9.1.1. Data Grabbers¶
DataGrabbers for datasets’ data description.
- enum junifer.datagrabber.AOMICSpace(value)¶
Accepted spaces for AOMIC.
- Member Type:
Valid values are as follows:
- Native = <AOMICSpace.Native: 'native'>¶
- MNI152NLin2009cAsym = <AOMICSpace.MNI152NLin2009cAsym: 'MNI152NLin2009cAsym'>¶
- enum junifer.datagrabber.AOMICTask(value)¶
Accepted tasks for AOMIC.
- Member Type:
Valid values are as follows:
- RestingState = <AOMICTask.RestingState: 'restingstate'>¶
- Anticipation = <AOMICTask.Anticipation: 'anticipation'>¶
- EmoMatching = <AOMICTask.EmoMatching: 'emomatching'>¶
- Faces = <AOMICTask.Faces: 'faces'>¶
- Gstroop = <AOMICTask.Gstroop: 'gstroop'>¶
- WorkingMemory = <AOMICTask.WorkingMemory: 'workingmemory'>¶
- StopSignal = <AOMICTask.StopSignal: 'stopsignal'>¶
- pydantic model junifer.datagrabber.BaseDataGrabber¶
Abstract base class for data fetcher.
For every datagrabber, one needs to provide a concrete implementation of this abstract class.
- Parameters:
- types
DataTypeorlistofvariants The data type(s) to grab.
- datadir
pathlib.Path The path where the data is or will be stored.
- types
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
Show JSON schema
{ "title": "BaseDataGrabber", "description": "Abstract base class for data fetcher.\n\nFor every datagrabber, one needs to provide a concrete\nimplementation of this abstract class.\n\nParameters\n----------\ntypes : :enum:`.DataType` or list of variants\n The data type(s) to grab.\ndatadir : pathlib.Path\n The path where the data is or will be stored.", "type": "object", "properties": { "types": { "anyOf": [ { "$ref": "#/$defs/DataType" }, { "items": { "$ref": "#/$defs/DataType" }, "type": "array" } ], "title": "Types" }, "datadir": { "format": "path", "title": "Datadir", "type": "string" } }, "$defs": { "DataType": { "description": "Accepted data type.", "enum": [ "T1w", "T2w", "BOLD", "Warp", "VBM_GM", "VBM_WM", "VBM_CSF", "fALFF", "GCOR", "LCOR", "DWI", "FreeSurfer" ], "title": "DataType", "type": "string" } }, "additionalProperties": false, "required": [ "types", "datadir" ] }
- Config:
extra: str = forbid
use_enum_values: bool = True
- Fields:
datadir (pathlib.Path)types (junifer.datagrabber.base.DataType | list[junifer.datagrabber.base.DataType])
- field types: Annotated[DataType | list[DataType], BeforeValidator(func=ensure_list, json_schema_input_type=PydanticUndefined)] [Required]¶
- Constraints:
func = <function ensure_list at 0x712131602660>
json_schema_input_type = PydanticUndefined
- filter(selection)¶
Filter elements to be grabbed.
- Parameters:
- selection
Elements The list of partial or complete element selectors to filter using, with the same number of values.
- selection
- Yields:
- object
An element that can be indexed by the DataGrabber.
- Raises:
ValueErrorIf the selectors have different numbers of values.
- abstractmethod get_element_keys()¶
Get element keys.
For each item in the
elementtuple passed to__getitem__(), this method returns the corresponding key(s).
- abstractmethod get_elements()¶
Get elements.
- Returns:
listList of elements that can be grabbed. The elements can be strings or tuples of strings to index the DataGrabber.
- abstractmethod get_item(**element)¶
Get the specified item from the dataset.
- get_type_element_keys(data_type)¶
Get the element keys that the data of a data type depends on.
The element keys (see
get_element_keys()) are the keys that the data types to grab depend on.
- model_post_init(context)¶
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- select_elements(selection)¶
Select the elements to grab.
All the selectors must have the same number of values (a single value counts as a tuple of one value), so they are either all complete elements or all partial selectors:
Complete elements have a value for each element key, in the order of
get_element_keys(), e.g.,("sub-01", "rest")for the keys["subject", "task"]. They are the elements to grab, so the elements of the dataset are not needed (they are not searched for). Repeated elements are only returned once.Partial selectors have fewer values, e.g.,
"sub-01"or("sub-01",). They select the elements of the dataset that match them, i.e., that have all their values for any key (seefilter()). The elements of the dataset are searched for once, and each element is returned once, even if several selectors match it: with["sub-01", "rest"], the element("sub-01", "rest")matches both selectors and is returned once, together with the other tasks ofsub-01and theresttask of the other subjects.
- Parameters:
- selection
Elements The list of partial or complete element selectors.
- selection
- Returns:
- Raises:
ValueErrorIf the selectors have different numbers of values.
RuntimeErrorIf a partial selector does not match any element.
- validate_datagrabber_params()¶
Run extra logical validation for datagrabber.
Subclasses can override to provide validation.
- property fulldir: Path¶
Get complete data directory path.
- Returns:
pathlib.PathComplete path to the data directory. Can be overridden by subclasses.
- enum junifer.datagrabber.ConfoundsFormat(value)¶
Accepted confounds format.
- Member Type:
Valid values are as follows:
- FMRIPrep = <ConfoundsFormat.FMRIPrep: 'fmriprep'>¶
- AdHoc = <ConfoundsFormat.AdHoc: 'adhoc'>¶
- pydantic model junifer.datagrabber.DMCC13Benchmark¶
Concrete implementation for datalad-based data fetching of DMCC13.
- Parameters:
- types{“BOLD”, “T1w”, “VBM_CSF”, “VBM_GM”, “VBM_WM”, “Warp”} or
listof the options, optional The data type(s) to grab.
- datadir
pathlib.Path, optional That path where the datalad dataset will be cloned. If not specified, the datalad dataset will be cloned into a temporary directory.
- sessions{“ses-wave1bas”, “ses-wave1pro”, “ses-wave1rea”} or
listof the options, optional DMCC sessions. By default, all available sessions are selected.
- tasks{“Rest”, “Axcpt”, “Cuedts”, “Stern”, “Stroop”} or
listof the options, optional DMCC tasks. By default, all available tasks are selected.
- phase_encodings{“AP”, “PA”} or
listof the options, optional DMCC phase encoding directions. By default, all available phase encodings are selected.
- runs{“1”, “2”} or
listof the options, optional DMCC runs. By default, all available runs are selected.
- native_t1wbool, optional
Whether to use T1w in native space (default False).
- types{“BOLD”, “T1w”, “VBM_CSF”, “VBM_GM”, “VBM_WM”, “Warp”} or
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
Show JSON schema
{ "title": "DMCC13Benchmark", "description": "Concrete implementation for datalad-based data fetching of DMCC13.\n\nParameters\n----------\ntypes : {\"BOLD\", \"T1w\", \"VBM_CSF\", \"VBM_GM\", \"VBM_WM\", \"Warp\"} or list of the options, optional\n The data type(s) to grab.\ndatadir : pathlib.Path, optional\n That path where the datalad dataset will be cloned.\n If not specified, the datalad dataset will be cloned into a temporary\n directory.\nsessions : {\"ses-wave1bas\", \"ses-wave1pro\", \"ses-wave1rea\"} or list of the options, optional\n DMCC sessions.\n By default, all available sessions are selected.\ntasks : {\"Rest\", \"Axcpt\", \"Cuedts\", \"Stern\", \"Stroop\"} or list of the options, optional\n DMCC tasks.\n By default, all available tasks are selected.\nphase_encodings : {\"AP\", \"PA\"} or list of the options, optional\n DMCC phase encoding directions.\n By default, all available phase encodings are selected.\nruns : {\"1\", \"2\"} or list of the options, optional\n DMCC runs.\n By default, all available runs are selected.\nnative_t1w : bool, optional\n Whether to use T1w in native space (default False).", "type": "object", "properties": { "types": { "anyOf": [ { "enum": [ "BOLD", "T1w", "VBM_CSF", "VBM_GM", "VBM_WM", "Warp" ], "type": "string" }, { "items": { "enum": [ "BOLD", "T1w", "VBM_CSF", "VBM_GM", "VBM_WM", "Warp" ], "type": "string" }, "type": "array" } ], "default": [ "BOLD", "T1w", "VBM_CSF", "VBM_GM", "VBM_WM" ], "title": "Types" }, "datadir": { "format": "path", "title": "Datadir", "type": "string" }, "patterns": { "additionalProperties": { "anyOf": [ { "additionalProperties": { "anyOf": [ { "additionalProperties": { "type": "string" }, "type": "object" }, { "type": "string" } ] }, "type": "object" }, { "additionalProperties": { "type": "string" }, "type": "object" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "default": { "BOLD": { "confounds": { "format": "fmriprep", "pattern": "derivatives/fmriprep-1.3.2/{subject}/{session}/func/{subject}_{session}_task-{task}_acq-mb4{phase_encoding}_run-{run}_desc-confounds_regressors.tsv" }, "mask": { "pattern": "derivatives/fmriprep-1.3.2/{subject}/{session}/func/{subject}_{session}_task-{task}_acq-mb4{phase_encoding}_run-{run}_space-MNI152NLin2009cAsym_desc-brain_mask.nii.gz", "space": "MNI152NLin2009cAsym" }, "pattern": "derivatives/fmriprep-1.3.2/{subject}/{session}/func/{subject}_{session}_task-{task}_acq-mb4{phase_encoding}_run-{run}_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz", "space": "MNI152NLin2009cAsym" }, "T1w": { "mask": { "pattern": "derivatives/fmriprep-1.3.2/{subject}/anat/{subject}_space-MNI152NLin2009cAsym_desc-brain_mask.nii.gz", "space": "MNI152NLin2009cAsym" }, "pattern": "derivatives/fmriprep-1.3.2/{subject}/anat/{subject}_space-MNI152NLin2009cAsym_desc-preproc_T1w.nii.gz", "space": "MNI152NLin2009cAsym" }, "VBM_CSF": { "pattern": "derivatives/fmriprep-1.3.2/{subject}/anat/{subject}_space-MNI152NLin2009cAsym_label-CSF_probseg.nii.gz", "space": "MNI152NLin2009cAsym" }, "VBM_GM": { "pattern": "derivatives/fmriprep-1.3.2/{subject}/anat/{subject}_space-MNI152NLin2009cAsym_label-GM_probseg.nii.gz", "space": "MNI152NLin2009cAsym" }, "VBM_WM": { "pattern": "derivatives/fmriprep-1.3.2/{subject}/anat/{subject}_space-MNI152NLin2009cAsym_label-WM_probseg.nii.gz", "space": "MNI152NLin2009cAsym" } }, "title": "Patterns", "type": "object" }, "replacements": { "default": [ "subject", "session", "task", "phase_encoding", "run" ], "items": { "type": "string" }, "title": "Replacements", "type": "array" }, "confounds_format": { "$ref": "#/$defs/ConfoundsFormat", "default": "fmriprep" }, "partial_pattern_ok": { "default": false, "title": "Partial Pattern Ok", "type": "boolean" }, "uri": { "default": "https://github.com/OpenNeuroDatasets/ds003452.git", "format": "uri", "minLength": 1, "title": "Uri", "type": "string" }, "rootdir": { "default": ".", "format": "path", "title": "Rootdir", "type": "string" }, "datalad_dirty": { "default": false, "title": "Datalad Dirty", "type": "boolean" }, "datalad_commit_id": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "title": "Datalad Commit Id" }, "datalad_id": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "title": "Datalad Id" }, "sessions": { "anyOf": [ { "enum": [ "ses-wave1bas", "ses-wave1pro", "ses-wave1rea" ], "type": "string" }, { "items": { "enum": [ "ses-wave1bas", "ses-wave1pro", "ses-wave1rea" ], "type": "string" }, "type": "array" } ], "default": [ "ses-wave1bas", "ses-wave1pro", "ses-wave1rea" ], "title": "Sessions" }, "tasks": { "anyOf": [ { "enum": [ "Rest", "Axcpt", "Cuedts", "Stern", "Stroop" ], "type": "string" }, { "items": { "enum": [ "Rest", "Axcpt", "Cuedts", "Stern", "Stroop" ], "type": "string" }, "type": "array" } ], "default": [ "Rest", "Axcpt", "Cuedts", "Stern", "Stroop" ], "title": "Tasks" }, "phase_encodings": { "anyOf": [ { "enum": [ "AP", "PA" ], "type": "string" }, { "items": { "enum": [ "AP", "PA" ], "type": "string" }, "type": "array" } ], "default": [ "AP", "PA" ], "title": "Phase Encodings" }, "runs": { "anyOf": [ { "enum": [ "1", "2" ], "type": "string" }, { "items": { "enum": [ "1", "2" ], "type": "string" }, "type": "array" } ], "default": [ "1", "2" ], "title": "Runs" }, "native_t1w": { "default": false, "title": "Native T1W", "type": "boolean" } }, "$defs": { "ConfoundsFormat": { "description": "Accepted confounds format.", "enum": [ "fmriprep", "adhoc" ], "title": "ConfoundsFormat", "type": "string" } }, "additionalProperties": true }
- Config:
extra: str = allow
use_enum_values: bool = True
- Fields:
confounds_format (junifer.datagrabber.pattern.ConfoundsFormat)native_t1w (bool)patterns (dict[str, dict[str, dict[str, str] | str] | dict[str, str] | list[dict[str, str]]])phase_encodings (Annotated[Literal[junifer.datagrabber.dmcc13_benchmark.DMCCPhaseEncoding.AP, junifer.datagrabber.dmcc13_benchmark.DMCCPhaseEncoding.PA] | list[Literal[junifer.datagrabber.dmcc13_benchmark.DMCCPhaseEncoding.AP, junifer.datagrabber.dmcc13_benchmark.DMCCPhaseEncoding.PA]], pydantic.functional_validators.BeforeValidator(func=junifer.utils.helpers.ensure_list, json_schema_input_type=PydanticUndefined)])replacements (list[str])runs (Annotated[Literal[junifer.datagrabber.dmcc13_benchmark.DMCCRun.One, junifer.datagrabber.dmcc13_benchmark.DMCCRun.Two] | list[Literal[junifer.datagrabber.dmcc13_benchmark.DMCCRun.One, junifer.datagrabber.dmcc13_benchmark.DMCCRun.Two]], pydantic.functional_validators.BeforeValidator(func=junifer.utils.helpers.ensure_list, json_schema_input_type=PydanticUndefined)])sessions (Annotated[Literal[junifer.datagrabber.dmcc13_benchmark.DMCCSession.Wave1Bas, junifer.datagrabber.dmcc13_benchmark.DMCCSession.Wave1Pro, junifer.datagrabber.dmcc13_benchmark.DMCCSession.Wave1Rea] | list[Literal[junifer.datagrabber.dmcc13_benchmark.DMCCSession.Wave1Bas, junifer.datagrabber.dmcc13_benchmark.DMCCSession.Wave1Pro, junifer.datagrabber.dmcc13_benchmark.DMCCSession.Wave1Rea]], pydantic.functional_validators.BeforeValidator(func=junifer.utils.helpers.ensure_list, json_schema_input_type=PydanticUndefined)])tasks (Annotated[Literal[junifer.datagrabber.dmcc13_benchmark.DMCCTask.Rest, junifer.datagrabber.dmcc13_benchmark.DMCCTask.Axcpt, junifer.datagrabber.dmcc13_benchmark.DMCCTask.Cuedts, junifer.datagrabber.dmcc13_benchmark.DMCCTask.Stern, junifer.datagrabber.dmcc13_benchmark.DMCCTask.Stroop] | list[Literal[junifer.datagrabber.dmcc13_benchmark.DMCCTask.Rest, junifer.datagrabber.dmcc13_benchmark.DMCCTask.Axcpt, junifer.datagrabber.dmcc13_benchmark.DMCCTask.Cuedts, junifer.datagrabber.dmcc13_benchmark.DMCCTask.Stern, junifer.datagrabber.dmcc13_benchmark.DMCCTask.Stroop]], pydantic.functional_validators.BeforeValidator(func=junifer.utils.helpers.ensure_list, json_schema_input_type=PydanticUndefined)])types (Annotated[Literal[junifer.datagrabber.base.DataType.BOLD, junifer.datagrabber.base.DataType.T1w, junifer.datagrabber.base.DataType.VBM_CSF, junifer.datagrabber.base.DataType.VBM_GM, junifer.datagrabber.base.DataType.VBM_WM, junifer.datagrabber.base.DataType.Warp] | list[Literal[junifer.datagrabber.base.DataType.BOLD, junifer.datagrabber.base.DataType.T1w, junifer.datagrabber.base.DataType.VBM_CSF, junifer.datagrabber.base.DataType.VBM_GM, junifer.datagrabber.base.DataType.VBM_WM, junifer.datagrabber.base.DataType.Warp]], pydantic.functional_validators.BeforeValidator(func=junifer.utils.helpers.ensure_list, json_schema_input_type=PydanticUndefined)])uri (pydantic.networks.AnyUrl)
- Validators:
- field confounds_format: ConfoundsFormat = <ConfoundsFormat.FMRIPrep: 'fmriprep'>¶
- field patterns: dict[str, dict[str, dict[str, str] | str] | dict[str, str] | list[dict[str, str]]] = {'BOLD': {'confounds': {'format': 'fmriprep', 'pattern': 'derivatives/fmriprep-1.3.2/{subject}/{session}/func/{subject}_{session}_task-{task}_acq-mb4{phase_encoding}_run-{run}_desc-confounds_regressors.tsv'}, 'mask': {'pattern': 'derivatives/fmriprep-1.3.2/{subject}/{session}/func/{subject}_{session}_task-{task}_acq-mb4{phase_encoding}_run-{run}_space-MNI152NLin2009cAsym_desc-brain_mask.nii.gz', 'space': 'MNI152NLin2009cAsym'}, 'pattern': 'derivatives/fmriprep-1.3.2/{subject}/{session}/func/{subject}_{session}_task-{task}_acq-mb4{phase_encoding}_run-{run}_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz', 'space': 'MNI152NLin2009cAsym'}, 'T1w': {'mask': {'pattern': 'derivatives/fmriprep-1.3.2/{subject}/anat/{subject}_space-MNI152NLin2009cAsym_desc-brain_mask.nii.gz', 'space': 'MNI152NLin2009cAsym'}, 'pattern': 'derivatives/fmriprep-1.3.2/{subject}/anat/{subject}_space-MNI152NLin2009cAsym_desc-preproc_T1w.nii.gz', 'space': 'MNI152NLin2009cAsym'}, 'VBM_CSF': {'pattern': 'derivatives/fmriprep-1.3.2/{subject}/anat/{subject}_space-MNI152NLin2009cAsym_label-CSF_probseg.nii.gz', 'space': 'MNI152NLin2009cAsym'}, 'VBM_GM': {'pattern': 'derivatives/fmriprep-1.3.2/{subject}/anat/{subject}_space-MNI152NLin2009cAsym_label-GM_probseg.nii.gz', 'space': 'MNI152NLin2009cAsym'}, 'VBM_WM': {'pattern': 'derivatives/fmriprep-1.3.2/{subject}/anat/{subject}_space-MNI152NLin2009cAsym_label-WM_probseg.nii.gz', 'space': 'MNI152NLin2009cAsym'}}¶
- field phase_encodings: Annotated[Literal[DMCCPhaseEncoding.AP, DMCCPhaseEncoding.PA] | list[Literal[DMCCPhaseEncoding.AP, DMCCPhaseEncoding.PA]], BeforeValidator(func=ensure_list, json_schema_input_type=PydanticUndefined)] = [DMCCPhaseEncoding.AP, DMCCPhaseEncoding.PA]¶
- Constraints:
func = <function ensure_list at 0x712131602660>
json_schema_input_type = PydanticUndefined
- field runs: Annotated[Literal[DMCCRun.One, DMCCRun.Two] | list[Literal[DMCCRun.One, DMCCRun.Two]], BeforeValidator(func=ensure_list, json_schema_input_type=PydanticUndefined)] = [DMCCRun.One, DMCCRun.Two]¶
- Constraints:
func = <function ensure_list at 0x712131602660>
json_schema_input_type = PydanticUndefined
- field sessions: Annotated[Literal[DMCCSession.Wave1Bas, DMCCSession.Wave1Pro, DMCCSession.Wave1Rea] | list[Literal[DMCCSession.Wave1Bas, DMCCSession.Wave1Pro, DMCCSession.Wave1Rea]], BeforeValidator(func=ensure_list, json_schema_input_type=PydanticUndefined)] = [DMCCSession.Wave1Bas, DMCCSession.Wave1Pro, DMCCSession.Wave1Rea]¶
- Constraints:
func = <function ensure_list at 0x712131602660>
json_schema_input_type = PydanticUndefined
- field tasks: Annotated[Literal[DMCCTask.Rest, DMCCTask.Axcpt, DMCCTask.Cuedts, DMCCTask.Stern, DMCCTask.Stroop] | list[Literal[DMCCTask.Rest, DMCCTask.Axcpt, DMCCTask.Cuedts, DMCCTask.Stern, DMCCTask.Stroop]], BeforeValidator(func=ensure_list, json_schema_input_type=PydanticUndefined)] = [DMCCTask.Rest, DMCCTask.Axcpt, DMCCTask.Cuedts, DMCCTask.Stern, DMCCTask.Stroop]¶
- Constraints:
func = <function ensure_list at 0x712131602660>
json_schema_input_type = PydanticUndefined
- field types: Annotated[Literal[DataType.BOLD, DataType.T1w, DataType.VBM_CSF, DataType.VBM_GM, DataType.VBM_WM, DataType.Warp] | list[Literal[DataType.BOLD, DataType.T1w, DataType.VBM_CSF, DataType.VBM_GM, DataType.VBM_WM, DataType.Warp]], BeforeValidator(func=ensure_list, json_schema_input_type=PydanticUndefined)] = [<DataType.BOLD: 'BOLD'>, <DataType.T1w: 'T1w'>, <DataType.VBM_CSF: 'VBM_CSF'>, <DataType.VBM_GM: 'VBM_GM'>, <DataType.VBM_WM: 'VBM_WM'>]¶
- Constraints:
func = <function ensure_list at 0x712131602660>
json_schema_input_type = PydanticUndefined
- model_post_init(context)¶
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- validate_datagrabber_params()¶
Run extra logical validation for datagrabber.
- enum junifer.datagrabber.DMCCPhaseEncoding(value)¶
Accepted DMCC phase encoding directions.
- Member Type:
Valid values are as follows:
- AP = <DMCCPhaseEncoding.AP: 'AP'>¶
- PA = <DMCCPhaseEncoding.PA: 'PA'>¶
- enum junifer.datagrabber.DMCCRun(value)¶
Accepted DMCC runs.
- Member Type:
Valid values are as follows:
- One = <DMCCRun.One: '1'>¶
- Two = <DMCCRun.Two: '2'>¶
- enum junifer.datagrabber.DMCCSession(value)¶
Accepted DMCC sessions.
- Member Type:
Valid values are as follows:
- Wave1Bas = <DMCCSession.Wave1Bas: 'ses-wave1bas'>¶
- Wave1Pro = <DMCCSession.Wave1Pro: 'ses-wave1pro'>¶
- Wave1Rea = <DMCCSession.Wave1Rea: 'ses-wave1rea'>¶
- enum junifer.datagrabber.DMCCTask(value)¶
Accepted DMCC tasks.
- Member Type:
Valid values are as follows:
- Rest = <DMCCTask.Rest: 'Rest'>¶
- Axcpt = <DMCCTask.Axcpt: 'Axcpt'>¶
- Cuedts = <DMCCTask.Cuedts: 'Cuedts'>¶
- Stern = <DMCCTask.Stern: 'Stern'>¶
- Stroop = <DMCCTask.Stroop: 'Stroop'>¶
- enum junifer.datagrabber.DataType(value)¶
Accepted data type.
- Member Type:
Valid values are as follows:
- T1w = <DataType.T1w: 'T1w'>¶
- T2w = <DataType.T2w: 'T2w'>¶
- BOLD = <DataType.BOLD: 'BOLD'>¶
- Warp = <DataType.Warp: 'Warp'>¶
- VBM_GM = <DataType.VBM_GM: 'VBM_GM'>¶
- VBM_WM = <DataType.VBM_WM: 'VBM_WM'>¶
- VBM_CSF = <DataType.VBM_CSF: 'VBM_CSF'>¶
- FALFF = <DataType.FALFF: 'fALFF'>¶
- GCOR = <DataType.GCOR: 'GCOR'>¶
- LCOR = <DataType.LCOR: 'LCOR'>¶
- DWI = <DataType.DWI: 'DWI'>¶
- FreeSurfer = <DataType.FreeSurfer: 'FreeSurfer'>¶
- class junifer.datagrabber.DataTypeManager¶
Class for managing data types.
Overridden to make the class singleton.
- clear()¶
Not implemented.
- popitem()¶
Not implemented.
- setdefault(key, value=None)¶
Not implemented.
- class junifer.datagrabber.DataTypeSchema¶
Data type schema.
- pydantic model junifer.datagrabber.DataladAOMICID1000¶
Concrete implementation for datalad-based data fetching of AOMIC ID1000.
- Parameters:
- types{“BOLD”, “T1w”, “VBM_CSF”, “VBM_GM”, “VBM_WM”, “DWI”, “FreeSurfer”, “Warp”} or
listof the options, optional The data type(s) to grab.
- datadir
pathlib.Path, optional That path where the datalad dataset will be cloned. If not specified, the datalad dataset will be cloned into a temporary directory.
- space{“native”, “MNI152NLin2009cAsym”}, optional
AOMIC space (default
AOMICSpace.MNI152NLin2009cAsym).
- types{“BOLD”, “T1w”, “VBM_CSF”, “VBM_GM”, “VBM_WM”, “DWI”, “FreeSurfer”, “Warp”} or
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
Show JSON schema
{ "title": "DataladAOMICID1000", "description": "Concrete implementation for datalad-based data fetching of AOMIC ID1000.\n\nParameters\n----------\ntypes : {\"BOLD\", \"T1w\", \"VBM_CSF\", \"VBM_GM\", \"VBM_WM\", \"DWI\", \"FreeSurfer\", \"Warp\"} or list of the options, optional\n The data type(s) to grab.\ndatadir : pathlib.Path, optional\n That path where the datalad dataset will be cloned.\n If not specified, the datalad dataset will be cloned into a temporary\n directory.\nspace : {\"native\", \"MNI152NLin2009cAsym\"}, optional\n AOMIC space (default ``AOMICSpace.MNI152NLin2009cAsym``).", "type": "object", "properties": { "types": { "anyOf": [ { "enum": [ "BOLD", "T1w", "VBM_CSF", "VBM_GM", "VBM_WM", "DWI", "FreeSurfer", "Warp" ], "type": "string" }, { "items": { "enum": [ "BOLD", "T1w", "VBM_CSF", "VBM_GM", "VBM_WM", "DWI", "FreeSurfer", "Warp" ], "type": "string" }, "type": "array" } ], "default": [ "BOLD", "T1w", "VBM_CSF", "VBM_GM", "VBM_WM", "DWI", "FreeSurfer", "Warp" ], "title": "Types" }, "datadir": { "format": "path", "title": "Datadir", "type": "string" }, "patterns": { "additionalProperties": { "anyOf": [ { "additionalProperties": { "anyOf": [ { "additionalProperties": { "type": "string" }, "type": "object" }, { "type": "string" } ] }, "type": "object" }, { "additionalProperties": { "type": "string" }, "type": "object" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "default": { "BOLD": { "confounds": { "format": "fmriprep", "pattern": "derivatives/fmriprep/{subject}/func/{subject}_task-moviewatching_desc-confounds_regressors.tsv" }, "mask": { "pattern": "derivatives/fmriprep/{subject}/func/{subject}_task-moviewatching_{sp_func_desc}desc-brain_mask.nii.gz" }, "pattern": "derivatives/fmriprep/{subject}/func/{subject}_task-moviewatching_{sp_func_desc}desc-preproc_bold.nii.gz", "reference": { "pattern": "derivatives/fmriprep/{subject}/func/{subject}_task-moviewatching_{sp_func_desc}boldref.nii.gz" } }, "T1w": { "mask": { "pattern": "derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}desc-brain_mask.nii.gz" }, "pattern": "derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}desc-preproc_T1w.nii.gz" }, "VBM_CSF": { "pattern": "derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}label-CSF_probseg.nii.gz" }, "VBM_GM": { "pattern": "derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}label-GM_probseg.nii.gz" }, "VBM_WM": { "pattern": "derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}label-WM_probseg.nii.gz" }, "DWI": { "pattern": "derivatives/dwipreproc/{subject}/dwi/{subject}_desc-preproc_dwi.nii.gz" }, "FreeSurfer": { "aseg": { "pattern": "derivatives/freesurfer/[!f]{subject}/mri/aseg.mg[z]" }, "lh_pial": { "pattern": "derivatives/freesurfer/[!f]{subject}/surf/lh.pia[l]" }, "lh_white": { "pattern": "derivatives/freesurfer/[!f]{subject}/surf/lh.whit[e]" }, "norm": { "pattern": "derivatives/freesurfer/[!f]{subject}/mri/norm.mg[z]" }, "pattern": "derivatives/freesurfer/[!f]{subject}/mri/T1.mg[z]", "rh_pial": { "pattern": "derivatives/freesurfer/[!f]{subject}/surf/rh.pia[l]" }, "rh_white": { "pattern": "derivatives/freesurfer/[!f]{subject}/surf/rh.whit[e]" } }, "Warp": [ { "dst": "native", "pattern": "derivatives/fmriprep/{subject}/anat/{subject}_from-MNI152NLin2009cAsym_to-T1w_mode-image_xfm.h5", "src": "MNI152NLin2009cAsym", "warper": "ants" }, { "dst": "MNI152NLin2009cAsym", "pattern": "derivatives/fmriprep/{subject}/anat/{subject}_from-T1w_to-MNI152NLin2009cAsym_mode-image_xfm.h5", "src": "native", "warper": "ants" } ] }, "title": "Patterns", "type": "object" }, "replacements": { "default": [ "subject" ], "items": { "type": "string" }, "title": "Replacements", "type": "array" }, "confounds_format": { "$ref": "#/$defs/ConfoundsFormat", "default": "fmriprep" }, "partial_pattern_ok": { "default": false, "title": "Partial Pattern Ok", "type": "boolean" }, "uri": { "default": "https://github.com/OpenNeuroDatasets/ds003097.git", "format": "uri", "minLength": 1, "title": "Uri", "type": "string" }, "rootdir": { "default": ".", "format": "path", "title": "Rootdir", "type": "string" }, "datalad_dirty": { "default": false, "title": "Datalad Dirty", "type": "boolean" }, "datalad_commit_id": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "title": "Datalad Commit Id" }, "datalad_id": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "title": "Datalad Id" }, "space": { "$ref": "#/$defs/AOMICSpace", "default": "MNI152NLin2009cAsym" } }, "$defs": { "AOMICSpace": { "description": "Accepted spaces for AOMIC.", "enum": [ "native", "MNI152NLin2009cAsym" ], "title": "AOMICSpace", "type": "string" }, "ConfoundsFormat": { "description": "Accepted confounds format.", "enum": [ "fmriprep", "adhoc" ], "title": "ConfoundsFormat", "type": "string" } }, "additionalProperties": true }
- Config:
extra: str = allow
use_enum_values: bool = True
- Fields:
confounds_format (junifer.datagrabber.pattern.ConfoundsFormat)patterns (dict[str, dict[str, dict[str, str] | str] | dict[str, str] | list[dict[str, str]]])replacements (list[str])space (junifer.datagrabber.aomic._types.AOMICSpace)types (Annotated[Literal[junifer.datagrabber.base.DataType.BOLD, junifer.datagrabber.base.DataType.T1w, junifer.datagrabber.base.DataType.VBM_CSF, junifer.datagrabber.base.DataType.VBM_GM, junifer.datagrabber.base.DataType.VBM_WM, junifer.datagrabber.base.DataType.DWI, junifer.datagrabber.base.DataType.FreeSurfer, junifer.datagrabber.base.DataType.Warp] | list[Literal[junifer.datagrabber.base.DataType.BOLD, junifer.datagrabber.base.DataType.T1w, junifer.datagrabber.base.DataType.VBM_CSF, junifer.datagrabber.base.DataType.VBM_GM, junifer.datagrabber.base.DataType.VBM_WM, junifer.datagrabber.base.DataType.DWI, junifer.datagrabber.base.DataType.FreeSurfer, junifer.datagrabber.base.DataType.Warp]], pydantic.functional_validators.BeforeValidator(func=junifer.utils.helpers.ensure_list, json_schema_input_type=PydanticUndefined)])uri (pydantic.networks.AnyUrl)
- Validators:
- field confounds_format: ConfoundsFormat = <ConfoundsFormat.FMRIPrep: 'fmriprep'>¶
- field patterns: dict[str, dict[str, dict[str, str] | str] | dict[str, str] | list[dict[str, str]]] = {'BOLD': {'confounds': {'format': 'fmriprep', 'pattern': 'derivatives/fmriprep/{subject}/func/{subject}_task-moviewatching_desc-confounds_regressors.tsv'}, 'mask': {'pattern': 'derivatives/fmriprep/{subject}/func/{subject}_task-moviewatching_{sp_func_desc}desc-brain_mask.nii.gz'}, 'pattern': 'derivatives/fmriprep/{subject}/func/{subject}_task-moviewatching_{sp_func_desc}desc-preproc_bold.nii.gz', 'reference': {'pattern': 'derivatives/fmriprep/{subject}/func/{subject}_task-moviewatching_{sp_func_desc}boldref.nii.gz'}}, 'DWI': {'pattern': 'derivatives/dwipreproc/{subject}/dwi/{subject}_desc-preproc_dwi.nii.gz'}, 'FreeSurfer': {'aseg': {'pattern': 'derivatives/freesurfer/[!f]{subject}/mri/aseg.mg[z]'}, 'lh_pial': {'pattern': 'derivatives/freesurfer/[!f]{subject}/surf/lh.pia[l]'}, 'lh_white': {'pattern': 'derivatives/freesurfer/[!f]{subject}/surf/lh.whit[e]'}, 'norm': {'pattern': 'derivatives/freesurfer/[!f]{subject}/mri/norm.mg[z]'}, 'pattern': 'derivatives/freesurfer/[!f]{subject}/mri/T1.mg[z]', 'rh_pial': {'pattern': 'derivatives/freesurfer/[!f]{subject}/surf/rh.pia[l]'}, 'rh_white': {'pattern': 'derivatives/freesurfer/[!f]{subject}/surf/rh.whit[e]'}}, 'T1w': {'mask': {'pattern': 'derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}desc-brain_mask.nii.gz'}, 'pattern': 'derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}desc-preproc_T1w.nii.gz'}, 'VBM_CSF': {'pattern': 'derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}label-CSF_probseg.nii.gz'}, 'VBM_GM': {'pattern': 'derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}label-GM_probseg.nii.gz'}, 'VBM_WM': {'pattern': 'derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}label-WM_probseg.nii.gz'}, 'Warp': [{'dst': 'native', 'pattern': 'derivatives/fmriprep/{subject}/anat/{subject}_from-MNI152NLin2009cAsym_to-T1w_mode-image_xfm.h5', 'src': 'MNI152NLin2009cAsym', 'warper': 'ants'}, {'dst': 'MNI152NLin2009cAsym', 'pattern': 'derivatives/fmriprep/{subject}/anat/{subject}_from-T1w_to-MNI152NLin2009cAsym_mode-image_xfm.h5', 'src': 'native', 'warper': 'ants'}]}¶
- field space: AOMICSpace = AOMICSpace.MNI152NLin2009cAsym¶
- field types: Annotated[Literal[DataType.BOLD, DataType.T1w, DataType.VBM_CSF, DataType.VBM_GM, DataType.VBM_WM, DataType.DWI, DataType.FreeSurfer, DataType.Warp] | list[Literal[DataType.BOLD, DataType.T1w, DataType.VBM_CSF, DataType.VBM_GM, DataType.VBM_WM, DataType.DWI, DataType.FreeSurfer, DataType.Warp]], BeforeValidator(func=ensure_list, json_schema_input_type=PydanticUndefined)] = [<DataType.BOLD: 'BOLD'>, <DataType.T1w: 'T1w'>, <DataType.VBM_CSF: 'VBM_CSF'>, <DataType.VBM_GM: 'VBM_GM'>, <DataType.VBM_WM: 'VBM_WM'>, <DataType.DWI: 'DWI'>, <DataType.FreeSurfer: 'FreeSurfer'>, <DataType.Warp: 'Warp'>]¶
- Constraints:
func = <function ensure_list at 0x712131602660>
json_schema_input_type = PydanticUndefined
- model_post_init(context)¶
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- validate_datagrabber_params()¶
Run extra logical validation for datagrabber.
- pydantic model junifer.datagrabber.DataladAOMICPIOP1¶
Concrete implementation for pattern-based data fetching of AOMIC PIOP1.
- Parameters:
- types{“BOLD”, “T1w”, “VBM_CSF”, “VBM_GM”, “VBM_WM”, “DWI”, “FreeSurfer”, “Warp”} or
listof the options, optional The data type(s) to grab.
- datadir
pathlib.Path, optional That path where the datalad dataset will be cloned. If not specified, the datalad dataset will be cloned into a temporary directory.
- tasks{“restingstate”, “anticipation”, “emomatching”, “faces”, “gstroop”, “workingmemory”} or
listof the options, optional AOMIC PIOP1 task sessions. By default, all available task sessions are selected.
- space{“native”, “MNI152NLin2009cAsym”}, optional
AOMIC space (default
AOMICSpace.MNI152NLin2009cAsym).
- types{“BOLD”, “T1w”, “VBM_CSF”, “VBM_GM”, “VBM_WM”, “DWI”, “FreeSurfer”, “Warp”} or
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
Show JSON schema
{ "title": "DataladAOMICPIOP1", "description": "Concrete implementation for pattern-based data fetching of AOMIC PIOP1.\n\nParameters\n----------\ntypes : {\"BOLD\", \"T1w\", \"VBM_CSF\", \"VBM_GM\", \"VBM_WM\", \"DWI\", \"FreeSurfer\", \"Warp\"} or list of the options, optional\n The data type(s) to grab.\ndatadir : pathlib.Path, optional\n That path where the datalad dataset will be cloned.\n If not specified, the datalad dataset will be cloned into a temporary\n directory.\ntasks : {\"restingstate\", \"anticipation\", \"emomatching\", \"faces\", \"gstroop\", \"workingmemory\"} or list of the options, optional\n AOMIC PIOP1 task sessions.\n By default, all available task sessions are selected.\nspace : {\"native\", \"MNI152NLin2009cAsym\"}, optional\n AOMIC space (default ``AOMICSpace.MNI152NLin2009cAsym``).", "type": "object", "properties": { "types": { "anyOf": [ { "enum": [ "BOLD", "T1w", "VBM_CSF", "VBM_GM", "VBM_WM", "DWI", "FreeSurfer", "Warp" ], "type": "string" }, { "items": { "enum": [ "BOLD", "T1w", "VBM_CSF", "VBM_GM", "VBM_WM", "DWI", "FreeSurfer", "Warp" ], "type": "string" }, "type": "array" } ], "default": [ "BOLD", "T1w", "VBM_CSF", "VBM_GM", "VBM_WM", "DWI", "FreeSurfer", "Warp" ], "title": "Types" }, "datadir": { "format": "path", "title": "Datadir", "type": "string" }, "patterns": { "additionalProperties": { "anyOf": [ { "additionalProperties": { "anyOf": [ { "additionalProperties": { "type": "string" }, "type": "object" }, { "type": "string" } ] }, "type": "object" }, { "additionalProperties": { "type": "string" }, "type": "object" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "default": { "BOLD": { "confounds": { "format": "fmriprep", "pattern": "derivatives/fmriprep/{subject}/func/{subject}_task-{task}_acq-*_desc-confounds_regressors.tsv" }, "mask": { "pattern": "derivatives/fmriprep/{subject}/func/{subject}_task-{task}_acq-*_{sp_func_desc}desc-brain_mask.nii.gz" }, "pattern": "derivatives/fmriprep/{subject}/func/{subject}_task-{task}_acq-*_{sp_func_desc}desc-preproc_bold.nii.gz", "reference": { "pattern": "derivatives/fmriprep/{subject}/func/{subject}_task-{task}_acq-*_{sp_func_desc}boldref.nii.gz" } }, "T1w": { "mask": { "pattern": "derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}desc-brain_mask.nii.gz" }, "pattern": "derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}desc-preproc_T1w.nii.gz" }, "VBM_CSF": { "pattern": "derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}label-CSF_probseg.nii.gz" }, "VBM_GM": { "pattern": "derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}label-GM_probseg.nii.gz" }, "VBM_WM": { "pattern": "derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}label-WM_probseg.nii.gz" }, "DWI": { "pattern": "derivatives/dwipreproc/{subject}/dwi/{subject}_desc-preproc_dwi.nii.gz" }, "FreeSurfer": { "aseg": { "pattern": "derivatives/freesurfer/[!f]{subject}/mri/aseg.mg[z]" }, "lh_pial": { "pattern": "derivatives/freesurfer/[!f]{subject}/surf/lh.pia[l]" }, "lh_white": { "pattern": "derivatives/freesurfer/[!f]{subject}/surf/lh.whit[e]" }, "norm": { "pattern": "derivatives/freesurfer/[!f]{subject}/mri/norm.mg[z]" }, "pattern": "derivatives/freesurfer/[!f]{subject}/mri/T1.mg[z]", "rh_pial": { "pattern": "derivatives/freesurfer/[!f]{subject}/surf/rh.pia[l]" }, "rh_white": { "pattern": "derivatives/freesurfer/[!f]{subject}/surf/rh.whit[e]" } }, "Warp": [ { "dst": "native", "pattern": "derivatives/fmriprep/{subject}/anat/{subject}_from-MNI152NLin2009cAsym_to-T1w_mode-image_xfm.h5", "src": "MNI152NLin2009cAsym", "warper": "ants" }, { "dst": "MNI152NLin2009cAsym", "pattern": "derivatives/fmriprep/{subject}/anat/{subject}_from-T1w_to-MNI152NLin2009cAsym_mode-image_xfm.h5", "src": "native", "warper": "ants" } ] }, "title": "Patterns", "type": "object" }, "replacements": { "default": [ "subject", "task" ], "items": { "type": "string" }, "title": "Replacements", "type": "array" }, "confounds_format": { "$ref": "#/$defs/ConfoundsFormat", "default": "fmriprep" }, "partial_pattern_ok": { "default": false, "title": "Partial Pattern Ok", "type": "boolean" }, "uri": { "default": "https://github.com/OpenNeuroDatasets/ds002785", "format": "uri", "minLength": 1, "title": "Uri", "type": "string" }, "rootdir": { "default": ".", "format": "path", "title": "Rootdir", "type": "string" }, "datalad_dirty": { "default": false, "title": "Datalad Dirty", "type": "boolean" }, "datalad_commit_id": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "title": "Datalad Commit Id" }, "datalad_id": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "title": "Datalad Id" }, "tasks": { "anyOf": [ { "enum": [ "restingstate", "anticipation", "emomatching", "faces", "gstroop", "workingmemory" ], "type": "string" }, { "items": { "enum": [ "restingstate", "anticipation", "emomatching", "faces", "gstroop", "workingmemory" ], "type": "string" }, "type": "array" } ], "default": [ "restingstate", "anticipation", "emomatching", "faces", "gstroop", "workingmemory" ], "title": "Tasks" }, "space": { "$ref": "#/$defs/AOMICSpace", "default": "MNI152NLin2009cAsym" } }, "$defs": { "AOMICSpace": { "description": "Accepted spaces for AOMIC.", "enum": [ "native", "MNI152NLin2009cAsym" ], "title": "AOMICSpace", "type": "string" }, "ConfoundsFormat": { "description": "Accepted confounds format.", "enum": [ "fmriprep", "adhoc" ], "title": "ConfoundsFormat", "type": "string" } }, "additionalProperties": true }
- Config:
extra: str = allow
use_enum_values: bool = True
- Fields:
confounds_format (junifer.datagrabber.pattern.ConfoundsFormat)patterns (dict[str, dict[str, dict[str, str] | str] | dict[str, str] | list[dict[str, str]]])replacements (list[str])space (junifer.datagrabber.aomic._types.AOMICSpace)tasks (Annotated[Literal[junifer.datagrabber.aomic._types.AOMICTask.RestingState, junifer.datagrabber.aomic._types.AOMICTask.Anticipation, junifer.datagrabber.aomic._types.AOMICTask.EmoMatching, junifer.datagrabber.aomic._types.AOMICTask.Faces, junifer.datagrabber.aomic._types.AOMICTask.Gstroop, junifer.datagrabber.aomic._types.AOMICTask.WorkingMemory] | list[Literal[junifer.datagrabber.aomic._types.AOMICTask.RestingState, junifer.datagrabber.aomic._types.AOMICTask.Anticipation, junifer.datagrabber.aomic._types.AOMICTask.EmoMatching, junifer.datagrabber.aomic._types.AOMICTask.Faces, junifer.datagrabber.aomic._types.AOMICTask.Gstroop, junifer.datagrabber.aomic._types.AOMICTask.WorkingMemory]], pydantic.functional_validators.BeforeValidator(func=junifer.utils.helpers.ensure_list, json_schema_input_type=PydanticUndefined)])types (Annotated[Literal[junifer.datagrabber.base.DataType.BOLD, junifer.datagrabber.base.DataType.T1w, junifer.datagrabber.base.DataType.VBM_CSF, junifer.datagrabber.base.DataType.VBM_GM, junifer.datagrabber.base.DataType.VBM_WM, junifer.datagrabber.base.DataType.DWI, junifer.datagrabber.base.DataType.FreeSurfer, junifer.datagrabber.base.DataType.Warp] | list[Literal[junifer.datagrabber.base.DataType.BOLD, junifer.datagrabber.base.DataType.T1w, junifer.datagrabber.base.DataType.VBM_CSF, junifer.datagrabber.base.DataType.VBM_GM, junifer.datagrabber.base.DataType.VBM_WM, junifer.datagrabber.base.DataType.DWI, junifer.datagrabber.base.DataType.FreeSurfer, junifer.datagrabber.base.DataType.Warp]], pydantic.functional_validators.BeforeValidator(func=junifer.utils.helpers.ensure_list, json_schema_input_type=PydanticUndefined)])uri (pydantic.networks.AnyUrl)
- Validators:
- field confounds_format: ConfoundsFormat = <ConfoundsFormat.FMRIPrep: 'fmriprep'>¶
- field patterns: dict[str, dict[str, dict[str, str] | str] | dict[str, str] | list[dict[str, str]]] = {'BOLD': {'confounds': {'format': 'fmriprep', 'pattern': 'derivatives/fmriprep/{subject}/func/{subject}_task-{task}_acq-*_desc-confounds_regressors.tsv'}, 'mask': {'pattern': 'derivatives/fmriprep/{subject}/func/{subject}_task-{task}_acq-*_{sp_func_desc}desc-brain_mask.nii.gz'}, 'pattern': 'derivatives/fmriprep/{subject}/func/{subject}_task-{task}_acq-*_{sp_func_desc}desc-preproc_bold.nii.gz', 'reference': {'pattern': 'derivatives/fmriprep/{subject}/func/{subject}_task-{task}_acq-*_{sp_func_desc}boldref.nii.gz'}}, 'DWI': {'pattern': 'derivatives/dwipreproc/{subject}/dwi/{subject}_desc-preproc_dwi.nii.gz'}, 'FreeSurfer': {'aseg': {'pattern': 'derivatives/freesurfer/[!f]{subject}/mri/aseg.mg[z]'}, 'lh_pial': {'pattern': 'derivatives/freesurfer/[!f]{subject}/surf/lh.pia[l]'}, 'lh_white': {'pattern': 'derivatives/freesurfer/[!f]{subject}/surf/lh.whit[e]'}, 'norm': {'pattern': 'derivatives/freesurfer/[!f]{subject}/mri/norm.mg[z]'}, 'pattern': 'derivatives/freesurfer/[!f]{subject}/mri/T1.mg[z]', 'rh_pial': {'pattern': 'derivatives/freesurfer/[!f]{subject}/surf/rh.pia[l]'}, 'rh_white': {'pattern': 'derivatives/freesurfer/[!f]{subject}/surf/rh.whit[e]'}}, 'T1w': {'mask': {'pattern': 'derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}desc-brain_mask.nii.gz'}, 'pattern': 'derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}desc-preproc_T1w.nii.gz'}, 'VBM_CSF': {'pattern': 'derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}label-CSF_probseg.nii.gz'}, 'VBM_GM': {'pattern': 'derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}label-GM_probseg.nii.gz'}, 'VBM_WM': {'pattern': 'derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}label-WM_probseg.nii.gz'}, 'Warp': [{'dst': 'native', 'pattern': 'derivatives/fmriprep/{subject}/anat/{subject}_from-MNI152NLin2009cAsym_to-T1w_mode-image_xfm.h5', 'src': 'MNI152NLin2009cAsym', 'warper': 'ants'}, {'dst': 'MNI152NLin2009cAsym', 'pattern': 'derivatives/fmriprep/{subject}/anat/{subject}_from-T1w_to-MNI152NLin2009cAsym_mode-image_xfm.h5', 'src': 'native', 'warper': 'ants'}]}¶
- field space: AOMICSpace = AOMICSpace.MNI152NLin2009cAsym¶
- field tasks: Annotated[Literal[AOMICTask.RestingState, AOMICTask.Anticipation, AOMICTask.EmoMatching, AOMICTask.Faces, AOMICTask.Gstroop, AOMICTask.WorkingMemory] | list[Literal[AOMICTask.RestingState, AOMICTask.Anticipation, AOMICTask.EmoMatching, AOMICTask.Faces, AOMICTask.Gstroop, AOMICTask.WorkingMemory]], BeforeValidator(func=ensure_list, json_schema_input_type=PydanticUndefined)] = [AOMICTask.RestingState, AOMICTask.Anticipation, AOMICTask.EmoMatching, AOMICTask.Faces, AOMICTask.Gstroop, AOMICTask.WorkingMemory]¶
- Constraints:
func = <function ensure_list at 0x712131602660>
json_schema_input_type = PydanticUndefined
- field types: Annotated[Literal[DataType.BOLD, DataType.T1w, DataType.VBM_CSF, DataType.VBM_GM, DataType.VBM_WM, DataType.DWI, DataType.FreeSurfer, DataType.Warp] | list[Literal[DataType.BOLD, DataType.T1w, DataType.VBM_CSF, DataType.VBM_GM, DataType.VBM_WM, DataType.DWI, DataType.FreeSurfer, DataType.Warp]], BeforeValidator(func=ensure_list, json_schema_input_type=PydanticUndefined)] = [<DataType.BOLD: 'BOLD'>, <DataType.T1w: 'T1w'>, <DataType.VBM_CSF: 'VBM_CSF'>, <DataType.VBM_GM: 'VBM_GM'>, <DataType.VBM_WM: 'VBM_WM'>, <DataType.DWI: 'DWI'>, <DataType.FreeSurfer: 'FreeSurfer'>, <DataType.Warp: 'Warp'>]¶
- Constraints:
func = <function ensure_list at 0x712131602660>
json_schema_input_type = PydanticUndefined
- model_post_init(context)¶
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- validate_datagrabber_params()¶
Run extra logical validation for datagrabber.
- pydantic model junifer.datagrabber.DataladAOMICPIOP2¶
Concrete implementation for pattern-based data fetching of AOMIC PIOP2.
- Parameters:
- types{“BOLD”, “T1w”, “VBM_CSF”, “VBM_GM”, “VBM_WM”, “DWI”, “FreeSurfer”, “Warp”} or
listof the options, optional The data type(s) to grab.
- datadir
pathlib.Path, optional That path where the datalad dataset will be cloned. If not specified, the datalad dataset will be cloned into a temporary directory.
- tasks{“restingstate”, “stopsignal”, “workingmemory”, “emomatching”} or
listof the options, optional AOMIC PIOP2 task sessions. By default, all available task sessions are selected.
- space{“native”, “MNI152NLin2009cAsym”}, optional
AOMIC space (default
AOMICSpace.MNI152NLin2009cAsym).
- types{“BOLD”, “T1w”, “VBM_CSF”, “VBM_GM”, “VBM_WM”, “DWI”, “FreeSurfer”, “Warp”} or
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
Show JSON schema
{ "title": "DataladAOMICPIOP2", "description": "Concrete implementation for pattern-based data fetching of AOMIC PIOP2.\n\nParameters\n----------\ntypes : {\"BOLD\", \"T1w\", \"VBM_CSF\", \"VBM_GM\", \"VBM_WM\", \"DWI\", \"FreeSurfer\", \"Warp\"} or list of the options, optional\n The data type(s) to grab.\ndatadir : pathlib.Path, optional\n That path where the datalad dataset will be cloned.\n If not specified, the datalad dataset will be cloned into a temporary\n directory.\ntasks : {\"restingstate\", \"stopsignal\", \"workingmemory\", \"emomatching\"} or list of the options, optional\n AOMIC PIOP2 task sessions.\n By default, all available task sessions are selected.\nspace : {\"native\", \"MNI152NLin2009cAsym\"}, optional\n AOMIC space (default ``AOMICSpace.MNI152NLin2009cAsym``).", "type": "object", "properties": { "types": { "anyOf": [ { "enum": [ "BOLD", "T1w", "VBM_CSF", "VBM_GM", "VBM_WM", "DWI", "FreeSurfer", "Warp" ], "type": "string" }, { "items": { "enum": [ "BOLD", "T1w", "VBM_CSF", "VBM_GM", "VBM_WM", "DWI", "FreeSurfer", "Warp" ], "type": "string" }, "type": "array" } ], "default": [ "BOLD", "T1w", "VBM_CSF", "VBM_GM", "VBM_WM", "DWI", "FreeSurfer", "Warp" ], "title": "Types" }, "datadir": { "format": "path", "title": "Datadir", "type": "string" }, "patterns": { "additionalProperties": { "anyOf": [ { "additionalProperties": { "anyOf": [ { "additionalProperties": { "type": "string" }, "type": "object" }, { "type": "string" } ] }, "type": "object" }, { "additionalProperties": { "type": "string" }, "type": "object" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "default": { "BOLD": { "confounds": { "format": "fmriprep", "pattern": "derivatives/fmriprep/{subject}/func/{subject}_task-{task}_acq-seq_desc-confounds_regressors.tsv" }, "mask": { "pattern": "derivatives/fmriprep/{subject}/func/{subject}_task-{task}_acq-seq_{sp_func_desc}desc-brain_mask.nii.gz" }, "pattern": "derivatives/fmriprep/{subject}/func/{subject}_task-{task}_acq-seq_{sp_func_desc}desc-preproc_bold.nii.gz", "reference": { "pattern": "derivatives/fmriprep/{subject}/func/{subject}_task-{task}_acq-seq_{sp_func_desc}boldref.nii.gz" } }, "T1w": { "mask": { "pattern": "derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}desc-brain_mask.nii.gz" }, "pattern": "derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}desc-preproc_T1w.nii.gz" }, "VBM_CSF": { "pattern": "derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}label-CSF_probseg.nii.gz" }, "VBM_GM": { "pattern": "derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}label-GM_probseg.nii.gz" }, "VBM_WM": { "pattern": "derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}label-WM_probseg.nii.gz" }, "DWI": { "pattern": "derivatives/dwipreproc/{subject}/dwi/{subject}_desc-preproc_dwi.nii.gz" }, "FreeSurfer": { "aseg": { "pattern": "derivatives/freesurfer/[!f]{subject}/mri/aseg.mg[z]" }, "lh_pial": { "pattern": "derivatives/freesurfer/[!f]{subject}/surf/lh.pia[l]" }, "lh_white": { "pattern": "derivatives/freesurfer/[!f]{subject}/surf/lh.whit[e]" }, "norm": { "pattern": "derivatives/freesurfer/[!f]{subject}/mri/norm.mg[z]" }, "pattern": "derivatives/freesurfer/[!f]{subject}/mri/T1.mg[z]", "rh_pial": { "pattern": "derivatives/freesurfer/[!f]{subject}/surf/rh.pia[l]" }, "rh_white": { "pattern": "derivatives/freesurfer/[!f]{subject}/surf/rh.whit[e]" } }, "Warp": [ { "dst": "native", "pattern": "derivatives/fmriprep/{subject}/anat/{subject}_from-MNI152NLin2009cAsym_to-T1w_mode-image_xfm.h5", "src": "MNI152NLin2009cAsym", "warper": "ants" }, { "dst": "MNI152NLin2009cAsym", "pattern": "derivatives/fmriprep/{subject}/anat/{subject}_from-T1w_to-MNI152NLin2009cAsym_mode-image_xfm.h5", "src": "native", "warper": "ants" } ] }, "title": "Patterns", "type": "object" }, "replacements": { "default": [ "subject", "task" ], "items": { "type": "string" }, "title": "Replacements", "type": "array" }, "confounds_format": { "$ref": "#/$defs/ConfoundsFormat", "default": "fmriprep" }, "partial_pattern_ok": { "default": false, "title": "Partial Pattern Ok", "type": "boolean" }, "uri": { "default": "https://github.com/OpenNeuroDatasets/ds002790", "format": "uri", "minLength": 1, "title": "Uri", "type": "string" }, "rootdir": { "default": ".", "format": "path", "title": "Rootdir", "type": "string" }, "datalad_dirty": { "default": false, "title": "Datalad Dirty", "type": "boolean" }, "datalad_commit_id": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "title": "Datalad Commit Id" }, "datalad_id": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "title": "Datalad Id" }, "tasks": { "anyOf": [ { "enum": [ "restingstate", "stopsignal", "emomatching", "workingmemory" ], "type": "string" }, { "items": { "enum": [ "restingstate", "stopsignal", "emomatching", "workingmemory" ], "type": "string" }, "type": "array" } ], "default": [ "restingstate", "stopsignal", "emomatching", "workingmemory" ], "title": "Tasks" }, "space": { "$ref": "#/$defs/AOMICSpace", "default": "MNI152NLin2009cAsym" } }, "$defs": { "AOMICSpace": { "description": "Accepted spaces for AOMIC.", "enum": [ "native", "MNI152NLin2009cAsym" ], "title": "AOMICSpace", "type": "string" }, "ConfoundsFormat": { "description": "Accepted confounds format.", "enum": [ "fmriprep", "adhoc" ], "title": "ConfoundsFormat", "type": "string" } }, "additionalProperties": true }
- Config:
extra: str = allow
use_enum_values: bool = True
- Fields:
confounds_format (junifer.datagrabber.pattern.ConfoundsFormat)patterns (dict[str, dict[str, dict[str, str] | str] | dict[str, str] | list[dict[str, str]]])replacements (list[str])space (junifer.datagrabber.aomic._types.AOMICSpace)tasks (Annotated[Literal[junifer.datagrabber.aomic._types.AOMICTask.RestingState, junifer.datagrabber.aomic._types.AOMICTask.StopSignal, junifer.datagrabber.aomic._types.AOMICTask.EmoMatching, junifer.datagrabber.aomic._types.AOMICTask.WorkingMemory] | list[Literal[junifer.datagrabber.aomic._types.AOMICTask.RestingState, junifer.datagrabber.aomic._types.AOMICTask.StopSignal, junifer.datagrabber.aomic._types.AOMICTask.EmoMatching, junifer.datagrabber.aomic._types.AOMICTask.WorkingMemory]], pydantic.functional_validators.BeforeValidator(func=junifer.utils.helpers.ensure_list, json_schema_input_type=PydanticUndefined)])types (Annotated[Literal[junifer.datagrabber.base.DataType.BOLD, junifer.datagrabber.base.DataType.T1w, junifer.datagrabber.base.DataType.VBM_CSF, junifer.datagrabber.base.DataType.VBM_GM, junifer.datagrabber.base.DataType.VBM_WM, junifer.datagrabber.base.DataType.DWI, junifer.datagrabber.base.DataType.FreeSurfer, junifer.datagrabber.base.DataType.Warp] | list[Literal[junifer.datagrabber.base.DataType.BOLD, junifer.datagrabber.base.DataType.T1w, junifer.datagrabber.base.DataType.VBM_CSF, junifer.datagrabber.base.DataType.VBM_GM, junifer.datagrabber.base.DataType.VBM_WM, junifer.datagrabber.base.DataType.DWI, junifer.datagrabber.base.DataType.FreeSurfer, junifer.datagrabber.base.DataType.Warp]], pydantic.functional_validators.BeforeValidator(func=junifer.utils.helpers.ensure_list, json_schema_input_type=PydanticUndefined)])uri (pydantic.networks.AnyUrl)
- Validators:
- field confounds_format: ConfoundsFormat = <ConfoundsFormat.FMRIPrep: 'fmriprep'>¶
- field patterns: dict[str, dict[str, dict[str, str] | str] | dict[str, str] | list[dict[str, str]]] = {'BOLD': {'confounds': {'format': 'fmriprep', 'pattern': 'derivatives/fmriprep/{subject}/func/{subject}_task-{task}_acq-seq_desc-confounds_regressors.tsv'}, 'mask': {'pattern': 'derivatives/fmriprep/{subject}/func/{subject}_task-{task}_acq-seq_{sp_func_desc}desc-brain_mask.nii.gz'}, 'pattern': 'derivatives/fmriprep/{subject}/func/{subject}_task-{task}_acq-seq_{sp_func_desc}desc-preproc_bold.nii.gz', 'reference': {'pattern': 'derivatives/fmriprep/{subject}/func/{subject}_task-{task}_acq-seq_{sp_func_desc}boldref.nii.gz'}}, 'DWI': {'pattern': 'derivatives/dwipreproc/{subject}/dwi/{subject}_desc-preproc_dwi.nii.gz'}, 'FreeSurfer': {'aseg': {'pattern': 'derivatives/freesurfer/[!f]{subject}/mri/aseg.mg[z]'}, 'lh_pial': {'pattern': 'derivatives/freesurfer/[!f]{subject}/surf/lh.pia[l]'}, 'lh_white': {'pattern': 'derivatives/freesurfer/[!f]{subject}/surf/lh.whit[e]'}, 'norm': {'pattern': 'derivatives/freesurfer/[!f]{subject}/mri/norm.mg[z]'}, 'pattern': 'derivatives/freesurfer/[!f]{subject}/mri/T1.mg[z]', 'rh_pial': {'pattern': 'derivatives/freesurfer/[!f]{subject}/surf/rh.pia[l]'}, 'rh_white': {'pattern': 'derivatives/freesurfer/[!f]{subject}/surf/rh.whit[e]'}}, 'T1w': {'mask': {'pattern': 'derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}desc-brain_mask.nii.gz'}, 'pattern': 'derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}desc-preproc_T1w.nii.gz'}, 'VBM_CSF': {'pattern': 'derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}label-CSF_probseg.nii.gz'}, 'VBM_GM': {'pattern': 'derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}label-GM_probseg.nii.gz'}, 'VBM_WM': {'pattern': 'derivatives/fmriprep/{subject}/anat/{subject}_{sp_anat_desc}label-WM_probseg.nii.gz'}, 'Warp': [{'dst': 'native', 'pattern': 'derivatives/fmriprep/{subject}/anat/{subject}_from-MNI152NLin2009cAsym_to-T1w_mode-image_xfm.h5', 'src': 'MNI152NLin2009cAsym', 'warper': 'ants'}, {'dst': 'MNI152NLin2009cAsym', 'pattern': 'derivatives/fmriprep/{subject}/anat/{subject}_from-T1w_to-MNI152NLin2009cAsym_mode-image_xfm.h5', 'src': 'native', 'warper': 'ants'}]}¶
- field space: AOMICSpace = AOMICSpace.MNI152NLin2009cAsym¶
- field tasks: Annotated[Literal[AOMICTask.RestingState, AOMICTask.StopSignal, AOMICTask.EmoMatching, AOMICTask.WorkingMemory] | list[Literal[AOMICTask.RestingState, AOMICTask.StopSignal, AOMICTask.EmoMatching, AOMICTask.WorkingMemory]], BeforeValidator(func=ensure_list, json_schema_input_type=PydanticUndefined)] = [AOMICTask.RestingState, AOMICTask.StopSignal, AOMICTask.EmoMatching, AOMICTask.WorkingMemory]¶
- Constraints:
func = <function ensure_list at 0x712131602660>
json_schema_input_type = PydanticUndefined
- field types: Annotated[Literal[DataType.BOLD, DataType.T1w, DataType.VBM_CSF, DataType.VBM_GM, DataType.VBM_WM, DataType.DWI, DataType.FreeSurfer, DataType.Warp] | list[Literal[DataType.BOLD, DataType.T1w, DataType.VBM_CSF, DataType.VBM_GM, DataType.VBM_WM, DataType.DWI, DataType.FreeSurfer, DataType.Warp]], BeforeValidator(func=ensure_list, json_schema_input_type=PydanticUndefined)] = [<DataType.BOLD: 'BOLD'>, <DataType.T1w: 'T1w'>, <DataType.VBM_CSF: 'VBM_CSF'>, <DataType.VBM_GM: 'VBM_GM'>, <DataType.VBM_WM: 'VBM_WM'>, <DataType.DWI: 'DWI'>, <DataType.FreeSurfer: 'FreeSurfer'>, <DataType.Warp: 'Warp'>]¶
- Constraints:
func = <function ensure_list at 0x712131602660>
json_schema_input_type = PydanticUndefined
- model_post_init(context)¶
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- validate_datagrabber_params()¶
Run extra logical validation for datagrabber.
- pydantic model junifer.datagrabber.DataladDataGrabber¶
Abstract base class for datalad-based data fetching.
Defines a DataGrabber that gets data from a datalad sibling.
- Parameters:
- uri
pydantic.AnyUrl URI of the datalad sibling.
- rootdir
pathlib.Path, optional The path within the datalad dataset to the root directory (default Path(“.”)).
- datadir
pathlib.Path, optional That path where the datalad dataset will be cloned. If not specified, the datalad dataset will be cloned into a temporary directory.
- uri
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.See also
BaseDataGrabberAbstract base class for DataGrabber.
PatternDataGrabberConcrete implementation for pattern-based data fetching.
PatternDataladDataGrabberConcrete 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.
If the
datadiris specified and has the stem prefix as"datalad"and the stem suffix as"juniferauto", it will be automatically deleted after use.Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
Show JSON schema
{ "title": "DataladDataGrabber", "description": "Abstract base class for datalad-based data fetching.\n\nDefines a DataGrabber that gets data from a datalad sibling.\n\nParameters\n----------\nuri : pydantic.AnyUrl\n URI of the datalad sibling.\nrootdir : pathlib.Path, optional\n The path within the datalad dataset to the root directory\n (default Path(\".\")).\ndatadir : pathlib.Path, optional\n That path where the datalad dataset will be cloned.\n If not specified, the datalad dataset will be cloned into a temporary\n directory.\n\nMethods\n-------\ninstall:\n Installs (clones) the datalad dataset into the ``datadir``. This method\n is called automatically when the datagrabber is used within a context.\nremove:\n Removes the datalad dataset from the ``datadir``. This method is called\n automatically when the datagrabber is used within a context.\n\nSee Also\n--------\nBaseDataGrabber:\n Abstract base class for DataGrabber.\nPatternDataGrabber:\n Concrete implementation for pattern-based data fetching.\nPatternDataladDataGrabber:\n Concrete implementation for pattern and datalad based data fetching.\n\nNotes\n-----\nThis class is intended to be used as a superclass of a subclass\nwith multiple inheritance.\n\nIf the ``datadir`` is specified and has the stem prefix as ``\"datalad\"``\nand the stem suffix as ``\"juniferauto\"``, it will be automatically\ndeleted after use.", "type": "object", "properties": { "types": { "anyOf": [ { "$ref": "#/$defs/DataType" }, { "items": { "$ref": "#/$defs/DataType" }, "type": "array" } ], "title": "Types" }, "datadir": { "format": "path", "title": "Datadir", "type": "string" }, "uri": { "format": "uri", "minLength": 1, "title": "Uri", "type": "string" }, "rootdir": { "default": ".", "format": "path", "title": "Rootdir", "type": "string" }, "datalad_dirty": { "default": false, "title": "Datalad Dirty", "type": "boolean" }, "datalad_commit_id": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "title": "Datalad Commit Id" }, "datalad_id": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "title": "Datalad Id" } }, "$defs": { "DataType": { "description": "Accepted data type.", "enum": [ "T1w", "T2w", "BOLD", "Warp", "VBM_GM", "VBM_WM", "VBM_CSF", "fALFF", "GCOR", "LCOR", "DWI", "FreeSurfer" ], "title": "DataType", "type": "string" } }, "additionalProperties": false, "required": [ "types", "uri" ] }
- Config:
extra: str = forbid
use_enum_values: bool = True
- Fields:
datadir (pathlib.Path)datalad_commit_id (str | None)datalad_dirty (bool)datalad_id (str | None)rootdir (pathlib.Path)uri (pydantic.networks.AnyUrl)
- Validators:
disable_tag»datalad_dirtywarn_existing_datalad_autodir»datadir
- validator disable_tag » datalad_dirty¶
Disable setting datalad_dirty directly.
- validator warn_existing_datalad_autodir » datadir¶
Warn if existing datalad autodir exists.
- cleanup()¶
Cleanup the datalad dataset.
- install()¶
Installs the datalad dataset.
- Raises:
ValueErrorIf the dataset is already installed but with a different ID.
datalad.support.exceptions.IncompleteResultsErrorIf there is a datalad-related problem while cloning dataset.
- model_post_init(context)¶
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- validate_datagrabber_params()¶
Run extra logical validation for datagrabber.
- property fulldir: Path¶
Get complete data directory path.
- Returns:
pathlib.PathComplete path to the data directory.
- pydantic model junifer.datagrabber.DataladHCP1200¶
Concrete implementation for datalad-based data fetching of HCP1200.
- Parameters:
- types{“BOLD”, “T1w”, “Warp”} or
listof the options, optional The data type(s) to grab.
- datadir
pathlib.Path, optional That path where the datalad dataset will be cloned. If not specified, the datalad dataset will be cloned into a temporary directory.
- tasks{“REST1”, “REST2”, “SOCIAL”, “WM”, “RELATIONAL”, “EMOTION”, “LANGUAGE”, “GAMBLING”, “MOTOR”} or
listof the options, optional HCP task sessions. By default, all available task sessions are selected.
- phase_encodings{“LR”, “RL”} or
listof the options, optional HCP phase encoding directions. By default, all are used.
- ica_fixbool, optional
Whether to retrieve data that was processed with ICA+FIX. Only
HCP1200Task.REST1andHCP1200Task.REST2tasks are available with ICA+FIX (default False).
- types{“BOLD”, “T1w”, “Warp”} or
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
Show JSON schema
{ "title": "DataladHCP1200", "description": "Concrete implementation for datalad-based data fetching of HCP1200.\n\nParameters\n----------\ntypes : {\"BOLD\", \"T1w\", \"Warp\"} or list of the options, optional\n The data type(s) to grab.\ndatadir : pathlib.Path, optional\n That path where the datalad dataset will be cloned.\n If not specified, the datalad dataset will be cloned into a temporary\n directory.\ntasks : {\"REST1\", \"REST2\", \"SOCIAL\", \"WM\", \"RELATIONAL\", \"EMOTION\", \"LANGUAGE\", \"GAMBLING\", \"MOTOR\"} or list of the options, optional\n HCP task sessions.\n By default, all available task sessions are selected.\nphase_encodings : {\"LR\", \"RL\"} or list of the options, optional\n HCP phase encoding directions.\n By default, all are used.\nica_fix : bool, optional\n Whether to retrieve data that was processed with ICA+FIX.\n Only ``HCP1200Task.REST1`` and ``HCP1200Task.REST2`` tasks\n are available with ICA+FIX\n (default False).", "type": "object", "properties": { "types": { "anyOf": [ { "enum": [ "BOLD", "T1w", "Warp" ], "type": "string" }, { "items": { "enum": [ "BOLD", "T1w", "Warp" ], "type": "string" }, "type": "array" } ], "default": [ "BOLD", "T1w", "Warp" ], "title": "Types" }, "datadir": { "format": "path", "title": "Datadir", "type": "string" }, "patterns": { "additionalProperties": { "anyOf": [ { "additionalProperties": { "anyOf": [ { "additionalProperties": { "type": "string" }, "type": "object" }, { "type": "string" } ] }, "type": "object" }, { "additionalProperties": { "type": "string" }, "type": "object" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "default": { "BOLD": { "pattern": "{subject}/MNINonLinear/Results/?fMRI_{task}_{phase_encoding}/?fMRI_{task}_{phase_encoding}{suffix}.nii.gz", "space": "MNI152NLin6Asym" }, "T1w": { "pattern": "{subject}/T1w/T1w_acpc_dc_restore.nii.gz", "space": "native" }, "Warp": [ { "dst": "native", "pattern": "{subject}/MNINonLinear/xfms/standard2acpc_dc.nii.gz", "src": "MNI152NLin6Asym", "warper": "fsl" }, { "dst": "MNI152NLin6Asym", "pattern": "{subject}/MNINonLinear/xfms/acpc_dc2standard.nii.gz", "src": "native", "warper": "fsl" } ] }, "title": "Patterns", "type": "object" }, "replacements": { "default": [ "subject", "task", "phase_encoding" ], "items": { "type": "string" }, "title": "Replacements", "type": "array" }, "confounds_format": { "anyOf": [ { "$ref": "#/$defs/ConfoundsFormat" }, { "type": "null" } ], "default": null }, "partial_pattern_ok": { "default": false, "title": "Partial Pattern Ok", "type": "boolean" }, "tasks": { "anyOf": [ { "enum": [ "REST1", "REST2", "SOCIAL", "WM", "RELATIONAL", "EMOTION", "LANGUAGE", "GAMBLING", "MOTOR" ], "type": "string" }, { "items": { "enum": [ "REST1", "REST2", "SOCIAL", "WM", "RELATIONAL", "EMOTION", "LANGUAGE", "GAMBLING", "MOTOR" ], "type": "string" }, "type": "array" } ], "default": [ "REST1", "REST2", "SOCIAL", "WM", "RELATIONAL", "EMOTION", "LANGUAGE", "GAMBLING", "MOTOR" ], "title": "Tasks" }, "phase_encodings": { "anyOf": [ { "enum": [ "RL", "LR" ], "type": "string" }, { "items": { "enum": [ "RL", "LR" ], "type": "string" }, "type": "array" } ], "default": [ "RL", "LR" ], "title": "Phase Encodings" }, "ica_fix": { "default": false, "title": "Ica Fix", "type": "boolean" }, "uri": { "default": "https://github.com/datalad-datasets/human-connectome-project-openaccess.git", "format": "uri", "minLength": 1, "title": "Uri", "type": "string" }, "rootdir": { "default": "HCP1200", "format": "path", "title": "Rootdir", "type": "string" }, "datalad_dirty": { "default": false, "title": "Datalad Dirty", "type": "boolean" }, "datalad_commit_id": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "title": "Datalad Commit Id" }, "datalad_id": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "title": "Datalad Id" } }, "$defs": { "ConfoundsFormat": { "description": "Accepted confounds format.", "enum": [ "fmriprep", "adhoc" ], "title": "ConfoundsFormat", "type": "string" } }, "additionalProperties": false }
- Config:
extra: str = forbid
use_enum_values: bool = True
- Fields:
rootdir (pathlib.Path)types (Annotated[Literal[junifer.datagrabber.base.DataType.BOLD, junifer.datagrabber.base.DataType.T1w, junifer.datagrabber.base.DataType.Warp] | list[Literal[junifer.datagrabber.base.DataType.BOLD, junifer.datagrabber.base.DataType.T1w, junifer.datagrabber.base.DataType.Warp]], pydantic.functional_validators.BeforeValidator(func=junifer.utils.helpers.ensure_list, json_schema_input_type=PydanticUndefined)])uri (pydantic.networks.AnyUrl)
- Validators:
- field types: Annotated[Literal[DataType.BOLD, DataType.T1w, DataType.Warp] | list[Literal[DataType.BOLD, DataType.T1w, DataType.Warp]], BeforeValidator(func=ensure_list, json_schema_input_type=PydanticUndefined)] = [<DataType.BOLD: 'BOLD'>, <DataType.T1w: 'T1w'>, <DataType.Warp: 'Warp'>]¶
- Constraints:
func = <function ensure_list at 0x712131602660>
json_schema_input_type = PydanticUndefined
- field uri: AnyUrl = AnyUrl('https://github.com/datalad-datasets/human-connectome-project-openaccess.git')¶
- get_elements()¶
Implement fetching list of elements in the dataset.
The subjects are sub-datasets, whose files are only there once they are installed, so the files are not searched: the elements are all the subjects (the directories of the dataset) with the tasks and phase encodings to grab, even if some of them have no data.
- Returns:
listThe list of elements that can be grabbed in the dataset.
- model_post_init(context)¶
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- pydantic model junifer.datagrabber.HCP1200¶
Concrete implementation for pattern-based data fetching of HCP1200.
- Parameters:
- types{“BOLD”, “T1w”, “Warp”} or
listof the options, optional The data type(s) to grab.
- datadir
pathlib.Path The path where the data is stored.
- tasks{“REST1”, “REST2”, “SOCIAL”, “WM”, “RELATIONAL”, “EMOTION”, “LANGUAGE”, “GAMBLING”, “MOTOR”} or
listof the options, optional HCP task sessions. By default, all available task sessions are selected.
- phase_encodings{“LR”, “RL”} or
listof the options, optional HCP phase encoding directions. By default, all are used.
- ica_fixbool, optional
Whether to retrieve data that was processed with ICA+FIX. Only
HCP1200Task.REST1andHCP1200Task.REST2tasks are available with ICA+FIX (default False).
- types{“BOLD”, “T1w”, “Warp”} or
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
Show JSON schema
{ "title": "HCP1200", "description": "Concrete implementation for pattern-based data fetching of HCP1200.\n\nParameters\n----------\ntypes : {\"BOLD\", \"T1w\", \"Warp\"} or list of the options, optional\n The data type(s) to grab.\ndatadir : pathlib.Path\n The path where the data is stored.\ntasks : {\"REST1\", \"REST2\", \"SOCIAL\", \"WM\", \"RELATIONAL\", \"EMOTION\", \"LANGUAGE\", \"GAMBLING\", \"MOTOR\"} or list of the options, optional\n HCP task sessions.\n By default, all available task sessions are selected.\nphase_encodings : {\"LR\", \"RL\"} or list of the options, optional\n HCP phase encoding directions.\n By default, all are used.\nica_fix : bool, optional\n Whether to retrieve data that was processed with ICA+FIX.\n Only ``HCP1200Task.REST1`` and ``HCP1200Task.REST2`` tasks\n are available with ICA+FIX\n (default False).", "type": "object", "properties": { "types": { "anyOf": [ { "enum": [ "BOLD", "T1w", "Warp" ], "type": "string" }, { "items": { "enum": [ "BOLD", "T1w", "Warp" ], "type": "string" }, "type": "array" } ], "default": [ "BOLD", "T1w", "Warp" ], "title": "Types" }, "datadir": { "format": "path", "title": "Datadir", "type": "string" }, "patterns": { "additionalProperties": { "anyOf": [ { "additionalProperties": { "anyOf": [ { "additionalProperties": { "type": "string" }, "type": "object" }, { "type": "string" } ] }, "type": "object" }, { "additionalProperties": { "type": "string" }, "type": "object" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "default": { "BOLD": { "pattern": "{subject}/MNINonLinear/Results/?fMRI_{task}_{phase_encoding}/?fMRI_{task}_{phase_encoding}{suffix}.nii.gz", "space": "MNI152NLin6Asym" }, "T1w": { "pattern": "{subject}/T1w/T1w_acpc_dc_restore.nii.gz", "space": "native" }, "Warp": [ { "dst": "native", "pattern": "{subject}/MNINonLinear/xfms/standard2acpc_dc.nii.gz", "src": "MNI152NLin6Asym", "warper": "fsl" }, { "dst": "MNI152NLin6Asym", "pattern": "{subject}/MNINonLinear/xfms/acpc_dc2standard.nii.gz", "src": "native", "warper": "fsl" } ] }, "title": "Patterns", "type": "object" }, "replacements": { "default": [ "subject", "task", "phase_encoding" ], "items": { "type": "string" }, "title": "Replacements", "type": "array" }, "confounds_format": { "anyOf": [ { "$ref": "#/$defs/ConfoundsFormat" }, { "type": "null" } ], "default": null }, "partial_pattern_ok": { "default": false, "title": "Partial Pattern Ok", "type": "boolean" }, "tasks": { "anyOf": [ { "enum": [ "REST1", "REST2", "SOCIAL", "WM", "RELATIONAL", "EMOTION", "LANGUAGE", "GAMBLING", "MOTOR" ], "type": "string" }, { "items": { "enum": [ "REST1", "REST2", "SOCIAL", "WM", "RELATIONAL", "EMOTION", "LANGUAGE", "GAMBLING", "MOTOR" ], "type": "string" }, "type": "array" } ], "default": [ "REST1", "REST2", "SOCIAL", "WM", "RELATIONAL", "EMOTION", "LANGUAGE", "GAMBLING", "MOTOR" ], "title": "Tasks" }, "phase_encodings": { "anyOf": [ { "enum": [ "RL", "LR" ], "type": "string" }, { "items": { "enum": [ "RL", "LR" ], "type": "string" }, "type": "array" } ], "default": [ "RL", "LR" ], "title": "Phase Encodings" }, "ica_fix": { "default": false, "title": "Ica Fix", "type": "boolean" } }, "$defs": { "ConfoundsFormat": { "description": "Accepted confounds format.", "enum": [ "fmriprep", "adhoc" ], "title": "ConfoundsFormat", "type": "string" } }, "additionalProperties": false, "required": [ "datadir" ] }
- Config:
extra: str = forbid
use_enum_values: bool = True
- Fields:
ica_fix (bool)patterns (dict[str, dict[str, dict[str, str] | str] | dict[str, str] | list[dict[str, str]]])phase_encodings (Annotated[Literal[junifer.datagrabber.hcp1200.hcp1200.HCP1200PhaseEncoding.RL, junifer.datagrabber.hcp1200.hcp1200.HCP1200PhaseEncoding.LR] | list[Literal[junifer.datagrabber.hcp1200.hcp1200.HCP1200PhaseEncoding.RL, junifer.datagrabber.hcp1200.hcp1200.HCP1200PhaseEncoding.LR]], pydantic.functional_validators.BeforeValidator(func=junifer.utils.helpers.ensure_list, json_schema_input_type=PydanticUndefined)])replacements (list[str])tasks (Annotated[Literal[junifer.datagrabber.hcp1200.hcp1200.HCP1200Task.REST1, junifer.datagrabber.hcp1200.hcp1200.HCP1200Task.REST2, junifer.datagrabber.hcp1200.hcp1200.HCP1200Task.SOCIAL, junifer.datagrabber.hcp1200.hcp1200.HCP1200Task.WM, junifer.datagrabber.hcp1200.hcp1200.HCP1200Task.RELATIONAL, junifer.datagrabber.hcp1200.hcp1200.HCP1200Task.EMOTION, junifer.datagrabber.hcp1200.hcp1200.HCP1200Task.LANGUAGE, junifer.datagrabber.hcp1200.hcp1200.HCP1200Task.GAMBLING, junifer.datagrabber.hcp1200.hcp1200.HCP1200Task.MOTOR] | list[Literal[junifer.datagrabber.hcp1200.hcp1200.HCP1200Task.REST1, junifer.datagrabber.hcp1200.hcp1200.HCP1200Task.REST2, junifer.datagrabber.hcp1200.hcp1200.HCP1200Task.SOCIAL, junifer.datagrabber.hcp1200.hcp1200.HCP1200Task.WM, junifer.datagrabber.hcp1200.hcp1200.HCP1200Task.RELATIONAL, junifer.datagrabber.hcp1200.hcp1200.HCP1200Task.EMOTION, junifer.datagrabber.hcp1200.hcp1200.HCP1200Task.LANGUAGE, junifer.datagrabber.hcp1200.hcp1200.HCP1200Task.GAMBLING, junifer.datagrabber.hcp1200.hcp1200.HCP1200Task.MOTOR]], pydantic.functional_validators.BeforeValidator(func=junifer.utils.helpers.ensure_list, json_schema_input_type=PydanticUndefined)])types (Annotated[Literal[junifer.datagrabber.base.DataType.BOLD, junifer.datagrabber.base.DataType.T1w, junifer.datagrabber.base.DataType.Warp] | list[Literal[junifer.datagrabber.base.DataType.BOLD, junifer.datagrabber.base.DataType.T1w, junifer.datagrabber.base.DataType.Warp]], pydantic.functional_validators.BeforeValidator(func=junifer.utils.helpers.ensure_list, json_schema_input_type=PydanticUndefined)])
- field patterns: dict[str, dict[str, dict[str, str] | str] | dict[str, str] | list[dict[str, str]]] = {'BOLD': {'pattern': '{subject}/MNINonLinear/Results/?fMRI_{task}_{phase_encoding}/?fMRI_{task}_{phase_encoding}{suffix}.nii.gz', 'space': 'MNI152NLin6Asym'}, 'T1w': {'pattern': '{subject}/T1w/T1w_acpc_dc_restore.nii.gz', 'space': 'native'}, 'Warp': [{'dst': 'native', 'pattern': '{subject}/MNINonLinear/xfms/standard2acpc_dc.nii.gz', 'src': 'MNI152NLin6Asym', 'warper': 'fsl'}, {'dst': 'MNI152NLin6Asym', 'pattern': '{subject}/MNINonLinear/xfms/acpc_dc2standard.nii.gz', 'src': 'native', 'warper': 'fsl'}]}¶
- field phase_encodings: Annotated[Literal[HCP1200PhaseEncoding.RL, HCP1200PhaseEncoding.LR] | list[Literal[HCP1200PhaseEncoding.RL, HCP1200PhaseEncoding.LR]], BeforeValidator(func=ensure_list, json_schema_input_type=PydanticUndefined)] = [HCP1200PhaseEncoding.RL, HCP1200PhaseEncoding.LR]¶
- Constraints:
func = <function ensure_list at 0x712131602660>
json_schema_input_type = PydanticUndefined
- field tasks: Annotated[Literal[HCP1200Task.REST1, HCP1200Task.REST2, HCP1200Task.SOCIAL, HCP1200Task.WM, HCP1200Task.RELATIONAL, HCP1200Task.EMOTION, HCP1200Task.LANGUAGE, HCP1200Task.GAMBLING, HCP1200Task.MOTOR] | list[Literal[HCP1200Task.REST1, HCP1200Task.REST2, HCP1200Task.SOCIAL, HCP1200Task.WM, HCP1200Task.RELATIONAL, HCP1200Task.EMOTION, HCP1200Task.LANGUAGE, HCP1200Task.GAMBLING, HCP1200Task.MOTOR]], BeforeValidator(func=ensure_list, json_schema_input_type=PydanticUndefined)] = [HCP1200Task.REST1, HCP1200Task.REST2, HCP1200Task.SOCIAL, HCP1200Task.WM, HCP1200Task.RELATIONAL, HCP1200Task.EMOTION, HCP1200Task.LANGUAGE, HCP1200Task.GAMBLING, HCP1200Task.MOTOR]¶
- Constraints:
func = <function ensure_list at 0x712131602660>
json_schema_input_type = PydanticUndefined
- field types: Annotated[Literal[DataType.BOLD, DataType.T1w, DataType.Warp] | list[Literal[DataType.BOLD, DataType.T1w, DataType.Warp]], BeforeValidator(func=ensure_list, json_schema_input_type=PydanticUndefined)] = [<DataType.BOLD: 'BOLD'>, <DataType.T1w: 'T1w'>, <DataType.Warp: 'Warp'>]¶
- Constraints:
func = <function ensure_list at 0x712131602660>
json_schema_input_type = PydanticUndefined
- validate_datagrabber_params()¶
Run extra logical validation for datagrabber.
- enum junifer.datagrabber.HCP1200PhaseEncoding(value)¶
Accepted HCP1200 phase encoding directions.
- Member Type:
Valid values are as follows:
- LR = <HCP1200PhaseEncoding.LR: 'LR'>¶
- RL = <HCP1200PhaseEncoding.RL: 'RL'>¶
- enum junifer.datagrabber.HCP1200Task(value)¶
Accepted HCP1200 tasks.
- Member Type:
Valid values are as follows:
- REST1 = <HCP1200Task.REST1: 'REST1'>¶
- REST2 = <HCP1200Task.REST2: 'REST2'>¶
- SOCIAL = <HCP1200Task.SOCIAL: 'SOCIAL'>¶
- WM = <HCP1200Task.WM: 'WM'>¶
- RELATIONAL = <HCP1200Task.RELATIONAL: 'RELATIONAL'>¶
- EMOTION = <HCP1200Task.EMOTION: 'EMOTION'>¶
- LANGUAGE = <HCP1200Task.LANGUAGE: 'LANGUAGE'>¶
- GAMBLING = <HCP1200Task.GAMBLING: 'GAMBLING'>¶
- MOTOR = <HCP1200Task.MOTOR: 'MOTOR'>¶
- pydantic model junifer.datagrabber.MultipleDataGrabber¶
Concrete implementation for multi sourced data fetching.
Implements a DataGrabber which can be used to fetch data from multiple DataGrabbers, e.g., from different datasets:
Each data type is grabbed by one DataGrabber (set with their
types), except for the DataGrabbers that only grab the nested data types of a data type (without its main file, e.g., only theconfoundsofBOLD), which replace the nested data types of the other one.The element keys are the ones of the first DataGrabber, followed by the other keys of the other DataGrabbers, which can have different keys (e.g.,
subjectandtaskforBOLD, and onlysubjectforVBM_GM). The keys with the same name must have the same values in all the DataGrabbers.The elements are the ones available in all the DataGrabbers, joined on their common keys, and each DataGrabber grabs an element with its keys.
- Parameters:
- datagrabbers
listof DataGrabber-like objects The DataGrabbers to use for fetching data.
- **kwargs
Keyword arguments passed to superclass.
- datagrabbers
- Raises:
RuntimeErrorIf more than one DataGrabber grabs the main file of a data type or the same nested data type without the main file.
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
Show JSON schema
{ "title": "MultipleDataGrabber", "description": "Concrete implementation for multi sourced data fetching.\n\nImplements a DataGrabber which can be used to fetch data from multiple\nDataGrabbers, e.g., from different datasets:\n\n* Each data type is grabbed by one DataGrabber (set with their ``types``),\n except for the DataGrabbers that only grab the nested data types of a\n data type (without its main file, e.g., only the ``confounds`` of\n ``BOLD``), which replace the nested data types of the other one.\n* The element keys are the ones of the first DataGrabber, followed by the\n other keys of the other DataGrabbers, which can have different keys\n (e.g., ``subject`` and ``task`` for ``BOLD``, and only ``subject`` for\n ``VBM_GM``). The keys with the same name must have the same values in\n all the DataGrabbers.\n* The elements are the ones available in all the DataGrabbers, joined on\n their common keys, and each DataGrabber grabs an element with its keys.\n\nParameters\n----------\ndatagrabbers : list of DataGrabber-like objects\n The DataGrabbers to use for fetching data.\n**kwargs\n Keyword arguments passed to superclass.\n\nRaises\n------\nRuntimeError\n If more than one DataGrabber grabs the main file of a data type or\n the same nested data type without the main file.", "type": "object", "properties": { "types": { "anyOf": [ { "$ref": "#/$defs/DataType" }, { "items": { "$ref": "#/$defs/DataType" }, "type": "array" } ], "default": [], "title": "Types" }, "datadir": { "default": ".", "format": "path", "title": "Datadir", "type": "string" }, "datagrabbers": { "items": { "anyOf": [ {}, { "$ref": "#/$defs/PatternDataGrabber" }, { "$ref": "#/$defs/PatternDataladDataGrabber" } ] }, "title": "Datagrabbers", "type": "array" } }, "$defs": { "ConfoundsFormat": { "description": "Accepted confounds format.", "enum": [ "fmriprep", "adhoc" ], "title": "ConfoundsFormat", "type": "string" }, "DataType": { "description": "Accepted data type.", "enum": [ "T1w", "T2w", "BOLD", "Warp", "VBM_GM", "VBM_WM", "VBM_CSF", "fALFF", "GCOR", "LCOR", "DWI", "FreeSurfer" ], "title": "DataType", "type": "string" }, "PatternDataGrabber": { "additionalProperties": false, "description": "Concrete implementation for pattern-based data fetching.\n\nImplements a DataGrabber that understands patterns to grab data.\n\nParameters\n----------\ntypes : :enum:`.DataType` or list of variants\n The data type(s) to grab.\ndatadir : pathlib.Path\n The path where the data is stored.\npatterns : ``DataGrabberPatterns``\n The datagrabber patterns. Check :class:`.DataTypeSchema` for the schema.\nreplacements : list of str or dict\n All possible replacements in ``patterns.<data_type>.pattern``. As a\n dictionary, the keys are the replacements and the values are the lists\n of values to grab (or None to grab all of them), e.g.,\n ``{\"subject\": None, \"task\": [\"rest\", \"movie\"]}`` to grab all the\n subjects and only these tasks.\nconfounds_format : :enum:`.ConfoundsFormat` or None, optional\n The format of the confounds for the dataset (default None).\npartial_pattern_ok : bool, optional\n Whether to raise error if partial pattern for a data type is found.\n This allows to bypass mandatory key check and issue a warning\n instead of raising error. This allows one to have a DataGrabber\n with data types without the corresponding mandatory keys and is\n powerful when used with :class:`.MultipleDataGrabber`\n (default True).\n\nAttributes\n----------\nskip_file_check", "properties": { "types": { "anyOf": [ { "$ref": "#/$defs/DataType" }, { "items": { "$ref": "#/$defs/DataType" }, "type": "array" } ], "title": "Types" }, "datadir": { "format": "path", "title": "Datadir", "type": "string" }, "patterns": { "additionalProperties": { "anyOf": [ { "additionalProperties": { "anyOf": [ { "additionalProperties": { "type": "string" }, "type": "object" }, { "type": "string" } ] }, "type": "object" }, { "additionalProperties": { "type": "string" }, "type": "object" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "title": "Patterns", "type": "object" }, "replacements": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "additionalProperties": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ] }, "type": "object" } ], "title": "Replacements" }, "confounds_format": { "anyOf": [ { "$ref": "#/$defs/ConfoundsFormat" }, { "type": "null" } ], "default": null }, "partial_pattern_ok": { "default": false, "title": "Partial Pattern Ok", "type": "boolean" } }, "required": [ "types", "datadir", "patterns", "replacements" ], "title": "PatternDataGrabber", "type": "object" }, "PatternDataladDataGrabber": { "additionalProperties": true, "description": "Concrete implementation for pattern and datalad based data fetching.\n\nImplements a DataGrabber that gets data from a datalad sibling,\ninterpreting patterns.\n\nParameters\n----------\nuri : pydantic.AnyUrl\n URI of the datalad sibling.\ntypes : enum:`.DataType` or list of variants\n The data type(s) to grab.\npatterns : ``DataGrabberPatterns``\n The datagrabber patterns. Check :class:`DataTypeSchema` for the schema.\nreplacements : list of str\n All possible replacements in ``patterns.<data_type>.pattern``.\nrootdir : pathlib.Path, optional\n The path within the datalad dataset to the root directory\n (default Path(\".\")).\nconfounds_format : :enum:`.ConfoundsFormat` or None, optional\n The format of the confounds for the dataset (default None).\ndatadir : pathlib.Path, optional\n That path where the datalad dataset will be cloned.\n If not specified, the datalad dataset will be cloned into a temporary\n directory.\n\n\nSee Also\n--------\nDataladDataGrabber:\n Abstract base class for datalad-based data fetching.\nPatternDataGrabber:\n Concrete implementation for pattern-based data fetching.", "properties": { "types": { "anyOf": [ { "$ref": "#/$defs/DataType" }, { "items": { "$ref": "#/$defs/DataType" }, "type": "array" } ], "title": "Types" }, "datadir": { "format": "path", "title": "Datadir", "type": "string" }, "patterns": { "additionalProperties": { "anyOf": [ { "additionalProperties": { "anyOf": [ { "additionalProperties": { "type": "string" }, "type": "object" }, { "type": "string" } ] }, "type": "object" }, { "additionalProperties": { "type": "string" }, "type": "object" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "title": "Patterns", "type": "object" }, "replacements": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "additionalProperties": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ] }, "type": "object" } ], "title": "Replacements" }, "confounds_format": { "anyOf": [ { "$ref": "#/$defs/ConfoundsFormat" }, { "type": "null" } ], "default": null }, "partial_pattern_ok": { "default": false, "title": "Partial Pattern Ok", "type": "boolean" }, "uri": { "format": "uri", "minLength": 1, "title": "Uri", "type": "string" }, "rootdir": { "default": ".", "format": "path", "title": "Rootdir", "type": "string" }, "datalad_dirty": { "default": false, "title": "Datalad Dirty", "type": "boolean" }, "datalad_commit_id": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "title": "Datalad Commit Id" }, "datalad_id": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "title": "Datalad Id" } }, "required": [ "types", "patterns", "replacements", "uri" ], "title": "PatternDataladDataGrabber", "type": "object" } }, "additionalProperties": true, "required": [ "datagrabbers" ] }
- Config:
extra: str = allow
use_enum_values: bool = True
- Fields:
datadir (pathlib.Path)datagrabbers (list[type[BaseDataGrabber] | junifer.datagrabber.pattern.PatternDataGrabber | junifer.datagrabber.pattern_datalad.PatternDataladDataGrabber])types (Annotated[junifer.datagrabber.base.DataType | list[junifer.datagrabber.base.DataType], pydantic.functional_validators.BeforeValidator(func=junifer.utils.helpers.ensure_list, json_schema_input_type=PydanticUndefined)])
- field datagrabbers: list[type[BaseDataGrabber] | PatternDataGrabber | PatternDataladDataGrabber] [Required]¶
- field types: Annotated[DataType | list[DataType], BeforeValidator(func=ensure_list, json_schema_input_type=PydanticUndefined)] = []¶
- Constraints:
func = <function ensure_list at 0x712131602660>
json_schema_input_type = PydanticUndefined
- get_element_keys()¶
Get element keys.
For each item in the
elementtuple passed to__getitem__(), this method returns the corresponding key(s): the keys of the first DataGrabber, followed by the other keys of the other DataGrabbers.
- get_elements()¶
Get elements.
The elements of the DataGrabbers are joined on their common keys, as the elements of the data types of a
PatternDataGrabber, so the elements are the ones available in all the DataGrabbers.- Returns:
listList 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:
- element
dict The element to be indexed.
- element
- Returns:
dictDictionary 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_type_element_keys(data_type)¶
Get the element keys that the data of a data type depends on.
- validate_datagrabber_params()¶
Run extra logical validation for datagrabber.
- class junifer.datagrabber.OptionalTypeSchema¶
Optional type schema.
- pydantic model junifer.datagrabber.PatternDataGrabber¶
Concrete implementation for pattern-based data fetching.
Implements a DataGrabber that understands patterns to grab data.
- Parameters:
- types
DataTypeorlistofvariants The data type(s) to grab.
- datadir
pathlib.Path The path where the data is stored.
- patterns
DataGrabberPatterns The datagrabber patterns. Check
DataTypeSchemafor the schema.- replacements
listofstrordict All possible replacements in
patterns.<data_type>.pattern. As a dictionary, the keys are the replacements and the values are the lists of values to grab (or None to grab all of them), e.g.,{"subject": None, "task": ["rest", "movie"]}to grab all the subjects and only these tasks.- confounds_format
ConfoundsFormatorNone, optional The format of the confounds for the dataset (default None).
- partial_pattern_okbool, optional
Whether to raise error if partial pattern for a data type is found. This allows to bypass mandatory key check and issue a warning instead of raising error. This allows one to have a DataGrabber with data types without the corresponding mandatory keys and is powerful when used with
MultipleDataGrabber(default True).
- types
- Attributes:
skip_file_checkSkip file check existence.
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
Show JSON schema
{ "title": "PatternDataGrabber", "description": "Concrete implementation for pattern-based data fetching.\n\nImplements a DataGrabber that understands patterns to grab data.\n\nParameters\n----------\ntypes : :enum:`.DataType` or list of variants\n The data type(s) to grab.\ndatadir : pathlib.Path\n The path where the data is stored.\npatterns : ``DataGrabberPatterns``\n The datagrabber patterns. Check :class:`.DataTypeSchema` for the schema.\nreplacements : list of str or dict\n All possible replacements in ``patterns.<data_type>.pattern``. As a\n dictionary, the keys are the replacements and the values are the lists\n of values to grab (or None to grab all of them), e.g.,\n ``{\"subject\": None, \"task\": [\"rest\", \"movie\"]}`` to grab all the\n subjects and only these tasks.\nconfounds_format : :enum:`.ConfoundsFormat` or None, optional\n The format of the confounds for the dataset (default None).\npartial_pattern_ok : bool, optional\n Whether to raise error if partial pattern for a data type is found.\n This allows to bypass mandatory key check and issue a warning\n instead of raising error. This allows one to have a DataGrabber\n with data types without the corresponding mandatory keys and is\n powerful when used with :class:`.MultipleDataGrabber`\n (default True).\n\nAttributes\n----------\nskip_file_check", "type": "object", "properties": { "types": { "anyOf": [ { "$ref": "#/$defs/DataType" }, { "items": { "$ref": "#/$defs/DataType" }, "type": "array" } ], "title": "Types" }, "datadir": { "format": "path", "title": "Datadir", "type": "string" }, "patterns": { "additionalProperties": { "anyOf": [ { "additionalProperties": { "anyOf": [ { "additionalProperties": { "type": "string" }, "type": "object" }, { "type": "string" } ] }, "type": "object" }, { "additionalProperties": { "type": "string" }, "type": "object" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "title": "Patterns", "type": "object" }, "replacements": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "additionalProperties": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ] }, "type": "object" } ], "title": "Replacements" }, "confounds_format": { "anyOf": [ { "$ref": "#/$defs/ConfoundsFormat" }, { "type": "null" } ], "default": null }, "partial_pattern_ok": { "default": false, "title": "Partial Pattern Ok", "type": "boolean" } }, "$defs": { "ConfoundsFormat": { "description": "Accepted confounds format.", "enum": [ "fmriprep", "adhoc" ], "title": "ConfoundsFormat", "type": "string" }, "DataType": { "description": "Accepted data type.", "enum": [ "T1w", "T2w", "BOLD", "Warp", "VBM_GM", "VBM_WM", "VBM_CSF", "fALFF", "GCOR", "LCOR", "DWI", "FreeSurfer" ], "title": "DataType", "type": "string" } }, "additionalProperties": false, "required": [ "types", "datadir", "patterns", "replacements" ] }
- Config:
extra: str = forbid
use_enum_values: bool = True
- Fields:
confounds_format (junifer.datagrabber.pattern.ConfoundsFormat | None)partial_pattern_ok (bool)patterns (dict[str, dict[str, dict[str, str] | str] | dict[str, str] | list[dict[str, str]]])replacements (list[str] | dict[str, list[str] | None])
- field confounds_format: ConfoundsFormat | None = None¶
- field patterns: dict[str, dict[str, dict[str, str] | str] | dict[str, str] | list[dict[str, str]]] [Required]¶
- get_element_keys()¶
Get element keys.
For each item in the “element” tuple, this functions returns the corresponding key: the replacements in the patterns of the data types to grab (see
get_type_element_keys()), e.g., withouttaskif only anatomical data types are grabbed.
- get_elements()¶
Implement fetching list of elements in the dataset.
It will use regex to search for “replacements” in the “patterns” and join the results for each type on their common replacements i.e., build a list of elements that have all the required types.
- Returns:
listThe list of elements that can be grabbed in the dataset.
- get_item(**element)¶
Get the specified item from the dataset.
This method constructs a real path to the requested item’s data, by replacing the
patternswith actual values passed via**element.
- get_replacement_values()¶
Get the values of the replacements to grab.
The values are given by
replacements(as a dictionary) or by the fields of the replacements (see_REPLACEMENT_FIELDS).- Returns:
dictThe lists of values of the replacements to grab, by replacement. The replacements without values (to grab all of them) are not included.
- get_type_element_keys(data_type)¶
Get the element keys that the data of a data type depends on.
- validate_datagrabber_params()¶
Run extra logical validation for datagrabber.
- pydantic model junifer.datagrabber.PatternDataladDataGrabber¶
Concrete implementation for pattern and datalad based data fetching.
Implements a DataGrabber that gets data from a datalad sibling, interpreting patterns.
- Parameters:
- uri
pydantic.AnyUrl URI of the datalad sibling.
- typesenum:.DataType or list of variants
The data type(s) to grab.
- patterns
DataGrabberPatterns The datagrabber patterns. Check
DataTypeSchemafor the schema.- replacements
listofstr All possible replacements in
patterns.<data_type>.pattern.- rootdir
pathlib.Path, optional The path within the datalad dataset to the root directory (default Path(“.”)).
- confounds_format
ConfoundsFormatorNone, optional The format of the confounds for the dataset (default None).
- datadir
pathlib.Path, optional That path where the datalad dataset will be cloned. If not specified, the datalad dataset will be cloned into a temporary directory.
- uri
See also
DataladDataGrabberAbstract base class for datalad-based data fetching.
PatternDataGrabberConcrete implementation for pattern-based data fetching.
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
Show JSON schema
{ "title": "PatternDataladDataGrabber", "description": "Concrete implementation for pattern and datalad based data fetching.\n\nImplements a DataGrabber that gets data from a datalad sibling,\ninterpreting patterns.\n\nParameters\n----------\nuri : pydantic.AnyUrl\n URI of the datalad sibling.\ntypes : enum:`.DataType` or list of variants\n The data type(s) to grab.\npatterns : ``DataGrabberPatterns``\n The datagrabber patterns. Check :class:`DataTypeSchema` for the schema.\nreplacements : list of str\n All possible replacements in ``patterns.<data_type>.pattern``.\nrootdir : pathlib.Path, optional\n The path within the datalad dataset to the root directory\n (default Path(\".\")).\nconfounds_format : :enum:`.ConfoundsFormat` or None, optional\n The format of the confounds for the dataset (default None).\ndatadir : pathlib.Path, optional\n That path where the datalad dataset will be cloned.\n If not specified, the datalad dataset will be cloned into a temporary\n directory.\n\n\nSee Also\n--------\nDataladDataGrabber:\n Abstract base class for datalad-based data fetching.\nPatternDataGrabber:\n Concrete implementation for pattern-based data fetching.", "type": "object", "properties": { "types": { "anyOf": [ { "$ref": "#/$defs/DataType" }, { "items": { "$ref": "#/$defs/DataType" }, "type": "array" } ], "title": "Types" }, "datadir": { "format": "path", "title": "Datadir", "type": "string" }, "patterns": { "additionalProperties": { "anyOf": [ { "additionalProperties": { "anyOf": [ { "additionalProperties": { "type": "string" }, "type": "object" }, { "type": "string" } ] }, "type": "object" }, { "additionalProperties": { "type": "string" }, "type": "object" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "title": "Patterns", "type": "object" }, "replacements": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "additionalProperties": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ] }, "type": "object" } ], "title": "Replacements" }, "confounds_format": { "anyOf": [ { "$ref": "#/$defs/ConfoundsFormat" }, { "type": "null" } ], "default": null }, "partial_pattern_ok": { "default": false, "title": "Partial Pattern Ok", "type": "boolean" }, "uri": { "format": "uri", "minLength": 1, "title": "Uri", "type": "string" }, "rootdir": { "default": ".", "format": "path", "title": "Rootdir", "type": "string" }, "datalad_dirty": { "default": false, "title": "Datalad Dirty", "type": "boolean" }, "datalad_commit_id": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "title": "Datalad Commit Id" }, "datalad_id": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "title": "Datalad Id" } }, "$defs": { "ConfoundsFormat": { "description": "Accepted confounds format.", "enum": [ "fmriprep", "adhoc" ], "title": "ConfoundsFormat", "type": "string" }, "DataType": { "description": "Accepted data type.", "enum": [ "T1w", "T2w", "BOLD", "Warp", "VBM_GM", "VBM_WM", "VBM_CSF", "fALFF", "GCOR", "LCOR", "DWI", "FreeSurfer" ], "title": "DataType", "type": "string" } }, "additionalProperties": true, "required": [ "types", "patterns", "replacements", "uri" ] }
- Config:
extra: str = allow
use_enum_values: bool = True
- Fields:
datadir (pathlib.Path)datalad_commit_id (str | None)datalad_dirty (bool)datalad_id (str | None)rootdir (pathlib.Path)types (Annotated[DataType | list[DataType], BeforeValidator(ensure_list)])uri (pydantic.networks.AnyUrl)
- Validators:
disable_tag»datalad_dirtywarn_existing_datalad_autodir»datadir
- field types: Annotated[DataType | list[DataType], BeforeValidator(ensure_list)] [Required]¶
- Constraints:
func = <function ensure_list at 0x712131602660>
json_schema_input_type = PydanticUndefined
- install()¶
Install the datalad dataset and check the patterns.
The elements are found by searching the files of the patterns, but the files of a sub-dataset are only there once the sub-dataset is installed, so the patterns cannot search the files of the sub-datasets (e.g., with one sub-dataset per subject).
- Raises:
RuntimeErrorIf a pattern searches the files of a sub-dataset.
- model_post_init(context)¶
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- validate_datagrabber_params()¶
Run extra logical validation for datagrabber.
- class junifer.datagrabber.PatternValidationMixin¶
Mixin class for pattern validation.
- validate_patterns(types, replacements, patterns, partial_pattern_ok=False)¶
Validate the patterns.
- Parameters:
- types
listofDataType The data type(s) to check patterns of.
- replacements
listofstr The replacements to be replaced in the
patterns.- patterns
DataGrabberPatterns The patterns to validate.
- partial_pattern_okbool, optional
Whether to raise error if partial pattern for a data type is found. If False, a warning is issued instead of raising an error (default False).
- types
- Raises:
ValueErrorIf length of
typesandpatternsare different or ifpatternsis missing entries fromtypesor if unknown data type is found inpatternsor if data type pattern key contains ‘*’ as value.
- junifer.datagrabber.register_confounds_format(name, alias)¶
Register custom confounds format.