mvpa2.datasets.miscfx.SequenceStats

class mvpa2.datasets.miscfx.SequenceStats(seq, order=2)

Simple helper to provide representation of sequence statistics

Matlab analog: https://cfn.upenn.edu/aguirre/wiki/public:m_sequences_code:mtest.m

WARNING: Experimental – API might change without warning! Current implementation is ugly!

Methods

clear()
copy()
fromkeys(S[,v])	v defaults to None.
get(k[,d])
has_key(k)
items()
iteritems()
iterkeys()
itervalues()
keys()
plot()	Plot correlation coefficients
pop(k[,d])	If key is not found, d is returned if given, otherwise KeyError is raised
popitem()	2-tuple; but raise KeyError if D is empty.
setdefault(k[,d])
update([E, ]**F)	If E present and has a .keys() method, does: for k in E: D[k] = E[k]
values()
viewitems()
viewkeys()
viewvalues()

Initialize SequenceStats

Parameters:

seq : list or ndarray

Actual sequence of targets

order : int

Maximal order of counter-balancing check. For perfect counterbalancing all matrices should be identical

Methods

clear()
copy()
fromkeys(S[,v])	v defaults to None.
get(k[,d])
has_key(k)
items()
iteritems()
iterkeys()
itervalues()
keys()
plot()	Plot correlation coefficients
pop(k[,d])	If key is not found, d is returned if given, otherwise KeyError is raised
popitem()	2-tuple; but raise KeyError if D is empty.
setdefault(k[,d])
update([E, ]**F)	If E present and has a .keys() method, does: for k in E: D[k] = E[k]
values()
viewitems()
viewkeys()
viewvalues()

plot()

Plot correlation coefficients