Bayesian fitting vs. classical fitting

Here are more advanced examples that show step-by-step the differences between a classical fit and a Bayesian fit on a model spectrum. You will thus first learn how to model a spectrum and then fit a model spectrum with one line, two resolved lines and two unresolved lines: this is when the bayesian fitting algorithm becomes intersting ;)

Calibrating your data

A data cube can be recalibrated using mode specific algorithm which depends on the type of data you have observed. You might want to give a try to these examples to see if you can get a better calibration for your data.

Advanced fitting

These examples show advanced fitting procedures for: * constraining line ratios (e.g. [NII]6548,6584 or [OIII]5007,4959) * fitting regions with mapped input parameters (e.g. velocity/broadening maps as input)