API¶

`tedana.workflows`: Common workflows¶

`tedana.workflows`
`tedana.workflows.tedana_workflow`(data, tes)	Run the “canonical” TE-Dependent ANAlysis workflow.
`tedana.workflows.t2smap_workflow`(data, tes)	Estimate T2 and S0, and optimally combine data across TEs.

`tedana.model`
`tedana.model.fitmodels_direct`(catd, mmix, …)	Fit TE-dependence and -independence models to components.
`tedana.model.fit`	Fit models.

`tedana.decomposition`
`tedana.decomposition.tedpca`(catd, OCcatd, …)	Use principal components analysis (PCA) to identify and remove thermal noise from multi-echo data.
`tedana.decomposition.tedica`(n_components, …)	Performs ICA on dd and returns mixing matrix
`tedana.decomposition._utils`	Utility functions for tedana decomposition

Functions to optimally combine data across echoes.

`tedana.combine`	Functions to optimally combine data across echoes.
`tedana.combine.make_optcom`(data, tes, mask)	Optimally combine BOLD data across TEs.
`tedana.combine`	Functions to optimally combine data across echoes.

Functions to estimate S0 and T2* from multi-echo data.

`tedana.decay`	Functions to estimate S0 and T2* from multi-echo data.
`tedana.decay.fit_decay`(data, tes, mask, masksum)	Fit voxel-wise monoexponential decay models to data
`tedana.decay.fit_decay_ts`(data, tes, mask, …)	Fit voxel- and timepoint-wise monoexponential decay models to data
`tedana.decay`	Functions to estimate S0 and T2* from multi-echo data.

`tedana.selection`
`tedana.selection.selcomps`(seldict, …)	Classify components in seldict as “accepted,” “rejected,” “midk,” or “ignored.”
`tedana.selection._utils`	Utility functions for tedana.selection

Functions to handle file input/output

`tedana.io`	Functions to handle file input/output
`tedana.io.split_ts`(data, mmix, mask, acc)	Splits data time series into accepted component time series and remainder
`tedana.io.ctabsel`(ctabfile)	Loads a pre-existing component table file
`tedana.io.filewrite`(data, filename, ref_img)	Writes data to filename in format of ref_img
`tedana.io.gscontrol_mmix`(optcom_ts, mmix, …)	Perform global signal regression.
`tedana.io.load_data`(data[, n_echos])	Coerces input data files to required 3D array output
`tedana.io.new_nii_like`(ref_img, data[, …])	Coerces data into NiftiImage format like ref_img
`tedana.io.write_split_ts`(data, mmix, mask, …)	Splits data into denoised / noise / ignored time series and saves to disk
`tedana.io.writect`
`tedana.io.writefeats`(data, mmix, mask, ref_img)	Converts data to component space with mmix and saves to disk
`tedana.io.writeresults`(ts, mask, comptable, …)	Denoises ts and saves all resulting files to disk
`tedana.io.writeresults_echoes`(catd, mmix, …)	Saves individually denoised echos to disk
`tedana.io`	Functions to handle file input/output

Utilities for tedana package

`tedana.utils`	Utilities for tedana package
`tedana.utils.andb`(arrs)	Sums arrays in arrs
`tedana.utils.dice`(arr1, arr2)	Compute Dice’s similarity index between two numpy arrays.
`tedana.utils.fitgaussian`(data)	Returns estimated gaussian parameters of a 2D distribution found by a fit
`tedana.utils.gaussian`(height, center_x, …)	Returns gaussian function
`tedana.utils.get_dtype`(data)	Determines neuroimaging format of data
`tedana.utils.getfbounds`(n_echos)	Gets F-statistic boundaries based on number of echos
`tedana.utils.load_image`(data)	Takes input data and returns a sample x time array
`tedana.utils.make_adaptive_mask`(data[, …])	Makes map of data specifying longest echo a voxel can be sampled with
`tedana.utils.make_min_mask`(data[, roi])	Generates a 3D mask of data
`tedana.utils.moments`(data)	Returns gaussian parameters of a 2D distribution by calculating its moments
`tedana.utils.unmask`(data, mask)	Unmasks data using non-zero entries of mask
`tedana.utils`	Utilities for tedana package