refactor: statistical functions

[weibullguy]

Does this pull request introduce a breaking change?

• Yes
• No

Purpose of this pull request

This pull request refactors the statistical functions across multiple distribution types (normal, exponential, lognormal, and Weibull) to improve maintainability, abstraction, and testing. The key goals include:

• Abstracting shared functionality: Common statistical calculations such as hazard rate, MTBF, and survival functions have been abstracted into a central location to reduce code duplication.
• Refining distribution-specific methods: Refactored the get_* functions in each distribution to call the abstracted methods without breaking the existing API.
• Enhancing unit test coverage: Added new test cases for edge conditions (e.g., zero, negative, and very large parameters) to ensure robustness and consistency across distributions.

Benefits of the pull request

• Increased code maintainability: By centralizing shared logic, the overall complexity of distribution-specific files has been reduced, improving readability and ease of future modifications.
• Consistency across distributions: The refactoring ensures a more consistent approach to how statistical methods are implemented and tested for different distributions.
• Better test coverage: New test cases have been introduced for boundary values and edge cases, increasing confidence in the reliability of these statistical functions.
• Prevention of breaking changes: The refactored functions maintain the same public API, ensuring that existing code relying on these functions remains unaffected.

Any particular area(s) reviewers should focus on

• Abstracted functions in the new statistical utility file: Reviewers should ensure that the logic for shared functionality is sound and correctly handles all distribution types.
• Tests for edge cases: Special attention should be given to the newly added tests for negative, zero, and large values for distribution parameters to verify their correctness.
• Distribution-specific files: Make sure the refactored get_* functions correctly call the centralized methods without introducing unintended side effects.

Any other pertinent information

Pull Request Checklist

• Code Style

  • Code is following code style guidelines.

• Static Checks

  • Failing static checks are only applicable to code outside the scope of
    this PR.

• Tests

  • At least one test for all newly created functions/methods?

• Chores

  • Issue(s) have been raised for problem areas outside the scope of
    this PR. These problem areas have been decorated with an ISSUE: # comment.

Summary by Sourcery

Refactor statistical functions to centralize shared logic for hazard rate, MTBF, and survival calculations, improving maintainability and consistency across distribution types. Enhance unit test coverage by adding new test cases for edge conditions to ensure robustness.

Enhancements:

• Refactor statistical functions to centralize shared logic for hazard rate, MTBF, and survival calculations across multiple distribution types, improving maintainability and consistency.

Tests:

• Enhance unit test coverage by adding new test cases for edge conditions, including zero, negative, and very large parameters, to ensure robustness and consistency across distributions.

[sourcery-ai]

Reviewer's Guide by Sourcery

This pull request refactors the statistical functions across multiple distribution types (normal, exponential, lognormal, and Weibull) to improve maintainability, abstraction, and testing. The key changes include centralizing common statistical calculations, refining distribution-specific methods, and enhancing unit test coverage.

Class diagram for refactored statistical functions

classDiagram
    class Distributions {
        +calculate_hazard_rate(time: float, location: float, scale: Optional[float], shape: Optional[float], dist_type: str) float
        +calculate_mtbf(shape: float, location: float, scale: float, dist_type: str) float
        +calculate_survival(shape: float, time: float, location: float, scale: float, dist_type: str) float
    }

    class Normal {
        +get_hazard_rate(location: float, scale: float, time: float) float
        +get_mtbf(location: float, scale: float) float
        +get_survival(location: float, scale: float, time: float) float
        +do_fit(data, kwargs) Tuple[float, float]
    }

    class Exponential {
        +get_hazard_rate(scale: float, location: float) float
        +get_mtbf(rate: float, location: float) float
        +get_survival(scale: float, time: float, location: float) float
        +do_fit(data, kwargs) Tuple[float, float]
    }

    class Lognormal {
        +get_hazard_rate(shape: float, location: float, scale: float, time: float) float
        +get_mtbf(shape: float, location: float, scale: float) float
        +get_survival(shape: float, location: float, scale: float, time: float) float
        +do_fit(data, kwargs) Tuple[float, float, float]
    }

    class Weibull {
        +get_hazard_rate(shape: float, location: float, scale: float, time: float) float
        +get_mtbf(shape: float, location: float, scale: float) float
        +get_survival(shape: float, location: float, scale: float, time: float) float
        +do_fit(data, kwargs) Tuple[float, float, float]
    }

    Distributions <|-- Normal
    Distributions <|-- Exponential
    Distributions <|-- Lognormal
    Distributions <|-- Weibull

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File-Level Changes

Change	Details	Files
Centralized common statistical calculations into a new distributions.py file	Created calculate_hazard_rate(), calculate_mtbf(), and calculate_survival() functions Implemented support for exponential, lognormal, normal, and Weibull distributions Added error handling for unsupported distribution types	src/ramstk/analyses/statistics/distributions.py
Refactored distribution-specific files to use the new centralized functions	Updated get_hazard_rate(), get_mtbf(), and get_survival() functions to use the new centralized calculations Simplified do_fit() functions by removing redundant code Improved error handling for empty datasets	src/ramstk/analyses/statistics/exponential.py src/ramstk/analyses/statistics/lognormal.py src/ramstk/analyses/statistics/normal.py src/ramstk/analyses/statistics/weibull.py
Enhanced unit tests for all distribution types	Added tests for edge cases (e.g., zero, negative, and very large parameter values) Implemented tests for empty datasets and invalid distribution types Updated existing tests to use pytest.approx() for float comparisons	tests/analyses/statistics/bounds_unit_test.py tests/analyses/statistics/exponential_unit_test.py tests/analyses/statistics/lognormal_unit_test.py tests/analyses/statistics/normal_unit_test.py tests/analyses/statistics/weibull_unit_test.py tests/analyses/statistics/distributions_unit_test.py
Improved code style and documentation	Updated function docstrings for clarity and consistency Standardized import statements across files Removed redundant comments and improved existing ones	src/ramstk/analyses/statistics/bounds.py src/ramstk/analyses/statistics/exponential.py src/ramstk/analyses/statistics/lognormal.py src/ramstk/analyses/statistics/normal.py src/ramstk/analyses/statistics/weibull.py

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[sourcery-ai]

Hey @weibullguy - I've reviewed your changes and they look great!

Here's what I looked at during the review

• 🟡 General issues: 1 issue found
• 🟢 Security: all looks good
• 🟢 Testing: all looks good
• 🟢 Complexity: all looks good
• 🟢 Documentation: all looks good

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[sourcery-ai]

issue (bug_risk): Potential bug in _z_norm calculation for alpha <= 1.0

The calculation for _z_norm when alpha <= 1.0 seems incorrect. It should probably use stats.norm.ppf() here as well, with appropriate scaling of alpha.