Fiber photometry is a powerful approach that combines genetically encoded calcium (Ca2+) indicators and multimode optical fibers to monitor neuronal activity in freely moving animals, which is critical to understand how specific groups of neurons play in directing or responding to an action or a stimulus. Fiber photometry experiments can easily produce a large amount of raw Ca2+ signals that need to be processed, corrected for artifacts, aligned with events, and visualized. Here we propose a Python package of functions, which facilitates the processing of fiber photometry recordings from multiple sites, and data integration with many behavioral events for commonly used tests with rodents. The signal processing includes removal of slow wave changes from the signal, e.g. caused by bleaching, correction for artefacts using Ca2+ -independent signal, and signal standardization to allow comparison of results from different recordings. Individual events, such as air puffs, foot-shocks, or licks during sucrose consumption tests, can be aligned to photometry recordings based on the triggers recorded by a photometry software, or based on time stamps tracked by a behavioral software, e.g. ANY-maze, Med Associates. Measures such as freezing scores or movement velocity, even when recorded at different frame rate, can be aligned with photometry recordings as well. Finally, our package allows to create summary results for individual animals and combined results when multiple animals are used. Overall, the package includes the functions that facilitate the whole analysis from data pre-processing to final plots.