data Transformation

Dataset.argo.point2profile([drop])	Transform a collection of points into a collection of profiles
Dataset.argo.profile2point()	Transform a collection of profiles to a collection of points
Dataset.argo.interp_std_levels(std_lev[, axis])	Interpolate measurements to standard pressure levels
Dataset.argo.groupby_pressure_bins(bins[, ...])	Group measurements by pressure bins
Dataset.argo.datamode.compute(indexfs)	Compute <PARAM>_DATA_MODE variables
Dataset.argo.datamode.filter([dm, params, ...])	Filter measurements according to parameters data mode
Dataset.argo.datamode.merge([params, errors])	Merge <PARAM> and <PARAM>_ADJUSTED variables according to DATA_MODE or <PARAM>_DATA_MODE
Dataset.argo.datamode.split()	Convert PARAMETER_DATA_MODE(N_PROF, N_PARAM) into several <PARAM>_DATA_MODE(N_PROF) variables

Data Filters

Dataset.argo.filter_qc([QC_list, QC_fields, ...])	Filter measurements according to QC values
Dataset.argo.filter_scalib_pres([force, inplace])	Filter variables according to OWC salinity calibration software requirements
Dataset.argo.filter_researchmode()	Filter dataset for research user mode
Dataset.argo.datamode.filter([dm, params, ...])	Filter measurements according to parameters data mode

Extensions

You can create your own extension to an Argo dataset for specific features. It should be registered by inheriting from argopy.extensions.ArgoAccessorExtension and decorated with argopy.extensions.register_argo_accessor.

argopy comes with the following extensions:

General purposes

Dataset.argo.teos10([vlist, inplace])	Add TEOS10 variables to the dataset
Dataset.argo.datamode(**kwargs)	Utilities for Argo parameters data mode
Dataset.argo.create_float_source([path, ...])	Preprocess data for OWC software calibration

BGC specifics

Dataset.argo.canyon_med(**kwargs)	Nutrients and Carbonate System Variables predictor with CANYON-MED
Dataset.argo.canyon_med.predict([params])	Make predictions using the CANYON-MED neural network
Dataset.argo.canyon_med.input_list	List of parameters required to make predictions with CANYON-MED
Dataset.argo.canyon_med.output_list	List of all possible output variables for CANYON-MED
Dataset.argo.canyon_b(**kwargs)	Nutrients and Carbonate System Variables predictor with CANYON-B
Dataset.argo.canyon_b.predict([params, ...])	Make predictions using the CANYON-B method.
Dataset.argo.canyon_b.input_list	List of parameters required to make predictions with CANYON-B
Dataset.argo.canyon_b.output_list	List of all possible output variables for CANYON-B
Dataset.argo.content(**kwargs)	Nutrients and Carbonate System Variables predictor made consistent with chemistry constraints with CONTENT
Dataset.argo.content.predict([epres, etemp, ...])	Make predictions using the CONTENT method.
Dataset.argo.content.input_list	List of parameters required to make predictions with CONTENT
Dataset.argo.content.output_list	List of all possible output variables for CONTENT
Dataset.argo.optic(**kwargs)	Optical modeling of the upper ocean
Dataset.argo.optic.Zeu([axis, par, method, ...])	Depth of the euphotic zone from PAR
Dataset.argo.optic.Zpd(*args, **kwargs)	First optical depth from depth of the euphotic zone
Dataset.argo.optic.Z_iPAR_threshold([axis, ...])	Depth where PAR reaches some threshold value (closest point)
Dataset.argo.optic.DCM([axis, chla, bbp, ...])	Search and qualify Deep Chlorophyll Maxima

Misc

Dataset.argo.index	Basic profile index
Dataset.argo.domain	Space/time domain of the dataset
Dataset.argo.list_WMO_CYC	Return a tuple with all (PLATFORM_NUMBER, CYCLE_NUMBER) in the dataset
Dataset.argo.uid(wmo_or_uid[, cyc, direction])	UID encoder/decoder
Dataset.argo.cast_types(**kwargs)	Make sure variables are of the appropriate types according to Argo
Dataset.argo.N_POINTS	Number of measurement points
Dataset.argo.to_zarr(*args, **kwargs)	Write Argo dataset content to a zarr group
Dataset.argo.reduce_profile(func[, params])	Apply a vectorized function for unlabeled arrays for each Argo profiles

ArgoFloat (data and meta-data fetcher for one float)

ArgoFloat(*args, **kwargs)

Argo GDAC float store

Extension: Plot

ArgoFloat.plot

Extension providing plot methods

ArgoFloat.plot.trajectory(**kwargs)	Quick map of the float trajectory
ArgoFloat.plot.map(param[, ds, pres, ...])	Plot a map of one dataset parameter, possibly sliced at a given pressure value.
ArgoFloat.plot.scatter(param[, ds])	Scatter plot for a 2-dimensional dataset parameter

Extension: Configuration and launch configuration parameters

ArgoFloat.config

Extension providing access to configuration parameters

ArgoFloat.config.n_params	Total number of parameters
ArgoFloat.config.parameters	List of configuration parameter names
ArgoFloat.config.n_missions	Total number of missions
ArgoFloat.config.missions	List of mission numbers
ArgoFloat.config.cycles	A dictionary mapping cycle on mission numbers
ArgoFloat.config.for_cycles(param, cycle_numbers)	Retrieve a configuration parameter for cycle number(s)
ArgoFloat.config.to_dataframe([missions])	Export configuration parameters to a class:pandas.DataFrame

ArgoFloat.launchconfig

Extension providing access to LAUNCH configuration parameters

ArgoFloat.launchconfig.n_params	Total number of parameters
ArgoFloat.launchconfig.parameters	List of configuration parameter names
ArgoFloat.launchconfig.to_dataframe()	Export launch configuration parameters to a class:pandas.DataFrame

ArgoIndex (explore Argo files index)

ArgoIndex(**kwargs)

Argo GDAC index store

List of extensions:

ArgoIndex.query	Extension providing search methods to query index entries
ArgoIndex.plot	Extension providing plot methods

Search on a single property of a file record:

ArgoIndex.query.wmo(WMOs[, nrows, composed])	Search index for floats defined by WMO
ArgoIndex.query.cyc(CYCs[, nrows, composed])	Search index for cycle numbers
ArgoIndex.query.lon([BOX, nrows, composed])	Search index for a meridional band
ArgoIndex.query.lat([BOX, nrows, composed])	Search index for a zonal band
ArgoIndex.query.date([BOX, nrows, composed])	Search index for a date range
ArgoIndex.query.params(PARAMs[, logical, ...])	Search index for one or a list of parameters
ArgoIndex.query.parameter_data_mode(PARAMs)	Search index for profiles with a parameter in a specific data mode
ArgoIndex.query.profiler_type(profiler_type)	Search index for profiler types
ArgoIndex.query.profiler_label(profiler_label)	Search index for profiler types with a given string in their long name
ArgoIndex.query.institution_code(...[, ...])	Search index for institution codes
ArgoIndex.query.institution_name(...[, ...])	Search index for institutions with a given string in their long name
ArgoIndex.query.dac(dac[, nrows, composed])	Search index for DAC

Search on at least two properties of a file record:

ArgoIndex.query.wmo_cyc(WMOs, CYCs[, nrows, ...])	Search index for floats defined by their WMO and specific cycle numbers
ArgoIndex.query.lon_lat(BOX[, nrows, composed])	Search index for a rectangular longitude/latitude domain
ArgoIndex.query.box(BOX[, nrows, composed])	Search index for a box: a rectangular latitude/longitude domain and time range
ArgoIndex.query.compose(query[, nrows])	Compose query with multiple search methods

Plotting methods:

ArgoIndex.plot.trajectory([index])	Quick map of profile index trajectories
ArgoIndex.plot.bar([by, index])	Bar plot of one index property

Argo vocabulary: referencing system

ArgoReferenceValue(name[, reference])	A class to work with an Argo Reference Value, i.e. a NVS vocabulary "concept".
ArgoReferenceTable([identifier])	A class to work with an Argo reference table, i.e. a NVS "vocabulary".
ArgoReferenceMapping(sub, obj, **kwargs)	A class to work with Argo Reference Value Relationships, i.e. a NVS "mapping".

Other Argo related data

status	alias of monitor_status
ArgoDocs([docid, cache])	ADMT documentation access and discovery
ArgoDOI([hashtag, fs, cache])	Argo GDAC snapshot DOI access and discovery
OceanOPSDeployments([box, deployed_only])	Argo floats deployment plans
CTDRefDataFetcher	alias of Fetch_box
TopoFetcher(box[, ds, cache, cachedir, ...])	Fetch topographic data through an ERDDAP server for an ocean rectangle

GDAC file stores

gdacfs([path, cache, cachedir])

Argo file system for any GDAC path

Data visualisation

Visualisation functions available at the argopy module level:

dashboard([wmo, cyc, type, url_only, width, ...])	Insert an Argo dashboard page in a notebook cell, or return the corresponding url
ArgoColors([name, N])	Class to manage discrete coloring for Argo related variables

All other visualisation functions are in the argopy.plot submodule:

open_sat_altim_report([WMO, embed])	Insert the CLS Satellite Altimeter Report figure in notebook cell
scatter_map(data[, x, y, hue, markersize, ...])	Try-to-be generic function to create a scatter plot on a map from argopy xarray.Dataset or pandas.DataFrame data
bar_plot(df[, by, style, with_seaborn])	Create a bar plot for an Argo index dataframe
scatter_plot(ds, param[, x, y, figsize, ...])	Argo parameter scatter plot
latlongrid(ax[, dx, dy, fontsize, ...])	Add latitude/longitude grid line and labels to a cartopy geoaxes

Utilities

Function under the argopy.utils submodule.

Lists

list_available_data_src()	List all available data sources
list_available_index_src()	List all available index sources
list_standard_variables([ds])	List of dataset variables possibly return in standard user mode
list_multiprofile_file_variables()	List of all 🟡 core + 🔵 deep variables that can be found in a multi-profile netcdf file
list_core_parameters()	List of all 🟡 core + 🔵 deep parameters that can be found in mono and multi-profile netcdf files
list_bgc_s_variables()	List of all 🟢 BGC mission variables that can be found in a BGC Synthetic netcdf files
list_bgc_s_parameters()	List of all 🟢 BGC mission parameters that can be found in a BGC Synthetic netcdf files
list_radiometry_variables()	List of all 🟢 BGC mission variables related to radiometry that can be found in a BGC Synthetic netcdf files
list_radiometry_parameters()	List of all 🟢 BGC mission parameters related to radiometry that can be found in a BGC Synthetic netcdf files
list_gdac_servers()	List of official Argo GDAC servers

Checkers

check_wmo(lst[, errors])	Validate a WMO option and returned it as a list of integers
check_cyc(lst[, errors])	Validate a CYC option and returned it as a list of integers
check_gdac_path(path[, errors, ignore_knowns])	Check if a path has the expected GDAC structure
isconnected([host, maxtry])	Check if an URL is alive
urlhaskeyword([url, keyword, maxtry])	Check if a keyword is in the content of a URL
isalive([api_server_check])	Check if an API is alive or not
isAPIconnected([src, data])	Check if a source API is alive or not

Misc

float_wmo(WMO_number[, errors])	Argo float WMO number object
Registry([initlist, dtype, invalid])	A list manager that can validate item type
Chunker(request[, chunks, chunksize])	To chunk fetcher requests
drop_variables_not_in_all_datasets(ds_collection)	Drop variables that are not in all datasets (the lowest common denominator)
fill_variables_not_in_all_datasets(ds_collection)	Add empty variables to dataset so that all the collection have the same xarray.Dataset.data_vars and xarray.Dataset.coords
GreenCoding()	GreenCoding API helper class for argopy carbon footprint
Github([repo, owner])	Github repository helper class
optical_modeling	Utility module for optical modeling diagnostics.

Argopy helpers

set_options(**kwargs)	Set options for argopy
clear_cache([fs])	Delete argopy cache folder content
tutorial.open_dataset(name)	Open a dataset from the argopy online data repository (requires internet).
show_versions([file, conda])	Print the versions of argopy and its dependencies
xarray.ArgoEngine()	Backend for Argo netCDF files based on the xarray netCDF4 engine

Internals

File systems

gdacfs([path, cache, cachedir])	Argo file system for any GDAC path
stores.ArgoStoreProto([cache, cachedir])	Argo File System Prototype
stores.filestore([cache, cachedir])	Argo local file system
stores.httpstore(*args, **kwargs)	Argo http file system
stores.httpstore_erddap([url, cache, cachedir])	Argo http file system that automatically try to login to an Erddap server
stores.memorystore([cache, cachedir])	Argo in-memory file system (global)
stores.ftpstore(*args, **kwargs)	Argo ftp file system
stores.s3store(*args, **kwargs)	Argo s3 file system
stores.ArgoKerchunker([store, root, ...])	Argo netcdf file kerchunk helper

Fetcher sources

ERDDAP

data_fetchers.erddap_data.ErddapArgoDataFetcher([...])	Manage access to Argo data through Ifremer ERDDAP
data_fetchers.erddap_data.Fetch_wmo([ds, ...])	Manage access to Argo data through Ifremer ERDDAP for: a list of WMOs
data_fetchers.erddap_data.Fetch_box([ds, ...])	Manage access to Argo data through Ifremer ERDDAP for: an ocean rectangle

GDAC

data_fetchers.gdac_data.GDACArgoDataFetcher([...])	Manage access to Argo data from a GDAC server
data_fetchers.gdac_data.Fetch_wmo([ds, ...])	Manage access to GDAC Argo data for: a list of WMOs.
data_fetchers.gdac_data.Fetch_box([ds, ...])	Manage access to GDAC Argo data for: a rectangular space/time domain.

Argovis

data_fetchers.argovis_data.ArgovisDataFetcher([...])
data_fetchers.argovis_data.Fetch_wmo([ds, ...])
data_fetchers.argovis_data.Fetch_box([ds, ...])