API¶
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, mask) |
Optimally combine BOLD data across TEs. |
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.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.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.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.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.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 |