How to use the nimare.extract.utils._get_dataset_dir function in NiMARE

package's default path for downloaded data.
    overwrite : :obj:`bool`, optional
        Whether to overwrite existing files or not. Default is False.
    verbose : :obj:`int`, optional
        Default is 1.

    Returns
    -------
    data_dir : :obj:`str`
        Updated data directory pointing to dataset files.
    """
    url = 'https://neurovault.org/collections/1425/download'

    dataset_name = 'nidm_21pain'

    data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir, verbose=verbose)
    desc_file = op.join(data_dir, 'description.txt')
    if op.isfile(desc_file) and overwrite is False:
        return data_dir

    # Download
    fname = op.join(data_dir, url.split('/')[-1])
    _download_zipped_file(url, filename=fname)

    # Unzip
    with zipfile.ZipFile(fname, 'r') as zip_ref:
        zip_ref.extractall(data_dir)

    collection_folders = [f for f in glob(op.join(data_dir, '*')) if '.nidm' not in f]
    collection_folders = [f for f in collection_folders if op.isdir(f)]
    if len(collection_folders) > 1:
        raise Exception('More than one folder found: '

def download_peaks2maps_model(data_dir=None, overwrite=False, verbose=1):
    """
    Download the trained Peaks2Maps model from OHBM 2018.
    """
    url = "https://zenodo.org/record/1257721/files/ohbm2018_model.tar.xz?download=1"

    temp_dataset_name = 'peaks2maps_model_ohbm2018__temp'
    temp_data_dir = _get_dataset_dir(temp_dataset_name, data_dir=data_dir, verbose=verbose)

    dataset_name = 'peaks2maps_model_ohbm2018'
    data_dir = temp_data_dir.replace(temp_dataset_name, dataset_name)

    desc_file = op.join(data_dir, 'description.txt')
    if op.isfile(desc_file) and overwrite is False:
        shutil.rmtree(temp_data_dir)
        return data_dir

    LGR.info('Downloading the model (this is a one-off operation)...')
    # Streaming, so we can iterate over the response.
    r = requests.get(url, stream=True)
    f = BytesIO()

    # Total size in bytes.
    total_size = int(r.headers.get('content-length', 0))

Location in which to place MALLET. Default is None, which uses the
        package's default path for downloaded data.
    overwrite : :obj:`bool`, optional
        Whether to overwrite existing files or not. Default is False.
    verbose : :obj:`int`, optional
        Default is 1.

    Returns
    -------
    data_dir : :obj:`str`
        Updated data directory pointing to MALLET files.
    """
    url = 'http://mallet.cs.umass.edu/dist/mallet-2.0.7.tar.gz'

    temp_dataset_name = 'mallet__temp'
    temp_data_dir = _get_dataset_dir(temp_dataset_name, data_dir=data_dir, verbose=verbose)

    dataset_name = 'mallet'
    data_dir = temp_data_dir.replace(temp_dataset_name, dataset_name)

    desc_file = op.join(data_dir, 'description.txt')
    if op.isfile(desc_file) and overwrite is False:
        shutil.rmtree(temp_data_dir)
        return data_dir

    mallet_file = op.join(temp_data_dir, op.basename(url))
    _download_zipped_file(url, mallet_file)

    with tarfile.open(mallet_file) as tf:
        tf.extractall(path=temp_data_dir)

    os.rename(op.join(temp_data_dir, 'mallet-2.0.7'), data_dir)

def __init__(self, text_df, text_column='abstract', n_topics=50,
                 n_iters=1000, alpha='auto', beta=0.001):
        mallet_dir = download_mallet()
        mallet_bin = op.join(mallet_dir, 'bin/mallet')

        model_dir = utils._get_dataset_dir('mallet_model')
        text_dir = op.join(model_dir, 'texts')

        if not op.isdir(model_dir):
            os.mkdir(model_dir)

        if alpha == 'auto':
            alpha = 50. / n_topics
        elif not isinstance(alpha, float):
            raise ValueError('Argument alpha must be float or "auto"')

        self.params = {
            'n_topics': n_topics,
            'n_iters': n_iters,
            'alpha': alpha,
            'beta': beta,
        }

verbose : :obj:`int`, optional
        Default is 1.

    Returns
    -------
    out_dict : :obj:`dict`
        Dictionary with two keys: 'ids' and 'relationships'. Each points to a
        csv file. The 'ids' file contains CogAt identifiers, canonical names,
        and aliases, sorted by alias length (number of characters).
        The 'relationships' file contains associations between CogAt items,
        with three columns: input, output, and rel_type (relationship type).
    """
    from cognitiveatlas.api import get_concept, get_task, get_disorder

    dataset_name = 'cognitive_atlas'
    data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir, verbose=verbose)

    ids_file = op.join(data_dir, 'cogat_aliases.csv')
    rels_file = op.join(data_dir, 'cogat_relationships.csv')
    if overwrite or not all([op.isfile(f) for f in [ids_file, rels_file]]):
        concepts = get_concept(silent=True).pandas
        tasks = get_task(silent=True).pandas
        disorders = get_disorder(silent=True).pandas

        # Identifiers and aliases
        long_concepts = _longify(concepts)
        long_tasks = _longify(tasks)

        # Disorders currently lack aliases
        disorders['name'] = disorders['name'].str.lower()
        disorders = disorders.assign(alias=disorders['name'])
        disorders = disorders[['id', 'name', 'alias']]