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The application of FY2 operational products which produced by the National Satellite Meteorological Center (NSMC) and distributed by CMACast, especial for good quantitative products, is needed to be strengthened. In this study, we successfully decoded and visualized the FY2G AMV and OLR products automatically using Python, and compared the results with SWAP software. The results show that the images produced by Python and SWAP are quite the same, but wind bars are more evenly distributed for the AMV discrete field data produced by Python, and the products produced by the Gouraud Shading method supported by Python is more smooth than those by the Flat Shading method used in SWAP for the OLR grid field data. According to the automatic and aesthetic advantages of Python visualized reality, the method has great potential in the operational meteorological application.