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We present the PyHST2 code which is in service at ESRF for phase-contrast andabsorption tomography. This code has been engineered to sustain the high dataflow typical of the third generation synchrotron facilities (10 terabytes perexperiment) by adopting a distributed and pipelined architecture. The codeimplements, beside a default filtered backprojection reconstruction, iterativereconstruction techniques with a-priori knowledge. These latter are used toimprove the reconstruction quality or in order to reduce the required datavolume and reach a given quality goal. The implemented a-priori knowledgetechniques are based on the total variation penalisation and a new recentlyfound convex functional which is based on overlapping patches.  We give details of the different methods and their implementations while thecode is distributed under free license.  We provide methods for estimating, in the absence of ground-truth data, theoptimal parameters values for a-priori techniques.

PyHST2: an hybrid distributed code for high speed tomographic reconstruction with iterative reconstruction and a priori knowledge capabilities