API

tedana.workflows: Common workflows

Command line interfaces and workflows.

tedana.workflows.tedana_workflow(data, tes)	Run the "canonical" TE-Dependent ANAlysis workflow.
tedana.workflows.ica_reclassify_workflow(...)	Run the post-tedana manual classification 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.

tedana.decomposition: Data decomposition

Functions for decomposing BOLD signals.

tedana.decomposition.tedpca(data_cat, ...[, ...])	Use principal components analysis (PCA) to identify and remove thermal noise from data.
tedana.decomposition.tedica(data, ...[, ...])	Perform ICA on data and return mixing matrix.

tedana.metrics: Computing TE-dependence metrics

TE-dependence and TE-independence metrics.

tedana.metrics.collect	Tools to collect and generate metrics.
tedana.metrics.dependence	Metrics evaluating component TE-dependence or -independence.

tedana.selection: Component selection

TEDANA selection methods.

tedana.selection.component_selector.ComponentSelector(tree)	Load and classify components based on a specified tree.
tedana.selection.component_selector.TreeError	Passes errors that are raised when validate_tree fails.
tedana.selection.component_selector.load_config(tree)	Load the json file with the decision tree and validate the fields in the decision tree.
tedana.selection.component_selector.validate_tree(tree)	Confirm that provided tree is a valid decision tree.

tedana.selection.selection_nodes	Functions that will be used as steps in a decision tree.
tedana.selection.selection_utils	Utility functions for tedana.selection.
tedana.selection.tedica	Functions to identify TE-dependent and TE-independent components.
tedana.selection.tedpca	Functions to identify TE-dependent and TE-independent components.

tedana.gscontrol: Global signal control

Global signal control methods.

tedana.gscontrol.gscontrol_raw(catd, optcom, ...)	Remove global signal from individual echo catd and optcom time series.
tedana.gscontrol.minimum_image_regression(...)	Perform minimum image regression (MIR) to remove T1-like effects from BOLD-like components.

tedana.io: Reading and writing data

Handle 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.InputHarvester(path)	Class for turning a registry file into a lookup table to get previous data.
tedana.io.CustomEncoder(*[, skipkeys, ...])	Class for converting some types because of JSON serialization and numpy incompatibilities.
tedana.io.load_data(data[, n_echos])	Coerce input data files to required 3D array output.
tedana.io.load_json(path)	Load a json file from path.
tedana.io.get_fields(name)	Identify all fields in an unformatted string.
tedana.io.new_nii_like(ref_img, data[, ...])	Coerce data into NiftiImage format like ref_img.
tedana.io.prep_data_for_json(d)	Attempt to create a JSON serializable dictionary from a data dictionary.
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)	Split data time series into accepted component time series and remainder.
tedana.io.write_split_ts(data, mmix, mask, ...)	Split data into denoised / noise / ignored time series and save to disk.
tedana.io.writeresults(ts, mask, comptable, ...)	Denoise ts and save all resulting files to disk.
tedana.io.writeresults_echoes(catd, mmix, ...)	Save individually denoised echos to disk.

tedana.stats: Statistical functions

Statistical functions.

tedana.stats.get_coeffs(data, x[, mask, ...])	Perform least-squares fit of x against data.
tedana.stats.computefeats2(data, mmix[, ...])	Convert data to component space using mmix.
tedana.stats.getfbounds(n_echos)	Get F-statistic boundaries based on number of echos.

tedana.bibtex: Tools for working with BibTeX files

Utilities for managing the tedana bibliography.

tedana.bibtex.find_braces(string)	Search a string for matched braces.
tedana.bibtex.reduce_idx(idx_list)	Identify outermost brace indices in list of indices.
tedana.bibtex.index_bibtex_identifiers(...)	Identify the BibTeX entry identifier before each entry.
tedana.bibtex.find_citations(description)	Find citations in a text description.
tedana.bibtex.reduce_references(citations, ...)	Reduce the list of references to only include ones associated with requested citations.
tedana.bibtex.get_description_references(...)	Find BibTeX references for citations in a methods description.

tedana.utils: Utility functions

Utilities for tedana package.

tedana.utils.andb(arrs)	Sum arrays in arrs.
tedana.utils.dice(arr1, arr2[, axis])	Compute Dice's similarity index between two numpy arrays.
tedana.utils.get_spectrum(data[, tr])	Return the power spectrum and corresponding frequencies.
tedana.utils.reshape_niimg(data)	Take input data and return a sample x time array.
tedana.utils.make_adaptive_mask(data[, ...])	Make 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.