def tortoise(*args):
        print args
        print (
            'Fitting a "Fixed Effect" GLM for merging LR and RL '
            'phase-encoding directions for subject %s ...' % (
                subject_data.subject_id))
        fmri_glm = FMRILinearModel(subject_data.func,
                                   [design_matrix.matrix
                                    for design_matrix in design_matrices],
                                   mask='compute'
                                   )
        fmri_glm.fit(do_scaling=True, model='ar1')
        print "... done.\r\n"

        # save computed mask
        mask_path = os.path.join(subject_data.output_dir, "mask.nii")
        print "Saving mask image to %s ..." % mask_path
        nibabel.save(fmri_glm.mask, mask_path)
        print "... done.\r\n"

        z_maps = {}
        effects_maps = {}
        map_dirs = {}
        try:
            for contrast_id, contrast_val in contrasts.iteritems():
                print "\tcontrast id: %s" % contrast_id
                z_map, eff_map = fmri_glm.contrast(
                    contrast_val,
                    con_id=contrast_id,
                    output_z=True,
                    output_effects=True
                    )

                # store stat maps to disk
                for map_type, out_map in zip(['z', 'effects'],
                                             [z_map, eff_map]):
                    map_dir = os.path.join(
                        subject_data.output_dir, '%s_maps' % map_type)
                    map_dirs[map_type] = map_dir
                    if not os.path.exists(map_dir):
                        os.makedirs(map_dir)
                    map_path = os.path.join(
                        map_dir, '%s_%s.nii' % (map_type, contrast_id))
                    print "\t\tWriting %s ..." % map_path

                    nibabel.save(out_map, map_path)

                    # collect zmaps for contrasts we're interested in
                    if map_type == 'z':
                        z_maps[contrast_id] = map_path

                    if map_type == 'effects':
                        effects_maps[contrast_id] = map_path

            return effects_maps, z_maps, mask_path, map_dirs
        except:
            return None

def _first_level_glm(study_dir, subject_id, model_id,
                     hrf_model='canonical', drift_model='cosine',
                     glm_model='ar1', mask='compute', verbose=1):

    study_id = os.path.split(study_dir)[1]
    subject_dir = os.path.join(study_dir, subject_id)

    if verbose > 0:
        print '%[email protected]%s: first level glm' % (subject_id, study_id)

    tr = get_study_tr(study_dir)
    images, n_scans = get_subject_bold_images(subject_dir)
    motion = get_subject_motion_per_session(subject_dir)
    contrasts = get_task_contrasts(study_dir, subject_dir, model_id)
    events = get_subject_events(study_dir, subject_dir)

    design_matrices = make_design_matrices(events, n_scans, tr,
                                           hrf_model, drift_model, motion)

    glm = FMRILinearModel(images, design_matrices, mask=mask)
    glm.fit(do_scaling=True, model=glm_model)

    for contrast_id in contrasts:

        con_val = []
        for session_con, session_dm in zip(contrasts[contrast_id],
                                           design_matrices):
            con = np.zeros(session_dm.shape[1])
            con[:len(session_con)] = session_con
            con_val.append(con)

        z_map, t_map, c_map, var_map = glm.contrast(
            con_val,
            con_id=contrast_id,
            output_z=True,
            output_stat=True,
            output_effects=True,
            output_variance=True,)

        model_dir = os.path.join(subject_dir, 'model',  model_id)

        for dtype, img in zip(['z', 't', 'c', 'var'],
                              [z_map, t_map, c_map, var_map]):

            map_dir = os.path.join(model_dir, '%s_maps' % dtype)

            if not os.path.exists(map_dir):
                os.makedirs(map_dir)

            path = os.path.join(
                map_dir, '%s.nii.gz' % normalize_name(contrast_id))
            nb.save(img, path)

    nb.save(glm.mask, os.path.join(model_dir, 'mask.nii.gz'))

def _apply_glm(out_dir, data, design_matrices,
               contrasts, mask='compute', model_id=None, resample=True):

    # print out_dir
    bold_dir = os.path.join(out_dir, 'fmri')
    if not os.path.exists(bold_dir):
        os.makedirs(bold_dir)
    for i, img in enumerate(data):
        if type(img) is str:
            img = nb.load(img)
        nb.save(img, os.path.join(bold_dir, 'bold_session_%i.nii.gz' % i))

    # fit glm
    glm = FMRILinearModel(data, design_matrices, mask=mask)
    glm.fit(do_scaling=True, model='ar1')

    nb.save(glm.mask, os.path.join(out_dir, 'mask.nii.gz'))
    if resample:
        resample_niimg(os.path.join(out_dir, 'mask.nii.gz'))

    stat_maps = {}
    for contrast_id in contrasts:
        stat_maps[contrast_id] = {}
        z_map, t_map, c_map, var_map = glm.contrast(
            contrasts[contrast_id],
            con_id=contrast_id,
            output_z=True,
            output_stat=True,
            output_effects=True,
            output_variance=True,)

        for dtype, out_map in zip(['z', 't', 'c', 'variance'],
                                  [z_map, t_map, c_map, var_map]):
            map_dir = os.path.join(out_dir, '%s_maps' % dtype)
            if not os.path.exists(map_dir):
                os.makedirs(map_dir)
            if model_id:
                map_path = os.path.join(
                    map_dir, '%s_%s.nii.gz' % (model_id, contrast_id))
            else:
                map_path = os.path.join(
                    map_dir, '%s.nii.gz' % contrast_id)
            nb.save(out_map, map_path)
            if resample:
                resample_niimg(map_path)

            stat_maps[contrast_id][dtype] = map_path


    return stat_maps

def _first_level(out_dir, data, design_matrices, contrasts,
                 glm_model='ar1', mask='compute', verbose=1):
    if verbose:
        print '%s:' % out_dir

    data = check_niimgs(data)
    design_matrices = check_design_matrices(design_matrices)
    contrasts = check_contrasts(contrasts)
    glm = FMRILinearModel(data, design_matrices, mask=mask)
    glm.fit(do_scaling=True, model=glm_model)

    for i, contrast_id in enumerate(contrasts):
        if verbose:
            print '  %s/%s - %s ' % (i, len(contrasts), contrast_id)

        con_val = []
        for session_con, session_dm in zip(contrasts[contrast_id],
                                           design_matrices):

            con = np.zeros(session_dm.shape[1])
            con[:len(session_con)] = session_con
            con_val.append(con)

        z_map, t_map, c_map, var_map = glm.contrast(
            con_val,
            con_id=contrast_id,
            output_z=True,
            output_stat=True,
            output_effects=True,
            output_variance=True,)

        for dtype, img in zip(['z', 't', 'c', 'var'],
                              [z_map, t_map, c_map, var_map]):

            map_dir = os.path.join(out_dir, '%s_maps' % dtype)

            if not os.path.exists(map_dir):
                os.makedirs(map_dir)

            path = os.path.join(
                map_dir, '%s.nii.gz' % remove_special(contrast_id))
            nb.save(img, path)

    nb.save(glm.mask, os.path.join(out_dir, 'mask.nii.gz'))

        for subject_glm_results in group_glm_inputs]
    contrasts = group_glm_inputs[0][0]
    sujects_effects_maps = [subject_glm_results[1]
                           for subject_glm_results in group_glm_inputs]
    group_level_z_maps = {}
    design_matrix = np.ones(len(sujects_effects_maps)
                            )[:, np.newaxis]  # only the intercept
    for contrast_id in contrasts:
        print "\tcontrast id: %s" % contrast_id

        # effects maps will be the input to the second level GLM
        first_level_image = nibabel.concat_images(
            [x[contrast_id] for x in sujects_effects_maps])

        # fit 2nd level GLM for given contrast
        group_model = FMRILinearModel(first_level_image,
                                    design_matrix, group_mask)
        group_model.fit(do_scaling=False, model='ols')

        # specify and estimate the contrast
        contrast_val = np.array(([[1.]]))  # the only possible contrast !
        z_map, = group_model.contrast(contrast_val,
                                    con_id='one_sample %s' % contrast_id,
                                    output_z=True)

        # save map
        map_dir = os.path.join(output_dir, 'z_maps')
        if not os.path.exists(map_dir):
            os.makedirs(map_dir)
        map_path = os.path.join(map_dir, '2nd_level_%s.nii.gz' % (
                contrast_id))
        print "\t\tWriting %s ..." % map_path

def group_one_sample_t_test(masks, effects_maps, contrasts, output_dir,
                            start_time=base_reporter.pretty_time(),
                            **kwargs):
    """
    Runs a one-sample t-test procedure for group analysis. Here, we are
    for each experimental condition, only interested refuting the null
    hypothesis H0: "The average effect accross the subjects is zero!"

    Parameters
    ----------
    masks: list of strings or nibabel image objects
        subject masks, one per subject

    effects_maps: list of dicts of lists
        effects maps from subject-level GLM; each entry is a dictionary;
        each entry (indexed by condition id) of this dictionary is the
        filename (or correspinding nibabel image object) for the effects
        maps for that condition (aka contrast),for that subject

    contrasts: dictionary of array_likes
        contrasts vectors, indexed by condition id

    kwargs: dict_like
        parameters can be regular `nipy.labs.viz.plot_map` parameters
        (e.g slicer="y") or any parameter we want be reported (e.g
        fwhm=[5, 5, 5])

    """

    # make output directory
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

    assert len(masks) == len(effects_maps), (len(masks), len(effects_maps))

    # compute group mask
    group_mask = nibabel.Nifti1Image(
        intersect_masks(masks).astype(np.int8),
        (nibabel.load(masks[0]) if isinstance(
                masks[0], basestring) else masks[0]).get_affine())

    # construct design matrix (only one covariate, namely the "mean effect")
    design_matrix = np.ones(len(effects_maps)
                            )[:, np.newaxis]  # only the intercept

    group_level_z_maps = {}
    group_level_t_maps = {}
    for contrast_id in contrasts:
        print "\tcontrast id: %s" % contrast_id

        # effects maps will be the input to the second level GLM
        first_level_image = nibabel.concat_images(
            [x[contrast_id] for x in effects_maps])

        # fit 2nd level GLM for given contrast
        group_model = FMRILinearModel(first_level_image,
                                      design_matrix, group_mask)
        group_model.fit(do_scaling=False, model='ols')

        # specify and estimate the contrast
        contrast_val = np.array(([[1.]])
                                )  # the only possible contrast !
        z_map, t_map = group_model.contrast(contrast_val,
                                      con_id='one_sample %s' % contrast_id,
                                      output_z=True,
                                      output_stat=True)

        # save map
        for map_type, map_img in zip(["z", "t"], [z_map, t_map]):
            map_dir = os.path.join(output_dir, '%s_maps' % map_type)
            if not os.path.exists(map_dir):
                os.makedirs(map_dir)
            map_path = os.path.join(map_dir, 'group_level_%s.nii.gz' % (
                    contrast_id))
            print "\t\tWriting %s ..." % map_path
            nibabel.save(map_img, map_path)
            if map_type == "z":
                group_level_z_maps[contrast_id] = map_path
            elif map_type == "t":
                group_level_z_maps[contrast_id] = map_path

    # do stats report
    stats_report_filename = os.path.join(
        output_dir, "report_stats.html")
    generate_subject_stats_report(stats_report_filename, contrasts,
                                  group_level_z_maps, group_mask,
                                  start_time=start_time,
                                  **kwargs)

    print "\r\nStatistic report written to %s\r\n" % (
        stats_report_filename)

    return group_level_z_maps

#.........这里部分代码省略.........

    # compute effects
    mask_path = os.path.join(subject_output_dir, "mask.nii.gz")
    skip = os.path.isfile(mask_path)
    if skip:
        for contrast_id, contrast_val in contrasts.iteritems():
            for map_type in ['z', 'effects']:
                map_dir = os.path.join(
                    subject_output_dir, '%s_maps' % map_type)
                if not os.path.exists(map_dir):
                    os.makedirs(map_dir)
                map_path = os.path.join(
                    map_dir, '%s.nii.gz' % contrast_id)
                if not os.path.exists(map_path):
                    skip = 0
                    break

                # collect zmaps for contrasts we're interested in
                if map_type == 'z':
                    z_maps[contrast_id] = map_path

                if map_type == 'effects':
                    effects_maps[contrast_id] = map_path

            if skip:
                print "Skipping subject %s..." % (
                    subject_id)

    # fit GLM
    if not skip:
        print (
            'Fitting a "Fixed Effect" GLM for merging LR and RL phase-encoding '
            'directions for subject %s ...' % subject_id)
        fmri_glm = FMRILinearModel(fmri_files,
                                   [design_matrix.matrix
                                    for design_matrix in design_matrices],
                                   mask='compute'
                                   )
        fmri_glm.fit(do_scaling=True, model='ar1')
        print "... done.\r\n"

        # save computed mask
        mask_path = os.path.join(subject_output_dir, "mask.nii.gz")
        print "Saving mask image to %s ..." % mask_path
        nibabel.save(fmri_glm.mask, mask_path)
        print "... done.\r\n"

        # compute effects
        for contrast_id, contrast_val in contrasts.iteritems():
            print "\tcontrast id: %s" % contrast_id
            z_map, eff_map = fmri_glm.contrast(
                contrast_val,
                con_id=contrast_id,
                output_z=True,
                output_effects=True
                )

            # store stat maps to disk
            for map_type, out_map in zip(['z', 'effects'],
                                         [z_map, eff_map]):
                map_dir = os.path.join(
                    subject_output_dir, '%s_maps' % map_type)
                if not os.path.exists(map_dir):
                    os.makedirs(map_dir)
                map_path = os.path.join(
                    map_dir, '%s.nii.gz' % contrast_id)

def do_glm_for_subject(subject_id, bold_base_folder, trial_base_folder,
                       output_base_folder):

    subject_dir = path(bold_base_folder) / ("sub%03d" % subject_id)
    output_dir = (path(output_base_folder) / ("sub%03d" % subject_id) /
                  "model001")
    print output_dir
    if not output_dir.exists():
        output_dir.makedirs()


    anat_file = subject_dir / "highres001.nii"
    anat = nb.load(anat_file)
    run_ids = range(1, 10)

    task_bold_files = [subject_dir.glob("task001_run%03d/rbold*.nii"
                                        % rid)[0]
                       for rid in run_ids]
    task_mvt_files = [subject_dir.glob("task001_run%03d/rp_bold*.txt" % 
                                       rid)[0]
                      for rid in run_ids]

    trial_files = [(path(trial_base_folder) / ("Sub%02d" % subject_id) /
                   "BOLD" / "Trials" / ("run_%02d_spmdef.txt" % rid))
                   for rid in range(1, 10)]
    stats_start_time = pretty_time()
    paradigms = []
    design_matrices = []
    n_scans = []
    all_frametimes = []
    list_of_contrast_dicts = []  # one dict per run

    for bold_file, mvt_file, trial_file in zip(task_bold_files, 
                                               task_mvt_files,
                                               trial_files):

        _n_scans = nb.load(bold_file).shape[-1]
        n_scans.append(_n_scans)
        paradigm = make_paradigm(trial_file)
        paradigms.append(paradigm)
        movements = np.loadtxt(mvt_file)

        tr = 2.
        drift_model = "Cosine"
        hrf_model = "Canonical With Derivative"
        hfcut = 128.

        frametimes = np.linspace(0, (_n_scans - 1) * tr, _n_scans)

        design_matrix = make_dmtx(
            frametimes,
            paradigm,
            hrf_model=hrf_model,
            drift_model=drift_model,
            hfcut=hfcut,
            add_regs=movements,
            add_reg_names=[
                "Tx", "Ty", "Tz", "R1", "R2", "R3"])

        design_matrices.append(design_matrix)
        all_frametimes.append(frametimes)

        # specify contrasts
        contrasts = {}
        n_columns = len(design_matrix.names)
        for i in xrange(paradigm.n_conditions):
            contrasts['%s' % design_matrix.names[2 * i]] = np.eye(
                n_columns)[2 * i]

        # more interesting contrasts"""
        contrasts['Famous-Unfamiliar'] = contrasts[
            'Famous'] - contrasts['Unfamiliar']
        contrasts['Unfamiliar-Famous'] = -contrasts['Famous-Unfamiliar']
        contrasts['Famous-Scrambled'] = contrasts[
            'Famous'] - contrasts['Scrambled']
        contrasts['Scrambled-Famous'] = -contrasts['Famous-Scrambled']
        contrasts['Unfamiliar-Scrambled'] = contrasts[
            'Unfamiliar'] - contrasts['Scrambled']
        contrasts['Scrambled-Unfamiliar'] = -contrasts['Unfamiliar-Scrambled']

        list_of_contrast_dicts.append(contrasts)


    # importat maps
    z_maps = {}
    effects_maps = {}

    fmri_glm = FMRILinearModel(task_bold_files,
                               [dm.matrix for dm in design_matrices],
                               mask="compute")
    fmri_glm.fit(do_scaling=True, model="ar1")

    # replicate contrasts across runs
    contrasts = dict((cid, [contrasts[cid]
                            for contrasts in list_of_contrast_dicts])
                     for cid, cval in contrasts.iteritems())

    # compute effects
    for contrast_id, contrast_val in contrasts.iteritems():
        print "\tcontrast id: %s" % contrast_id
#.........这里部分代码省略.........

def do_subject_glm(subject_data):
    """FE analysis for a single subject."""
    subject_id = subject_data['subject_id']
    output_dir = subject_data["output_dir"]
    func_files = subject_data['func']
    anat = subject_data['anat']
    onset_files = subject_data['onset']
    # subject_id = os.path.basename(subject_dir)
    # subject_output_dir = os.path.join(output_dir, subject_id)
    mem = Memory(os.path.join(output_dir, "cache"))
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

    # glob files: anat, session func files, session onset files
    # anat = glob.glob(os.path.join(subject_dir, anat_wildcard))
    # assert len(anat) == 1
    # anat = anat[0]
    # onset_files = sorted([glob.glob(os.path.join(subject_dir, session))[0]
    #                       for session in session_onset_wildcards])
    # func_files = sorted([sorted(glob.glob(os.path.join(subject_dir, session)))
    #                      for session in session_func_wildcards])

    ### Preprocess data #######################################################
    if 0:
        subject_data = mem.cache(do_subject_preproc)(
            dict(func=func_files, anat=anat, output_dir=output_dir))
        func_files = subject_data['func']
        anat = subject_data['anat']

        # reslice func images
        func_files = [mem.cache(reslice_vols)(
            sess_func,
            target_affine=nibabel.load(sess_func[0]).get_affine())
                      for sess_func in func_files]

    ### GLM: loop on (session_bold, onse_file) pairs over the various sessions
    design_matrices = []
    for session, (func_file, onset_file) in enumerate(zip(func_files,
                                                          onset_files)):
        if isinstance(func_file, str):
            bold = nibabel.load(func_file)
        else:
            if len(func_file) == 1:
                func_file = func_file[0]
                bold = nibabel.load(func_file)
                assert len(bold.shape) == 4
                n_scans = bold.shape[-1]
                del bold
            else:
                n_scans = len(func_file)
        frametimes = np.linspace(0, (n_scans - 1) * tr, n_scans)
        conditions, onsets, durations, amplitudes = parse_onset_file(
            onset_file)
        onsets *= tr
        durations *= tr
        paradigm = BlockParadigm(con_id=conditions, onset=onsets,
                                 duration=durations, amplitude=amplitudes)
        design_matrices.append(make_dmtx(frametimes,
                                         paradigm, hrf_model=hrf_model,
                                         drift_model=drift_model,
                                         hfcut=hfcut))

    # specify contrasts
    n_columns = len(design_matrices[0].names)
    contrasts = {}
    for i in xrange(paradigm.n_conditions):
        contrasts['%s' % design_matrices[0].names[2 * i]
                  ] = np.eye(n_columns)[2 * i]

    # more interesting contrasts
    contrasts['faces-scrambled'] = contrasts['faces'
                                             ] - contrasts['scrambled']
    contrasts['scrambled-faces'] = -contrasts['faces-scrambled']
    contrasts['effects_of_interest'] = contrasts['faces'
                                                 ] + contrasts['scrambled']

    # effects of interest F-test
    diff_contrasts = []
    for i in xrange(paradigm.n_conditions - 1):
        a = contrasts[design_matrices[0].names[2 * i]]
        b = contrasts[design_matrices[0].names[2 * (i + 1)]]
        diff_contrasts.append(a - b)
    contrasts["diff"] = diff_contrasts

    # fit GLM
    print 'Fitting a GLM (this takes time)...'
    fmri_glm = FMRILinearModel([nibabel.concat_images(sess_func,
                                                      check_affines=False)
                                for sess_func in func_files],
                               [design_matrix.matrix
                                for design_matrix in design_matrices],
                               mask='compute'
                               )
    fmri_glm.fit(do_scaling=True, model='ar1')

    # save computed mask
    mask_path = os.path.join(output_dir, "mask.nii.gz")

    print "Saving mask image %s" % mask_path
    nibabel.save(fmri_glm.mask, mask_path)
#.........这里部分代码省略.........

contrasts["left-right"] = contrasts["left"] - contrasts["right"]
contrasts["right-left"] = contrasts["right"] - contrasts["left"]
contrasts["audio-video"] = contrasts["audio"] - contrasts["video"]
contrasts["video-audio"] = contrasts["video"] - contrasts["audio"]
contrasts["computation-sentences"] = contrasts["computation"] -  \
                                     contrasts["sentences"]
contrasts["reading-visual"] = contrasts["sentences"] * 2 - \
                              contrasts["damier_H"] - contrasts["damier_V"]
contrasts['effects_of_interest'] = np.eye(25)[:20:2]

########################################
# Perform a GLM analysis
########################################

print 'Fitting a GLM (this takes time)...'
fmri_glm = FMRILinearModel(data_path, design_matrix.matrix,
                           mask='compute')
fmri_glm.fit(do_scaling=True, model='ar1')

#########################################
# Estimate the contrasts
#########################################

print 'Computing contrasts...'
for index, (contrast_id, contrast_val) in enumerate(contrasts.iteritems()):
    print '  Contrast % 2i out of %i: %s' % (
        index + 1, len(contrasts), contrast_id)
    # save the z_image
    image_path = path.join(write_dir, '%s_z_map.nii' % contrast_id)
    z_map, = fmri_glm.contrast(contrast_val, con_id=contrast_id, output_z=True)
    save(z_map, image_path)

design_matrix = make_dmtx(frametimes, paradigm, hrf_model=hrf_model,
                          drift_model=drift_model, hfcut=hfcut)

ax = design_matrix.show()
ax.set_position([.05, .25, .9, .65])
ax.set_title('Design matrix')

plt.savefig(path.join(write_dir, 'design_matrix.png'))
dim = design_matrix.matrix.shape[1]

########################################
# Perform a GLM analysis
########################################

print 'Fitting a GLM (this takes time)...'
fmri_glm = FMRILinearModel(data_path, design_matrix.matrix,
                           mask='compute')
fmri_glm.fit(do_scaling=True, model='ar1')

########################################
# Output beta and variance images
########################################
beta_hat = fmri_glm.glms[0].get_beta()  # Least-squares estimates of the beta
variance_hat = fmri_glm.glms[0].get_mse() # Estimates of the variance
mask = fmri_glm.mask.get_data() > 0

# output beta images
beta_map = np.tile(mask.astype(np.float)[..., np.newaxis], dim)
beta_map[mask] = beta_hat.T
beta_image = Nifti1Image(beta_map, fmri_glm.affine)
beta_image.get_header()['descrip'] = (
    'Parameter estimates of the localizer dataset')

        exit(1)
    try:
        cvect = [float(arg) for arg in args]
    except ValueError:
        print(USAGE)
        exit(1)

# Input files
fmri_files = [example_data.get_filename('fiac', 'fiac0', run)
              for run in ['run1.nii.gz', 'run2.nii.gz']]
design_files = [example_data.get_filename('fiac', 'fiac0', run)
                for run in ['run1_design.npz', 'run2_design.npz']]
mask_file = example_data.get_filename('fiac', 'fiac0', 'mask.nii.gz')

# Load all the data
multi_session_model = FMRILinearModel(fmri_files, design_files, mask_file)

# GLM fitting
multi_session_model.fit(do_scaling=True, model='ar1')

# Compute the required contrast
print('Computing test contrast image...')
n_regressors = [np.load(f)['X'].shape[1] for f in design_files]
con = [np.hstack((cvect, np.zeros(nr - len(cvect)))) for nr in n_regressors]
z_map, = multi_session_model.contrast(con)

# Show Z-map image
mean_map = multi_session_model.means[0]
plot_map(z_map.get_data(),
         z_map.get_affine(),
         anat=mean_map.get_data(),

def first_level(subject_dic):
    # experimental paradigm meta-params
    stats_start_time = time.ctime()
    tr = 2.4
    drift_model = 'blank'
    hrf_model = 'canonical'  # hemodynamic reponse function
    hfcut = 128.
    n_scans = 128

    # make design matrices
    mask_images = []
    design_matrices = []
    fmri_files = subject_dic['func']

    for x in xrange(len(fmri_files)):
        paradigm = paradigm_contrasts.localizer_paradigm()

        # build design matrix
        frametimes = np.linspace(0, (n_scans - 1) * tr, n_scans)
        design_matrix = make_dmtx(
            frametimes,
            paradigm, hrf_model=hrf_model,
            drift_model=drift_model, hfcut=hfcut,
            )
        design_matrices.append(design_matrix)

    # Specify contrasts
    contrasts = paradigm_contrasts.localizer_contrasts(design_matrix)

    #create output directory
    subject_session_output_dir = os.path.join(subject_dic['output_dir'],
                                                  'res_stats')

    if not os.path.exists(subject_session_output_dir):
             os.makedirs(subject_session_output_dir)

    # Fit GLM
    print 'Fitting a GLM (this takes time)...'
    fmri_glm = FMRILinearModel(fmri_files,
                               [design_matrix.matrix
                                for design_matrix in design_matrices],
                               mask='compute'
                               )
    fmri_glm.fit(do_scaling=True, model='ar1')

    # save computed mask
    mask_path = os.path.join(subject_session_output_dir,
                             "mask.nii.gz")
    print "Saving mask image %s" % mask_path
    nibabel.save(fmri_glm.mask, mask_path)
    mask_images.append(mask_path)

    # compute contrasts
    z_maps = {}
    effects_maps = {}
    for contrast_id, contrast_val in contrasts.iteritems():
        print "\tcontrast id: %s" % contrast_id
        z_map, t_map, effects_map, var_map = fmri_glm.contrast(
            [contrast_val] * 1,
            con_id=contrast_id,
            output_z=True,
            output_stat=True,
            output_effects=True,
            output_variance=True
            )

        # store stat maps to disk
        for map_type, out_map in zip(['z', 't', 'effects', 'variance'],
                                  [z_map, t_map, effects_map, var_map]):
            map_dir = os.path.join(
                subject_session_output_dir, '%s_maps' % map_type)
            if not os.path.exists(map_dir):
                os.makedirs(map_dir)
            map_path = os.path.join(
                map_dir, '%s%s.nii.gz' %(subject_dic['subject_id'], contrast_id))
            print "\t\tWriting %s ..." % map_path
            nibabel.save(out_map, map_path)

            # collect zmaps for contrasts we're interested in
            if map_type == 'z':
                z_maps[contrast_id] = map_path
            if map_type == 'effects':
                effects_maps[contrast_id] = map_path

    # do stats report
    anat_img = nibabel.load(subject_dic['anat'])
    stats_report_filename = os.path.join(subject_session_output_dir,
                                         "report_stats.html")
                                         
    generate_subject_stats_report(
        stats_report_filename,
        contrasts,
        z_maps,
        fmri_glm.mask,
        threshold=2.3,
        cluster_th=15,
        anat=anat_img,
        anat_affine=anat_img.get_affine(),
        design_matrices=design_matrix,
        subject_id="sub001",
#.........这里部分代码省略.........

def do_subject_glm(subject_data):
    """FE analysis for a single subject."""
    subject_id = subject_data['subject_id']
    output_dir = subject_data["output_dir"]
    func_files = subject_data['func']
    anat = subject_data['anat']
    onset_files = subject_data['onset']
    tr = subject_data['TR']
    time_units = subject_data['time_units'].lower()
    assert time_units in ["seconds", "tr", "milliseconds"]
    drift_model = subject_data['drift_model']
    hrf_model = subject_data["hrf_model"]
    hfcut = subject_data["hfcut"]
    mem = Memory(os.path.join(output_dir, "cache"))
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

    if 0:
        subject_data = mem.cache(do_subject_preproc)(
            dict(func=func_files, anat=anat, output_dir=output_dir))
        func_files = subject_data['func']
        anat = subject_data['anat']

        # reslice func images
        func_files = [mem.cache(reslice_vols)(
            sess_func, target_affine=nibabel.load(sess_func[0]).get_affine())
                      for sess_func in func_files]

    ### GLM: loop on (session_bold, onse_file) pairs over the various sessions
    design_matrices = []
    for func_file, onset_file in zip(func_files, onset_files):
        if isinstance(func_file, str):
            bold = nibabel.load(func_file)
        else:
            if len(func_file) == 1:
                func_file = func_file[0]
                bold = nibabel.load(func_file)
                assert len(bold.shape) == 4
                n_scans = bold.shape[-1]
                del bold
            else:
                n_scans = len(func_file)
        frametimes = np.linspace(0, (n_scans - 1) * tr, n_scans)
        conditions, onsets, durations, amplitudes = parse_onset_file(
            onset_file)
        if time_units == "tr":
            onsets *= tr
            durations *= tr
        elif time_units in ["milliseconds"]:
            onsets *= 1e-3
            durations *= 1e-3
        paradigm = BlockParadigm(con_id=conditions, onset=onsets,
                                 duration=durations, amplitude=amplitudes)
        design_matrices.append(make_dmtx(
                frametimes,
                paradigm, hrf_model=hrf_model,
                drift_model=drift_model, hfcut=hfcut))

    # specify contrasts
    n_columns = len(design_matrices[0].names)
    contrasts = {}
    for i in range(paradigm.n_conditions):
        contrasts['%s' % design_matrices[0].names[2 * i]
                  ] = np.eye(n_columns)[2 * i]

    # effects of interest F-test
    diff_contrasts = []
    for i in range(paradigm.n_conditions - 1):
        a = contrasts[design_matrices[0].names[2 * i]]
        b = contrasts[design_matrices[0].names[2 * (i + 1)]]
        diff_contrasts.append(a - b)
    contrasts["diff"] = diff_contrasts

    # fit GLM
    print('Fitting a GLM (this takes time)...')
    fmri_glm = FMRILinearModel([nibabel.concat_images(sess_func,
                                                      check_affines=False)
                                for sess_func in func_files],
                               [design_matrix.matrix
                                for design_matrix in design_matrices],
                               mask='compute'
                               )
    fmri_glm.fit(do_scaling=True, model='ar1')

    # save computed mask
    mask_path = os.path.join(output_dir, "mask.nii.gz")

    print("Saving mask image %s" % mask_path)
    nibabel.save(fmri_glm.mask, mask_path)

    # compute contrasts
    z_maps = {}
    effects_maps = {}
    for contrast_id, contrast_val in contrasts.items():
        print("\tcontrast id: %s" % contrast_id)
        if np.ndim(contrast_val) > 1:
            contrast_type = "t"
        else:
            contrast_type = "F"
        z_map, t_map, effects_map, var_map = fmri_glm.contrast(
#.........这里部分代码省略.........

#########################################

# simplest ones
contrasts = {}
n_columns = len(design_matrix.names)
for i in range(paradigm.n_conditions):
    contrasts['%s' % design_matrix.names[2 * i]] = np.eye(n_columns)[2 * i]

# Our contrast of interest
reading_vs_visual = contrasts["phrasevideo"] - contrasts["damier_H"]

########################################
# Perform a GLM analysis on H1
########################################

fmri_glm = FMRILinearModel(fmri_data,
                           design_matrix.matrix, mask='compute')
fmri_glm.fit(do_scaling=True, model='ar1')

# Estimate the contrast
z_map, = fmri_glm.contrast(reading_vs_visual, output_z=True)

# Plot the contrast
vmax = max(-z_map.get_data().min(), z_map.get_data().max())
plot_map(z_map.get_data(), z_map.get_affine(),
            cmap=cm.cold_hot, vmin=-vmax, vmax=vmax,
            slicer='z', black_bg=True, threshold=2.5,
            title='Reading vs visual')

# Count all the clusters for |Z| > 2.5
Z = z_map.get_data()
from scipy import ndimage

#.........这里部分代码省略.........
        # plt.show()

        paradigms.append(paradigm)
        design_matrices.append(design_matrix)
        frametimes_list.append(frametimes)
        n_scans.append(_n_scans)

        # specify contrasts
        contrasts = {}
        n_columns = len(design_matrix.names)
        for i in xrange(paradigm.n_conditions):
            contrasts['%s' % design_matrix.names[2 * i]] = np.eye(
                n_columns)[2 * i]

        # more interesting contrasts"""
        contrasts['Famous-Unfamiliar'] = contrasts[
            'Famous'] - contrasts['Unfamiliar']
        contrasts['Unfamiliar-Famous'] = -contrasts['Famous-Unfamiliar']
        contrasts['Famous-Scrambled'] = contrasts[
            'Famous'] - contrasts['Scrambled']
        contrasts['Scrambled-Famous'] = -contrasts['Famous-Scrambled']
        contrasts['Unfamiliar-Scrambled'] = contrasts[
            'Unfamiliar'] - contrasts['Scrambled']
        contrasts['Scrambled-Unfamiliar'] = -contrasts['Unfamiliar-Scrambled']

        list_of_contrast_dicts.append(contrasts)

    # importat maps
    z_maps = {}
    effects_maps = {}

    # fit GLM
    print('\r\nFitting a GLM (this takes time) ..')
    fmri_glm = FMRILinearModel([nibabel.concat_images(sess_func)
                                for sess_func in subject_data.func],
                               [design_matrix.matrix
                                for design_matrix in design_matrices],
                               mask='compute')
    fmri_glm.fit(do_scaling=True, model='ar1')

    print "... done.\r\n"

    # save computed mask
    mask_path = os.path.join(subject_data.output_dir, "mask.nii.gz")

    print "Saving mask image to %s ..." % mask_path
    nibabel.save(fmri_glm.mask, mask_path)
    print "... done.\r\n"

    # replicate contrasts across runs
    contrasts = dict((cid, [contrasts[cid]
                            for contrasts in list_of_contrast_dicts])
                     for cid, cval in contrasts.iteritems())

    # compute effects
    for contrast_id, contrast_val in contrasts.iteritems():
        print "\tcontrast id: %s" % contrast_id
        z_map, eff_map = fmri_glm.contrast(
            contrast_val,
            con_id=contrast_id,
            output_z=True,
            output_stat=False,
            output_effects=True,
            output_variance=False
            )

    for i in xrange(paradigm.n_conditions):
        contrasts['%s' % design_matrix.names[2 * i]
                  ] = np.eye(n_columns)[2 * i]

    # more interesting contrasts
    contrasts['faces-scrambled'] = contrasts['faces'
                                             ] - contrasts['scrambled']
    contrasts['scrambled-faces'] = -contrasts['faces-scrambled']
    contrasts['effects_of_interest'] = contrasts['faces'
                                                 ] + contrasts['scrambled']

    # fit GLM
    print 'Fitting a GLM for %s (this takes time)...' % (
        subject_data.session_id[x])
    fmri_glm = FMRILinearModel(nibabel.concat_images(subject_data.func[x]),
                               design_matrix.matrix,
                               mask='compute'
                               )
    fmri_glm.fit(do_scaling=True, model='ar1')

    # save computed mask
    mask_path = os.path.join(subject_session_output_dir,
                             "mask.nii.gz")
    print "Saving mask image %s" % mask_path
    nibabel.save(fmri_glm.mask, mask_path)
    mask_images.append(mask_path)

    # compute contrasts
    z_maps = {}
    effects_maps = {}
    for contrast_id, contrast_val in contrasts.iteritems():
        print "\tcontrast id: %s" % contrast_id