  X-ray Ptychography is an advanced computational microscopy technique which is
delivering exceptionally detailed quantitative imaging of biological and
nanotechnology specimens. However coarse parametrisation in propagation
distance, position errors and partial coherence frequently menaces the
experiment viability. In this work we formally introduced these actors, solving
the whole reconstruction as an optimisation problem. A modern Deep Learning
framework is used to correct autonomously the setup incoherences, thus
improving the quality of a ptychography reconstruction. Automatic procedures
are indeed crucial to reduce the time for a reliable analysis, which has a
significant impact on all the fields that use this kind of microscopy. We
implemented our algorithm in our software framework, SciComPty, releasing it as
open-source. We tested our system on both synthetic datasets and also on real
data acquired at the TwinMic beamline of the Elettra synchrotron facility.
