25-08-2025, 09:27 PM
I'm going to post maybe my last post about the topics detected with NMF. I don't know if this can help with the MS deciphering or understanding. I post this figure, which I found interesting and funny at the same time.
![[Image: 2wpDwmC.jpeg]](https://i.imgur.com/2wpDwmC.jpeg)
The figure shows a timeline of NMF topics across the manuscript, binned by folio (and side).
Each vertical slice corresponds to one folio and side; the stacked areas in that slice sum to 100% and show the mixture of topics detected on that folio. Colors are unique per topic (T0, T1, …), and the letters at the top mark the section of the manuscript for the corresponding range of folios (H=Herbal, P=Pharmaceutical, A=Astronomical, B=Biological, T=Text-only, R=Recipes).
Rare topics are suppressed. I used a 15% threshold (topics contributing <15% in a bin are set to 0). The 15% threshold emphasizes dominant topics in each bin; lowering it will reintroduce rare topics (more bands), raising it will focus on only the strongest themes.
What I think is interesting is that, overall, this timeline compresses the manuscript’s topic structure into a single, readable view, showing where topics dominate, where they mix, and how they evolve across folios and sections.
If anyone finds this useful, I will be happy.
Either way, I’ll keep pulling on this topic-detection thread and see what I get.
The figure shows a timeline of NMF topics across the manuscript, binned by folio (and side).
Each vertical slice corresponds to one folio and side; the stacked areas in that slice sum to 100% and show the mixture of topics detected on that folio. Colors are unique per topic (T0, T1, …), and the letters at the top mark the section of the manuscript for the corresponding range of folios (H=Herbal, P=Pharmaceutical, A=Astronomical, B=Biological, T=Text-only, R=Recipes).
Rare topics are suppressed. I used a 15% threshold (topics contributing <15% in a bin are set to 0). The 15% threshold emphasizes dominant topics in each bin; lowering it will reintroduce rare topics (more bands), raising it will focus on only the strongest themes.
What I think is interesting is that, overall, this timeline compresses the manuscript’s topic structure into a single, readable view, showing where topics dominate, where they mix, and how they evolve across folios and sections.
If anyone finds this useful, I will be happy.
Either way, I’ll keep pulling on this topic-detection thread and see what I get.