In the past year, pygwinc has expanded to support not just fundamental noise calculations (e.g., quantum, thermal) but also any number of user-defined noises. These custom noise definitions can do anything, from evaluating an empirical model (e.g., electronics, suspension) to loading real noise measurements (e.g., laser AM/PM noise). Here is an example of the framework applied to H1.
Starting with the BHD review-era noises, I have set up the 40m pygwinc fork with a working noise budget which we can easily expand. Specific actions:
I set up our fork in this way to keep the 40m separate from the main pygwinc code (i.e., not added to as a built-in IFO type). With the 40m code all contained within one root-level directory (with a 40m-specific name), we should now always be able to upgrade to the latest pygwinc without creating intractable merge conflicts.