Parameter Selection

Most baseline algorithms in pybaselines have several parameters that can be adjusted. While this allows for fine-tuning each algorithm to work in a wide array of cases, it can also present a difficulty for new users. It is suggested to start by adjusting only one or two main parameters, and then change other parameters as needed. Due to the variable nature of baselines, it is highly recommended to not assume the default parameters will work for your data! Below are the suggested parameters to begin adjusting for each family of algorithms within pybaselines:

• Polynomial methods

  • poly_order controls the curvature of the baseline.

• Whittaker-smoothing-based methods

  • lam controls the curvature of the baseline. See this example to get an idea of how lam effects the baseline. The optimal lam value for each algorithm is not typically the same.

• Morphological methods

  • half_window controls the general fit of the baseline. See this example to get an idea of how half_window effects the baseline. The optimal half_window value for each algorithm is not typically the same.

• Spline methods

  • lam controls the curvature of the baseline. The Whittaker example also generally applies to spline methods.

• Smoothing-based methods

  • half_window controls the general fit of the baseline. The Morphological example also generally applies to smoothing methods.

• Baseline/Peak Classification methods

  • Algorithm dependent

• Optimizers

  • Algorithm dependent

• Miscellaneous methods

  • Algorithm dependent