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, Dn 4000, Hα, and Hβ well. The star formation rate derived by piXedfit is consistent with that derived from Hα 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).
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