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Paper companion notebook — Jones et al.
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Tutorial 6 — Exporting to hctsa (MATLAB)
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Tutorial 5 — catch22 feature extraction
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Tutorial 3 — Circadian analysis
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Tutorial 2 — HMM: hidden behavioural states
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Tutorial 1 — Overview: working with ethoscope data
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Getting started
Installing ethoscopy as a docker container with ethoscope-lab (recommended). The ethoscope-lab do...
About
Behavpy is the default object in ethoscopy, a way of storing your metadata and data in a single s...
Basic methods
Behavpy has lots of built in methods to manipulate your data. The next few sections will walk you...
Loading the data
Setting up To begin you need three paths saved as variables: the path to the metadata .csv fi...
Visualising your data
Once the behavpy object is created, the print function will just show your data structure. If yo...
Jupyter tutorials
Ethoscopy is best used in a Jupyter instance. We provide a pre-baked docker container that comes ...
Visualising mAGO data
Within the Gilestro lab we have special adaptations to the ethoscope which includes the mAGO, a m...
Visualising with the HMM
The best way to get to grips with your newly trained HMM is to decode some data and has a look at...
Periodograms
Periodograms are essential for definitely showing periodicity in a quantifiable way. Periodogra...
Circadian methods and plots
The below methods and plots should give a good insight into your specimens circadian rhythm. If y...
Training an HMM
Behavpy_HMM is an extension of behavpy that revolves around the use of Hidden Markov Models (HMM)...
Metadata design
What is the metadata? The metadata is a simple .csv file that contains the information to (1) fi...