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: Modeling TE-dependence

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

tedana.decomposition: Data decomposition

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

tedana.combine: Combine time series

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.

tedana.decay: Signal decay

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: Component selection

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

tedana.io: Reading and writing data

Functions to handle file input/output

tedana.io	Functions to handle file input/output
tedana.io.split_ts(data, mmix, mask, comptable)	Splits data time series into accepted component time series and remainder
tedana.io.filewrite(data, filename, ref_img)	Writes data to filename in format of ref_img
tedana.io.gscontrol_mmix
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.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

tedana.utils: Utility functions

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.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.unmask(data, mask)	Unmasks data using non-zero entries of mask
tedana.utils	Utilities for tedana package