modalities.fmri.design_matrix¶
Module: modalities.fmri.design_matrix
¶
Inheritance diagram for nipy.modalities.fmri.design_matrix
:
This module implements fMRI Design Matrix creation.
The DesignMatrix object is just a container that represents the design matrix. Computations of the different parts of the design matrix are confined to the make_dmtx() function, that instantiates the DesignMatrix object. All the remainder are just ancillary functions.
Design matrices contain three different types of regressors:
 Taskrelated regressors, that result from the convolution of the experimental paradigm regressors with hemodynamic models
 Userspecified regressors, that represent information available on the data, e.g. motion parameters, physiological data resampled at the acquisition rate, or sinusoidal regressors that model the signal at a frequency of interest.
 Drift regressors, that represent low_frequency phenomena of no interest in the data; they need to be included to reduce variance estimates.
Author: Bertrand Thirion, 20092011
Class¶
DesignMatrix
¶

class
nipy.modalities.fmri.design_matrix.
DesignMatrix
(matrix, names, frametimes=None)¶ This is a container for a lightweight class for design matrices
This class is only used to make IO and visualization.
Attributes
matrix: array of shape (n_scans, n_regressors) the numerical specification of the matrix. names: list of len (n_regressors) the names associated with the columns. frametimes: array of shape (n_scans), optional the occurrence time of the matrix rows. 
__init__
(matrix, names, frametimes=None)¶

show
(rescale=True, ax=None, cmap=None)¶ Visualization of a design matrix
Parameters: rescale: bool, optional
rescale columns magnitude for visualization or not.
ax: axis handle, optional
Handle to axis onto which we will draw design matrix.
cmap: colormap, optional
Matplotlib colormap to use, passed to imshow.
Returns: ax: axis handle

show_contrast
(contrast, ax=None, cmap=None)¶ Plot a contrast for a design matrix.
Parameters: contrast : np.float
Array forming contrast with respect to the design matrix.
ax: axis handle, optional
Handle to axis onto which we will draw design matrix.
cmap: colormap, optional
Matplotlib colormap to use, passed to imshow.
Returns: ax: axis handle

write_csv
(path)¶ write self.matrix as a csv file with appropriate column names
Parameters: path: string, path of the resulting csv file Notes
The frametimes are not written

Functions¶

nipy.modalities.fmri.design_matrix.
dmtx_from_csv
(path, frametimes=None)¶ Return a DesignMatrix instance from a csv file
Parameters: path: string, path of the .csv file Returns: A DesignMatrix instance

nipy.modalities.fmri.design_matrix.
dmtx_light
(frametimes, paradigm=None, hrf_model='canonical', drift_model='cosine', hfcut=128, drift_order=1, fir_delays=[0], add_regs=None, add_reg_names=None, min_onset=24, path=None)¶ Make a design matrix while avoiding framework
Parameters: see make_dmtx, plus
path: string, optional: a path to write the output
Returns: dmtx array of shape(nreg, nbframes):
the sampled design matrix
names list of strings of len (nreg)
the names of the columns of the design matrix

nipy.modalities.fmri.design_matrix.
make_dmtx
(frametimes, paradigm=None, hrf_model='canonical', drift_model='cosine', hfcut=128, drift_order=1, fir_delays=[0], add_regs=None, add_reg_names=None, min_onset=24)¶ Generate a design matrix from the input parameters
Parameters: frametimes: array of shape(nbframes), the timing of the scans
paradigm: Paradigm instance, optional
description of the experimental paradigm
hrf_model: string, optional,
that specifies the hemodynamic response function. Can be one of {‘canonical’, ‘canonical with derivative’, ‘fir’, ‘spm’, ‘spm_time’, ‘spm_time_dispersion’}.
drift_model: string, optional
specifies the desired drift model, to be chosen among ‘polynomial’, ‘cosine’, ‘blank’
hfcut: float, optional
cut period of the lowpass filter
drift_order: int, optional
order of the drift model (in case it is polynomial)
fir_delays: array of shape(nb_onsets) or list, optional,
in case of FIR design, yields the array of delays used in the FIR model
add_regs: array of shape(nbframes, naddreg), optional
additional usersupplied regressors
add_reg_names: list of (naddreg) regressor names, optional
if None, while naddreg>0, these will be termed ‘reg_%i’,i=0..naddreg1
min_onset: float, optional
minimal onset relative to frametimes[0] (in seconds) events that start before frametimes[0] + min_onset are not considered
Returns: DesignMatrix instance