tedana.utils.utils

Utilities for tedana package

Functions

andb(arrs)	Sums arrays in arrs
dice(arr1, arr2)	Compute Dice’s similarity index between two numpy arrays.
filewrite(data, filename, ref_img[, gzip, …])	Writes data to filename in format of ref_img
fitgaussian(data)	Returns estimated gaussian parameters of a 2D distribution found by a fit
gaussian(height, center_x, center_y, …)	Returns gaussian function
get_dtype(data)	Determines neuroimaging format of data
getfbounds(n_echos)	Gets estimated F-statistic boundaries based on number of echos
load_data(data[, n_echos])	Coerces input data files to required 3D array output
load_image(data)	Takes input data and returns a sample x time array
make_adaptive_mask(data[, mask, minimum, getsum])	Makes map of data specifying longest echo a voxel can be sampled with
make_min_mask(data[, roi])	Generates a 3D mask of data
moments(data)	Returns gaussian parameters of a 2D distribution by calculating its moments
new_nii_like(ref_img, data[, affine, …])	Coerces data into NiftiImage format like ref_img
unmask(data, mask)	Unmasks data using non-zero entries of mask

andb(arrs)

Sums arrays in arrs

Parameters:arrs (list) – List of boolean or integer arrays to be summed Returns:result – Integer array of summed arrs Return type:numpy.ndarray

dice(arr1, arr2)

Compute Dice’s similarity index between two numpy arrays. Arrays will be binarized before comparison.

Parameters:arr2 (arr1,) – Input arrays, arrays to binarize and compare. Returns:dsi – Dice-Sorenson index. Return type:float

References

REF

filewrite(data, filename, ref_img, gzip=False, copy_header=True)

Writes data to filename in format of ref_img

Parameters:

• data ((S [x T]) array_like) – Data to be saved
• filename (str) – Filepath where data should be saved to
• ref_img (str or img_like) – Reference image
• gzip (bool, optional) – Whether to gzip output (if not specified in filename). Only applies if output dtype is NIFTI. Default: False
• copy_header (bool, optional) – Whether to copy header from ref_img to new image. Default: True

Returns:

name – Path of saved image (with added extensions, as appropriate)

Return type:

str

fitgaussian(data)

Returns estimated gaussian parameters of a 2D distribution found by a fit

Parameters:data (array_like) – 2D data array Returns:p – Array with height, center_x, center_y, width_x, width_y of data Return type:array_like

References

Scipy Cookbook

gaussian(height, center_x, center_y, width_x, width_y)

Returns gaussian function

Parameters:

• height (float) –
• center_x (float) –
• center_y (float) –
• width_x (float) –
• width_y (float) –

Returns:

Gaussian function with provided parameters

Return type:

lambda

References

Scipy Cookbook

get_dtype(data)

Determines neuroimaging format of data

Parameters:data (list of str or str or img_like) – Data to determine format of Returns:dtype – Format of input data Return type:{‘NIFTI’, ‘OTHER’} str

getfbounds(n_echos)

Gets estimated F-statistic boundaries based on number of echos

Parameters:n_echos (int) – Number of echoes Returns:fmin, fmid, fmax – Minimum, mid, and max F bounds Return type:float

load_data(data, n_echos=None)

Coerces input data files to required 3D array output

Parameters:

• data ((X x Y x M x T) array_like or list of img_like) – Input multi-echo data array, where X and Y are spatial dimensions, M is the Z-spatial dimensions with all the input echos concatenated, and T is time. A list of image-like objects (e.g., .nii) are accepted, as well
• n_echos (int, optional) – Number of echos in provided data array. Only necessary if data is array_like. Default: None

Returns:

• fdata ((S x E x T) numpy.ndarray) – Output data where S is samples, E is echos, and T is time
• ref_img (str or numpy.ndarray) – Filepath to reference image for saving output files or NIFTI-like array

load_image(data)

Takes input data and returns a sample x time array

Parameters:data ((X x Y x Z [x T]) array_like or img_like object) – Data array or data file to be loaded and reshaped Returns:fdata – Reshaped data, where S is samples and T is time Return type:(S [x T]) numpy.ndarray

make_adaptive_mask(data, mask=None, minimum=True, getsum=False)

Makes map of data specifying longest echo a voxel can be sampled with

Parameters:

• data ((S x E x T) array_like) – Multi-echo data array, where S is samples, E is echos, and T is time
• mask (str or img_like, optional) – Binary mask for voxels to consider in TE Dependent ANAlysis. Default is to generate mask from data with good signal across echoes
• minimum (bool, optional) – Use make_min_mask() instead of generating a map with echo-specific times. Default: True
• getsum (bool, optional) – Return masksum in addition to mask. Default: False

Returns:

• mask ((S,) numpy.ndarray) – Boolean array of voxels that have sufficient signal in at least one echo
• masksum ((S,) numpy.ndarray) – Valued array indicating the number of echos with sufficient signal in a given voxel. Only returned if getsum = True

make_min_mask(data, roi=None)

Generates a 3D mask of data

Only samples that are consistently (i.e., across time AND echoes) non-zero in data are True in output

Parameters:

• data ((S x E x T) array_like) – Multi-echo data array, where S is samples, E is echos, and T is time
• roi (str, optional) – Binary mask for region-of-interest to consider in TE Dependent ANAlysis

Returns:

mask – Boolean array

Return type:

(S,) numpy.ndarray

moments(data)

Returns gaussian parameters of a 2D distribution by calculating its moments

Parameters:data (array_like) – 2D data array Returns:

• height (float)
• center_x (float)
• center_y (float)
• width_x (float)
• width_y (float)

References

Scipy Cookbook

new_nii_like(ref_img, data, affine=None, copy_header=True)

Coerces data into NiftiImage format like ref_img

Parameters:

• ref_img (str or img_like) – Reference image
• data ((S [x T]) array_like) – Data to be saved
• affine ((4 x 4) array_like, optional) – Transformation matrix to be used. Default: ref_img.affine
• copy_header (bool, optional) – Whether to copy header from ref_img to new image. Default: True

Returns:

nii – NiftiImage

Return type:

nibabel.nifti1.Nifti1Image

unmask(data, mask)

Unmasks data using non-zero entries of mask

Parameters:

• data ((M [x E [x T]]) array_like) – Masked array, where M is the number of True values in mask
• mask ((S,) array_like) – Boolean array of S samples that was used to mask data. It should have exactly M True values.

Returns:

out – Unmasked data array

Return type:

(S [x E [x T]]) numpy.ndarray