# Source code for dipy.data.fetcher

    import contextlib
    from hashlib import md5
    import json
    import logging
    import os
    import os.path as op
    from os.path import join as pjoin
    import random
    from shutil import copyfileobj
    import tarfile
    import tempfile
    from urllib.request import Request, urlopen
    import zipfile

    import nibabel as nib
    import numpy as np
    from tqdm.auto import tqdm

    from dipy.core.gradients import (
        gradient_table,
        gradient_table_from_gradient_strength_bvecs,
    )
    from dipy.io.gradients import read_bvals_bvecs
    from dipy.io.image import load_nifti, load_nifti_data, save_nifti
    from dipy.io.streamline import load_trk
    from dipy.testing.decorators import warning_for_keywords
    from dipy.utils.optpkg import TripWire, optional_package

    # Set a user-writeable file-system location to put files:
    if "DIPY_HOME" in os.environ:
        dipy_home = os.environ["DIPY_HOME"]
    else:
        dipy_home = pjoin(op.expanduser("~"), ".dipy")

    # The URL to the University of Washington Researchworks repository:
    UW_RW_URL = "https://digital.lib.washington.edu/researchworks/bitstream/handle/"

    boto3, has_boto3, _ = optional_package("boto3")

    HEADER_LIST = [
        {"User-Agent": "Mozilla/5.0 (Windows NT 6.1; Win64; x64)"},
        # Firefox 77 Mac
        {
            "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:77.0) Gecko/20100101 Firefox/77.0",  # noqa
            "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8",  # noqa
            "Accept-Language": "en-US,en;q=0.5",
            "Referer": "https://www.google.com/",
            "DNT": "1",
            "Connection": "keep-alive",
            "Upgrade-Insecure-Requests": "1",
        },
        # Firefox 77 Windows
        {
            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:77.0) Gecko/20100101 Firefox/77.0",  # noqa
            "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8",  # noqa
            "Accept-Language": "en-US,en;q=0.5",
            "Accept-Encoding": "gzip, deflate, br",
            "Referer": "https://www.google.com/",
            "DNT": "1",
            "Connection": "keep-alive",
            "Upgrade-Insecure-Requests": "1",
        },
        # Chrome 83 Mac
        {
            "Connection": "keep-alive",
            "DNT": "1",
            "Upgrade-Insecure-Requests": "1",
            "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36",  # noqa
            "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9",  # noqa
            "Sec-Fetch-Site": "none",
            "Sec-Fetch-Mode": "navigate",
            "Sec-Fetch-Dest": "document",
            "Referer": "https://www.google.com/",
            "Accept-Encoding": "gzip, deflate, br",
            "Accept-Language": "en-GB,en-US;q=0.9,en;q=0.8",
        },
        # Chrome 83 Windows
        {
            "Connection": "keep-alive",
            "Upgrade-Insecure-Requests": "1",
            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36",  # noqa
            "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9",  # noqa
            "Sec-Fetch-Site": "same-origin",
            "Sec-Fetch-Mode": "navigate",
            "Sec-Fetch-User": "?1",
            "Sec-Fetch-Dest": "document",
            "Referer": "https://www.google.com/",
            "Accept-Encoding": "gzip, deflate, br",
            "Accept-Language": "en-US,en;q=0.9",
        },
    ]

    class FetcherError(Exception):
        pass

    def _log(msg):
        """Helper function used as short hand for logging."""
        logger = logging.getLogger(__name__)
        logger.info(msg)

    @warning_for_keywords()
    def copyfileobj_withprogress(fsrc, fdst, total_length, *, length=16 * 1024):
        for _ in tqdm(
            range(0, int(total_length), length),
            unit=" MB",
            bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt}{unit} [{elapsed}]",
        ):
            buf = fsrc.read(length)
            if not buf:
                break
            fdst.write(buf)

    def _already_there_msg(folder):
        """
        Prints a message indicating that a certain data-set is already in place
        """
        msg = "Dataset is already in place. If you want to fetch it again "
        msg += f"please first remove the folder {folder} "
        _log(msg)

    def _get_file_md5(filename):
        """Compute the md5 checksum of a file"""
        md5_data = md5()
        with open(filename, "rb") as f:
            for chunk in iter(lambda: f.read(128 * md5_data.block_size), b""):
                md5_data.update(chunk)
        return md5_data.hexdigest()

    @warning_for_keywords()
    def check_md5(filename, *, stored_md5=None):
        """
        Computes the md5 of filename and check if it matches with the supplied
        string md5

        Parameters
        ----------
        filename : string
            Path to a file.
        md5 : string
            Known md5 of filename to check against. If None (default), checking is
            skipped
        """
        if stored_md5 is not None:
            computed_md5 = _get_file_md5(filename)
            if stored_md5 != computed_md5:
                msg = f"""The downloaded file, {filename}, does not have the expected md5
       checksum of "{stored_md5}". Instead, the md5 checksum was: "{computed_md5}". This
       could mean that something is wrong with the file or that the upstream file has been
       updated. You can try downloading the file again or updating to the newest version of
       dipy."""
                raise FetcherError(msg)

    def _get_file_data(fname, url, *, use_headers=False):
        """Get data from url and write it to file.

        Parameters
        ----------
        fname : str
            The filename to write the data to.
        url : str
            The URL to get the data from.
        use_headers : bool, optional
            Whether to use headers when downloading files.

        """
        req = url
        if use_headers:
            hdr = random.choice(HEADER_LIST)
            req = Request(url, headers=hdr)
        with contextlib.closing(urlopen(req)) as opener:
            try:
                response_size = opener.headers["content-length"]
            except KeyError:
                response_size = None

            with open(fname, "wb") as data:
                if response_size is None:
                    copyfileobj(opener, data)
                else:
                    copyfileobj_withprogress(opener, data, response_size)

    @warning_for_keywords()
    def fetch_data(files, folder, *, data_size=None, use_headers=False):
        """Downloads files to folder and checks their md5 checksums

        Parameters
        ----------
        files : dictionary
            For each file in `files` the value should be (url, md5). The file will
            be downloaded from url if the file does not already exist or if the
            file exists but the md5 checksum does not match.
        folder : str
            The directory where to save the file, the directory will be created if
            it does not already exist.
        data_size : str, optional
            A string describing the size of the data (e.g. "91 MB") to be logged to
            the screen. Default does not produce any information about data size.
        use_headers : bool, optional
            Whether to use headers when downloading files.

        Raises
        ------
        FetcherError
            Raises if the md5 checksum of the file does not match the expected
            value. The downloaded file is not deleted when this error is raised.

        """
        if not op.exists(folder):
            _log(f"Creating new folder {folder}")
            os.makedirs(folder)

        if data_size is not None:
            _log(f"Data size is approximately {data_size}")

        all_skip = True
        for f in files:
            url, md5 = files[f]
            fullpath = pjoin(folder, f)
            if op.exists(fullpath) and (_get_file_md5(fullpath) == md5):
                continue
            all_skip = False
            _log(f'Downloading "{f}" to {folder}')
            _log(f"From: {url}")
            _get_file_data(fullpath, url, use_headers=use_headers)
            check_md5(fullpath, stored_md5=md5)
        if all_skip:
            _already_there_msg(folder)
        else:
            _log(f"Files successfully downloaded to {folder}")

    @warning_for_keywords()
    def _make_fetcher(
        name,
        folder,
        baseurl,
        remote_fnames,
        local_fnames,
        *,
        md5_list=None,
        optional_fnames=None,
        doc="",
        data_size=None,
        msg=None,
        unzip=False,
        use_headers=False,
    ):
        """Create a new fetcher

        Parameters
        ----------
        name : str
            The name of the fetcher function.
        folder : str
            The full path to the folder in which the files would be placed locally.
            Typically, this is something like 'pjoin(dipy_home, 'foo')'
        baseurl : str
            The URL from which this fetcher reads files
        remote_fnames : list of strings
            The names of the files in the baseurl location
        local_fnames : list of strings
            The names of the files to be saved on the local filesystem
        md5_list : list of strings, optional
            The md5 checksums of the files. Used to verify the content of the
            files. Default: None, skipping checking md5.
        optional_fnames : str, optional
            The name of the optional file to be saved on the local filesystem.
        doc : str, optional.
            Documentation of the fetcher.
        data_size : str, optional.
            If provided, is sent as a message to the user before downloading
            starts.
        msg : str, optional.
            A message to print to screen when fetching takes place. Default (None)
            is to print nothing
        unzip : bool, optional
            Whether to unzip the file(s) after downloading them. Supports zip, gz,
            and tar.gz files.
        use_headers : bool, optional
            Whether to use headers when downloading files.

        returns
        -------
        fetcher : function
            A function that, when called, fetches data according to the designated
            inputs
        """
        optional_fnames = optional_fnames or []

        def fetcher(*, include_optional=False):
            files = {}
            for (
                i,
                (f, n),
            ) in enumerate(zip(remote_fnames, local_fnames)):
                if n in optional_fnames and not include_optional:
                    continue
                files[n] = (baseurl + f, md5_list[i] if md5_list is not None else None)

            fetch_data(files, folder, data_size=data_size, use_headers=use_headers)

            if msg is not None:
                _log(msg)
            if unzip:
                for f in local_fnames:
                    split_ext = op.splitext(f)
                    if split_ext[-1] in (".gz", ".bz2"):
                        if op.splitext(split_ext[0])[-1] == ".tar":
                            ar = tarfile.open(pjoin(folder, f))
                            ar.extractall(path=folder)
                            ar.close()
                        else:
                            raise ValueError("File extension is not recognized")
                    elif split_ext[-1] == ".zip":
                        z = zipfile.ZipFile(pjoin(folder, f), "r")
                        files[f] += (tuple(z.namelist()),)
                        z.extractall(folder)
                        z.close()
                    else:
                        raise ValueError("File extension is not recognized")

            return files, folder

        fetcher.__name__ = name
        fetcher.__doc__ = doc
        return fetcher

    fetch_isbi2013_2shell = _make_fetcher(
        "fetch_isbi2013_2shell",
        pjoin(dipy_home, "isbi2013"),
        UW_RW_URL + "1773/38465/",
        ["phantom64.nii.gz", "phantom64.bval", "phantom64.bvec"],
        ["phantom64.nii.gz", "phantom64.bval", "phantom64.bvec"],
        md5_list=[
            "42911a70f232321cf246315192d69c42",
            "90e8cf66e0f4d9737a3b3c0da24df5ea",
            "4b7aa2757a1ccab140667b76e8075cb1",
        ],
        doc="Download a 2-shell software phantom dataset",
        data_size="",
    )

    fetch_stanford_labels = _make_fetcher(
        "fetch_stanford_labels",
        pjoin(dipy_home, "stanford_hardi"),
        "https://stacks.stanford.edu/file/druid:yx282xq2090/",
        ["aparc-reduced.nii.gz", "label_info.txt"],
        ["aparc-reduced.nii.gz", "label_info.txt"],
        md5_list=["742de90090d06e687ce486f680f6d71a", "39db9f0f5e173d7a2c2e51b07d5d711b"],
        doc="Download reduced freesurfer aparc image from stanford web site",
    )

    fetch_sherbrooke_3shell = _make_fetcher(
        "fetch_sherbrooke_3shell",
        pjoin(dipy_home, "sherbrooke_3shell"),
        UW_RW_URL + "1773/38475/",
        ["HARDI193.nii.gz", "HARDI193.bval", "HARDI193.bvec"],
        ["HARDI193.nii.gz", "HARDI193.bval", "HARDI193.bvec"],
        md5_list=[
            "0b735e8f16695a37bfbd66aab136eb66",
            "e9b9bb56252503ea49d31fb30a0ac637",
            "0c83f7e8b917cd677ad58a078658ebb7",
        ],
        doc="Download a 3shell HARDI dataset with 192 gradient direction",
    )

    fetch_stanford_hardi = _make_fetcher(
        "fetch_stanford_hardi",
        pjoin(dipy_home, "stanford_hardi"),
        "https://stacks.stanford.edu/file/druid:yx282xq2090/",
        ["dwi.nii.gz", "dwi.bvals", "dwi.bvecs"],
        ["HARDI150.nii.gz", "HARDI150.bval", "HARDI150.bvec"],
        md5_list=[
            "0b18513b46132b4d1051ed3364f2acbc",
            "4e08ee9e2b1d2ec3fddb68c70ae23c36",
            "4c63a586f29afc6a48a5809524a76cb4",
        ],
        doc="Download a HARDI dataset with 160 gradient directions",
    )

    fetch_resdnn_tf_weights = _make_fetcher(
        "fetch_resdnn_tf_weights",
        pjoin(dipy_home, "histo_resdnn_weights"),
        "https://ndownloader.figshare.com/files/",
        ["22736240"],
        ["resdnn_weights_mri_2018.h5"],
        md5_list=["f0e118d72ab804a464494bd9015227f4"],
        doc="Download ResDNN Tensorflow model weights for Nath et. al 2018",
    )

    fetch_resdnn_torch_weights = _make_fetcher(
        "fetch_resdnn_torch_weights",
        pjoin(dipy_home, "histo_resdnn_weights"),
        "https://ndownloader.figshare.com/files/",
        ["50019429"],
        ["histo_weights.pth"],
        md5_list=["ca13692bbbaea725ff8b5df2d3a2779a"],
        doc="Download ResDNN Pytorch model weights for Nath et. al 2018",
    )

    fetch_synb0_weights = _make_fetcher(
        "fetch_synb0_weights",
        pjoin(dipy_home, "synb0"),
        "https://ndownloader.figshare.com/files/",
        ["36379914", "36379917", "36379920", "36379923", "36379926"],
        [
            "synb0_default_weights1.h5",
            "synb0_default_weights2.h5",
            "synb0_default_weights3.h5",
            "synb0_default_weights4.h5",
            "synb0_default_weights5.h5",
        ],
        md5_list=[
            "a9362c75bc28616167a11a42fe5d004e",
            "9dc9353d6ff741d8e22b8569f157c56e",
            "e548f341e4f12d63dfbed306233fddce",
            "8cb7a3492d08e4c9b8938277d6fd9b75",
            "5e796f892605b3bdb9cb9678f1c6ac11",
        ],
        doc="Download Synb0 model weights for Schilling et. al 2019",
    )

    fetch_synb0_test = _make_fetcher(
        "fetch_synb0_test",
        pjoin(dipy_home, "synb0"),
        "https://ndownloader.figshare.com/files/",
        ["36379911", "36671850"],
        ["test_input_synb0.npz", "test_output_synb0.npz"],
        md5_list=["987203aa73de2dac8770f39ed506dc0c", "515544fbcafd9769785502821b47b661"],
        doc="Download Synb0 test data for Schilling et. al 2019",
    )

    fetch_deepn4_tf_weights = _make_fetcher(
        "fetch_deepn4_tf_weights",
        pjoin(dipy_home, "deepn4"),
        "https://ndownloader.figshare.com/files/",
        ["44673313"],
        ["model_weights.h5"],
        md5_list=["ef264edd554177a180cf99162dbd2745"],
        doc="Download DeepN4 model weights for Kanakaraj et. al 2024",
    )

    fetch_deepn4_torch_weights = _make_fetcher(
        "fetch_deepn4_torch_weights",
        pjoin(dipy_home, "deepn4"),
        "https://ndownloader.figshare.com/files/",
        ["52285805"],
        ["deepn4_torch_weights"],
        md5_list=["97c5a5f8356a3d0eeca1c6bb7949c8b8"],
        doc="Download DeepN4 model weights for Kanakaraj et. al 2024",
    )

    fetch_deepn4_test = _make_fetcher(
        "fetch_deepn4_test",
        pjoin(dipy_home, "deepn4"),
        "https://ndownloader.figshare.com/files/",
        ["48842938", "52454531"],
        ["test_input_deepn4.npz", "new_test_output_deepn4.npz"],
        md5_list=["07aa7cc7c7f839683a0aad5bb853605b", "6da15c4358fd13c99773eedeb93953c7"],
        doc="Download DeepN4 test data for Kanakaraj et. al 2024",
    )

    fetch_evac_tf_weights = _make_fetcher(
        "fetch_evac_tf_weights",
        pjoin(dipy_home, "evac"),
        "https://ndownloader.figshare.com/files/",
        ["43037191"],
        ["evac_default_weights.h5"],
        md5_list=["491cfa4f9a2860fad6c19f2b71b918e1"],
        doc="Download EVAC+ model weights for Park et. al 2022",
    )

    fetch_evac_torch_weights = _make_fetcher(
        "fetch_evac_torch_weights",
        pjoin(dipy_home, "evac"),
        "https://ndownloader.figshare.com/files/",
        ["50019432"],
        ["evac_weights.pth"],
        md5_list=["b2bab512a0f899f089d9dd9ffc44f65b"],
        doc="Download EVAC+ model weights for Park et. al 2022",
    )

    fetch_evac_test = _make_fetcher(
        "fetch_evac_test",
        pjoin(dipy_home, "evac"),
        "https://ndownloader.figshare.com/files/",
        ["48891958"],
        ["evac_test_data.npz"],
        md5_list=["072a0dd6d2cddf8a3697b6a772e06e29"],
        doc="Download EVAC+ test data for Park et. al 2022",
    )

    fetch_stanford_t1 = _make_fetcher(
        "fetch_stanford_t1",
        pjoin(dipy_home, "stanford_hardi"),
        "https://stacks.stanford.edu/file/druid:yx282xq2090/",
        ["t1.nii.gz"],
        ["t1.nii.gz"],
        md5_list=["a6a140da6a947d4131b2368752951b0a"],
    )

    fetch_stanford_pve_maps = _make_fetcher(
        "fetch_stanford_pve_maps",
        pjoin(dipy_home, "stanford_hardi"),
        "https://stacks.stanford.edu/file/druid:yx282xq2090/",
        ["pve_csf.nii.gz", "pve_gm.nii.gz", "pve_wm.nii.gz"],
        ["pve_csf.nii.gz", "pve_gm.nii.gz", "pve_wm.nii.gz"],
        md5_list=[
            "2c498e4fed32bca7f726e28aa86e9c18",
            "1654b20aeb35fc2734a0d7928b713874",
            "2e244983cf92aaf9f9d37bc7716b37d5",
        ],
    )

    fetch_stanford_tracks = _make_fetcher(
        "fetch_stanford_tracks",
        pjoin(dipy_home, "stanford_hardi"),
        "https://raw.githubusercontent.com/dipy/dipy_datatest/main/",
        [
            "hardi-lr-superiorfrontal.trk",
        ],
        [
            "hardi-lr-superiorfrontal.trk",
        ],
        md5_list=[
            "2d49aaf6ad6c10d8d069bfb319bf3541",
        ],
        doc="Download stanford track for examples",
        data_size="1.4MB",
    )

    fetch_taiwan_ntu_dsi = _make_fetcher(
        "fetch_taiwan_ntu_dsi",
        pjoin(dipy_home, "taiwan_ntu_dsi"),
        UW_RW_URL + "1773/38480/",
        ["DSI203.nii.gz", "DSI203.bval", "DSI203.bvec", "DSI203_license.txt"],
        ["DSI203.nii.gz", "DSI203.bval", "DSI203.bvec", "DSI203_license.txt"],
        md5_list=[
            "950408c0980a7154cb188666a885a91f",
            "602e5cb5fad2e7163e8025011d8a6755",
            "a95eb1be44748c20214dc7aa654f9e6b",
            "7fa1d5e272533e832cc7453eeba23f44",
        ],
        doc="Download a DSI dataset with 203 gradient directions",
        msg="See DSI203_license.txt for LICENSE. For the complete datasets"
        + " please visit https://dsi-studio.labsolver.org",
        data_size="91MB",
    )

    fetch_syn_data = _make_fetcher(
        "fetch_syn_data",
        pjoin(dipy_home, "syn_test"),
        UW_RW_URL + "1773/38476/",
        ["t1.nii.gz", "b0.nii.gz"],
        ["t1.nii.gz", "b0.nii.gz"],
        md5_list=["701bda02bb769655c7d4a9b1df2b73a6", "e4b741f0c77b6039e67abb2885c97a78"],
        data_size="12MB",
        doc="Download t1 and b0 volumes from the same session",
    )

    fetch_mni_template = _make_fetcher(
        "fetch_mni_template",
        pjoin(dipy_home, "mni_template"),
        "https://ndownloader.figshare.com/files/",
        [
            "5572676?private_link=4b8666116a0128560fb5",
            "5572673?private_link=93216e750d5a7e568bda",
            "5572670?private_link=33c92d54d1afb9aa7ed2",
            "5572661?private_link=584319b23e7343fed707",
        ],
        [
            "mni_icbm152_t2_tal_nlin_asym_09a.nii",
            "mni_icbm152_t1_tal_nlin_asym_09a.nii",
            "mni_icbm152_t1_tal_nlin_asym_09c_mask.nii",
            "mni_icbm152_t1_tal_nlin_asym_09c.nii",
        ],
        md5_list=[
            "f41f2e1516d880547fbf7d6a83884f0d",
            "1ea8f4f1e41bc17a94602e48141fdbc8",
            "a243e249cd01a23dc30f033b9656a786",
            "3d5dd9b0cd727a17ceec610b782f66c1",
        ],
        doc="fetch the MNI 2009a T1 and T2, and 2009c T1 and T1 mask files",
        data_size="70MB",
    )

    fetch_scil_b0 = _make_fetcher(
        "fetch_scil_b0",
        dipy_home,
        UW_RW_URL + "1773/38479/",
        ["datasets_multi-site_all_companies.zip"],
        ["datasets_multi-site_all_companies.zip"],
        md5_list=["e9810fa5bf21b99da786647994d7d5b7"],
        doc="Download b=0 datasets from multiple MR systems (GE, Philips, "
        + "Siemens) and different magnetic fields (1.5T and 3T)",
        data_size="9.2MB",
        unzip=True,
    )

    fetch_bundles_2_subjects = _make_fetcher(
        "fetch_bundles_2_subjects",
        pjoin(dipy_home, "exp_bundles_and_maps"),
        UW_RW_URL + "1773/38477/",
        ["bundles_2_subjects.tar.gz"],
        ["bundles_2_subjects.tar.gz"],
        md5_list=["97756fbef11ce2df31f1bedf1fc7aac7"],
        data_size="234MB",
        doc="Download 2 subjects from the SNAIL dataset with their bundles",
        unzip=True,
    )

    fetch_ivim = _make_fetcher(
        "fetch_ivim",
        pjoin(dipy_home, "ivim"),
        "https://ndownloader.figshare.com/files/",
        ["5305243", "5305246", "5305249"],
        ["ivim.nii.gz", "ivim.bval", "ivim.bvec"],
        md5_list=[
            "cda596f89dc2676af7d9bf1cabccf600",
            "f03d89f84aa9a9397103a400e43af43a",
            "fb633a06b02807355e49ccd85cb92565",
        ],
        doc="Download IVIM dataset",
    )

    fetch_cfin_multib = _make_fetcher(
        "fetch_cfin_multib",
        pjoin(dipy_home, "cfin_multib"),
        UW_RW_URL + "/1773/38488/",
        [
            "T1.nii",
            "__DTI_AX_ep2d_2_5_iso_33d_20141015095334_4.nii",
            "__DTI_AX_ep2d_2_5_iso_33d_20141015095334_4.bval",
            "__DTI_AX_ep2d_2_5_iso_33d_20141015095334_4.bvec",
        ],
        [
            "T1.nii",
            "__DTI_AX_ep2d_2_5_iso_33d_20141015095334_4.nii",
            "__DTI_AX_ep2d_2_5_iso_33d_20141015095334_4.bval",
            "__DTI_AX_ep2d_2_5_iso_33d_20141015095334_4.bvec",
        ],
        md5_list=[
            "889883b5e7d93a6e372bc760ea887e7c",
            "9daea1d01d68fd0055a3b34f5ffd5f6e",
            "3ee44135fde7ea5c9b8c801414bdde2c",
            "948373391de950e7cc1201ba9f696bf0",
        ],
        doc="Download CFIN multi b-value diffusion data",
        msg=(
            "This data was provided by Brian Hansen and Sune Jespersen"
            + " More details about the data are available in their paper: "
            + " https://www.nature.com/articles/sdata201672"
        ),
    )

    fetch_file_formats = _make_fetcher(
        "bundle_file_formats_example",
        pjoin(dipy_home, "bundle_file_formats_example"),
        "https://zenodo.org/record/3352379/files/",
        [
            "cc_m_sub.trk",
            "laf_m_sub.tck",
            "lpt_m_sub.fib",
            "raf_m_sub.vtk",
            "rpt_m_sub.dpy",
            "template0.nii.gz",
        ],
        [
            "cc_m_sub.trk",
            "laf_m_sub.tck",
            "lpt_m_sub.fib",
            "raf_m_sub.vtk",
            "rpt_m_sub.dpy",
            "template0.nii.gz",
        ],
        md5_list=[
            "78ed7bead3e129fb4b4edd6da1d7e2d2",
            "20009796ccd43dc8d2d5403b25dff717",
            "8afa8419e2efe04ede75cce1f53c77d8",
            "9edcbea30c7a83b467c3cdae6ce963c8",
            "42bff2538a650a7ff1e57bfd9ed90ad6",
            "99c37a2134026d2c4bbb7add5088ddc6",
        ],
        doc="Download 5 bundles in various file formats and their reference",
        data_size="25MB",
    )

    fetch_bundle_atlas_hcp842 = _make_fetcher(
        "fetch_bundle_atlas_hcp842",
        pjoin(dipy_home, "bundle_atlas_hcp842"),
        "https://ndownloader.figshare.com/files/",
        ["13638644"],
        ["Atlas_80_Bundles.zip"],
        md5_list=["78331d527a10ec000d4f33bac472e099"],
        doc="Download atlas tractogram from the hcp842 dataset with 80 bundles",
        data_size="300MB",
        unzip=True,
    )

    fetch_30_bundle_atlas_hcp842 = _make_fetcher(
        "fetch_30_bundle_atlas_hcp842",
        pjoin(dipy_home, "bundle_atlas_hcp842"),
        "https://ndownloader.figshare.com/files/",
        ["26842853"],
        ["Atlas_30_Bundles.zip"],
        md5_list=["f3922cdbea4216823798fade128d6782"],
        doc="Download atlas tractogram from the hcp842 dataset with 30 bundles",
        data_size="207.09MB",
        unzip=True,
    )

    fetch_target_tractogram_hcp = _make_fetcher(
        "fetch_target_tractogram_hcp",
        pjoin(dipy_home, "target_tractogram_hcp"),
        "https://ndownloader.figshare.com/files/",
        ["12871127"],
        ["hcp_tractogram.zip"],
        md5_list=["fa25ef19c9d3748929b6423397963b6a"],
        doc="Download tractogram of one of the hcp dataset subjects",
        data_size="541MB",
        unzip=True,
    )

    fetch_bundle_fa_hcp = _make_fetcher(
        "fetch_bundle_fa_hcp",
        pjoin(dipy_home, "bundle_fa_hcp"),
        "https://ndownloader.figshare.com/files/",
        ["14035265"],
        ["hcp_bundle_fa.nii.gz"],
        md5_list=["2d5c0036b0575597378ddf39191028ea"],
        doc=("Download map of FA within two bundles in one of the hcp dataset subjects"),
        data_size="230kb",
    )

    fetch_qtdMRI_test_retest_2subjects = _make_fetcher(
        "fetch_qtdMRI_test_retest_2subjects",
        pjoin(dipy_home, "qtdMRI_test_retest_2subjects"),
        "https://zenodo.org/record/996889/files/",
        [
            "subject1_dwis_test.nii.gz",
            "subject2_dwis_test.nii.gz",
            "subject1_dwis_retest.nii.gz",
            "subject2_dwis_retest.nii.gz",
            "subject1_ccmask_test.nii.gz",
            "subject2_ccmask_test.nii.gz",
            "subject1_ccmask_retest.nii.gz",
            "subject2_ccmask_retest.nii.gz",
            "subject1_scheme_test.txt",
            "subject2_scheme_test.txt",
            "subject1_scheme_retest.txt",
            "subject2_scheme_retest.txt",
        ],
        [
            "subject1_dwis_test.nii.gz",
            "subject2_dwis_test.nii.gz",
            "subject1_dwis_retest.nii.gz",
            "subject2_dwis_retest.nii.gz",
            "subject1_ccmask_test.nii.gz",
            "subject2_ccmask_test.nii.gz",
            "subject1_ccmask_retest.nii.gz",
            "subject2_ccmask_retest.nii.gz",
            "subject1_scheme_test.txt",
            "subject2_scheme_test.txt",
            "subject1_scheme_retest.txt",
            "subject2_scheme_retest.txt",
        ],
        md5_list=[
            "ebd7441f32c40e25c28b9e069bd81981",
            "dd6a64dd68c8b321c75b9d5fb42c275a",
            "830a7a028a66d1b9812f93309a3f9eae",
            "d7f1951e726c35842f7ea0a15d990814",
            "ddb8dfae908165d5e82c846bcc317cab",
            "5630c06c267a0f9f388b07b3e563403c",
            "02e9f92b31e8980f658da99e532e14b5",
            "6e7ce416e7cfda21cecce3731f81712b",
            "957cb969f97d89e06edd7a04ffd61db0",
            "5540c0c9bd635c29fc88dd599cbbf5e6",
            "5540c0c9bd635c29fc88dd599cbbf5e6",
            "5540c0c9bd635c29fc88dd599cbbf5e6",
        ],
        doc="Downloads test-retest qt-dMRI acquisitions of two C57Bl6 mice.",
        data_size="298.2MB",
    )

    fetch_gold_standard_io = _make_fetcher(
        "fetch_gold_standard_io",
        pjoin(dipy_home, "gold_standard_io"),
        "https://zenodo.org/record/7767654/files/",
        [
            "gs.trk",
            "gs.tck",
            "gs.trx",
            "gs.fib",
            "gs.dpy",
            "gs.nii",
            "gs_3mm.nii",
            "gs_rasmm_space.txt",
            "gs_voxmm_space.txt",
            "gs_vox_space.txt",
            "points_data.txt",
            "streamlines_data.txt",
        ],
        [
            "gs.trk",
            "gs.tck",
            "gs.trx",
            "gs.fib",
            "gs.dpy",
            "gs.nii",
            "gs_3mm.nii",
            "gs_rasmm_space.txt",
            "gs_voxmm_space.txt",
            "gs_vox_space.txt",
            "points_data.json",
            "streamlines_data.json",
        ],
        md5_list=[
            "3acf565779f4d5107f96b2ef90578d64",
            "151a30cf356c002060d720bf9d577245",
            "a6587f1a3adc4df076910c4d72eb4161",
            "e9818e07bef5bd605dea0877df14a2b0",
            "248606297e400d1a9b1786845aad8de3",
            "a2d4d8f62d1de0ab9927782c7d51cb27",
            "217b3ae0712a02b2463b8eedfe9a0a68",
            "ca193a5508d3313d542231aaf262960f",
            "3284de59dfd9ca3130e6e01258ed9022",
            "a2a89c387f45adab733652a92f6602d5",
            "4bcca0c6195871fc05e93cdfabec22b4",
            "578f29052ac03a6d8a98580eb7c70d97",
        ],
        doc="Downloads the gold standard for streamlines io testing.",
        data_size="47.KB",
    )

    fetch_qte_lte_pte = _make_fetcher(
        "fetch_qte_lte_pte",
        pjoin(dipy_home, "qte_lte_pte"),
        "https://zenodo.org/record/4624866/files/",
        ["lte-pte.nii.gz", "lte-pte.bval", "lte-pte.bvec", "mask.nii.gz"],
        ["lte-pte.nii.gz", "lte-pte.bval", "lte-pte.bvec", "mask.nii.gz"],
        md5_list=[
            "f378b2cd9f57625512002b9e4c0f1660",
            "5c25d24dd3df8590582ed690507a8769",
            "31abe55dfda7ef5fdf5015d0713be9b0",
            "1b7b83b8a60295f52d80c3855a12b275",
        ],
        doc="Download QTE data with linear and planar tensor encoding.",
        data_size="41.5 MB",
    )

    fetch_cti_rat1 = _make_fetcher(
        "fetch_cti_rat1",
        pjoin(dipy_home, "cti_rat1"),
        "https://zenodo.org/record/8276773/files/",
        [
            "Rat1_invivo_cti_data.nii",
            "bvals1.bval",
            "bvec1.bvec",
            "bvals2.bval",
            "bvec2.bvec",
            "Rat1_mask.nii",
        ],
        [
            "Rat1_invivo_cti_data.nii",
            "bvals1.bval",
            "bvec1.bvec",
            "bvals2.bval",
            "bvec2.bvec",
            "Rat1_mask.nii",
        ],
        md5_list=[
            "2f855e7826f359d80cfd6f094d3a7008",
            "1deed2a91e20104ca42d7482cc096a9a",
            "40a4f5131b8a64608d16b0c6c5ad0837",
            "1979c7dc074e00f01103cbdf83ed78db",
            "653d9344060803d5576f43c65ce45ccb",
            "34bc3d5acea9442d05ef185717780440",
        ],
        doc="Download Rat Brain DDE data for CTI reconstruction"
        + " (Rat #1 data from Henriques et al. MRM 2021).",
        data_size="152.92 MB",
        msg=(
            "More details about the data are available in the paper: "
            + "https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.28938"
        ),
    )

    fetch_fury_surface = _make_fetcher(
        "fetch_fury_surface",
        pjoin(dipy_home, "fury_surface"),
        "https://raw.githubusercontent.com/fury-gl/fury-data/master/surfaces/",
        ["100307_white_lh.vtk"],
        ["100307_white_lh.vtk"],
        md5_list=["dbec91e29af15541a5cb36d80977b26b"],
        doc="Surface for testing and examples",
        data_size="11MB",
    )

    fetch_DiB_70_lte_pte_ste = _make_fetcher(
        "fetch_DiB_70_lte_pte_ste",
        pjoin(dipy_home, "DiB_70_lte_pte_ste"),
        "https://github.com/filip-szczepankiewicz/Szczepankiewicz_DIB_2019/"
        "raw/master/DATA/brain/NII_Boito_SubSamples/",
        [
            "DiB_70_lte_pte_ste/DiB_70_lte_pte_ste.nii.gz",
            "DiB_70_lte_pte_ste/bval_DiB_70_lte_pte_ste.bval",
            "DiB_70_lte_pte_ste/bvec_DiB_70_lte_pte_ste.bvec",
            "DiB_mask.nii.gz",
        ],
        [
            "DiB_70_lte_pte_ste.nii.gz",
            "bval_DiB_70_lte_pte_ste.bval",
            "bvec_DiB_70_lte_pte_ste.bvec",
            "DiB_mask.nii.gz",
        ],
        doc="Download QTE data with linear, planar, "
        + "and spherical tensor encoding. If using this data please cite "
        + "F Szczepankiewicz, S Hoge, C-F Westin. Linear, planar and "
        + "spherical tensor-valued diffusion MRI data by free waveform "
        + "encoding in healthy brain, water, oil and liquid crystals. "
        + "Data in Brief (2019),"
        + "DOI: https://doi.org/10.1016/j.dib.2019.104208",
        md5_list=[
            "11f2e0d53e19061654eb3cdfc8fe9827",
            "15021885b4967437c8cf441c09045c25",
            "1e6b867182da249f81aa9abd50e8b9f7",
            "2ea48d80b6ae1c3da50cb44e615b09e5",
        ],
        data_size="51.1 MB",
    )

    fetch_DiB_217_lte_pte_ste = _make_fetcher(
        "fetch_DiB_217_lte_pte_ste",
        pjoin(dipy_home, "DiB_217_lte_pte_ste"),
        "https://github.com/filip-szczepankiewicz/Szczepankiewicz_DIB_2019/"
        "raw/master/DATA/brain/NII_Boito_SubSamples/",
        [
            "DiB_217_lte_pte_ste/DiB_217_lte_pte_ste_1.nii.gz",
            "DiB_217_lte_pte_ste/DiB_217_lte_pte_ste_2.nii.gz",
            "DiB_217_lte_pte_ste/bval_DiB_217_lte_pte_ste.bval",
            "DiB_217_lte_pte_ste/bvec_DiB_217_lte_pte_ste.bvec",
            "DiB_mask.nii.gz",
        ],
        [
            "DiB_217_lte_pte_ste_1.nii.gz",
            "DiB_217_lte_pte_ste_2.nii.gz",
            "bval_DiB_217_lte_pte_ste.bval",
            "bvec_DiB_217_lte_pte_ste.bvec",
            "DiB_mask.nii.gz",
        ],
        doc="Download QTE data with linear, planar, "
        + "and spherical tensor encoding. If using this data please cite "
        + "F Szczepankiewicz, S Hoge, C-F Westin. Linear, planar and "
        + "spherical tensor-valued diffusion MRI data by free waveform "
        + "encoding in healthy brain, water, oil and liquid crystals. "
        + "Data in Brief (2019),"
        + "DOI: https://doi.org/10.1016/j.dib.2019.104208",
        md5_list=[
            "424e9cf75b20bc1f7ae1acde26b26da0",
            "8e70d14fb8f08065a7a0c4d3033179c6",
            "1f657215a475676ce333299038df3a39",
            "4220ca92f2906c97ed4c7287eb62c6f0",
            "2ea48d80b6ae1c3da50cb44e615b09e5",
        ],
        data_size="166.3 MB",
    )

    fetch_ptt_minimal_dataset = _make_fetcher(
        "fetch_ptt_minimal_dataset",
        pjoin(dipy_home, "ptt_dataset"),
        "https://raw.githubusercontent.com/dipy/dipy_datatest/main/",
        ["ptt_fod.nii", "ptt_seed_coords.txt", "ptt_seed_image.nii"],
        ["ptt_fod.nii", "ptt_seed_coords.txt", "ptt_seed_image.nii"],
        md5_list=[
            "6e454f8088b64e7b85218c71010d8dbe",
            "8c2d71fb95020e2bb1743623eb11c2a6",
            "9cb88f88d664019ba80c0b372c8bafec",
        ],
        doc="Download FOD and seeds for PTT testing and examples",
        data_size="203KB",
    )

    fetch_bundle_warp_dataset = _make_fetcher(
        "fetch_bundle_warp_dataset",
        pjoin(dipy_home, "bundle_warp"),
        "https://ndownloader.figshare.com/files/",
        ["40026343", "40026346"],
        [
            "m_UF_L.trk",
            "s_UF_L.trk",
        ],
        md5_list=["4db38ca1e80c16d6e3a97f88f0611187", "c1499005baccfab865ce38368d7a4c7f"],
        doc="Download Bundle Warp dataset",
    )

    fetch_disco1_dataset = _make_fetcher(
        "fetch_disco1_dataset",
        pjoin(dipy_home, "disco", "disco_1"),
        "https://data.mendeley.com/public-files/datasets/fgf86jdfg6/files/",
        [
            "028147aa-f17f-4514-80e6-24c7419da75e/file_downloaded",
            "e714ff30-be78-4284-b282-84ed011885de/file_downloaded",
            "a45ac785-4977-434b-b05e-c97ed4a1e6c7/file_downloaded",
            "c3d1de3e-6dcf-4142-99bf-4dd42d445a0a/file_downloaded",
            "35f025f6-d4ab-45e6-9f58-e7685862117f/file_downloaded",
            "02ad744f-4910-40a2-870b-5507d6195926/file_downloaded",
            "c030a2dc-6b80-43d7-a63c-87a28c77c73b/file_downloaded",
            "c6a54096-d7ea-4d05-a921-6aeb0c1bc625/file_downloaded",
            "d95bfd9a-8886-48ec-acc5-5526f3bca32c/file_downloaded",
            "8ef464f7-e648-4583-a208-3ee87a08a2a1/file_downloaded",
            "cbaa3708-7f39-46ec-8cea-51285956bf5a/file_downloaded",
            "964040aa-e1f0-48f7-8eb7-87553f4190e5/file_downloaded",
            "e8bea304-35ce-45de-95be-c51e2ed917f6/file_downloaded",
            "ec5da4f1-53bd-4103-8722-ea3211223150/file_downloaded",
            "c1ddba00-9ea1-435a-946a-bca43079eadf/file_downloaded",
            "0aa2d1a0-ae07-4eff-af6a-22183249e9fa/file_downloaded",
            "744d468f-bd0e-44a8-8395-893889c16462/file_downloaded",
            "311afc50-9bf5-469a-a298-091d252940c9/file_downloaded",
            "68f37ddf-8f06-4d3e-80c5-842b495516ba/file_downloaded",
            "d272c9aa-85d9-40d1-9a87-1044d361da0c/file_downloaded",
            "0ba92dbc-4489-4626-8bfd-5d56b7557699/file_downloaded",
            "099c3f98-4574-4315-be5e-a2b183695e71/file_downloaded",
            "231bbeac-bdec-4815-9238-9a2370bdca6b/file_downloaded",
            "cd073c2f-c3b0-4a46-9c3d-63a853a8af49/file_downloaded",
            "470f235c-04f5-4544-bf4b-f11ac0d8fbd2/file_downloaded",
            "276b637b-6244-4927-b1af-8c1ca12b8a0c/file_downloaded",
            "1c524b3f-ca6b-4f6f-9e6f-25431496000a/file_downloaded",
            "24f8fe7c-8ab2-40c8-baec-4d1f29eba4f2/file_downloaded",
            "5cc5fb58-6058-40dd-9969-019f7814a4a1/file_downloaded",
            "aabcc286-b607-46e1-9229-80638630d518/file_downloaded",
            "87b6cee7-0645-4405-b5b3-af89c04a2ac9/file_downloaded",
            "ad47bf18-8b69-4b0c-8706-ada53365fd99/file_downloaded",
            "417505fb-3a7a-4cbc-ac8a-92acc26bb958/file_downloaded",
            "7708e776-f07c-47a9-9982-b5fbd9dd4390/file_downloaded",
            "654863c6-6154-4da6-9f4c-3b0c76535dd7/file_downloaded",
            "5f8d028c-82a1-4837-b34f-ad8251c0685b/file_downloaded",
            "bd2fa6f3-f050-44a6-ad18-6c31bb037eb7/file_downloaded",
            "b432035b-5429-4255-8e21-88184802d005/file_downloaded",
            "1f1a4391-9740-4e17-89c3-79430aab0f91/file_downloaded",
            "6c90c685-ae16-4f21-ae13-71e89ca9eb66/file_downloaded",
            "f8878550-061b-40e0-a0c7-5aac5a33e542/file_downloaded",
        ],
        [
            "DiSCo_gradients.bvals",
            "DiSCo_gradients_dipy.bvecs",
            "DiSCo_gradients_fsl.bvecs",
            "DiSCo_gradients_mrtrix.b",
            "DiSCo_gradients.scheme",
            "lowRes_DiSCo1_DWI_RicianNoise-snr40.nii.gz",
            "lowRes_DiSCo1_ROIs.nii.gz",
            "lowRes_DiSCo1_mask.nii.gz",
            "lowRes_DiSCo1_DWI_RicianNoise-snr50.nii.gz",
            "lowRes_DiSCo1_ROIs-mask.nii.gz",
            "lowRes_DiSCo1_DWI_Intra.nii.gz",
            "lowRes_DiSCo1_Strand_Bundle_Count.nii.gz",
            "lowRes_DiSCo1_Strand_Count.nii.gz",
            "lowRes_DiSCo1_Strand_Intra_Volume_Fraction.nii.gz",
            "lowRes_DiSCo1_DWI_RicianNoise-snr30.nii.gz",
            "lowRes_DiSCo1_DWI_RicianNoise-snr20.nii.gz",
            "lowRes_DiSCo1_DWI.nii.gz",
            "lowRes_DiSCo1_Strand_ODFs.nii.gz",
            "lowRes_DiSCo1_Strand_Average_Diameter.nii.gz",
            "lowRes_DiSCo1_DWI_RicianNoise-snr10.nii.gz",
            "highRes_DiSCo1_Strand_ODFs.nii.gz",
            "highRes_DiSCo1_DWI_RicianNoise-snr50.nii.gz",
            "highRes_DiSCo1_DWI.nii.gz",
            "highRes_DiSCo1_ROIs.nii.gz",
            "highRes_DiSCo1_DWI_RicianNoise-snr40.nii.gz",
            "highRes_DiSCo1_mask.nii.gz",
            "highRes_DiSCo1_Strand_Bundle_Count.nii.gz",
            "highRes_DiSCo1_DWI_RicianNoise-snr20.nii.gz",
            "highRes_DiSCo1_DWI_RicianNoise-snr30.nii.gz",
            "highRes_DiSCo1_Strand_Average_Diameter.nii.gz",
            "highRes_DiSCo1_Strand_Streamline_Count.nii.gz",
            "highRes_DiSCo1_DWI_Intra.nii.gz",
            "highRes_DiSCo1_DWI_RicianNoise-snr10.nii.gz",
            "highRes_DiSCo1_Strand_Intra_Volume_Fraction.nii.gz",
            "highRes_DiSCo1_ROIs-mask.nii.gz",
            "DiSCo1_Connectivity_Matrix_Strands_Count.txt",
            "DiSCo1_Connectivity_Matrix_Cross-Sectional_Area.txt",
            "DiSCo1_Strands_ROIs_Pairs.txt",
            "DiSCo1_Strands_Diameters.txt",
            "DiSCo1_Strands_Trajectories.tck",
            "DiSCo1_Strands_Trajectories.trk",
        ],
        md5_list=[
            "c03cfec8ee605a54866ef09c3c8ba31d",
            "b8443aee0a2d2b60ee658a2342cccf0f",
            "4ac749bb584e6963851ee170e137e3ec",
            "207d57a6c16cdf230b45d4df5b4e1dee",
            "2346de6ad563a10e40b8ebec3ac4e0ff",
            "1981ac168ea76ce4d70e3dcd1d749f13",
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            "ac9900538f4563c39032dcc289c36a51",
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            "b967371fe44c4d736edcd65b8da12b93",
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            "580d72144a5e19a119e6839d8f1c623d",
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            "7cf61f1088971a8799df80c29dc7a9f1",
            "da762d46e6b7d513be0411ac66769c5c",
            "5d7bac8737a4543eee60f7aa26073df9",
            "370bdf6571aa061daaa654a107eae6fd",
            "50fd6e201536b4a4815dd102d9c80974",
            "8269aed23fc643bb98c36945462e866a",
            "86becb33b54a3c4291f0cd77ec87bb3e",
            "d240cccd9d84e69bf146a5d06bb898bb",
            "e75a1838ff8e5860bdbde1162e5aa80f",
            "f7d9507884960f86918f6328962d98d2",
            "be4f8221ccdd5a139f16ac18353d12c6",
            "d8d33dbdc820289f5575b5c064c6dfe9",
            "53740e3d2851d5f301d93730268928f2",
            "ed98e0f26abc85af5735fe228cb86b4c",
            "50a9385f567138c23df4ddf333c785c0",
            "2d845131db87b6a2230a325e076bdd7f",
            "c53f6e42cfd796a561b1af434a61c91d",
            "f97730469c53deaa5b827effebef3e78",
            "d34fd49147cd061b9417328118567d1c",
            "f0d28f1d7eec1fad866607ee59686476",
            "e475641a08ebafeecb79a21e618d7081",
            "8c2a338606c0cb7de6e34b8317f59cd6",
        ],
        optional_fnames=[
            "DiSCo_gradients_fsl.bvecs",
            "DiSCo_gradients_mrtrix.b",
            "DiSCo_gradients.scheme",
            "highRes_DiSCo1_DWI.nii.gz",
            "lowRes_DiSCo1_DWI_RicianNoise-snr40.nii.gz",
            "lowRes_DiSCo1_ROIs.nii.gz",
            "lowRes_DiSCo1_mask.nii.gz",
            "lowRes_DiSCo1_DWI_RicianNoise-snr50.nii.gz",
            "lowRes_DiSCo1_ROIs-mask.nii.gz",
            "lowRes_DiSCo1_DWI_Intra.nii.gz",
            "lowRes_DiSCo1_Strand_Bundle_Count.nii.gz",
            "lowRes_DiSCo1_Strand_Count.nii.gz",
            "lowRes_DiSCo1_Strand_Intra_Volume_Fraction.nii.gz",
            "lowRes_DiSCo1_DWI_RicianNoise-snr30.nii.gz",
            "lowRes_DiSCo1_DWI_RicianNoise-snr20.nii.gz",
            "lowRes_DiSCo1_DWI.nii.gz",
            "lowRes_DiSCo1_Strand_ODFs.nii.gz",
            "lowRes_DiSCo1_Strand_Average_Diameter.nii.gz",
            "lowRes_DiSCo1_DWI_RicianNoise-snr10.nii.gz",
            "highRes_DiSCo1_Strand_ODFs.nii.gz",
            "highRes_DiSCo1_DWI_RicianNoise-snr50.nii.gz",
            "highRes_DiSCo1_DWI_RicianNoise-snr40.nii.gz",
            "highRes_DiSCo1_Strand_Bundle_Count.nii.gz",
            "highRes_DiSCo1_DWI_RicianNoise-snr20.nii.gz",
            "highRes_DiSCo1_DWI_RicianNoise-snr30.nii.gz",
            "highRes_DiSCo1_Strand_Average_Diameter.nii.gz",
            "highRes_DiSCo1_Strand_Streamline_Count.nii.gz",
            "highRes_DiSCo1_DWI_Intra.nii.gz",
            "highRes_DiSCo1_Strand_Intra_Volume_Fraction.nii.gz",
            "DiSCo1_Connectivity_Matrix_Strands_Count.txt",
            "DiSCo1_Strands_ROIs_Pairs.txt",
            "DiSCo1_Strands_Diameters.txt",
            "DiSCo1_Strands_Trajectories.trk",
        ],
        doc=(
            "Download DISCO 1 dataset: The Diffusion-Simulated Connectivity "
            "Dataset. DOI: 10.17632/fgf86jdfg6.3"
        ),
        data_size="1.05 GB",
        use_headers=True,
    )

    fetch_disco2_dataset = _make_fetcher(
        "fetch_disco2_dataset",
        pjoin(dipy_home, "disco", "disco_2"),
        "https://data.mendeley.com/public-files/datasets/fgf86jdfg6/files/",
        [
            "028147aa-f17f-4514-80e6-24c7419da75e/file_downloaded",
            "e714ff30-be78-4284-b282-84ed011885de/file_downloaded",
            "a45ac785-4977-434b-b05e-c97ed4a1e6c7/file_downloaded",
            "c3d1de3e-6dcf-4142-99bf-4dd42d445a0a/file_downloaded",
            "35f025f6-d4ab-45e6-9f58-e7685862117f/file_downloaded",
            "e7a50803-a58d-47dc-b026-6a6e20602546/file_downloaded",
            "12158d68-0050-4528-88db-5fb606b2151d/file_downloaded",
            "361df9ec-0542-4e05-8f2d-1c605a5a915a/file_downloaded",
            "2bcf46ae-0b07-4dd0-baf8-654a05a4aca7/file_downloaded",
            "6a514a78-8a8a-4cdf-947a-d1b01ced4b0d/file_downloaded",
            "a387810f-54fe-4493-8842-f3fbeea687fe/file_downloaded",
            "2401238c-ae37-4a99-81a8-50ba90110650/file_downloaded",
            "980a6702-c192-4a4e-b706-f3ec2fb5e800/file_downloaded",
            "c5691dae-5b10-4c67-8a58-2df8314c85ec/file_downloaded",
            "d4eabb32-8b28-461c-8248-a0bacf4e44f2/file_downloaded",
            "dd98fb48-c48f-4d72-85a9-38a2d8b2bb3f/file_downloaded",
            "fed31d0d-7c75-4073-82e5-2c9ba30daa5a/file_downloaded",
            "012853cb-0d7f-4135-8407-a99c4e022024/file_downloaded",
            "46d0466f-75bd-4646-af10-5215477339d8/file_downloaded",
            "d1adcbdd-1d11-43cd-a72a-6830e91b7c33/file_downloaded",
            "f3e9d4a5-dc9d-4112-8a67-b63b40c87e5c/file_downloaded",
            "825d2154-e892-474d-ae31-e490467dfa3c/file_downloaded",
            "bf62b3fd-fff7-4658-b143-9ec948fc256f/file_downloaded",
            "173dacc8-683c-42c0-b270-67f59c77d49d/file_downloaded",
            "f981cbad-84c9-42bb-bf19-978f12e6c972/file_downloaded",
            "d122bf02-98f7-46b2-9286-98473216d64e/file_downloaded",
            "50b68dd1-1037-4cb4-8d1a-025cff38da55/file_downloaded",
            "5f3e04b6-9bd5-4010-bc2f-ea16b7ac8b1c/file_downloaded",
            "f8d7ac40-7d0e-41ba-9788-4f973be5968b/file_downloaded",
            "d4adf4a7-1646-4530-85cb-66c2779e1cb6/file_downloaded",
            "6b1f740e-2b20-440f-9bec-344540d80d1d/file_downloaded",
            "7ea07af5-5cb8-4857-bf30-17a0d4f39f83/file_downloaded",
            "7ad1c5ab-9606-481c-a08e-0770798b224f/file_downloaded",
            "8d8c8fe9-7747-4ba2-b759-f27cf160b88f/file_downloaded",
            "143de683-9bce-43b9-b08e-2e16fea05a53/file_downloaded",
            "f3f5f798-0d01-41de-ad96-6a2e2d04eeb1/file_downloaded",
            "0dc1a52f-7770-4492-8dc7-27da1f9d2ec9/file_downloaded",
            "bba6f22f-1212-49c2-b07f-3b62ae435b4d/file_downloaded",
            "5928d70c-01f3-47ab-8f29-0484f9a014a2/file_downloaded",
            "6be306cb-1dfd-4fe8-912d-ec117ff0d70d/file_downloaded",
            "e49d38af-e262-46ef-ad44-c51c29ee2178/file_downloaded",
        ],
        [
            "DiSCo_gradients.bvals",
            "DiSCo_gradients_dipy.bvecs",
            "DiSCo_gradients_fsl.bvecs",
            "DiSCo_gradients_mrtrix.b",
            "DiSCo_gradients.scheme",
            "lowRes_DiSCo2_DWI_RicianNoise-snr50.nii.gz",
            "lowRes_DiSCo2_Strand_Average_Diameter.nii.gz",
            "lowRes_DiSCo2_DWI_RicianNoise-snr40.nii.gz",
            "lowRes_DiSCo2_DWI.nii.gz",
            "lowRes_DiSCo2_ROIs.nii.gz",
            "lowRes_DiSCo2_mask.nii.gz",
            "lowRes_DiSCo2_DWI_Intra.nii.gz",
            "lowRes_DiSCo2_ROIs-mask.nii.gz",
            "lowRes_DiSCo2_Strand_Count.nii.gz",
            "lowRes_DiSCo2_DWI_RicianNoise-snr20.nii.gz",
            "lowRes_DiSCo2_DWI_RicianNoise-snr30.nii.gz",
            "lowRes_DiSCo2_Strand_Intra_Volume_Fraction.nii.gz",
            "lowRes_DiSCo2_DWI_RicianNoise-snr10.nii.gz",
            "lowRes_DiSCo2_Strand_ODFs.nii.gz",
            "lowRes_DiSCo2_Strand_Bundle_Count.nii.gz",
            "highRes_DiSCo2_DWI_RicianNoise-snr40.nii.gz",
            "highRes_DiSCo2_DWI_RicianNoise-snr50.nii.gz",
            "highRes_DiSCo2_ROIs-mask.nii.gz",
            "highRes_DiSCo2_Strand_ODFs.nii.gz",
            "highRes_DiSCo2_Strand_Count.nii.gz",
            "highRes_DiSCo2_ROIs.nii.gz",
            "highRes_DiSCo2_mask.nii.gz",
            "highRes_DiSCo2_Strand_Average_Diameter.nii.gz",
            "highRes_DiSCo2_DWI_RicianNoise-snr30.nii.gz",
            "highRes_DiSCo2_DWI_Intra.nii.gz",
            "highRes_DiSCo2_DWI_RicianNoise-snr20.nii.gz",
            "highRes_DiSCo2_DWI.nii.gz",
            "highRes_DiSCo2_Strand_Bundle_Count.nii.gz",
            "highRes_DiSCo2_DWI_RicianNoise-snr10.nii.gz",
            "highRes_DiSCo2_Strand_Intra_Volume_Fraction.nii.gz",
            "DiSCo2_Strands_Diameters.txt",
            "DiSCo2_Connectivity_Matrix_Strands_Count.txt",
            "DiSCo2_Strands_Trajectories.trk",
            "DiSCo2_Strands_ROIs_Pairs.txt",
            "DiSCo2_Strands_Trajectories.tck",
            "DiSCo2_Connectivity_Matrix_Cross-Sectional_Area.txt",
        ],
        md5_list=[
            "c03cfec8ee605a54866ef09c3c8ba31d",
            "b8443aee0a2d2b60ee658a2342cccf0f",
            "4ac749bb584e6963851ee170e137e3ec",
            "207d57a6c16cdf230b45d4df5b4e1dee",
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            "19c037c7813c7574b809baedd9bb64fd",
            "77a1f4111eefc01b26d7ec8b6473894d",
            "cde18c2581ced8755958a153332c375a",
            "88b12d4c24bd833430b3125340c96801",
            "4d70e9b246101f5f2ba8a44d92beb2e8",
            "c01bb0a1d2bdb95565b92eb022ff2124",
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            "41a3155f6fb44da50c515bb867006ab1",
            "9494f6a6f5c4e44b88051f0284757517",
            "5d9f8bd4a540891082f2dda976f70478",
            "8e2e5900008ae6923fe56447d7b493c7",
            "e961d27613acc250b6407d29d5020154",
            "ef330fa395d4e48d0286652927116275",
            "00c620b144253ef114fee1fe8e95a87f",
            "7e4cf83496996aefd9f27c6077c71d74",
            "e6be6c73425337e331fda4e4bdcbe749",
            "0b6c1770d48f39f704986ba69970541e",
            "69da5bb232b41dbeeea9d409ca94955f",
            "0b43ffcc46217da86a472a7b1e02ac06",
            "4e774e280cc57b1336e4fa9e479ee4ee",
            "cc5442ea2ff2ddfcd1330016e2a68399",
            "7f276c0276561df13c86008776faf6b2",
        ],
        optional_fnames=[
            "DiSCo_gradients_fsl.bvecs",
            "DiSCo_gradients_mrtrix.b",
            "DiSCo_gradients.scheme",
            "lowRes_DiSCo2_DWI_RicianNoise-snr50.nii.gz",
            "lowRes_DiSCo2_Strand_Average_Diameter.nii.gz",
            "lowRes_DiSCo2_DWI_RicianNoise-snr40.nii.gz",
            "lowRes_DiSCo2_DWI.nii.gz",
            "lowRes_DiSCo2_ROIs.nii.gz",
            "lowRes_DiSCo2_mask.nii.gz",
            "lowRes_DiSCo2_DWI_Intra.nii.gz",
            "lowRes_DiSCo2_ROIs-mask.nii.gz",
            "lowRes_DiSCo2_Strand_Count.nii.gz",
            "lowRes_DiSCo2_DWI_RicianNoise-snr20.nii.gz",
            "lowRes_DiSCo2_DWI_RicianNoise-snr30.nii.gz",
            "lowRes_DiSCo2_Strand_Intra_Volume_Fraction.nii.gz",
            "lowRes_DiSCo2_DWI_RicianNoise-snr10.nii.gz",
            "lowRes_DiSCo2_Strand_ODFs.nii.gz",
            "lowRes_DiSCo2_Strand_Bundle_Count.nii.gz",
            "highRes_DiSCo2_DWI_RicianNoise-snr40.nii.gz",
            "highRes_DiSCo2_DWI_RicianNoise-snr50.nii.gz",
            "highRes_DiSCo2_Strand_ODFs.nii.gz",
            "highRes_DiSCo2_Strand_Count.nii.gz",
            "highRes_DiSCo2_Strand_Average_Diameter.nii.gz",
            "highRes_DiSCo2_DWI_RicianNoise-snr30.nii.gz",
            "highRes_DiSCo2_DWI_Intra.nii.gz",
            "highRes_DiSCo2_DWI_RicianNoise-snr20.nii.gz",
            "highRes_DiSCo2_Strand_Bundle_Count.nii.gz",
            "highRes_DiSCo2_Strand_Intra_Volume_Fraction.nii.gz",
            "highRes_DiSCo2_DWI.nii.gz",
            "DiSCo2_Strands_Diameters.txt",
            "DiSCo2_Connectivity_Matrix_Strands_Count.txt",
            "DiSCo2_Strands_ROIs_Pairs.txt",
            "DiSCo2_Strands_Trajectories.trk",
        ],
        doc=(
            "Download DISCO 2 dataset: The Diffusion-Simulated Connectivity "
            "Dataset. DOI: 10.17632/fgf86jdfg6.3"
        ),
        data_size="1.05 GB",
        use_headers=True,
    )

    fetch_disco3_dataset = _make_fetcher(
        "fetch_disco3_dataset",
        pjoin(dipy_home, "disco", "disco_3"),
        "https://data.mendeley.com/public-files/datasets/fgf86jdfg6/files/",
        [
            "028147aa-f17f-4514-80e6-24c7419da75e/file_downloaded",
            "e714ff30-be78-4284-b282-84ed011885de/file_downloaded",
            "a45ac785-4977-434b-b05e-c97ed4a1e6c7/file_downloaded",
            "c3d1de3e-6dcf-4142-99bf-4dd42d445a0a/file_downloaded",
            "35f025f6-d4ab-45e6-9f58-e7685862117f/file_downloaded",
            "3cc65986-2e64-4397-a34d-386dfe286a89/file_downloaded",
            "ac384df6-8e9a-4c44-89db-624f89c12015/file_downloaded",
            "7feb06e6-0bdc-4fba-b7df-c67c3cb43301/file_downloaded",
            "c8bddc43-f1f2-404c-98d4-9ab34380fc8f/file_downloaded",
            "00bdcd82-dde0-499d-9bef-581755ad7e57/file_downloaded",
            "8abbc092-9368-4154-9e20-b28e3b6d03b4/file_downloaded",
            "e0773f2f-7fbe-4139-8e71-2d3267360ab5/file_downloaded",
            "8670b9c4-9275-4da6-adbf-398889a606d7/file_downloaded",
            "ec2306f2-7d60-452a-82d8-66962ffb903d/file_downloaded",
            "00cf30d9-14fa-484b-b6e1-3d484e43a840/file_downloaded",
            "b61b83aa-522b-4239-9a7b-74c80aea3c07/file_downloaded",
            "75176143-5ddf-4dfa-b2cd-b87da0abfc7b/file_downloaded",
            "d5fe5abf-b72e-474a-9316-714e0739feec/file_downloaded",
            "45a139fc-2d99-4832-8d95-7a7df2acf711/file_downloaded",
            "68f30089-1bf9-45d9-82d8-d77855823229/file_downloaded",
            "4162d753-0d7c-4492-aa4f-88554ee64fed/file_downloaded",
            "5ca6e137-2e65-41cd-bb4b-e1d21dcb5a28/file_downloaded",
            "8e88dccf-d7b3-4692-ab0f-26810ba168e0/file_downloaded",
            "fd97249f-ab0b-4ffa-a3ba-c3d0b3b1c947/file_downloaded",
            "73636f0f-7fc6-48c9-a140-7a11df9847f2/file_downloaded",
            "186a2116-21a8-42fc-b6d1-f303b0ba0b69/file_downloaded",
            "e087b436-d1be-4c8d-8c56-3de9ef7fb054/file_downloaded",
            "305ae78b-725c-4bb1-b435-1bca70c18b6b/file_downloaded",
            "2e62eb11-660b-4f09-920d-f5ee1e25b02a/file_downloaded",
            "a35e33f5-f23a-48de-9c03-1560ce5fecbe/file_downloaded",
            "f1737350-0b46-468e-b78a-af9731eac720/file_downloaded",
            "86ba2b2c-2094-43ef-be3e-e286dca5c5bd/file_downloaded",
            "c5a4a685-c3d6-4ef2-9ec9-956fce821c9b/file_downloaded",
            "d84ec650-8c05-41a6-a4ef-a4e3a2f66dd3/file_downloaded",
            "7648b6ce-2bc8-4753-8e8c-399978d4e187/file_downloaded",
            "1b702c78-cbae-4ceb-a5f6-89c5cb0f4f88/file_downloaded",
            "835af619-7447-4247-8e5b-2e2895480add/file_downloaded",
            "99752985-840e-4ed2-b068-68f6be7af48f/file_downloaded",
            "c07c7b51-1eab-4852-8b36-bd9901f15d7a/file_downloaded",
            "af6f3bc0-a09a-461a-8e6b-6095d83110ea/file_downloaded",
            "482bcf8f-52eb-4bf8-83b4-765ed935ad81/file_downloaded",
        ],
        [
            "DiSCo_gradients.bvals",
            "DiSCo_gradients_dipy.bvecs",
            "DiSCo_gradients_fsl.bvecs",
            "DiSCo_gradients_mrtrix.b",
            "DiSCo_gradients.scheme",
            "lowRes_DiSCo3_Strand_ODFs.nii.gz",
            "lowRes_DiSCo3_DWI_RicianNoise-snr10.nii.gz",
            "lowRes_DiSCo3_Strand_Intra_Volume_Fraction.nii.gz",
            "lowRes_DiSCo3_DWI_RicianNoise-snr30.nii.gz",
            "lowRes_DiSCo3_DWI_RicianNoise-snr20.nii.gz",
            "lowRes_DiSCo3_Strand_Average_Diameter.nii.gz",
            "lowRes_DiSCo3_DWI_Intra.nii.gz",
            "lowRes_DiSCo3_Strand_Count.nii.gz",
            "lowRes_DiSCo3_DWI.nii.gz",
            "lowRes_DiSCo3_Strand_Bundle_Count.nii.gz",
            "lowRes_DiSCo3_ROIs-mask.nii.gz",
            "lowRes_DiSCo3_DWI_RicianNoise-snr40.nii.gz",
            "lowRes_DiSCo3_mask.nii.gz",
            "lowRes_DiSCo3_DWI_RicianNoise-snr50.nii.gz",
            "lowRes_DiSCo3_ROIs.nii.gz",
            "highRes_DiSCo3_DWI_RicianNoise-snr10.nii.gz",
            "highRes_DiSCo3_Strand_Average_Diameter.nii.gz",
            "highRes_DiSCo3_Strand_Count.nii.gz",
            "highRes_DiSCo3_DWI_RicianNoise-snr20.nii.gz",
            "highRes_DiSCo3_DWI.nii.gz",
            "highRes_DiSCo3_DWI_RicianNoise-snr30.nii.gz",
            "highRes_DiSCo3_ROIs-mask.nii.gz",
            "highRes_DiSCo3_Strand_Intra_Volume_Fraction.nii.gz",
            "highRes_DiSCo3_Strand_Bundle_Count.nii.gz",
            "highRes_DiSCo3_DWI_Intra.nii.gz",
            "highRes_DiSCo3_DWI_RicianNoise-snr50.nii.gz",
            "highRes_DiSCo3_Strand_ODFs.nii.gz",
            "highRes_DiSCo3_mask.nii.gz",
            "highRes_DiSCo3_ROIs.nii.gz",
            "highRes_DiSCo3_DWI_RicianNoise-snr40.nii.gz",
            "DiSCo3_Connectivity_Matrix_Cross-Sectional_Area.txt",
            "DiSCo3_Strands_Trajectories.tck",
            "DiSCo3_Strands_Trajectories.trk",
            "DiSCo3_Strands_ROIs_Pairs.txt",
            "DiSCo3_Connectivity_Matrix_Strands_Count.txt",
            "DiSCo3_Strands_Diameters.txt",
        ],
        md5_list=[
            "c03cfec8ee605a54866ef09c3c8ba31d",
            "b8443aee0a2d2b60ee658a2342cccf0f",
            "4ac749bb584e6963851ee170e137e3ec",
            "207d57a6c16cdf230b45d4df5b4e1dee",
            "2346de6ad563a10e40b8ebec3ac4e0ff",
            "acad92cebd780424280b5e38a5b41445",
            "c5ce15ea50d02621d2a3f27cde7cd208",
            "c827954b40279462b520edfb8cd2ced0",
            "f4e8fca1a434436ffe8fc4392d2e635b",
            "62606109e8a96f41208b45bffbe94aae",
            "a03bc8afe4d0348841c9fed5f03695b4",
            "85f60f649c6f3d4e6ae6bcf88053058d",
            "7fdec9c4f4fb740f595191a4d0202de7",
            "df04eb8f1ce0138f3c7f20488f4dbc00",
            "0bde3cefd74e4c3b4d28ec0d90bcadc9",
            "aa7bfbfc9ff454e7a466a01229b1377b",
            "218561e3ebbee2f08d4f3040ec2f12a9",
            "577ae9e407bb225dae48482a7f88bafb",
            "e85e6edf80bd07030f5a6c1769c84546",
            "49ed07d5fc60ee17a4a4019d7bea7d97",
            "aa86d752bec702172a54c1e8efe51d78",
            "df0dde6428f5c5146b5ce8f353865bca",
            "0f0b68b956415bbe06766e497ee77388",
            "44adc18b39d03b8d83f48cc572aaab78",
            "0460e6420739ae31b7076f5e1be5a1f1",
            "80c07379496d0f2aab66d8041a9c53b1",
            "0e3763e51612b56fa8dc933efc8d1971",
            "676f88a792d9034015a9613001c9bf02",
            "6f399bee3158613c74c1330cee5d9b0d",
            "534e64906cc2bf58ff4cf98ecd301dd5",
            "7d0f7513c85d766c45581f9fa8ab34a5",
            "01b4fe6c6e6459eac293a62428680e3b",
            "2bb5a0b24139f5448cdd51167ce33906",
            "bf6c5a26cb1061be91bec7fa73153cec",
            "e8911b580e7fc7e2d94d238f2c81c43b",
            "647714842c8dea4d192bdd7c2b41fd3b",
            "bba1e4283ed853eb484497db4b6da460",
            "d761384f8fed31c53197ce4916f32296",
            "3b119c1b8487da71412f3275616003dc",
            "47c300fcb48b882c124d69086b224956",
            "9e47185ae517f6f35daa1aa16c51ba45",
        ],
        optional_fnames=[
            "DiSCo_gradients_fsl.bvecs",
            "DiSCo_gradients_mrtrix.b",
            "DiSCo_gradients.scheme",
            "lowRes_DiSCo3_Strand_ODFs.nii.gz",
            "lowRes_DiSCo3_DWI_RicianNoise-snr10.nii.gz",
            "lowRes_DiSCo3_Strand_Intra_Volume_Fraction.nii.gz",
            "lowRes_DiSCo3_DWI_RicianNoise-snr30.nii.gz",
            "lowRes_DiSCo3_DWI_RicianNoise-snr20.nii.gz",
            "lowRes_DiSCo3_Strand_Average_Diameter.nii.gz",
            "lowRes_DiSCo3_DWI_Intra.nii.gz",
            "lowRes_DiSCo3_Strand_Count.nii.gz",
            "lowRes_DiSCo3_DWI.nii.gz",
            "lowRes_DiSCo3_Strand_Bundle_Count.nii.gz",
            "lowRes_DiSCo3_ROIs-mask.nii.gz",
            "lowRes_DiSCo3_DWI_RicianNoise-snr40.nii.gz",
            "lowRes_DiSCo3_mask.nii.gz",
            "lowRes_DiSCo3_DWI_RicianNoise-snr50.nii.gz",
            "lowRes_DiSCo3_ROIs.nii.gz",
            "highRes_DiSCo3_Strand_Average_Diameter.nii.gz",
            "highRes_DiSCo3_Strand_Count.nii.gz",
            "highRes_DiSCo3_DWI_RicianNoise-snr20.nii.gz",
            "highRes_DiSCo3_DWI_RicianNoise-snr30.nii.gz",
            "highRes_DiSCo3_Strand_Intra_Volume_Fraction.nii.gz",
            "highRes_DiSCo3_Strand_Bundle_Count.nii.gz",
            "highRes_DiSCo3_DWI_Intra.nii.gz",
            "highRes_DiSCo3_DWI_RicianNoise-snr50.nii.gz",
            "highRes_DiSCo3_Strand_ODFs.nii.gz",
            "highRes_DiSCo3_DWI_RicianNoise-snr40.nii.gz",
            "highRes_DiSCo3_DWI.nii.gz",
            "DiSCo3_Strands_ROIs_Pairs.txt",
            "DiSCo3_Connectivity_Matrix_Strands_Count.txt",
            "DiSCo3_Strands_Diameters.txt",
            "DiSCo3_Strands_Trajectories.trk",
        ],
        doc=(
            "Download DISCO 3 dataset: The Diffusion-Simulated Connectivity "
            "Dataset. DOI: 10.17632/fgf86jdfg6.3"
        ),
        data_size="1.05 GB",
        use_headers=True,
    )

    def fetch_disco_dataset(*, include_optional=False):
        """Download All DISCO datasets.

        Notes
        -----
        see DOI: 10.17632/fgf86jdfg6.3

        """
        files_1, folder_1 = fetch_disco1_dataset(include_optional=include_optional)
        files_2, folder_2 = fetch_disco2_dataset(include_optional=include_optional)
        files_3, folder_3 = fetch_disco3_dataset(include_optional=include_optional)

        all_path = (
            [pjoin(folder_1, f) for f in files_1]
            + [pjoin(folder_2, f) for f in files_2]
            + [pjoin(folder_3, f) for f in files_3]
        )

        return all_path

    @warning_for_keywords()
    def get_fnames(*, name="small_64D", include_optional=False):
        """Provide full paths to example or test datasets.

        Parameters
        ----------
        name : str
            the filename/s of which dataset to return, one of:

            - 'small_64D' small region of interest nifti,bvecs,bvals 64 directions
            - 'small_101D' small region of interest nifti, bvecs, bvals
              101 directions
            - 'aniso_vox' volume with anisotropic voxel size as Nifti
            - 'fornix' 300 tracks in Trackvis format (from Pittsburgh
              Brain Competition)
            - 'gqi_vectors' the scanner wave vectors needed for a GQI acquisitions
              of 101 directions tested on Siemens 3T Trio
            - 'small_25' small ROI (10x8x2) DTI data (b value 2000, 25 directions)
            - 'test_piesno' slice of N=8, K=14 diffusion data
            - 'reg_c' small 2D image used for validating registration
            - 'reg_o' small 2D image used for validation registration
            - 'cb_2' two vectorized cingulum bundles
        include_optional : bool, optional
            If True, include optional datasets.

        Returns
        -------
        fnames : tuple
            filenames for dataset

        Examples
        --------
        >>> import numpy as np
        >>> from dipy.io.image import load_nifti
        >>> from dipy.data import get_fnames
        >>> fimg, fbvals, fbvecs = get_fnames(name='small_101D')
        >>> bvals=np.loadtxt(fbvals)
        >>> bvecs=np.loadtxt(fbvecs).T
        >>> data, affine = load_nifti(fimg)
        >>> data.shape == (6, 10, 10, 102)
        True
        >>> bvals.shape == (102,)
        True
        >>> bvecs.shape == (102, 3)
        True

        """
        DATA_DIR = pjoin(op.dirname(__file__), "files")
        if name == "small_64D":
            fbvals = pjoin(DATA_DIR, "small_64D.bval")
            fbvecs = pjoin(DATA_DIR, "small_64D.bvec")
            fimg = pjoin(DATA_DIR, "small_64D.nii")
            return fimg, fbvals, fbvecs
        if name == "55dir_grad":
            fbvals = pjoin(DATA_DIR, "55dir_grad.bval")
            fbvecs = pjoin(DATA_DIR, "55dir_grad.bvec")
            return fbvals, fbvecs
        if name == "small_101D":
            fbvals = pjoin(DATA_DIR, "small_101D.bval")
            fbvecs = pjoin(DATA_DIR, "small_101D.bvec")
            fimg = pjoin(DATA_DIR, "small_101D.nii.gz")
            return fimg, fbvals, fbvecs
        if name == "aniso_vox":
            return pjoin(DATA_DIR, "aniso_vox.nii.gz")
        if name == "ascm_test":
            return pjoin(DATA_DIR, "ascm_out_test.nii.gz")
        if name == "fornix":
            return pjoin(DATA_DIR, "tracks300.trk")
        if name == "gqi_vectors":
            return pjoin(DATA_DIR, "ScannerVectors_GQI101.txt")
        if name == "dsi515btable":
            return pjoin(DATA_DIR, "dsi515_b_table.txt")
        if name == "dsi4169btable":
            return pjoin(DATA_DIR, "dsi4169_b_table.txt")
        if name == "grad514":
            return pjoin(DATA_DIR, "grad_514.txt")
        if name == "small_25":
            fbvals = pjoin(DATA_DIR, "small_25.bval")
            fbvecs = pjoin(DATA_DIR, "small_25.bvec")
            fimg = pjoin(DATA_DIR, "small_25.nii.gz")
            return fimg, fbvals, fbvecs
        if name == "small_25_streamlines":
            fstreamlines = pjoin(DATA_DIR, "EuDX_small_25.trk")
            return fstreamlines
        if name == "S0_10":
            fimg = pjoin(DATA_DIR, "S0_10slices.nii.gz")
            return fimg
        if name == "test_piesno":
            fimg = pjoin(DATA_DIR, "test_piesno.nii.gz")
            return fimg
        if name == "reg_c":
            return pjoin(DATA_DIR, "C.npy")
        if name == "reg_o":
            return pjoin(DATA_DIR, "circle.npy")
        if name == "cb_2":
            return pjoin(DATA_DIR, "cb_2.npz")
        if name == "minimal_bundles":
            return pjoin(DATA_DIR, "minimal_bundles.zip")
        if name == "t1_coronal_slice":
            return pjoin(DATA_DIR, "t1_coronal_slice.npy")
        if name == "t-design":
            N = 45
            return pjoin(DATA_DIR, f"tdesign{N}.txt")
        if name == "scil_b0":
            files, folder = fetch_scil_b0()
            files = files["datasets_multi-site_all_companies.zip"][2]
            files = [pjoin(folder, f) for f in files]
            return [f for f in files if op.isfile(f)]
        if name == "stanford_hardi":
            files, folder = fetch_stanford_hardi()
            fraw = pjoin(folder, "HARDI150.nii.gz")
            fbval = pjoin(folder, "HARDI150.bval")
            fbvec = pjoin(folder, "HARDI150.bvec")
            return fraw, fbval, fbvec
        if name == "taiwan_ntu_dsi":
            files, folder = fetch_taiwan_ntu_dsi()
            fraw = pjoin(folder, "DSI203.nii.gz")
            fbval = pjoin(folder, "DSI203.bval")
            fbvec = pjoin(folder, "DSI203.bvec")
            return fraw, fbval, fbvec
        if name == "sherbrooke_3shell":
            files, folder = fetch_sherbrooke_3shell()
            fraw = pjoin(folder, "HARDI193.nii.gz")
            fbval = pjoin(folder, "HARDI193.bval")
            fbvec = pjoin(folder, "HARDI193.bvec")
            return fraw, fbval, fbvec
        if name == "isbi2013_2shell":
            files, folder = fetch_isbi2013_2shell()
            fraw = pjoin(folder, "phantom64.nii.gz")
            fbval = pjoin(folder, "phantom64.bval")
            fbvec = pjoin(folder, "phantom64.bvec")
            return fraw, fbval, fbvec
        if name == "stanford_labels":
            files, folder = fetch_stanford_labels()
            return pjoin(folder, "aparc-reduced.nii.gz")
        if name == "syn_data":
            files, folder = fetch_syn_data()
            t1_name = pjoin(folder, "t1.nii.gz")
            b0_name = pjoin(folder, "b0.nii.gz")
            return t1_name, b0_name
        if name == "stanford_t1":
            files, folder = fetch_stanford_t1()
            return pjoin(folder, "t1.nii.gz")
        if name == "stanford_pve_maps":
            files, folder = fetch_stanford_pve_maps()
            f_pve_csf = pjoin(folder, "pve_csf.nii.gz")
            f_pve_gm = pjoin(folder, "pve_gm.nii.gz")
            f_pve_wm = pjoin(folder, "pve_wm.nii.gz")
            return f_pve_csf, f_pve_gm, f_pve_wm
        if name == "ivim":
            files, folder = fetch_ivim()
            fraw = pjoin(folder, "ivim.nii.gz")
            fbval = pjoin(folder, "ivim.bval")
            fbvec = pjoin(folder, "ivim.bvec")
            return fraw, fbval, fbvec
        if name == "tissue_data":
            files, folder = fetch_tissue_data()
            t1_name = pjoin(folder, "t1_brain.nii.gz")
            t1d_name = pjoin(folder, "t1_brain_denoised.nii.gz")
            ap_name = pjoin(folder, "power_map.nii.gz")
            return t1_name, t1d_name, ap_name
        if name == "cfin_multib":
            files, folder = fetch_cfin_multib()
            t1_name = pjoin(folder, "T1.nii")
            fraw = pjoin(folder, "__DTI_AX_ep2d_2_5_iso_33d_20141015095334_4.nii")
            fbval = pjoin(folder, "__DTI_AX_ep2d_2_5_iso_33d_20141015095334_4.bval")
            fbvec = pjoin(folder, "__DTI_AX_ep2d_2_5_iso_33d_20141015095334_4.bvec")
            return fraw, fbval, fbvec, t1_name
        if name == "target_tractrogram_hcp":
            files, folder = fetch_target_tractogram_hcp()
            return pjoin(
                folder, "target_tractogram_hcp", "hcp_tractogram", "streamlines.trk"
            )
        if name == "bundle_atlas_hcp842":
            files, folder = fetch_bundle_atlas_hcp842()
            return get_bundle_atlas_hcp842()
        if name == "30_bundle_atlas_hcp842":
            files, folder = fetch_30_bundle_atlas_hcp842()
            return get_bundle_atlas_hcp842(size=30)
        if name == "qte_lte_pte":
            _, folder = fetch_qte_lte_pte()
            fdata = pjoin(folder, "lte-pte.nii.gz")
            fbval = pjoin(folder, "lte-pte.bval")
            fbvec = pjoin(folder, "lte-pte.bvec")
            fmask = pjoin(folder, "mask.nii.gz")
            return fdata, fbval, fbvec, fmask
        if name == "cti_rat1":
            _, folder = fetch_cti_rat1()
            fdata = pjoin(folder, "Rat1_invivo_cti_data.nii")
            fbval1 = pjoin(folder, "bvals1.bval")
            fbvec1 = pjoin(folder, "bvec1.bvec")
            fbval2 = pjoin(folder, "bvals2.bval")
            fbvec2 = pjoin(folder, "bvec2.bvec")
            fmask = pjoin(folder, "Rat1_mask.nii")
            return fdata, fbval1, fbvec1, fbval2, fbvec2, fmask
        if name == "fury_surface":
            files, folder = fetch_fury_surface()
            surface_name = pjoin(folder, "100307_white_lh.vtk")
            return surface_name
        if name == "histo_resdnn_tf_weights":
            files, folder = fetch_resdnn_tf_weights()
            wraw = pjoin(folder, "resdnn_weights_mri_2018.h5")
            return wraw
        if name == "histo_resdnn_torch_weights":
            files, folder = fetch_resdnn_torch_weights()
            wraw = pjoin(folder, "histo_weights.pth")
            return wraw
        if name == "synb0_default_weights":
            _, folder = fetch_synb0_weights()
            w1 = pjoin(folder, "synb0_default_weights1.h5")
            w2 = pjoin(folder, "synb0_default_weights2.h5")
            w3 = pjoin(folder, "synb0_default_weights3.h5")
            w4 = pjoin(folder, "synb0_default_weights4.h5")
            w5 = pjoin(folder, "synb0_default_weights5.h5")
            return w1, w2, w3, w4, w5
        if name == "synb0_test_data":
            files, folder = fetch_synb0_test()
            input_array = pjoin(folder, "test_input_synb0.npz")
            target_array = pjoin(folder, "test_output_synb0.npz")
            return input_array, target_array
        if name == "deepn4_default_tf_weights":
            _, folder = fetch_deepn4_tf_weights()
            w1 = pjoin(folder, "model_weights.h5")
            return w1
        if name == "deepn4_default_torch_weights":
            _, folder = fetch_deepn4_torch_weights()
            w1 = pjoin(folder, "deepn4_torch_weights")
            return w1
        if name == "deepn4_test_data":
            files, folder = fetch_deepn4_test()
            input_array = pjoin(folder, "test_input_deepn4.npz")
            target_array = pjoin(folder, "new_test_output_deepn4.npz")
            return input_array, target_array
        if name == "evac_default_tf_weights":
            files, folder = fetch_evac_tf_weights()
            weight = pjoin(folder, "evac_default_weights.h5")
            return weight
        if name == "evac_default_torch_weights":
            files, folder = fetch_evac_torch_weights()
            weight = pjoin(folder, "evac_weights.pth")
            return weight
        if name == "evac_test_data":
            files, folder = fetch_evac_test()
            test_data = pjoin(folder, "evac_test_data.npz")
            return test_data
        if name == "DiB_70_lte_pte_ste":
            _, folder = fetch_DiB_70_lte_pte_ste()
            fdata = pjoin(folder, "DiB_70_lte_pte_ste.nii.gz")
            fbval = pjoin(folder, "bval_DiB_70_lte_pte_ste.bval")
            fbvec = pjoin(folder, "bvec_DiB_70_lte_pte_ste.bvec")
            fmask = pjoin(folder, "DiB_mask.nii.gz")
            return fdata, fbval, fbvec, fmask
        if name == "DiB_217_lte_pte_ste":
            _, folder = fetch_DiB_217_lte_pte_ste()
            fdata_1 = pjoin(folder, "DiB_217_lte_pte_ste_1.nii.gz")
            fdata_2 = pjoin(folder, "DiB_217_lte_pte_ste_2.nii.gz")
            fbval = pjoin(folder, "bval_DiB_217_lte_pte_ste.bval")
            fbvec = pjoin(folder, "bvec_DiB_217_lte_pte_ste.bvec")
            fmask = pjoin(folder, "DiB_mask.nii.gz")
            return fdata_1, fdata_2, fbval, fbvec, fmask
        if name == "ptt_minimal_dataset":
            files, folder = fetch_ptt_minimal_dataset()
            fod_name = pjoin(folder, "ptt_fod.nii")
            seed_coords_name = pjoin(folder, "ptt_seed_coords.txt")
            seed_image_name = pjoin(folder, "ptt_seed_image.nii")
            return fod_name, seed_coords_name, seed_image_name
        if name == "gold_standard_tracks":
            filepath_dix = {}
            files, folder = fetch_gold_standard_io()
            for filename in files:
                filepath_dix[filename] = os.path.join(folder, filename)

            with open(filepath_dix["points_data.json"]) as json_file:
                points_data = dict(json.load(json_file))

            with open(filepath_dix["streamlines_data.json"]) as json_file:
                streamlines_data = dict(json.load(json_file))

            return filepath_dix, points_data, streamlines_data
        if name in ["disco", "disco1", "disco2", "disco3"]:
            local_fetcher = globals().get(f"fetch_{name}_dataset")
            files, folder = local_fetcher(include_optional=include_optional)
            return [pjoin(folder, f) for f in files]

    def read_qtdMRI_test_retest_2subjects():
        r"""Load test-retest qt-dMRI acquisitions of two C57Bl6 mice.

        These datasets were used to study test-retest reproducibility of
        time-dependent q-space indices (q$\tau$-indices) in the corpus callosum of
        two mice :footcite:p:`Fick2018`. The data itself and its details are
        publicly available and can be cited at :footcite:p:`Wassermann2017`.

        The test-retest diffusion MRI spin echo sequences were acquired from two
        C57Bl6 wild-type mice on an 11.7 Tesla Bruker scanner. The test and retest
        acquisition were taken 48 hours from each other. The (processed) data
        consists of 80x160x5 voxels of size 110x110x500μm. Each data set consists
        of 515 Diffusion-Weighted Images (DWIs) spread over 35 acquisition shells.
        The shells are spread over 7 gradient strength shells with a maximum
        gradient strength of 491 mT/m, 5 pulse separation shells between
        [10.8 - 20.0]ms, and a pulse length of 5ms. We manually created a brain
        mask and corrected the data from eddy currents and motion artifacts using
        FSL's eddy. A region of interest was then drawn in the middle slice in the
        corpus callosum, where the tissue is reasonably coherent.

        Returns
        -------
        data : list of length 4
            contains the dwi datasets ordered as
            (subject1_test, subject1_retest, subject2_test, subject2_retest)
        cc_masks : list of length 4
            contains the corpus callosum masks ordered in the same order as data.
        gtabs : list of length 4
            contains the qt-dMRI gradient tables of the data sets.

        References
        ----------
        .. footbibliography::
        """
        data = []
        data_names = [
            "subject1_dwis_test.nii.gz",
            "subject1_dwis_retest.nii.gz",
            "subject2_dwis_test.nii.gz",
            "subject2_dwis_retest.nii.gz",
        ]
        for data_name in data_names:
            data_loc = pjoin(dipy_home, "qtdMRI_test_retest_2subjects", data_name)
            data.append(load_nifti_data(data_loc))

        cc_masks = []
        mask_names = [
            "subject1_ccmask_test.nii.gz",
            "subject1_ccmask_retest.nii.gz",
            "subject2_ccmask_test.nii.gz",
            "subject2_ccmask_retest.nii.gz",
        ]
        for mask_name in mask_names:
            mask_loc = pjoin(dipy_home, "qtdMRI_test_retest_2subjects", mask_name)
            cc_masks.append(load_nifti_data(mask_loc))

        gtabs = []
        gtab_txt_names = [
            "subject1_scheme_test.txt",
            "subject1_scheme_retest.txt",
            "subject2_scheme_test.txt",
            "subject2_scheme_retest.txt",
        ]
        for gtab_txt_name in gtab_txt_names:
            txt_loc = pjoin(dipy_home, "qtdMRI_test_retest_2subjects", gtab_txt_name)
            qtdmri_scheme = np.loadtxt(txt_loc, skiprows=1)
            bvecs = qtdmri_scheme[:, 1:4]
            G = qtdmri_scheme[:, 4] / 1e3  # because dipy takes T/mm not T/m
            small_delta = qtdmri_scheme[:, 5]
            big_delta = qtdmri_scheme[:, 6]
            gtab = gradient_table_from_gradient_strength_bvecs(
                G, bvecs, big_delta, small_delta
            )
            gtabs.append(gtab)

        return data, cc_masks, gtabs

    def read_scil_b0():
        """Load GE 3T b0 image form the scil b0 dataset.

        Returns
        -------
        img : obj,
            Nifti1Image

        """
        fnames = get_fnames(name="scil_b0")
        return nib.load(fnames[0])

    def read_siemens_scil_b0():
        """Load Siemens 1.5T b0 image from the scil b0 dataset.

        Returns
        -------
        img : obj,
            Nifti1Image

        """
        fnames = get_fnames(name="scil_b0")
        return nib.load(fnames[1])

    def read_isbi2013_2shell():
        """Load ISBI 2013 2-shell synthetic dataset.

        Returns
        -------
        img : obj,
            Nifti1Image
        gtab : obj,
            GradientTable

        """
        fraw, fbval, fbvec = get_fnames(name="isbi2013_2shell")
        bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
        gtab = gradient_table(bvals, bvecs=bvecs)
        img = nib.load(fraw)
        return img, gtab

    def read_sherbrooke_3shell():
        """Load Sherbrooke 3-shell HARDI dataset.

        Returns
        -------
        img : obj,
            Nifti1Image
        gtab : obj,
            GradientTable

        """
        fraw, fbval, fbvec = get_fnames(name="sherbrooke_3shell")
        bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
        gtab = gradient_table(bvals, bvecs=bvecs)
        img = nib.load(fraw)
        return img, gtab

    def read_stanford_labels():
        """Read stanford hardi data and label map."""
        # First get the hardi data
        hard_img, gtab = read_stanford_hardi()
        # Fetch and load
        labels_file = get_fnames(name="stanford_labels")
        labels_img = nib.load(labels_file)
        return hard_img, gtab, labels_img

    def read_stanford_hardi():
        """Load Stanford HARDI dataset.

        Returns
        -------
        img : obj,
            Nifti1Image
        gtab : obj,
            GradientTable

        """
        fraw, fbval, fbvec = get_fnames(name="stanford_hardi")
        bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
        gtab = gradient_table(bvals, bvecs=bvecs)
        img = nib.load(fraw)
        return img, gtab

    def read_stanford_t1():
        f_t1 = get_fnames(name="stanford_t1")
        img = nib.load(f_t1)
        return img

    def read_stanford_pve_maps():
        f_pve_csf, f_pve_gm, f_pve_wm = get_fnames(name="stanford_pve_maps")
        img_pve_csf = nib.load(f_pve_csf)
        img_pve_gm = nib.load(f_pve_gm)
        img_pve_wm = nib.load(f_pve_wm)
        return img_pve_csf, img_pve_gm, img_pve_wm

    def read_taiwan_ntu_dsi():
        """Load Taiwan NTU dataset.

        Returns
        -------
        img : obj,
            Nifti1Image
        gtab : obj,
            GradientTable

        """
        fraw, fbval, fbvec = get_fnames(name="taiwan_ntu_dsi")
        bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
        bvecs[1:] = bvecs[1:] / np.sqrt(np.sum(bvecs[1:] * bvecs[1:], axis=1))[:, None]
        gtab = gradient_table(bvals, bvecs=bvecs)
        img = nib.load(fraw)
        return img, gtab

    def read_syn_data():
        """Load t1 and b0 volumes from the same session.

        Returns
        -------
        t1 : obj,
            Nifti1Image
        b0 : obj,
            Nifti1Image

        """
        t1_name, b0_name = get_fnames(name="syn_data")
        t1 = nib.load(t1_name)
        b0 = nib.load(b0_name)
        return t1, b0

    def fetch_tissue_data(*, include_optional=False):
        """Download images to be used for tissue classification"""

        t1 = "https://ndownloader.figshare.com/files/6965969"
        t1d = "https://ndownloader.figshare.com/files/6965981"
        ap = "https://ndownloader.figshare.com/files/6965984"

        folder = pjoin(dipy_home, "tissue_data")

        md5_list = [
            "99c4b77267a6855cbfd96716d5d65b70",  # t1
            "4b87e1b02b19994fbd462490cc784fa3",  # t1d
            "c0ea00ed7f2ff8b28740f18aa74bff6a",
        ]  # ap

        url_list = [t1, t1d, ap]
        fname_list = ["t1_brain.nii.gz", "t1_brain_denoised.nii.gz", "power_map.nii.gz"]

        if not op.exists(folder):
            _log(f"Creating new directory {folder}")
            os.makedirs(folder)
            msg = "Downloading 3 Nifti1 images (9.3MB)..."
            _log(msg)

            for i in range(len(md5_list)):
                _get_file_data(pjoin(folder, fname_list[i]), url_list[i])
                check_md5(pjoin(folder, fname_list[i]), md5_list[i])

            _log("Done.")
            _log(f"Files copied in folder {folder}")
        else:
            _already_there_msg(folder)

        return fname_list, folder

    @warning_for_keywords()
    def read_tissue_data(*, contrast="T1"):
        """Load images to be used for tissue classification

        Parameters
        ----------
        contrast : str
            'T1', 'T1 denoised' or 'Anisotropic Power'

        Returns
        -------
        image : obj,
            Nifti1Image

        """
        folder = pjoin(dipy_home, "tissue_data")
        t1_name = pjoin(folder, "t1_brain.nii.gz")
        t1d_name = pjoin(folder, "t1_brain_denoised.nii.gz")
        ap_name = pjoin(folder, "power_map.nii.gz")

        md5_dict = {
            "t1": "99c4b77267a6855cbfd96716d5d65b70",
            "t1d": "4b87e1b02b19994fbd462490cc784fa3",
            "ap": "c0ea00ed7f2ff8b28740f18aa74bff6a",
        }

        check_md5(t1_name, md5_dict["t1"])
        check_md5(t1d_name, md5_dict["t1d"])
        check_md5(ap_name, md5_dict["ap"])

        if contrast == "T1 denoised":
            return nib.load(t1d_name)
        elif contrast == "Anisotropic Power":
            return nib.load(ap_name)
        else:
            return nib.load(t1_name)

    mni_notes = """
        Notes
        -----
        The templates were downloaded from the MNI (McGill University)
        `website <https://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009>`_
        in July 2015.

        The following publications should be referenced when using these templates:

        - :footcite:t:`Fonov2013`
        - :footcite:t:`Fonov2009`

        **License for the MNI templates:**

        Copyright (C) 1993-2004, Louis Collins McConnell Brain Imaging Centre,
        Montreal Neurological Institute, McGill University. Permission to use,
        copy, modify, and distribute this software and its documentation for any
        purpose and without fee is hereby granted, provided that the above
        copyright notice appear in all copies. The authors and McGill University
        make no representations about the suitability of this software for any
        purpose. It is provided "as is" without express or implied warranty. The
        authors are not responsible for any data loss, equipment damage, property
        loss, or injury to subjects or patients resulting from the use or misuse
        of this software package.

        References
        ----------
        .. footbibliography::
    """

    @warning_for_keywords()
    def read_mni_template(*, version="a", contrast="T2"):
        """Read the MNI template from disk.

        Parameters
        ----------
        version: string
            There are two MNI templates 2009a and 2009c, so options available are:
            "a" and "c".
        contrast : list or string, optional
            Which of the contrast templates to read. For version "a" two contrasts
            are available: "T1" and "T2". Similarly for version "c" there are two
            options, "T1" and "mask". You can input contrast as a string or a list

        Returns
        -------
        list : contains the nibabel.Nifti1Image objects requested, according to the
            order they were requested in the input.

        Examples
        --------
        >>> # Get only the T1 file for version c:
        >>> T1 = read_mni_template("c", contrast = "T1") # doctest: +SKIP
        >>> # Get both files in this order for version a:
        >>> T1, T2 = read_mni_template(contrast = ["T1", "T2"]) # doctest: +SKIP

        """
        files, folder = fetch_mni_template()
        file_dict_a = {
            "T1": pjoin(folder, "mni_icbm152_t1_tal_nlin_asym_09a.nii"),
            "T2": pjoin(folder, "mni_icbm152_t2_tal_nlin_asym_09a.nii"),
        }

        file_dict_c = {
            "T1": pjoin(folder, "mni_icbm152_t1_tal_nlin_asym_09c.nii"),
            "mask": pjoin(folder, "mni_icbm152_t1_tal_nlin_asym_09c_mask.nii"),
        }

        if contrast == "T2" and version == "c":
            raise ValueError("No T2 image for MNI template 2009c")

        if contrast == "mask" and version == "a":
            raise ValueError("No template mask available for MNI 2009a")

        if not isinstance(contrast, str) and version == "c":
            for k in contrast:
                if k == "T2":
                    raise ValueError("No T2 image for MNI template 2009c")

        if version == "a":
            if isinstance(contrast, str):
                return nib.load(file_dict_a[contrast])
            else:
                out_list = []
                for k in contrast:
                    out_list.append(nib.load(file_dict_a[k]))
        elif version == "c":
            if isinstance(contrast, str):
                return nib.load(file_dict_c[contrast])
            else:
                out_list = []
                for k in contrast:
                    out_list.append(nib.load(file_dict_c[k]))
        else:
            raise ValueError("Only 2009a and 2009c versions are available")
        return out_list

    # Add the references to both MNI-related functions:
    read_mni_template.__doc__ += mni_notes
    fetch_mni_template.__doc__ += mni_notes

    @warning_for_keywords()
    def fetch_cenir_multib(*, with_raw=False, **kwargs):
        """Fetch 'HCP-like' data, collected at multiple b-values.

        Parameters
        ----------
        with_raw : bool
            Whether to fetch the raw data. Per default, this is False, which means
            that only eddy-current/motion corrected data is fetched
        """
        folder = pjoin(dipy_home, "cenir_multib")

        fname_list = [
            "4D_dwi_eddycor_B200.nii.gz",
            "dwi_bvals_B200",
            "dwi_bvecs_B200",
            "4D_dwieddycor_B400.nii.gz",
            "bvals_B400",
            "bvecs_B400",
            "4D_dwieddycor_B1000.nii.gz",
            "bvals_B1000",
            "bvecs_B1000",
            "4D_dwieddycor_B2000.nii.gz",
            "bvals_B2000",
            "bvecs_B2000",
            "4D_dwieddycor_B3000.nii.gz",
            "bvals_B3000",
            "bvecs_B3000",
        ]

        md5_list = [
            "fd704aa3deb83c1c7229202cb3db8c48",
            "80ae5df76a575fe5bf9f1164bb0d4cfb",
            "18e90f8a3e6a4db2457e5b1ba1cc98a9",
            "3d0f2b8ef7b6a4a3aa5c4f7a90c9cfec",
            "c38056c40c9cc42372232d6e75c47f54",
            "810d79b4c30cb7dff3b2000017d5f72a",
            "dde8037601a14436b2173f4345b5fd17",
            "97de6a492ae304f39e0b418b6ebac64c",
            "f28a0faa701bdfc66e31bde471a5b992",
            "c5e4b96e3afdee99c0e994eff3b2331a",
            "9c83b8d5caf9c3def240f320f2d2f56c",
            "05446bd261d57193d8dbc097e06db5ff",
            "f0d70456ce424fda2cecd48e64f3a151",
            "336accdb56acbbeff8dac1748d15ceb8",
            "27089f3baaf881d96f6a9da202e3d69b",
        ]
        if with_raw:
            fname_list.extend(
                [
                    "4D_dwi_B200.nii.gz",
                    "4D_dwi_B400.nii.gz",
                    "4D_dwi_B1000.nii.gz",
                    "4D_dwi_B2000.nii.gz",
                    "4D_dwi_B3000.nii.gz",
                ]
            )
            md5_list.extend(
                [
                    "a8c36e76101f2da2ca8119474ded21d5",
                    "a0e7939f6d977458afbb2f4659062a79",
                    "87fc307bdc2e56e105dffc81b711a808",
                    "7c23e8a5198624aa29455f0578025d4f",
                    "4e4324c676f5a97b3ded8bbb100bf6e5",
                ]
            )

        files = {}
        baseurl = UW_RW_URL + "1773/33311/"
        for f, m in zip(fname_list, md5_list):
            files[f] = (baseurl + f, m)

        fetch_data(files, folder)
        return files, folder

    @warning_for_keywords()
    def read_cenir_multib(*, bvals=None):
        """Read CENIR multi b-value data.

        Parameters
        ----------
        bvals : list or int
            The b-values to read from file (200, 400, 1000, 2000, 3000).

        Returns
        -------
        gtab : a GradientTable class instance
        img : nibabel.Nifti1Image

        """
        files, folder = fetch_cenir_multib(with_raw=False)
        if bvals is None:
            bvals = [200, 400, 1000, 2000, 3000]
        if isinstance(bvals, int):
            bvals = [bvals]
        file_dict = {
            200: {
                "DWI": pjoin(folder, "4D_dwi_eddycor_B200.nii.gz"),
                "bvals": pjoin(folder, "dwi_bvals_B200"),
                "bvecs": pjoin(folder, "dwi_bvecs_B200"),
            },
            400: {
                "DWI": pjoin(folder, "4D_dwieddycor_B400.nii.gz"),
                "bvals": pjoin(folder, "bvals_B400"),
                "bvecs": pjoin(folder, "bvecs_B400"),
            },
            1000: {
                "DWI": pjoin(folder, "4D_dwieddycor_B1000.nii.gz"),
                "bvals": pjoin(folder, "bvals_B1000"),
                "bvecs": pjoin(folder, "bvecs_B1000"),
            },
            2000: {
                "DWI": pjoin(folder, "4D_dwieddycor_B2000.nii.gz"),
                "bvals": pjoin(folder, "bvals_B2000"),
                "bvecs": pjoin(folder, "bvecs_B2000"),
            },
            3000: {
                "DWI": pjoin(folder, "4D_dwieddycor_B3000.nii.gz"),
                "bvals": pjoin(folder, "bvals_B3000"),
                "bvecs": pjoin(folder, "bvecs_B3000"),
            },
        }
        data = []
        bval_list = []
        bvec_list = []
        for bval in bvals:
            data.append(load_nifti_data(file_dict[bval]["DWI"]))
            bval_list.extend(np.loadtxt(file_dict[bval]["bvals"]))
            bvec_list.append(np.loadtxt(file_dict[bval]["bvecs"]))

        # All affines are the same, so grab the last one:
        aff = nib.load(file_dict[bval]["DWI"]).affine
        return (
            nib.Nifti1Image(np.concatenate(data, -1), aff),
            gradient_table(bval_list, bvecs=np.concatenate(bvec_list, -1)),
        )

    CENIR_notes = """
        Notes
        -----
        Details of the acquisition and processing, and additional meta-data are
        available through UW researchworks:

        https://digital.lib.washington.edu/researchworks/handle/1773/33311
        """

    fetch_cenir_multib.__doc__ += CENIR_notes
    read_cenir_multib.__doc__ += CENIR_notes

    @warning_for_keywords()
    def read_bundles_2_subjects(
        *, subj_id="subj_1", metrics=("fa",), bundles=("af.left", "cst.right", "cc_1")
    ):
        r"""Read images and streamlines from 2 subjects of the SNAIL dataset.

        See :footcite:p:`Renauld2016` and :footcite:p:`Garyfallidis2015` for further
        details about the dataset and processing pipeline.

        Parameters
        ----------
        subj_id : string
            Either ``subj_1`` or ``subj_2``.
        metrics : array-like
            Either ['fa'] or ['t1'] or ['fa', 't1']
        bundles : array-like
            E.g., ['af.left', 'cst.right', 'cc_1']. See all the available bundles
            in the ``exp_bundles_maps/bundles_2_subjects`` directory of your
            ``DIPY_HOME`` of ``$HOME/.dipy`` folder.

        Returns
        -------
        dix : dict
            Dictionary with data of the metrics and the bundles as keys.

        Notes
        -----
        If you are using these datasets please cite the following publications.

        References
        ----------
        .. footbibliography::

        """
        dname = pjoin(dipy_home, "exp_bundles_and_maps", "bundles_2_subjects")

        from dipy.io.streamline import load_tractogram
        from dipy.tracking.streamline import Streamlines

        res = {}

        if "t1" in metrics:
            data, affine = load_nifti(pjoin(dname, subj_id, "t1_warped.nii.gz"))
            res["t1"] = data

        if "fa" in metrics:
            fa, affine = load_nifti(pjoin(dname, subj_id, "fa_1x1x1.nii.gz"))
            res["fa"] = fa

        res["affine"] = affine

        for bun in bundles:
            streams = load_tractogram(
                pjoin(dname, subj_id, "bundles", f"bundles_{bun}.trk"),
                "same",
                bbox_valid_check=False,
            ).streamlines

            streamlines = Streamlines(streams)
            res[bun] = streamlines

        return res

    def read_ivim():
        """Load IVIM dataset.

        Returns
        -------
        img : obj,
            Nifti1Image
        gtab : obj,
            GradientTable

        """
        fraw, fbval, fbvec = get_fnames(name="ivim")
        bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
        gtab = gradient_table(bvals, bvecs=bvecs, b0_threshold=0)
        img = nib.load(fraw)
        return img, gtab

    def read_cfin_dwi():
        """Load CFIN multi b-value DWI data.

        Returns
        -------
        img : obj,
            Nifti1Image
        gtab : obj,
            GradientTable

        """
        fraw, fbval, fbvec, _ = get_fnames(name="cfin_multib")
        bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
        gtab = gradient_table(bvals, bvecs=bvecs)
        img = nib.load(fraw)
        return img, gtab

    def read_cfin_t1():
        """Load CFIN T1-weighted data.

        Returns
        -------
        img : obj,
            Nifti1Image

        """
        _, _, _, fraw = get_fnames(name="cfin_multib")
        img = nib.load(fraw)
        return img  # , gtab

    def get_file_formats():
        """

        Returns
        -------
        bundles_list : all bundles (list)
        ref_anat : reference
        """
        ref_anat = pjoin(dipy_home, "bundle_file_formats_example", "template0.nii.gz")
        bundles_list = []
        for filename in [
            "cc_m_sub.trk",
            "laf_m_sub.tck",
            "lpt_m_sub.fib",
            "raf_m_sub.vtk",
            "rpt_m_sub.dpy",
        ]:
            bundles_list.append(pjoin(dipy_home, "bundle_file_formats_example", filename))

        return bundles_list, ref_anat

    @warning_for_keywords()
    def get_bundle_atlas_hcp842(*, size=80):
        """
        Returns
        -------
        file1 : string
        file2 : string
        """
        size = 80 if size not in [80, 30] else size

        file1 = pjoin(
            dipy_home,
            "bundle_atlas_hcp842",
            f"Atlas_{size}_Bundles",
            "whole_brain",
            "whole_brain_MNI.trk",
        )

        file2 = pjoin(
            dipy_home, "bundle_atlas_hcp842", f"Atlas_{size}_Bundles", "bundles", "*.trk"
        )

        return file1, file2

    def get_two_hcp842_bundles():
        """
        Returns
        -------
        file1 : string
        file2 : string
        """
        file1 = pjoin(
            dipy_home, "bundle_atlas_hcp842", "Atlas_80_Bundles", "bundles", "AF_L.trk"
        )

        file2 = pjoin(
            dipy_home, "bundle_atlas_hcp842", "Atlas_80_Bundles", "bundles", "CST_L.trk"
        )

        return file1, file2

    def get_target_tractogram_hcp():
        """
        Returns
        -------
        file1 : string
        """
        file1 = pjoin(
            dipy_home, "target_tractogram_hcp", "hcp_tractogram", "streamlines.trk"
        )

        return file1

    def read_qte_lte_pte():
        """Read q-space trajectory encoding data with linear and planar tensor
        encoding.

        Returns
        -------
        data_img : nibabel.nifti1.Nifti1Image
            dMRI data image.
        mask_img : nibabel.nifti1.Nifti1Image
            Brain mask image.
        gtab : dipy.core.gradients.GradientTable
            Gradient table.
        """
        fdata, fbval, fbvec, fmask = get_fnames(name="qte_lte_pte")
        data_img = nib.load(fdata)
        mask_img = nib.load(fmask)
        bvals = np.loadtxt(fbval)
        bvecs = np.loadtxt(fbvec)
        btens = np.array(["LTE" for i in range(61)] + ["PTE" for i in range(61)])
        gtab = gradient_table(bvals, bvecs=bvecs, btens=btens)
        return data_img, mask_img, gtab

    def read_DiB_70_lte_pte_ste():
        """Read q-space trajectory encoding data with 70 between linear, planar,
        and spherical tensor encoding measurements.

        Returns
        -------
        data_img : nibabel.nifti1.Nifti1Image
            dMRI data image.
        mask_img : nibabel.nifti1.Nifti1Image
            Brain mask image.
        gtab : dipy.core.gradients.GradientTable
            Gradient table.
        """
        fdata, fbval, fbvec, fmask = get_fnames(name="DiB_70_lte_pte_ste")
        data_img = nib.load(fdata)
        mask_img = nib.load(fmask)
        bvals = np.loadtxt(fbval)
        bvecs = np.loadtxt(fbvec)
        btens = np.array(
            ["LTE" for i in range(1)]
            + ["LTE" for i in range(4)]
            + ["PTE" for i in range(3)]
            + ["STE" for i in range(3)]
            + ["STE" for i in range(4)]
            + ["LTE" for i in range(8)]
            + ["PTE" for i in range(5)]
            + ["STE" for i in range(5)]
            + ["LTE" for i in range(21)]
            + ["PTE" for i in range(10)]
            + ["STE" for i in range(6)]
        )
        gtab = gradient_table(bvals, bvecs=bvecs, btens=btens)
        return data_img, mask_img, gtab

    def read_DiB_217_lte_pte_ste():
        """Read q-space trajectory encoding data with 217 between linear,
        planar, and spherical tensor encoding.

        Returns
        -------
        data_img : nibabel.nifti1.Nifti1Image
            dMRI data image.
        mask_img : nibabel.nifti1.Nifti1Image
            Brain mask image.
        gtab : dipy.core.gradients.GradientTable
            Gradient table.
        """
        fdata_1, fdata_2, fbval, fbvec, fmask = get_fnames(name="DiB_217_lte_pte_ste")
        _, folder = fetch_DiB_217_lte_pte_ste()
        if op.isfile(pjoin(folder, "DiB_217_lte_pte_ste.nii.gz")):
            data_img = nib.load(pjoin(folder, "DiB_217_lte_pte_ste.nii.gz"))
        else:
            data_1, affine = load_nifti(fdata_1)
            data_2, _ = load_nifti(fdata_2)
            data = np.concatenate((data_1, data_2), axis=3)
            save_nifti(pjoin(folder, "DiB_217_lte_pte_ste.nii.gz"), data, affine)
            data_img = nib.load(pjoin(folder, "DiB_217_lte_pte_ste.nii.gz"))
        mask_img = nib.load(fmask)
        bvals = np.loadtxt(fbval)
        bvecs = np.loadtxt(fbvec)
        btens = np.array(
            ["LTE" for i in range(13)]
            + ["LTE" for i in range(10)]
            + ["PTE" for i in range(10)]
            + ["STE" for i in range(10)]
            + ["LTE" for i in range(10)]
            + ["PTE" for i in range(10)]
            + ["STE" for i in range(10)]
            + ["LTE" for i in range(16)]
            + ["PTE" for i in range(16)]
            + ["STE" for i in range(10)]
            + ["LTE" for i in range(46)]
            + ["PTE" for i in range(46)]
            + ["STE" for i in range(10)]
        )
        gtab = gradient_table(bvals, bvecs=bvecs, btens=btens)
        return data_img, mask_img, gtab

    def extract_example_tracts(out_dir):
        """Extract 5 'AF_L','CST_R' and 'CC_ForcepsMajor' trk files in out_dir
        folder.

        Parameters
        ----------
        out_dir : str
            Folder in which to extract the files.

        """

        fname = get_fnames(name="minimal_bundles")

        with zipfile.ZipFile(fname, "r") as zip_obj:
            zip_obj.extractall(out_dir)

    def read_five_af_bundles():
        """Load 5 small left arcuate fasciculus bundles.

        Returns
        -------
        bundles: list of ArraySequence
            List with loaded bundles.

        """

        subjects = ["sub_1", "sub_2", "sub_3", "sub_4", "sub_5"]

        with tempfile.TemporaryDirectory() as temp_dir:
            extract_example_tracts(temp_dir)

            bundles = []
            for sub in subjects:
                fname = pjoin(temp_dir, sub, "AF_L.trk")
                bundle_obj = load_trk(fname, "same", bbox_valid_check=False)
                bundles.append(bundle_obj.streamlines)

        return bundles

    @warning_for_keywords()
    def to_bids_description(
        path, *, fname="dataset_description.json", BIDSVersion="1.4.0", **kwargs
    ):
        """Dumps a dict into a bids description at the given location"""
        kwargs.update({"BIDSVersion": BIDSVersion})
        desc_file = op.join(path, fname)
        with open(desc_file, "w") as outfile:
            json.dump(kwargs, outfile)

    @warning_for_keywords()
    def fetch_hcp(
        subjects,
        *,
        hcp_bucket="hcp-openaccess",
        profile_name="hcp",
        path=None,
        study="HCP_1200",
        aws_access_key_id=None,
        aws_secret_access_key=None,
    ):
        """
        Fetch HCP diffusion data and arrange it in a manner that resembles the
        BIDS specification.

        See :footcite:p:`Gorgolewski2016` for details about the BIDS specification.

        Parameters
        ----------
        subjects : list
            Each item is an integer, identifying one of the HCP subjects
        hcp_bucket : string, optional
            The name of the HCP S3 bucket.
        profile_name : string, optional
            The name of the AWS profile used for access.
        path : string, optional
            Path to save files into. Defaults to the value of the ``DIPY_HOME``
            environment variable is set; otherwise, defaults to ``$HOME/.dipy``.
        study : string, optional
            Which HCP study to grab.
        aws_access_key_id : string, optional
            AWS credentials to HCP AWS S3. Will only be used if `profile_name` is
            set to False.
        aws_secret_access_key : string, optional
            AWS credentials to HCP AWS S3. Will only be used if `profile_name` is
            set to False.

        Returns
        -------
        dict with remote and local names of these files,
        path to BIDS derivative dataset

        Notes
        -----
        To use this function with its default setting, you need to have a
        file '~/.aws/credentials', that includes a section:

        [hcp]
        AWS_ACCESS_KEY_ID=XXXXXXXXXXXXXXXX
        AWS_SECRET_ACCESS_KEY=XXXXXXXXXXXXXXXX

        The keys are credentials that you can get from HCP
        (see https://wiki.humanconnectome.org/docs/How%20to%20Get%20Access%20to%20the%20HCP%20OpenAccess%20Amazon%20S3%20Bucket.md)

        Local filenames are changed to match our expected conventions.

        References
        ----------
        .. footbibliography::
        """  # noqa: E501
        if not has_boto3:
            raise ValueError(
                "'fetch_hcp' requires boto3 and it is"
                " not currently installed. Please install"
                "it using `pip install boto3`. "
            )

        if profile_name:
            boto3.setup_default_session(profile_name=profile_name)
        elif aws_access_key_id is not None and aws_secret_access_key is not None:
            boto3.setup_default_session(
                aws_access_key_id=aws_access_key_id,
                aws_secret_access_key=aws_secret_access_key,
            )
        else:
            raise ValueError(
                "Must provide either a `profile_name` or ",
                "both `aws_access_key_id` and ",
                "`aws_secret_access_key` as input to 'fetch_hcp'",
            )

        s3 = boto3.resource("s3")
        bucket = s3.Bucket(hcp_bucket)

        if path is None:
            if not op.exists(dipy_home):
                os.mkdir(dipy_home)
            my_path = dipy_home
        else:
            my_path = path

        base_dir = pjoin(my_path, study, "derivatives", "hcp_pipeline")

        if not op.exists(base_dir):
            os.makedirs(base_dir, exist_ok=True)

        data_files = {}
        # If user provided incorrect input, these are typical failures that
        # are easy to recover from:
        if isinstance(subjects, (int, str)):
            subjects = [subjects]

        for subject in subjects:
            sub_dir = pjoin(base_dir, f"sub-{subject}")
            if not op.exists(sub_dir):
                os.makedirs(pjoin(sub_dir, "dwi"), exist_ok=True)
                os.makedirs(pjoin(sub_dir, "anat"), exist_ok=True)
            data_files[pjoin(sub_dir, "dwi", f"sub-{subject}_dwi.bval")] = (
                f"{study}/{subject}/T1w/Diffusion/bvals"
            )
            data_files[pjoin(sub_dir, "dwi", f"sub-{subject}_dwi.bvec")] = (
                f"{study}/{subject}/T1w/Diffusion/bvecs"
            )
            data_files[pjoin(sub_dir, "dwi", f"sub-{subject}_dwi.nii.gz")] = (
                f"{study}/{subject}/T1w/Diffusion/data.nii.gz"
            )
            data_files[pjoin(sub_dir, "anat", f"sub-{subject}_T1w.nii.gz")] = (
                f"{study}/{subject}/T1w/T1w_acpc_dc.nii.gz"
            )
            data_files[pjoin(sub_dir, "anat", f"sub-{subject}_aparc+aseg_seg.nii.gz")] = (
                f"{study}/{subject}/T1w/aparc+aseg.nii.gz"
            )

        download_files = {}
        for k in data_files.keys():
            if not op.exists(k):
                download_files[k] = data_files[k]
        if len(download_files.keys()):
            with tqdm(total=len(download_files.keys())) as pbar:
                for k in download_files.keys():
                    pbar.set_description_str(f"Downloading {k}")
                    bucket.download_file(download_files[k], k)
                    pbar.update()

        # Create the BIDS dataset description file text
        hcp_acknowledgements = (
            "Data were provided by the Human Connectome Project, WU-Minn Consortium"
            " (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657)"
            " funded by the 16 NIH Institutes and Centers that support the NIH Blueprint"
            " for Neuroscience Research; and by the McDonnell Center for Systems"
            " Neuroscience at Washington University.",
        )
        to_bids_description(
            pjoin(my_path, study),
            **{
                "Name": study,
                "Acknowledgements": hcp_acknowledgements,
                "Subjects": subjects,
            },
        )

        # Create the BIDS derivatives description file text
        to_bids_description(
            base_dir,
            **{
                "Name": study,
                "Acknowledgements": hcp_acknowledgements,
                "GeneratedBy": [{"Name": "hcp_pipeline"}],
            },
        )

        return data_files, pjoin(my_path, study)

    def _hbn_downloader(my_path, derivative, subjects, client):
        base_dir = op.join(my_path, "HBN", "derivatives", derivative)

        if not os.path.exists(base_dir):
            os.makedirs(base_dir, exist_ok=True)

        data_files = {}

        for subject in subjects:
            initial_query = client.list_objects(
                Bucket="fcp-indi", Prefix=f"data/Projects/HBN/BIDS_curated/sub-{subject}/"
            )
            ses = initial_query.get("Contents", None)
            if ses is None:
                raise ValueError(f"Could not find data for subject {subject}")
            else:
                ses = ses[0]["Key"].split("/")[5]

            query = client.list_objects(
                Bucket="fcp-indi",
                Prefix=f"data/Projects/HBN/BIDS_curated/derivatives/{derivative}/sub-{subject}/",
            )  # noqa
            query_content = query.get("Contents", None)
            if query_content is None:
                raise ValueError(f"Could not find derivatives data for subject {subject}")
            file_list = [kk["Key"] for kk in query["Contents"]]
            sub_dir = op.join(base_dir, f"sub-{subject}")
            ses_dir = op.join(sub_dir, ses)
            if derivative == "qsiprep":
                if not os.path.exists(sub_dir):
                    os.makedirs(os.path.join(sub_dir, "anat"), exist_ok=True)
                    os.makedirs(os.path.join(sub_dir, "figures"), exist_ok=True)
                    os.makedirs(os.path.join(ses_dir, "dwi"), exist_ok=True)
                    os.makedirs(os.path.join(ses_dir, "anat"), exist_ok=True)
            if derivative == "afq":
                if not os.path.exists(sub_dir):
                    os.makedirs(os.path.join(ses_dir, "bundles"), exist_ok=True)
                    os.makedirs(os.path.join(ses_dir, "clean_bundles"), exist_ok=True)
                    os.makedirs(os.path.join(ses_dir, "ROIs"), exist_ok=True)
                    os.makedirs(os.path.join(ses_dir, "tract_profile_plots"), exist_ok=True)
                    os.makedirs(os.path.join(ses_dir, "viz_bundles"), exist_ok=True)

            for remote in file_list:
                full = remote.split("Projects")[-1][1:].replace("/BIDS_curated", "")
                local = op.join(my_path, full)
                data_files[local] = remote

        download_files = {}
        for k in data_files.keys():
            if not op.exists(k):
                download_files[k] = data_files[k]

        if len(download_files.keys()):
            with tqdm(total=len(download_files.keys())) as pbar:
                for k in download_files.keys():
                    pbar.set_description_str(f"Downloading {k}")
                    client.download_file("fcp-indi", download_files[k], k)
                    pbar.update()

        # Create the BIDS dataset description file text
        to_bids_description(
            op.join(my_path, "HBN"), **{"Name": "HBN", "Subjects": subjects}
        )

        # Create the BIDS derivatives description file text
        to_bids_description(
            base_dir, **{"Name": "HBN", "PipelineDescription": {"Name": "qsiprep"}}
        )

        return data_files

    def fetch_hbn(subjects, *, path=None, include_afq=False):
        """
        Fetch preprocessed data from the Healthy Brain Network POD2 study.

        See :footcite:p:`Alexander2017` and :footcite:p:`RichieHalford2022` for
        further details about the study and processing pipeline.

        Parameters
        ----------
        subjects : list
            Identifiers of the subjects to download.
            For example: ["NDARAA948VFH", "NDAREK918EC2"].

        path : string, optional
            Path to save files into. Defaults to the value of the ``DIPY_HOME``
            environment variable is set; otherwise, defaults to ``$HOME/.dipy``.

        include_afq : bool, optional
            Whether to include pyAFQ derivatives

        Returns
        -------
        dict with remote and local names of these files,
        path to BIDS derivative dataset

        References
        ----------
        .. footbibliography::

        """

        if has_boto3:
            from botocore import UNSIGNED
            from botocore.client import Config
        else:
            TripWire(
                "The `fetch_hbn` function requires the boto3"
                + " library, but that is not installed."
            )

        # Anonymous access:
        client = boto3.client("s3", config=Config(signature_version=UNSIGNED))

        if path is None:
            if not op.exists(dipy_home):
                os.mkdir(dipy_home)
            my_path = dipy_home
        else:
            my_path = path

        # If user provided incorrect input, these are typical failures that
        # are easy to recover from:
        if isinstance(subjects, (int, str)):
            subjects = [subjects]

        data_files = _hbn_downloader(my_path, "qsiprep", subjects, client)

        if include_afq:
            data_files.update(_hbn_downloader(my_path, "afq", subjects, client))

        return data_files, pjoin(my_path, "HBN")