# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
    Change directory to provide relative paths for doctests
    >>> import os
    >>> filepath = os.path.dirname( os.path.realpath( __file__ ) )
    >>> datadir = os.path.realpath(os.path.join(filepath, '../../testing/data'))
    >>> os.chdir(datadir)

"""

from nipype.interfaces.base import (
    BaseInterface, BaseInterfaceInputSpec, traits,
    File, TraitedSpec, Directory, isdefined)
import os
import os.path as op
import numpy as np
import nibabel as nb
import networkx as nx
import shutil
from nipype.utils.misc import package_check
import warnings

from ... import logging
iflogger = logging.getLogger('interface')

have_cmp = True
try:
    package_check('cmp')
except Exception, e:
    have_cmp = False
else:
    import cmp
    from cmp.util import runCmd


def create_annot_label(subject_id, subjects_dir, fs_dir, parcellation_name):
    iflogger.info("Create the cortical labels necessary for our ROIs")
    iflogger.info("=================================================")
    fs_label_dir = op.join(op.join(subjects_dir, subject_id), 'label')
    output_dir = op.abspath(op.curdir)
    paths = []
    cmp_config = cmp.configuration.PipelineConfiguration()
    cmp_config.parcellation_scheme = "Lausanne2008"
    for hemi in ['lh', 'rh']:
        spath = cmp_config._get_lausanne_parcellation(
            'Lausanne2008')[parcellation_name]['fs_label_subdir_name'] % hemi
        paths.append(spath)
    for p in paths:
        try:
            os.makedirs(op.join('.', p))
        except:
            pass
    if '33' in parcellation_name:
        comp = [
            ('rh', 'myatlas_36_rh.gcs', 'rh.myaparc_36.annot',
             'regenerated_rh_36', 'myaparc_36'),
            ('rh', 'myatlas_60_rh.gcs', 'rh.myaparc_60.annot',
             'regenerated_rh_60', 'myaparc_60'),
            ('lh', 'myatlas_36_lh.gcs', 'lh.myaparc_36.annot',
             'regenerated_lh_36', 'myaparc_36'),
            ('lh', 'myatlas_60_lh.gcs', 'lh.myaparc_60.annot',
             'regenerated_lh_60', 'myaparc_60'),
        ]
    elif '60' in parcellation_name:
        comp = [
            ('rh', 'myatlas_60_rh.gcs', 'rh.myaparc_60.annot',
             'regenerated_rh_60', 'myaparc_60'),
            ('lh', 'myatlas_60_lh.gcs', 'lh.myaparc_60.annot',
             'regenerated_lh_60', 'myaparc_60'),
        ]
    elif '125' in parcellation_name:
        comp = [
            ('rh', 'myatlas_125_rh.gcs', 'rh.myaparc_125.annot',
             'regenerated_rh_125', 'myaparc_125'),
            ('rh', 'myatlas_60_rh.gcs', 'rh.myaparc_60.annot',
             'regenerated_rh_60', 'myaparc_60'),
            ('lh', 'myatlas_125_lh.gcs', 'lh.myaparc_125.annot',
             'regenerated_lh_125', 'myaparc_125'),
            ('lh', 'myatlas_60_lh.gcs', 'lh.myaparc_60.annot',
             'regenerated_lh_60', 'myaparc_60'),
        ]
    elif '250' in parcellation_name:
        comp = [
            ('rh', 'myatlas_250_rh.gcs', 'rh.myaparc_250.annot',
             'regenerated_rh_250', 'myaparc_250'),
            ('rh', 'myatlas_60_rh.gcs', 'rh.myaparc_60.annot',
             'regenerated_rh_60', 'myaparc_60'),
            ('lh', 'myatlas_250_lh.gcs', 'lh.myaparc_250.annot',
             'regenerated_lh_250', 'myaparc_250'),
            ('lh', 'myatlas_60_lh.gcs', 'lh.myaparc_60.annot',
             'regenerated_lh_60', 'myaparc_60'),
        ]
    else:
        comp = [
            ('rh', 'myatlas_36_rh.gcs', 'rh.myaparc_36.annot',
             'regenerated_rh_36', 'myaparc_36'),
            ('rh', 'myatlasP1_16_rh.gcs', 'rh.myaparcP1_16.annot',
             'regenerated_rh_500', 'myaparcP1_16'),
            ('rh', 'myatlasP17_28_rh.gcs', 'rh.myaparcP17_28.annot',
             'regenerated_rh_500', 'myaparcP17_28'),
            ('rh', 'myatlasP29_36_rh.gcs', 'rh.myaparcP29_36.annot',
             'regenerated_rh_500', 'myaparcP29_36'),
            ('rh', 'myatlas_60_rh.gcs', 'rh.myaparc_60.annot',
             'regenerated_rh_60', 'myaparc_60'),
            ('rh', 'myatlas_125_rh.gcs', 'rh.myaparc_125.annot',
             'regenerated_rh_125', 'myaparc_125'),
            ('rh', 'myatlas_250_rh.gcs', 'rh.myaparc_250.annot',
             'regenerated_rh_250', 'myaparc_250'),
            ('lh', 'myatlas_36_lh.gcs', 'lh.myaparc_36.annot',
             'regenerated_lh_36', 'myaparc_36'),
            ('lh', 'myatlasP1_16_lh.gcs', 'lh.myaparcP1_16.annot',
             'regenerated_lh_500', 'myaparcP1_16'),
            ('lh', 'myatlasP17_28_lh.gcs', 'lh.myaparcP17_28.annot',
             'regenerated_lh_500', 'myaparcP17_28'),
            ('lh', 'myatlasP29_36_lh.gcs', 'lh.myaparcP29_36.annot',
             'regenerated_lh_500', 'myaparcP29_36'),
            ('lh', 'myatlas_60_lh.gcs', 'lh.myaparc_60.annot',
             'regenerated_lh_60', 'myaparc_60'),
            ('lh', 'myatlas_125_lh.gcs', 'lh.myaparc_125.annot',
             'regenerated_lh_125', 'myaparc_125'),
            ('lh', 'myatlas_250_lh.gcs', 'lh.myaparc_250.annot',
             'regenerated_lh_250', 'myaparc_250'),
        ]

    log = cmp_config.get_logger()

    for out in comp:
        mris_cmd = 'mris_ca_label %s %s "%s/surf/%s.sphere.reg" "%s" "%s" ' % (subject_id, out[0],
                                                                               op.join(subjects_dir, subject_id), out[0], cmp_config.get_lausanne_atlas(out[1]), op.join(fs_label_dir, out[2]))
        runCmd(mris_cmd, log)
        iflogger.info('-----------')

        annot = '--annotation "%s"' % out[4]

        mri_an_cmd = 'mri_annotation2label --subject %s --hemi %s --outdir "%s" %s' % (subject_id, out[0], op.join(output_dir, out[3]), annot)
        iflogger.info(mri_an_cmd)
        runCmd(mri_an_cmd, log)
        iflogger.info('-----------')
        iflogger.info(os.environ['SUBJECTS_DIR'])
    # extract cc and unknown to add to tractography mask, we do not want this as a region of interest
    # in FS 5.0, unknown and corpuscallosum are not available for the 35 scale (why?),
    # but for the other scales only, take the ones from _60
        rhun = op.join(output_dir, 'rh.unknown.label')
        lhun = op.join(output_dir, 'lh.unknown.label')
        rhco = op.join(output_dir, 'rh.corpuscallosum.label')
        lhco = op.join(output_dir, 'lh.corpuscallosum.label')
    shutil.copy(
        op.join(output_dir, 'regenerated_rh_60', 'rh.unknown.label'), rhun)
    shutil.copy(
        op.join(output_dir, 'regenerated_lh_60', 'lh.unknown.label'), lhun)
    shutil.copy(op.join(
        output_dir, 'regenerated_rh_60', 'rh.corpuscallosum.label'), rhco)
    shutil.copy(op.join(
        output_dir, 'regenerated_lh_60', 'lh.corpuscallosum.label'), lhco)

    mri_cmd = """mri_label2vol --label "%s" --label "%s" --label "%s" --label "%s" --temp "%s" --o  "%s" --identity """ % (rhun, lhun, rhco, lhco, op.join(op.join(subjects_dir, subject_id), 'mri', 'orig.mgz'), op.join(fs_label_dir, 'cc_unknown.nii.gz') )
    runCmd(mri_cmd, log)
    runCmd('mris_volmask %s' % subject_id, log)
    mri_cmd = 'mri_convert -i "%s/mri/ribbon.mgz" -o "%s/mri/ribbon.nii.gz"' % (op.join(subjects_dir, subject_id), op.join(subjects_dir, subject_id))
    runCmd(mri_cmd, log)
    mri_cmd = 'mri_convert -i "%s/mri/aseg.mgz" -o "%s/mri/aseg.nii.gz"' % (
        op.join(subjects_dir, subject_id), op.join(subjects_dir, subject_id))
    runCmd(mri_cmd, log)

    iflogger.info("[ DONE ]")


def create_roi(subject_id, subjects_dir, fs_dir, parcellation_name, dilation):
    """ Creates the ROI_%s.nii.gz files using the given parcellation information
    from networks. Iteratively create volume. """
    iflogger.info("Create the ROIs:")
    output_dir = op.abspath(op.curdir)
    fs_dir = op.join(subjects_dir, subject_id)
    cmp_config = cmp.configuration.PipelineConfiguration()
    cmp_config.parcellation_scheme = "Lausanne2008"
    log = cmp_config.get_logger()
    parval = cmp_config._get_lausanne_parcellation(
        'Lausanne2008')[parcellation_name]
    pgpath = parval['node_information_graphml']
    aseg = nb.load(op.join(fs_dir, 'mri', 'aseg.nii.gz'))
    asegd = aseg.get_data()

    # identify cortical voxels, right (3) and left (42) hemispheres
    idxr = np.where(asegd == 3)
    idxl = np.where(asegd == 42)
    xx = np.concatenate((idxr[0], idxl[0]))
    yy = np.concatenate((idxr[1], idxl[1]))
    zz = np.concatenate((idxr[2], idxl[2]))

    # initialize variables necessary for cortical ROIs dilation
    # dimensions of the neighbourhood for rois labels assignment (choose odd dimensions!)
    shape = (25, 25, 25)
    center = np.array(shape) // 2
    # dist: distances from the center of the neighbourhood
    dist = np.zeros(shape, dtype='float32')
    for x in range(shape[0]):
        for y in range(shape[1]):
            for z in range(shape[2]):
                distxyz = center - [x, y, z]
                dist[x, y, z] = np.sqrt(np.sum(np.multiply(distxyz, distxyz)))

    iflogger.info("Working on parcellation: ")
    iflogger.info(cmp_config._get_lausanne_parcellation(
        'Lausanne2008')[parcellation_name])
    iflogger.info("========================")
    pg = nx.read_graphml(pgpath)
    # each node represents a brain region
    # create a big 256^3 volume for storage of all ROIs
    rois = np.zeros((256, 256, 256), dtype=np.int16)

    count = 0
    for brk, brv in pg.nodes_iter(data=True):
        count = count + 1
        iflogger.info(brv)
        iflogger.info(brk)
        if brv['dn_hemisphere'] == 'left':
            hemi = 'lh'
        elif brv['dn_hemisphere'] == 'right':
            hemi = 'rh'
        if brv['dn_region'] == 'subcortical':
            iflogger.info(brv)
            iflogger.info("---------------------")
            iflogger.info("Work on brain region: %s" % (brv['dn_region']))
            iflogger.info("Freesurfer Name: %s" % brv['dn_fsname'])
            iflogger.info("Region %s of %s " % (count, pg.number_of_nodes()))
            iflogger.info("---------------------")
            # if it is subcortical, retrieve roi from aseg
            idx = np.where(asegd == int(brv['dn_fs_aseg_val']))
            rois[idx] = int(brv['dn_correspondence_id'])

        elif brv['dn_region'] == 'cortical':
            iflogger.info(brv)
            iflogger.info("---------------------")
            iflogger.info("Work on brain region: %s" % (brv['dn_region']))
            iflogger.info("Freesurfer Name: %s" % brv['dn_fsname'])
            iflogger.info("Region %s of %s " % (count, pg.number_of_nodes()))
            iflogger.info("---------------------")

            labelpath = op.join(
                output_dir, parval['fs_label_subdir_name'] % hemi)
            # construct .label file name

            fname = '%s.%s.label' % (hemi, brv['dn_fsname'])

            # execute fs mri_label2vol to generate volume roi from the label file
            # store it in temporary file to be overwritten for each region

            mri_cmd = 'mri_label2vol --label "%s" --temp "%s" --o "%s" --identity' % (op.join(labelpath, fname),
                                                                                      op.join(fs_dir, 'mri', 'orig.mgz'), op.join(output_dir, 'tmp.nii.gz'))
            runCmd(mri_cmd, log)

            tmp = nb.load(op.join(output_dir, 'tmp.nii.gz'))
            tmpd = tmp.get_data()

            # find voxel and set them to intensityvalue in rois
            idx = np.where(tmpd == 1)
            rois[idx] = int(brv['dn_correspondence_id'])

        # store volume eg in ROI_scale33.nii.gz
        out_roi = op.abspath('ROI_%s.nii.gz' % parcellation_name)

        # update the header
        hdr = aseg.get_header()
        hdr2 = hdr.copy()
        hdr2.set_data_dtype(np.uint16)

        log.info("Save output image to %s" % out_roi)
        img = nb.Nifti1Image(rois, aseg.get_affine(), hdr2)
        nb.save(img, out_roi)

    iflogger.info("[ DONE ]")
    # dilate cortical regions
    if (dilation == True):
        iflogger.info("Dilating cortical regions...")
        # loop throughout all the voxels belonging to the aseg GM volume
        for j in range(xx.size):
            if rois[xx[j], yy[j], zz[j]] == 0:
                local = extract(
                    rois, shape, position=(xx[j], yy[j], zz[j]), fill=0)
                mask = local.copy()
                mask[np.nonzero(local > 0)] = 1
                thisdist = np.multiply(dist, mask)
                thisdist[np.nonzero(thisdist == 0)] = np.amax(thisdist)
                value = np.int_(
                    local[np.nonzero(thisdist == np.amin(thisdist))])
                if value.size > 1:
                    counts = np.bincount(value)
                    value = np.argmax(counts)
                rois[xx[j], yy[j], zz[j]] = value

        # store volume eg in ROIv_scale33.nii.gz
        out_roi = op.abspath('ROIv_%s.nii.gz' % parcellation_name)
        iflogger.info("Save output image to %s" % out_roi)
        img = nb.Nifti1Image(rois, aseg.get_affine(), hdr2)
        nb.save(img, out_roi)

        iflogger.info("[ DONE ]")


def create_wm_mask(subject_id, subjects_dir, fs_dir, parcellation_name):
    iflogger.info("Create white matter mask")
    fs_dir = op.join(subjects_dir, subject_id)
    cmp_config = cmp.configuration.PipelineConfiguration()
    cmp_config.parcellation_scheme = "Lausanne2008"
    pgpath = cmp_config._get_lausanne_parcellation(
        'Lausanne2008')[parcellation_name]['node_information_graphml']
    # load ribbon as basis for white matter mask
    fsmask = nb.load(op.join(fs_dir, 'mri', 'ribbon.nii.gz'))
    fsmaskd = fsmask.get_data()

    wmmask = np.zeros(fsmaskd.shape)
    # extract right and left white matter
    idx_lh = np.where(fsmaskd == 120)
    idx_rh = np.where(fsmaskd == 20)

    wmmask[idx_lh] = 1
    wmmask[idx_rh] = 1

    # remove subcortical nuclei from white matter mask
    aseg = nb.load(op.join(fs_dir, 'mri', 'aseg.nii.gz'))
    asegd = aseg.get_data()

    try:
        import scipy.ndimage.morphology as nd
    except ImportError:
        raise Exception('Need scipy for binary erosion of white matter mask')

    # need binary erosion function
    imerode = nd.binary_erosion

    # ventricle erosion
    csfA = np.zeros(asegd.shape)
    csfB = np.zeros(asegd.shape)

    # structuring elements for erosion
    se1 = np.zeros((3, 3, 5))
    se1[1, :, 2] = 1
    se1[:, 1, 2] = 1
    se1[1, 1, :] = 1
    se = np.zeros((3, 3, 3))
    se[1, :, 1] = 1
    se[:, 1, 1] = 1
    se[1, 1, :] = 1

    # lateral ventricles, thalamus proper and caudate
    # the latter two removed for better erosion, but put back afterwards
    idx = np.where((asegd == 4) |
                  (asegd == 43) |
                  (asegd == 11) |
                  (asegd == 50) |
                  (asegd == 31) |
                  (asegd == 63) |
                  (asegd == 10) |
                  (asegd == 49))
    csfA[idx] = 1
    csfA = imerode(imerode(csfA, se1), se)

    # thalmus proper and cuadate are put back because they are not lateral ventricles
    idx = np.where((asegd == 11) |
                  (asegd == 50) |
                  (asegd == 10) |
                  (asegd == 49))
    csfA[idx] = 0

    # REST CSF, IE 3RD AND 4TH VENTRICULE AND EXTRACEREBRAL CSF
    idx = np.where((asegd == 5) |
                  (asegd == 14) |
                  (asegd == 15) |
                  (asegd == 24) |
                  (asegd == 44) |
                  (asegd == 72) |
                  (asegd == 75) |
                  (asegd == 76) |
                  (asegd == 213) |
                  (asegd == 221))
    # 43 ??, 4??  213?, 221?
    # more to discuss.
    for i in [5, 14, 15, 24, 44, 72, 75, 76, 213, 221]:
        idx = np.where(asegd == i)
        csfB[idx] = 1

    # do not remove the subthalamic nucleus for now from the wm mask
    # 23, 60
    # would stop the fiber going to the segmented "brainstem"

    # grey nuclei, either with or without erosion
    gr_ncl = np.zeros(asegd.shape)

    # with erosion
    for i in [10, 11, 12, 49, 50, 51]:
        idx = np.where(asegd == i)
        # temporary volume
        tmp = np.zeros(asegd.shape)
        tmp[idx] = 1
        tmp = imerode(tmp, se)
        idx = np.where(tmp == 1)
        gr_ncl[idx] = 1

    # without erosion
    for i in [13, 17, 18, 26, 52, 53, 54, 58]:
        idx = np.where(asegd == i)
        gr_ncl[idx] = 1

    # remove remaining structure, e.g. brainstem
    remaining = np.zeros(asegd.shape)
    idx = np.where(asegd == 16)
    remaining[idx] = 1

    # now remove all the structures from the white matter
    idx = np.where(
        (csfA != 0) | (csfB != 0) | (gr_ncl != 0) | (remaining != 0))
    wmmask[idx] = 0
    iflogger.info("Removing lateral ventricles and eroded grey nuclei and brainstem from white matter mask")

    # ADD voxels from 'cc_unknown.nii.gz' dataset
    ccun = nb.load(op.join(fs_dir, 'label', 'cc_unknown.nii.gz'))
    ccund = ccun.get_data()
    idx = np.where(ccund != 0)
    iflogger.info("Add corpus callosum and unknown to wm mask")
    wmmask[idx] = 1

    # check if we should subtract the cortical rois from this parcellation
    iflogger.info("Loading %s to subtract cortical ROIs from white matter mask" % ('ROI_%s.nii.gz' % parcellation_name))
    roi = nb.load(op.join(op.curdir, 'ROI_%s.nii.gz' % parcellation_name))
    roid = roi.get_data()
    assert roid.shape[0] == wmmask.shape[0]
    pg = nx.read_graphml(pgpath)
    for brk, brv in pg.nodes_iter(data=True):
        if brv['dn_region'] == 'cortical':
            iflogger.info("Subtracting region %s with intensity value %s" %
                          (brv['dn_region'], brv['dn_correspondence_id']))
            idx = np.where(roid == int(brv['dn_correspondence_id']))
            wmmask[idx] = 0

    # output white matter mask. crop and move it afterwards
    wm_out = op.join(fs_dir, 'mri', 'fsmask_1mm.nii.gz')
    img = nb.Nifti1Image(wmmask, fsmask.get_affine(), fsmask.get_header())
    iflogger.info("Save white matter mask: %s" % wm_out)
    nb.save(img, wm_out)


def crop_and_move_datasets(subject_id, subjects_dir, fs_dir, parcellation_name, out_roi_file,dilation):
    fs_dir = op.join(subjects_dir, subject_id)
    cmp_config = cmp.configuration.PipelineConfiguration()
    cmp_config.parcellation_scheme = "Lausanne2008"
    log = cmp_config.get_logger()
    output_dir = op.abspath(op.curdir)

    iflogger.info("Cropping and moving datasets to %s" % output_dir)
    ds = [
        (op.join(fs_dir, 'mri', 'aseg.nii.gz'),
         op.abspath('aseg.nii.gz')),
        (op.join(fs_dir, 'mri', 'ribbon.nii.gz'),
         op.abspath('ribbon.nii.gz')),
        (op.join(fs_dir, 'mri', 'fsmask_1mm.nii.gz'),
         op.abspath('fsmask_1mm.nii.gz')),
        (op.join(fs_dir, 'label', 'cc_unknown.nii.gz'),
         op.abspath('cc_unknown.nii.gz'))
    ]

    ds.append((op.abspath('ROI_%s.nii.gz' % parcellation_name),
              op.abspath('ROI_HR_th.nii.gz')))
    if(dilation==True):
    	ds.append((op.abspath('ROIv_%s.nii.gz' % parcellation_name),
            op.abspath('ROIv_HR_th.nii.gz')))
    orig = op.join(fs_dir, 'mri', 'orig', '001.mgz')
    for d in ds:
        iflogger.info("Processing %s:" % d[0])
        if not op.exists(d[0]):
            raise Exception('File %s does not exist.' % d[0])
        # reslice to original volume because the roi creation with freesurfer
        # changed to 256x256x256 resolution
        mri_cmd = 'mri_convert -rl "%s" -rt nearest "%s" -nc "%s"' % (
            orig, d[0], d[1])
        runCmd(mri_cmd, log)


def extract(Z, shape, position, fill):
    """ Extract voxel neighbourhood
Parameters
----------
Z: the original data
shape: tuple containing neighbourhood dimensions
position: tuple containing central point indexes
fill: value for the padding of Z
Returns
-------
R: the neighbourhood of the specified point in Z
"""
    R = np.ones(shape, dtype=Z.dtype) * \
        fill  # initialize output block to the fill value
    P = np.array(
        list(position)).astype(int)  # position coordinates(numpy array)
    Rs = np.array(
        list(R.shape)).astype(int)  # output block dimensions (numpy array)
    Zs = np.array(
        list(Z.shape)).astype(int)  # original volume dimensions (numpy array)

    R_start = np.zeros(len(shape)).astype(int)
    R_stop = np.array(list(shape)).astype(int)
    Z_start = (P - Rs // 2)
    Z_start_cor = (np.maximum(Z_start, 0)).tolist()  # handle borders
    R_start = R_start + (Z_start_cor - Z_start)
    Z_stop = (P + Rs // 2) + Rs % 2
    Z_stop_cor = (np.minimum(Z_stop, Zs)).tolist()  # handle borders
    R_stop = R_stop - (Z_stop - Z_stop_cor)

    R[R_start[0]:R_stop[0], R_start[1]:R_stop[1], R_start[2]:R_stop[2]] = Z[Z_start_cor[0]:Z_stop_cor[0], Z_start_cor[1]:Z_stop_cor[1], Z_start_cor[2]:Z_stop_cor[2]]

    return R


class ParcellateInputSpec(BaseInterfaceInputSpec):
    subject_id = traits.String(mandatory=True, desc='Subject ID')
    parcellation_name = traits.Enum('scale500', ['scale33', 'scale60', 'scale125', 'scale250', 'scale500'], usedefault=True)
    freesurfer_dir = Directory(exists=True, desc='Freesurfer main directory')
    subjects_dir = Directory(exists=True, desc='Freesurfer subjects directory')
    out_roi_file = File(
        genfile=True, desc='Region of Interest file for connectivity mapping')
    dilation = traits.Bool(False, usedefault=True,
                           desc='Dilate cortical parcels? Useful for fMRI connectivity')


class ParcellateOutputSpec(TraitedSpec):
    roi_file = File(
        exists=True, desc='Region of Interest file for connectivity mapping')
    roiv_file = File(desc='Region of Interest file for fMRI connectivity mapping')
    white_matter_mask_file = File(exists=True, desc='White matter mask file')
    cc_unknown_file = File(
        desc='Image file with regions labelled as unknown cortical structures',
                    exists=True)
    ribbon_file = File(desc='Image file detailing the cortical ribbon',
                    exists=True)
    aseg_file = File(
        desc='Automated segmentation file converted from Freesurfer "subjects" directory',
                    exists=True)
    roi_file_in_structural_space = File(
        desc='ROI image resliced to the dimensions of the original structural image',
                    exists=True)
    dilated_roi_file_in_structural_space = File(
        desc='dilated ROI image resliced to the dimensions of the original structural image')


class Parcellate(BaseInterface):
    """Subdivides segmented ROI file into smaller subregions

    This interface implements the same procedure as in the ConnectomeMapper's
    parcellation stage (cmp/stages/parcellation/maskcreation.py) for a single
    parcellation scheme (e.g. 'scale500').

    Example
    -------

    >>> import nipype.interfaces.cmtk as cmtk
    >>> parcellate = cmtk.Parcellate()
    >>> parcellate.inputs.freesurfer_dir = '.'
    >>> parcellate.inputs.subjects_dir = '.'
    >>> parcellate.inputs.subject_id = 'subj1'
    >>> parcellate.inputs.dilation = True
    >>> parcellate.inputs.parcellation_name = 'scale500'
    >>> parcellate.run()                 # doctest: +SKIP
    """

    input_spec = ParcellateInputSpec
    output_spec = ParcellateOutputSpec

    def _run_interface(self, runtime):
        if self.inputs.subjects_dir:
            os.environ.update({'SUBJECTS_DIR': self.inputs.subjects_dir})

        if not os.path.exists(op.join(self.inputs.subjects_dir, self.inputs.subject_id)):
            raise Exception
        iflogger.info("ROI_HR_th.nii.gz / fsmask_1mm.nii.gz CREATION")
        iflogger.info("=============================================")
        create_annot_label(self.inputs.subject_id, self.inputs.subjects_dir, self.inputs.freesurfer_dir, self.inputs.parcellation_name)
        create_roi(self.inputs.subject_id, self.inputs.subjects_dir, self.inputs.freesurfer_dir, self.inputs.parcellation_name, self.inputs.dilation)
        create_wm_mask(self.inputs.subject_id, self.inputs.subjects_dir, self.inputs.freesurfer_dir, self.inputs.parcellation_name)
        crop_and_move_datasets(self.inputs.subject_id, self.inputs.subjects_dir, self.inputs.freesurfer_dir, self.inputs.parcellation_name, self.inputs.out_roi_file,self.inputs.dilation)
        return runtime

    def _list_outputs(self):
        outputs = self._outputs().get()
        if isdefined(self.inputs.out_roi_file):
            outputs['roi_file'] = op.abspath(self.inputs.out_roi_file)
        else:
            outputs['roi_file'] = op.abspath(
                self._gen_outfilename('nii.gz', 'ROI'))
        if(self.inputs.dilation==True):
            outputs['roiv_file'] = op.abspath(self._gen_outfilename(
            'nii.gz', 'ROIv'))
        outputs['white_matter_mask_file'] = op.abspath('fsmask_1mm.nii.gz')
        outputs['cc_unknown_file'] = op.abspath('cc_unknown.nii.gz')
        outputs['ribbon_file'] = op.abspath('ribbon.nii.gz')
        outputs['aseg_file'] = op.abspath('aseg.nii.gz')
        outputs['roi_file_in_structural_space'] = op.abspath(
            'ROI_HR_th.nii.gz')
        if(self.inputs.dilation==True):
            outputs['dilated_roi_file_in_structural_space'] = op.abspath(
            'ROIv_HR_th.nii.gz')
        return outputs

    def _gen_outfilename(self, ext, prefix='ROI'):
        return prefix + '_' + self.inputs.parcellation_name + '.' + ext
