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(() -> None.  Remove all items from D.)
copy(() -> a shallow copy of D)
fromkeys(...)	v defaults to None.
get((k[,d]) -> D[k] if k in D, ...)
has_key((k) -> True if D has a key k, else False)
items(() -> list of D’s (key, value) pairs, ...)
iteritems(() -> an iterator over the (key, ...)
iterkeys(() -> an iterator over the keys of D)
itervalues(...)
keys(() -> list of D’s keys)
plot()	Plot correlation coefficients
pop((k[,d]) -> v, ...)	If key is not found, d is returned if given, otherwise KeyError is raised
popitem(() -> (k, v), ...)	2-tuple; but raise KeyError if D is empty.
setdefault((k[,d]) -> D.get(k,d), ...)
update(([E, ...)	If E present and has a .keys() method, does: for k in E: D[k] = E[k]
values(() -> list of D’s 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(() -> None.  Remove all items from D.)
copy(() -> a shallow copy of D)
fromkeys(...)	v defaults to None.
get((k[,d]) -> D[k] if k in D, ...)
has_key((k) -> True if D has a key k, else False)
items(() -> list of D’s (key, value) pairs, ...)
iteritems(() -> an iterator over the (key, ...)
iterkeys(() -> an iterator over the keys of D)
itervalues(...)
keys(() -> list of D’s keys)
plot()	Plot correlation coefficients
pop((k[,d]) -> v, ...)	If key is not found, d is returned if given, otherwise KeyError is raised
popitem(() -> (k, v), ...)	2-tuple; but raise KeyError if D is empty.
setdefault((k[,d]) -> D.get(k,d), ...)
update(([E, ...)	If E present and has a .keys() method, does: for k in E: D[k] = E[k]
values(() -> list of D’s values)
viewitems(...)
viewkeys(...)
viewvalues(...)

plot()

Plot correlation coefficients