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)[source]¶ Bases:
objectThis is a container for a light-weight 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.
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write_csv(path)[source]¶ 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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show(rescale=True, ax=None, cmap=None)[source]¶ 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)[source]¶ 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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Functions¶
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nipy.modalities.fmri.design_matrix.dmtx_from_csv(path, frametimes=None)[source]¶ 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)[source]¶ 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)[source]¶ 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