Loading functions

These package is used to perform operation of reading and writing information about data you need to use.

The data must be organized in a standard way. `` experiment_folder/ – 0_results/ where analysis results will be placed – derivatives/ same as above (for compatibility with BIDS data format) – subjN/ single subject folder —- ``

• sekupy.io.bids module

• sekupy.io.configuration module

• sekupy.io.connectivity module

• sekupy.io.loader module

• sekupy.io.mambo module

• sekupy.io.mapper module

• sekupy.io.subjects module

sekupy.io.base module

sekupy.io.base.add_attributes(ds, attr)

Add sample attributes to the dataset.

This function adds additional sample attributes from a dictionary to the dataset’s sample attribute collection.

Parameters:

• ds (Dataset) – The dataset to add attributes to

• attr (dict) – Dictionary containing attribute names as keys and attribute values as values

Returns:

Dataset with additional sample attributes added

Return type:

Dataset

sekupy.io.base.add_events(ds)

Add event information to the dataset.

This function extracts event information from the dataset’s targets and chunks attributes and adds event-related attributes.

Parameters:

ds (Dataset) – The dataset to add event information to

Returns:

Dataset with event information added to attributes

Return type:

Dataset

sekupy.io.base.add_filename(ds, fmri_list)

Add filename information to dataset sample attributes.

This function adds file provenance information to each sample in the dataset, tracking which file each volume came from.

Parameters:

• ds (Dataset) – The dataset to add filename information to

• fmri_list (list) – List of neuroimaging files (nibabel images)

Returns:

Dataset with filename sample attribute added

Return type:

Dataset

sekupy.io.base.find_roi(path, roi_list)

sekupy.io.base.load_attributes(path, subj, task, **kwargs)

Loads attribute files from path and selected subject.

Parameters:

• path ([type]) – [description]

• subj ([type]) – [description]

• task ([type]) – [description]

Returns:

[description]

Return type:

[type]

Raises:

FileNotFoundError – [description]

sekupy.io.base.load_dataset(path, subj, folder, **kwargs)

Load a 2d dataset.

The function needs the image path, the subject and the main folder of the data.

Parameters:

• path (string) – specification of filepath to load

• subj (string) – subject name (in general it specifies a subfolder under path)

• folder (string) – subfolder under subject folder (in general is the experiment name)

• kwargs (keyword arguments) – Keyword arguments to format-specific load

Returns:

ds – Instance of sekupy.dataset.base.Dataset

Return type:

Dataset

sekupy.io.base.load_filelist(path, name, folder, **kwargs)

Load file given the filename.

Parameters:

• path (string) – specification of filepath to load

• name (string) – subject name (in general it specifies a subfolder under path)

• folder (string) – subfolder under subject folder (in general is the experiment name)

• kwargs (keyword arguments) – Keyword arguments to format-specific load

Returns:

file_list – list of strings indicating the file pathname

Return type:

string list

sekupy.io.base.load_fmri(filelist)

Load data specified in the file list as nibabel image.

Parameters:

filelist (list) – List of pathnames specifying the location of image to be loaded

Returns:

List of nibabel images.

Return type:

fmri_list

sekupy.io.base.load_mask(path, **kwargs)

Load the mask from the input path.

Parameters:

path ([type]) – [description]

Returns:

[description]

Return type:

[type]

sekupy.io.base.load_roi_labels(roi_labels)

Module contents

sekupy.io.dataset_wizard(X, y=None, **kwargs)

sekupy.io.load_ds(conf_file, task, extra_sa=None, loader=<function load_dataset>, prepro=None, n_subjects=None, selected_subjects=None, **kwargs)

This is function loads a PyMVPA dataset given the configuration file and a loader.

Parameters:

• conf_file (str) – Path of the configuration file (see more in `sekupy.io.configuration.read_configuration`)

• task (str) – name of the task that is used, this should be contatined in configuration file

• extra_sa (dictionary, optional) – set of extra sample attributes to be attached to the dataset, by default None

• loader (function, optional) – The function used to load the data in a correct way, by default load_dataset

• prepro (`sekupy.preprocessing.Pipeline` object) – or list of `sekupy.preprocessing.Transformer`, optional Preprocessing pipeline to be performed at dataset-level, by default None

• n_subjects (int, optional) – number of subjects to be loaded, by default None

• selected_subjects (list of string, optional) – name of the subjects to be loaded, by default None

Returns:

The loaded dataset

Return type:

`sekupy.dataset.base.Dataset`