This repository contains Various Jupyter notebooks to illustrate some PyMoDAQ features:
Data in PyMoDAQ are featured with a lot of metadata, allowing their proper description and enabling seamlessly saving/loading to hdf5 files. But what about representation? Analysis? Exploration?
With Python you usually have to write a script to manipulate and plot your data using your favorite backend (matplotlib, plotly, qt, tkinter, …) However because PyMoDAQ is highly graphical you won't need that. PyMoDAQ is featured with various data viewers allowing you to plot any kind of data. You'll see below some nice examples of how to plot your PyMoDAQ's data using the builtin data viewers.
Notebook : Plotting Data
PyMoDAQ's data are also advanced objects including a lot of properties and methods to explore and manipulate them. A practical example of data analysis from a physics experiment is presented in the following notebook. You'll see how to crop, average, fit... your data for an easier analysis.
Notebook : Analyzing Data
The following notebook illustrates the use of Bayesian Optimisation of Black box functions using Gaussian Processes and Utility functions. Such an algorithm is used in the PyMoDAQ extension: Bayesian Optimisation.
Notebook : Bayesian Optimisation