sparse-ir: Optimal compression and sparse sampling of many-body propagators

We introduce sparse-ir, a collection of libraries to efficiently handle imaginary-time propagators, central object in finite-temperature quantum many-body calculations. leverage two concepts: firstly, the intermediate representation (IR), an optimal compression propagator with robust a-priori error estimates, and secondly, sparse sampling, near-optimal grids imaginary time frequency from which can be reconstructed on diagrammatic equations solved. IR sampling are packaged into stand-alone, easy-to-use Python, Julia Fortran libraries, readily included existing software. also include extensive set sample codes showcasing library for typical ab initio methods.