AstroCatR reconstructs celestial objects' time series data for astronomical catalogs. It is a command-line program running on the Linux platform and is implemented in C and Python; AstroCatR's capabilities are based on specialized sky partitioning and MPI parallel programming. The package contains three parts: ETL (extract-transform-load) pre-processing, TS-matching calculation, and time series data retrieval. Once the user obtains the original catalogs, running ETL pre-processing generates a sky zoning file. The TS-matching module marks celestial objects, and finally, running the Query program searches celestial objects from the time series datasets which matched with the target.

HCGrid maps non-uniform radio astronomy data onto a uniformly distributed grid using a convolution-based algorithm on CPU-GPU heterogeneous platforms. The package has three modules; the initialization module initializes parameters needed for the calculation process, such as setting the size of the sampling space and output resolution. The gridding module uses a parallel ordering algorithm to pre-order the sampling points based on HEALPix on the CPU platform and uses an efficient two-level lookup table to speed up the acquisition of sampling points; it then accelerates convolution by using the high parallelism of GPU and through related performance optimization strategies based on CUDA architecture to further improve the gridding performance. The third module processes the results; it visualizes the gridding and exports the final products as FITS files.