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Here are some observations about the documentation.
We can categorize the documentation into three main sections: User, Developer, and a dedicated Testing section.
For the User Documentation,
We encountered some difficulties navigating the command-line interface (CLI) section. We believe it would be clearer if there were direct links to the relevant class documentation (currently located in the API Reference), allowing for easier access to detailed information about image processing methods, including specifics like applied formulas.
Regarding the Developer Documentation,
To better understand function inputs and outputs, it is essential to include type annotations for both expected input parameters and returned objects. This would also help developers become more familiar with the API, especially as it becomes accessible to users through NumPy objects.
Lastly, on the Testing Documentation,
The tests can serve as a demonstration of the library's capabilities. However, current tests have minimal documentation and lack context about what is being tested and the expected results. The tests appear to be fairly general, focusing primarily on issues such as missing a required argument, rather than demonstrating the function's capability to perform the intended processing.
In fact in svf and hillshade calculations, we noticed that test.run_test(check_outputs=False)is executed within the test with compare=False, so the results of both functions are not compared to reference outputs. Could we have more information about this?
The text was updated successfully, but these errors were encountered:
Here are some observations about the documentation.
We can categorize the documentation into three main sections: User, Developer, and a dedicated Testing section.
For the User Documentation,
We encountered some difficulties navigating the command-line interface (CLI) section. We believe it would be clearer if there were direct links to the relevant class documentation (currently located in the API Reference), allowing for easier access to detailed information about image processing methods, including specifics like applied formulas.
Regarding the Developer Documentation,
To better understand function inputs and outputs, it is essential to include type annotations for both expected input parameters and returned objects. This would also help developers become more familiar with the API, especially as it becomes accessible to users through NumPy objects.
Lastly, on the Testing Documentation,
The tests can serve as a demonstration of the library's capabilities. However, current tests have minimal documentation and lack context about what is being tested and the expected results. The tests appear to be fairly general, focusing primarily on issues such as missing a required argument, rather than demonstrating the function's capability to perform the intended processing.
In fact in svf and hillshade calculations, we noticed that test.run_test(check_outputs=False)is executed within the test with compare=False, so the results of both functions are not compared to reference outputs. Could we have more information about this?
The text was updated successfully, but these errors were encountered: