CIMS
The Python implementation of the CIMS economic climate model.
https://github.com/emrg-sfu/cims
Category: Climate Change
Sub Category: Earth and Climate Modeling
Last synced: about 2 hours ago
JSON representation
Repository metadata
- Host: GitHub
- URL: https://github.com/emrg-sfu/cims
- Owner: EMRG-SFU
- License: mit
- Created: 2023-09-19T00:02:21.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2026-02-10T19:47:41.000Z (10 days ago)
- Last Synced: 2026-02-14T04:52:51.509Z (6 days ago)
- Language: Python
- Homepage:
- Size: 80.1 MB
- Stars: 5
- Watchers: 2
- Forks: 0
- Open Issues: 77
- Releases: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
README.md
CIMS
CIMS is a Python package providing the Python implementation of the CIMS
economic climate model.
βοΈ Installation
CIMS is not currently available on PyPi or other package indexes. Follow the installation guide to get CIMS running on your machine.
π§βπ» Usage
Once you've installed CIMS you can call its functions and classes from within your own Python script, notebook, or program. Follow the quickstart guide to familiarize yourself with CIMS's key functionality.
import CIMS
model_file = 'path/to/model.xlsb'
my_reader = CIMS.ModelReader(infile=model_file)
my_model = CIMS.Model(my_reader)
my_model.run()
π Contributing
Contributions to CIMS are welcome, in many different forms:
- Issues β If you identify a bug, error in the documentation, or a potential improvement to CIMS, consider putting this information into an issue. First, search the list of existing issues to see if there is an ongoing discussion to join. If a relevant issue doesn't already exist, please create a new issue.
- Code β If you are comfortable writing code feel free to make a Pull Request (PR) with your changes. If you've tackled a large feature request or bug, please also create a new issue, or mention an existing issue within your PR.
- Documentation β If you notice typos, out-of-date information, or opportunities for improvements in the documentation (and are comfortable writing Markdown), please consider making a PR with changes.
Any kind of contribution, whether its fixing a small typo, refactoring existing code, or the implementation of a brand new module helps improve this project.
π Citation
π Acknowledgements
Bradford Griffin and Jillian Anderson are the project's lead researcher and lead technical developer, respectively.
In addition, contributions to the codebase have been made by members of Simon Fraser University's Big Data Hub and Research Computing Group:
- Steven Bergner
- Rashid Barket
- Maude Lachaine
- Adriena Wong
- Daisy Yu
- Kacy Wu
Finally, thank you to the numerous EMRG graduate students who have attended meetings, submitted features requests, and flagged bugs:
- Thomas Budd
- Aaron Pardy
- Emma Starke
- Kaitlin Thompson
- Heather Chambers
- Ryan Safton
βοΈ License
The CIMS Python library is licensed under the MIT License. For more information about this license, checkout this overview.
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: CIMS energy-economy model
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Brad
family-names: Griffin
email: bradford_griffin@sfu.ca
affiliation: Simon Fraser University
- given-names: Jillian
family-names: Anderson
email: jillian_anderson@sfu.ca
affiliation: Simon Fraser University
orcid: 'https://orcid.org/0000-0002-2528-2580'
- given-names: Matthew
family-names: Sniatynski
email: matt@exmath.org
affiliation: The Laboratory for Experimental Mathematics Inc.
- given-names: Thomas
family-names: Budd
email: tbudd@sfu.ca
affiliation: Simon Fraser University
- given-names: Emma
family-names: Starke
email: emma_starke@sfu.ca
affiliation: Simon Fraser University
repository-code: 'https://github.com/EMRG-SFU/cims'
repository: 'https://github.com/EMRG-SFU/cims-models'
abstract: >-
CIMS is an integrated energy-economy model designed to
provide information to policy makers on the likely
response of firms and households to policies that
influence their technology acquisition and use decisions.
As a technology simulation model, it reflects how people
actually behave rather than how they ought to behave (as
in cost minimization modelling). CIMS is able to provide
policy makers with information on: (1) the ability of
policies to achieve specific objectives (like greenhouse
gas emissions reductions), (2) the likely costs of
achieving these objectives, and (3) the uncertainties
associated with the modelβs simulations.
CIMS is maintained and operated by the Energy and
Materials Research Group at the School of Resource and
Environmental Management at Simon Fraser University in
Burnaby, B.C., Canada.
Funding for the development of the initial version of this
project was provided by the Pacific Institute for Climate
Solutions Opportunity Projects Program, Natural Resources
Canada, and Environment and Climate Change Canada.
keywords:
- energy
- emissions
- policy
- simulation
- jupyter-notebook
- networkx
- polars
- ipwidgets
- plotly
license: MIT
version: '1.0'
date-released: '2025-10-21'
Owner metadata
- Name: EMRG-SFU
- Login: EMRG-SFU
- Email:
- Kind: organization
- Description:
- Website:
- Location:
- Twitter:
- Company:
- Icon url: https://avatars.githubusercontent.com/u/92178114?v=4
- Repositories: 1
- Last ynced at: 2025-07-04T18:26:44.154Z
- Profile URL: https://github.com/EMRG-SFU
GitHub Events
Total
- Release event: 1
- Delete event: 5
- Pull request event: 10
- Issues event: 22
- Watch event: 1
- Issue comment event: 9
- Push event: 12
- Public event: 1
- Pull request review comment event: 6
- Pull request review event: 3
- Create event: 3
Last Year
- Release event: 1
- Delete event: 5
- Pull request event: 10
- Issues event: 22
- Watch event: 1
- Issue comment event: 9
- Push event: 12
- Public event: 1
- Pull request review comment event: 6
- Pull request review event: 3
- Create event: 3
Committers metadata
Last synced: 1 day ago
Total Commits: 568
Total Committers: 25
Avg Commits per committer: 22.72
Development Distribution Score (DDS): 0.472
Commits in past year: 30
Committers in past year: 3
Avg Commits per committer in past year: 10.0
Development Distribution Score (DDS) in past year: 0.533
| Name | Commits | |
|---|---|---|
| Jillian Anderson | 1****8 | 300 |
| Brad Griffin | b****n@s****a | 159 |
| Kacy Wu | K****2@g****m | 20 |
| mlachain | m****n@s****a | 18 |
| rbarket | b****d@h****m | 16 |
| βyya188β | β****8@s****β | 12 |
| Matt | m****t@M****P | 8 |
| Maude Lachaine | m****e@S****l | 6 |
| Steven Bergner | g****b | 4 |
| Maude Lachaine | m****e@d****a | 4 |
| rbarket | 5****t@u****a | 4 |
| Daisy Yu | 1****8@u****a | 2 |
| Maude Lachaine | m****e@S****e | 2 |
| yya188 | y****8@s****a | 2 |
| Kacy Wu | 5****8@u****a | 1 |
| Maude Lachaine | m****e@M****e | 1 |
| Maude Lachaine | m****e@d****a | 1 |
| Maude Lachaine | m****e@d****a | 1 |
| Maude Lachaine | m****e@d****a | 1 |
| Maude Lachaine | m****e@d****a | 1 |
| Maude Lachaine | m****e@d****a | 1 |
| Maude Lachaine | m****e@d****a | 1 |
| adrienaw | 5****w@u****a | 1 |
| EvanDungate | e****e@g****m | 1 |
| matt-exmath | 1****h | 1 |
Committer domains:
- users.noreply.gitlab.rcg.sfu.ca: 4
- sfu.ca: 3
- d207-023-223-118.wireless.sfu.ca: 1
- d207-023-208-135.wireless.sfu.ca: 1
- d207-023-175-154.wireless.sfu.ca: 1
- d207-023-172-121.wireless.sfu.ca: 1
- d142-058-076-252.wireless.sfu.ca: 1
- d142-058-076-174.wireless.sfu.ca: 1
- maudes-mbp.hitronhub.home: 1
- semaphosmacbook.hitronhub.home: 1
- d142-058-079-066.wireless.sfu.ca: 1
- sfu.caβ: 1
Issue and Pull Request metadata
Last synced: 5 days ago
Total issues: 80
Total pull requests: 39
Average time to close issues: about 2 months
Average time to close pull requests: about 1 month
Total issue authors: 6
Total pull request authors: 3
Average comments per issue: 1.26
Average comments per pull request: 0.46
Merged pull request: 33
Bot issues: 0
Bot pull requests: 0
Past year issues: 56
Past year pull requests: 22
Past year average time to close issues: about 1 month
Past year average time to close pull requests: 11 days
Past year issue authors: 5
Past year pull request authors: 3
Past year average comments per issue: 0.46
Past year average comments per pull request: 0.41
Past year merged pull request: 17
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
- brad-griffin (40)
- jillianderson8 (31)
- matt-exmath (3)
- tcbudd (3)
- gabi-diner-cer (2)
- ees88 (1)
Top Pull Request Authors
- jillianderson8 (25)
- brad-griffin (10)
- matt-exmath (4)
Top Issue Labels
- bug (15)
- enhancement (15)
- refactoring (6)
- low priority (1)
- on hold (1)
Top Pull Request Labels
- bug (6)
- enhancement (5)
- refactoring (3)
- documentation (1)
Dependencies
- jupyter
- matplotlib
- networkx
- numpy
- pandas >=2
- pip
- polars
- pyarrow
- python >=3.11
- scipy
- seaborn >=0.13.2
- setuptools
- xlrd
- matplotlib *
- networkx *
- numpy *
- packaging *
- pandas >=1.2
- polars *
- pyarrow *
- pyxlsb *
- requests *
- scipy *
- seaborn >=0.13.2
- tqdm *
- wasabi *
- xlrd *
Score: 7.6255950721324535