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

tedana.metrics.dependence_metrics(catd, …) Fit TE-dependence and -independence models to components.
tedana.metrics.kundu_metrics(comptable, …) Compute metrics used by Kundu v2.5 and v3.2 decision trees.

tedana.selection: Component selection

tedana.selection.manual_selection(comptable) Perform manual selection of components.
tedana.selection.kundu_selection_v2(…) 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
tedana.gscontrol.gscontrol_mmix(optcom_ts, …) Perform global signal regression.

tedana.io: Reading and writing data

Functions to handle file input/output

tedana.io.load_data(data[, n_echos]) Coerces input data files to required 3D array output
tedana.io.filewrite(data, filename, ref_img) Writes data to filename in format of ref_img
tedana.io.new_nii_like(ref_img, data[, …]) Coerces data into NiftiImage format like ref_img
tedana.io.save_comptable(df, filename[, …]) Save pandas DataFrame as a BIDS Derivatives-compatible json file.
tedana.io.load_comptable(filename) Load a BIDS Derivatives decomposition json file into a pandas DataFrame.
tedana.io.add_decomp_prefix(comp_num, …) Create component name with leading zeros matching number of components
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.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.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
tedana.stats.getfbounds(n_echos) Gets F-statistic boundaries based on number of echos

tedana.utils: Utility functions

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.get_spectrum(data, tr) Returns the power spectrum and corresponding frequencies when provided with a component time course and repitition time.
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.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
tedana.utils.sec2millisec(arr) Convert seconds to milliseconds.
tedana.utils.millisec2sec(arr) Convert milliseconds to seconds.