Docs/joss paper review #18
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Refined the tile processing logic to accommodate buffered tiles and handle cases with no tile data. Included input validation in several modules, added interpolation options for calculating CHM, restructured imports for clarity, and improved error handling for the raster dimension and spatial extent checks. This ensures accurate and efficient processing, particularly when handling large EPT point clouds.
add code coverage to main workflow
Update main.yml
Reorganize image files into a more logical directory hierarchy under `docs/images` for improved maintainability. Remove the obsolete ReadTheDocs configuration and unnecessary documentation files, such as `api_reference.rst`, `benchmarks.rst`, `conf.py`, and `demo.ipynb`, to streamline the project. The changes aim to enhance the clarity and efficiency of the documentation build process.
Include a new LAZ file, 20191210_5QKB020880.laz, in the documentation's example data for demonstration purposes. This addition aims to provide users with a sample dataset to facilitate testing and understanding of data processing workflows.
Moved CONTRIBUTING.md to docs/contributing.md for better organization and added a new example notebook for large point clouds processing. Updated the README to reflect the added GDAL dependency. Enhanced index.md with clearer package features and simplified forest metrics processing example.
The `plot_metric` function now includes an optional `metric_name` parameter to specify the label for the colorbar. If `metric_name` is not provided, the plot title will be used as the default label. This enhancement allows for more descriptive or unit-specific labels on the colorbar.
The documentation for pyforestscan has been restructured for better organization and clarity. New sections are added for specific topics like Canopy Height Models, Digital Terrain Models, etc. This refactor enhances accessibility and usability for users seeking particular information.
This commit introduces several new files, including images, example data, and API documentation for various modules. Additionally, a Code of Conduct is established, fostering a welcoming community environment. The example notebook on calculating forest metrics has been updated to improve clarity and efficiency in demonstrating the use of the PyForestScan library.
Introduce a CNAME for custom domain hosting and add necessary documentation dependencies. Update the GitHub Actions workflow to support multiple Python versions for testing. Increment the version from 0.1.7 to 0.2.0 to reflect these changes.
Update to v0.2.0
The interpolation parameter now supports a 'None' option, ensuring no interpolation is performed if desired. Adjusted the tests accordingly to accommodate the changes in CHM calculation, including verification for voxel processing and grid orientation. Additionally, revised test cases to handle larger datasets and improved CHM height checks.
Enhance CHM interpolation logic and update tests.
This update reflects minor changes or improvements to the package. Ensure dependencies and configurations are compatible with this version update.
Deleted the empty 'Untitled.ipynb' file from the docs/examples directory as it contained no code or markdown content. Updated the Binder links in 'index.md' and 'README.md' to point to the correct getting-started notebook path. This helps streamline the repository and improves documentation accuracy.
This update enhances the existing GitHub Actions workflow by introducing steps to build, tag, and push a Docker image alongside the Python package. The Docker image is built from the repository, tagged, and pushed to Docker Hub, ensuring continuous deployment and consistency across environments.
Switch conda channel to conda-forge for package installation and rename visualization function to a more general term. These changes ensure compatibility with conda-forge packages and improve code readability by using a generic function name for plotting metrics.
Created paper.bib containing the project's bibliography and paper.md with the project's title, tags, authors, affiliations, summary, statement of need, detailed equations for calculating forest structural metrics from LiDAR data, and references. This documentation provides essential background on the project's goals and methodologies, contributing to transparent and reproducible research.
Include appreciation for Juan Guerra-Hernandez and Adrian Pascual for providing a crucial dataset used in testing and validating the PyForestScan library. This added section improves the completeness and transparency of the research acknowledgments.
Revised citations to include an additional relevant source on tropical forest estimation and updated an existing reference to a more recent publication. These changes enhance the credibility and currency of the referenced research.
Introduced a GitHub Actions workflow to automate the generation of JOSS paper drafts. This workflow triggers on pushes to the relevant files and creates a draft PDF, which is then uploaded as an artifact.
Changed the 'Build draft PDF' action to use the 'master' branch for the latest updates and removed unnecessary whitespace. This ensures compatibility with the latest features and fixes in the GitHub actions used.
Switched from align to equation environment to enhance the readability and formatting of mathematical expressions. This change ensures better spacing and alignment, especially for equations with multiple components or tags.
Inserted line breaks between successive LaTeX equations for improved readability and formatting in the paper. This change ensures that the rendered document will display these equations more clearly, enhancing the overall presentation of mathematical content.
Updated the affiliation of the second and third authors and corrected a reference citation typo in the statement of need. These changes ensure the accuracy of author information and reference consistency.
Updated the workflow to use a nested directory for the paper artifact. This ensures the paper is stored under a more organized file structure.
Annotated missing affiliation for Benjamin Palsa Leamon in the paper. This ensures all authors are correctly attributed.
- Add colons between author names and the year of publication in inline references. - Add semicolons between references with multiple publications - Update capitalization in .bib file so that titles are in title case, proper nouns are protected.
Added new references and acknowledged the National Science Foundation award. This enhances the credibility of the research and indicates additional funding sources and recognition.
Update the capitalization of "Python" and "lidar" in the title and main text for consistency. Ensure all metric names like Plant Area Index (PAI) and Foliage Height Diversity (FHD) follow proper capitalization to enhance readability and professionalism.
This update includes the citation for the PDAL library in the references section of the paper. The citation is added for the mention of PDAL in the summary and statement of need sections, providing proper attribution to the contributing authors and source.
Removed Ryan Perroy from author list. Enhanced the summary and statement of need sections to clarify functionalities and emphasize forest structure metric calculation. Clarified the challenges associated with size and the features that the library provides. Added new sections: usage and contributing.
This commit adds new references in the bibliography and updates citations in the paper's markdown file. It includes precise URLs for Jupyter notebooks and additional acknowledgment for Ryan Perroy's contributions. The .gitignore file is also updated to exclude .pdf and .jats files.
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This PR includes several major changes -- most of which are done to address the reviewer comments (#11 #12 #13 #14).
Change Summary
Major changes include the bump to version v0.2.1, which has improved functionality to read EPT files and tiling on large EPT datasets. Minor changes include updates to visualization functions and documentation, including the deprecation of readthedocs in favor of mkdocs, more comprehensive notebook examples, binder integration, and Docker integration.