xmipp_ml_refine3d (v3.0)


Separate structurally heterogenous data sets into homogeneous classes by a multi-reference 3D-angular refinement using a maximum-likelihood(ML) target function.


-i <input_file>
Metadata or stack with input images
--nref <int=1>
Number of references to generate automatically (recommended)
--ref <reference_file=>
Image, stack or metadata with initial(s) references(s)
--oroot <rootname=ml2d>
Output rootname
Also check mirror image of each reference
--thr <N=1>
Use N parallel threads
--iem <blocks=1>
Number of blocks to be used with IEM
--ang <float=10>
Angular sampling (degrees)

ML additional options:

--eps <float=5e-5>
Stopping criterium
--iter <int=25>
Maximum number of iterations to perform
--psi_step <float=5.>
In-plane rotation sampling interval [deg]
--noise <float=1>
Expected standard deviation for pixel noise
--offset <float=3.>
Expected standard deviation for origin offset [pix]
--frac <docfile=>
Docfile with expected model fractions (default: even distr.)
-C <double=1e-12>
Significance criterion for fast approach
Kick-start the fast algorithm from all-zero offsets
Do not re-estimate the standard deviation in the pixel noise
Do not re-estimate the standard deviation in the origin offsets
Do not re-estimate the model fractions
--student <df=6>
Use t-distributed instead of Gaussian model for the noise df = Degrees of freedom for the t-distribution
Refined normalization parameters for each particle
Save memory A(deprecated)
Save memory B(deprecated)

Additional options:

--recons <recons_type=wlsART>
Reconstruction method to be used
where <recons_type> can be:
  • wlsART <params=> wlsART parameters
  • fourier <params=> fourier parameters
Start wlsART reconstructions from all-zero volumes
--sym <symfile=c1>
Symmetry group
--low_pass <freq=-1>
Low-pass filter volume every iteration
--sym_mask <maskfile=>
Local symmetry (only inside mask)
--tilt <min=-91.> <max=91.>
Minimum and maximum values for restriction tilt angle search
Randomly perturb reference projection directions

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