arcosparse 0.5.0

Helper to download and subset sparse data that has been Arcoified and are available through STAC and sqlite formated data

arcosparse: A Python library for ARCO sparse datasets subsetting

Disclaimer

It is not recommended to use the arcosparse library directly. Instead, if you want to work with sparse datasets, use the copernicusmarine Toolbox or tools like earthkit.

Issues on the repository are welcome and we will do our best to answer them.

Usage

[!WARNING] This library is still in development. Breaking changes might be introduced from version 0.y.z to 0.y+1.z.

Main functions

arcosparse.subset_and_return_dataframe

Subset the data based on the input and return a dataframe.

arcosparse.subset_and_save

Subset the data based on the input and return data as a partitioned parquet file. It means that the data is saved in one folder and in this folder there are many small parquet files. Though, you can open all the data at once.

To open the data into a dataframe, use this snippet:

import glob

output_path = "some_folder"

# Get all partitioned Parquet files
parquet_files = glob.glob(f"{output_path}/*.parquet")

# # Read all files into a single dataframe
df = pd.concat(pd.read_parquet(file) for file in parquet_files)

arcosparse.get_entities

A function to get the metadata about the entities that are available in the dataset. Since all the information is retrieved from the metadata, the argument is the url_metadata, the same used for the subset. Returns a list of arcosparse.Entity. It contains information about the entities available in the dataset:

• entity_id: same as the entity_id column in the result of a subset.
• entity_type: same as the entity_type column in the result of a subset.
• doi: the DOI of the entity.
• institution: the institution associated with the entity.

arcosparse.get_dataset_metadata

A function to get the metadata about the dataset. Since all the information is retrieved from the metadata, the argument is the url_metadata, the same used for the subset.

Returns an object arcosparse.Dataset. It contains information about the dataset:

• dataset_id: the ID of the dataset.
• variables: a list of the names of the variables available in the dataset.
• assets: a list of the names of the assets available in the dataset.
• coordinates: a list of arcosparse.DatasetCoordinate objects. Each object contains the following information:
  • coordinate_id: the ID of the coordinate.
  • unit: the unit of the coordinate.
  • minimum: the minimum value of the coordinate.
  • maximum: the maximum value of the coordinate.
  • step: the step of the coordinate.
  • values: the values of the coordinate.

Authentication

You may need to authenticate to access some datasets, particularly when working with ECMWF data.

To do so, use the user_configuration argument, which accepts an arcosparse.UserConfiguration instance containing the following fields:

• auth_token: The token used to authenticate requests. It is passed as the Authorization: Bearer {auth_token} header.

Example:

import arcosparse

user_configuration = arcosparse.UserConfiguration(
    auth_token="my_token"
)
df = arcosparse.subset_and_return_dataframe(
    url_metadata="https://example.com/metadata.json",
    minimum_latitude=10,
    maximum_latitude=20,
    minimum_longitude=30,
    maximum_longitude=40,
    minimum_time="2020-01-01T00:00:00Z",
    maximum_time="2020-12-31T23:59:59Z",
    minimum_elevation=0,
    maximum_elevation=1000,
    variables=["temperature", "precipitation"],
    user_configuration=user_configuration
)

Note that STAC catalogues are typically public, so arcosparse will request the catalogue without authentication. However, any asset links found within the catalogue will be authenticated using the token provided in auth_token, if one is supplied.

Changelog

0.5.0

0.5.0: Breaking Changes

• Deleted disable_progress_bar argument in the functions subset_and_return_dataframe and subset_and_save. Use progress_bar_configuration={"disable": True} instead.

0.5.0: New features

• pandas>=3 is now available.
• Add a way to handle metadata in chunks. Now capable of reading overflow chunks.
• Change license to EUPL-1.2.
• Can authenticate the requests to the assets with a token provided in auth_token in user_configuration. It is passed as the Authorization: Bearer {auth_token} header. See the "Authentication" section in the doc for more details.
• arcosparse got public. The repository is now open.

0.4.2

0.4.2: Bug fixes

• Fix a bug where dates in the metadata like "2025-06-25T07:43:54.514180Z" would not be parsed and raised an error. Now, it uses dateutil.parser to parse the date strings correctly.

0.4.1

0.4.1: New features

• Added function get_dataset_metadata. It returns an arcosparse.Dataset object.

0.4.0

Breaking Changes

• Deleted function get_entities_ids. Use get_entities as a replacement. Example:

# old code
my_entities = get_entities_ids(url_metadata)

# new code
my_entities = [entity.entity_id for entity in get_entities(url_metadata)]

New features

• Added function get_entities. It returns a list of Entity objects.

Bug fixes

• Fix a bug where arcosparse would modify the dict that users input in the columns_rename argument. Now, it deepcopy it to modify it after that.

0.3.5

• Return all the columns even if full of NaNs.

0.3.4

• Deleted deprecated get_platforms_names function
• Fix an issue when query on the chunk would not be correct if the requested subset is 0.

0.3.3

• Add GPLv3 license

0.3.2

• Fixes an issue on Windows where deleting a file is not permited if we don't close explicitly the sql connection.

0.3.1

• Reindex when concatenate. Fixes issue when indexes wouldn't be unique.
• Fixes an issue on Windows where datetime.to_timestamp does not support dates before 1970-1-1 (i.e. negative values for timestamps).
• Fixes an issue on Windows where a temporary sqlite file cannot be opened while it's already open in the process.

0.3.0

• Change columns output: from "platform_id" to "entity_id" and from "platform_type" to "entity_type".
• Document the expected column names in the doc of the functions.
• Add columns_rename argument to subset_and_return_dataframe and subset_and_save to be able to choose the names of the columns in the output.