We present piXedfit, pixelized spectral energy distribution (SED) fitting, a Python package that provides tools for analyzing spatially resolved properties of galaxies using multiband imaging data alone or in combination with integral field spectroscopy (IFS) data. It has six modules that can handle all tasks in the spatially resolved SED fitting. The SED-fitting module uses the Bayesian inference technique with two kinds of posterior sampling methods: Markov Chain Monte Carlo (MCMC) and random dense sampling of parameter space (RDSPS). We test the performance of the SED-fitting module using mock SEDs of simulated galaxies from IllustrisTNG. The SED fitting with both posterior sampling methods can recover physical properties and star formation histories of the IllustrisTNG galaxies well. We further test the performance of piXedfit modules by analyzing 20 galaxies observed by the CALIFA and MaNGA surveys. The data are comprised of 12-band imaging data from the Galaxy Evolution Explorer, SDSS, 2MASS, and WISE and the IFS data from CALIFA or MaNGA. The piXedfit package can spatially match (in resolution and sampling) the imaging and IFS data. By fitting only the photometric SEDs, piXedfit can predict the spectral continuum, D_n 4000, H _α , and H _β well. The star formation rate derived by piXedfit is consistent with that derived from H _α emission. The RDSPS method gives equally good fitting results as the MCMC and is much faster. As a versatile tool, piXedfit is equipped with a parallel computing module for efficient analysis of large data sets and will be made publicly available (https://github.com/aabdurrouf/piXedfit).

我们提出piXedfit，像素化光谱能量分布 (SED) 拟合，这是一个 Python 包，它提供了使用多波段成像数据单独或结合积分场光谱 (IFS) 数据分析星系空间分辨特性的工具。它有六个模块，可以处理空间分辨 SED 拟合中的所有任务。SED 拟合模块使用贝叶斯推理技术和两种后验采样方法：马尔可夫链蒙特卡罗 (MCMC) 和参数空间随机密集采样 (RDSPS)。我们使用来自 IllustrisTNG 的模拟星系的模拟 SED 来测试 SED 拟合模块的性能。两种后验采样方法的 SED 拟合都可以很好地恢复 IllustrisTNG 星系的物理特性和恒星形成历史。我们进一步测试piXedfit的性能通过分析 CALIFA 和 MaNGA 调查观测到的 20 个星系来构建模块。这些数据包括来自 Galaxy Evolution Explorer、SDSS、2MASS 和 WISE 的 12 波段成像数据以及来自 CALIFA 或 MaNGA 的 IFS 数据。所述piXedfit包可以在空间上匹配（在分辨率和采样）成像和IFS数据。通过仅拟合光度 SED，piXedfit可以很好地预测光谱连续谱、D _n 4000、H _α和 H _β。piXedfit推导出的恒星形成率与 H _α发射推导出的恒星形成率一致。RDSPS 方法提供与 MCMC 一样好的拟合结果，而且速度要快得多。作为多功能工具，piXedfit 配备并行计算模块，用于高效分析大型数据集，并将公开提供（https://github.com/aabdurrouf/piXedfit）。