9.2.8. On-the-fly¶

Utilities for on-the-fly analyses.

junifer.onthefly.read_transform(storage, transform, feature_name=None, feature_md5=None, nan_policy='bypass', 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).

nan_policystr, optional

The policy to handle NaN values (default “ignore”). Options are:

“bypass”: Do nothing and pass NaN values to the transform function.
“drop_element”: Drop (skip) elements with NaN values.
“drop_rows”: Drop (skip) rows with NaN values.
“drop_columns”: Drop (skip) columns with NaN values.
“drop_symmetric”: Drop (skip) symmetric pairs with NaN values.

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.

Raises:

ValueError: If nan_policy is invalid or if package is invalid.
RuntimeError: If package is bctpy and stored data kind is not matrix.
ImportError: If bctpy cannot be imported.
AttributeError: If function to be invoked in invalid.

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.