.. AUTO-GENERATED FILE -- DO NOT EDIT!

.. _example_match_distribution:


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

.. image:: ../pics/ex_match_distribution.*
   :align: center
   :alt: Matching distributions for a random voxel


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