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:
- Task-related regressors, that result from the convolution of the experimental paradigm regressors with hemodynamic models
- User-specified 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, 2009-2011
Class¶
DesignMatrix
¶
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class
nipy.modalities.fmri.design_matrix.
DesignMatrix
(matrix, names, frametimes=None)¶ This is a container for a light-weight class for design matrices This class is only used to make IO and visualization
Methods
show
([rescale, ax, cmap])Visualization of a design matrix show_contrast
(contrast[, ax, cmap])Plot a contrast for a design matrix. write_csv
(path)write self.matrix as a csv file with appropriate column names -
__init__
(matrix, names, frametimes=None)¶
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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 :
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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 :
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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
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Functions¶
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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 :
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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
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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 low-pass 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 user-supplied regressors
add_reg_names: list of (naddreg) regressor names, optional :
if None, while naddreg>0, these will be termed ‘reg_%i’,i=0..naddreg-1
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 :