9.1.1. Data Grabbers#

Provide imports for datagrabber sub-package.

class junifer.datagrabber.BaseDataGrabber(types, datadir)#

Abstract base class for DataGrabber.

For every interface that is required, one needs to provide a concrete implementation of this abstract class.

Parameters:

typeslist of str: The types of data to be grabbed.
datadirstr or pathlib.Path: The directory where the data is / will be stored.

Attributes:

datadirpathlib.Path: Get data directory path.

property datadir: Path#

Get data directory path.

Returns:

pathlib.Path: Path to the data directory. Can be overridden by subclasses.

abstract get_element_keys()#

Get element keys.

For each item in the element tuple passed to __getitem__(), this method returns the corresponding key(s).

Returns:

list of str: The element keys.

abstract get_elements()#

Get elements.

Returns:

list: List of elements that can be grabbed. The elements can be strings, tuples or any object that will be then used as a key to index the DataGrabber.

abstract get_item(**element)#

Get the specified item from the dataset.

Parameters:

elementdict: The element to be indexed.

Returns:

dict: Dictionary of paths for each type of data required for the specified element.

get_types()#

Get types.

Returns:

list of str: The types of data to be grabbed.

class junifer.datagrabber.DataladAOMICID1000(datadir=None, types=None)#

Concrete implementation for datalad-based data fetching of AOMIC ID1000.

Parameters:

datadirstr or Path or None, optional: The directory where the datalad dataset will be cloned. If None, the datalad dataset will be cloned into a temporary directory (default None).
types: {“BOLD”, “BOLD_confounds”, “T1w”, “probseg_CSF”, “probseg_GM”, “probseg_WM”, “DWI”} or a list of the options, optional: AOMIC data types. If None, all available data types are selected. (default None).

get_item(subject)#

Index one element in the dataset.

Parameters:

subjectstr: The subject ID.

Returns:

outdict: Dictionary of paths for each type of data required for the specified element.

class junifer.datagrabber.DataladAOMICPIOP1(datadir=None, types=None, tasks=None)#

Concrete implementation for pattern-based data fetching of AOMIC PIOP1.

Parameters:

datadirstr or Path or None, optional: The directory where the datalad dataset will be cloned. If None, the datalad dataset will be cloned into a temporary directory (default None).
types: {“BOLD”, “BOLD_confounds”, “T1w”, “probseg_CSF”, “probseg_GM”, “probseg_WM”, “DWI”} or a list of the options, optional: AOMIC data types. If None, all available data types are selected. (default None).
tasks{“restingstate”, “anticipation”, “emomatching”, “faces”, “gstroop”, “workingmemory”} or list of the options, optional: AOMIC PIOP1 task sessions. If None, all available task sessions are selected (default None).

get_elements()#

Implement fetching list of subjects in the dataset.

Returns:

list of str: The list of subjects in the dataset.

get_item(subject, task)#

Index one element in the dataset.

Parameters:

subjectstr: The subject ID.
task{“restingstate”, “anticipation”, “emomatching”, “faces”, “gstroop”, “workingmemory”}: The task to get.

Returns:

outdict: Dictionary of paths for each type of data required for the specified element.

class junifer.datagrabber.DataladAOMICPIOP2(datadir=None, types=None, tasks=None)#

Concrete implementation for pattern-based data fetching of AOMIC PIOP2.

Parameters:

datadirstr or Path or None, optional: The directory where the datalad dataset will be cloned. If None, the datalad dataset will be cloned into a temporary directory (default None).
types: {“BOLD”, “BOLD_confounds”, “T1w”, “probseg_CSF”, “probseg_GM”, “probseg_WM”, “DWI”} or a list of the options, optional: AOMIC data types. If None, all available data types are selected. (default None).
tasks{“restingstate”, “stopsignal”, “workingmemory”} or list of the options, optional: AOMIC PIOP2 task sessions. If None, all available task sessions are selected (default None).

get_elements()#

Implement fetching list of elements in the dataset.

Returns:

list: The list of elements that can be grabbed in the dataset after imposing constraints based on specified tasks.

get_item(subject, task)#

Index one element in the dataset.

Parameters:

subjectstr: The subject ID.
task{“restingstate”, “stopsignal”, “workingmemory”}: The task to get.

Returns:

outdict: Dictionary of paths for each type of data required for the specified element.

class junifer.datagrabber.DataladDataGrabber(rootdir='.', datadir=None, uri=None, **kwargs)#

Abstract base class for datalad-based data fetching.

Defines a DataGrabber that gets data from a datalad sibling.

Parameters:

rootdirstr or pathlib.Path, optional: The path within the datalad dataset to the root directory (default “.”).
datadirstr or pathlib.Path or None, optional: That directory where the datalad dataset will be cloned. If None, the datalad dataset will be cloned into a temporary directory (default None).
uristr or None, optional: URI of the datalad sibling (default None).
**kwargs: Keyword arguments passed to superclass.

See also

BaseDataGrabber: Abstract base class for DataGrabber.
PatternDataGrabber: Concrete implementation for pattern-based data fetching.
PatternDataladDataGrabber: Concrete implementation for pattern and datalad based data fetching.

Notes

This class is intended to be used as a superclass of a subclass with multiple inheritance.

Methods

install:	Installs (clones) the datalad dataset into the `datadir`. This method is called automatically when the datagrabber is used within a context.
remove:	Removes the datalad dataset from the `datadir`. This method is called automatically when the datagrabber is used within a context.

cleanup()#

Cleanup the datalad dataset.

property datadir: Path#

Get data directory path.

Returns:

pathlib.Path: Path to the data directory.

install()#

Install the datalad dataset into the datadir.

Raises:

ValueError: If the dataset is already installed but with a different ID.

class junifer.datagrabber.DataladHCP1200(datadir=None, tasks=None, phase_encodings=None, ica_fix=False)#

Concrete implementation for datalad-based data fetching of HCP1200.

Parameters:

datadirstr or Path or None, optional: The directory where the datalad dataset will be cloned. If None, the datalad dataset will be cloned into a temporary directory (default None).
tasks{“REST1”, “REST2”, “SOCIAL”, “WM”, “RELATIONAL”, “EMOTION”, “LANGUAGE”, “GAMBLING”, “MOTOR”} or list of the options or None , optional: HCP task sessions. If None, all available task sessions are selected (default None).
phase_encodings{“LR”, “RL”} or list of the options or None, optional: HCP phase encoding directions. If None, both will be used (default None).
ica_fixbool, optional: Whether to retrieve data that was processed with ICA+FIX. Only “REST1” and “REST2” tasks are available with ICA+FIX (default False).

property skip_file_check: bool#: Skip file check existence.

class junifer.datagrabber.HCP1200(datadir, tasks=None, phase_encodings=None, ica_fix=False, **kwargs)#

Concrete implementation for pattern-based data fetching of HCP1200.

Parameters:

datadirstr or Path, optional: The directory where the data is / will be stored.
tasks{“REST1”, “REST2”, “SOCIAL”, “WM”, “RELATIONAL”, “EMOTION”, “LANGUAGE”, “GAMBLING”, “MOTOR”} or list of the options or None , optional: HCP task sessions. If None, all available task sessions are selected (default None).
phase_encodings{“LR”, “RL”} or list of the options or None, optional: HCP phase encoding directions. If None, both will be used (default None).
ica_fixbool, optional: Whether to retrieve data that was processed with ICA+FIX. Only “REST1” and “REST2” tasks are available with ICA+FIX (default False).
**kwargs: Keyword arguments passed to superclass.

get_elements()#

Implement fetching list of elements in the dataset.

Returns:

list: The list of elements that can be grabbed in the dataset.

get_item(subject, task, phase_encoding)#

Implement single element indexing in the database.

Parameters:

subjectstr: The subject ID.
task{“REST1”, “REST2”, “SOCIAL”, “WM”, “RELATIONAL”, “EMOTION”, “LANGUAGE”, “GAMBLING”, “MOTOR”}: The task.
phase_encoding{“LR”, “RL”}: The phase encoding.

Returns:

dict: Dictionary of dictionaries for each type of data required for the specified element.

class junifer.datagrabber.MultipleDataGrabber(datagrabbers, **kwargs)#

Concrete implementation for multi sourced data fetching.

Implements a DataGrabber which can be used to fetch data from multiple DataGrabbers.

Parameters:

datagrabberslist of DataGrabber-like objects: The DataGrabbers to use for fetching data.
**kwargs: Keyword arguments passed to superclass.

get_element_keys()#

Get element keys.

For each item in the element tuple passed to __getitem__(), this method returns the corresponding key(s).

Returns:

list of str: The element keys.

get_elements()#

Get elements.

Returns:

list: List of elements that can be grabbed. The elements can be strings, tuples or any object that will be then used as a key to index the the DataGrabber. The element should be present in all of the related DataGrabbers.

get_item(**_)#

Get the specified item from the dataset.

Parameters:

elementdict: The element to be indexed.

Returns:

dict: Dictionary of paths for each type of data required for the specified element.

Notes

This function is not implemented for this class as it is useless.

get_types()#

Get types.

Returns:

list of list of str: The types of data to be grabbed.

class junifer.datagrabber.PatternDataGrabber(types, patterns, replacements, datadir, confounds_format=None)#

Concrete implementation for pattern-based data fetching.

Implements a DataGrabber that understands patterns to grab data.

Parameters:

typeslist of str: The types of data to be grabbed.
patternsdict: Patterns for each type of data as a dictionary. The keys are the types and the values are the patterns. Each occurrence of the string {subject} in the pattern will be replaced by the indexed element.
replacementsstr or list of str: Replacements in the patterns for each item in the “element” tuple.
datadirstr or pathlib.Path: The directory where the data is / will be stored.
confounds_format{“fmriprep”, “adhoc”} or None, optional: The format of the confounds for the dataset (default None).

get_element_keys()#

Get element keys.

For each item in the “element” tuple, this functions returns the corresponding key, that is, the replacements of patterns defined in the constructor.

Returns:

list of str: The element keys.

get_elements()#

Implement fetching list of elements in the dataset.

It will use regex to search for “replacements” in the “patterns” and return the intersection of the results for each type i.e., build a list of elements that have all the required types.

Returns:

list: The list of elements that can be grabbed in the dataset.

get_item(**element)#

Implement single element indexing in the database.

This method constructs a real path to the requested item’s data, by replacing the patterns with actual values passed via **element.

Parameters:

elementdict: The element to be indexed. The keys must be the same as the replacements.

Returns:

dict: Dictionary of dictionaries for each type of data required for the specified element.

property skip_file_check: bool#: Skip file check existence.

class junifer.datagrabber.PatternDataladDataGrabber(types, patterns, **kwargs)#

Concrete implementation for pattern and datalad based data fetching.

Implements a DataGrabber that gets data from a datalad sibling, interpreting patterns.

Parameters:

typeslist of str: The types of data to be grabbed.
patternsdict: Patterns for each type of data as a dictionary. The keys are the types and the values are the patterns. Each occurrence of the string {subject} in the pattern will be replaced by the indexed element.
**kwargs: Keyword arguments passed to superclass.