In this study we introduce a symbolic dataset composed of non-metric Iranian classical music, and algorithms for structural parsing of this music, and generation of variations. The corpus comprises MIDI files and data sheets of Dastgah Shour from Radif Mirza Abdollah, the foundational repertoire of Iranian classical music. Furthermore, we apply our previously-introduced algorithm for parsing melodic structure (Kanani et al., 2023b)to the dataset. Unlike much Western music, this type of non-metric music does not follow bar-centric organisation. The non-metric organisation can be captured well by our parsing algorithm. We parse each tune (Gusheh) into a grammar to identify motifs and phrases. These grammar representations can be useful for educational and ethnomusicological purposes. We also further develop a previously-introduced method of creating melodic variations (Kanani et al., 2023b). After parsing an existing tune to produce a grammar, by applying mutations to this grammar, we generate a new grammar. Expanding this new version yields a variation of the original tune. Variations are assessed by a domain-expert listener. Additionally, we conduct a statistical analysis of mutation with different representation setups for our parsing and generation algorithms. The overarching conclusion is that the system successfully produces acceptable variations post-mutation. While our case study focuses on Iranian classical music, the methodology can be adapted for Arabic or Turkish classical music.