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Behavpy_HMM is an extension of behavpy that revolves around the use of Hidden Markov Models (HMM) to segment and predict sleep and awake stages in Drosophila. A good introduction to HMMs is available here: https://web.stanford.edu/~jurafsky/slp3/A.pdf.  The p...

The best way to get to grips with your newly trained HMM is to decode some data and has a look at it visually. Single plots # Like plot_overtime() this method will take a single variable and trained hmm, and plot them over time. # If you're using behavpy_H...

Within the Gilestro lab we have special adaptations to the ethoscope which includes the mAGO, a module that can sleep deprive flies manually and also deliver a puff of an odour of choice after periods of rest. See the documentation here: ethoscope_mAGO.  If y...