EMAN 1.8

EMAN is a suite of scientific image processing tools aimed primarily at the transmission electron microscopy community.
EMAN is a suite of scientific image processing tools aimed primarily at the transmission electron microscopy community, though it is beginning to be used in other fields as well. It has a particular focus on performing a task known as single particle reconstruction.

In this method, images of nanoscale molecules and molecular assemblies embedded in vitreous (glassy) ice are collected on a transmission electron microscope, then processed using EMAN to produce a complete 3-D recosntruction at resolutions now approaching atomic resolution. For low resolution structures (~2 nm), this may require ~8 hours of computer processing and a few thousand particles.

For structures aimed at ~0.5 nm or better resolution, hundreds of thousands of particles and hundreds of thousands of CPU-hours (on large computational clusters) may be required. Indeed, EMAN is often used in supercomputing facilities as a test application for large-scale computing.

Scientific image processing is distinguished from typical 'Photoshop' image processing in that it is analyitical in nature. Images processed in EMAN are floating point greyscale images. That is, the pixel values in the images are represented as real numbers, not as small integers (typical GIF/JPG/PNG images are limited to integral values from 0-255 for each pixel).

Processing often includes complex image processing operations in Fourier or Wavelet space. EMAN was first released in 1999, and has been under continuous development since. It consists of a C++ library of hundreds of different image/volume processing algorithms with bindings into the popular Python scripting language. In new EMAN development, all user-level programs (of which there are over 200 in EMAN 1.8) are developed in Python, allowing the knowledgable end-user to make modifications without having to download or compile any of the C++ source code.

What's New in This Release:

· Substantial improvements were made in refine2d.py.
· Using refine2d.py on all new data sets is strongly suggested.
· Some programs use the EMAN2 style of arguments, i.e. "program < file > --option=value", rather than the old style, "program < file > option=value".
· There is a new HDF5 format compatible with EMAN2.
· New AIRS programs were added, such as "skeleton".
· New options were added to make refinement work better on large icosahedral objects.
· The parallelism infrastructure was improved, though network-related problems may still exist.
· Random model generation in makeinitialmodel.py was fixed.

last updated on:
February 28th, 2007, 17:13 GMT
license type:
GPL (GNU General Public License) 
developed by:
Steve Ludtke
ROOT \ Science
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