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| '''''UNDER CONSTRUCTION''''' | The EMAN2 python library has many similarities to EMAN1. The main image data class is even called EMData, just like EMAN1. The most significant change is the change in naming convention from CamelCase to underscore_notation. That is, in EMAN1, you might say image.doFFT(), is image.do_fft() in EMAN2. The addition of a modular system with full introspection for various classes of algorithms is also a big change. For example, rather than having a dedicated low-pass filter method, in EMAN2, you would: * image.process_inplace("filter.lowpass.gauss",{"cutoff_freq":.125}) For the most part, the Python methods/functions directly follow the C++ counterparts, so [[http://blake.bcm.edu/eman2/doxygen_html|the autogenerated C++ documentation]] serves as a good reference for python programmers as well. For those learning, we are gradually adding both [[EMAN2/Tutorials|written tutorials]] as well as a set of [[EMAN2/VideoTutorials|video mini-tutorials]] which cover programming as well as basic use of EMAN2. |
EMAN2 scripting for EMAN1 users
The EMAN2 python library has many similarities to EMAN1. The main image data class is even called EMData, just like EMAN1. The most significant change is the change in naming convention from CamelCase to underscore_notation. That is, in EMAN1, you might say image.doFFT(), is image.do_fft() in EMAN2. The addition of a modular system with full introspection for various classes of algorithms is also a big change. For example, rather than having a dedicated low-pass filter method, in EMAN2, you would:
- image.process_inplace("filter.lowpass.gauss",{"cutoff_freq":.125})
For the most part, the Python methods/functions directly follow the C++ counterparts, so the autogenerated C++ documentation serves as a good reference for python programmers as well.
For those learning, we are gradually adding both written tutorials as well as a set of video mini-tutorials which cover programming as well as basic use of EMAN2.