Component selection breaks when no BOLD components are found

[tsalo]

Summary

A recent NeuroStars post uncovered a bug in the component selection decision tree in which it attempts to find an elbow on an empty array (ncls). I think that this probably occurs when no potential BOLD components based on the absolute measures in Step 1, which leads to no non-outlier varex components (ncls). Then the decision tree tries to find an elbow on what ends up being an empty array here, raising an exception.

I think that we should include the linked snippet in Step 1 as well as where it's currently implemented.

Additional Detail

Here is the output and error originally posted:

/u/local/apps/python/3.6.1/lib/python3.6/site-packages/h5py/init.py:34: FutureWarning: Conversion of the second argument of issubdtype from float to np.floating is deprecated. In future, it will be treated as np.float64 == np.dtype(float).type.
from ._conv import register_converters as _register_converters
/u/home/d/darag/.local/lib/python3.6/site-packages/sklearn/externals/joblib/init.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.
warnings.warn(msg, category=DeprecationWarning)
INFO:tedana.workflows.tedana:Using output directory: /u/scratch/d/darag/multiecho/11055_2/TED.mcf_ts_brain
INFO:tedana.workflows.tedana:Loading input data: [‘te1_mcf_ts_brain.nii’, ‘te2_mcf_ts_brain.nii’, ‘te3_mcf_ts_brain.nii’]
INFO:tedana.workflows.tedana:Computing EPI mask from first echo
WARNING:tedana.utils:279 voxels in user-defined mask do not have good signal. Removing voxels from mask.
INFO:tedana.workflows.tedana:Computing T2* map
INFO:tedana.combine:Optimally combining data with voxel-wise T2 estimates
INFO:tedana.decomposition.eigendecomp:Computing PCA of optimally combined multi-echo data
INFO:tedana.decomposition.eigendecomp:Making initial component selection guess from PCA results
INFO:tedana.combine:Optimally combining data with voxel-wise T2 estimates
INFO:tedana.model.fit:Fitting TE- and S0-dependent models to components
INFO:tedana.decomposition.eigendecomp:Saving PCA results to: /u/scratch/d/darag/multiecho/11055_2/TED.mcf_ts_brain/pcastate.pkl
INFO:tedana.decomposition.eigendecomp:Selected 283 components with MLE dimensionality detection
WARNING:tedana.decomposition.eigendecomp:ICA attempt 1 failed to converge after 500 iterations
WARNING:tedana.decomposition.eigendecomp:Random seed updated to 43
WARNING:tedana.decomposition.eigendecomp:ICA attempt 2 failed to converge after 500 iterations
WARNING:tedana.decomposition.eigendecomp:Random seed updated to 44
WARNING:tedana.decomposition.eigendecomp:ICA attempt 3 failed to converge after 500 iterations
WARNING:tedana.decomposition.eigendecomp:Random seed updated to 45
WARNING:tedana.decomposition.eigendecomp:ICA attempt 4 failed to converge after 500 iterations
WARNING:tedana.decomposition.eigendecomp:Random seed updated to 46
WARNING:tedana.decomposition.eigendecomp:ICA attempt 5 failed to converge after 500 iterations
WARNING:tedana.decomposition.eigendecomp:Random seed updated to 47
WARNING:tedana.decomposition.eigendecomp:ICA attempt 6 failed to converge after 500 iterations
WARNING:tedana.decomposition.eigendecomp:Random seed updated to 48
WARNING:tedana.decomposition.eigendecomp:ICA attempt 7 failed to converge after 500 iterations
WARNING:tedana.decomposition.eigendecomp:Random seed updated to 49
WARNING:tedana.decomposition.eigendecomp:ICA attempt 8 failed to converge after 500 iterations
WARNING:tedana.decomposition.eigendecomp:Random seed updated to 50
WARNING:tedana.decomposition.eigendecomp:ICA attempt 9 failed to converge after 500 iterations
WARNING:tedana.decomposition.eigendecomp:Random seed updated to 51
WARNING:tedana.decomposition.eigendecomp:ICA attempt 10 failed to converge after 500 iterations
INFO:tedana.workflows.tedana:Making second component selection guess from ICA results
INFO:tedana.combine:Optimally combining data with voxel-wise T2 estimates
INFO:tedana.model.fit:Fitting TE- and S0-dependent models to components
INFO:tedana.model.fit:Performing spatial clustering of components
Traceback (most recent call last):
File “/u/home/d/darag/.local/bin/tedana”, line 11, in 
sys.exit(_main())
File “/u/home/d/darag/.local/lib/python3.6/site-packages/tedana/workflows/tedana.py”, line 453, in _main
tedana_workflow(**vars(options))
File “/u/home/d/darag/.local/lib/python3.6/site-packages/tedana/workflows/tedana.py”, line 379, in tedana_workflow
n_echos)
File “/u/home/d/darag/.local/lib/python3.6/site-packages/tedana/selection/select_comps.py”, line 250, in selcomps
rho_elbow = np.mean((getelbow(comptable.loc[ncls, ‘rho’], return_val=True),
File “/u/home/d/darag/.local/lib/python3.6/site-packages/tedana/selection/_utils.py”, line 71, in getelbow
p = coords - coords[:, 0].reshape(2, 1)
IndexError: index 0 is out of bounds for axis 1 with size 0