High-level algorithms

This section describes the high-level HDRL algorithms as they are exposed in PyHDRL. It corresponds to the Functionalities chapters of the HDRL Pipeline Developer Manual (ESO-299492, HDRL 1.6.0).

Each page gives a short description of the algorithm, the Python classes and functions that implement it, and examples taken from the PyHDRL bindings and unit tests. Parameter lists, return values and exceptions are documented in the API Reference.

The algorithm theory, figures and recipe-level cpl_parameterlist helpers remain in the HDRL Pipeline Developer Manual. They are not repeated here.

There is no dedicated Bias or Dark class in PyHDRL. Those chapters use hdrl.func.Collapse and hdrl.core.ImageList.collapse, as in the C library.

• Overscan
• Bias
• Dark
• Flat
• Bad-pixel detection
• Strehl
• Fringing
• Object catalogue generation
• Efficiency
• Response
• Differential Atmospheric Refraction
• Effective air mass
• Fixed Pattern Noise
• Image and cube resampling
• Limiting magnitude
• Barycentric correction