Skip to main content

Digital service environmental footprint model

Project description

E-footprint model

A toolkit for exhaustively modeling the environmental impact of digital services.

The current perimeter is the carbon footprint associated with the fabrication and usage of servers, storage, network (usage only) and end-user devices necessary for the existence of a digital service. Other environmental impacts (water, rare earth metals, etc.) will be added soon through an integration with the Boavizta API, and the lifecycle phases of device transportation and end of life are currently considered negligible.

Getting started

Installation

Check out INSTALL.md.

Documentation

Here is the link to the e-footprint documentation. There you will find a description of all the e-footprint objects, their parameters, the relationship between the objects and the calculated attributes and their graphs.

Graphical interface

You can explore the model’s graphical interface. This interface allows for a powerful use of the model but is still in beta for now. Please send an email to e-footprint’s main maintainer, Vincent Villet if you wish to give feedback and / or be notified when the interface gets to a first stable version !

Modeling examples

Checkout our open source e-footprint modeling use cases.

Tutorial

pip install efootprint

You can then run the jupyter notebook tutorial to familiarize yourself with the object logic and generate an object relationship graph and a calculation graph as HTML files in the current folder.

object relationships graph
Object relationships graph: usage patterns in deep blue, user journey in blue, user journey steps in pale blue, jobs in gold, infra hardware in red. Hover over a node to get the numerical values of its environmental and technical attributes. For simplifying the graph the Network and Hardware nodes are not shown.
simple calculation graph
Calculation graph: user inputs in gold, hypothesis in darkred, and intermediate calculations in pale blue. Hover over a node to read the formula.

To launch jupyter:

# Todo once to setup jupyter kernel
poetry run ipython kernel install --user --name=efootprint-kernel
# Start Jupyter server with poetry
poetry run jupyter notebook tutorial.ipynb

Dev setup

Check out INSTALL.md.

Code logic

The code has been architectured to separate modeling from optimization from API logic. The goal is to make contribution to the modeling logic as straightforward as possible.

  • Scripts that deal with modeling logic are located in efootprint/core.
  • Optimizations (having the model rerun the right calculations whenever an input attribute or a link between objects changes) are dealt with in efootprint/abstract_modeling_classes.
  • The API doesn’t exist yet but will be also decoupled from the modeling and optimization logics.

Contributing

Check out CONTRIBUTING.md

License

GNU Affero General Public License v3.0

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

efootprint-8.0.1.tar.gz (84.2 kB view details)

Uploaded Source

Built Distribution

efootprint-8.0.1-py3-none-any.whl (100.9 kB view details)

Uploaded Python 3

File details

Details for the file efootprint-8.0.1.tar.gz.

File metadata

  • Download URL: efootprint-8.0.1.tar.gz
  • Upload date:
  • Size: 84.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.2 CPython/3.12.7 Darwin/24.1.0

File hashes

Hashes for efootprint-8.0.1.tar.gz
Algorithm Hash digest
SHA256 6e572ea1c3140c483f47ca5170884d411b41abca60c75060412ddcbd6501622e
MD5 ca7aba5a0f96300842f5e585a1c7e301
BLAKE2b-256 278d3ec0b47b6688c3b397f5c05d685661eddb2d568f76c0fb1d46e424bf3fa1

See more details on using hashes here.

File details

Details for the file efootprint-8.0.1-py3-none-any.whl.

File metadata

  • Download URL: efootprint-8.0.1-py3-none-any.whl
  • Upload date:
  • Size: 100.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.2 CPython/3.12.7 Darwin/24.1.0

File hashes

Hashes for efootprint-8.0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 fed5a9b417efd1497ef6087b8f8e1c22512f1a871a552d8ddcec103270e3b254
MD5 69d72fbf6eb2750ae39410d48c1236c3
BLAKE2b-256 932760bd2037b6a8362e953c068edcbcd9011c974e0478088b9d8d7a627f8d2f

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page