  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. piXedfit 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 posteriors sampling methods:
Markov Chain Monte Carlo (MCMC) and random densely-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 posteriors
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 comprises of 12-band imaging data from GALEX, SDSS, 2MASS, and WISE, and
the IFS data from CALIFA or MaNGA. piXedfit can spatially match (in resolution
and sampling) of the imaging and IFS data. By fitting only the photometric
SEDs, piXedfit can predict the spectral continuum, $\text{D}_{\rm n}4000$,
$H_{\alpha}$, and $H_{\beta}$ well. The star formation rate (SFR) derived by
piXedfit is consistent with that derived from $H_{\alpha}$ emission. The RDSPS
method gives equally good fitting results as the MCMC and it is much faster
than the MCMC. piXedfit is a versatile tool equipped with a parallel computing
module for efficient analysis of large datasets, and will be made publicly
available (https://github.com/aabdurrouf/piXedfit).
