"""
Downloading NeuroImaging datasets: functional datasets (task + resting-state)
"""
import warnings
import os
import re
import numpy as np
import nibabel
from sklearn.datasets.base import Bunch

from .utils import (_get_dataset_dir, _fetch_files, _get_dataset_descr,
                    _read_md5_sum_file, _tree, _filter_columns)

from .._utils.compat import BytesIO, _basestring, _urllib
from .._utils.numpy_conversions import csv_to_array


def fetch_haxby_simple(data_dir=None, url=None, resume=True, verbose=1):
    """Download and load a simple example haxby dataset.

    Parameters
    ----------
    data_dir: string, optional
        Path of the data directory. Used to force data storage in a specified
        location. Default: None

    Returns
    -------
    data: sklearn.datasets.base.Bunch
        Dictionary-like object, interest attributes are:
        'func': list of string.  Path to nifti file with bold data.
        'session_target': list of string. Path to text file containing session and
        target data.
        'mask': string. Path to nifti mask file.
        'session': list of string. Path to text file containing labels
        (can be used for LeaveOneLabelOut cross validation for example).

    References
    ----------
    `Haxby, J., Gobbini, M., Furey, M., Ishai, A., Schouten, J.,
    and Pietrini, P. (2001). Distributed and overlapping representations of
    faces and objects in ventral temporal cortex. Science 293, 2425-2430.`

    Notes
    -----
    PyMVPA provides a tutorial using this dataset :
    http://www.pymvpa.org/tutorial.html

    More informations about its structure :
    http://dev.pymvpa.org/datadb/haxby2001.html

    See `additional information
    <http://www.sciencemag.org/content/293/5539/2425>`_
    """
    # URL of the dataset. It is optional because a test uses it to test dataset
    # downloading
    if url is None:
        url = 'http://www.pymvpa.org/files/pymvpa_exampledata.tar.bz2'

    opts = {'uncompress': True}
    files = [
            (os.path.join('pymvpa-exampledata', 'attributes.txt'), url, opts),
            (os.path.join('pymvpa-exampledata', 'bold.nii.gz'), url, opts),
            (os.path.join('pymvpa-exampledata', 'mask.nii.gz'), url, opts),
            (os.path.join('pymvpa-exampledata', 'attributes_literal.txt'),
             url, opts),
    ]

    dataset_name = 'haxby2001_simple'
    data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir,
                                verbose=verbose)
    files = _fetch_files(data_dir, files, resume=resume, verbose=verbose)

    # There is a common file for the two versions of Haxby
    fdescr = _get_dataset_descr('haxby2001')

    # List of length 1 are used because haxby_simple is single-subject
    return Bunch(func=[files[1]], session_target=[files[0]], mask=files[2],
                 conditions_target=[files[3]], description=fdescr)


def fetch_haxby(data_dir=None, n_subjects=1, fetch_stimuli=False,
                url=None, resume=True, verbose=1):
    """Download and loads complete haxby dataset

    Parameters
    ----------
    data_dir: string, optional
        Path of the data directory. Used to force data storage in a specified
        location. Default: None

    n_subjects: int, optional
        Number of subjects, from 1 to 6.

    fetch_stimuli: boolean, optional
        Indicate if stimuli images must be downloaded. They will be presented
        as a dictionnary of categories.

    Returns
    -------
    data: sklearn.datasets.base.Bunch
        Dictionary-like object, the interest attributes are :
        'anat': string list. Paths to anatomic images.
        'func': string list. Paths to nifti file with bold data.
        'session_target': string list. Paths to text file containing
        session and target data.
        'mask': string. Path to fullbrain mask file.
        'mask_vt': string list. Paths to nifti ventral temporal mask file.
        'mask_face': string list. Paths to nifti ventral temporal mask file.
        'mask_house': string list. Paths to nifti ventral temporal mask file.
        'mask_face_little': string list. Paths to nifti ventral temporal
        mask file.
        'mask_house_little': string list. Paths to nifti ventral temporal
        mask file.

    References
    ----------
    `Haxby, J., Gobbini, M., Furey, M., Ishai, A., Schouten, J.,
    and Pietrini, P. (2001). Distributed and overlapping representations of
    faces and objects in ventral temporal cortex. Science 293, 2425-2430.`

    Notes
    -----
    PyMVPA provides a tutorial making use of this dataset:
    http://www.pymvpa.org/tutorial.html

    More information about its structure:
    http://dev.pymvpa.org/datadb/haxby2001.html

    See `additional information
    <http://www.sciencemag.org/content/293/5539/2425>`

    Run 8 in subject 5 does not contain any task labels.
    The anatomical image for subject 6 is unavailable.
    """

    if n_subjects > 6:
        warnings.warn('Warning: there are only 6 subjects')
        n_subjects = 6

    dataset_name = 'haxby2001'
    data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir,
                                verbose=verbose)

    # Get the mask
    url_mask = 'https://www.nitrc.org/frs/download.php/7868/mask.nii.gz'
    mask = _fetch_files(data_dir, [('mask.nii.gz', url_mask, {})],
                        verbose=verbose)[0]

    # Dataset files
    if url is None:
        url = 'http://data.pymvpa.org/datasets/haxby2001/'
    md5sums = _fetch_files(data_dir, [('MD5SUMS', url + 'MD5SUMS', {})],
                           verbose=verbose)[0]
    md5sums = _read_md5_sum_file(md5sums)

    # definition of dataset files
    sub_files = ['bold.nii.gz', 'labels.txt',
                 'mask4_vt.nii.gz', 'mask8b_face_vt.nii.gz',
                 'mask8b_house_vt.nii.gz', 'mask8_face_vt.nii.gz',
                 'mask8_house_vt.nii.gz', 'anat.nii.gz']
    n_files = len(sub_files)

    files = [
            (os.path.join('subj%d' % i, sub_file),
             url + 'subj%d-2010.01.14.tar.gz' % i,
             {'uncompress': True,
              'md5sum': md5sums.get('subj%d-2010.01.14.tar.gz' % i, None)})
            for i in range(1, n_subjects + 1)
            for sub_file in sub_files
            if not (sub_file == 'anat.nii.gz' and i == 6)  # no anat for sub. 6
    ]

    files = _fetch_files(data_dir, files, resume=resume, verbose=verbose)

    if n_subjects == 6:
        files.append(None)  # None value because subject 6 has no anat

    kwargs = {}
    if fetch_stimuli:
        stimuli_files = [(os.path.join('stimuli', 'README'),
                          url + 'stimuli-2010.01.14.tar.gz',
                          {'uncompress': True})]
        readme = _fetch_files(data_dir, stimuli_files, resume=resume,
                              verbose=verbose)[0]
        kwargs['stimuli'] = _tree(os.path.dirname(readme), pattern='*.jpg',
                                  dictionary=True)

    fdescr = _get_dataset_descr(dataset_name)

    # return the data
    return Bunch(
            anat=files[7::n_files],
            func=files[0::n_files],
            session_target=files[1::n_files],
            mask_vt=files[2::n_files],
            mask_face=files[3::n_files],
            mask_house=files[4::n_files],
            mask_face_little=files[5::n_files],
            mask_house_little=files[6::n_files],
            mask=mask,
            description=fdescr,
            **kwargs)


def fetch_nyu_rest(n_subjects=None, sessions=[1], data_dir=None, resume=True,
                   verbose=1):
    """Download and loads the NYU resting-state test-retest dataset.

    Parameters
    ----------
    n_subjects: int, optional
        The number of subjects to load. If None is given, all the
        subjects are used.

    sessions: iterable of int, optional
        The sessions to load. Load only the first session by default.

    data_dir: string, optional
        Path of the data directory. Used to force data storage in a specified
        location. Default: None

    Returns
    -------
    data: sklearn.datasets.base.Bunch
        Dictionary-like object, the interest attributes are :
        'func': string list. Paths to functional images.
        'anat_anon': string list. Paths to anatomic images.
        'anat_skull': string. Paths to skull-stripped images.
        'session': numpy array. List of ids corresponding to images sessions.

    Notes
    ------
    This dataset is composed of 3 sessions of 26 participants (11 males).
    For each session, three sets of data are available:

    - anatomical:

      * anonymized data (defaced thanks to BIRN defacer)
      * skullstripped data (using 3DSkullStrip from AFNI)

    - functional

    For each participant, 3 resting-state scans of 197 continuous EPI
    functional volumes were collected :

    - 39 slices
    - matrix = 64 x 64
    - acquisition voxel size = 3 x 3 x 3 mm

    Sessions 2 and 3 were conducted in a single scan session, 45 min
    apart, and were 5-16 months after Scan 1.

    All details about this dataset can be found here :
    http://cercor.oxfordjournals.org/content/19/10/2209.full

    References
    ----------
    :Documentation:
        http://www.nitrc.org/docman/?group_id=274

    :Download:
        http://www.nitrc.org/frs/?group_id=274

    :Paper to cite:
        `The Resting Brain: Unconstrained yet Reliable
        <http://cercor.oxfordjournals.org/content/19/10/2209>`_
        Z. Shehzad, A.M.C. Kelly, P.T. Reiss, D.G. Gee, K. Gotimer,
        L.Q. Uddin, S.H. Lee, D.S. Margulies, A.K. Roy, B.B. Biswal,
        E. Petkova, F.X. Castellanos and M.P. Milham.

    :Other references:
        * `The oscillating brain: Complex and Reliable
          <http://dx.doi.org/10.1016/j.neuroimage.2009.09.037>`_
          X-N. Zuo, A. Di Martino, C. Kelly, Z. Shehzad, D.G. Gee,
          D.F. Klein, F.X. Castellanos, B.B. Biswal, M.P. Milham

        * `Reliable intrinsic connectivity networks: Test-retest
          evaluation using ICA and dual regression approach
          <http://dx.doi.org/10.1016/j.neuroimage.2009.10.080>`_,
          X-N. Zuo, C. Kelly, J.S. Adelstein, D.F. Klein,
          F.X. Castellanos, M.P. Milham

    """
    fa1 = 'http://www.nitrc.org/frs/download.php/1071/NYU_TRT_session1a.tar.gz'
    fb1 = 'http://www.nitrc.org/frs/download.php/1072/NYU_TRT_session1b.tar.gz'
    fa2 = 'http://www.nitrc.org/frs/download.php/1073/NYU_TRT_session2a.tar.gz'
    fb2 = 'http://www.nitrc.org/frs/download.php/1074/NYU_TRT_session2b.tar.gz'
    fa3 = 'http://www.nitrc.org/frs/download.php/1075/NYU_TRT_session3a.tar.gz'
    fb3 = 'http://www.nitrc.org/frs/download.php/1076/NYU_TRT_session3b.tar.gz'
    fa1_opts = {'uncompress': True,
                'move': os.path.join('session1', 'NYU_TRT_session1a.tar.gz')}
    fb1_opts = {'uncompress': True,
                'move': os.path.join('session1', 'NYU_TRT_session1b.tar.gz')}
    fa2_opts = {'uncompress': True,
                'move': os.path.join('session2', 'NYU_TRT_session2a.tar.gz')}
    fb2_opts = {'uncompress': True,
                'move': os.path.join('session2', 'NYU_TRT_session2b.tar.gz')}
    fa3_opts = {'uncompress': True,
                'move': os.path.join('session3', 'NYU_TRT_session3a.tar.gz')}
    fb3_opts = {'uncompress': True,
                'move': os.path.join('session3', 'NYU_TRT_session3b.tar.gz')}

    p_anon = os.path.join('anat', 'mprage_anonymized.nii.gz')
    p_skull = os.path.join('anat', 'mprage_skullstripped.nii.gz')
    p_func = os.path.join('func', 'lfo.nii.gz')

    subs_a = ['sub05676', 'sub08224', 'sub08889', 'sub09607', 'sub14864',
              'sub18604', 'sub22894', 'sub27641', 'sub33259', 'sub34482',
              'sub36678', 'sub38579', 'sub39529']
    subs_b = ['sub45463', 'sub47000', 'sub49401', 'sub52738', 'sub55441',
              'sub58949', 'sub60624', 'sub76987', 'sub84403', 'sub86146',
              'sub90179', 'sub94293']

    # Generate the list of files by session
    anat_anon_files = [
        [(os.path.join('session1', sub, p_anon), fa1, fa1_opts)
            for sub in subs_a]
        + [(os.path.join('session1', sub, p_anon), fb1, fb1_opts)
            for sub in subs_b],
        [(os.path.join('session2', sub, p_anon), fa2, fa2_opts)
            for sub in subs_a]
        + [(os.path.join('session2', sub, p_anon), fb2, fb2_opts)
            for sub in subs_b],
        [(os.path.join('session3', sub, p_anon), fa3, fa3_opts)
            for sub in subs_a]
        + [(os.path.join('session3', sub, p_anon), fb3, fb3_opts)
            for sub in subs_b]]

    anat_skull_files = [
        [(os.path.join('session1', sub, p_skull), fa1, fa1_opts)
            for sub in subs_a]
        + [(os.path.join('session1', sub, p_skull), fb1, fb1_opts)
            for sub in subs_b],
        [(os.path.join('session2', sub, p_skull), fa2, fa2_opts)
            for sub in subs_a]
        + [(os.path.join('session2', sub, p_skull), fb2, fb2_opts)
            for sub in subs_b],
        [(os.path.join('session3', sub, p_skull), fa3, fa3_opts)
            for sub in subs_a]
        + [(os.path.join('session3', sub, p_skull), fb3, fb3_opts)
            for sub in subs_b]]

    func_files = [
        [(os.path.join('session1', sub, p_func), fa1, fa1_opts)
            for sub in subs_a]
        + [(os.path.join('session1', sub, p_func), fb1, fb1_opts)
            for sub in subs_b],
        [(os.path.join('session2', sub, p_func), fa2, fa2_opts)
            for sub in subs_a]
        + [(os.path.join('session2', sub, p_func), fb2, fb2_opts)
            for sub in subs_b],
        [(os.path.join('session3', sub, p_func), fa3, fa3_opts)
            for sub in subs_a]
        + [(os.path.join('session3', sub, p_func), fb3, fb3_opts)
            for sub in subs_b]]

    max_subjects = len(subs_a) + len(subs_b)
    # Check arguments
    if n_subjects is None:
        n_subjects = len(subs_a) + len(subs_b)
    if n_subjects > max_subjects:
        warnings.warn('Warning: there are only %d subjects' % max_subjects)
        n_subjects = 25

    anat_anon = []
    anat_skull = []
    func = []
    session = []
    for i in sessions:
        if not (i in [1, 2, 3]):
            raise ValueError('NYU dataset session id must be in [1, 2, 3]')
        anat_anon += anat_anon_files[i - 1][:n_subjects]
        anat_skull += anat_skull_files[i - 1][:n_subjects]
        func += func_files[i - 1][:n_subjects]
        session += [i] * n_subjects

    dataset_name = 'nyu_rest'
    data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir,
                                verbose=verbose)
    anat_anon = _fetch_files(data_dir, anat_anon, resume=resume,
                             verbose=verbose)
    anat_skull = _fetch_files(data_dir, anat_skull, resume=resume,
                              verbose=verbose)
    func = _fetch_files(data_dir, func, resume=resume,
                        verbose=verbose)

    fdescr = _get_dataset_descr(dataset_name)

    return Bunch(anat_anon=anat_anon, anat_skull=anat_skull, func=func,
                 session=session, description=fdescr)


def fetch_adhd(n_subjects=30, data_dir=None, url=None, resume=True,
               verbose=1):
    """Download and load the ADHD resting-state dataset.

    Parameters
    ----------
    n_subjects: int, optional
        The number of subjects to load from maximum of 40 subjects.
        By default, 30 subjects will be loaded. If None is given,
        all 40 subjects will be loaded.

    data_dir: string, optional
        Path of the data directory. Used to force data storage in a specified
        location. Default: None

    url: string, optional
        Override download URL. Used for test only (or if you setup a mirror of
        the data). Default: None

    Returns
    -------
    data: sklearn.datasets.base.Bunch
        Dictionary-like object, the interest attributes are :
         - 'func': Paths to functional resting-state images
         - 'phenotypic': Explanations of preprocessing steps
         - 'confounds': CSV files containing the nuisance variables

    References
    ----------
    :Download:
        ftp://www.nitrc.org/fcon_1000/htdocs/indi/adhd200/sites/ADHD200_40sub_preprocessed.tgz

    """

    if url is None:
        url = 'https://www.nitrc.org/frs/download.php/'

    # Preliminary checks and declarations
    dataset_name = 'adhd'
    data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir,
                                verbose=verbose)
    ids = ['0010042', '0010064', '0010128', '0021019', '0023008', '0023012',
           '0027011', '0027018', '0027034', '0027037', '1019436', '1206380',
           '1418396', '1517058', '1552181', '1562298', '1679142', '2014113',
           '2497695', '2950754', '3007585', '3154996', '3205761', '3520880',
           '3624598', '3699991', '3884955', '3902469', '3994098', '4016887',
           '4046678', '4134561', '4164316', '4275075', '6115230', '7774305',
           '8409791', '8697774', '9744150', '9750701']
    nitrc_ids = range(7782, 7822)
    max_subjects = len(ids)
    if n_subjects is None:
        n_subjects = max_subjects
    if n_subjects > max_subjects:
        warnings.warn('Warning: there are only %d subjects' % max_subjects)
        n_subjects = max_subjects
    ids = ids[:n_subjects]
    nitrc_ids = nitrc_ids[:n_subjects]

    opts = dict(uncompress=True)

    # Dataset description
    fdescr = _get_dataset_descr(dataset_name)

    # First, get the metadata
    phenotypic = ('ADHD200_40subs_motion_parameters_and_phenotypics.csv',
        url + '7781/adhd40_metadata.tgz', opts)

    phenotypic = _fetch_files(data_dir, [phenotypic], resume=resume,
                              verbose=verbose)[0]

    # Load the csv file
    phenotypic = np.genfromtxt(phenotypic, names=True, delimiter=',',
                               dtype=None)

    # Keep phenotypic information for selected subjects
    int_ids = np.asarray(ids, dtype=int)
    phenotypic = phenotypic[[np.where(phenotypic['Subject'] == i)[0][0]
                             for i in int_ids]]

    # Download dataset files

    archives = [url + '%i/adhd40_%s.tgz' % (ni, ii)
                for ni, ii in zip(nitrc_ids, ids)]
    functionals = ['data/%s/%s_rest_tshift_RPI_voreg_mni.nii.gz' % (i, i)
                   for i in ids]
    confounds = ['data/%s/%s_regressors.csv' % (i, i) for i in ids]

    functionals = _fetch_files(
        data_dir, zip(functionals, archives, (opts,) * n_subjects),
        resume=resume, verbose=verbose)

    confounds = _fetch_files(
        data_dir, zip(confounds, archives, (opts,) * n_subjects),
        resume=resume, verbose=verbose)

    return Bunch(func=functionals, confounds=confounds,
                 phenotypic=phenotypic, description=fdescr)


def fetch_miyawaki2008(data_dir=None, url=None, resume=True, verbose=1):
    """Download and loads Miyawaki et al. 2008 dataset (153MB)

    Returns
    -------
    data: Bunch
        Dictionary-like object, the interest attributes are :

        - 'func': string list
            Paths to nifti file with bold data
        - 'label': string list
            Paths to text file containing session and target data
        - 'mask': string
            Path to nifti mask file to define target volume in visual
            cortex

    References
    ----------
    `Visual image reconstruction from human brain activity
    using a combination of multiscale local image decoders
    <http://www.cell.com/neuron/abstract/S0896-6273%2808%2900958-6>`_,
    Miyawaki, Y., Uchida, H., Yamashita, O., Sato, M. A.,
    Morito, Y., Tanabe, H. C., ... & Kamitani, Y. (2008).
    Neuron, 60(5), 915-929.

    Notes
    -----
    This dataset is available on the `brainliner website
    <http://brainliner.jp/data/brainliner-admin/Reconstruct>`_

    See `additional information
    <http://www.cns.atr.jp/dni/en/downloads/
    fmri-data-set-for-visual-image-reconstruction/>`_
    """

    url = 'https://www.nitrc.org/frs/download.php' \
          '/5899/miyawaki2008.tgz?i_agree=1&download_now=1'
    opts = {'uncompress': True}

    # Dataset files

    # Functional MRI:
    #   * 20 random scans (usually used for training)
    #   * 12 figure scans (usually used for testing)

    func_figure = [(os.path.join('func', 'data_figure_run%02d.nii.gz' % i),
                    url, opts) for i in range(1, 13)]

    func_random = [(os.path.join('func', 'data_random_run%02d.nii.gz' % i),
                    url, opts) for i in range(1, 21)]

    # Labels, 10x10 patches, stimuli shown to the subject:
    #   * 20 random labels
    #   * 12 figure labels (letters and shapes)

    label_filename = 'data_%s_run%02d_label.csv'
    label_figure = [(os.path.join('label', label_filename % ('figure', i)),
                     url, opts) for i in range(1, 13)]

    label_random = [(os.path.join('label', label_filename % ('random', i)),
                     url, opts) for i in range(1, 21)]

    # Masks

    file_mask = [
        'mask.nii.gz',
        'LHlag0to1.nii.gz',
        'LHlag10to11.nii.gz',
        'LHlag1to2.nii.gz',
        'LHlag2to3.nii.gz',
        'LHlag3to4.nii.gz',
        'LHlag4to5.nii.gz',
        'LHlag5to6.nii.gz',
        'LHlag6to7.nii.gz',
        'LHlag7to8.nii.gz',
        'LHlag8to9.nii.gz',
        'LHlag9to10.nii.gz',
        'LHV1d.nii.gz',
        'LHV1v.nii.gz',
        'LHV2d.nii.gz',
        'LHV2v.nii.gz',
        'LHV3A.nii.gz',
        'LHV3.nii.gz',
        'LHV4v.nii.gz',
        'LHVP.nii.gz',
        'RHlag0to1.nii.gz',
        'RHlag10to11.nii.gz',
        'RHlag1to2.nii.gz',
        'RHlag2to3.nii.gz',
        'RHlag3to4.nii.gz',
        'RHlag4to5.nii.gz',
        'RHlag5to6.nii.gz',
        'RHlag6to7.nii.gz',
        'RHlag7to8.nii.gz',
        'RHlag8to9.nii.gz',
        'RHlag9to10.nii.gz',
        'RHV1d.nii.gz',
        'RHV1v.nii.gz',
        'RHV2d.nii.gz',
        'RHV2v.nii.gz',
        'RHV3A.nii.gz',
        'RHV3.nii.gz',
        'RHV4v.nii.gz',
        'RHVP.nii.gz'
    ]

    file_mask = [(os.path.join('mask', m), url, opts) for m in file_mask]

    file_names = func_figure + func_random + \
                 label_figure + label_random + \
                 file_mask

    dataset_name = 'miyawaki2008'
    data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir,
                                verbose=verbose)
    files = _fetch_files(data_dir, file_names, resume=resume, verbose=verbose)

    fdescr = _get_dataset_descr(dataset_name)

    # Return the data
    return Bunch(
        func=files[:32],
        label=files[32:64],
        mask=files[64],
        mask_roi=files[65:],
        description=fdescr)


def fetch_localizer_contrasts(contrasts, n_subjects=None, get_tmaps=False,
                              get_masks=False, get_anats=False,
                              data_dir=None, url=None, resume=True, verbose=1):
    """Download and load Brainomics Localizer dataset (94 subjects).

    "The Functional Localizer is a simple and fast acquisition
    procedure based on a 5-minute functional magnetic resonance
    imaging (fMRI) sequence that can be run as easily and as
    systematically as an anatomical scan. This protocol captures the
    cerebral bases of auditory and visual perception, motor actions,
    reading, language comprehension and mental calculation at an
    individual level. Individual functional maps are reliable and
    quite precise. The procedure is decribed in more detail on the
    Functional Localizer page."
    (see http://brainomics.cea.fr/localizer/)

    "Scientific results obtained using this dataset are described in
    Pinel et al., 2007" [1]

    Parameters
    ----------
    contrasts: list of str
        The contrasts to be fetched (for all 94 subjects available).
        Allowed values are::

            {"checkerboard",
            "horizontal checkerboard",
            "vertical checkerboard",
            "horizontal vs vertical checkerboard",
            "vertical vs horizontal checkerboard",
            "sentence listening",
            "sentence reading",
            "sentence listening and reading",
            "sentence reading vs checkerboard",
            "calculation (auditory cue)",
            "calculation (visual cue)",
            "calculation (auditory and visual cue)",
            "calculation (auditory cue) vs sentence listening",
            "calculation (visual cue) vs sentence reading",
            "calculation vs sentences",
            "calculation (auditory cue) and sentence listening",
            "calculation (visual cue) and sentence reading",
            "calculation and sentence listening/reading",
            "calculation (auditory cue) and sentence listening vs "
            "calculation (visual cue) and sentence reading",
            "calculation (visual cue) and sentence reading vs checkerboard",
            "calculation and sentence listening/reading vs button press",
            "left button press (auditory cue)",
            "left button press (visual cue)",
            "left button press",
            "left vs right button press",
            "right button press (auditory cue)",
            "right button press (visual cue)",
            "right button press",
            "right vs left button press",
            "button press (auditory cue) vs sentence listening",
            "button press (visual cue) vs sentence reading",
            "button press vs calculation and sentence listening/reading"}

        or equivalently on can use the original names::

            {"checkerboard",
            "horizontal checkerboard",
            "vertical checkerboard",
            "horizontal vs vertical checkerboard",
            "vertical vs horizontal checkerboard",
            "auditory sentences",
            "visual sentences",
            "auditory&visual sentences",
            "visual sentences vs checkerboard",
            "auditory calculation",
            "visual calculation",
            "auditory&visual calculation",
            "auditory calculation vs auditory sentences",
            "visual calculation vs sentences",
            "auditory&visual calculation vs sentences",
            "auditory processing",
            "visual processing",
            "visual processing vs auditory processing",
            "auditory processing vs visual processing",
            "visual processing vs checkerboard",
            "cognitive processing vs motor",
            "left auditory click",
            "left visual click",
            "left auditory&visual click",
            "left auditory & visual click vs right auditory&visual click",
            "right auditory click",
            "right visual click",
            "right auditory&visual click",
            "right auditory & visual click vs left auditory&visual click",
            "auditory click vs auditory sentences",
            "visual click vs visual sentences",
            "auditory&visual motor vs cognitive processing"}

    n_subjects: int, optional
        The number of subjects to load. If None is given,
        all 94 subjects are used.

    get_tmaps: boolean
        Whether t maps should be fetched or not.

    get_masks: boolean
        Whether individual masks should be fetched or not.

    get_anats: boolean
        Whether individual structural images should be fetched or not.

    data_dir: string, optional
        Path of the data directory. Used to force data storage in a specified
        location.

    url: string, optional
        Override download URL. Used for test only (or if you setup a mirror of
        the data).

    resume: bool
        Whether to resume download of a partly-downloaded file.

    verbose: int
        Verbosity level (0 means no message).

    Returns
    -------
    data: Bunch
        Dictionary-like object, the interest attributes are :

        - 'cmaps': string list
            Paths to nifti contrast maps
        - 'tmaps' string list (if 'get_tmaps' set to True)
            Paths to nifti t maps
        - 'masks': string list
            Paths to nifti files corresponding to the subjects individual masks
        - 'anats': string
            Path to nifti files corresponding to the subjects structural images

    References
    ----------
    Pinel, Philippe, et al.
    "Fast reproducible identification and large-scale databasing of
    individual functional cognitive networks."
    BMC neuroscience 8.1 (2007): 91.

    """
    if isinstance(contrasts, _basestring):
        raise ValueError('Contrasts should be a list of strings, but '
                         'a single string was given: "%s"' % contrasts)
    if n_subjects is None:
        n_subjects = 94  # 94 subjects available
    if (n_subjects > 94) or (n_subjects < 1):
        warnings.warn("Wrong value for \'n_subjects\' (%d). The maximum "
                      "value will be used instead (\'n_subjects=94\')")
        n_subjects = 94  # 94 subjects available

    # we allow the user to use alternatives to Brainomics contrast names
    contrast_name_wrapper = {
        # Checkerboard
        "checkerboard": "checkerboard",
        "horizontal checkerboard": "horizontal checkerboard",
        "vertical checkerboard": "vertical checkerboard",
        "horizontal vs vertical checkerboard":
            "horizontal vs vertical checkerboard",
        "vertical vs horizontal checkerboard":
            "vertical vs horizontal checkerboard",
        # Sentences
        "sentence listening": "auditory sentences",
        "sentence reading": "visual sentences",
        "sentence listening and reading": "auditory&visual sentences",
        "sentence reading vs checkerboard": "visual sentences vs checkerboard",
        # Calculation
        "calculation (auditory cue)": "auditory calculation",
        "calculation (visual cue)": "visual calculation",
        "calculation (auditory and visual cue)": "auditory&visual calculation",
        "calculation (auditory cue) vs sentence listening":
            "auditory calculation vs auditory sentences",
        "calculation (visual cue) vs sentence reading":
            "visual calculation vs sentences",
        "calculation vs sentences": "auditory&visual calculation vs sentences",
        # Calculation + Sentences
        "calculation (auditory cue) and sentence listening":
            "auditory processing",
        "calculation (visual cue) and sentence reading":
            "visual processing",
        "calculation (visual cue) and sentence reading vs "
        "calculation (auditory cue) and sentence listening":
            "visual processing vs auditory processing",
        "calculation (auditory cue) and sentence listening vs "
        "calculation (visual cue) and sentence reading":
            "auditory processing vs visual processing",
        "calculation (visual cue) and sentence reading vs checkerboard":
            "visual processing vs checkerboard",
        "calculation and sentence listening/reading vs button press":
            "cognitive processing vs motor",
        # Button press
        "left button press (auditory cue)": "left auditory click",
        "left button press (visual cue)": "left visual click",
        "left button press": "left auditory&visual click",
        "left vs right button press": "left auditory & visual click vs "
            + "right auditory&visual click",
        "right button press (auditory cue)": "right auditory click",
        "right button press (visual cue)": "right visual click",
        "right button press": "right auditory & visual click",
        "right vs left button press": "right auditory & visual click "
           + "vs left auditory&visual click",
        "button press (auditory cue) vs sentence listening":
            "auditory click vs auditory sentences",
        "button press (visual cue) vs sentence reading":
            "visual click vs visual sentences",
        "button press vs calculation and sentence listening/reading":
            "auditory&visual motor vs cognitive processing"}
    allowed_contrasts = list(contrast_name_wrapper.values())
    # convert contrast names
    contrasts_wrapped = []
    # get a unique ID for each contrast. It is used to give a unique name to
    # each download file and avoid name collisions.
    contrasts_indices = []
    for contrast in contrasts:
        if contrast in allowed_contrasts:
            contrasts_wrapped.append(contrast)
            contrasts_indices.append(allowed_contrasts.index(contrast))
        elif contrast in contrast_name_wrapper:
            name = contrast_name_wrapper[contrast]
            contrasts_wrapped.append(name)
            contrasts_indices.append(allowed_contrasts.index(name))
        else:
            raise ValueError("Contrast \'%s\' is not available" % contrast)

    # It is better to perform several small requests than a big one because:
    # - Brainomics server has no cache (can lead to timeout while the archive
    #   is generated on the remote server)
    # - Local (cached) version of the files can be checked for each contrast
    opts = {'uncompress': True}
    subject_ids = ["S%02d" % s for s in range(1, n_subjects + 1)]
    subject_id_max = subject_ids[-1]
    data_types = ["c map"]
    if get_tmaps:
        data_types.append("t map")
    rql_types = str.join(", ", ["\"%s\"" % x for x in data_types])
    root_url = "http://brainomics.cea.fr/localizer/"

    base_query = ("Any X,XT,XL,XI,XF,XD WHERE X is Scan, X type XT, "
                  "X concerns S, "
                  "X label XL, X identifier XI, "
                  "X format XF, X description XD, "
                  'S identifier <= "%s", ' % (subject_id_max, ) +
                  'X type IN(%(types)s), X label "%(label)s"')

    urls = ["%sbrainomics_data_%d.zip?rql=%s&vid=data-zip"
            % (root_url, i,
               _urllib.parse.quote(base_query % {"types": rql_types,
                                          "label": c},
                            safe=',()'))
            for c, i in zip(contrasts_wrapped, contrasts_indices)]
    filenames = []
    for subject_id in subject_ids:
        for data_type in data_types:
            for contrast_id, contrast in enumerate(contrasts_wrapped):
                name_aux = str.replace(
                    str.join('_', [data_type, contrast]), ' ', '_')
                file_path = os.path.join(
                    "brainomics_data", subject_id, "%s.nii.gz" % name_aux)
                file_tarball_url = urls[contrast_id]
                filenames.append((file_path, file_tarball_url, opts))
    # Fetch masks if asked by user
    if get_masks:
        urls.append("%sbrainomics_data_masks.zip?rql=%s&vid=data-zip"
                    % (root_url,
                       _urllib.parse.quote(base_query % {"types": '"boolean mask"',
                                                  "label": "mask"},
                                    safe=',()')))
        for subject_id in subject_ids:
            file_path = os.path.join(
                "brainomics_data", subject_id, "boolean_mask_mask.nii.gz")
            file_tarball_url = urls[-1]
            filenames.append((file_path, file_tarball_url, opts))
    # Fetch anats if asked by user
    if get_anats:
        urls.append("%sbrainomics_data_anats.zip?rql=%s&vid=data-zip"
                    % (root_url,
                       _urllib.parse.quote(base_query % {"types": '"normalized T1"',
                                                  "label": "anatomy"},
                                    safe=',()')))
        for subject_id in subject_ids:
            file_path = os.path.join(
                "brainomics_data", subject_id,
                "normalized_T1_anat_defaced.nii.gz")
            file_tarball_url = urls[-1]
            filenames.append((file_path, file_tarball_url, opts))
    # Fetch subject characteristics (separated in two files)
    if url is None:
        url_csv = ("%sdataset/cubicwebexport.csv?rql=%s&vid=csvexport"
                   % (root_url, _urllib.parse.quote("Any X WHERE X is Subject")))
        url_csv2 = ("%sdataset/cubicwebexport2.csv?rql=%s&vid=csvexport"
                    % (root_url,
                       _urllib.parse.quote("Any X,XI,XD WHERE X is QuestionnaireRun, "
                                    "X identifier XI, X datetime "
                                    "XD", safe=',')
                       ))
    else:
        url_csv = "%s/cubicwebexport.csv" % url
        url_csv2 = "%s/cubicwebexport2.csv" % url
    filenames += [("cubicwebexport.csv", url_csv, {}),
                  ("cubicwebexport2.csv", url_csv2, {})]

    # Actual data fetching
    dataset_name = 'brainomics_localizer'
    data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir,
                                verbose=verbose)
    fdescr = _get_dataset_descr(dataset_name)
    files = _fetch_files(data_dir, filenames, verbose=verbose)
    anats = None
    masks = None
    tmaps = None
    # combine data from both covariates files into one single recarray
    from numpy.lib.recfunctions import join_by
    ext_vars_file2 = files[-1]
    csv_data2 = np.recfromcsv(ext_vars_file2, delimiter=';')
    files = files[:-1]
    ext_vars_file = files[-1]
    csv_data = np.recfromcsv(ext_vars_file, delimiter=';')
    files = files[:-1]
    # join_by sorts the output along the key
    csv_data = join_by('subject_id', csv_data, csv_data2,
                       usemask=False, asrecarray=True)[:n_subjects]
    if get_anats:
        anats = files[-n_subjects:]
        files = files[:-n_subjects]
    if get_masks:
        masks = files[-n_subjects:]
        files = files[:-n_subjects]
    if get_tmaps:
        tmaps = files[1::2]
        files = files[::2]
    return Bunch(cmaps=files, tmaps=tmaps, masks=masks, anats=anats,
                 ext_vars=csv_data, description=fdescr)


def fetch_localizer_calculation_task(n_subjects=None, data_dir=None, url=None,
                                     verbose=1):
    """Fetch calculation task contrast maps from the localizer.

    This function is only a caller for the fetch_localizer_contrasts in order
    to simplify examples reading and understanding.
    The 'calculation (auditory and visual cue)' contrast is used.

    Parameters
    ----------
    n_subjects: int, optional
        The number of subjects to load. If None is given,
        all 94 subjects are used.

    data_dir: string, optional
        Path of the data directory. Used to force data storage in a specified
        location.

    url: string, optional
        Override download URL. Used for test only (or if you setup a mirror of
        the data).

    verbose: int, optional
        verbosity level (0 means no message).

    Returns
    -------
    data: Bunch
        Dictionary-like object, the interest attributes are :
        'cmaps': string list, giving paths to nifti contrast maps

    """
    data = fetch_localizer_contrasts(["calculation (auditory and visual cue)"],
                                     n_subjects=n_subjects,
                                     get_tmaps=False, get_masks=False,
                                     get_anats=False, data_dir=data_dir,
                                     url=url, resume=True, verbose=verbose)
    data.pop('tmaps')
    data.pop('masks')
    data.pop('anats')
    return data


def fetch_abide_pcp(data_dir=None, n_subjects=None, pipeline='cpac',
                    band_pass_filtering=False, global_signal_regression=False,
                    derivatives=['func_preproc'],
                    quality_checked=True, url=None, verbose=1, **kwargs):
    """ Fetch ABIDE dataset

    Fetch the Autism Brain Imaging Data Exchange (ABIDE) dataset wrt criteria
    that can be passed as parameter. Note that this is the preprocessed
    version of ABIDE provided by the preprocess connectome projects (PCP).

    Parameters
    ----------

    data_dir: string, optional
        Path of the data directory. Used to force data storage in a specified
        location. Default: None

    n_subjects: int, optional
        The number of subjects to load. If None is given,
        all 94 subjects are used.

    pipeline: string, optional
        Possible pipelines are "ccs", "cpac", "dparsf" and "niak"

    band_pass_filtering: boolean, optional
        Due to controversies in the literature, band pass filtering is
        optional. If true, signal is band filtered between 0.01Hz and 0.1Hz.

    global_signal_regression: boolean optional
        Indicates if global signal regression should be applied on the
        signals.

    derivatives: string list, optional
        Types of downloaded files. Possible values are: alff, degree_binarize,
        degree_weighted, dual_regression, eigenvector_binarize,
        eigenvector_weighted, falff, func_mask, func_mean, func_preproc, lfcd,
        reho, rois_aal, rois_cc200, rois_cc400, rois_dosenbach160, rois_ez,
        rois_ho, rois_tt, and vmhc. Please refer to the PCP site for more
        details.

    quality_checked: boolean, optional
        if true (default), restrict the list of the subjects to the one that
        passed quality assessment for all raters.

    kwargs: parameter list, optional
        Any extra keyword argument will be used to filter downloaded subjects
        according to the CSV phenotypic file. Some examples of filters are
        indicated below.

    SUB_ID: list of integers in [50001, 50607], optional
        Ids of the subjects to be loaded.

    DX_GROUP: integer in {1, 2}, optional
        1 is autism, 2 is control

    DSM_IV_TR: integer in [0, 4], optional
        O is control, 1 is autism, 2 is Asperger, 3 is PPD-NOS,
        4 is Asperger or PPD-NOS

    AGE_AT_SCAN: float in [6.47, 64], optional
        Age of the subject

    SEX: integer in {1, 2}, optional
        1 is male, 2 is female

    HANDEDNESS_CATEGORY: string in {'R', 'L', 'Mixed', 'Ambi'}, optional
        R = Right, L = Left, Ambi = Ambidextrous

    HANDEDNESS_SCORE: integer in [-100, 100], optional
        Positive = Right, Negative = Left, 0 = Ambidextrous

    Notes
    -----
    Code and description of preprocessing pipelines are provided on the
    `PCP website <http://preprocessed-connectomes-project.github.io/>`.

    References
    ----------
    Nielsen, Jared A., et al. "Multisite functional connectivity MRI
    classification of autism: ABIDE results." Frontiers in human neuroscience
    7 (2013).
    """
    # People keep getting it wrong and submiting a string instead of a
    # list of strings. We'll make their life easy
    if isinstance(derivatives, _basestring):
        derivatives = [derivatives, ]

    # Parameter check
    for derivative in derivatives:
        if derivative not in [
                'alff', 'degree_binarize', 'degree_weighted',
                'dual_regression', 'eigenvector_binarize',
                'eigenvector_weighted', 'falff', 'func_mask', 'func_mean',
                'func_preproc', 'lfcd', 'reho', 'rois_aal', 'rois_cc200',
                'rois_cc400', 'rois_dosenbach160', 'rois_ez', 'rois_ho',
                'rois_tt', 'vmhc']:
            raise KeyError('%s is not a valid derivative' % derivative)

    strategy = ''
    if not band_pass_filtering:
        strategy += 'no'
    strategy += 'filt_'
    if not global_signal_regression:
        strategy += 'no'
    strategy += 'global'

    # General file: phenotypic information
    dataset_name = 'ABIDE_pcp'
    data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir,
                                verbose=verbose)
    if url is None:
        url = ('https://s3.amazonaws.com/fcp-indi/data/Projects/'
               'ABIDE_Initiative')

    if quality_checked:
        kwargs['qc_rater_1'] = 'OK'
        kwargs['qc_anat_rater_2'] = ['OK', 'maybe']
        kwargs['qc_func_rater_2'] = ['OK', 'maybe']
        kwargs['qc_anat_rater_3'] = 'OK'
        kwargs['qc_func_rater_3'] = 'OK'

    # Fetch the phenotypic file and load it
    csv = 'Phenotypic_V1_0b_preprocessed1.csv'
    path_csv = _fetch_files(data_dir, [(csv, url + '/' + csv, {})],
                            verbose=verbose)[0]

    # Note: the phenotypic file contains string that contains comma which mess
    # up numpy array csv loading. This is why I do a pass to remove the last
    # field. This can be
    # done simply with pandas but we don't want such dependency ATM
    # pheno = pandas.read_csv(path_csv).to_records()
    with open(path_csv, 'r') as pheno_f:
        pheno = ['i' + pheno_f.readline()]

        # This regexp replaces commas between double quotes
        for line in pheno_f:
            pheno.append(re.sub(r',(?=[^"]*"(?:[^"]*"[^"]*")*[^"]*$)', ";", line))

    # bytes (encode()) needed for python 2/3 compat with numpy
    pheno = '\n'.join(pheno).encode()
    pheno = BytesIO(pheno)
    pheno = np.recfromcsv(pheno, comments='$', case_sensitive=True)

    # First, filter subjects with no filename
    pheno = pheno[pheno['FILE_ID'] != b'no_filename']
    # Apply user defined filters
    user_filter = _filter_columns(pheno, kwargs)
    pheno = pheno[user_filter]

    # Go into specific data folder and url
    data_dir = os.path.join(data_dir, pipeline, strategy)
    url = '/'.join([url, 'Outputs', pipeline, strategy])

    # Get the files
    results = {}
    file_ids = [file_id.decode() for file_id in pheno['FILE_ID']]
    if n_subjects is not None:
        file_ids = file_ids[:n_subjects]
        pheno = pheno[:n_subjects]

    results['description'] = _get_dataset_descr(dataset_name)
    results['phenotypic'] = pheno
    for derivative in derivatives:
        ext = '.1D' if derivative.startswith('rois') else '.nii.gz'
        files = [(file_id + '_' + derivative + ext,
                  '/'.join([url, derivative, file_id + '_' + derivative + ext]),
                  {}) for file_id in file_ids]
        files = _fetch_files(data_dir, files, verbose=verbose)
        # Load derivatives if needed
        if ext == '.1D':
            files = [np.loadtxt(f) for f in files]
        results[derivative] = files
    return Bunch(**results)


def _load_mixed_gambles(zmap_imgs):
    """Ravel zmaps (one per subject) along time axis, resulting,
    in a n_subjects * n_trials 3D niimgs and, and then make
    gain vector y of same length.
    """
    X = []
    y = []
    mask = []
    for zmap_img in zmap_imgs:
        # load subject data
        this_X = zmap_img.get_data()
        affine = zmap_img.get_affine()
        finite_mask = np.all(np.isfinite(this_X), axis=-1)
        this_mask = np.logical_and(np.all(this_X != 0, axis=-1),
                                   finite_mask)
        this_y = np.array([np.arange(1, 9)] * 6).ravel()

        # gain levels
        if len(this_y) != this_X.shape[-1]:
            raise RuntimeError("%s: Expecting %i volumes, got %i!" % (
                zmap_img, len(this_y), this_X.shape[-1]))

        # standardize subject data
        this_X -= this_X.mean(axis=-1)[..., np.newaxis]
        std = this_X.std(axis=-1)
        std[std == 0] = 1
        this_X /= std[..., np.newaxis]

        # commit subject data
        X.append(this_X)
        y.extend(this_y)
        mask.append(this_mask)
    y = np.array(y)
    X = np.concatenate(X, axis=-1)
    mask = np.sum(mask, axis=0) > .5 * len(zmap_imgs)
    mask = np.logical_and(mask, np.all(np.isfinite(X), axis=-1))
    X = X[mask, :].T
    tmp = np.zeros(list(mask.shape) + [len(X)])
    tmp[mask, :] = X.T
    mask_img = nibabel.Nifti1Image(mask.astype(np.int), affine)
    X = nibabel.four_to_three(nibabel.Nifti1Image(tmp, affine))
    return X, y, mask_img


def fetch_mixed_gambles(n_subjects=1, data_dir=None, url=None, resume=True,
                        return_raw_data=False, verbose=0):
    """Fetch Jimura "mixed gambles" dataset.

    Parameters
    ----------
    n_subjects: int, optional (default 1)
        The number of subjects to load. If None is given, all the
        subjects are used.

    data_dir: string, optional (default None)
        Path of the data directory. Used to force data storage in a specified
        location. Default: None.

    url: string, optional (default None)
        Override download URL. Used for test only (or if you setup a mirror of
        the data).

    resume: bool, optional (default True)
        If true, try resuming download if possible.

    verbose: int, optional (default 0)
        Defines the level of verbosity of the output.

    return_raw_data: bool, optional (default True)
        If false, then the data will transformed into and (X, y) pair, suitable
        for machine learning routines. X is a list of n_subjects * 48
        Nifti1Image objects (where 48 is the number of trials),
        and y is an array of shape (n_subjects * 48,).

    smooth: float, or list of 3 floats, optional (default 0.)
        Size of smoothing kernel to apply to the loaded zmaps.

    Returns
    -------
    data: Bunch
        Dictionary-like object, the interest attributes are :
        'zmaps': string list
            Paths to realigned gain betamaps (one nifti per subject).
        'gain': ..
            If make_Xy is true, this is a list of n_subjects * 48
            Nifti1Image objects, else it is None.
        'y': array of shape (n_subjects * 48,) or None
            If make_Xy is true, then this is an array of shape
            (n_subjects * 48,), else it is None.

    References
    ----------
    [1] K. Jimura and R. Poldrack, "Analyses of regional-average activation
        and multivoxel pattern information tell complementary stories",
        Neuropsychologia, vol. 50, page 544, 2012
    """
    if n_subjects > 16:
        warnings.warn('Warning: there are only 16 subjects!')
        n_subjects = 16
    if url is None:
        url = ("https://www.nitrc.org/frs/download.php/7229/"
               "jimura_poldrack_2012_zmaps.zip")
    opts = dict(uncompress=True)
    files = [("zmaps%ssub%03i_zmaps.nii.gz" % (os.sep, (j + 1)), url, opts)
             for j in range(n_subjects)]
    data_dir = _get_dataset_dir('jimura_poldrack_2012_zmaps',
                                data_dir=data_dir)
    zmap_fnames = _fetch_files(data_dir, files, resume=resume, verbose=verbose)
    data = Bunch(zmaps=zmap_fnames)
    if not return_raw_data:
        X, y, mask_img = _load_mixed_gambles(map(nibabel.load, data.zmaps))
        data.zmaps, data.gain, data.mask_img = X, y, mask_img
    return data


def fetch_megatrawls_netmats(dimensionality=100, timeseries='eigen_regression',
                             matrices='partial_correlation', data_dir=None,
                             resume=True, verbose=1):
    """Downloads and returns Network Matrices data from MegaTrawls release in HCP.

    This data can be used to predict relationships between imaging data and
    non-imaging behavioural measures such as age, sex, education, etc.
    The network matrices are estimated from functional connectivity
    datasets of 461 subjects. Full technical details in [1] [2].

    .. versionadded:: 0.2.2

    Parameters
    ----------
    dimensionality: int, optional
        Valid inputs are 25, 50, 100, 200, 300. By default, network matrices
        estimated using Group ICA brain parcellations of 100 components/dimensions
        will be returned.

    timeseries: str, optional
        Valid inputs are 'multiple_spatial_regression' or 'eigen_regression'. By
        default 'eigen_regression', matrices estimated using first principal
        eigen component timeseries signals extracted from each subject data
        parcellations will be returned. Otherwise, 'multiple_spatial_regression'
        matrices estimated using spatial regressor based timeseries signals
        extracted from each subject data parcellations will be returned.

    matrices: str, optional
        Valid inputs are 'full_correlation' or 'partial_correlation'. By default,
        partial correlation matrices will be returned otherwise if selected
        full correlation matrices will be returned.

    data_dir: str, default is None, optional
        Path of the data directory. Used to force data storage in a specified
        location.

    resume: bool, default is True
        This parameter is required if a partially downloaded file is needed
        to be resumed to download again.

    verbose: int, default is 1
        This parameter is used to set the verbosity level to print the message
        to give information about the processing.
        0 indicates no information will be given.

    Returns
    -------
    data: Bunch
        dictionary-like object, the attributes are :

        - 'dimensions': int, consists of given input in dimensions.

        - 'timeseries': str, consists of given input in timeseries method.

        - 'matrices': str, consists of given type of specific matrices.

        - 'correlation_matrices': ndarray, consists of correlation matrices
          based on given type of matrices. Array size will depend on given
          dimensions (n, n).
        - 'description': data description

    References
    ----------
    [1] Stephen Smith et al, HCP beta-release of the Functional Connectivity
        MegaTrawl.
        April 2015 "HCP500-MegaTrawl" release.
        https://db.humanconnectome.org/megatrawl/

    [2] Smith, S.M. et al. Nat. Neurosci. 18, 1565-1567 (2015).

    [3] N.Filippini, et al. Distinct patterns of brain activity in young
        carriers of the APOE-e4 allele.
        Proc Natl Acad Sci USA (PNAS), 106::7209-7214, 2009.

    [4] S.Smith, et al. Methods for network modelling from high quality rfMRI data.
        Meeting of the Organization for Human Brain Mapping. 2014

    [5] J.X. O'Reilly et al. Distinct and overlapping functional zones in the
        cerebellum defined by resting state functional connectivity.
        Cerebral Cortex, 2009.

    Note: See description for terms & conditions on data usage.

    """
    url = "http://www.nitrc.org/frs/download.php/8037/Megatrawls.tgz"
    opts = {'uncompress': True}

    error_message = "Invalid {0} input is provided: {1}, choose one of them {2}"
    # standard dataset terms
    dimensionalities = [25, 50, 100, 200, 300]
    if dimensionality not in dimensionalities:
        raise ValueError(error_message.format('dimensionality', dimensionality,
                                              dimensionalities))
    timeseries_methods = ['multiple_spatial_regression', 'eigen_regression']
    if timeseries not in timeseries_methods:
        raise ValueError(error_message.format('timeseries', timeseries,
                                              timeseries_methods))
    output_matrices_names = ['full_correlation', 'partial_correlation']
    if matrices not in output_matrices_names:
        raise ValueError(error_message.format('matrices', matrices,
                                              output_matrices_names))

    dataset_name = 'Megatrawls'
    data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir, verbose=verbose)
    description = _get_dataset_descr(dataset_name)

    timeseries_map = dict(multiple_spatial_regression='ts2', eigen_regression='ts3')
    matrices_map = dict(full_correlation='Znet1.txt', partial_correlation='Znet2.txt')
    filepath = [(os.path.join(
        '3T_Q1-Q6related468_MSMsulc_d%d_%s' % (dimensionality, timeseries_map[timeseries]),
        matrices_map[matrices]), url, opts)]

    # Fetch all the files
    files = _fetch_files(data_dir, filepath, resume=resume, verbose=verbose)

    # Load the files into arrays
    correlation_matrices = csv_to_array(files[0])

    return Bunch(
        dimensions=dimensionality,
        timeseries=timeseries,
        matrices=matrices,
        correlation_matrices=correlation_matrices,
        description=description)
