usage: __main__.py [-h] (--metrics_dir dir | --metrics file [file ...])
[--bin] [--normalize_weights] [--indent INDENT]
[--sort_keys] [-v [{DEBUG,INFO,WARNING}]] [-f]
in_mask
Compute the statistics (mean, std) of scalar maps, which can represent
diffusion metrics, in a ROI. Prints the results.
The mask can either be a binary mask, or a weighting mask. If the mask is
a weighting mask it should either contain floats between 0 and 1 or should be
normalized with --normalize_weights. IMPORTANT: if the mask contains weights
(and not 0 and 1 exclusively), the standard deviation will also be weighted.
positional arguments:
in_mask Mask volume filename.
Can be a binary mask or a weighted mask.
options:
-h, --help show this help message and exit
--bin If set, will consider every value of the mask higherthan 0 to be
part of the mask (equivalent weighting for every voxel).
--normalize_weights If set, the weights will be normalized to the [0,1] range.
-v [{DEBUG,INFO,WARNING}]
Produces verbose output depending on the provided level.
Default level is warning, default when using -v is info.
-f Force overwriting of the output files.
Metrics input options:
--metrics_dir dir Name of the directory containing metrics files: we will
load all nifti files.
--metrics file [file ...]
Metrics nifti filename. List of the names of the metrics file,
in nifti format.
Json options:
--indent INDENT Indent for json pretty print.
--sort_keys Sort keys in output json.