示例#1
0
文件: roi.py 项目: TheChymera/chyMRI
def ts(img_path,
	mask=False,
	substitution={},
	):
	"""
	Return the mean and median of a Region of Interest (ROI) time course.

	Parameters
	----------

	img_path : str
		Path to NIfTI file from which the ROI is to be extracted.
	maks : nilearn.NiftiMasker or str, optional
		Nilearn `nifti1.Nifti1Image` object to use for masking the desired ROI, or a string specifying the path of a maskfile.
	substitution : dict, optional
		A dictionary with keys which include 'subject' and 'session'.
	"""
	if substitution:
		img_path = img_path.format(**substitution)
	img_path = path.abspath(path.expanduser(img_path))
	img = nib.load(img_path)
	try:
		masked_data = mask.fit_transform(img)
	except:
		mask = path.abspath(path.expanduser(mask))
		mask = NiftiMasker(mask_img=mask)
		masked_data = mask.fit_transform(img).T
	ts_means = np.mean(masked_data, axis=0)
	ts_medians = np.mean(masked_data, axis=0)
	return ts_means, ts_medians
示例#2
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    def transform(self, imgs, confounds=None):
        """

        Parameters
        ----------
        imgs: list of Niimg-like objects
        """
        self._check_fitted()

        if self.smoothing_fwhm:
            imgs = smooth_img(imgs, self.smoothing_fwhm)

        imgs = [_utils.check_niimg_3d(img) for img in imgs]

        for i, roi in enumerate(self.mask_img_):
            masker = NiftiMasker(mask_img=roi)
            x = masker.fit_transform(imgs)
            if self.extract_funcs is not None:
                x = np.array([FDICT[f][0](x, **FDICT[f][1]) for f in self.extract_funcs])
            if i == 0:
                X = x
            else:
                X = np.concatenate((X, x), axis=0)

        return X.swapaxes(0, 1)
def create_rois_from_clusters(contrast_tmap, mask, threshold=3.09,
                              height_control='brute', cluster_threshold=10,
                              save_path=None):
    if save_path is not None:
        if not os.path.exists(save_path):
            os.makedirs(save_path)

    thresholded = map_threshold(contrast_tmap, mask, threshold,
                                height_control, cluster_threshold)
    cluster_map, n_cluster = label(thresholded.get_data() > 0)

    clusters = []
    masker = NiftiMasker(mask_img=mask)
    masker.fit()
    mask_affine = nib.load(mask).get_affine()
    for label_ in range(1, n_cluster + 1):
        cluster = cluster_map.copy()
        cluster[cluster_map != label_] = 0
        cluster[cluster_map == label_] = 1
        cluster = nib.Nifti1Image(cluster, mask_affine)
        clusters.append(cluster)
        if save_path is not None:
            nib.save(cluster, os.path.join(save_path,
                     'cluster_{0}.nii'.format(label_)))

    return clusters
示例#4
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def preprocess(num, subj, subj_dir, subj_warp_dir, force_warp=False):
    bold_path = 'BOLD/task001_run00%i/bold_dico_bold7Tp1_to_subjbold7Tp1.nii.gz' % (num+1)
    bold_path = os.path.join(DATA_DIR, subj, bold_path)
    template_path = os.path.join(DATA_DIR, 'templates', 'grpbold7Tp1', 'brain.nii.gz')
    warp_path = os.path.join(DATA_DIR, subj, 'templates', 'bold7Tp1', 'in_grpbold7Tp1', 'subj2tmpl_warp.nii.gz')

    output_path = os.path.join(subj_warp_dir, 'run00%i.nii.gz' % num)

    if force_warp or not os.path.exists(output_path):
        print 'Warping image #%i...' % num
        subprocess.call(['fsl5.0-applywarp', '-i', bold_path, '-o', output_path, '-r', template_path, '-w', warp_path, '-d', 'float'])
    else:
        print 'Reusing cached warp image #%i' % num

    print 'Loading image #%i...' % num
    bold = load(output_path)

    masker = NiftiMasker(load(MASK_FILE))
    # masker = niftimasker(load(MASK_FILE), detrend=true, smoothing_fwhm=4.0,
    #                     high_pass=0.01, t_r=2.0, standardize=true)
    masker.fit()
    print 'Removing confounds from image #%i...' % num
    data = masker.transform(bold, confounds(num, subj))
    print 'Detrending image #%i...' % num
    filtered = np.float32(savgol_filter(data, 61, 5, axis=0))
    img = masker.inverse_transform(data-filtered)
    print 'Smoothing image #%i...' % num
    img = image.smooth_img(img, 4.0)
    print 'Saving image #%i...' % num
    save(img, os.path.join(subj_dir, 'run00%i.nii.gz' % num))
    print 'Finished with image #%i' % num
示例#5
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def MaskFlatten(concat_dict, mask, iter_n):
    '''Mask image data, convert to 2D feature matrix'''
    nifti_masker = NiftiMasker(mask_img=mask)
    masked_dict = {}
    for i in range(iter_n):
        masked_dict[i] = nifti_masker.fit_transform(concat_dict[i])
    return masked_dict
示例#6
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def nilearn_denoise(in_file, brain_mask, wm_mask, csf_mask,
                      motreg_file, outlier_file,
                      bandpass, tr ):
    """Clean time series using Nilearn high_variance_confounds to extract 
    CompCor regressors and NiftiMasker for regression of all nuissance regressors,
    detrending, normalziation and bandpass filtering.
    """
    import numpy as np
    import nibabel as nb
    import os
    from nilearn.image import high_variance_confounds
    from nilearn.input_data import NiftiMasker
    from nipype.utils.filemanip import split_filename

    # reload niftis to round affines so that nilearn doesn't complain
    wm_nii=nb.Nifti1Image(nb.load(wm_mask).get_data(), np.around(nb.load(wm_mask).get_affine(), 2), nb.load(wm_mask).get_header())
    csf_nii=nb.Nifti1Image(nb.load(csf_mask).get_data(), np.around(nb.load(csf_mask).get_affine(), 2), nb.load(csf_mask).get_header())
    time_nii=nb.Nifti1Image(nb.load(in_file).get_data(),np.around(nb.load(in_file).get_affine(), 2), nb.load(in_file).get_header())
        
    # infer shape of confound array
    # not ideal
    confound_len = nb.load(in_file).get_data().shape[3]
    
    # create outlier regressors
    outlier_regressor = np.empty((confound_len,1))
    try:
        outlier_val = np.genfromtxt(outlier_file)
    except IOError:
        outlier_val = np.empty((0))
    for index in np.atleast_1d(outlier_val):
        outlier_vector = np.zeros((confound_len, 1))
        outlier_vector[index] = 1
        outlier_regressor = np.hstack((outlier_regressor, outlier_vector))
    
    outlier_regressor = outlier_regressor[:,1::]
        
    # load motion regressors
    motion_regressor=np.genfromtxt(motreg_file)
    
    # extract high variance confounds in wm/csf masks from motion corrected data
    wm_regressor=high_variance_confounds(time_nii, mask_img=wm_nii, detrend=True)
    csf_regressor=high_variance_confounds(time_nii, mask_img=csf_nii, detrend=True)
    
    # create Nifti Masker for denoising
    denoiser=NiftiMasker(mask_img=brain_mask, standardize=True, detrend=True, high_pass=bandpass[1], low_pass=bandpass[0], t_r=tr)
    
    # denoise and return denoise data to img
    confounds=np.hstack((outlier_regressor,wm_regressor, csf_regressor, motion_regressor))
    denoised_data=denoiser.fit_transform(in_file, confounds=confounds)
    denoised_img=denoiser.inverse_transform(denoised_data)
        
    # save  
    _, base, _ = split_filename(in_file)
    img_fname = base + '_denoised.nii.gz'
    nb.save(denoised_img, img_fname)
    
    confound_fname = os.path.join(os.getcwd(), "all_confounds.txt")
    np.savetxt(confound_fname, confounds, fmt="%.10f")
    
    return os.path.abspath(img_fname), confound_fname
	def _run_interface(self, runtime):
		from nilearn.input_data import NiftiMasker, NiftiLabelsMasker
		from nipype.utils.filemanip import split_filename
		import nibabel as nib
		import os

		functional_filename = self.inputs.in_file
		atlas_filename = self.inputs.atlas_filename
		mask_filename = self.inputs.mask_filename

		# Extracting the ROI signals
		masker = NiftiLabelsMasker(labels_img=atlas_filename,
                           background_label = 0,
                           standardize=True,
                           detrend = True,
                           verbose = 1
                           )
		time_series = masker.fit_transform(functional_filename)

		# Removing the ROI signal from the time series
		nifti_masker = NiftiMasker(mask_img=mask_filename)
		masked_data = nifti_masker.fit_transform(functional_filename, confounds=time_series[...,0])
		masked_img = nifti_masker.inverse_transform(masked_data)

		# Saving the result to disk
		outputs = self._outputs().get()
		fname = self.inputs.in_file
		_, base, _ = split_filename(fname)
		nib.save(masked_img, os.path.abspath(base + '_regressed.nii.gz'))
		return runtime
示例#8
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    def apply_mask(self, mask):
        """ Mask Brain_Data instance

        Args:
            mask: mask (Brain_Data or nifti object)
            
        """

        if isinstance(mask,Brain_Data):
            mask = mask.to_nifti() # convert to nibabel
        if not isinstance(mask, nib.Nifti1Image):
            if type(mask) is str:
                if os.path.isfile(mask):
                    mask = nib.load(mask)
               # Check if mask need to be resampled into Brain_Data mask space
                if not ((self.mask.get_affine()==mask.get_affine()).all()) & (self.mask.shape[0:3]==mask.shape[0:3]):
                    mask = resample_img(mask,target_affine=self.mask.get_affine(),target_shape=self.mask.shape)
            else:
                raise ValueError("Mask is not a nibabel instance, Brain_Data instance, or a valid file name.")

        masked = deepcopy(self)
        nifti_masker = NiftiMasker(mask_img=mask)
        masked.data = nifti_masker.fit_transform(self.to_nifti())
        if len(self.data.shape) > 2:
            masked.data = masked.data.squeeze()
        masked.nifti_masker = nifti_masker
        return masked
def extract_brain_rad(db, rad_column, rad_dir, stat, include_chim=False):
    """Replaces radiation presence by stat on whole brain ROI.

    Assumes brain mask and radiation nifti file is in rad_dir."""
    brain_mask_file = 'BrainMask_to_rd.nii.gz'
    extracted_rad_stat = {}  # Memoization of radiation statistic
    for idx, row in db.iterrows():
        if row[rad_column] == 1:
            sub_id = row['patient']
            if sub_id in extracted_rad_stat:
                db.loc[idx, rad_column] = extracted_rad_stat[sub_id]
            else:
                mask_path = os.path.join(rad_dir, sub_id, brain_mask_file)
                mask_check = os.path.isfile(mask_path)
                rad_path = os.path.join(rad_dir, sub_id, sub_id + '.nii')
                rad_check = os.path.isfile(rad_path)
                if mask_check and rad_check:
                    masker = NiftiMasker(mask_path)
                    rad_stat = stat(masker.fit_transform(rad_path))
                    extracted_rad_stat[sub_id] = rad_stat
                    db.loc[idx, rad_column] = rad_stat
                else:
                    db.loc[idx, rad_column] = None
        elif not include_chim:
            db.loc[idx, rad_column] = None

    db = db[db[rad_column].notnull()]
    return db
示例#10
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文件: snr.py 项目: TheChymera/chyMRI
def significant_signal(data_path,
	substitution={},
	mask_path='',
	exclude_ones=False,
	):
	"""Return the mean and median inverse logarithm of a p-value map.

	Parameters
	----------

	data_path : str
		Path to a p-value map in NIfTI format.
	mask_path : str
		Path to a region of interest map in NIfTI format.
		THIS IS ALMOST ALWAYS REQUIRED, as NIfTI statistic images populate the whole 3D circumscribed space around your structure of interest,
		and commonly assign null values to the background.
		In an inverse logarithm computation, null corresponds to infinity, which can considerably bias the evaluation.
	substitution : dict
		Dictionary whose keys are format identifiers present in `data_path` and whose values are strings.

	Returns
	-------

	mean : float
	median : float
	"""

	if substitution:
		data_path = data_path.format(**substitution)
	data_path = path.abspath(path.expanduser(data_path))
	try:
		img = nib.load(data_path)
	except FileNotFoundError:
		return float('NaN'), float('NaN')
	if mask_path:
		mask_path = path.abspath(path.expanduser(mask_path))
		masker = NiftiMasker(mask_img=mask_path)
		masked_data = masker.fit_transform(img).T
		data = masked_data[~np.isnan(masked_data)]
	else:
		data = img.get_data()
		data = data[~np.isnan(data)]
	# We interpret zero as the lowest p-value, and conservatively estimate it to be equal to just under half of the smallest value in the defined range
	nonzero = data[np.nonzero(data)]
	data_min = np.min(nonzero)
	data_min = data_min*0.49
	data[data == 0] = data_min
	if exclude_ones:
		data = data[data!=1]
	data = -np.log10(data)
	# We use np.ma.median() because life is complicated:
	# https://github.com/numpy/numpy/issues/7330
	median = np.ma.median(data, axis=None)
	mean = np.mean(data)

	return mean, median
示例#11
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    def similarity(self, image, method='correlation'):
        """ Calculate similarity of Brain_Data() instance with single Brain_Data or Nibabel image

            Args:
                self: Brain_Data instance of data to be applied
                image: Brain_Data or Nibabel instance of weight map

            Returns:
                pexp: Outputs a vector of pattern expression values

        """

        if not isinstance(image, Brain_Data):
            if isinstance(image, nib.Nifti1Image):
                image = Brain_Data(image)
            else:
                raise ValueError("Image is not a Brain_Data or nibabel instance")
        dim = image.shape()

        # Check to make sure masks are the same for each dataset and if not create a union mask
        # This might be handy code for a new Brain_Data method
        if np.sum(self.nifti_masker.mask_img.get_data()==1)!=np.sum(image.nifti_masker.mask_img.get_data()==1):
            new_mask = intersect_masks([self.nifti_masker.mask_img, image.nifti_masker.mask_img], threshold=1, connected=False)
            new_nifti_masker = NiftiMasker(mask_img=new_mask)
            data2 = new_nifti_masker.fit_transform(self.to_nifti())
            image2 = new_nifti_masker.fit_transform(image.to_nifti())
        else:
            data2 = self.data
            image2 = image.data


        # Calculate pattern expression
        if method is 'dot_product':
            if len(image2.shape) > 1:
                if image2.shape[0]>1:
                    pexp = []
                    for i in range(image2.shape[0]):
                        pexp.append(np.dot(data2, image2[i,:]))
                    pexp = np.array(pexp)
                else:
                    pexp = np.dot(data2, image2)
            else:
                pexp = np.dot(data2, image2)
        elif method is 'correlation':
            if len(image2.shape) > 1:
                if image2.shape[0]>1:
                    pexp = []
                    for i in range(image2.shape[0]):
                        pexp.append(pearson(image2[i,:], data2))
                    pexp = np.array(pexp)
                else:
                    pexp = pearson(image2, data2)
            else:
                pexp = pearson(image2, data2)
        return pexp
示例#12
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def _vectorize_nii(in_data_file, mask_file, parcellation_path, fwhm):
    from nilearn.input_data import NiftiMasker, NiftiLabelsMasker
    import nibabel as nib

    if parcellation_path is None:
        masker = NiftiMasker(mask_img=mask_file, smoothing_fwhm=fwhm)
    else:
        masker = NiftiLabelsMasker(labels_img=parcellation_path, smoothing_fwhm=fwhm)

    vectorized_data = masker.fit_transform(in_data_file)
    return vectorized_data, masker
def map_threshold(stat_img, mask_img, threshold, height_control='fpr',
                  cluster_threshold=0):
    """ Threshold the provvided map

    Parameters
    ----------
    stat_img : Niimg-like object,
       statistical image (presumably in z scale)

    mask_img : Niimg-like object,
        mask image

    threshold: float,
        cluster forming threshold (either a p-value or z-scale value)

    height_control: string
        false positive control meaning of cluster forming
        threshold: 'fpr'|'fdr'|'bonferroni'|'none'

    cluster_threshold : float, optional
        cluster size threshold

    Returns
    -------
    thresholded_map : Nifti1Image,
        the stat_map theresholded at the prescribed voxel- and cluster-level
    """
    # Masking
    masker = NiftiMasker(mask_img=mask_img)
    stats = np.ravel(masker.fit_transform(stat_img))
    n_voxels = np.size(stats)

    # Thresholding
    if height_control == 'fpr':
        z_th = norm.isf(threshold)
    elif height_control == 'fdr':
        z_th = fdr_threshold(stats, threshold)
    elif height_control == 'bonferroni':
        z_th = norm.isf(threshold / n_voxels)
    else:  # Brute-force thresholding
        z_th = threshold
    stats *= (stats > z_th)

    stat_map = masker.inverse_transform(stats).get_data()

    # Extract connected components above threshold
    label_map, n_labels = label(stat_map > z_th)
    labels = label_map[(masker.mask_img_.get_data() > 0)]
    for label_ in range(1, n_labels + 1):
        if np.sum(labels == label_) < cluster_threshold:
            stats[labels == label_] = 0

    return masker.inverse_transform(stats)
示例#14
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def make_ttest(reg1, reg2):
    masker = NiftiMasker(nib.load(MASK_FILE), standardize=False)
    masker.fit()

    subjects = [1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]

    a = np.arctanh(join_all_subjects(reg1, subjects, masker))
    b = np.arctanh(join_all_subjects(reg2, subjects, masker))
    t, prob = ttest_rel(a, b)

    tt = masker.inverse_transform(t)
    pp = masker.inverse_transform(prob)
    return tt, pp
示例#15
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def load_data():
    with open(expanduser('~/data/HCP_unmasked/data.json'), 'r') as f:
        data = json.load(f)
        for this_data in data:
            this_data['array'] += '.npy'
        mask_img = expanduser('~/data/HCP_mask/mask_img.nii.gz')
    masker = NiftiMasker(mask_img=mask_img, smoothing_fwhm=4,
                         standardize=True)
    masker.fit()
    smith2009 = fetch_atlas_smith_2009()
    init = smith2009.rsn70
    dict_init = masker.transform(init)
    return masker, dict_init, sorted(data, key=lambda t: t['filename'])
示例#16
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def preprocess_varpar(num, subj, subj_dir, **kwargs):
    from nistats.design_matrix import make_design_matrix
    from nistats.first_level_model import run_glm
    bold_path = 'BOLD/task001_run00%i/bold_dico_bold7Tp1_to_subjbold7Tp1.nii.gz' % (num+1)
    bold_path = os.path.join(DATA_DIR, subj, bold_path)
    mask = os.path.join(DATA_DIR, subj, 'templates', 'bold7Tp1', 'brain_mask.nii.gz')
    bold = load(bold_path)
    masker = NiftiMasker(mask)
    data = masker.fit_transform(bold)
    dmat = make_design_matrix(np.arange(data.shape[0])*TR, hrf_model='fir', drift_order=5,
                              **kwargs)
    labels, results = run_glm(data, dmat, noise_model='ols', verbose=1)
    img = masker.inverse_transform(StandardScaler().fit_transform(results[0.0].resid))
#    return StandardScaler().fit_transform(results[0.0].resid)
    save(img, os.path.join(subj_dir, 'run00%i.nii.gz' % num))
示例#17
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class SmoothResampleMasker(BaseMasker):

    def __init__(self, mask_img=None, smoothing_fwhm=None, resampling=None, searchlight=False):

        self.mask_img = mask_img
        self.smoothing_fwhm = smoothing_fwhm
        self.resampling = resampling
        self.searchlight = searchlight

        self.masker = None

    def fit(self):

        if self.resampling is not None:
            self.mask_img = resample_img(self.mask_img, target_affine=np.diag(self.resampling * np.ones(3)))
        self.masker = NiftiMasker(mask_img=self.mask_img)
        self.masker.fit()

        return self

    def transform(self, imgs, confounds=None):

        smooth_prefix = '' if self.smoothing_fwhm is None else 's%g' % self.smoothing_fwhm
        resample_prefix = '' if self.smoothing_fwhm is None else 'r%g' % self.smoothing_fwhm

        if not isinstance(imgs, list):
            imgs = [imgs]

        path_first = imgs[0] if isinstance(imgs[0], str) else imgs[0].get_filename()

        path_first_resampled = os.path.join(os.path.dirname(path_first), resample_prefix + os.path.basename(path_first))
        path_first_smoothed = os.path.join(os.path.dirname(path_first), smooth_prefix + resample_prefix + os.path.basename(path_first))

        if self.resampling is not None and self.smoothing_fwhm is not None:
            if self.resampling is not None:
                if not os.path.exists(path_first_resampled) and not os.path.exists(path_first_smoothed):
                    imgs = resample_img(imgs, target_affine=np.diag(self.resampling * np.ones(3)))
                else:
                    imgs = []
            if self.smoothing_fwhm is not None:
                if not os.path.exists(path_first_smoothed):
                    imgs = smooth_img(imgs, self.smoothing_fwhm)
                else:
                    imgs = []
        else:
            imgs = [check_niimg_3d(img) for img in imgs]

        return self.masker.transform(imgs)
示例#18
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class signal_extractor():

    def __init__(self, dataset = None):
        self.dataset = dataset
        if dataset.has_key('mask'):
            self.masker = NiftiMasker(mask_img = self.dataset.mask,
                                low_pass = .1,
                                high_pass = .01,
                                smoothing_fwhm =6.,
                                t_r = 1.05,
                                detrend = True,
                                standardize = False,
                                memory_level = 0,
                                verbose=5)
        else:
            self.masker = NiftiMasker(
                                low_pass = .1,
                                high_pass = .01,
                                smoothing_fwhm =6.,
                                t_r = 1.05,
                                detrend = True,
                                standardize = False,
                                memory_level = 0,
                                verbose=5)
    def extract(self):
        for idx, func in enumerate([self.dataset.func1]):
            #add mask, smoothing, filter and detrending


            for i in range(len(self.dataset.subjects)):
                tic = time.clock()
                #extract signal to x
                x = self.masker.fit_transform(func[i])
                print "loading time : "+ str(time.clock() - tic)
                yield x, self.masker
示例#19
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def loader(anat, downsample, target_affine, dataroot, subject, maskpath, nrun,
           niifilename, labels, **kwargs):
    ''' 
    All parameters are submitted as cfg dictionary.
    Given parameters in cfg, return masked and concatenated over runs data 
    
    Input
    anat: MNI template
    downsample: 1 or 0
    target_affine: downsampling matrix
    dataroot: element of path to data
    subject: folder in dataroot with subject data
    maskpath: path to mask
    nrun: number of runs
    niifilename: how is the data file called
    labels: labels from load_labels function
    
    Output
    dict(nii_func=nii_func,nii_mean=nii_mean,masker=masker,nii_mask=nii_mask)
    nii_func: 4D data
    nii_mean: mean over 4th dimension
    masker: masker object from nibabel
    nii_mask: 3D mask
    '''
    nii_func = list()
    for r in range(nrun):
        fname = '{0}/{1}/run{2}/{3}'.format(dataroot, subject, r+1, niifilename) # Assumption about file location
        nii_img = load(fname, mmap=False)
        nii_img.set_sform(anat.get_sform())
        # Get mean over 4D
        nii_mean = mean_img(nii_img)
        # Masking
        nii_mask = load(maskpath)
        nii_mask.set_sform(anat.get_sform())
        # Binarize the mask
        nii_mask = check_binary(nii_mask)
        if downsample:
            nii_img = resample_img(nii_img, target_affine=target_affine)
            nii_mask = resample_img(nii_mask, target_affine=target_affine, interpolation='nearest')
        masker = NiftiMasker(nii_mask, standardize=True)
        nii_img = masker.fit_transform(nii_img)
        # Drop zero timepoints, zscore
        nii_img = drop_labels(nii_img, labels.get('to_drop_zeros')[r])
        nii_func.append(stats.zscore(nii_img, axis=0)) # zscore over time
    # throw data together
    nii_func = np.concatenate(nii_func)
    return dict(nii_func=nii_func, nii_mean=nii_mean, masker=masker, nii_mask=nii_mask)
示例#20
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    def __init__(self, data=None, Y=None, X=None, mask=None, output_file=None, **kwargs):
        if mask is not None:
            if not isinstance(mask, nib.Nifti1Image):
                if type(mask) is str:
                    if os.path.isfile(mask):
                        mask = nib.load(mask)
            else:
                raise ValueError("mask is not a nibabel instance")
            self.mask = mask
        else:
            self.mask = nib.load(os.path.join(get_resource_path(),'MNI152_T1_2mm_brain_mask.nii.gz'))
        self.nifti_masker = NiftiMasker(mask_img=self.mask)

        if data is not None:
            if type(data) is str:
                data=nib.load(data)
                self.data = self.nifti_masker.fit_transform(data)
            elif type(data) is list:
                # Load and transform each image in list separately (nib.concat_images(data) can't handle images of different sizes)
                self.data = []
                for i in data:
                    if isinstance(i,six.string_types):
                        self.data.append(self.nifti_masker.fit_transform(nib.load(i)))
                    elif isinstance(i,nib.Nifti1Image):
                        self.data.append(self.nifti_masker.fit_transform(i))
                self.data = np.array(self.data)
            elif not isinstance(data, nib.Nifti1Image):
                raise ValueError("data is not a nibabel instance")

            # Collapse any extra dimension
            if any([x==1 for x in self.data.shape]):
                self.data=self.data.squeeze()
        else:
            self.data = np.array([])

        if Y is not None:
            if type(Y) is str:
                if os.path.isfile(Y):
                    Y=pd.read_csv(Y,header=None,index_col=None)
            if isinstance(Y, pd.DataFrame):
                if self.data.shape[0]!= len(Y):
                    raise ValueError("Y does not match the correct size of data")
                self.Y = Y
            else:
                raise ValueError("Make sure Y is a pandas data frame.")
        else:
            self.Y = pd.DataFrame()

        if X is not None:
            if self.data.shape[0]!= X.shape[0]:
                raise ValueError("X does not match the correct size of data")
            self.X = X
        else:
            self.X = pd.DataFrame()

        if output_file is not None:
            self.file_name = output_file
        else:
            self.file_name = []
示例#21
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def test_multi_pca_score():
    shape = (6, 8, 10, 5)
    affine = np.eye(4)
    rng = np.random.RandomState(0)

    # Create a "multi-subject" dataset
    imgs = []
    for i in range(8):
        this_img = rng.normal(size=shape)
        imgs.append(nibabel.Nifti1Image(this_img, affine))

    mask_img = nibabel.Nifti1Image(np.ones(shape[:3], dtype=np.int8), affine)

    # Assert that score is between zero and one
    multi_pca = MultiPCA(mask=mask_img, random_state=0, memory_level=0,
                         n_components=3)
    multi_pca.fit(imgs)
    s = multi_pca.score(imgs)
    assert_true(np.all(s <= 1))
    assert_true(np.all(0 <= s))

    # Assert that score does not fail with single subject data
    multi_pca = MultiPCA(mask=mask_img, random_state=0, memory_level=0,
                         n_components=3)
    multi_pca.fit(imgs[0])
    s = multi_pca.score(imgs[0])
    assert_true(isinstance(s, float))
    assert(0. <= s <= 1.)

    # Assert that score is one for n_components == n_sample
    # in single subject configuration
    multi_pca = MultiPCA(mask=mask_img, random_state=0, memory_level=0,
                         n_components=5)
    multi_pca.fit(imgs[0])
    s = multi_pca.score(imgs[0])
    assert_almost_equal(s, 1., 1)

    # Per component score
    multi_pca = MultiPCA(mask=mask_img, random_state=0, memory_level=0,
                         n_components=5)
    multi_pca.fit(imgs[0])
    masker = NiftiMasker(mask_img).fit()
    s = multi_pca._raw_score(masker.transform(imgs[0]), per_component=True)
    assert_equal(s.shape, (5,))
    assert_true(np.all(s <= 1))
    assert_true(np.all(0 <= s))
示例#22
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    def fit(self):

        if self.resampling is not None:
            self.mask_img = resample_img(self.mask_img, target_affine=np.diag(self.resampling * np.ones(3)))
        self.masker = NiftiMasker(mask_img=self.mask_img)
        self.masker.fit()

        return self
示例#23
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def residualize_imgs(in_file, mask_file, confounds_file):
    '''
    * takes 4d file, mask file & confounds as np.array
    * regresses out confounds (only within mask)
    * writes residualized nii
    '''
    from nilearn.input_data import NiftiMasker
    import os
    import numpy as np

    confounds = np.loadtxt(confounds_file)
    masker = NiftiMasker(mask_img=mask_file)
    brain_data_2d = masker.fit_transform(in_file, confounds=confounds)
    out_file = os.path.join(os.getcwd(), 'residualized_data.nii.gz')
    out_img = masker.inverse_transform(brain_data_2d)
    out_img.to_filename(out_file)
    return out_file
示例#24
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    def multivariate_similarity(self, images, method='ols'):
        """ Predict spatial distribution of Brain_Data() instance from linear combination of other Brain_Data() instances or Nibabel images

            Args:
                self: Brain_Data instance of data to be applied
                images: Brain_Data instance of weight map

            Returns:
                out: dictionary of regression statistics in Brain_Data instances {'beta','t','p','df','residual'}

        """
        ## Notes:  Should add ridge, and lasso, elastic net options options

        if len(self.shape()) > 1:
            raise ValueError("This method can only decompose a single brain image.")

        if not isinstance(images, Brain_Data):
            raise ValueError("Images are not a Brain_Data instance")
        dim = images.shape()

        # Check to make sure masks are the same for each dataset and if not create a union mask
        # This might be handy code for a new Brain_Data method
        if np.sum(self.nifti_masker.mask_img.get_data()==1)!=np.sum(images.nifti_masker.mask_img.get_data()==1):
            new_mask = intersect_masks([self.nifti_masker.mask_img, images.nifti_masker.mask_img], threshold=1, connected=False)
            new_nifti_masker = NiftiMasker(mask_img=new_mask)
            data2 = new_nifti_masker.fit_transform(self.to_nifti())
            image2 = new_nifti_masker.fit_transform(images.to_nifti())
        else:
            data2 = self.data
            image2 = images.data

        # Add intercept and transpose
        image2 = np.vstack((np.ones(image2.shape[1]),image2)).T

        # Calculate pattern expression
        if method is 'ols':
            b = np.dot(np.linalg.pinv(image2), data2)
            res = data2 - np.dot(image2,b)
            sigma = np.std(res,axis=0)
            stderr = np.dot(np.matrix(np.diagonal(np.linalg.inv(np.dot(image2.T,image2)))**.5).T,np.matrix(sigma))
            t_out = b /stderr
            df = image2.shape[0]-image2.shape[1]
            p = 2*(1-t.cdf(np.abs(t_out),df))

        return {'beta':b, 't':t_out, 'p':p, 'df':df, 'sigma':sigma, 'residual':res}
示例#25
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def read_data_haxby(subject, tr=2.5, masker=False):
    haxby_dataset = fetch_haxby(subjects=[subject])

    # Load fmri data
    fmri_filename = haxby_dataset.func[0]
    fmri = load_img(fmri_filename)
    # mask = haxby_dataset.mask_vt[0]
    masker = NiftiMasker(mask_strategy='epi', standardize=True, detrend=True,
                         high_pass=0.01, t_r=tr, smoothing_fwhm=5)
    fmri = masker.fit_transform(fmri)
    fmri = fmri.reshape(12, -1, fmri.shape[-1])

    # Load stimuli data
    classes = np.array(['rest', 'face', 'house', 'bottle', 'cat', 'chair',
                        'scissors', 'shoe', 'scrambledpix'])
    labels = np.recfromcsv(
        haxby_dataset.session_target[0], delimiter=" ")['labels'].reshape(
            12, -1)
    stimuli, onsets, conditions = (np.zeros((
        12, len(labels[0]), len(classes))), [], [])
    stimuli[:, 0, 0] = 1
    for session in range(12):
        onsets.append([])
        conditions.append([])
        for scan in range(1, len(fmri[session])):
            if (labels[session][scan - 1] == 'rest' and
                labels[session][scan] != 'rest'):
                label = labels[session][scan]
                stimuli[session, scan, np.where(classes == label)[0][0]] = 1
                conditions[session].append(label)
                onsets[session].append(scan * tr)
            else:
                stimuli[session, scan, 0] = 1

    if subject == 5:
        fmri = np.vstack((fmri[:8], fmri[9:]))
        stimuli = np.vstack((stimuli[:8], stimuli[9:]))
        onsets = np.vstack((onsets[:8], onsets[9:]))
        conditions = np.vstack((conditions[:8], conditions[9:]))

    if masker:
        return fmri, stimuli, onsets, conditions, masker

    return fmri, stimuli, onsets, conditions
示例#26
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def apply_mask(data=None, weight_map=None, mask=None, method='dot_product', save_output=False, output_dir='.'):
    """ Apply Nifti weight map to Nifti Images. 
 
        Args:
            data: nibabel instance of data to be applied
            weight_map: nibabel instance of weight map
            mask: binary nibabel mask
            method: type of pattern expression (e.g,. 'dot_product','correlation')
            save_output: Boolean indicating whether or not to save output to csv file.
            output_dir: Directory to use for writing all outputs
            **kwargs: Additional parameters to pass

        Returns:
            pexp: Outputs a vector of pattern expression values

    """ 

    if mask is not None:
        if type(mask) is not nib.nifti1.Nifti1Image:
            raise ValueError("Mask is not a nibabel instance")
    else:
        mask = nib.load(os.path.join(resource_dir,'MNI152_T1_2mm_brain_mask_dil.nii.gz'))
    
    if type(data) is not nib.nifti1.Nifti1Image:
        raise ValueError("Data is not a nibabel instance")
    
    nifti_masker = NiftiMasker(mask_img=mask)
    data_masked = nifti_masker.fit_transform(data)

    if type(weight_map) is not nib.nifti1.Nifti1Image:
        raise ValueError("Weight_map is not a nibabel instance")
    
    weight_map_masked = nifti_masker.fit_transform(weight_map)

    # Calculate pattern expression
    if method is 'dot_product':
        pexp = np.dot(data_masked,np.transpose(weight_map_masked)).squeeze()
    elif method is 'correlation':
        pexp = pearson(data_masked,weight_map_masked)

    if save_output:
        np.savetxt(os.path.join(output_dir,"Pattern_Expression_" + method + ".csv"), pexp, delimiter=",")

    return pexp
示例#27
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def test_dict_learning():
    data, mask_img, components, rng = _make_canica_test_data(n_subjects=8)
    mask = NiftiMasker(mask_img=mask_img).fit()
    dict_init = mask.inverse_transform(components)
    dict_learning = DictLearning(n_components=4, random_state=0,
                                 dict_init=dict_init,
                                 mask=mask_img,
                                 smoothing_fwhm=0., alpha=1)

    dict_learning_auto_init = DictLearning(n_components=4, random_state=0,
                                           mask=mask_img,
                                           smoothing_fwhm=0., n_epochs=10,
                                           alpha=1)
    maps = {}
    for estimator in [dict_learning,
                      dict_learning_auto_init]:
        estimator.fit(data)
        maps[estimator] = estimator.masker_. \
            inverse_transform(estimator.components_).get_data()
        maps[estimator] = np.reshape(np.rollaxis(maps[estimator], 3, 0),
                                     (4, 400))

    for this_dict_learning in [dict_learning]:
        these_maps = maps[this_dict_learning]
        S = np.sqrt(np.sum(components ** 2, axis=1))
        S[S == 0] = 1
        components /= S[:, np.newaxis]

        S = np.sqrt(np.sum(these_maps ** 2, axis=1))
        S[S == 0] = 1
        these_maps /= S[:, np.newaxis]

        K = np.abs(components.dot(these_maps.T))
        recovered_maps = np.sum(K > 0.9)
        assert(recovered_maps >= 2)

    # Smoke test n_epochs > 1
    dict_learning = DictLearning(n_components=4, random_state=0,
                                 dict_init=dict_init,
                                 mask=mask_img,
                                 smoothing_fwhm=0., n_epochs=2, alpha=1)
    dict_learning.fit(data)
示例#28
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def main(output_dir, n_jobs):
    dir_list = [join(output_dir, f) for f in os.listdir(output_dir) if
                os.path.isdir(join(output_dir, f))]

    mask, func_filenames = get_hcp_data(raw=True)

    masker = NiftiMasker(mask_img=mask, smoothing_fwhm=None,
                         standardize=False)
    masker.fit()

    test_data = func_filenames[(-n_test_records * 2)::2]

    n_samples, n_voxels = np.load(test_data[-1], mmap_mode='r').shape
    X = np.empty((n_test_records * n_samples, n_voxels))

    for i, this_data in enumerate(test_data):
        X[i * n_samples:(i + 1) * n_samples] = np.load(this_data,
                                                       mmap_mode='r')

    Parallel(n_jobs=n_jobs, verbose=1, temp_folder='/dev/shm')(
        delayed(analyse_dir)(dir_name, X, masker) for dir_name in dir_list)
示例#29
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 def __init__(self, dataset = None):
     self.dataset = dataset
     if dataset.has_key('mask'):
         self.masker = NiftiMasker(mask_img = self.dataset.mask,
                             low_pass = .1,
                             high_pass = .01,
                             smoothing_fwhm =6.,
                             t_r = 1.05,
                             detrend = True,
                             standardize = False,
                             memory_level = 0,
                             verbose=5)
     else:
         self.masker = NiftiMasker(
                             low_pass = .1,
                             high_pass = .01,
                             smoothing_fwhm =6.,
                             t_r = 1.05,
                             detrend = True,
                             standardize = False,
                             memory_level = 0,
                             verbose=5)
示例#30
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 def __init__(self, mask=None, metric='wavelet', regu='tv', lbda=1, detrend=True,
              low_pass=.1, high_pass=.01, t_r=1.05, smoothing_fwhm=6.,
              memory='', memory_level=0, n_jobs=1, nb_vanishmoment=2,
              norm=1, q=np.array(2), nbvoies=None,
              distn=1, wtype=1, j1=2, j2=8):
     self.metric = metric
     self.mask = mask
     self.n_jobs = n_jobs
     self.nb_vanishmoment = nb_vanishmoment
     self.norm = norm
     self.q = q
     self.nbvoies = nbvoies
     self.distn = distn
     self.wtype = wtype
     self.j1 = j1
     self.j2 = j2
     self.regu = regu
     self.lbda = lbda
     
     if self.mask is None:
             self.masker = NiftiMasker(detrend=detrend,
                                 low_pass=low_pass,
                                 high_pass=high_pass,
                                 t_r=t_r,
                                 smoothing_fwhm=smoothing_fwhm,
                                 standardize=False,
                                 memory_level=memory_level,
                                 verbose=0)
     else:
         self.masker = NiftiMasker(mask_img=self.mask,
                                 detrend=detrend,
                                 low_pass=low_pass,
                                 high_pass=high_pass,
                                 t_r=t_r,
                                 smoothing_fwhm=smoothing_fwhm,
                                 standardize=False,
                                 memory_level=memory_level,
                                 verbose=0)
         self.masker.fit(self.mask)
示例#31
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    def fit(self, second_level_input, confounds=None, design_matrix=None):
        """ Fit the second-level GLM

        1. create design matrix
        2. do a masker job: fMRI_data -> Y
        3. fit regression to (Y, X)

        Parameters
        ----------
        second_level_input: list of `FirstLevelModel` objects or pandas
                            DataFrame or list of Niimg-like objects.

            Giving FirstLevelModel objects will allow to easily compute
            the second level contast of arbitrary first level contrasts thanks
            to the first_level_contrast argument of the compute_contrast
            method. Effect size images will be computed for each model to
            contrast at the second level.

            If a pandas DataFrame, then they have to contain subject_label,
            map_name and effects_map_path. It can contain multiple maps that
            would be selected during contrast estimation with the argument
            first_level_contrast of the compute_contrast function. The
            DataFrame will be sorted based on the subject_label column to avoid
            order inconsistencies when extracting the maps. So the rows of the
            automatically computed design matrix, if not provided, will
            correspond to the sorted subject_label column.
 
            If list of Niimg-like objects then this is taken literally as Y
            for the model fit and design_matrix must be provided.

        confounds: pandas DataFrame, optional
            Must contain a subject_label column. All other columns are
            considered as confounds and included in the model. If
            design_matrix is provided then this argument is ignored.
            The resulting second level design matrix uses the same column
            names as in the given DataFrame for confounds. At least two columns
            are expected, "subject_label" and at least one confound.

        design_matrix: pandas DataFrame, optional
            Design matrix to fit the GLM. The number of rows
            in the design matrix must agree with the number of maps derived
            from second_level_input.
            Ensure that the order of maps given by a second_level_input
            list of Niimgs matches the order of the rows in the design matrix.

        """
        # Check parameters
        # check first level input
        if isinstance(second_level_input, list):
            if len(second_level_input) < 2:
                raise ValueError('A second level model requires a list with at'
                                 'least two first level models or niimgs')
            # Check FirstLevelModel objects case
            if isinstance(second_level_input[0], FirstLevelModel):
                models_input = enumerate(second_level_input)
                for model_idx, first_level_model in models_input:
                    if (first_level_model.labels_ is None
                            or first_level_model.results_ is None):
                        raise ValueError(
                            'Model %s at index %i has not been fit yet'
                            '' % (first_level_model.subject_label, model_idx))
                    if not isinstance(first_level_model, FirstLevelModel):
                        raise ValueError(' object at idx %d is %s instead of'
                                         ' FirstLevelModel object' %
                                         (model_idx, type(first_level_model)))
                    if confounds is not None:
                        if first_level_model.subject_label is None:
                            raise ValueError(
                                'In case confounds are provided, first level '
                                'objects need to provide the attribute '
                                'subject_label to match rows appropriately.'
                                'Model at idx %d does not provide it. '
                                'To set it, you can do '
                                'first_level_model.subject_label = "01"'
                                '' % (model_idx))
            # Check niimgs case
            elif isinstance(second_level_input[0], (str, Nifti1Image)):
                if design_matrix is None:
                    raise ValueError('List of niimgs as second_level_input'
                                     ' require a design matrix to be provided')
                for model_idx, niimg in enumerate(second_level_input):
                    if not isinstance(niimg, (str, Nifti1Image)):
                        raise ValueError(' object at idx %d is %s instead of'
                                         ' Niimg-like object' %
                                         (model_idx, type(niimg)))
        # Check pandas dataframe case
        elif isinstance(second_level_input, pd.DataFrame):
            for col in ['subject_label', 'map_name', 'effects_map_path']:
                if col not in second_level_input.columns:
                    raise ValueError('second_level_input DataFrame must have'
                                     ' columns subject_label, map_name and'
                                     ' effects_map_path')
            # Make sure subject_label contain strings
            second_level_columns = second_level_input.columns.tolist()
            labels_index = second_level_columns.index('subject_label')
            labels_dtype = second_level_input.dtypes[labels_index]
            if not isinstance(labels_dtype, np.object):
                raise ValueError('subject_label column must be of dtype '
                                 'object instead of dtype %s' % labels_dtype)
        elif isinstance(second_level_input, (str, Nifti1Image)):
            if design_matrix is None:
                raise ValueError('List of niimgs as second_level_input'
                                 ' require a design matrix to be provided')
            second_level_input = check_niimg(niimg=second_level_input,
                                             ensure_ndim=4)
        else:
            raise ValueError('second_level_input must be a list of'
                             ' `FirstLevelModel` objects, a pandas DataFrame'
                             ' or a list Niimg-like objects. Instead %s '
                             'was provided' % type(second_level_input))

        # check confounds
        if confounds is not None:
            if not isinstance(confounds, pd.DataFrame):
                raise ValueError('confounds must be a pandas DataFrame')
            if 'subject_label' not in confounds.columns:
                raise ValueError('confounds DataFrame must contain column'
                                 '"subject_label"')
            if len(confounds.columns) < 2:
                raise ValueError('confounds should contain at least 2 columns'
                                 'one called "subject_label" and the other'
                                 'with a given confound')
            # Make sure subject_label contain strings
            labels_index = confounds.columns.tolist().index('subject_label')
            labels_dtype = confounds.dtypes[labels_index]
            if not isinstance(labels_dtype, np.object):
                raise ValueError('subject_label column must be of dtype '
                                 'object instead of dtype %s' % labels_dtype)

        # check design matrix
        if design_matrix is not None:
            if not isinstance(design_matrix, pd.DataFrame):
                raise ValueError('design matrix must be a pandas DataFrame')

        # sort a pandas dataframe by subject_label to avoid inconsistencies
        # with the design matrix row order when automatically extracting maps
        if isinstance(second_level_input, pd.DataFrame):
            columns = second_level_input.columns.tolist()
            column_index = columns.index('subject_label')
            sorted_matrix = sorted(second_level_input.values,
                                   key=lambda x: x[column_index])
            sorted_input = pd.DataFrame(sorted_matrix, columns=columns)
            second_level_input = sorted_input

        self.second_level_input_ = second_level_input
        self.confounds_ = confounds

        # Report progress
        t0 = time.time()
        if self.verbose > 0:
            sys.stderr.write("Fitting second level model. "
                             "Take a deep breath\r")

        # Select sample map for masker fit and get subjects_label for design
        if isinstance(second_level_input, pd.DataFrame):
            sample_map = second_level_input['effects_map_path'][0]
            labels = second_level_input['subject_label']
            subjects_label = labels.values.tolist()
        elif isinstance(second_level_input, Nifti1Image):
            sample_map = mean_img(second_level_input)
        elif isinstance(second_level_input[0], FirstLevelModel):
            sample_model = second_level_input[0]
            sample_condition = sample_model.design_matrices_[0].columns[0]
            sample_map = sample_model.compute_contrast(
                sample_condition, output_type='effect_size')
            labels = [model.subject_label for model in second_level_input]
            subjects_label = labels
        else:
            # In this case design matrix had to be provided
            sample_map = mean_img(second_level_input)

        # Create and set design matrix, if not given
        if design_matrix is None:
            design_matrix = make_second_level_design_matrix(
                subjects_label, confounds)
        self.design_matrix_ = design_matrix

        # Learn the mask. Assume the first level imgs have been masked.
        if not isinstance(self.mask, NiftiMasker):
            self.masker_ = NiftiMasker(mask_img=self.mask,
                                       smoothing_fwhm=self.smoothing_fwhm,
                                       memory=self.memory,
                                       verbose=max(0, self.verbose - 1),
                                       memory_level=self.memory_level)
        else:
            self.masker_ = clone(self.mask)
            for param_name in ['smoothing_fwhm', 'memory', 'memory_level']:
                our_param = getattr(self, param_name)
                if our_param is None:
                    continue
                if getattr(self.masker_, param_name) is not None:
                    warn('Parameter %s of the masker overriden' % param_name)
                setattr(self.masker_, param_name, our_param)
        self.masker_.fit(sample_map)

        # Report progress
        if self.verbose > 0:
            sys.stderr.write("\nComputation of second level model done in "
                             "%i seconds\n" % (time.time() - t0))

        return self
示例#32
0
class SecondLevelModel(BaseEstimator, TransformerMixin, CacheMixin):
    """ Implementation of the General Linear Model for multiple subject
    fMRI data

    Parameters
    ----------

    mask: Niimg-like, NiftiMasker or MultiNiftiMasker object, optional,
        Mask to be used on data. If an instance of masker is passed,
        then its mask will be used. If no mask is given,
        it will be computed automatically by a MultiNiftiMasker with default
        parameters. Automatic mask computation assumes first level imgs have
        already been masked.

    smoothing_fwhm: float, optional
        If smoothing_fwhm is not None, it gives the size in millimeters of the
        spatial smoothing to apply to the signal.

    memory: string, optional
        Path to the directory used to cache the masking process and the glm
        fit. By default, no caching is done. Creates instance of joblib.Memory.

    memory_level: integer, optional
        Rough estimator of the amount of memory used by caching. Higher value
        means more memory for caching.

    verbose : integer, optional
        Indicate the level of verbosity. By default, nothing is printed.
        If 0 prints nothing. If 1 prints final computation time.
        If 2 prints masker computation details.

    n_jobs : integer, optional
        The number of CPUs to use to do the computation. -1 means
        'all CPUs', -2 'all CPUs but one', and so on.

    minimize_memory : boolean, optional
        Gets rid of some variables on the model fit results that are not
        necessary for contrast computation and would only be useful for
        further inspection of model details. This has an important impact
        on memory consumption. True by default.

    """
    def __init__(self,
                 mask=None,
                 smoothing_fwhm=None,
                 memory=Memory(None),
                 memory_level=1,
                 verbose=0,
                 n_jobs=1,
                 minimize_memory=True):
        self.mask = mask
        self.smoothing_fwhm = smoothing_fwhm
        if isinstance(memory, _basestring):
            self.memory = Memory(memory)
        else:
            self.memory = memory
        self.memory_level = memory_level
        self.verbose = verbose
        self.n_jobs = n_jobs
        self.minimize_memory = minimize_memory
        self.second_level_input_ = None
        self.confounds_ = None

    def fit(self, second_level_input, confounds=None, design_matrix=None):
        """ Fit the second-level GLM

        1. create design matrix
        2. do a masker job: fMRI_data -> Y
        3. fit regression to (Y, X)

        Parameters
        ----------
        second_level_input: list of `FirstLevelModel` objects or pandas
                            DataFrame or list of Niimg-like objects.

            Giving FirstLevelModel objects will allow to easily compute
            the second level contast of arbitrary first level contrasts thanks
            to the first_level_contrast argument of the compute_contrast
            method. Effect size images will be computed for each model to
            contrast at the second level.

            If a pandas DataFrame, then they have to contain subject_label,
            map_name and effects_map_path. It can contain multiple maps that
            would be selected during contrast estimation with the argument
            first_level_contrast of the compute_contrast function. The
            DataFrame will be sorted based on the subject_label column to avoid
            order inconsistencies when extracting the maps. So the rows of the
            automatically computed design matrix, if not provided, will
            correspond to the sorted subject_label column.
 
            If list of Niimg-like objects then this is taken literally as Y
            for the model fit and design_matrix must be provided.

        confounds: pandas DataFrame, optional
            Must contain a subject_label column. All other columns are
            considered as confounds and included in the model. If
            design_matrix is provided then this argument is ignored.
            The resulting second level design matrix uses the same column
            names as in the given DataFrame for confounds. At least two columns
            are expected, "subject_label" and at least one confound.

        design_matrix: pandas DataFrame, optional
            Design matrix to fit the GLM. The number of rows
            in the design matrix must agree with the number of maps derived
            from second_level_input.
            Ensure that the order of maps given by a second_level_input
            list of Niimgs matches the order of the rows in the design matrix.

        """
        # Check parameters
        # check first level input
        if isinstance(second_level_input, list):
            if len(second_level_input) < 2:
                raise ValueError('A second level model requires a list with at'
                                 'least two first level models or niimgs')
            # Check FirstLevelModel objects case
            if isinstance(second_level_input[0], FirstLevelModel):
                models_input = enumerate(second_level_input)
                for model_idx, first_level_model in models_input:
                    if (first_level_model.labels_ is None
                            or first_level_model.results_ is None):
                        raise ValueError(
                            'Model %s at index %i has not been fit yet'
                            '' % (first_level_model.subject_label, model_idx))
                    if not isinstance(first_level_model, FirstLevelModel):
                        raise ValueError(' object at idx %d is %s instead of'
                                         ' FirstLevelModel object' %
                                         (model_idx, type(first_level_model)))
                    if confounds is not None:
                        if first_level_model.subject_label is None:
                            raise ValueError(
                                'In case confounds are provided, first level '
                                'objects need to provide the attribute '
                                'subject_label to match rows appropriately.'
                                'Model at idx %d does not provide it. '
                                'To set it, you can do '
                                'first_level_model.subject_label = "01"'
                                '' % (model_idx))
            # Check niimgs case
            elif isinstance(second_level_input[0], (str, Nifti1Image)):
                if design_matrix is None:
                    raise ValueError('List of niimgs as second_level_input'
                                     ' require a design matrix to be provided')
                for model_idx, niimg in enumerate(second_level_input):
                    if not isinstance(niimg, (str, Nifti1Image)):
                        raise ValueError(' object at idx %d is %s instead of'
                                         ' Niimg-like object' %
                                         (model_idx, type(niimg)))
        # Check pandas dataframe case
        elif isinstance(second_level_input, pd.DataFrame):
            for col in ['subject_label', 'map_name', 'effects_map_path']:
                if col not in second_level_input.columns:
                    raise ValueError('second_level_input DataFrame must have'
                                     ' columns subject_label, map_name and'
                                     ' effects_map_path')
            # Make sure subject_label contain strings
            second_level_columns = second_level_input.columns.tolist()
            labels_index = second_level_columns.index('subject_label')
            labels_dtype = second_level_input.dtypes[labels_index]
            if not isinstance(labels_dtype, np.object):
                raise ValueError('subject_label column must be of dtype '
                                 'object instead of dtype %s' % labels_dtype)
        elif isinstance(second_level_input, (str, Nifti1Image)):
            if design_matrix is None:
                raise ValueError('List of niimgs as second_level_input'
                                 ' require a design matrix to be provided')
            second_level_input = check_niimg(niimg=second_level_input,
                                             ensure_ndim=4)
        else:
            raise ValueError('second_level_input must be a list of'
                             ' `FirstLevelModel` objects, a pandas DataFrame'
                             ' or a list Niimg-like objects. Instead %s '
                             'was provided' % type(second_level_input))

        # check confounds
        if confounds is not None:
            if not isinstance(confounds, pd.DataFrame):
                raise ValueError('confounds must be a pandas DataFrame')
            if 'subject_label' not in confounds.columns:
                raise ValueError('confounds DataFrame must contain column'
                                 '"subject_label"')
            if len(confounds.columns) < 2:
                raise ValueError('confounds should contain at least 2 columns'
                                 'one called "subject_label" and the other'
                                 'with a given confound')
            # Make sure subject_label contain strings
            labels_index = confounds.columns.tolist().index('subject_label')
            labels_dtype = confounds.dtypes[labels_index]
            if not isinstance(labels_dtype, np.object):
                raise ValueError('subject_label column must be of dtype '
                                 'object instead of dtype %s' % labels_dtype)

        # check design matrix
        if design_matrix is not None:
            if not isinstance(design_matrix, pd.DataFrame):
                raise ValueError('design matrix must be a pandas DataFrame')

        # sort a pandas dataframe by subject_label to avoid inconsistencies
        # with the design matrix row order when automatically extracting maps
        if isinstance(second_level_input, pd.DataFrame):
            columns = second_level_input.columns.tolist()
            column_index = columns.index('subject_label')
            sorted_matrix = sorted(second_level_input.values,
                                   key=lambda x: x[column_index])
            sorted_input = pd.DataFrame(sorted_matrix, columns=columns)
            second_level_input = sorted_input

        self.second_level_input_ = second_level_input
        self.confounds_ = confounds

        # Report progress
        t0 = time.time()
        if self.verbose > 0:
            sys.stderr.write("Fitting second level model. "
                             "Take a deep breath\r")

        # Select sample map for masker fit and get subjects_label for design
        if isinstance(second_level_input, pd.DataFrame):
            sample_map = second_level_input['effects_map_path'][0]
            labels = second_level_input['subject_label']
            subjects_label = labels.values.tolist()
        elif isinstance(second_level_input, Nifti1Image):
            sample_map = mean_img(second_level_input)
        elif isinstance(second_level_input[0], FirstLevelModel):
            sample_model = second_level_input[0]
            sample_condition = sample_model.design_matrices_[0].columns[0]
            sample_map = sample_model.compute_contrast(
                sample_condition, output_type='effect_size')
            labels = [model.subject_label for model in second_level_input]
            subjects_label = labels
        else:
            # In this case design matrix had to be provided
            sample_map = mean_img(second_level_input)

        # Create and set design matrix, if not given
        if design_matrix is None:
            design_matrix = make_second_level_design_matrix(
                subjects_label, confounds)
        self.design_matrix_ = design_matrix

        # Learn the mask. Assume the first level imgs have been masked.
        if not isinstance(self.mask, NiftiMasker):
            self.masker_ = NiftiMasker(mask_img=self.mask,
                                       smoothing_fwhm=self.smoothing_fwhm,
                                       memory=self.memory,
                                       verbose=max(0, self.verbose - 1),
                                       memory_level=self.memory_level)
        else:
            self.masker_ = clone(self.mask)
            for param_name in ['smoothing_fwhm', 'memory', 'memory_level']:
                our_param = getattr(self, param_name)
                if our_param is None:
                    continue
                if getattr(self.masker_, param_name) is not None:
                    warn('Parameter %s of the masker overriden' % param_name)
                setattr(self.masker_, param_name, our_param)
        self.masker_.fit(sample_map)

        # Report progress
        if self.verbose > 0:
            sys.stderr.write("\nComputation of second level model done in "
                             "%i seconds\n" % (time.time() - t0))

        return self

    def compute_contrast(self,
                         second_level_contrast=None,
                         first_level_contrast=None,
                         second_level_stat_type=None,
                         output_type='z_score'):
        """Generate different outputs corresponding to
        the contrasts provided e.g. z_map, t_map, effects and variance.

        Parameters
        ----------
        second_level_contrast: str or array of shape (n_col), optional
            Where ``n_col`` is the number of columns of the design matrix,
            The string can be a formula compatible with the linear constraint
            of the Patsy library. Basically one can use the name of the
            conditions as they appear in the design matrix of
            the fitted model combined with operators /\*+- and numbers.
            Please check the patsy documentation for formula examples:
            http://patsy.readthedocs.io/en/latest/API-reference.html#patsy.DesignInfo.linear_constraint
            The default (None) is accepted if the design matrix has a single
            column, in which case the only possible contrast array([1]) is
            applied; when the design matrix has multiple columns, an error is
            raised.

        first_level_contrast: str or array of shape (n_col) with respect to
                              FirstLevelModel, optional
                              
            In case a list of FirstLevelModel was provided as
            second_level_input, we have to provide a contrast to apply to
            the first level models to get the corresponding list of images
            desired, that would be tested at the second level. In case a
            pandas DataFrame was provided as second_level_input this is the
            map name to extract from the pandas dataframe map_name column.
            It has to be a 't' contrast.

        second_level_stat_type: {'t', 'F'}, optional
            Type of the second level contrast

        output_type: str, optional
            Type of the output map. Can be 'z_score', 'stat', 'p_value',
            'effect_size' or 'effect_variance'

        Returns
        -------
        output_image: Nifti1Image
            The desired output image

        """
        if self.second_level_input_ is None:
            raise ValueError('The model has not been fit yet')

        # first_level_contrast check
        if isinstance(self.second_level_input_[0], FirstLevelModel):
            if first_level_contrast is None:
                raise ValueError('If second_level_input was a list of '
                                 'FirstLevelModel, then first_level_contrast '
                                 'is mandatory. It corresponds to the '
                                 'second_level_contrast argument of the '
                                 'compute_contrast method of FirstLevelModel')

        # check contrast definition
        if second_level_contrast is None:
            if self.design_matrix_.shape[1] == 1:
                second_level_contrast = np.ones([1])
            else:
                raise ValueError('No second-level contrast is specified.')
        if isinstance(second_level_contrast, np.ndarray):
            con_val = second_level_contrast
            if np.all(con_val == 0):
                raise ValueError('Contrast is null')
        else:
            design_info = DesignInfo(self.design_matrix_.columns.tolist())
            constraint = design_info.linear_constraint(second_level_contrast)
            con_val = constraint.coefs
        # check output type
        if isinstance(output_type, _basestring):
            if output_type not in [
                    'z_score', 'stat', 'p_value', 'effect_size',
                    'effect_variance'
            ]:
                raise ValueError(
                    'output_type must be one of "z_score", "stat"'
                    ', "p_value", "effect_size" or "effect_variance"')
        else:
            raise ValueError('output_type must be one of "z_score", "stat",'
                             ' "p_value", "effect_size" or "effect_variance"')

        # Get effect_maps appropriate for chosen contrast
        effect_maps = _infer_effect_maps(self.second_level_input_,
                                         first_level_contrast)
        # Check design matrix X and effect maps Y agree on number of rows
        if len(effect_maps) != self.design_matrix_.shape[0]:
            raise ValueError(
                'design_matrix does not match the number of maps considered. '
                '%i rows in design matrix do not match with %i maps' %
                (self.design_matrix_.shape[0], len(effect_maps)))

        # Fit an Ordinary Least Squares regression for parametric statistics
        Y = self.masker_.transform(effect_maps)
        if self.memory:
            mem_glm = self.memory.cache(run_glm, ignore=['n_jobs'])
        else:
            mem_glm = run_glm
        labels, results = mem_glm(Y,
                                  self.design_matrix_.values,
                                  n_jobs=self.n_jobs,
                                  noise_model='ols')
        # We save memory if inspecting model details is not necessary
        if self.minimize_memory:
            for key in results:
                results[key] = SimpleRegressionResults(results[key])
        self.labels_ = labels
        self.results_ = results

        # We compute contrast object
        if self.memory:
            mem_contrast = self.memory.cache(compute_contrast)
        else:
            mem_contrast = compute_contrast
        contrast = mem_contrast(self.labels_, self.results_, con_val,
                                second_level_stat_type)

        # We get desired output from contrast object
        estimate_ = getattr(contrast, output_type)()

        # Prepare the returned images
        output = self.masker_.inverse_transform(estimate_)
        contrast_name = str(con_val)
        output.header['descrip'] = ('%s of contrast %s' %
                                    (output_type, contrast_name))
        return output
示例#33
0
from kmapper import KeplerMapper, Cover
from sklearn.cluster import DBSCAN
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from umap.umap_ import UMAP

from dyneusr import DyNeuGraph
from dyneusr.tools import visualize_mapper_stages
from dyneusr.mapper.utils import optimize_dbscan

# Fetch dataset, extract time-series from ventral temporal (VT) mask
dataset = fetch_haxby()
masker = NiftiMasker(dataset.mask_vt[0],
                     standardize=True,
                     detrend=True,
                     smoothing_fwhm=4.0,
                     low_pass=0.09,
                     high_pass=0.008,
                     t_r=2.5,
                     memory="nilearn_cache")
X = masker.fit_transform(dataset.func[0])

# Encode labels as integers
df = pd.read_csv(dataset.session_target[0], sep=" ")
target, labels = pd.factorize(df.labels.values)
y = pd.DataFrame({l: (target == i).astype(int) for i, l in enumerate(labels)})

# Extract sessions 4-5
mask_sessions = df.chunks.add(1).isin([4, 5])
X = X[mask_sessions]
y = y.loc[mask_sessions, :]
target = target[mask_sessions]
    ###########################################################################
    # Real data loading
    t_r = 0.735
    n_times_valid = args.n_time_frames - hrf_time_frames + 1

    h = double_gamma_hrf(t_r, hrf_time_frames)
    D = (np.eye(n_times_valid, k=-1) - np.eye(n_times_valid, k=0))[:, :-1]
    H = make_toeplitz(h, n_times_valid).T

    # load data
    sub1_img = 'data/6025086_20227_MNI_RS.nii.gz'
    sub2_img = 'data/6025837_20227_MNI_RS.nii.gz'

    masker = NiftiMasker(standardize=True,
                         detrend=True,
                         low_pass=0.1,
                         high_pass=0.01,
                         t_r=t_r,
                         memory='__cache_dir__')  # noqa: E128
    masker.fit([sub1_img, sub2_img])

    y_train = masker.inverse_transform(masker.transform(sub1_img)).get_data()
    y_test = masker.inverse_transform(masker.transform(sub2_img)).get_data()

    # reduce dimensionality
    start_ = 10
    mask_roi = (slice(start_, start_ + nx), slice(start_, start_ + ny),
                slice(start_, start_ + nz), slice(0, args.n_time_frames))
    y_train = y_train[mask_roi]
    y_test = y_test[mask_roi]

    # lbda-max scale data
示例#35
0
from group_parcellation import (
    make_parcels, reproducibility_selection, parcel_selection, 
    parcel_cv, rate_atlas)

###############################################################################
# Get the data

contrasts = ['horizontal vs vertical checkerboard',
             'left vs right button press',
             'button press vs calculation and sentence listening/reading',
             'auditory processing vs visual processing',
             'auditory&visual calculation vs sentences',
             'sentence reading vs checkerboard']

nifti_masker = NiftiMasker('mask_GM_forFunc.nii')
grp_mask = load(nifti_masker.mask_img).get_data()
affine = load(nifti_masker.mask_img).get_affine()

# Create the data matrix
n_contrasts, n_subjects = len(contrasts), 40
subjects = ['S%02d' % i for i in range(n_subjects)]
n_voxels = grp_mask.sum()
X = np.zeros((n_voxels, n_contrasts, n_subjects))

for nc, contrast in enumerate(contrasts):
    imgs = datasets.fetch_localizer_contrasts(
        [contrast], n_subjects=n_subjects,
        data_dir='/tmp/')['cmaps']
    X[:, nc, :] = nifti_masker.fit_transform(imgs).T
示例#36
0
dummy_classifier = DummyClassifier()

# Make a data splitting object for cross validation
from sklearn.cross_validation import LeaveOneLabelOut, cross_val_score

cv = LeaveOneLabelOut(session_labels)

mask_names = ['mask_vt', 'mask_face', 'mask_house']

mask_scores = {}
mask_chance_scores = {}

for mask_name in mask_names:
    print "Working on mask %s" % mask_name
    # For decoding, standardizing is often very important
    masker = NiftiMasker(mask_img=data_files[mask_name][0], standardize=True)
    masked_timecourses = masker.fit_transform(
        data_files.func[0])[resting_state == False]

    mask_scores[mask_name] = {}
    mask_chance_scores[mask_name] = {}

    for category in categories:
        print "Processing %s %s" % (mask_name, category)
        classification_target = stimuli[resting_state == False] == category
        mask_scores[mask_name][category] = cross_val_score(
            classifier,
            masked_timecourses,
            classification_target,
            cv=cv,
            scoring="f1")
示例#37
0
plotting.plot_roi(resampled_mask_visual,
                  title='Visual regions mask extracted from atlas',
                  cut_coords=(8, -80, 9),
                  colorbar=True,
                  cmap='Paired')

###############################################################################
# Define a masker
# ---------------
# We define a nilearn masker that will be used to handle relevant data.
# For more information, visit :
# 'http://nilearn.github.io/manipulating_images/masker_objects.html'
#

from nilearn.input_data import NiftiMasker
roi_masker = NiftiMasker(mask_img=resampled_mask_visual).fit()

###############################################################################
# Prepare the data
# ----------------
# For each subject, for each task and conditions, our dataset contains two
# independent acquisitions, similar except for one acquisition parameter, the
# encoding phase used that was either Antero-Posterior (AP) or
# Postero-Anterior (PA). Although this induces small differences
# in the final data, we will take  advantage of these pseudo-duplicates to
# create a training and a testing set that contains roughly the same signals
# but acquired independently.
#

# The training set, used to learn alignment from source subject toward target:
# * source train: AP contrasts for subject sub-01
示例#38
0
The reduction process from 4D-images to feature vectors comes with the
loss of spatial structure (see Figure 1). It however allows to discard
uninformative voxels, such as the ones outside of the brain. Such voxels
that only carry noise and scanner artifacts would reduce SNR and affect
the quality of the estimation. The selected voxels form a brain mask.
Such a mask is often given along with the datasets or can be computed
with software tools such as FSL or SPM.

Nifti_masker is used for applying a mask to extract time-series from Niimg-like objects.

NiftiMasker is useful when preprocessing (detrending, standardization, resampling, etc.)
of in-mask voxels is necessary. Use case: working with time series of resting-state or task maps.
"""

nifti_masker = NiftiMasker(  # Applying a mask to extract time-series from Niimg-like objects.
    smoothing_fwhm=5,
    memory='nilearn_cache',
    memory_level=1)  # cache options
cmap_filenames = localizer_dataset.cmaps
fmri_masked = nifti_masker.fit_transform(cmap_filenames)

# Get Anova (parametric F-scores)
# F regression is a linear model for testing the individual effect of each of many regressors. This is a scoring function to be used in a feature seletion procedure, not a free standing feature selection procedure.

_, pvals_anova = f_regression(
    fmri_masked,
    tested_var,  # Univariate linear regression tests.
    center=False)  # do not remove intercept
pvals_anova *= fmri_masked.shape[1]
pvals_anova[np.isnan(pvals_anova)] = 1
pvals_anova[pvals_anova > 1] = 1
neg_log_pvals_anova = -np.log10(pvals_anova)
示例#39
0
    ###########################################################################
    ############################## Scatter plots ##############################
    ###########################################################################

    if 'scatter_plots' in args.analysis[0]:
        # retrieve default atlas  (= set of ROI)
        atlas = datasets.fetch_atlas_harvard_oxford(params.atlas)
        labels = atlas['labels']
        maps = nilearn.image.load_img(atlas['maps'])

        # extract data
        for index_mask in range(len(labels) - 1):
            mask = math_img('img > 50', img=index_img(maps, index_mask))
            masker = NiftiMasker(mask_img=mask,
                                 memory='nilearn_cache',
                                 verbose=5)
            masker.fit()
            for analysis in analysis_parameters['scatter_plots']:
                for subject in subjects:
                    subject = Subjects().get_subject(int(subject))
                    model1 = [
                        os.path.join(
                            paths.path2derivatives, source,
                            analysis['input_data_folder1'], language,
                            analysis['model1_folder'],
                            analysis['model1'] + '_' + subject + '.nii.gz')
                    ]
                    model2 = [
                        os.path.join(
                            paths.path2derivatives, source,
示例#40
0
labels = np.recfromcsv(data.session_target[0], delimiter=" ")

# scikit-learn >= 0.14 supports text labels. You can replace this line by:
# target = labels['labels']
_, target = np.unique(labels['labels'], return_inverse=True)

### Keep only data corresponding to faces or cat ##############################
condition_mask = np.logical_or(labels['labels'] == 'face',
                               labels['labels'] == 'cat')
target = target[condition_mask]

### Prepare the data: apply the mask ##########################################

from nilearn.input_data import NiftiMasker
# For decoding, standardizing is often very important
nifti_masker = NiftiMasker(mask=data.mask_vt[0], standardize=True)

# We give the nifti_masker a filename and retrieve a 2D array ready
# for machine learning with scikit-learn
fmri_masked = nifti_masker.fit_transform(data.func[0])

# Restrict the classification to the face vs house discrimination
fmri_masked = fmri_masked[condition_mask]

### Prediction ################################################################

# Here we use a Support Vector Classification, with a linear kernel
from sklearn.svm import SVC

svc = SVC(kernel='linear')
示例#41
0
behavioral = pd.read_csv(stim, sep="\t")
#grab conditional labels 
y = behavioral["Label"]


# In[ ]:


#restrict data to our target analysis 
condition_mask = behavioral["Label"].isin(['HF_HS_receipt', "h20_receipt"])
y = y[condition_mask]
#confirm we have the # of condtions needed
print(y.unique())


masker = NiftiMasker(mask_img=imag_mask, standardize=True, memory="nilearn_cache", memory_level=6)
X = masker.fit_transform(dataset)
# Apply our condition_mask
X = X[condition_mask]

from sklearn.svm import SVC
svc = SVC(kernel='linear')

from sklearn.feature_selection import SelectKBest, f_classif
feature_selection = SelectKBest(f_classif, k=500)

# We have our classifier (SVC), our feature selection (SelectKBest), and now,
# we can plug them together in a *pipeline* that performs the two operations
# successively:
from sklearn.pipeline import Pipeline
anova_svc = Pipeline([('anova', feature_selection), ('svc', svc)])
    for subject in subject_list:
        mask = (df.contrast == contrast).values *\
               (df.subject == subject).values *\
               (df.acquisition == 'ap')
        if len(df[mask]) == 0:
            print(subject, contrast)
        ap_paths.append(df[mask].path.values[-1])
        mask = (df.contrast == contrast).values *\
               (df.subject == subject).values *\
               (df.acquisition == 'pa')
        if len(df[mask]) == 0:
            print(subject, contrast)
        pa_paths.append(df[mask].path.values[-1])

# image masking
masker = NiftiMasker(
    mask_img=mask_gm, memory=write_dir, smoothing_fwhm=None).fit()
X1 = masker.transform(ap_paths).reshape(
    n_contrasts, int(n_subjects * n_voxels))
X2 = masker.transform(pa_paths).reshape(
    n_contrasts, int(n_subjects * n_voxels))

# learn a dictionary of elements
n_components = 20
alpha = .6
X1[np.isnan(X1)] = 0
X2[np.isnan(X2)] = 0

dictionary1, components_1 = make_dictionary(
    X1, n_components=n_components, alpha=alpha, write_dir=write_dir,
    contrasts=contrasts, method='multitask', l1_ratio=.25)
dictionary2, components_2 = make_dictionary(
示例#43
0
import numpy as np
from scipy import linalg
import matplotlib.pyplot as plt
from nilearn import datasets
from nilearn.input_data import NiftiMasker

n_subjects = 50  # more subjects requires more memory and more time

### Load Oasis dataset ########################################################
oasis_dataset = datasets.fetch_oasis_vbm(n_subjects=n_subjects)
gray_matter_map_filenames = oasis_dataset.gray_matter_maps
age = oasis_dataset.ext_vars['age'].astype(float)

### Preprocess data ###########################################################
nifti_masker = NiftiMasker(standardize=False,
                           smoothing_fwhm=0,
                           memory='nilearn_cache')  # cache options
# remove features with too low between-subject variance
gm_maps_masked = nifti_masker.fit_transform(gray_matter_map_filenames)
gm_maps_masked[:, gm_maps_masked.var(0) < 0.01] = 0.
# final masking
new_images = nifti_masker.inverse_transform(gm_maps_masked)
gm_maps_masked = nifti_masker.fit_transform(new_images)
n_samples, n_features = gm_maps_masked.shape
print n_samples, "subjects, ", n_features, "features"

### Inference with massively univariate model #################################
from nilearn.mass_univariate import permuted_ols

print "Massively univariate model"
neg_log_pvals, all_scores, _ = permuted_ols(
示例#44
0
from nilearn.image import concat_imgs
import joblib
import time

RES_NAME = 'nips3mm_serial'
WRITE_DIR = op.join(os.getcwd(), RES_NAME)
if not op.exists(WRITE_DIR):
    os.mkdir(WRITE_DIR)

##############################################################################
# load+preprocess data
##############################################################################

mask_img = 'grey10_icbm_3mm_bin.nii.gz'
nifti_masker = NiftiMasker(mask_img=mask_img,
                           smoothing_fwhm=False,
                           standardize=False)
nifti_masker.fit()
mask_nvox = nifti_masker.mask_img_.get_data().sum()

print('Loading data...')
X_task, labels = joblib.load('preload_HT_3mm')

labels = np.int32(labels)

# contrasts are IN ORDER -> shuffle!
new_inds = np.arange(0, X_task.shape[0])
np.random.shuffle(new_inds)
X_task = X_task[new_inds]
y = labels[new_inds]
import matplotlib.pyplot as plt
from nilearn import datasets
from nilearn.input_data import NiftiMasker
from nilearn.image import get_data

############################################################################
# Load Localizer contrast
n_samples = 20
localizer_dataset = datasets.fetch_localizer_calculation_task(
    n_subjects=n_samples)
tested_var = np.ones((n_samples, 1))

############################################################################
# Mask data
nifti_masker = NiftiMasker(smoothing_fwhm=5,
                           memory='nilearn_cache',
                           memory_level=1)  # cache options
cmap_filenames = localizer_dataset.cmaps
fmri_masked = nifti_masker.fit_transform(cmap_filenames)

############################################################################
# Anova (parametric F-scores)
from sklearn.feature_selection import f_regression
_, pvals_anova = f_regression(fmri_masked, tested_var,
                              center=False)  # do not remove intercept
pvals_anova *= fmri_masked.shape[1]
pvals_anova[np.isnan(pvals_anova)] = 1
pvals_anova[pvals_anova > 1] = 1
neg_log_pvals_anova = -np.log10(pvals_anova)
neg_log_pvals_anova_unmasked = nifti_masker.inverse_transform(
    neg_log_pvals_anova)
示例#46
0
class Simulator:
    def __init__(self, brain_mask=None, output_dir=None):  # no scoring param
        # self.resource_folder = os.path.join(os.getcwd(),'resources')
        if output_dir is None:
            self.output_dir = os.path.join(os.getcwd())
        else:
            self.output_dir = output_dir

        if isinstance(brain_mask, str):
            brain_mask = nib.load(brain_mask)
        elif brain_mask is None:
            brain_mask = nib.load(resolve_mni_path(MNI_Template)['mask'])
        elif ~isinstance(brain_mask, nib.nifti1.Nifti1Image):
            raise ValueError(
                "brain_mask is not a string or a nibabel instance")
        self.brain_mask = brain_mask
        self.nifti_masker = NiftiMasker(mask_img=self.brain_mask)

    def gaussian(self, mu, sigma, i_tot):
        """ create a 3D gaussian signal normalized to a given intensity

        Args:
            mu: average value of the gaussian signal (usually set to 0)
            sigma: standard deviation
            i_tot: sum total of activation (numerical integral over the gaussian returns this value)
        """
        x, y, z = np.mgrid[0:self.brain_mask.shape[0],
                           0:self.brain_mask.shape[1],
                           0:self.brain_mask.shape[2]]

        # Need an (N, 3) array of (x, y) pairs.
        xyz = np.column_stack([x.flat, y.flat, z.flat])

        covariance = np.diag(sigma**2)
        g = multivariate_normal.pdf(xyz, mean=mu, cov=covariance)

        # Reshape back to a 3D grid.
        g = g.reshape(x.shape).astype(float)

        # select only the regions within the brain mask
        g = np.multiply(self.brain_mask.get_data(), g)
        # adjust total intensity of gaussian
        g = np.multiply(i_tot / np.sum(g), g)

        return g

    def sphere(self, r, p):
        """ create a sphere of given radius at some point p in the brain mask

        Args:
            r: radius of the sphere
            p: point (in coordinates of the brain mask) of the center of the sphere
        """
        dims = self.brain_mask.shape

        x, y, z = np.ogrid[-p[0]:dims[0] - p[0], -p[1]:dims[1] - p[1],
                           -p[2]:dims[2] - p[2]]
        mask = x * x + y * y + z * z <= r * r

        activation = np.zeros(dims)
        activation[mask] = 1
        activation = np.multiply(activation, self.brain_mask.get_data())
        activation = nib.Nifti1Image(activation, affine=np.eye(4))

        # return the 3D numpy matrix of zeros containing the sphere as a region of ones
        return activation.get_data()

    def normal_noise(self, mu, sigma):
        """ produce a normal noise distribution for all all points in the brain mask

        Args:
            mu: average value of the gaussian signal (usually set to 0)
            sigma: standard deviation
        """

        self.nifti_masker.fit(self.brain_mask)
        vlength = int(np.sum(self.brain_mask.get_data()))
        if sigma != 0:
            n = np.random.normal(mu, sigma, vlength)
        else:
            n = [mu] * vlength
        m = self.nifti_masker.inverse_transform(n)

        # return the 3D numpy matrix of zeros containing the brain mask filled with noise produced over a normal distribution
        return m.get_data()

    def to_nifti(self, m):
        """ convert a numpy matrix to the nifti format and assign to it the brain_mask's affine matrix

        Args:
            m: the 3D numpy matrix we wish to convert to .nii
        """
        if not (type(m) == np.ndarray and len(m.shape) >= 3):  # try 4D
            # if not (type(m) == np.ndarray and len(m.shape) == 3):
            raise ValueError(
                "ERROR: need 3D np.ndarray matrix to create the nifti file")
        m = m.astype(np.float32)
        ni = nib.Nifti1Image(m, affine=self.brain_mask.affine)
        return ni

    def n_spheres(self, radius, center):
        """ generate a set of spheres in the brain mask space

        Args:
            radius: vector of radius.  Will create multiple spheres if len(radius) > 1
            centers: a vector of sphere centers of the form [px, py, pz] or [[px1, py1, pz1], ..., [pxn, pyn, pzn]]
        """
        # initialize useful values
        dims = self.brain_mask.get_data().shape

        # Initialize Spheres with options for multiple radii and centers of the spheres (or just an int and a 3D list)
        if isinstance(radius, int):
            radius = [radius]
        if center is None:
            center = [[dims[0] / 2, dims[1] / 2, dims[2] / 2] * len(radius)
                      ]  # default value for centers
        elif isinstance(center, list) and isinstance(center[0],
                                                     int) and len(radius) == 1:
            centers = [center]
        if (type(radius)) is list and (type(center) is
                                       list) and (len(radius) == len(center)):
            A = np.zeros_like(self.brain_mask.get_data())
            for i in range(len(radius)):
                A = np.add(A, self.sphere(radius[i], center[i]))
            return A
        else:
            raise ValueError(
                "Data type for sphere or radius(ii) or center(s) not recognized."
            )

    def create_data(self,
                    levels,
                    sigma,
                    radius=5,
                    center=None,
                    reps=1,
                    output_dir=None):
        """ create simulated data with integers

        Args:
            levels: vector of intensities or class labels
            sigma: amount of noise to add
            radius: vector of radius.  Will create multiple spheres if len(radius) > 1
            center: center(s) of sphere(s) of the form [px, py, pz] or [[px1, py1, pz1], ..., [pxn, pyn, pzn]]
            reps: number of data repetitions useful for trials or subjects
            output_dir: string path of directory to output data.  If None, no data will be written
            **kwargs: Additional keyword arguments to pass to the prediction algorithm

        """

        # Create reps
        nlevels = len(levels)
        y = levels
        rep_id = [1] * len(levels)
        for i in range(reps - 1):
            y = y + levels
            rep_id.extend([i + 2] * nlevels)

        # Initialize Spheres with options for multiple radii and centers of the spheres (or just an int and a 3D list)
        A = self.n_spheres(radius, center)

        # for each intensity
        A_list = []
        for i in y:
            A_list.append(np.multiply(A, i))

        # generate a different gaussian noise profile for each mask
        mu = 0  # values centered around 0
        N_list = []
        for i in range(len(y)):
            N_list.append(self.normal_noise(mu, sigma))

        # add noise and signal together, then convert to nifti files
        NF_list = []
        for i in range(len(y)):
            NF_list.append(self.to_nifti(np.add(N_list[i], A_list[i])))
        NF_list = Brain_Data(NF_list)

        # Assign variables to object
        self.data = NF_list
        self.y = pd.DataFrame(data=y)
        self.rep_id = pd.DataFrame(data=rep_id)

        dat = self.data
        dat.Y = self.y

        # Write Data to files if requested
        if output_dir is not None and isinstance(output_dir, six.string_types):
            NF_list.write(os.path.join(output_dir, 'data.nii.gz'))
            self.y.to_csv(os.path.join(output_dir, 'y.csv'),
                          index=None,
                          header=False)
            self.rep_id.to_csv(os.path.join(output_dir, 'rep_id.csv'),
                               index=None,
                               header=False)
        return dat

    def create_cov_data(self,
                        cor,
                        cov,
                        sigma,
                        mask=None,
                        reps=1,
                        n_sub=1,
                        output_dir=None):
        """ create continuous simulated data with covariance

        Args:
            cor: amount of covariance between each voxel and Y variable
            cov: amount of covariance between voxels
            sigma: amount of noise to add
            radius: vector of radius.  Will create multiple spheres if len(radius) > 1
            center: center(s) of sphere(s) of the form [px, py, pz] or [[px1, py1, pz1], ..., [pxn, pyn, pzn]]
            reps: number of data repetitions
            n_sub: number of subjects to simulate
            output_dir: string path of directory to output data.  If None, no data will be written
            **kwargs: Additional keyword arguments to pass to the prediction algorithm

        """

        if mask is None:
            # Initialize Spheres with options for multiple radii and centers of the spheres (or just an int and a 3D list)
            A = self.n_spheres(10, None)  # parameters are (radius, center)
            mask = nib.Nifti1Image(A.astype(np.float32),
                                   affine=self.brain_mask.affine)

        # Create n_reps with cov for each voxel within sphere
        # Build covariance matrix with each variable correlated with y amount 'cor' and each other amount 'cov'
        flat_sphere = self.nifti_masker.fit_transform(mask)

        n_vox = np.sum(flat_sphere == 1)
        cov_matrix = np.ones([n_vox + 1, n_vox + 1]) * cov
        cov_matrix[0, :] = cor  # set covariance with y
        cov_matrix[:, 0] = cor  # set covariance with all other voxels
        np.fill_diagonal(cov_matrix, 1)  # set diagonal to 1
        mv_sim = np.random.multivariate_normal(np.zeros([n_vox + 1]),
                                               cov_matrix,
                                               size=reps)
        print(mv_sim)
        y = mv_sim[:, 0]
        self.y = y
        mv_sim = mv_sim[:, 1:]
        new_dat = np.ones([mv_sim.shape[0], flat_sphere.shape[1]])
        new_dat[:, np.where(flat_sphere == 1)[1]] = mv_sim
        self.data = self.nifti_masker.inverse_transform(
            np.add(new_dat,
                   np.random.standard_normal(size=new_dat.shape) *
                   sigma))  # add noise scaled by sigma
        self.rep_id = [1] * len(y)
        if n_sub > 1:
            self.y = list(self.y)
            for s in range(1, n_sub):
                self.data = nib.concat_images(
                    [
                        self.data,
                        self.nifti_masker.inverse_transform(
                            np.add(
                                new_dat,
                                np.random.standard_normal(size=new_dat.shape) *
                                sigma))
                    ],
                    axis=3)  # add noise scaled by sigma
                noise_y = list(y + np.random.randn(len(y)) * sigma)
                self.y = self.y + noise_y
                self.rep_id = self.rep_id + [s + 1] * len(mv_sim[:, 0])
            self.y = np.array(self.y)

        # # Old method in 4 D space - much slower
        # x,y,z = np.where(A==1)
        # cov_matrix = np.ones([len(x)+1,len(x)+1]) * cov
        # cov_matrix[0,:] = cor # set covariance with y
        # cov_matrix[:,0] = cor # set covariance with all other voxels
        # np.fill_diagonal(cov_matrix,1) # set diagonal to 1
        # mv_sim = np.random.multivariate_normal(np.zeros([len(x)+1]),cov_matrix, size=reps) # simulate data from multivariate covar
        # self.y = mv_sim[:,0]
        # mv_sim = mv_sim[:,1:]
        # A_4d = np.resize(A,(reps,A.shape[0],A.shape[1],A.shape[2]))
        # for i in xrange(len(x)):
        #     A_4d[:,x[i],y[i],z[i]]=mv_sim[:,i]
        # A_4d = np.rollaxis(A_4d,0,4) # reorder shape of matrix so that time is in 4th dimension
        # self.data = self.to_nifti(np.add(A_4d,np.random.standard_normal(size=A_4d.shape)*sigma)) # add noise scaled by sigma
        # self.rep_id = ???  # need to add this later

        # Write Data to files if requested
        if output_dir is not None:
            if isinstance(output_dir, six.string_types):
                if not os.path.isdir(output_dir):
                    os.makedirs(output_dir)
                self.data.to_filename(
                    os.path.join(
                        output_dir, 'maskdata_cor' + str(cor) + "_cov" +
                        str(cov) + '_sigma' + str(sigma) + '.nii.gz'))
                y_file = open(os.path.join(output_dir, 'y.csv'), 'wb')
                wr = csv.writer(y_file, quoting=csv.QUOTE_ALL)
                wr.writerow(self.y)

                rep_id_file = open(os.path.join(output_dir, 'rep_id.csv'),
                                   'wb')
                wr = csv.writer(rep_id_file, quoting=csv.QUOTE_ALL)
                wr.writerow(self.rep_id)

    def create_ncov_data(self,
                         cor,
                         cov,
                         sigma,
                         masks=None,
                         reps=1,
                         n_sub=1,
                         output_dir=None):
        """ create continuous simulated data with covariance

        Args:
            cor: amount of covariance between each voxel and Y variable (an int or a vector)
            cov: amount of covariance between voxels (an int or a matrix)
            sigma: amount of noise to add
            mask: region(s) where we will have activations (list if more than one)
            reps: number of data repetitions
            n_sub: number of subjects to simulate
            output_dir: string path of directory to output data.  If None, no data will be written
            **kwargs: Additional keyword arguments to pass to the prediction algorithm

        """

        if masks is None:
            # Initialize Spheres with options for multiple radii and centers of the spheres (or just an int and a 3D list)
            A = self.n_spheres(10, None)  # parameters are (radius, center)
            masks = nib.Nifti1Image(A.astype(np.float32),
                                    affine=self.brain_mask.affine)

        if type(masks) is nib.nifti1.Nifti1Image:
            masks = [masks]
        if type(cor) is float or type(cor) is int:
            cor = [cor]
        if type(cov) is float or type(cor) is int:
            cov = [cov]
        if not len(cor) == len(masks):
            raise ValueError(
                "cor matrix has incompatible dimensions for mask list of length "
                + str(len(masks)))
        if not len(cov) == len(masks) or len(masks) == 0 or not len(
                cov[0]) == len(masks):
            raise ValueError(
                "cov matrix has incompatible dimensions for mask list of length "
                + str(len(masks)))

        # Create n_reps with cov for each voxel within sphere
        # Build covariance matrix with each variable correlated with y amount 'cor' and each other amount 'cov'
        flat_masks = self.nifti_masker.fit_transform(masks)

        print("Building correlation/covariation matrix...")
        n_vox = np.sum(
            flat_masks == 1, axis=1
        )  # this is a list, each entry contains number voxels for given mask
        if 0 in n_vox:
            raise ValueError(
                "one or more processing mask does not fit inside the brain mask"
            )

        cov_matrix = np.zeros([np.sum(n_vox) + 1,
                               np.sum(n_vox) + 1])  # one big covariance matrix
        for i, nv in enumerate(n_vox):
            cstart = np.sum(n_vox[:i]) + 1
            cstop = cstart + nv
            cov_matrix[0, cstart:cstop] = cor[i]  # set covariance with y
            cov_matrix[cstart:cstop,
                       0] = cor[i]  # set covariance with all other voxels
            for j in range(len(masks)):
                rstart = np.sum(n_vox[:j]) + 1
                rstop = rstart + nv
                cov_matrix[cstart:cstop, rstart:rstop] = cov[i][
                    j]  # set covariance of this mask's voxels with each of other masks
        np.fill_diagonal(cov_matrix, 1)  # set diagonal to 1

        # these operations happen in one vector that we'll later split into the separate regions
        print("Generating multivariate normal distribution...")
        mv_sim_l = np.random.multivariate_normal(np.zeros([np.sum(n_vox) + 1]),
                                                 cov_matrix,
                                                 size=reps)
        print(mv_sim_l)

        self.y = mv_sim_l[:, 0]
        mv_sim = mv_sim_l[:, 1:]
        new_dats = np.ones([mv_sim.shape[0], flat_masks.shape[1]])

        for rep in range(reps):
            for mask_i in range(len(masks)):
                start = int(np.sum(n_vox[:mask_i]))
                stop = int(start + n_vox[mask_i])
                print(rep, start, stop)
                new_dats[rep, np.where(
                    flat_masks[mask_i, :] == 1)] = mv_sim[rep, start:stop]

        noise = np.random.standard_normal(size=new_dats.shape[1]) * sigma
        self.data = self.nifti_masker.inverse_transform(
            np.add(new_dats, noise))  # append 3d simulated data to list
        self.rep_id = [1] * len(self.y)

        print("Generating subject-level noise...")
        print("y == " + str(self.y.shape))
        if n_sub > 1:
            self.y = list(self.y)
            y = list(self.y)
            for s in range(1, n_sub):
                # ask Luke about this new version
                noise = np.random.standard_normal(
                    size=new_dats.shape[1]) * sigma
                next_subj = self.nifti_masker.inverse_transform(
                    np.add(new_dats, noise))
                self.data = nib.concat_images([self.data, next_subj], axis=3)

                y += list(self.y + np.random.randn(len(self.y)) * sigma)
                print("y == " + str(len(y)))
                self.rep_id += [s + 1] * len(mv_sim[:, 0])
            self.y = np.array(y)

        print("Saving to " + str(output_dir))
        print("dat == " + str(self.data.shape))
        print("y == " + str(self.y.shape))
        if output_dir is not None:
            if type(output_dir) is str:
                if not os.path.isdir(output_dir):
                    os.makedirs(output_dir)
                self.data.to_filename(
                    os.path.join(
                        output_dir, 'simulated_data_' + str(sigma) + 'sigma_' +
                        str(n_sub) + 'subj.nii.gz'))
                y_file = open(os.path.join(output_dir, 'y.csv'), 'wb')
                wr = csv.writer(y_file, quoting=csv.QUOTE_ALL)
                wr.writerow(self.y)

                rep_id_file = open(os.path.join(output_dir, 'rep_id.csv'),
                                   'wb')
                wr = csv.writer(rep_id_file, quoting=csv.QUOTE_ALL)
                wr.writerow(self.rep_id)
示例#47
0
from nilearn.input_data import NiftiMasker
masker = NiftiMasker()
masker.fit(func_file)

示例#48
0
文件: fmri2001.py 项目: FF120/python
## 使用感兴趣区域选择的体素,使得体素数目较少
###############################################
haxby_dataset = datasets.fetch_haxby()

labels = np.recfromcsv(haxby_dataset.session_target[0], delimiter=" ")

target = labels['labels']
# Keep only data corresponding to faces or cats
condition_mask = np.logical_or(labels['labels'] == b'face',
                               labels['labels'] == b'cat')
#准备好了标签Y
target = target[condition_mask]
#模版文件的路径
mask_filename = haxby_dataset.mask_vt[0]
#加载模版并标准化
nifti_masker = NiftiMasker(mask_img=mask_filename, standardize=True)

func_filename = haxby_dataset.func[0]

fmri_masked = nifti_masker.fit_transform(func_filename)
#准备好了特征
fmri_masked = fmri_masked[condition_mask]

#使用SVM分类和预测
svc = SVC(kernel='linear')
#训练模型
#svc.fit(fmri_masked, target)
#预测
#prediction = svc.predict(fmri_masked)

#使用训练的数据预测结果很可能达到100%;
示例#49
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def map_threshold(stat_img,
                  mask_img=None,
                  threshold=.001,
                  height_control='fpr',
                  cluster_threshold=0):
    """ Threshold the provided map

    Parameters
    ----------
    stat_img : Niimg-like object,
       statistical image (presumably in z scale)

    mask_img : Niimg-like object, optional,
        mask image

    threshold: float, optional
        cluster forming threshold (either a p-value or z-scale value)

    height_control: string, optional
        false positive control meaning of cluster forming
        threshold: 'fpr'|'fdr'|'bonferroni'|'none'

    cluster_threshold : float, optional
        cluster size threshold

    Returns
    -------
    thresholded_map : Nifti1Image,
        the stat_map theresholded at the prescribed voxel- and cluster-level

    threshold: float,
        the voxel-level threshold used actually
    """
    # Masking
    if mask_img is None:
        masker = NiftiMasker(mask_strategy='background').fit(stat_img)
    else:
        masker = NiftiMasker(mask_img=mask_img).fit()
    stats = np.ravel(masker.transform(stat_img))
    n_voxels = np.size(stats)

    # Thresholding
    if height_control == 'fpr':
        z_th = norm.isf(threshold)
    elif height_control == 'fdr':
        z_th = fdr_threshold(stats, threshold)
    elif height_control == 'bonferroni':
        z_th = norm.isf(threshold / n_voxels)
    else:  # Brute-force thresholding
        z_th = threshold
    stats *= (stats > z_th)

    # embed it back to 3D grid
    stat_map = masker.inverse_transform(stats).get_data()

    # Extract connected components above threshold
    label_map, n_labels = label(stat_map > z_th)
    labels = label_map[masker.mask_img_.get_data() > 0]

    for label_ in range(1, n_labels + 1):
        if np.sum(labels == label_) < cluster_threshold:
            stats[labels == label_] = 0

    return masker.inverse_transform(stats), z_th
示例#50
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def denoise(img_file, tsv_file, out_path, col_names=False, hp_filter=False, lp_filter=False, out_figure_path=False):
    nii_ext = '.nii.gz'
    FD_thr = [.5]
    sc_range = np.arange(-1, 3)
    constant = 'constant'

    # read in files
    img = load_niimg(img_file)
    # get file info
    img_name = os.path.basename(img.get_filename())
    file_base = img_name[0:img_name.find('.')]
    save_img_file = pjoin(out_path, file_base + \
                          '_NR' + nii_ext)
    data = img.get_data()
    df_orig = pandas.read_csv(tsv_file, '\t', na_values='n/a')
    df = copy.deepcopy(df_orig)
    Ntrs = df.as_matrix().shape[0]
    print('# of TRs: ' + str(Ntrs))
    assert (Ntrs == data.shape[len(data.shape) - 1])

    # select columns to use as nuisance regressors
    if col_names:
        df = df[col_names]
        str_append = '  [SELECTED regressors in CSV]'
    else:
        col_names = df.columns.tolist()
        str_append = '  [ALL regressors in CSV]'

    # fill in missing nuisance values with mean for that variable
    for col in df.columns:
        if sum(df[col].isnull()) > 0:
            print('Filling in ' + str(sum(df[col].isnull())) + ' NaN value for ' + col)
            df[col] = df[col].fillna(np.mean(df[col]))
    print('# of Confound Regressors: ' + str(len(df.columns)) + str_append)

    # implement HP filter in regression
    TR = img.header.get_zooms()[-1]
    frame_times = np.arange(Ntrs) * TR
    if hp_filter:
        hp_filter = float(hp_filter)
        assert (hp_filter > 0)
        period_cutoff = 1. / hp_filter
        df = make_first_level_design_matrix(frame_times, period_cut=period_cutoff, add_regs=df.as_matrix(),
                                add_reg_names=df.columns.tolist())
        # fn adds intercept into dm

        hp_cols = [col for col in df.columns if 'drift' in col]
        print('# of High-pass Filter Regressors: ' + str(len(hp_cols)))
    else:
        # add in intercept column into data frame
        df[constant] = 1
        print('No High-pass Filter Applied')

    dm = df.as_matrix()

    # prep data
    data = np.reshape(data, (-1, Ntrs))
    data_mean = np.mean(data, axis=1)
    Nvox = len(data_mean)

    # setup and run regression
    model = regression.OLSModel(dm)
    results = model.fit(data.T)
    if not hp_filter:
        results_orig_resid = copy.deepcopy(results.resid)  # save for rsquared computation

    # apply low-pass filter
    if lp_filter:
        # input to butterworth fn is time x voxels
        low_pass = float(lp_filter)
        Fs = 1. / TR
        if low_pass >= Fs / 2:
            raise ValueError('Low pass filter cutoff if too close to the Nyquist frequency (%s)' % (Fs / 2))

        temp_img_file = pjoin(out_path, file_base + \
                              '_temp' + nii_ext)
        temp_img = nb.Nifti1Image(np.reshape(results.resid.T + np.reshape(data_mean, (Nvox, 1)), img.shape).astype('float32'),
                                  img.affine, header=img.header)
        temp_img.to_filename(temp_img_file)
        results.resid = butterworth(results.resid, sampling_rate=Fs, low_pass=low_pass, high_pass=None)
        print('Low-pass Filter Applied: < ' + str(low_pass) + ' Hz')

    # add mean back into data
    clean_data = results.resid.T + np.reshape(data_mean, (Nvox, 1))  # add mean back into residuals

    # save out new data file
    print('Saving output file...')
    clean_data = np.reshape(clean_data, img.shape).astype('float32')
    new_img = nb.Nifti1Image(clean_data, img.affine, header=img.header)
    new_img.to_filename(save_img_file)

    ######### generate Rsquared map for confounds only
    if hp_filter:
        # first remove low-frequency information from data
        hp_cols.append(constant)
        model_first = regression.OLSModel(df[hp_cols].as_matrix())
        results_first = model_first.fit(data.T)
        results_first_resid = copy.deepcopy(results_first.resid)
        del results_first, model_first

        # compute sst - borrowed from matlab
        sst = np.square(np.linalg.norm(results_first_resid -
                                       np.mean(results_first_resid, axis=0), axis=0))

        # now regress out 'true' confounds to estimate their Rsquared
        nr_cols = [col for col in df.columns if 'drift' not in col]
        model_second = regression.OLSModel(df[nr_cols].as_matrix())
        results_second = model_second.fit(results_first_resid)

        # compute sse - borrowed from matlab
        sse = np.square(np.linalg.norm(results_second.resid, axis=0))

        del results_second, model_second, results_first_resid

    elif not hp_filter:
        # compute sst - borrowed from matlab
        sst = np.square(np.linalg.norm(data.T -
                                       np.mean(data.T, axis=0), axis=0))

        # compute sse - borrowed from matlab
        sse = np.square(np.linalg.norm(results_orig_resid, axis=0))

        del results_orig_resid

    # compute rsquared of nuisance regressors
    zero_idx = scipy.logical_and(sst == 0, sse == 0)
    sse[zero_idx] = 1
    sst[zero_idx] = 1  # would be NaNs - become rsquared = 0
    rsquare = 1 - np.true_divide(sse, sst)
    rsquare[np.isnan(rsquare)] = 0

    ######### Visualizing DM & outputs
    fontsize = 12
    fontsize_title = 14
    def_img_size = 8

    if not out_figure_path:
        out_figure_path = save_img_file[0:save_img_file.find('.')] + '_figures'

    if not os.path.isdir(out_figure_path):
        os.mkdir(out_figure_path)
    png_append = '_' + img_name[0:img_name.find('.')] + '.png'
    print('Output directory: ' + out_figure_path)

    # DM corr matrix
    cm = df[df.columns[0:-1]].corr()
    curr_sz = copy.deepcopy(def_img_size)
    if cm.shape[0] > def_img_size:
        curr_sz = curr_sz + ((cm.shape[0] - curr_sz) * .3)
    mtx_scale = curr_sz * 100

    mask = np.zeros_like(cm, dtype=np.bool)
    mask[np.triu_indices_from(mask)] = True

    fig, ax = plt.subplots(figsize=(curr_sz, curr_sz))
    cmap = sns.diverging_palette(220, 10, as_cmap=True)
    sns.heatmap(cm, mask=mask, cmap=cmap, center=0, vmax=cm[cm < 1].max().max(), vmin=cm[cm < 1].min().min(),
                square=True, linewidths=.5, cbar_kws={"shrink": .6})
    ax.set_xticklabels(ax.get_xticklabels(), rotation=60, ha='right', fontsize=fontsize)
    ax.set_yticklabels(cm.columns.tolist(), rotation=-30, va='bottom', fontsize=fontsize)
    ax.set_title('Nuisance Corr. Matrix', fontsize=fontsize_title)
    plt.tight_layout()
    file_corr_matrix = 'Corr_matrix_regressors' + png_append
    fig.savefig(pjoin(out_figure_path, file_corr_matrix))
    plt.close(fig)
    del fig, ax

    # DM of Nuisance Regressors (all)
    tr_label = 'TR (Volume #)'
    fig, ax = plt.subplots(figsize=(curr_sz - 4.1, def_img_size))
    x_scale_html = ((curr_sz - 4.1) / def_img_size) * 890
    reporting.plot_design_matrix(df, ax=ax)
    ax.set_title('Nuisance Design Matrix', fontsize=fontsize_title)
    ax.set_xticklabels(ax.get_xticklabels(), rotation=60, ha='right', fontsize=fontsize)
    ax.set_yticklabels(ax.get_yticklabels(), fontsize=fontsize)
    ax.set_ylabel(tr_label, fontsize=fontsize)
    plt.tight_layout()
    file_design_matrix = 'Design_matrix' + png_append
    fig.savefig(pjoin(out_figure_path, file_design_matrix))
    plt.close(fig)
    del fig, ax

    # FD timeseries plot
    FD = 'FD'
    poss_names = ['FramewiseDisplacement', FD, 'framewisedisplacement', 'fd']
    fd_idx = [df_orig.columns.__contains__(i) for i in poss_names]
    if np.sum(fd_idx) > 0:
        FD_name = poss_names[fd_idx == True]
        if sum(df_orig[FD_name].isnull()) > 0:
            df_orig[FD_name] = df_orig[FD_name].fillna(np.mean(df_orig[FD_name]))
        y = df_orig[FD_name].as_matrix()
        Nremove = []
        sc_idx = []
        for thr_idx, thr in enumerate(FD_thr):
            idx = y >= thr
            sc_idx.append(copy.deepcopy(idx))
            for iidx in np.where(idx)[0]:
                for buffer in sc_range:
                    curr_idx = iidx + buffer
                    if curr_idx >= 0 and curr_idx <= len(idx):
                        sc_idx[thr_idx][curr_idx] = True
            Nremove.append(np.sum(sc_idx[thr_idx]))

        Nplots = len(FD_thr)
        sns.set(font_scale=1.5)
        sns.set_style('ticks')
        fig, axes = plt.subplots(Nplots, 1, figsize=(def_img_size * 1.5, def_img_size / 2), squeeze=False)
        sns.despine()
        bound = .4
        fd_mean = np.mean(y)
        for curr in np.arange(0, Nplots):
            axes[curr, 0].plot(y)
            axes[curr, 0].plot((-bound, Ntrs + bound), FD_thr[curr] * np.ones((1, 2))[0], '--', color='black')
            axes[curr, 0].scatter(np.arange(0, Ntrs), y, s=20)

            if Nremove[curr] > 0:
                info = scipy.ndimage.measurements.label(sc_idx[curr])
                for cluster in np.arange(1, info[1] + 1):
                    temp = np.where(info[0] == cluster)[0]
                    axes[curr, 0].axvspan(temp.min() - bound, temp.max() + bound, alpha=.5, color='red')

            axes[curr, 0].set_ylabel('Framewise Disp. (' + FD + ')')
            axes[curr, 0].set_title(FD + ': ' + str(100 * Nremove[curr] / Ntrs)[0:4]
                                    + '% of scan (' + str(Nremove[curr]) + ' volumes) would be scrubbed (FD thr.= ' +
                                    str(FD_thr[curr]) + ')')
            plt.text(Ntrs + 1, FD_thr[curr] - .01, FD + ' = ' + str(FD_thr[curr]), fontsize=fontsize)
            plt.text(Ntrs, fd_mean - .01, 'avg = ' + str(fd_mean), fontsize=fontsize)
            axes[curr, 0].set_xlim((-bound, Ntrs + 8))

        plt.tight_layout()
        axes[curr, 0].set_xlabel(tr_label)
        file_fd_plot = FD + '_timeseries' + png_append
        fig.savefig(pjoin(out_figure_path, file_fd_plot))
        plt.close(fig)
        del fig, axes
        print(FD + ' timeseries plot saved')

    else:
        print(FD + ' not found: ' + FD + ' timeseries not plotted')
        file_fd_plot = None

    # Carpet and DVARS plots - before & after nuisance regression

    # need to create mask file to input to DVARS function
    mask_file = pjoin(out_figure_path, 'mask_temp.nii.gz')
    nifti_masker = NiftiMasker(mask_strategy='epi', standardize=False)
    nifti_masker.fit(img)
    nifti_masker.mask_img_.to_filename(mask_file)

    # create 2 or 3 carpet plots, depending on if LP filter is also applied
    Ncarpet = 2
    total_sz = int(16)
    carpet_scale = 840
    y_labels = ['Input (voxels)', 'Output \'cleaned\'']
    imgs = [img, new_img]
    img_files = [img_file, save_img_file]
    color = ['red', 'salmon']
    labels = ['input', 'cleaned']
    if lp_filter:
        Ncarpet = 3
        total_sz = int(20)
        carpet_scale = carpet_scale * (9/8)
        y_labels = ['Input', 'Clean Pre-LP', 'Clean LP']
        imgs.insert(1, temp_img)
        img_files.insert(1, temp_img_file)
        color.insert(1, 'firebrick')
        labels.insert(1, 'clean pre-LP')
        labels[-1] = 'clean LP'

    dvars = []
    print('Computing dvars...')
    for in_file in img_files:
        temp = nac.compute_dvars(in_file=in_file, in_mask=mask_file)[1]
        dvars.append(np.hstack((temp.mean(), temp)))
        del temp

    small_sz = 2
    fig = plt.figure(figsize=(def_img_size * 1.5, def_img_size + ((Ncarpet - 2) * 1)))
    row_used = 0
    if np.sum(fd_idx) > 0:  # if FD data is available
        row_used = row_used + small_sz
        ax0 = plt.subplot2grid((total_sz, 1), (0, 0), rowspan=small_sz)
        ax0.plot(y)
        ax0.scatter(np.arange(0, Ntrs), y, s=10)
        curr = 0
        if Nremove[curr] > 0:
            info = scipy.ndimage.measurements.label(sc_idx[curr])
            for cluster in np.arange(1, info[1] + 1):
                temp = np.where(info[0] == cluster)[0]
                ax0.axvspan(temp.min() - bound, temp.max() + bound, alpha=.5, color='red')
        ax0.set_ylabel(FD)

        for side in ["top", "right", "bottom"]:
            ax0.spines[side].set_color('none')
            ax0.spines[side].set_visible(False)

        ax0.set_xticks([])
        ax0.set_xlim((-.5, Ntrs - .5))
        ax0.spines["left"].set_position(('outward', 10))

    ax_d = plt.subplot2grid((total_sz, 1), (row_used, 0), rowspan=small_sz)
    for iplot in np.arange(len(dvars)):
        ax_d.plot(dvars[iplot], color=color[iplot], label=labels[iplot])
    ax_d.set_ylabel('DVARS')
    for side in ["top", "right", "bottom"]:
        ax_d.spines[side].set_color('none')
        ax_d.spines[side].set_visible(False)
    ax_d.set_xticks([])
    ax_d.set_xlim((-.5, Ntrs - .5))
    ax_d.spines["left"].set_position(('outward', 10))
    ax_d.legend(fontsize=fontsize - 2)
    row_used = row_used + small_sz

    st = 0
    carpet_each = int((total_sz - row_used) / Ncarpet)
    for idx, img_curr in enumerate(imgs):
        ax_curr = plt.subplot2grid((total_sz, 1), (row_used + st, 0), rowspan=carpet_each)
        fig = plotting.plot_carpet(img_curr, figure=fig, axes=ax_curr)
        ax_curr.set_ylabel(y_labels[idx])
        for side in ["bottom", "left"]:
            ax_curr.spines[side].set_position(('outward', 10))

        if idx < len(imgs)-1:
            ax_curr.spines["bottom"].set_visible(False)
            ax_curr.set_xticklabels('')
            ax_curr.set_xlabel('')
            st = st + carpet_each

    file_carpet_plot = 'Carpet_plots' + png_append
    fig.savefig(pjoin(out_figure_path, file_carpet_plot))
    plt.close()
    del fig, ax0, ax_curr, ax_d, dvars
    os.remove(mask_file)
    print('Carpet/DVARS plots saved')
    if lp_filter:
        os.remove(temp_img_file)
        del temp_img

    # Display T-stat maps for nuisance regressors
    # create mean img
    img_size = (img.shape[0], img.shape[1], img.shape[2])
    mean_img = nb.Nifti1Image(np.reshape(data_mean, img_size), img.affine)
    mx = []
    for idx, col in enumerate(df.columns):
        if not 'drift' in col and not constant in col:
            con_vector = np.zeros((1, df.shape[1]))
            con_vector[0, idx] = 1
            con = results.Tcontrast(con_vector)
            mx.append(np.max(np.absolute([con.t.min(), con.t.max()])))
    mx = .8 * np.max(mx)
    t_png = 'Tstat_'
    file_tstat = []
    for idx, col in enumerate(df.columns):
        if not 'drift' in col and not constant in col:
            con_vector = np.zeros((1, df.shape[1]))
            con_vector[0, idx] = 1
            con = results.Tcontrast(con_vector)
            m_img = nb.Nifti1Image(np.reshape(con, img_size), img.affine)

            title_str = col + ' Tstat'
            fig = plotting.plot_stat_map(m_img, mean_img, threshold=3, colorbar=True, display_mode='z', vmax=mx,
                                         title=title_str,
                                         cut_coords=7)
            file_temp = t_png + col + png_append
            fig.savefig(pjoin(out_figure_path, file_temp))
            file_tstat.append({'name': col, 'file': file_temp})
            plt.close()
            del fig, file_temp
            print(title_str + ' map saved')

    # Display R-sq map for nuisance regressors
    m_img = nb.Nifti1Image(np.reshape(rsquare, img_size), img.affine)
    title_str = 'Nuisance Rsq'
    mx = .95 * rsquare.max()
    fig = plotting.plot_stat_map(m_img, mean_img, threshold=.2, colorbar=True, display_mode='z', vmax=mx,
                                 title=title_str,
                                 cut_coords=7)
    file_rsq_map = 'Rsquared' + png_append
    fig.savefig(pjoin(out_figure_path, file_rsq_map))
    plt.close()
    del fig
    print(title_str + ' map saved')

    ######### html report
    templateLoader = jinja2.FileSystemLoader(searchpath="/")
    templateEnv = jinja2.Environment(loader=templateLoader)

    templateVars = {"img_file": img_file,
                    "save_img_file": save_img_file,
                    "Ntrs": Ntrs,
                    "tsv_file": tsv_file,
                    "col_names": col_names,
                    "hp_filter": hp_filter,
                    "lp_filter": lp_filter,
                    "file_design_matrix": file_design_matrix,
                    "file_corr_matrix": file_corr_matrix,
                    "file_fd_plot": file_fd_plot,
                    "file_rsq_map": file_rsq_map,
                    "file_tstat": file_tstat,
                    "x_scale": x_scale_html,
                    "mtx_scale": mtx_scale,
                    "file_carpet_plot": file_carpet_plot,
                    "carpet_scale": carpet_scale
                    }

    TEMPLATE_FILE = pjoin(os.getcwd(), "report_template.html")
    template = templateEnv.get_template(TEMPLATE_FILE)

    outputText = template.render(templateVars)

    html_file = pjoin(out_figure_path, img_name[0:img_name.find('.')] + '.html')
    with open(html_file, "w") as f:
        f.write(outputText)

    print('')
    print('HTML report: ' + html_file)
    return new_img
# Restrict to faces and houses
condition_mask = np.logical_or(y == b'face', y == b'house')
y = y[condition_mask]

# We have 2 conditions
n_conditions = np.size(np.unique(y))

#############################################################################
# Prepare the fMRI data
from nilearn.input_data import NiftiMasker

mask_filename = haxby_dataset.mask
# For decoding, standardizing is often very important
nifti_masker = NiftiMasker(mask_img=mask_filename,
                           sessions=session,
                           smoothing_fwhm=4,
                           standardize=True,
                           memory="nilearn_cache",
                           memory_level=1)
func_filename = haxby_dataset.func[0]
X = nifti_masker.fit_transform(func_filename)
# Apply our condition_mask
X = X[condition_mask]
session = session[condition_mask]

#############################################################################
# Build the decoder

# Define the prediction function to be used.
# Here we use a Support Vector Classification, with a linear kernel
from sklearn.svm import SVC
svc = SVC(kernel='linear')
示例#52
0
def plot_melodic_components(
    melodic_dir,
    in_file,
    tr=None,
    out_file="melodic_reportlet.svg",
    compress="auto",
    report_mask=None,
    noise_components_file=None,
):
    """
    Plots the spatiotemporal components extracted by FSL MELODIC
    from functional MRI data.

    Parameters
    ----------
    melodic_dir : str
        Path pointing to the outputs of MELODIC
    in_file :  str
        Path pointing to the reference fMRI dataset. This file
        will be used to extract the TR value, if the ``tr`` argument
        is not set. This file will be used to calculate a mask
        if ``report_mask`` is not provided.
    tr : float
        Repetition time in seconds
    out_file : str
        Path where the resulting SVG file will be stored
    compress : ``'auto'`` or bool
        Whether SVG should be compressed. If ``'auto'``, compression
        will be executed if dependencies are installed (SVGO)
    report_mask : str
        Path to a brain mask corresponding to ``in_file``
    noise_components_file : str
        A CSV file listing the indexes of components classified as noise
        by some manual or automated (e.g. ICA-AROMA) procedure. If a
        ``noise_components_file`` is provided, then components will be
        plotted with red/green colors (correspondingly to whether they
        are in the file -noise components, red-, or not -signal, green-).
        When all or none of the components are in the file, a warning
        is printed at the top.

    """
    from nilearn.image import index_img, iter_img
    import nibabel as nb
    import numpy as np
    import pylab as plt
    import seaborn as sns
    from matplotlib.gridspec import GridSpec
    import os

    sns.set_style("white")
    current_palette = sns.color_palette()
    in_nii = nb.load(in_file)
    if not tr:
        tr = in_nii.header.get_zooms()[3]
        units = in_nii.header.get_xyzt_units()
        if units:
            if units[-1] == "msec":
                tr = tr / 1000.0
            elif units[-1] == "usec":
                tr = tr / 1000000.0
            elif units[-1] != "sec":
                NIWORKFLOWS_LOG.warning("Unknown repetition time units "
                                        "specified - assuming seconds")
        else:
            NIWORKFLOWS_LOG.warning(
                "Repetition time units not specified - assuming seconds")

    from nilearn.input_data import NiftiMasker
    from nilearn.plotting import cm

    if not report_mask:
        nifti_masker = NiftiMasker(mask_strategy="epi")
        nifti_masker.fit(index_img(in_nii, range(2)))
        mask_img = nifti_masker.mask_img_
    else:
        mask_img = nb.load(report_mask)

    mask_sl = []
    for j in range(3):
        mask_sl.append(transform_to_2d(mask_img.get_fdata(), j))

    timeseries = np.loadtxt(os.path.join(melodic_dir, "melodic_mix"))
    power = np.loadtxt(os.path.join(melodic_dir, "melodic_FTmix"))
    stats = np.loadtxt(os.path.join(melodic_dir, "melodic_ICstats"))
    n_components = stats.shape[0]
    Fs = 1.0 / tr
    Ny = Fs / 2
    f = Ny * (np.array(list(range(1, power.shape[0] + 1)))) / (power.shape[0])

    # Set default colors
    color_title = "k"
    color_time = current_palette[0]
    color_power = current_palette[1]
    classified_colors = None

    warning_row = 0  # Do not allocate warning row
    # Only if the components file has been provided, a warning banner will
    # be issued if all or none of the components were classified as noise
    if noise_components_file:
        noise_components = np.loadtxt(noise_components_file,
                                      dtype=int,
                                      delimiter=",",
                                      ndmin=1)
        # Activate warning row if pertinent
        warning_row = int(noise_components.size in (0, n_components))
        classified_colors = {True: "r", False: "g"}

    n_rows = int((n_components + (n_components % 2)) / 2)
    fig = plt.figure(figsize=(6.5 * 1.5, (n_rows + warning_row) * 0.85))
    gs = GridSpec(
        n_rows * 2 + warning_row,
        9,
        width_ratios=[1, 1, 1, 4, 0.001, 1, 1, 1, 4],
        height_ratios=[5] * warning_row + [1.1, 1] * n_rows,
    )

    if warning_row:
        ax = fig.add_subplot(gs[0, :])
        ncomps = "NONE of the"
        if noise_components.size == n_components:
            ncomps = "ALL"
        ax.annotate(
            "WARNING: {} components were classified as noise".format(ncomps),
            xy=(0.0, 0.5),
            xycoords="axes fraction",
            xytext=(0.01, 0.5),
            textcoords="axes fraction",
            size=12,
            color="#ea8800",
            bbox=dict(boxstyle="round", fc="#f7dcb7", ec="#FC990E"),
        )
        ax.axes.get_xaxis().set_visible(False)
        ax.axes.get_yaxis().set_visible(False)

    titlefmt = "C{id:d}{noise}: Tot. var. expl. {var:.2g}%".format
    for i, img in enumerate(
            iter_img(os.path.join(melodic_dir, "melodic_IC.nii.gz"))):

        col = i % 2
        row = i // 2
        l_row = row * 2 + warning_row
        is_noise = False

        if classified_colors:
            # If a noise components list is provided, assign red/green
            is_noise = (i + 1) in noise_components
            color_title = color_time = color_power = classified_colors[
                is_noise]

        data = img.get_fdata()
        for j in range(3):
            ax1 = fig.add_subplot(gs[l_row:l_row + 2, j + col * 5])
            sl = transform_to_2d(data, j)
            m = np.abs(sl).max()
            ax1.imshow(sl,
                       vmin=-m,
                       vmax=+m,
                       cmap=cm.cold_white_hot,
                       interpolation="nearest")
            ax1.contour(mask_sl[j], levels=[0.5], colors="k", linewidths=0.5)
            plt.axis("off")
            ax1.autoscale_view("tight")
            if j == 0:
                ax1.set_title(
                    titlefmt(id=i + 1,
                             noise=" [noise]" * is_noise,
                             var=stats[i, 1]),
                    x=0,
                    y=1.18,
                    fontsize=7,
                    horizontalalignment="left",
                    verticalalignment="top",
                    color=color_title,
                )

        ax2 = fig.add_subplot(gs[l_row, 3 + col * 5])
        ax3 = fig.add_subplot(gs[l_row + 1, 3 + col * 5])

        ax2.plot(
            np.arange(len(timeseries[:, i])) * tr,
            timeseries[:, i],
            linewidth=min(200 / len(timeseries[:, i]), 1.0),
            color=color_time,
        )
        ax2.set_xlim([0, len(timeseries[:, i]) * tr])
        ax2.axes.get_yaxis().set_visible(False)
        ax2.autoscale_view("tight")
        ax2.tick_params(axis="both", which="major", pad=0)
        sns.despine(left=True, bottom=True)
        for tick in ax2.xaxis.get_major_ticks():
            tick.label.set_fontsize(6)
            tick.label.set_color(color_time)

        ax3.plot(
            f[0:],
            power[0:, i],
            color=color_power,
            linewidth=min(100 / len(power[0:, i]), 1.0),
        )
        ax3.set_xlim([f[0], f.max()])
        ax3.axes.get_yaxis().set_visible(False)
        ax3.autoscale_view("tight")
        ax3.tick_params(axis="both", which="major", pad=0)
        for tick in ax3.xaxis.get_major_ticks():
            tick.label.set_fontsize(6)
            tick.label.set_color(color_power)
        sns.despine(left=True, bottom=True)

    plt.subplots_adjust(hspace=0.5)
    fig.savefig(
        out_file,
        dpi=300,
        format="svg",
        transparent=True,
        bbox_inches="tight",
        pad_inches=0.01,
    )
    fig.clf()
#Retrieve the behavioral targets, that we are going to predict in the decoding
y_mask = labels['labels']
subs = labels['subs']

# ---STEP 3---
#feature selection
#To keep only data corresponding to app food or unapp food, we create a mask of the samples belonging to the condition.
condition_mask = np.logical_or(y_mask == b'app',y_mask == b'unapp')
print(condition_mask.shape)
y = y_mask[condition_mask]
print(y)
n_conditions = np.size(np.unique(y))

#prepare the fxnl data. CHANGED IMAG_MASK TO IMG_MASK_NI1. CAN ONLY TAKE NIFTI1?
nifti_masker = NiftiMasker(mask_img=img_mask_ni1,
                           smoothing_fwhm=4,standardize=True,
                           memory="nilearn_cache",memory_level=1)

fmri_trans = nifti_masker.fit_transform(fmri_subjs)
print(fmri_trans)
X = fmri_trans[condition_mask]
subs = subs[condition_mask]


# ---STEP 4---
#setting prediction  & testing the classifer
svc = SVC(kernel='linear')
print(svc)

# Define the dimension reduction to be used.
# Here we use a classical univariate feature selection based on F-test, namely Anova. We set the number of features to be selected to 500
示例#54
0
def p_roi_masking(substitution, ts_file_template, beta_file_template,
                  p_file_template, design_file_template, event_file_template,
                  p_level, brain_mask):
    """Apply a substitution pattern to timecourse, beta, and design file templates - and mask the data of the former two according to a roi. Subsequently scale the design by the mean beta.

	Parameters
	----------

	substitution : dict
	A dictionary containing the template replacement fields as keys and identifiers as values.

	ts_file_template : string
	Timecourse file template with replacement fields. The file should be in NIfTI format.

	beta_file_template : string
	Beta file template with replacement fields. The file should be in NIfTI format.

	design_file_template : string
	Design file template with replacement fields. The file should be in CSV format.

	roi_path : string
	Path to the region of interest file based on which to create a mask for the time course and beta files. The file should be in NIfTI format.

	brain_mask : string
	Path to the a mask file in the *exact same* coordinate space as the input image. This is very important, as the mask is needed to crop out artefactual p=0 values. These cannot just be filtered out nummerically, since it is possible that the GLM resturns p=0 for the most significant results.

	Returns
	-------

	timecourse : array_like
	Numpy array containing the mean timecourse in the region of interest.

	design : array_like
	Numpy array containing the regressor scaled by the mean beta value of the region of interest..

	mask_map : data
	Nibabel image of the mask

	subplot_title : string
	Title for the subplot, computed from the substitution fields.
	"""

    ts_file = path.abspath(
        path.expanduser(ts_file_template.format(**substitution)))
    beta_file = path.abspath(
        path.expanduser(beta_file_template.format(**substitution)))
    p_file = path.abspath(
        path.expanduser(p_file_template.format(**substitution)))
    design_file = path.abspath(
        path.expanduser(design_file_template.format(**substitution)))
    event_file = path.abspath(
        path.expanduser(event_file_template.format(**substitution)))
    brain_mask = path.abspath(path.expanduser(brain_mask))
    try:
        img = nib.load(p_file)
        brain_mask = nib.load(brain_mask)
    except (FileNotFoundError, nib.py3k.FileNotFoundError):
        return None, None, None, None, None
    data = img.get_data()
    brain_mask = brain_mask.get_data()
    header = img.header
    affine = img.affine
    shape = data.shape
    data = data.flatten()
    brain_mask = brain_mask.flatten()
    brain_mask = brain_mask.astype(bool)
    brain_data = data[brain_mask]
    reject, nonzero_data, _, _ = multipletests(brain_data,
                                               p_level,
                                               method="fdr_bh")
    brain_mask[brain_mask] = reject
    brain_mask = brain_mask.astype(int)
    mask = brain_mask.reshape(shape)
    mask_map = nib.Nifti1Image(mask, affine, header)
    masker = NiftiMasker(mask_img=mask_map)
    try:
        timecourse = masker.fit_transform(ts_file).T
        betas = masker.fit_transform(beta_file).T
    except ValueError:
        return None, None, None, None, None
    subplot_title = "\n ".join(
        [str(substitution["subject"]),
         str(substitution["session"])])
    timecourse = np.mean(timecourse, axis=0)
    design = pd.read_csv(design_file,
                         skiprows=5,
                         sep="\t",
                         header=None,
                         index_col=False)
    design = design * np.mean(betas)
    event_df = pd.read_csv(event_file, sep="\t")

    return timecourse, design, mask_map, event_df, subplot_title
示例#55
0
    def fit(self, run_imgs, events=None, confounds=None, design_matrices=None):
        """ Fit the GLM

        For each run:
        1. create design matrix X
        2. do a masker job: fMRI_data -> Y
        3. fit regression to (Y, X)

        Parameters
        ----------
        run_imgs: Niimg-like object or list of Niimg-like objects,
            See http://nilearn.github.io/manipulating_images/input_output.html#inputing-data-file-names-or-image-objects
            Data on which the GLM will be fitted. If this is a list,
            the affine is considered the same for all.

        events: pandas Dataframe or string or list of pandas DataFrames or
                   strings
                   
            fMRI events used to build design matrices. One events object
            expected per run_img. Ignored in case designs is not None.
            If string, then a path to a csv file is expected.

        confounds: pandas Dataframe or string or list of pandas DataFrames or
                   strings
                   
            Each column in a DataFrame corresponds to a confound variable
            to be included in the regression model of the respective run_img.
            The number of rows must match the number of volumes in the
            respective run_img. Ignored in case designs is not None.
            If string, then a path to a csv file is expected.

        design_matrices: pandas DataFrame or list of pandas DataFrames,
            Design matrices that will be used to fit the GLM. If given it
            takes precedence over events and confounds.

        """
        # Check arguments
        # Check imgs type
        if events is not None:
            _check_events_file_uses_tab_separators(events_files=events)
        if not isinstance(run_imgs, (list, tuple)):
            run_imgs = [run_imgs]
        if design_matrices is None:
            if events is None:
                raise ValueError('events or design matrices must be provided')
            if self.t_r is None:
                raise ValueError('t_r not given to FirstLevelModel object'
                                 ' to compute design from events')
        else:
            design_matrices = _check_run_tables(run_imgs, design_matrices,
                                                'design_matrices')
        # Check that number of events and confound files match number of runs
        # Also check that events and confound files can be loaded as DataFrame
        if events is not None:
            events = _check_run_tables(run_imgs, events, 'events')
        if confounds is not None:
            confounds = _check_run_tables(run_imgs, confounds, 'confounds')

        # Learn the mask
        if self.mask_img is False:
            # We create a dummy mask to preserve functionality of api
            ref_img = check_niimg(run_imgs[0])
            self.mask_img = Nifti1Image(np.ones(ref_img.shape[:3]),
                                        ref_img.affine)
        if not isinstance(self.mask_img, NiftiMasker):
            self.masker_ = NiftiMasker(mask_img=self.mask_img,
                                       smoothing_fwhm=self.smoothing_fwhm,
                                       target_affine=self.target_affine,
                                       standardize=self.standardize,
                                       mask_strategy='epi',
                                       t_r=self.t_r,
                                       memory=self.memory,
                                       verbose=max(0, self.verbose - 2),
                                       target_shape=self.target_shape,
                                       memory_level=self.memory_level)
            self.masker_.fit(run_imgs[0])
        else:
            if self.mask_img.mask_img_ is None and self.masker_ is None:
                self.masker_ = clone(self.mask_img)
                for param_name in [
                        'target_affine', 'target_shape', 'smoothing_fwhm',
                        't_r', 'memory', 'memory_level'
                ]:
                    our_param = getattr(self, param_name)
                    if our_param is None:
                        continue
                    if getattr(self.masker_, param_name) is not None:
                        warn('Parameter %s of the masker'
                             ' overriden' % param_name)
                    setattr(self.masker_, param_name, our_param)
                self.masker_.fit(run_imgs[0])
            else:
                self.masker_ = self.mask_img

        # For each run fit the model and keep only the regression results.
        self.labels_, self.results_, self.design_matrices_ = [], [], []
        n_runs = len(run_imgs)
        t0 = time.time()
        for run_idx, run_img in enumerate(run_imgs):
            # Report progress
            if self.verbose > 0:
                percent = float(run_idx) / n_runs
                percent = round(percent * 100, 2)
                dt = time.time() - t0
                # We use a max to avoid a division by zero
                if run_idx == 0:
                    remaining = 'go take a coffee, a big one'
                else:
                    remaining = (100. - percent) / max(0.01, percent) * dt
                    remaining = '%i seconds remaining' % remaining

                sys.stderr.write("Computing run %d out of %d runs (%s)\n" %
                                 (run_idx + 1, n_runs, remaining))

            # Build the experimental design for the glm
            run_img = check_niimg(run_img, ensure_ndim=4)
            if design_matrices is None:
                n_scans = run_img.get_data().shape[3]
                if confounds is not None:
                    confounds_matrix = confounds[run_idx].values
                    if confounds_matrix.shape[0] != n_scans:
                        raise ValueError('Rows in confounds does not match'
                                         'n_scans in run_img at index %d' %
                                         (run_idx, ))
                    confounds_names = confounds[run_idx].columns.tolist()
                else:
                    confounds_matrix = None
                    confounds_names = None
                start_time = self.slice_time_ref * self.t_r
                end_time = (n_scans - 1 + self.slice_time_ref) * self.t_r
                frame_times = np.linspace(start_time, end_time, n_scans)
                design = make_first_level_design_matrix(
                    frame_times, events[run_idx], self.hrf_model,
                    self.drift_model, self.high_pass, self.drift_order,
                    self.fir_delays, confounds_matrix, confounds_names,
                    self.min_onset)
            else:
                design = design_matrices[run_idx]
            self.design_matrices_.append(design)

            # Mask and prepare data for GLM
            if self.verbose > 1:
                t_masking = time.time()
                sys.stderr.write('Starting masker computation \r')

            Y = self.masker_.transform(run_img)

            if self.verbose > 1:
                t_masking = time.time() - t_masking
                sys.stderr.write('Masker took %d seconds       \n' % t_masking)

            if self.signal_scaling:
                Y, _ = mean_scaling(Y, self.scaling_axis)
            if self.memory:
                mem_glm = self.memory.cache(run_glm, ignore=['n_jobs'])
            else:
                mem_glm = run_glm

            # compute GLM
            if self.verbose > 1:
                t_glm = time.time()
                sys.stderr.write('Performing GLM computation\r')
            labels, results = mem_glm(Y,
                                      design.values,
                                      noise_model=self.noise_model,
                                      bins=100,
                                      n_jobs=self.n_jobs)
            if self.verbose > 1:
                t_glm = time.time() - t_glm
                sys.stderr.write('GLM took %d seconds         \n' % t_glm)

            self.labels_.append(labels)
            # We save memory if inspecting model details is not necessary
            if self.minimize_memory:
                for key in results:
                    results[key] = SimpleRegressionResults(results[key])
            self.results_.append(results)
            del Y

        # Report progress
        if self.verbose > 0:
            sys.stderr.write(
                "\nComputation of %d runs done in %i seconds\n\n" %
                (n_runs, time.time() - t0))

        return self
示例#56
0
# on a homogeneous background

# Load Miyawaki dataset
from nilearn import datasets
miyawaki_dataset = datasets.fetch_miyawaki2008()

# print basic information on the dataset
print('First functional nifti image (4D) is located at: %s' %
      miyawaki_dataset.func[0])  # 4D data

miyawaki_filename = miyawaki_dataset.func[0]
miyawaki_mean_img = image.mean_img(miyawaki_filename)
plot_epi(miyawaki_mean_img, title='Mean EPI image')
###############################################################################
# A NiftiMasker with the default strategy
masker = NiftiMasker()
masker.fit(miyawaki_filename)

# Plot the generated mask using the mask_img_ attribute
plot_roi(masker.mask_img_,
         miyawaki_mean_img,
         title="Mask from already masked data")

###############################################################################
# Plot the generated mask using the .generate_report method
report = masker.generate_report()
report

###############################################################################
# Computing a mask from raw EPI data
###############################################################################
示例#57
0
            dim=0,  # title=subject,
            colorbar=False,
            view_type='filled_contours',
            linewidths=2.)
        axes.axis('off')
    fig.savefig(os.path.join(write_dir, 'snapshot_%s.pdf' % name),
                facecolor='k',
                dpi=300)
    # plt.close(fig)


db = data_parser(derivatives=SMOOTH_DERIVATIVES, conditions=CONTRASTS)
# db = db[db.task.isin(task_list)]
mask_gm = nib.load(os.path.join(DERIVATIVES, 'group', 'anat',
                                'gm_mask.nii.gz'))
masker = NiftiMasker(mask_img=mask_gm, memory=mem).fit()

write_dir = 'output'
if not os.path.exists(write_dir):
    os.mkdir(write_dir)
"""
task_contrast = [('ArchiSocial', 'false_belief-mechanistic_video'),
                 ('ArchiSocial', 'false_belief-mechanistic_audio'),
                 ('ArchiSocial', 'triangle_mental-random'),
                 ('hcp_social', 'mental-random')]
plot_contrasts(db, task_contrast, masker, write_dir, cut=-50, display_mode='x',
               name='social')

#plot_contrasts(db, task_contrast, masker, write_dir, cut=20, display_mode='z')
task_contrast = [('HcpWm', 'body-avg'),
                 ('HcpWm', 'face-avg'),
示例#58
0
def plot_melodic_components(melodic_dir,
                            in_file,
                            tr=None,
                            out_file='melodic_reportlet.svg',
                            compress='auto',
                            report_mask=None,
                            noise_components_file=None):
    from nilearn.image import index_img, iter_img
    import nibabel as nb
    import numpy as np
    import seaborn as sns
    from matplotlib.gridspec import GridSpec
    import os
    import re
    from io import StringIO
    sns.set_style("white")
    current_palette = sns.color_palette()
    in_nii = nb.load(in_file)
    if not tr:
        tr = in_nii.header.get_zooms()[3]
        units = in_nii.header.get_xyzt_units()
        if units:
            if units[-1] == 'msec':
                tr = tr / 1000.0
            elif units[-1] == 'usec':
                tr = tr / 1000000.0
            elif units[-1] != 'sec':
                NIWORKFLOWS_LOG.warning('Unknown repetition time units '
                                        'specified - assuming seconds')
        else:
            NIWORKFLOWS_LOG.warning(
                'Repetition time units not specified - assuming seconds')

    from nilearn.input_data import NiftiMasker
    from nilearn.plotting import cm

    if not report_mask:
        nifti_masker = NiftiMasker(mask_strategy='epi')
        nifti_masker.fit(index_img(in_nii, range(2)))
        mask_img = nifti_masker.mask_img_
    else:
        mask_img = nb.load(report_mask)

    mask_sl = []
    for j in range(3):
        mask_sl.append(transform_to_2d(mask_img.get_data(), j))

    timeseries = np.loadtxt(os.path.join(melodic_dir, "melodic_mix"))
    power = np.loadtxt(os.path.join(melodic_dir, "melodic_FTmix"))
    stats = np.loadtxt(os.path.join(melodic_dir, "melodic_ICstats"))
    n_components = stats.shape[0]
    Fs = 1.0 / tr
    Ny = Fs / 2
    f = Ny * (np.array(list(range(1, power.shape[0] + 1)))) / (power.shape[0])

    n_rows = int((n_components + (n_components % 2)) / 2)
    fig = plt.figure(figsize=(6.5 * 1.5, n_rows * 0.85))
    gs = GridSpec(n_rows * 2,
                  9,
                  width_ratios=[
                      1,
                      1,
                      1,
                      4,
                      0.001,
                      1,
                      1,
                      1,
                      4,
                  ],
                  height_ratios=[1.1, 1] * n_rows)

    noise_components = None
    if noise_components_file:
        noise_components = np.loadtxt(noise_components_file,
                                      dtype=int,
                                      delimiter=',',
                                      ndmin=1)

    for i, img in enumerate(
            iter_img(os.path.join(melodic_dir, "melodic_IC.nii.gz"))):

        col = i % 2
        row = int(i / 2)
        l_row = row * 2

        # Set default colors
        color_title = 'k'
        color_time = current_palette[0]
        color_power = current_palette[1]

        if noise_components is not None and noise_components.size > 0:
            # If a noise components list is provided, assign red/green
            color_title = color_time = color_power = ('r' if (
                i + 1) in noise_components else 'g')

        data = img.get_data()
        for j in range(3):
            ax1 = fig.add_subplot(gs[l_row:l_row + 2, j + col * 5])
            sl = transform_to_2d(data, j)
            m = np.abs(sl).max()
            ax1.imshow(sl,
                       vmin=-m,
                       vmax=+m,
                       cmap=cm.cold_white_hot,
                       interpolation="nearest")
            ax1.contour(mask_sl[j], levels=[0.5], colors='k', linewidths=0.5)
            plt.axis("off")
            ax1.autoscale_view('tight')
            if j == 0:
                ax1.set_title("C%d: Tot. var. expl. %.2g%%" %
                              (i + 1, stats[i, 1]),
                              x=0,
                              y=1.18,
                              fontsize=7,
                              horizontalalignment='left',
                              verticalalignment='top',
                              color=color_title)

        ax2 = fig.add_subplot(gs[l_row, 3 + col * 5])
        ax3 = fig.add_subplot(gs[l_row + 1, 3 + col * 5])

        ax2.plot(np.arange(len(timeseries[:, i])) * tr,
                 timeseries[:, i],
                 linewidth=min(200 / len(timeseries[:, i]), 1.0),
                 color=color_time)
        ax2.set_xlim([0, len(timeseries[:, i]) * tr])
        ax2.axes.get_yaxis().set_visible(False)
        ax2.autoscale_view('tight')
        ax2.tick_params(axis='both', which='major', pad=0)
        sns.despine(left=True, bottom=True)
        for tick in ax2.xaxis.get_major_ticks():
            tick.label.set_fontsize(6)
            tick.label.set_color(color_time)

        ax3.plot(f[0:],
                 power[0:, i],
                 color=color_power,
                 linewidth=min(100 / len(power[0:, i]), 1.0))
        ax3.set_xlim([f[0], f.max()])
        ax3.axes.get_yaxis().set_visible(False)
        ax3.autoscale_view('tight')
        ax3.tick_params(axis='both', which='major', pad=0)
        for tick in ax3.xaxis.get_major_ticks():
            tick.label.set_fontsize(6)
            tick.label.set_color(color_power)
        sns.despine(left=True, bottom=True)

    plt.subplots_adjust(hspace=0.5)

    image_buf = StringIO()
    fig.savefig(image_buf,
                dpi=300,
                format='svg',
                transparent=True,
                bbox_inches='tight',
                pad_inches=0.01)
    fig.clf()
    image_svg = image_buf.getvalue()

    if compress is True or compress == 'auto':
        image_svg = svg_compress(image_svg, compress)
    image_svg = re.sub(' height="[0-9]+[a-z]*"', '', image_svg, count=1)
    image_svg = re.sub(' width="[0-9]+[a-z]*"', '', image_svg, count=1)
    image_svg = re.sub(' viewBox',
                       ' preseveAspectRation="xMidYMid meet" viewBox',
                       image_svg,
                       count=1)

    with open(out_file, 'w' if PY3 else 'wb') as f:
        f.write(image_svg)
示例#59
0
def roi_masking(substitution, ts_file_template, beta_file_template,
                design_file_template, event_file_template, roi):
    """Apply a substitution pattern to timecourse, beta, and design file templates - and mask the data of the former two according to a roi. Subsequently scale the design by the mean beta.

	Parameters
	----------

	substitution : dict
	A dictionary containing the template replacement fields as keys and identifiers as values.

	ts_file_template : string
	Timecourse file template with replacement fields. The file should be in NIfTI format.

	beta_file_template : string
	Beta file template with replacement fields. The file should be in NIfTI format.

	design_file_template : string
	Design file template with replacement fields. The file should be in CSV format.

	roi_path : string
	Path to the region of interest file based on which to create a mask for the time course and beta files. The file should be in NIfTI format.

	Returns
	-------

	timecourse : array_like
	Numpy array containing the mean timecourse in the region of interest.

	design : array_like
	Numpy array containing the regressor scaled by the mean beta value of the region of interest..

	mask_map : data
	Nibabel image of the mask

	subplot_title : string
	Title for the subplot, computed from the substitution fields.
	"""

    ts_file = path.expanduser(ts_file_template.format(**substitution))
    beta_file = path.expanduser(beta_file_template.format(**substitution))
    design_file = path.expanduser(design_file_template.format(**substitution))
    event_file = path.expanduser(event_file_template.format(**substitution))

    masker = NiftiMasker(mask_img=roi)
    if isinstance(roi, str):
        mask_map = nib.load(roi)
    else:
        mask_map = roi
    try:
        timecourse = masker.fit_transform(ts_file).T
        betas = masker.fit_transform(beta_file).T
        design = pd.read_csv(design_file,
                             skiprows=5,
                             sep="\t",
                             header=None,
                             index_col=False)
        event_df = pd.read_csv(event_file, sep="\t")
    except ValueError:
        print('Not found', ts_file, beta_file, design_file, event_file)
        return None, None, None, None, None
    subplot_title = "Subject {} | Session {}".format(
        str(substitution["subject"]), str(substitution["session"]))
    timecourse = np.mean(timecourse, axis=0)
    design = design * np.mean(betas)

    return timecourse, design, mask_map, event_df, subplot_title
示例#60
0
#To keep only data corresponding to app food or unapp food, we create a mask of the samples belonging to the condition.

condition_mask = func_df["labels"].isin(['rest', 'app'])
#condition_mask = func_df["labels"].isin(['app', 'unapp', 'H2O'])
print(condition_mask.shape)
#y = y_mask[condition_mask]
y = y_mask[condition_mask]
print(y.shape)
n_conditions = np.size(np.unique(y))
print(n_conditions)
#n_conditions = np.size(np.unique(y))
print(y.unique())
#session = func_df[condition_mask].to_records(index=False)
#print(session.dtype.name)
nifti_masker = NiftiMasker(mask_img=imag_mask,
                           smoothing_fwhm=4,
                           standardize=True,
                           memory_level=0)
fmri_trans = nifti_masker.fit_transform(fmri_subjs)
print(fmri_trans)
X = fmri_trans[condition_mask]
subs = subs[condition_mask]

svc = SVC()
svc = SVC(kernel='linear', verbose=False)
print(svc)
from sklearn.feature_selection import SelectPercentile, f_classif
#feature_selection = SelectPercentile(f_classif, percentile=10)
feature_selection = SelectKBest(f_classif, k=1500)
np.warnings.filterwarnings('ignore')

anova_svc = Pipeline([('anova', feature_selection), ('svc', svc)])