This is an old revision of the document!
Separate Into Classes : Separates stacks of particle images into stacks for each class.
Usage in command line:
sp_separate_class.py input_class_avgs input_image_stack output_directory --filtrad=filter_radius --apix=pixel_size --shrink=shrink_factor --align_isac_dir=isac_directory --format=stack_format --verbose
The purpose of sp_separate_class.py is to:
: write particle-membership lists for each class : write separate stacks for each class, with an option to low-pass filter and/or downsample the images, and : optionally compute eigenimages (basis images) for each class
1. Standard usage: create separate stacks for each class:
sp_separate_class.py input_class_avgs input_image_stack output_directory
2. Apply a low-pass filter to the image stacks:
sp_separate_class.py input_class_avgs input_image_stack output_directory --filt=filter_radius --apix=pixel_size
Filter radius is in units of Angstroms. If apix parameter is not specified, program will assume units of pixels^-1.
3. Downsample output image stack:
sp_separate_class.py input_class_avgs input_image_stack output_directory --shrink=shrink_factor
4. Apply ISAC alignments to particles:
sp_separate_class.py input_class_avgs input_image_stack output_directory --align_isac_dir=isac_directory
If the input class averages are ordered_class_averages.hdf, the alignments applied to the ordered class averages will be applied to the particles.
5. Compute eigenimages (basis images) for each class:
sp_separate_class.py input_class_avgs input_image_stack output_directory --align_isac_dir=isac_directory --nvec=number_of_factors
The additional output, stkeigen.hdf, will contain the average, variance, and the requested number of eigenimages.
: Should allow filter types other than Gaussian low-pass
Tapu Shaikh
Category 1:: APPLICATIONS
sphire/bin/sp_separate_class.py
Beta:: Under evaluation and testing. Please let us know if there are any bugs.
There are no known bugs so far.