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#   See COPYING file distributed along with the PyMVPA package for the
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"""
Analysis of eye movement patterns
=================================

In this example we are going to look at a classification analysis of eye
movement patterns. Although complex preprocessing steps can be performed to
extract higher-order features from the raw coordinate timeseries provided by an
eye-tracker, we are keeping it simple.

Right after importing the PyMVPA suite, we load the data from a textfile.
It contains coordinate timeseries of 144 trials (recorded with 350 Hz), where
subjects either looked at upright or inverted images of human faces. Each
timeseries snippet covers 3 seconds. This data has been pre-processed to remove
eyeblink artefacts.

In addition to the coordinates we also load trial attributes from a second
textfile. These attributes indicate which image was shown, whether it was
showing a male or female face, and wether it was upright or inverted.
"""

from mvpa.suite import *

# where is the data
datapath = os.path.join(pymvpa_datadbroot,
                        'face_inversion_demo', 'face_inversion_demo')
# (X, Y, trial id) for all timepoints
data = np.loadtxt(os.path.join(datapath, 'gaze_coords.txt'))
# (orientation, gender, image id) for each trial
attribs = np.loadtxt(os.path.join(datapath, 'trial_attrs.txt'))

"""
As a first step we put the coordinate timeseries into a dataset, and labels each
timepoint with its associated trial ID. We also label the two features
accordingly.
"""

raw_ds = Dataset(data[:,:2],
                 sa = {'trial': data[:,2]},
                 fa = {'fid': ['rawX', 'rawY']})

"""
The second step is down-sampling the data to about 33 Hz, resampling each trial
timeseries individually (using the trial ID attribute to define dataset chunks).
"""

ds = fft_resample(raw_ds, 100, window='hann',
                  chunks_attr='trial', attr_strategy='sample')

"""
Now we can use a :class:`~mvpa.mappers.boxcar.BoxcarMapper` to turn each
trial-timeseries into an individual sample. We know that each sample consists
of 100 timepoints. After the dataset is mapped we can add all per-trial
attributes into the sample attribute collection.
"""

bm = BoxcarMapper(np.arange(len(ds.sa['trial'].unique)) * 100,
                  boxlength=100)
bm.train(ds)
ds=ds.get_mapped(bm)

ds.sa.update({'orient': attribs[:,0].astype(int),
              'gender': attribs[:,1].astype(int),
              'img_id': attribs[:,1].astype(int)})

"""
In comparison with upright faces, inverted ones had prominent features at very
different locations on the screen. Most notably, the eyes were flipped to the
bottom half. To prevent the classifier from using such differences, we flip the
Y-coordinates for trials with inverted to align the with the upright condition.
"""

ds.samples[ds.sa.orient == 1, :, 1] = \
        -1 * (ds.samples[ds.sa.orient == 1, :, 1] - 512) + 512

"""
The current dataset has 100 two-dimensional features, the X and Y
coordinate for each of the hundred timepoints. We use a
:class:`~mvpa.mappers.flatten.FlattenMapper` to convert each sample into a
one-dimensionl vector (of length 200). However, we also keep the original
dataset, because it will allow us to perform some plotting much easier.
"""

fm = FlattenMapper()
fm.train(ds)
# want to make a copy to keep the original pristine for later plotting
fds = ds.copy().get_mapped(fm)

# simplify the trial attribute
fds.sa['trial'] = [t[0] for t in ds.sa.trial]

"""
The last steps of preprocessing are Z-scoring all features
(coordinate-timepoints) and dividing the dataset into 8 chunks -- to simplify
a cross-validation analysis.
"""

zscore(fds, chunks_attr=None)

# for classification divide the data into chunks
nchunks =  8
chunks = np.zeros(len(fds), dtype='int')
for o in fds.sa['orient'].unique:
    chunks[fds.sa.orient == o] = np.arange(len(fds.sa.orient == o)) % nchunks
fds.sa['chunks'] = chunks

"""
Now everything is set and we can proceed to the classification analysis. We
are using a support vector machine that is going to be trained on the
``orient`` attribute, indicating trials with upright and inverted faces. We are
going to perform the analysis with a :class:`~mvpa.clfs.meta.SplitClassifier`,
because we are also interested in the temporal sensitivity profile. That one is
easily accessible via the corresponding sensitivity analyzer.
"""


clf = SVM(space='orient')
mclf = SplitClassifier(clf, space='orient',
                       enable_ca=['confusion'])
sensana = mclf.get_sensitivity_analyzer()
sens = sensana(fds)
print mclf.ca.confusion

"""
The 8-fold cross-validation shows a trial-wise classification accuracy of
over 80%. Now we can take a look at the sensitivity. We use the
:class:`~mvpa.mappers.flatten.FlattenMapper` that is stored in the dataset to
unmangle X and Y coordinate vectors in the sensitivity array.
"""

# split mean sensitivities into X and Y coordinate parts by reversing through
# the flatten mapper
xy_sens = fds.a.mapper[-2].reverse(sens).samples

"""
Plotting the results
--------------------

The analysis is done and we can compile a figure to visualize the results.
After some inital preparations, we plot an example image of a face that was
used in this experiment. We align the image coordinates with the original
on-screen coordinates to match them to the gaze track, and overlay the image
with the mean gaze track across all trials for each condition.
"""

# descriptive plots
pl.figure()
# original screen size was
axes = ('x', 'y')
screen_size = np.array((1280, 1024))
screen_center = screen_size / 2
colors = ('r','b')
fig = 1

pl.subplot(2, 2, fig)
pl.title('Mean Gaze Track')
face_img = pl.imread(os.path.join(datapath, 'demo_face.png'))
# determine the extend of the image in original screen coordinates
# to match with gaze position
orig_img_extent=(screen_center[0] - face_img.shape[1]/2,
                 screen_center[0] + face_img.shape[1]/2,
                 screen_center[1] + face_img.shape[0]/2,
                 screen_center[1] - face_img.shape[0]/2)
# show face image and put it with original pixel coordinates
pl.imshow(face_img,
          extent=orig_img_extent,
          cmap=pl.cm.gray)
pl.plot(np.mean(ds.samples[ds.sa.orient == 1,:,0], axis=0),
        np.mean(ds.samples[ds.sa.orient == 1,:,1], axis=0),
        colors[0], label='inverted')
pl.plot(np.mean(ds.samples[ds.sa.orient == 2,:,0], axis=0),
        np.mean(ds.samples[ds.sa.orient == 2,:,1], axis=0),
        colors[1], label='upright')
pl.axis(orig_img_extent)
pl.legend()
fig += 1

"""
The next two subplot contain the gaze coordinate over the peri-stimulus time
for both, X and Y axis respectively.
"""

pl.subplot(2, 2, fig)
pl.title('Gaze Position X-Coordinate')
plot_erp(ds.samples[ds.sa.orient == 1,:,1], pre=0, errtype = 'std',
         color=colors[0], SR=100./3.)
plot_erp(ds.samples[ds.sa.orient == 2,:,1], pre=0, errtype = 'std',
         color=colors[1], SR=100./3.)
pl.ylim(orig_img_extent[2:])
pl.xlabel('Peristimulus Time')
fig += 1

pl.subplot(2, 2, fig)
pl.title('Gaze Position Y-Coordinate')
plot_erp(ds.samples[ds.sa.orient == 1,:,0], pre=0, errtype = 'std',
         color=colors[0], SR=100./3.)
plot_erp(ds.samples[ds.sa.orient == 2,:,0], pre=0, errtype = 'std',
         color=colors[1], SR=100./3.)
pl.ylim(orig_img_extent[:2])
pl.xlabel('Peristimulus Time')
fig += 1

"""
The last panel has the associated sensitivity profile for both coordinate axes.
"""

pl.subplot(2, 2, fig)
pl.title('SVM-Sensitivity Profiles')
lines = plot_err_line(xy_sens[..., 0], linestyle='-', fmt='ko', errtype='std')
lines[0][0].set_label('X')
lines = plot_err_line(xy_sens[..., 1], linestyle='-', fmt='go', errtype='std')
lines[0][0].set_label('Y')
pl.legend()
pl.ylim((-0.1, 0.1))
pl.xlim(0,100)
pl.axhline(y=0, color='0.6', ls='--')
pl.xlabel('Timepoints')

from mvpa.base import cfg
if cfg.getboolean('examples', 'interactive', True):
    # show all the cool figures
    pl.show()

"""
The following figure is not exactly identical to the product of this code, but
rather shows the result of a few minutes of beautifications in Inkscape_.

.. _Inkscape: http://www.inkscape.org/

.. figure:: ../pics/ex_eyemovements.*
   :align: center

   Gaze track for viewing upright vs. inverted faces. The figure shows the mean
   gaze path for both conditions overlayed on an example face. The panels to
   the left and below show the X and Y coordinates over the trial timecourse
   (shaded aread corresponds to one standard deviation across all trials above
   and below the mean). The black curve depicts the associated temporal SVM
   weight profile for the classification of both conditions.

"""
