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[FEAT] Integrate variance code in Nada Numpy #62

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jcabrero opened this issue Aug 27, 2024 · 2 comments
Open

[FEAT] Integrate variance code in Nada Numpy #62

jcabrero opened this issue Aug 27, 2024 · 2 comments
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enhancement New feature or request good first issue Good for newcomers

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@jcabrero
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All the links in this issue point to the specific files that need to be modified to achieve this result

Thank you for taking the time to review the issues related to Nada Numpy. This particular issue involves integrating an existing implementation of variance functionality into Nada Numpy. The source code for the variance can be found here: Nada by Example link.

To complete this task, please follow these steps:

  1. Integrate the Variance Function:

    • In nada_numpy/array.py, integrate the variance class as a new member function. This will allow the variance to be called as array.var().
  2. Add a Wrapper in nada_numpy/funcs.py:

    • In Nada Numpy, functions can also be called in the form na.var(array). To support this, add a wrapper in nada_numpy/funcs.py. You can refer to the existing functions in this file to see how they simply wrap around array.var() in this context.
  3. Testing:

    • The tests/nada-tests folder contains the testing infrastructure for nada_numpy. You will need to create one or more scripts to test the variance functionality. Follow these steps for testing:
      1. Add a script to tests/nada-tests/nada-project.toml.
      2. Place your test script in tests/nada-tests/src/, where it will verify the expected behavior.
      3. Generate the test file by running nada generate-test --test-name variance variance and placing it in tests/nada-tests/tests/.
      4. Finally, add the script to the TESTS array in tests/test_all.py to integrate it with the CI/CD pipeline.
@jcabrero jcabrero added enhancement New feature or request good first issue Good for newcomers labels Aug 27, 2024
@ghost ghost mentioned this issue Oct 7, 2024
@ghost
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ghost commented Oct 7, 2024

Please take a look at PR 75.

@jcabrero
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jcabrero commented Oct 7, 2024

Thanks a lot for your PR. I have reviewed it. It still requires a bit of work, but I look forward to seeing your improvements on it.

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