********** User Guide ********** This is how to use Pylira: .. code:: from pylira import LIRADeconvolver from pylira.data import point_source_gauss_psf data = point_source_gauss_psf() data["flux_init"] = data["flux"] deconvolve = LIRADeconvolver( alpha_init=np.ones(np.log2(data["counts"].shape[0]).astype(int)) ) result = deconvolve.run(data=data) The ``data`` object is a simple Pythin ``dict`` containing the following quantities: ===================== ================================================= Quantity Definition ===================== ================================================= counts 2D Numpy array containing the counts image psf 2D Numpy array containing an image of the PSF exposure (optional) 2D Numpy array containing the exposure image background (optional) 2D Numpy array containing the background / baseline image ===================== ================================================= From these quantities the predicted number of counts is computed like: .. math:: N_{Pred} = \mathrm{PSF} \circledast (\mathcal{E} \cdot (F + B)) Where :math:`\mathcal{E}` is the exposure, :math:`F` the deconvovled flux image, :math:`B` the background and :math:`PSF` the PSF image. .. toctree:: :maxdepth: 2 :hidden: data tutorials/index