subtom_eigenvolumes_msa
Computes Eigendecomposition of X-Matrix covariance and projects data on Eigenvectors.
subtom_eigenvolumes_msa(
'all_motl_fn_prefix', all_motl_fn_prefix ('combinedmotl/allmotl'),
'ptcl_fn_prefix', ptcl_fn_prefix ('subtomograms/subtomo'),
'eig_vec_fn_prefix', eig_vec_fn_prefix ('class/eigvec_msa'),
'eig_val_fn_prefix', eig_val_fn_prefix ('class/eigval_msa'),
'xmatrix_fn_prefix', xmatrix_fn_prefix ('class/xmatrix_msa'),
'eig_vol_fn_prefix', eig_vol_fn_prefix ('class/eigvol_msa'),
'mask_fn', mask_fn ('none'),
'iteration', iteration (1),
'num_eigs', num_eigs (40),
'eigs_iterations', eigs_iterations ('default'),
'eigs_tolerance', eig_tolerance ('default'))
Calculates num_eigs weighted projections of particles onto the same number
of determined Eigenvectors, by means of a previously calculated X-matrix,
named as given by xmatrix_fn_prefix and iteration to produce Eigenvolumes which can
then be used to determine which vectors can best influence classification.
The Eigenvectors and Eigenvalues are also written out as specified by
eig_vec_fn_prefix, eig_val_fn_prefix, and iteration The
Eigenvolumes are also masked by the file specified by mask_fn. The output
weighted Eigenvolume will be written out as described by
eig_vol_fn_prefix, iteration and #, where the # is the particular
Eigenvolume being written out. Two options eigs_iterations and
eigs_tolerance are also available to tune how eigs is run. If the string
‘default’ is given for either the default values in eigs will be used.
Example
subtom_eigenvolumes_msa(...
'all_motl_fn_prefix', 'combinedmotl/allmotl', ...
'ptcl_fn_prefix', 'subtomograms/subtomo', ...
'eig_vec_fn_prefix', 'class/eigvec', ...
'eig_val_fn_prefix', 'class/eigval', ...
'xmatrix_fn_prefix', 'class/xmatrix', ...
'eig_vol_fn_prefix', 'class/eigvol', ...
'mask_fn', 'class/class_mask.em', ...
'iteration', 1, ...
'num_eigs', 40, ...
'eigs_iterations', 'default', ...
'eigs_tolerance', 'default')