More detailed energy and carbon stats

CHANGELOG.rst

@@ -15,6 +15,7 @@ New features and enhancements
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 * New function ``ensemble.partition.general_partition`` (:pull:`2035`)
 * Added a new ``xclim.indices.generic.bivariate_count_occurrences`` function to count instances where operations and performed and validated for two variables. (:pull:`2030`).
+* `xclim` now tracks energy usage and carbon emissions ("last run", "average", and "total") during CI workflows using the `eco-ci-energy-estimation` GitHub Action. (:pull:`2046`).
 
 Internal changes
 ^^^^^^^^^^^^^^^^

README.rst

@@ -11,7 +11,7 @@ xclim: Climate services library |logo| |logo-dark| |logo-light|
 +----------------------------+-----------------------------------------------------+
 | Coding Standards           | |black| |ruff| |pre-commit| |ossf-bp| |fossa|       |
 +----------------------------+-----------------------------------------------------+
-| Development Status         | |status| |build| |coveralls| |energy|               |
+| Development Status         | |status| |build| |coveralls|                        |
 +----------------------------+-----------------------------------------------------+
 
 `xclim` is an operational Python library for climate services, providing numerous climate-related indicator tools
@@ -20,7 +20,7 @@ adjustment of climate model simulations, as well as climate model ensemble analy
 
 `xclim` is built using `xarray`_ and can seamlessly benefit from the parallelization handling provided by `dask`_.
 Its objective is to make it as simple as possible for users to perform typical climate services data treatment workflows.
-Leveraging xarray and dask, users can easily bias-adjust climate simulations over large spatial domains or compute indices from large climate datasets.
+Leveraging `xarray` and `dask`, users can easily bias-adjust climate simulations over large spatial domains or compute indices from large climate datasets.
 
 For example, the following would compute monthly mean temperature from daily mean temperature:
 
@@ -32,7 +32,7 @@ For example, the following would compute monthly mean temperature from daily mea
     ds = xr.open_dataset(filename)
     tg = xclim.atmos.tg_mean(ds.tas, freq="MS")
 
-For applications where metadata and missing values are important to get right, xclim provides a class for each index
+For applications where metadata and missing values are important to get right, `xclim` provides a class for each index
 that validates inputs, checks for missing values, converts units and assigns metadata attributes to the output.
 This also provides a mechanism for users to customize the indices to their own specifications and preferences.
 `xclim` currently provides over 150 indices related to mean, minimum and maximum daily temperature, daily precipitation,
@@ -110,6 +110,19 @@ This is free software: you can redistribute it and/or modify it under the terms
 .. _Apache License 2.0: https://opensource.org/license/apache-2-0/
 .. _LICENSE: https://github.com/Ouranosinc/xclim/blob/main/LICENSE
 
+Energy and Carbon Usage
+-----------------------
+The `xclim` development team is interested in thoroughly testing our software while also reducing the environmental impact of the software we develop.
+This repository uses the `ECO-CI`_ tool to estimate and track the energy use and carbon emissions of our continuous integration workflows.
+
++---------------------------------------------------------------------------------------+
+| `ECO-CI`_ Energy Use and Carbon Emissions from CI Workflows (since November 2024)     |
++------------------+------------------+-------------------------------------------------+
+| Testing suite    | Energy Usage     | |energy-last| |energy-average| |energy-total|   |
+|                  +------------------+-------------------------------------------------+
+| (*main* branch)  | Carbon Emissions | |carbon-last| |carbon-average| |carbon-total|   |
++------------------+------------------+-------------------------------------------------+
+
 Credits
 -------
 `xclim` development is funded through Ouranos_, Environment and Climate Change Canada (ECCC_), the `Fonds vert`_ and the Fonds d'électrification et de changements climatiques (FECC_), the Canadian Foundation for Innovation (CFI_), and the Fonds de recherche du Québec (FRQ_).
@@ -120,6 +133,7 @@ This package was created with Cookiecutter_ and the `audreyfeldroy/cookiecutter-
 .. _CFI: https://www.innovation.ca/
 .. _Cookiecutter: https://github.com/cookiecutter/cookiecutter/
 .. _ECCC: https://www.canada.ca/en/environment-climate-change.html
+.. _ECO-CI: https://www.green-coding.io/
 .. _FECC: https://www.environnement.gouv.qc.ca/ministere/fonds-electrification-changements-climatiques/index.htm
 .. _Fonds vert: https://www.environnement.gouv.qc.ca/ministere/fonds-vert/index.htm
 .. _FRQ: https://frq.gouv.qc.ca/
@@ -169,10 +183,6 @@ This package was created with Cookiecutter_ and the `audreyfeldroy/cookiecutter-
         :target: https://bestpractices.coreinfrastructure.org/projects/6041
         :alt: Open Source Security Foundation Best Practices
 
-.. |energy| image:: https://api.green-coding.io/v1/ci/badge/get?repo=Ouranosinc/xclim&branch=main&workflow=47013755
-        :target: https://metrics.green-coding.io/ci.html?repo=Ouranosinc/xclim&branch=main&workflow=47013755
-        :alt: Energy Consumption
-
 .. |ossf-score| image:: https://api.securityscorecards.dev/projects/github.com/Ouranosinc/xclim/badge
         :target: https://securityscorecards.dev/viewer/?uri=github.com/Ouranosinc/xclim
         :alt: Open Source Security Foundation Scorecard
@@ -215,3 +225,30 @@ This package was created with Cookiecutter_ and the `audreyfeldroy/cookiecutter-
 .. |versions| image:: https://img.shields.io/pypi/pyversions/xclim.svg
         :target: https://pypi.python.org/pypi/xclim
         :alt: Supported Python Versions
+
+..
+    Energy Metrics by https://github.com/green-coding-solutions/eco-ci-energy-estimation
+
+.. |energy-total| image:: https://api.green-coding.io/v1/ci/badge/get?repo=Ouranosinc/xclim&branch=main&workflow=5828641&mode=totals
+        :target: https://metrics.green-coding.io/ci.html?repo=Ouranosinc/xclim&branch=main&workflow=5828641
+        :alt: Energy Consumption (Total)
+
+.. |energy-last| image:: https://api.green-coding.io/v1/ci/badge/get?repo=Ouranosinc/xclim&branch=main&workflow=5828641
+        :target: https://metrics.green-coding.io/ci.html?repo=Ouranosinc/xclim&branch=main&workflow=5828641
+        :alt: Energy Consumption (Last run)
+
+.. |energy-average| image:: https://api.green-coding.io/v1/ci/badge/get?repo=Ouranosinc/xclim&branch=main&workflow=5828641&mode=avg&duration_days=90
+        :target: https://metrics.green-coding.io/ci.html?repo=Ouranosinc/xclim&branch=main&workflow=5828641
+        :alt: Energy Consumption (Last 90 days moving average)
+
+.. |carbon-total| image:: https://api.green-coding.io/v1/ci/badge/get?repo=Ouranosinc/xclim&branch=main&workflow=5828641&mode=totals&metric=carbon
+        :target: https://metrics.green-coding.io/ci.html?repo=Ouranosinc/xclim&branch=festive-indices&workflow=5828641
+        :alt: Carbon Emissions (Total)
+
+.. |carbon-last| image:: https://api.green-coding.io/v1/ci/badge/get?repo=Ouranosinc/xclim&branch=main&workflow=5828641&metric=carbon
+        :target: https://metrics.green-coding.io/ci.html?repo=Ouranosinc/xclim&branch=main&workflow=5828641
+        :alt: Carbon Emissions (Last run)
+
+.. |carbon-average| image:: https://api.green-coding.io/v1/ci/badge/get?repo=Ouranosinc/xclim&branch=main&workflow=5828641&mode=avg&duration_days=90&metric=carbon
+        :target: https://metrics.green-coding.io/ci.html?repo=Ouranosinc/xclim&branch=main&workflow=5828641
+        :alt: Carbon Emissions (Last 90 days moving average)