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modalities.fmri.protocol

Module: modalities.fmri.protocol

Inheritance diagram for nipy.modalities.fmri.protocol:

Classes

ExperimentalFactor

class nipy.modalities.fmri.protocol.ExperimentalFactor(name, iterator, convolved=False, delta=True, dt=0.02)

Bases: nipy.modalities.fmri.protocol.ExperimentalRegressor, nipy.fixes.scipy.stats.models.formula.Factor

Return a factor that is a function of experimental time based on an iterator. If the delta attribute is False, it is assumed that the iterator returns rows of the form:

type, start, stop

Here, type is a hashable object and start and stop are floats.

If delta is True, then the events are assumed to be delta functions and the rows are assumed to be of the form:

type, start

where the events are (square wave) approximations of a delta function, non zero on [start, start+dt).

Notes

self[key] returns the __UNCONVOLVED__ factor, even if the ExperimentalFactor has been convolved with an HRF.

__init__(name, iterator, convolved=False, delta=True, dt=0.02)
Parameters:
name : TODO

TODO

iterator : TODO

TODO

convolved : bool

TODO

delta : bool

TODO

dt : float

TODO

fromiterator(iterator, delimiter=', ')

Determine an ExperimentalFactor from an iterator

Parameters:
iterator : TODO

TODO

delimiter : string

TODO

Returns:

None

main_effect()

Return the ‘main effect’ for an ExperimentalFactor.

Returns:ExperimentalQuantitative
names(keep=False)
Parameters:
keep : bool

TODO

Returns:

TODO

ExperimentalFormula

class nipy.modalities.fmri.protocol.ExperimentalFormula(termlist, namespace={})

Bases: nipy.fixes.scipy.stats.models.formula.Formula

A formula with no intercept.

__init__(termlist, namespace={})
Create a formula from either:
  1. a formula object
  2. a sequence of term instances
  3. one term
names(keep=False)
Parameters:
keep : bool

TODO

Returns:

TODO

ExperimentalQuantitative

class nipy.modalities.fmri.protocol.ExperimentalQuantitative(name, fn, termname=None, **keywords)

Bases: nipy.modalities.fmri.protocol.ExperimentalRegressor, nipy.fixes.scipy.stats.models.formula.Quantitative

Generate a regressor that is a function of time based on a function fn.

__init__(name, fn, termname=None, **keywords)

ExperimentalRegressor

class nipy.modalities.fmri.protocol.ExperimentalRegressor(convolved=False, namespace={'time': <nipy.modalities.fmri.protocol.ExperimentalQuantitative object at 0x957b4ac>}, termname='term')

Bases: object

__init__(convolved=False, namespace={'time': <nipy.modalities.fmri.protocol.ExperimentalQuantitative object at 0x957b4ac>}, termname='term')
Parameters:
convolved : bool

TODO

namespace : TODO

TODO

termname : string

TODO

convolve(IRF)
Parameters:
IRF : TODO

TODO

Returns:

self

convolved
names()
Returns:TODO

ExperimentalStepFunction

class nipy.modalities.fmri.protocol.ExperimentalStepFunction(name, iterator, **keywords)

Bases: nipy.modalities.fmri.protocol.ExperimentalQuantitative

This returns a step function from an iterator returning tuples

(start, stop, height)

with height defaulting to 1 if not present.

__init__(name, iterator, **keywords)
Parameters:
name : TODO

TODO

iterator : TODO

TODO

keywords : dict

Passed through as the keywords to ExperimentalQuantitative.__init__