nimare.dataset.Dataset

class Dataset(source, target='mni152_2mm', mask=None)

Storage container for a coordinate- and/or image-based meta-analytic dataset/database.

Parameters:

• source (str) – JSON file containing dictionary with database information or the dict() object
• target (str) – Desired coordinate space for coordinates. Names follow NIDM convention.
• mask (str, Nifti1Image, or any nilearn Masker) – Mask(er) to use. If None, uses the target space image, with all non-zero voxels included in the mask.

Methods

get(self, dict_)	Retrieve files and/or metadata from the current Dataset.
get_images(self[, ids, imtype])	Get images of a certain type for a subset of studies in the dataset.
get_labels(self[, ids])	Extract list of labels for which studies in Dataset have annotations.
get_metadata(self[, ids, field])	Get metadata from Dataset.
get_params(self[, deep])	Get parameters for this estimator.
get_studies_by_coordinate(self, xyz[, r])	Extract list of studies with at least one focus within radius r of requested coordinates.
get_studies_by_label(self[, labels, …])	Extract list of studies with a given label.
get_studies_by_mask(self, mask)	Extract list of studies with at least one coordinate in mask.
get_texts(self[, ids, text_type])	Extract list of texts of a given type for selected IDs.
load(filename[, compressed])	Load a pickled class instance from file.
save(self, filename[, compress])	Pickle the class instance to the provided file.
set_params(self, \*\*params)	Set the parameters of this estimator.
slice(self, ids)	Return a reduced dataset with only requested IDs.
update_path(self, new_path)	Update paths to images.

get(self, dict_)

Retrieve files and/or metadata from the current Dataset.

Parameters:dict (dict) – Dictionary specifying images or metadata to collect Returns:results – A dictionary of lists of requested data. Return type:dict

get_images(self, ids=None, imtype='z')

Get images of a certain type for a subset of studies in the dataset.

Parameters:

• ids (list, optional) – A list of IDs in the Dataset for which to find texts. Default is None, in which case all texts of requested type are returned.
• imtype (str, optional) – Type of image to extract. Corresponds to column name in Dataset.images DataFrame. Default is ‘z’.

Returns:

images – List of images of requested type for selected IDs.

Return type:

list

get_labels(self, ids=None)

Extract list of labels for which studies in Dataset have annotations.

Parameters:ids (list, optional) – A list of IDs in the Dataset for which to find labels. Default is None, in which case all labels are returned. Returns:labels – List of labels for which there are annotations in the Dataset. Return type:list

get_metadata(self, ids=None, field='sample_sizes')

Get metadata from Dataset.

Parameters:

• ids (list, optional) – A list of IDs in the Dataset for which to find texts. Default is None, in which case all texts of requested type are returned.
• field (str, optional) – Metadata field to extract. Corresponds to column name in Dataset.metadata DataFrame. Default is ‘sample_sizes’.

Returns:

metadata – List of values of requested type for selected IDs.

Return type:

list

get_params(self, deep=True)

Get parameters for this estimator.

Parameters:deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns:params – Parameter names mapped to their values. Return type:mapping of string to any

get_studies_by_coordinate(self, xyz, r=20)

Extract list of studies with at least one focus within radius r of requested coordinates.

Parameters:

• xyz ((X x 3) array_like) – List of coordinates against which to find studies.
• r (float, optional) – Radius (in mm) within which to find studies. Default is 20mm.

Returns:

found_ids – A list of IDs from the Dataset with at least one focus within radius r of requested coordinates.

Return type:

list

get_studies_by_label(self, labels=None, label_threshold=0.5)

Extract list of studies with a given label.

Parameters:

• labels (list, optional) – List of labels to use to search Dataset. If a contrast has all of the labels above the threshold, it will be returned. Default is None.
• label_threshold (float, optional) – Default is 0.5.

Returns:

found_ids – A list of IDs from the Dataset found by the search criteria.

Return type:

list

get_studies_by_mask(self, mask)

Extract list of studies with at least one coordinate in mask.

Parameters:mask (img_like) – Mask across which to search for coordinates. Returns:found_ids – A list of IDs from the Dataset with at least one focus in the mask. Return type:list

get_texts(self, ids=None, text_type='abstract')

Extract list of texts of a given type for selected IDs.

Parameters:

• ids (list, optional) – A list of IDs in the Dataset for which to find texts. Default is None, in which case all texts of requested type are returned.
• text_type (str, optional) – Type of text to extract. Corresponds to column name in Dataset.texts DataFrame. Default is ‘abstract’.

Returns:

texts – List of texts of requested type for selected IDs.

Return type:

list

classmethod load(filename, compressed=True)

Load a pickled class instance from file.

Parameters:

• filename (str) – Name of file containing object.
• compressed (bool, optional) – If True, the file is assumed to be compressed and gzip will be used to load it. Otherwise, it will assume that the file is not compressed. Default = True.

Returns:

obj – Loaded class object.

Return type:

class object

save(self, filename, compress=True)

Pickle the class instance to the provided file.

Parameters:

• filename (str) – File to which object will be saved.
• compress (bool, optional) – If True, the file will be compressed with gzip. Otherwise, the uncompressed version will be saved. Default = True.

set_params(self, **params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns: Return type:self

slice(self, ids)

Return a reduced dataset with only requested IDs.

Parameters:ids (array_like) – List of study IDs to include in new dataset Returns:new_dset – Redcued Dataset containing only requested studies. Return type:nimare.dataset.Dataset

update_path(self, new_path)

Update paths to images. Prepends new path to the relative path for files in Dataset.images.

Parameters:new_path (str) – Path to prepend to relative paths of files in Dataset.images.