9.2.11. On-the-fly

Utilities for on-the-fly analyses.

junifer.onthefly.read_transform(storage, transform, feature_name=None, feature_md5=None, transform_args=None, transform_kw_args=None)

Read stored feature and transform to specific statistical output.

Parameters:

storagestorage-like

The storage class, for example, SQLiteFeatureStorage.

transformstr

The kind of transform formatted as <package>_<function>, for example, bctpy_degrees_und.

feature_namestr, optional

Name of the feature to read (default None).

feature_md5str, optional

MD5 hash of the feature to read (default None).

transform_argstuple, optional

The positional arguments for the callable of transform (default None).

transform_kw_argsdict, optional

The keyword arguments for the callable of transform (default None).

Returns:

pandas.DataFrame

The transformed feature as a dataframe.

Notes

This function has been only tested for:

• bct.degrees_und

• bct.strengths_und

• bct.clustering_coef_wu

• bct.eigenvector_centrality_und

Using other functions may fail and require tweaking.

Provide onthefly functions for BrainPrint post-analysis.

junifer.onthefly.brainprint.normalize(storage, features, kind)

Read stored brainprint data and normalize either surfaces or volumes.

Parameters:

storagestorage-like

The storage class, for example, HDF5FeatureStorage.

featuresdict, optional

The feature names or MD5 hashes to read as dict. The dict should have the keys:

• "areas" (if kind="surface")

• "volumes" (if kind="volume")

• "eigenvalues"

and the corresponding value for each of the keys is again a dict with the keys:

• "feature_name" : str or None

• "feature_md5" : str or None

Either one of "feature_name" or "feature_md5" needs to be not None for each first-level key, but both keys are mandatory.

kind{“surface”, “volume”}

The kind of normalization.

Returns:

pandas.DataFrame

The transformed feature as a pandas.DataFrame.

Raises:

ValueError

If kind is invalid.

junifer.onthefly.brainprint.reweight(storage, feature_name=None, feature_md5=None)

Read stored brainprint data and reweight eigenvalues.

Parameters:

storagestorage-like

The storage class, for example, HDF5FeatureStorage.

feature_namestr, optional

Name of the feature to read (default None).

feature_md5str, optional

MD5 hash of the feature to read (default None).

Returns:

pandas.DataFrame

The transformed feature as a pandas.DataFrame.