#!/usr/bin/env python
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
#
#   See COPYING file distributed along with the PyMVPA package for the
#   copyright and license terms.
#
### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
"""
Simple Data-Exploration
=======================

Example showing some possibilities of data exploration
(i.e. to 'smell' data).
"""

from mvpa2.suite import *

# load example fmri dataset
ds = load_example_fmri_dataset()

# only use the first 5 chunks to save some cpu-cycles
ds = ds[ds.chunks < 5]

"""
It is always useful to have a quick look at the summary of the dataset and
verify that statistics (mean, standard deviation) are in the expected range,
that there is balance among targets/chunks, and that order is balanced (where
appropriate).
"""

print ds.summary()

"""
Now we can take a look at the distribution of the feature values in all
sample categories and chunks.
"""

pl.figure(figsize=(14, 14)) # larger figure
hist(ds, xgroup_attr='chunks', ygroup_attr='targets', noticks=None,
     bins=20, normed=True)

# next only works with floating point data
ds.samples = ds.samples.astype('float')

# look at sample similarity
# Note, the decreasing similarity with increasing temporal distance
# of the samples
pl.figure(figsize=(14, 6))
pl.subplot(121)
plot_samples_distance(ds, sortbyattr='chunks')
pl.title('Sample distances (sorted by chunks)')

# similar distance plot, but now samples sorted by their
# respective targets, i.e. samples with same targets are plotted
# in adjacent columns/rows.
# Note, that the first and largest group corresponds to the
# 'rest' condition in the dataset
pl.subplot(122)
plot_samples_distance(ds, sortbyattr='targets')
pl.title('Sample distances (sorted by targets)')

# z-score features individually per chunk
print 'Detrending data'
poly_detrend(ds, polyord=2, chunks_attr='chunks')
print 'Z-Scoring data'
zscore(ds)

pl.figure(figsize=(14, 6))
pl.subplot(121)
plot_samples_distance(ds, sortbyattr='chunks')
pl.title('Distances: z-scored, detrended (sorted by chunks)')
pl.subplot(122)
plot_samples_distance(ds, sortbyattr='targets')
pl.title('Distances: z-scored, detrended (sorted by targets)');
if cfg.getboolean('examples', 'interactive', True):
    pl.show()

# XXX add some more, maybe show effect of preprocessing

"""
Outputs of the example script. Data prior to preprocessing

.. image:: ../pics/ex_smellit2.*
   :align: center
   :alt: Data prior preprocessing

Data after minimal preprocessing

.. image:: ../pics/ex_smellit3.*
   :align: center
   :alt: Data after z-scoring and detrending

"""

#!/usr/bin/env python
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
#
#   See COPYING file distributed along with the PyMVPA package for the
#   copyright and license terms.
#
### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
"""
Simple Data-Exploration
=======================

Example showing some possibilities of data exploration
(i.e. to 'smell' data).
"""

from mvpa2.suite import *

# load example fmri dataset
ds = load_example_fmri_dataset()

# only use the first 5 chunks to save some cpu-cycles
ds = ds[ds.chunks < 5]

"""
It is always useful to have a quick look at the summary of the dataset and
verify that statistics (mean, standard deviation) are in the expected range,
that there is balance among targets/chunks, and that order is balanced (where
appropriate).
"""

print ds.summary()

"""
Now we can take a look at the distribution of the feature values in all
sample categories and chunks.
"""

pl.figure(figsize=(14, 14)) # larger figure
hist(ds, xgroup_attr='chunks', ygroup_attr='targets', noticks=None,
     bins=20, normed=True)

# next only works with floating point data
ds.samples = ds.samples.astype('float')

# look at sample similarity
# Note, the decreasing similarity with increasing temporal distance
# of the samples
pl.figure(figsize=(14, 6))
pl.subplot(121)
plot_samples_distance(ds, sortbyattr='chunks')
pl.title('Sample distances (sorted by chunks)')

# similar distance plot, but now samples sorted by their
# respective targets, i.e. samples with same targets are plotted
# in adjacent columns/rows.
# Note, that the first and largest group corresponds to the
# 'rest' condition in the dataset
pl.subplot(122)
plot_samples_distance(ds, sortbyattr='targets')
pl.title('Sample distances (sorted by targets)')

# z-score features individually per chunk
print 'Detrending data'
poly_detrend(ds, polyord=2, chunks_attr='chunks')
print 'Z-Scoring data'
zscore(ds)

pl.figure(figsize=(14, 6))
pl.subplot(121)
plot_samples_distance(ds, sortbyattr='chunks')
pl.title('Distances: z-scored, detrended (sorted by chunks)')
pl.subplot(122)
plot_samples_distance(ds, sortbyattr='targets')
pl.title('Distances: z-scored, detrended (sorted by targets)');
if cfg.getboolean('examples', 'interactive', True):
    pl.show()

# XXX add some more, maybe show effect of preprocessing

"""
Outputs of the example script. Data prior to preprocessing

.. image:: ../pics/ex_smellit2.*
   :align: center
   :alt: Data prior preprocessing

Data after minimal preprocessing

.. image:: ../pics/ex_smellit3.*
   :align: center
   :alt: Data after z-scoring and detrending

"""

#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # #   See COPYING file distributed along with the PyMVPA package for the #   copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## Simple Data-Exploration =======================  Example showing some possibilities of data exploration (i.e. to 'smell' data). # load example fmri dataset # only use the first 5 chunks to save some cpu-cycles It is always useful to have a quick look at the summary of the dataset and verify that statistics (mean, standard deviation) are in the expected range, that there is balance among targets/chunks, and that order is balanced (where appropriate). Now we can take a look at the distribution of the feature values in all sample categories and chunks. # larger figure # next only works with floating point data # look at sample similarity # Note, the decreasing similarity with increasing temporal distance # of the samples # similar distance plot, but now samples sorted by their # respective targets, i.e. samples with same targets are plotted # in adjacent columns/rows. # Note, that the first and largest group corresponds to the # 'rest' condition in the dataset # z-score features individually per chunk # XXX add some more, maybe show effect of preprocessing Outputs of the example script. Data prior to preprocessing  .. image:: ../pics/ex_smellit2.*    :align: center    :alt: Data prior preprocessing  Data after minimal preprocessing  .. image:: ../pics/ex_smellit3.*    :align: center    :alt: Data after z-scoring and detrending