Modern radio interferometric arrays offer high sensitivity, wide fields of view, and broad frequency coverage, but also pose significant data calibration challenges. Standard direction-independent calibration is insufficient to correct direction-dependent effects, such as ionospheric phase distortions and primary beam variations, which produce strong artifacts around bright sources and limit achievable image dynamic range. Built on standard CASA tasks, we present a Python-based direction-dependent calibration and peeling framework, demonstrated using radio continuum imaging data from the upgraded Giant Metrewave Radio Telescope (uGMRT). The framework efficiently subtracts bright-source models and suppresses their associated direction-dependent artifacts, producing significantly flatten backgrounds and improving image fidelity and faint-source detectability. We further introduce an optimized "model-restoration'' strategy that mitigates direction-dependent artifacts while preserving the overall flux densities of bright sources that are themselves of scientific interest. For fields containing multiple bright sources, sequential application of the framework systematically reduces background noise, thereby increasing sensitivity and faint-source detectability. The framework is Python-based, CASA-compatible, and readily applicable to other mid- and low-frequency interferometric arrays, with the code publicly released alongside this paper.
Peng et al. (2026) studied this question.