tedana.workflows: Common 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.decay: Modeling signal decay across echoes

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

tedana.decay.fit_decay(data, tes, mask, ...)

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.combine: Combining time series across echoes

Functions to optimally combine data across echoes.

tedana.combine.make_optcom(data, tes, ...[, ...])

Optimally combine BOLD data across TEs, using only those echos with reliable signal across at least three echos.

tedana.decomposition: Data decomposition

tedana.decomposition.tedpca(data_cat, ...[, ...])

Use principal components analysis (PCA) to identify and remove thermal noise from multi-echo data.

tedana.decomposition.tedica(data, ...[, ...])

Perform ICA on data and returns mixing matrix

tedana.metrics: Computing TE-dependence metrics

TE-dependence and TE-independence metrics.


Tools to collect and generate metrics.


Metrics evaluating component TE-dependence or -independence.

tedana.selection: Component selection


Perform manual selection of components.


Classify components as "accepted", "rejected", or "ignored" based on relevant metrics.

tedana.selection.kundu_tedpca(comptable, n_echos)

Select PCA components using Kundu's decision tree approach.

tedana.gscontrol: Global signal control

Global signal control methods

tedana.gscontrol.gscontrol_raw(catd, optcom, ...)

Removes global signal from individual echo catd and optcom time series


Perform minimum image regression (MIR) to remove T1-like effects from BOLD-like components.

tedana.io: Reading and writing data

The io module handles most file input and output in the tedana workflow.

Other functions in the module help write outputs which require multiple data sources, assist in writing per-echo verbose outputs, or act as helper functions for any of the above.

tedana.io.OutputGenerator(reference_img[, ...])

A class for managing tedana outputs.

tedana.io.load_data(data[, n_echos])

Coerce 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.add_decomp_prefix(comp_num, ...)

Create component name with leading zeros matching number of components

tedana.io.denoise_ts(data, mmix, mask, comptable)

Apply component classifications to data for denoising.

tedana.io.split_ts(data, mmix, mask, comptable)

Splits data time series into accepted component time series and remainder

tedana.io.write_split_ts(data, mmix, mask, ...)

Splits data into denoised / noise / ignored time series 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.stats: Statistical functions

Statistical functions

tedana.stats.get_coeffs(data, X[, mask, ...])

Performs least-squares fit of X against data

tedana.stats.computefeats2(data, mmix[, ...])

Converts data to component space using mmix


Gets F-statistic boundaries based on number of echos

tedana.bibtex: Tools for working with BibTeX files

Utilities for tedana package


Search a string for matched braces.


Identify outermost brace indices in list of indices.


Identify the BibTeX entry identifier before each entry.


Find citations in a text description.

tedana.bibtex.reduce_references(citations, ...)

Reduce the list of references to only include ones associated with requested citations.


Find BibTeX references for citations in a methods description.

tedana.utils: Utility functions

Utilities for tedana package


Sums arrays in arrs

tedana.utils.dice(arr1, arr2[, axis])

Compute Dice's similarity index between two numpy arrays.

tedana.utils.get_spectrum(data[, tr])

Returns the power spectrum and corresponding frequencies when provided with a component time course and repitition time.


Take input data and return 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.threshold_map(img, min_cluster_size)

Cluster-extent threshold and binarize image.

tedana.utils.unmask(data, mask)

Unmasks data using non-zero entries of mask


Convert seconds to milliseconds.


Convert milliseconds to seconds.