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OnStove

Calculates the net-benefits of different stove options in a given geography and compares all stoves to one another with regards to their net-benefit.
https://github.com/open-source-spatial-clean-cooking-tool/onstove

Category: Energy Systems
Sub Category: Renewable Energy Integration

Last synced: about 13 hours ago
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Repository metadata

This repository contain the general code for the Open Source Spatial Clean Cooking Tool OnStove

README.md

Documentation Status
Tests
License: MIT
codecov
DOI
Conda Version

This repository contains the general code for the geospatial cost-benefit clean cooking tool, OnStove. OnStove calculates the net-benefits of different stove options in a given geography and compares all stoves to one another with regards to their net-benefit.

Introduction

OnStove is developed by the division of Energy Systems at KTH together with partners. The tool is a geospatial, raster-based tool determining the net-benefit of different cooking solutions selected by the user for raster grid cell of a given study area. The tool takes into account four benefits of adopting clean cooking: reduced morbidity, mortality, emissions and time saved, as well as three costs: capital, fuel as well as operation and maintenance (O&M) costs. In each grid cell of the study area the stove with the highest net-benefit is chosen.

OnStove produces scenarios depicting the “true” cost of clean cooking. The scenarios benefits and costs of produced by the tool are to be interpreted as the benefits and costs one could expect if the clean cooking transition was to happen now (overnight change). Results from OnStove are to be interpreted as an upper bound of net-benefits following a switch to cleaner stoves. OnStove can be used by planners and policy makers to identify whether various combinations of interventions in their settings would be worth the potential benefits that could be captured

Installation

First, you need to install a python distribution using
Anaconda or
Miniconda (recomended).

Installing with conda

The easiest way of installing and using OnStove is through conda. After installing a distribution of conda,
Open an Anaconda Prompt and run:

> conda create -n onstove -c conda-forge onstove

Now you will have a new conda environment called onstove with OnStove installed on it. To use it open an Anaconda Prompt
in the root folder of your analysis and activate the environment with:

> conda activate onstove

Downloading the source code and installing the environment (for advanced users)

If you rather download the development version of OnStove and install the development environment, open an Anaconda Prompt
and download the source code with:

> conda install git
> git clone https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove.git

Then use the jupyter_env.yaml in the envs folder to install the environment by writing:

> cd OnStove
> conda env create --name onstove --file envs/jupyter_env.yaml
> conda activate onstove

Now your environment onstove is available to use. Note that you need to activate it
always before conducting any analysis.

Documentation

Access the latest documentation in read the docs.

Resources

Publication on sub-Saharan Africa

How to cite

Khavari, Babak, Camilo Ramirez, Marc Jeuland and Francesco Fuso Nerini (12 January 2023). 
"A geospatial approach to understanding clean cooking challenges in sub-Saharan Africa". 
Nature Sustainability. 1–11. ISSN 2398-9629. doi:10.1038/s41893-022-01039-8. 
Creative Commons CC‑BY‑4.0 license.

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Last synced: 12 days ago

Total Commits: 661
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Avg Commits per committer: 110.167
Development Distribution Score (DDS): 0.525

Commits in past year: 4
Committers in past year: 1
Avg Commits per committer in past year: 4.0
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Name Email Commits
Camilo Ramirez c****g@k****e 314
Babak k****i@k****e 292
Jeff Osundwa j****f@k****m 39
aliciaoberholzer a****5@g****m 11
manuelsalslz m****s@g****m 4
Jeremy j****9@g****m 1

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Last synced: about 1 month ago

Total issues: 28
Total pull requests: 12
Average time to close issues: about 1 year
Average time to close pull requests: 17 days
Total issue authors: 3
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Average comments per issue: 0.07
Average comments per pull request: 0.25
Merged pull request: 8
Bot issues: 0
Bot pull requests: 0

Past year issues: 16
Past year pull requests: 2
Past year average time to close issues: 20 days
Past year average time to close pull requests: 5 days
Past year issue authors: 2
Past year pull request authors: 1
Past year average comments per issue: 0.06
Past year average comments per pull request: 1.0
Past year merged pull request: 0
Past year bot issues: 0
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More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/open-source-spatial-clean-cooking-tool/onstove

Top Issue Authors

  • camiloramirezgo (14)
  • manuelsalslz (9)
  • babakkhavari (5)

Top Pull Request Authors

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Dependencies

.github/workflows/tests.yml actions
  • actions/checkout v3 composite
  • conda-incubator/setup-miniconda v2 composite
environment.yml pypi
setup.py pypi
  • dill *
  • geopandas *
  • jupyterlab *
  • matplotlib *
  • plotnine *
  • psutil *
  • psycopg2 *
  • python-decouple *
  • rasterio *
  • scikit-image *
  • svgpath2mpl *
  • svgpathtools *

Score: 5.834810737062606