| Multivariate Pattern Analysis in Python

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This content refers to the previous stable release of PyMVPA. Please visit www.pymvpa.org for the most recent version of PyMVPA and its documentation.

# Determine the Distribution of some Variable

This is an example demonstrating discovery of the distribution facility.

    from mvpa.suite import *

    verbose.level = 2
    if __debug__:
        # report useful debug information for the example
        debug.active += ['STAT', 'STAT_']

    report = Report(name='match_distribution_report',
                    title='PyMVPA Example: match_distribution.py')
    verbose.handlers += [report]     # Lets add verbose output to the report.
                                     # Similar action could be done to 'debug'
    #
    # Figure for just normal distribution
    #

    # generate random signal from normal distribution
    verbose(1, "Random signal with normal distribution")
    data = N.random.normal(size=(1000, 1))

    # find matching distributions
    # NOTE: since kstest is broken in older versions of scipy
    #       p-roc testing is done here, which aims to minimize
    #       false positives/negatives while doing H0-testing
    test = 'p-roc'
    figsize = (15, 10)
    verbose(1, "Find matching datasets")
    matches = matchDistribution(data, test=test, p=0.05)

    P.figure(figsize=figsize)
    P.subplot(2, 1, 1)
    plotDistributionMatches(data, matches, legend=1, nbest=5)
    P.title('Normal: 5 best distributions')

    P.subplot(2, 1, 2)
    plotDistributionMatches(data, matches, nbest=5, p=0.05,
                            tail='any', legend=4)
    P.title('Accept regions for two-tailed test')

    # we are done with the figure -- add it to report
    report.figure()

    #
    # Figure for fMRI data sample we have
    #
    verbose(1, "Load sample fMRI dataset")
    attr = SampleAttributes(os.path.join(pymvpa_dataroot, 'attributes.txt'))
    dataset = NiftiDataset(samples=os.path.join(pymvpa_dataroot, 'bold.nii.gz'),
                           labels=attr.labels,
                           chunks=attr.chunks,
                           mask=os.path.join(pymvpa_dataroot, 'mask.nii.gz'))
    # select random voxel
    dataset = dataset.selectFeatures(
                [int(N.random.uniform()*dataset.nfeatures)])

    verbose(2, "Minimal preprocessing to remove the bias per each voxel")
    detrend(dataset, perchunk=True, model='linear')
    zscore(dataset, perchunk=True, baselinelabels=[0],
           targetdtype='float32')

    # on all voxels at once, just for the sake of visualization
    data = dataset.samples.ravel()
    verbose(2, "Find matching distribution")
    matches = matchDistribution(data, test=test, p=0.05)

    P.figure(figsize=figsize)
    P.subplot(2, 1, 1)
    plotDistributionMatches(data, matches, legend=1, nbest=5)
    P.title('Random voxel: 5 best distributions')

    P.subplot(2, 1, 2)
    plotDistributionMatches(data, matches, nbest=5, p=0.05,
                            tail='any', legend=4)
    P.title('Accept regions for two-tailed test')
    report.figure()

    if cfg.getboolean('examples', 'interactive', True):
        # store the report
        report.save()
        # show the cool figure
        P.show()

Example output for a random voxel is

See also

The full source code of this example is included in the PyMVPA source distribution (doc/examples/match_distribution.py).

### Navigation

- index
- next |
- previous |
- ExpPsy Project |
- NeuroDebian Project |
- PyMVPA 0.4.x Home |
- PyMVPA Home |
- Documentation »
- Full Examples »