General purpose tools for longitudinal beam dynamics studies

Data acquisition and analysis are an essential part of studying and managing the longitudinal motion of the beam in the accelerators, diagnosing problems and improving the beam quality. Nevertheless, unified methods of data acquisition and analysis have never been implemented, resulting in the existence of many different ways to perform similar tasks. This can impact efficiency and reproducibility. My work addresses this issue by developing two python-based libraries that simplify and standardise both data acquisition and data analysis in beam studies and operations. The first library, PyDAq, focuses on data acquisition. It offers functionalities to either save or return live and logged data to the user, as well as the ability to perform parametric scans. It offers a simple interface, which is the same for both live and logged data. It allows grouping the data in a way that facilitates their subsequent analysis, and can save them in a well-defined and standardised structure. It also implements the novel possibility to track a single beam instance through different accelerators. The second library, PyLAn, is a collaborative library for longitudinal analysis. It contains a framework that allows users to chain functions one after the other and run them on large sets of data, to automate and simplify the execution of data analysis routines, and it provides tools to evaluate the speed and output of functions.