Description
Submitting Author: Name (@gparadis)
Package Name: ws3
One-Line Description of Package: ws3 (Wood Supply Simulation System) is a Python package for modeling landscape-level wood supply planning problems
Repository Link (if existing): https://github.com/UBC-FRESH/ws3
EiC: @coatless
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- I agree to abide by pyOpenSci's Code of Conduct during the review process and in maintaining my package after should it be accepted.
- I have read and will commit to package maintenance after the review as per the pyOpenSci Policies Guidelines.
Description
- Include a brief paragraph describing what your package does:
ws3
is an open-source Python package that provides a modular framework for forest estate modeling and wood supply simulation. It enables researchers and practitioners to analyze and optimize forest management strategies under different ecological and policy scenarios. As a transparent and extensible alternative to commercial tools like Remsoft Woodstock, ws3
advances reproducible science in forestry and land-use modeling. Its architecture facilitates collaborative development, standardized data flows, and integration with broader decision support pipelines.
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Scope
-
Please indicate which category or categories this package falls under:
- Data retrieval
- Data extraction
- Data processing/munging
- Data deposition
- Data validation and testing
- Data visualization
- Workflow automation
- Citation management and bibliometrics
- Scientific software wrappers
- Database interoperability
Domain Specific
- Geospatial
- Education
- Explain how and why the package falls under these categories (briefly, 1-2 sentences). For community partnerships, check also their specific guidelines as documented in the links above. Please note any areas you are unsure of:
Workflow automation: ws3
automates complex simulation and optimization workflows for forest estate modeling. While there is no practical manual alternative to many of its core modeling functions, ws3
structures these workflows using modular Python components that support transparent, reproducible planning and scenario analysis. In this sense, ws3
enables users to automate, inspect, and extend workflows that would otherwise be available only through proprietary software such as Remsoft Woodstock.
Scientific software wrapper: ws3
exposes reusable Python interfaces to core simulation and optimization logic, enabling integration with broader scientific pipelines (e.g., scenario generators, visualization tools, or external simulators). Unlike monolithic black-box tools, ws3
supports introspection and reuse by design.
Data visualization: While not the primary focus, ws3
includes utilities to visualize modeling inputs, landbase structure, and simulated outputs in a geospatial context using open-source tools like geopandas
and matplotlib
.
Geospatial: Forest estate models are inherently spatial. ws3
handles geospatial data inputs and outputs using standard formats (e.g., GeoJSON, raster), integrates with GIS workflows, and supports spatial partitioning of forested landscapes into planning units and yield regions.
- Who is the target audience and what are the scientific applications of this package?
The target audience includes researchers and professionals in forest management, ecological modeling, carbon accounting, and land-use policy. ws3
supports scientific applications such as sustainable harvest planning, habitat impact analysis, carbon offset scenario testing, and long-term forest policy evaluation.
- Are there other Python packages that accomplish similar things? If so, how does yours differ?
There are no other open-source Python packages that provide equivalent functionality for forest estate modeling. Existing alternatives such as Remsoft Woodstock or Patchworks are proprietary or closed-source, limiting transparency, reproducibility, and extensibility. ws3
is unique in offering a flexible, inspectable, and open framework for simulating and optimizing strategic forest planning problems.
- Any other questions or issues we should be aware of:
The package is relatively large and complex compared to many pyOpenSci submissions. However, we believe it fits within scope due to its modular architecture, test coverage, and potential to serve as a reusable modeling toolkit in the domain of forest and land-use science.
P.S. Have feedback/comments about our review process? Leave a comment here
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