xmipp_mlf_align2d (v3.0)


-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=mlf2d>
Output rootname
Also check mirror image of each reference
Use pre-centered images to pre-calculate significant orientations. If this flag is set part of the integration over all references, rotations and translations is skipped. The program will store all (N_imgs*N_refs) origin offsets that yield the maximum probability of observing each experimental image, given each of the references. In the first iterations a complete integration over all references, rotations and translations is performed for all images. In all subsequent iterations, for all combinations of experimental images, references and rotations, the probability of observing the image given the optimal origin offsets from the previous iteration is calculated. Then, if this probability is not considered "significant", we assume that none of the other translations will be significant, and we skip the integration over the translations. A combination of experimental image, reference and rotation is considered as "significant" if the probability at the corresponding optimal origin offsets is larger than C times the maximum of all these probabilities for that experimental image and reference (by default C=1e-12) This version may run up to ten times faster than the original, complete-search approach, while practically identical results may be obtained.
--thr <N=1>
Use N parallel threads
--no_ctf <pixel_size=1>
do not use any CTF correction, pixel size should be provided by defaut the CTF info is read from input images metadata

Additional options

--eps <float=5e-5>
Stopping criterium
--iter <int=100>
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
--restart <iter=1>
restart a run with all parameters as in the logfile
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)
--search_shift <int=3>
Limited translational searches (in pixels)
--reduce_snr <factor=1>
Use a value smaller than one to decrease the estimated SSNRs
Use this if the experimental images have not been phase flipped
Use this if the references (-ref) are not CTF-deconvoluted
--limit_resolution <first_high=0> <high=0> <low=999>
Exclude frequencies from P-calculations (in Ang) First value is highest frequency during first iteration. Second is the highest in following iterations and third is lowest
--fix_high <float=-1>


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