Warning

This is a review version of the documentation for PyOpenSci submission.

constants

The constants serve as predefined and standardized identifiers to ensure consistency and prevent potential spelling mistakes when referencing key elements, such as distributions and models, throughout the project. These constants act as safeguards, making the codebase more robust and less error-prone by eliminating the need for users to manually input or recall exact strings.

Instead of typing “gaussian” directly, users can use the predefined constant GAUSSIAN to avoid typos. Aliases such as NORMAL for GAUSSIAN ensure semantic clarity while maintaining consistency. This approach not only reduces the likelihood of errors but also improves code readability and maintainability.

EPSILON: float

A small value used to prevent division by zero.

Note

EPSILON = np.finfo(float).eps

GAUSSIAN: str

Specifies the Gaussian distribution type.

NORMAL: str

Alias for GAUSSIAN.

LOG_NORMAL: str

Specifies the Log-Normal distribution type.

SKEW_NORMAL: str

Specifies the Skew-Normal distribution type.

LAPLACE: str

Specifies the Laplace distribution type.

GAMMA_SR: str

Gamma distribution with shape-rate parameterization.

GAMMA_SS: str

Gamma distribution with shape-scale parameterization.

BETA: str

Specifies the Beta distribution type.

ARC_SINE: str

Specifies the ArcSine distribution type.

LINE: str

Specifies a linear model type.

LINEAR: str

Alias for LINE.

QUADRATIC: str

Specifies a quadratic model type.

CUBIC: str

Specifies a cubic model type.

How to use

They are available for various uses cases, and can be called directly from pyMultiFit using

from pymultifit import EPSILON
from pymultifit import GAUSSIAN, LAPLACE