CLI Reference

The following section outlines the CLI of shimming-toolbox.

shimmingtoolbox.cli.check_env.check_dcm2niix_installation()int

Checks that dcm2niix is installed.

This function calls which dcm2niix and checks the exit code to verify that dcm2niix is installed.

Returns

Exit code. 0 on success, nonzero on failure.

Return type

int

shimmingtoolbox.cli.check_env.check_prelude_installation()int

Checks that prelude is installed.

This function calls which prelude and checks the exit code to verify that prelude is installed.

Returns

Exit code. 0 on success, nonzero on failure.

Return type

int

shimmingtoolbox.cli.check_env.check_sct_installation()int

Checks that SCT is installed.

This function calls which sct_check_dependencies and checks the exit code to verify that sct is installed.

Returns

True if sct is installed, False if not.

Return type

bool

shimmingtoolbox.cli.check_env.get_dcm2niix_version()str

Gets the dcm2niix installation version.

This function calls dcm2niix --version and captures the output to obtain the installation version.

Returns

Version of the dcm2niix installation.

Return type

str

shimmingtoolbox.cli.check_env.get_env_info()str

Gets information about the environment.

This function gets information about the operating system, the host machine hardware, Python version & implementation, and Python location.

Returns

A multiline string containing environment info.

Return type

str

shimmingtoolbox.cli.check_env.get_pkg_info()str

Gets package version.

This function gets the version of shimming-toolbox.

Returns

The version number of the shimming-toolbox installation.

Return type

str

shimmingtoolbox.cli.check_env.get_prelude_version()str

Gets the prelude installation version.

This function calls prelude --help and parses the output to obtain the installation version.

Returns

Version of the prelude installation.

Return type

str

shimmingtoolbox.cli.check_env.get_sct_version()str

Gets the sct installation version.

This function calls sct_check_dependencies -short and captures the output to obtain the installation version.

Returns

Version of the SCT installation.

Return type

str

shimmingtoolbox.cli.check_env.print_line(string)

print without carriage return

mask

Create a mask based on a specified shape (box, rectangle, SpinalCord Toolbox mask) or based on the thresholding of an input image. Callable with the prefix ‘st’ in front of ‘mask’. (Example: ‘st_mask -h’).

mask [OPTIONS] COMMAND [ARGS]...

box

Create a box mask from the input file. The nifti file is converted to a numpy array. If this array is in 3D dimensions, then a binary mask is created from this array in the form of a box with lengths defined in ‘size’. This box is centered according to the 3 dimensions indicated in ‘center’. The mask is stored by default under the name ‘mask.nii.gz’ in the output folder.Return the filename for the output mask.

mask box [OPTIONS]

Options

-input <fname_input>

Required (str): Input path of the nifti file to mask. This nifti file must have 3D. Supported extensions are .nii or .nii.gz.

-output <output>

(str): Name of output mask. Supported extensions are .nii or .nii.gz. (default: (os.curdir, ‘mask.nii.gz’))

-size <size>

Required (int): Length of the side of the box along first, second and third dimension (in pixels). (nargs=3)

-center <center>

(int): Center of the box along first, second and third dimension (in pixels). If no center is provided (None), the middle is used. (nargs=3) (default: None, None, None)

rect

Create a rectangle mask from the input file. The nifti file is converted to a numpy array. If this array is in 2 dimensions, then a binary mask is created from this array in the form of a rectangle of lengths defined in ‘size’. This rectangle is centered according to the 2 dimensions indicated in ‘center’. If this array is in 3 dimensions, a binary mask is created in the shape of rectangle for each slice of the 3rd dimension of the array, in the same way as for a 2D array. The masks of all these slices are grouped in an array to form a binary mask in 3 dimensions. The mask is stored by default under the name ‘mask.nii.gz’ in the output folder.Return an output nifti file with square mask.

mask rect [OPTIONS]

Options

-input <fname_input>

Required (str): Input path of the nifti file to mask. This nifti file must have 2D or 3D. Supported extensions are .nii or .nii.gz.

-output <output>

(str): Name of output mask. Supported extensions are .nii or .nii.gz. (default: (os.curdir, ‘mask.nii.gz’))

-size <size>

Required (int): Length of the side of the box along first and second dimension (in pixels). (nargs=2)

-center <center>

(int): Center of the box along first and second dimension (in pixels). If no center is provided (None), the middle is used. (nargs=2) (default: None, None)

sct

Creates a mask around the spinal cord using the Spinal Cord Toolbox (SCT). The mask, which size can be specified, requires to identify the spinal cord centerline. The method of identification is specified by the flag ‘–centerline’. The output of this function is a NIfTI file containing the mask.

mask sct [OPTIONS]

Options

--input <fname_input>

Required Input nifti file to mask. Must be 3D. Supported extensions are .nii or .nii.gz. Example: data.nii.gz

--output <fname_output>

Name of output mask. Supported extensions are .nii or .nii.gz. Example: data.nii.

Default

./mask.nii.gz

--size <size>

Size of the mask in the axial plane, given in pixel (Example: 35) or in millimeter (Example: 35mm). If shape=gaussian, size corresponds to sigma (Example: 45).

Default

20

--shape <shape>

Shape of the mask.

Options

cylinder | box | gaussian

--contrast <contrast>

Type of image contrast.

Default

t2s

Options

t1 | t2 | t2s | dwi

--centerline <centerline>

Method used for extracting the centerline: - svm: Automatic detection using Support Vector Machine algorithm. - cnn: Automatic detection using Convolutional Neural Network. - viewer: Semi-automatic detection using manual selection of a few points with an interactive viewer followed by regularization. - file: Use an existing centerline (use with flag –file_centerline)

Default

svm

Options

svm | cnn | viewer | file

--file-centerline <file_centerline>

Input centerline file. This option is only valid with ‘–centerline file’. Example: t2_centerline_manual.nii.gz

--brain <brain>

Set to 1 if the image contains the brain (or part of it), set to 0 otherwise (to speed up the segmentation). This option is only valid with ‘–centerline cnn’.

--kernel <kernel>

Choice of kernel shape for the CNN. Segmentation with 3D kernels is slower than with 2D kernels.

Default

2d

Options

2d | 3d

--remove-tmp <remove_tmp>

Remove temporary files.

Default

True

--verbose <verbose>

Verbose: 0 = nothing, 1 = classic, 2 = expended.

Default

1

threshold

Create a threshold mask from the input file. The nifti file is converted into a numpy array. A binary mask is created from the thresholding of the array. The mask is stored by default under the name ‘mask.nii.gz’ in the output folder. Return an output nifti file with threshold mask.

mask threshold [OPTIONS]

Options

-input <fname_input>

Required (str): Input path of the nifti file to mask. Supported extensions are .nii or .nii.gz.

-output <output>

(str): Name of output mask. Supported extensions are .nii or .nii.gz. (default: (os.curdir, ‘mask.nii.gz’))

-thr <thr>

(int): Value to threshold the data: voxels will be set to zero if their value is equal or less than this threshold. (default: 30)