  Background and Objective: Data from electro-anatomical mapping (EAM) systems
are playing an increasingly important role in computational modeling studies
for the patient-specific calibration of digital twin models. However, data
exported from commercial EAM systems are challenging to access and parse.
Converting to data formats that are easily amenable to be viewed and analyzed
with commonly used cardiac simulation software tools such as openCARP remains
challenging. We therefore developed an open-source platform, pyCEPS, for
parsing and converting clinical EAM data conveniently to standard formats
widely adopted within the cardiac modeling community. Methods and Results:
pyCEPS is an open-source Python-based platform providing the following
functions: (i) access and interrogate the EAM data exported from clinical
mapping systems; (ii) efficient browsing of EAM data to preview mapping
procedures, electrograms (EGMs), and electro-cardiograms (ECGs); (iii)
conversion to modeling formats according to the openCARP standard, to be
amenable to analysis with standard tools and advanced workflows as used for in
silico EAM data. Documentation and training material to facilitate access to
this complementary research tool for new users is provided. We describe the
technological underpinnings and demonstrate the capabilities of pyCEPS first,
and showcase its use in an exemplary modeling application where we use clinical
imaging data to build a patient-specific anatomical model. Conclusion: With
pyCEPS we offer an open-source framework for accessing EAM data, and converting
these to cardiac modeling standard formats. pyCEPS provides the core
functionality needed to integrate EAM data in cardiac modeling research. We
detail how pyCEPS could be integrated into model calibration workflows
facilitating the calibration of a computational model based on EAM data.
