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dipy_align_affine
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usage: dipy_align_affine [-h] [--transform str] [--nbins int]
                     [--sampling_prop int] [--metric str]
                     [--level_iters [int [int ...]]]
                     [--sigmas [float [float ...]]]
                     [--factors [float [float ...]]] [--progressive]
                     [--save_metric] [--out_dir str] [--out_moved str]
                     [--out_affine str] [--out_quality str]
                     static_img_files moving_img_files

positional arguments:
  static_img_files      Path to the static image file. 
  moving_img_files      Path to the moving image file. 

optional arguments:
  -h, --help            show this help message and exit
  --transform str       com: center of mass, trans: translation, rigid: rigid body  affine: full affine including translation, rotation, shearing and  scaling (default 'affine'). 
  --nbins int           Number of bins to discretize the joint and marginal PDF  (default '32'). 
  --sampling_prop int   Number ([0-100]) of voxels for calculating the PDF.  'None' implies all voxels (default 'None'). 
  --metric str          Similarity metric for gathering mutual information  (default 'mi' , Mutual Information metric). 
  --level_iters [int [int ...]]
                        The number of iterations at each scale of the scale space.  `level_iters[0]` corresponds to the coarsest scale,  `level_iters[-1]` the finest, where n is the length of the   sequence. By default, a 3-level scale space with iterations   sequence equal to [10000, 1000, 100] will be used. 
  --sigmas [float [float ...]]
                        Custom smoothing parameter to build the scale space (one parameter  for each scale). By default, the sequence of sigmas will be  [3, 1, 0]. 
  --factors [float [float ...]]
                        Custom scale factors to build the scale space (one factor for each  scale). By default, the sequence of factors will be [4, 2, 1]. 
  --progressive         Enable/Disable the progressive registration (default 'True'). 
  --save_metric         If true, quality assessment metric are saved in 'quality_metric.txt' (default 'False'). 

output arguments(optional):
  --out_dir str         Directory to save the transformed image and the affine matrix  (default ''). 
  --out_moved str       Name for the saved transformed image  (default 'moved.nii.gz'). 
  --out_affine str      Name for the saved affine matrix  (default 'affine.txt'). 
  --out_quality str     Name of the file containing the saved quality  metric (default 'quality_metric.txt').

References: 
Garyfallidis, E., M. Brett, B. Amirbekian, A. Rokem, S. Van Der Walt, M. Descoteaux, and I. Nimmo-Smith. Dipy, a library for the analysis of diffusion MRI data. Frontiers in Neuroinformatics, 1-18, 2014.