Welcome to the pyCoAn GitHub Page
CoAn stands for
Correlative Analysis of electron microscopy data. CoAn
was originally designed for correlation-based docking of atomic models into
lower-resolution densities generated by electron microscopy and image
reconstruction. The distinguishing factor of the underlying docking methodology
is the use of correlation statistics which explicitly accounts for measurement
errors through cross-validation and allows the definition of confidence
intervals for the rotational and translational parameters, thus defining a
solution set of docked models, all of which are compatible with the data within
their margin of error.
Since its inception, CoAn has grown significantly in scope to incorporate
analysis algorithms including denoising, segmentation, and pattern recognition.
It has now evolved into a python-based design framework, pyCoAn,
similar in concept to Matlab, but tailor-made
for Computational Analysis of electron microscopy data. pyCoAn
not only incorporates much of the functionality of the original CoAn package and its extensions, it
also provides seamless access to a multitude of image- and data-processing
software packages with a unified interface. This allows the end-user great
flexibility in data analysis and allows them to focus on the questions at hand,
rather than spending time figuring out how to reformat data from package A into
something package B can use. The functionality of the pyCoAn
base distribution can be extended with separately distributed add-ons as well
as standard Python modules.
pyCoAn is in a constant
state of ongoing development, as new algorithms get incorporated and more of
the original algorithms are being refactored as Python modules, along with
continued work on parallelizing compute-intensive tasks.
Distribution page at https://github.com/pyCoAn/distro
Releases at https://github.com/pyCoAn/distro/releases