Esempio n. 1
0
def test_output_F():
    # Test output_F convenience function
    rng = np.random.RandomState(ord('F'))
    Y = rng.normal(size=(10,1)) * 10 + 100
    X = np.c_[rng.normal(size=(10,3)), np.ones((N,))]
    c1 = np.zeros((X.shape[1],))
    c1[0] = 1
    model = OLSModel(X)
    results = model.fit(Y)
    # Check we get required outputs
    exp_f = results.t(0) **2
    assert_array_almost_equal(exp_f, output_F(results, c1))
Esempio n. 2
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def run_model(subj, run):
    """
    Single subject fitting of FIAC model
    """
    #----------------------------------------------------------------------
    # Set initial parameters of the FIAC dataset
    #----------------------------------------------------------------------
    # Number of volumes in the fMRI data
    nvol = 121
    # The TR of the experiment
    TR = 2.5
    # The time of the first volume
    Tstart = 0.0
    # The array of times corresponding to each volume in the fMRI data
    volume_times = np.arange(nvol) * TR + Tstart
    # This recarray of times has one column named 't'.  It is used in the
    # function design.event_design to create the design matrices.
    volume_times_rec = make_recarray(volume_times, 't')
    # Get a path description dictionary that contains all the path data relevant
    # to this subject/run
    path_info = futil.path_info_run(subj,run)

    #----------------------------------------------------------------------
    # Experimental design
    #----------------------------------------------------------------------

    # Load the experimental description from disk.  We have utilities in futil
    # that reformat the original FIAC-supplied format into something where the
    # factorial structure of the design is more explicit.  This has already
    # been run once, and get_experiment_initial() will simply load the
    # newly-formatted design description files (.csv) into record arrays.
    experiment = futil.get_experiment(path_info)

    # Create design matrices for the "initial" and "experiment" factors, saving
    # the default contrasts.

    # The function event_design will create design matrices, which in the case
    # of "experiment" will have num_columns = (# levels of speaker) * (# levels
    # of sentence) * len(delay.spectral) = 2 * 2 * 2 = 8. For "initial", there
    # will be (# levels of initial) * len([hrf.glover]) = 1 * 1 = 1.

    # Here, delay.spectral is a sequence of 2 symbolic HRFs that are described
    # in:
    #
    # Liao, C.H., Worsley, K.J., Poline, J-B., Aston, J.A.D., Duncan, G.H.,
    #    Evans, A.C. (2002). \'Estimating the delay of the response in fMRI
    #    data.\' NeuroImage, 16:593-606.

    # The contrast definitions in ``cons_exper`` are a dictionary with keys
    # ['constant_0', 'constant_1', 'speaker_0', 'speaker_1', 'sentence_0',
    # 'sentence_1', 'sentence:speaker_0', 'sentence:speaker_1'] representing the
    # four default contrasts: constant, main effects + interactions, each
    # convolved with 2 HRFs in delay.spectral. For example, sentence:speaker_0
    # is the interaction of sentence and speaker convolved with the first (=0)
    # of the two HRF basis functions, and sentence:speaker_1 is the interaction
    # convolved with the second (=1) of the basis functions.

    # XXX use the hrf __repr__ for naming contrasts
    X_exper, cons_exper = design.block_design(experiment, volume_times_rec,
                                              hrfs=delay.spectral,
                                              level_contrasts=True)

    # In addition to factors, there is typically a "drift" term. In this case,
    # the drift is a natural cubic spline with a not at the midpoint
    # (volume_times.mean())
    vt = volume_times # shorthand
    drift = np.array( [vt**i for i in range(4)] +
                      [(vt-vt.mean())**3 * (np.greater(vt, vt.mean()))] )
    for i in range(drift.shape[0]):
        drift[i] /= drift[i].max()

    # We transpose the drift so that its shape is (nvol,5) so that it will have
    # the same number of rows as X_exper.
    drift = drift.T

    # There are helper functions to create these drifts: design.fourier_basis,
    # design.natural_spline.  Therefore, the above is equivalent (except for
    # the normalization by max for numerical stability) to
    #
    # >>> drift = design.natural_spline(t, [volume_times.mean()])

    # Stack all the designs, keeping the new contrasts which has the same keys
    # as cons_exper, but its values are arrays with 15 columns, with the
    # non-zero entries matching the columns of X corresponding to X_exper
    X, cons = design.stack_designs((X_exper, cons_exper),
                                   (drift, {}))

    # Sanity check: delete any non-estimable contrasts
    for k in cons.keys():
        if not isestimable(cons[k], X):
            del(cons[k])
            warnings.warn("contrast %s not estimable for this run" % k)

    # The default contrasts are all t-statistics.  We may want to output
    # F-statistics for 'speaker', 'sentence', 'speaker:sentence' based on the
    # two coefficients, one for each HRF in delay.spectral

    # We reproduce the same constrasts as in the data base
    # outputting an F using both HRFs, as well as the
    # t using only the first HRF

    for obj1, obj2 in [('face', 'scrambled'),
                       ('house', 'scrambled'),
                       ('chair', 'scrambled'),
                       ('face', 'house')]:
        cons['%s_vs_%s_F' % (obj1, obj2)] = \
            np.vstack([cons['object_%s_0' % obj1] - 
                       cons['object_%s_0' % obj2],
                       cons['object_%s_1' % obj1] - 
                       cons['object_%s_1' % obj2]])


        cons['%s_vs_%s_t' % (obj1, obj2)] = (cons['object_%s_0' % obj1] - 
                                             cons['object_%s_0' % obj2])

    #----------------------------------------------------------------------
    # Data loading
    #----------------------------------------------------------------------

    # Load in the fMRI data, saving it as an array.  It is transposed to have
    # time as the first dimension, i.e. fmri[t] gives the t-th volume.
    fmri_im = futil.get_fmri(path_info) # an Image
    fmri_im = rollimg(fmri_im, 't')
    fmri = fmri_im.get_data() # now, it's an ndarray

    nvol, volshape = fmri.shape[0], fmri.shape[1:]
    nx, sliceshape = volshape[0], volshape[1:]

    #----------------------------------------------------------------------
    # Model fit
    #----------------------------------------------------------------------

    # The model is a two-stage model, the first stage being an OLS (ordinary
    # least squares) fit, whose residuals are used to estimate an AR(1)
    # parameter for each voxel.
    m = OLSModel(X)
    ar1 = np.zeros(volshape)

    # Fit the model, storing an estimate of an AR(1) parameter at each voxel
    for s in range(nx):
        d = np.array(fmri[:,s])
        flatd = d.reshape((d.shape[0], -1))
        result = m.fit(flatd)
        ar1[s] = ((result.resid[1:] * result.resid[:-1]).sum(0) /
                  (result.resid**2).sum(0)).reshape(sliceshape)

    # We round ar1 to nearest one-hundredth and group voxels by their rounded
    # ar1 value, fitting an AR(1) model to each batch of voxels.

    # XXX smooth here?
    # ar1 = smooth(ar1, 8.0)
    ar1 *= 100
    ar1 = ar1.astype(np.int) / 100.

    # We split the contrasts into F-tests and t-tests.
    # XXX helper function should do this
    fcons = {}; tcons = {}
    for n, v in cons.items():
        v = np.squeeze(v)
        if v.ndim == 1:
            tcons[n] = v
        else:
            fcons[n] = v

    # Setup a dictionary to hold all the output
    # XXX ideally these would be memmap'ed Image instances
    output = {}
    for n in tcons:
        tempdict = {}
        for v in ['sd', 't', 'effect']:
            tempdict[v] = np.memmap(NamedTemporaryFile(prefix='%s%s.nii'
                                    % (n,v)), dtype=np.float,
                                    shape=volshape, mode='w+')
        output[n] = tempdict

    for n in fcons:
        output[n] = np.memmap(NamedTemporaryFile(prefix='%s%s.nii'
                                    % (n,v)), dtype=np.float,
                                    shape=volshape, mode='w+')

    # Loop over the unique values of ar1
    for val in np.unique(ar1):
        armask = np.equal(ar1, val)
        m = ARModel(X, val)
        d = fmri[:,armask]
        results = m.fit(d)

        # Output the results for each contrast
        for n in tcons:
            resT = results.Tcontrast(tcons[n])
            output[n]['sd'][armask] = resT.sd
            output[n]['t'][armask] = resT.t
            output[n]['effect'][armask] = resT.effect
        for n in fcons:
            output[n][armask] = results.Fcontrast(fcons[n]).F

    # Dump output to disk
    odir = futil.output_dir(path_info,tcons,fcons)
    # The coordmap for a single volume in the time series
    vol0_map = fmri_im[0].coordmap
    for n in tcons:
        for v in ['t', 'sd', 'effect']:
            im = Image(output[n][v], vol0_map)
            save_image(im, pjoin(odir, n, '%s.nii' % v))
    for n in fcons:
        im = Image(output[n], vol0_map)
        save_image(im, pjoin(odir, n, "F.nii"))
Esempio n. 3
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def run_model(subj, run):
    """
    Single subject fitting of OpenfMRI ds105 model
    """
    #----------------------------------------------------------------------
    # Set initial parameters of the OpenfMRI ds105 dataset
    #----------------------------------------------------------------------
    # Number of volumes in the fMRI data
    nvol = 121
    # The TR of the experiment
    TR = 2.5
    # The time of the first volume
    Tstart = 0.0
    # The array of times corresponding to each volume in the fMRI data
    volume_times = np.arange(nvol) * TR + Tstart
    # This recarray of times has one column named 't'.  It is used in the
    # function design.event_design to create the design matrices.
    volume_times_rec = make_recarray(volume_times, 't')
    # Get a path description dictionary that contains all the path data relevant
    # to this subject/run
    path_info = futil.path_info_run(subj, run)

    #----------------------------------------------------------------------
    # Experimental design
    #----------------------------------------------------------------------

    # Load the experimental description from disk.  We have utilities in futil
    # that reformat the original OpenfMRI ds105-supplied format into something
    # where the factorial structure of the design is more explicit.  This has
    # already been run once, and get_experiment_initial() will simply load the
    # newly-formatted design description files (.csv) into record arrays.
    experiment = futil.get_experiment(path_info)

    # Create design matrices for the "initial" and "experiment" factors, saving
    # the default contrasts.

    # The function event_design will create design matrices, which in the case
    # of "experiment" will have num_columns = (# levels of speaker) * (# levels
    # of sentence) * len(delay.spectral) = 2 * 2 * 2 = 8. For "initial", there
    # will be (# levels of initial) * len([hrf.glover]) = 1 * 1 = 1.

    # Here, delay.spectral is a sequence of 2 symbolic HRFs that are described
    # in:
    #
    # Liao, C.H., Worsley, K.J., Poline, J-B., Aston, J.A.D., Duncan, G.H.,
    #    Evans, A.C. (2002). \'Estimating the delay of the response in fMRI
    #    data.\' NeuroImage, 16:593-606.

    # The contrast definitions in ``cons_exper`` are a dictionary with keys
    # ['constant_0', 'constant_1', 'speaker_0', 'speaker_1', 'sentence_0',
    # 'sentence_1', 'sentence:speaker_0', 'sentence:speaker_1'] representing the
    # four default contrasts: constant, main effects + interactions, each
    # convolved with 2 HRFs in delay.spectral. For example, sentence:speaker_0
    # is the interaction of sentence and speaker convolved with the first (=0)
    # of the two HRF basis functions, and sentence:speaker_1 is the interaction
    # convolved with the second (=1) of the basis functions.

    # XXX use the hrf __repr__ for naming contrasts
    X_exper, cons_exper = design.block_design(experiment,
                                              volume_times_rec,
                                              hrfs=delay.spectral,
                                              level_contrasts=True)

    # In addition to factors, there is typically a "drift" term. In this case,
    # the drift is a natural cubic spline with a not at the midpoint
    # (volume_times.mean())
    vt = volume_times  # shorthand
    drift = np.array([vt**i for i in range(4)] + [(vt - vt.mean())**3 *
                                                  (np.greater(vt, vt.mean()))])
    for i in range(drift.shape[0]):
        drift[i] /= drift[i].max()

    # We transpose the drift so that its shape is (nvol,5) so that it will have
    # the same number of rows as X_exper.
    drift = drift.T

    # There are helper functions to create these drifts: design.fourier_basis,
    # design.natural_spline.  Therefore, the above is equivalent (except for
    # the normalization by max for numerical stability) to
    #
    # >>> drift = design.natural_spline(t, [volume_times.mean()])

    # Stack all the designs, keeping the new contrasts which has the same keys
    # as cons_exper, but its values are arrays with 15 columns, with the
    # non-zero entries matching the columns of X corresponding to X_exper
    X, cons = design.stack_designs((X_exper, cons_exper), (drift, {}))

    # Sanity check: delete any non-estimable contrasts
    for k in cons.keys():
        if not isestimable(cons[k], X):
            del (cons[k])
            warnings.warn("contrast %s not estimable for this run" % k)

    # The default contrasts are all t-statistics.  We may want to output
    # F-statistics for 'speaker', 'sentence', 'speaker:sentence' based on the
    # two coefficients, one for each HRF in delay.spectral

    # We reproduce the same constrasts as in the data base
    # outputting an F using both HRFs, as well as the
    # t using only the first HRF

    for obj1, obj2 in [('face', 'scrambled'), ('house', 'scrambled'),
                       ('chair', 'scrambled'), ('face', 'house')]:
        cons['%s_vs_%s_F' % (obj1, obj2)] = \
            np.vstack([cons['object_%s_0' % obj1] -
                       cons['object_%s_0' % obj2],
                       cons['object_%s_1' % obj1] -
                       cons['object_%s_1' % obj2]])

        cons['%s_vs_%s_t' % (obj1, obj2)] = (cons['object_%s_0' % obj1] -
                                             cons['object_%s_0' % obj2])

    #----------------------------------------------------------------------
    # Data loading
    #----------------------------------------------------------------------

    # Load in the fMRI data, saving it as an array.  It is transposed to have
    # time as the first dimension, i.e. fmri[t] gives the t-th volume.
    fmri_im = futil.get_fmri(path_info)  # an Image
    fmri_im = rollimg(fmri_im, 't')
    fmri = fmri_im.get_data()  # now, it's an ndarray

    nvol, volshape = fmri.shape[0], fmri.shape[1:]
    nx, sliceshape = volshape[0], volshape[1:]

    #----------------------------------------------------------------------
    # Model fit
    #----------------------------------------------------------------------

    # The model is a two-stage model, the first stage being an OLS (ordinary
    # least squares) fit, whose residuals are used to estimate an AR(1)
    # parameter for each voxel.
    m = OLSModel(X)
    ar1 = np.zeros(volshape)

    # Fit the model, storing an estimate of an AR(1) parameter at each voxel
    for s in range(nx):
        d = np.array(fmri[:, s])
        flatd = d.reshape((d.shape[0], -1))
        result = m.fit(flatd)
        ar1[s] = ((result.resid[1:] * result.resid[:-1]).sum(0) /
                  (result.resid**2).sum(0)).reshape(sliceshape)

    # We round ar1 to nearest one-hundredth and group voxels by their rounded
    # ar1 value, fitting an AR(1) model to each batch of voxels.

    # XXX smooth here?
    # ar1 = smooth(ar1, 8.0)
    ar1 *= 100
    ar1 = ar1.astype(np.int) / 100.

    # We split the contrasts into F-tests and t-tests.
    # XXX helper function should do this
    fcons = {}
    tcons = {}
    for n, v in cons.items():
        v = np.squeeze(v)
        if v.ndim == 1:
            tcons[n] = v
        else:
            fcons[n] = v

    # Setup a dictionary to hold all the output
    # XXX ideally these would be memmap'ed Image instances
    output = {}
    for n in tcons:
        tempdict = {}
        for v in ['sd', 't', 'effect']:
            tempdict[v] = np.memmap(NamedTemporaryFile(prefix='%s%s.nii' %
                                                       (n, v)),
                                    dtype=np.float,
                                    shape=volshape,
                                    mode='w+')
        output[n] = tempdict

    for n in fcons:
        output[n] = np.memmap(NamedTemporaryFile(prefix='%s%s.nii' % (n, v)),
                              dtype=np.float,
                              shape=volshape,
                              mode='w+')

    # Loop over the unique values of ar1
    for val in np.unique(ar1):
        armask = np.equal(ar1, val)
        m = ARModel(X, val)
        d = fmri[:, armask]
        results = m.fit(d)

        # Output the results for each contrast
        for n in tcons:
            resT = results.Tcontrast(tcons[n])
            output[n]['sd'][armask] = resT.sd
            output[n]['t'][armask] = resT.t
            output[n]['effect'][armask] = resT.effect
        for n in fcons:
            output[n][armask] = results.Fcontrast(fcons[n]).F

    # Dump output to disk
    odir = futil.output_dir(path_info, tcons, fcons)
    # The coordmap for a single volume in the time series
    vol0_map = fmri_im[0].coordmap
    for n in tcons:
        for v in ['t', 'sd', 'effect']:
            im = Image(output[n][v], vol0_map)
            save_image(im, pjoin(odir, n, '%s.nii' % v))
    for n in fcons:
        im = Image(output[n], vol0_map)
        save_image(im, pjoin(odir, n, "F.nii"))
Esempio n. 4
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import numpy as np

from nipy.algorithms.statistics.api  import OLSModel
from ..outputters import output_T, output_F

from nose.tools import assert_true, assert_equal, assert_raises

from numpy.testing import (assert_array_almost_equal,
                           assert_array_equal)


N = 10
X = np.c_[np.linspace(-1,1,N), np.ones((N,))]
RNG = np.random.RandomState(20110901)
Y = RNG.normal(size=(10,1)) * 10 + 100
MODEL = OLSModel(X)
RESULTS = MODEL.fit(Y)
C1 = [1, 0]


def test_model():
    # Check basics about the model fit
    # Check we fit the mean
    assert_array_almost_equal(RESULTS.theta[1], np.mean(Y))


def test_output_T():
    # Check we get required outputs
    res = RESULTS.Tcontrast(C1) # all return values
    # default is all return values
    assert_array_almost_equal([res.effect, res.sd, res.t],