View source for Tedana

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Usage: tedana.py [options]

Options:
  -h, --help            show this help message and exit
  -d DATA, --orig_data=DATA
                        Spatially Concatenated Multi-Echo Dataset
  -e TES, --TEs=TES     Echo times (in ms) ex: 15,39,63
  --mix=MIXM            Mixing matrix. If not provided, ME-PCA & ME-ICA (MDP)
                        is done.
  --manacc=MANACC       Comma separated list of manually accepted components
  --kdaw=KDAW           Dimensionality augmentation weight (Kappa). Default
                        10. -1 for low-dimensional ICA
  --rdaw=RDAW           Dimensionality augmentation weight (Rho). Default 1.
                        -1 for low-dimensional ICA
  --conv=CONV           Convergence limit. Default 2.5e-5
  --sourceTEs=STE       Source TEs for models. ex: -ste 2,3 ; -ste 0 for all,
                        -1 for opt. com. Default -1.
  --denoiseTE=E2D       TE to denoise. Default middle
  --initcost=INITCOST   Initial cost func. for ICA:
                        pow3,tanh(default),gaus,skew
  --finalcost=FINALCOST
                        Final cost func, same opts. as initial
  --stabilize           Stabilize convergence by reducing dimensionality, for
                        low quality data
  --noignored           Remove ignored components from denoised timeseries as
                        well.
  --fout                Output TE-dependence Kappa/Rho SPMs
  --label=LABEL         Label for output directory.
  --seed=SEED           Seed used for ICA. Default 42.
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