This is a Python package that implements the routines described in the paper
"HRF estimation improves sensitivity of fMRI encoding and decoding models", Fabian Pedregosa, Michael Eickenberg, Bertrand Thirion and Alexandre Gramfort, [PDF] [URL]
hrf_estimation is a pure Python package and can be installed through the Python Package Index (PYPI):
pip install -U hrf_estimationYou can also download the source code from the PYPI website or get the latest sources from github
The principal function is rank_one
def rank_one(X, Y, alpha, size_u, u0=None, v0=None, Z=None, rtol=1e-6, verbose=False, maxiter=1000):
"""
multi-target rank one model
||y - X vec(u v.T) - Z w||^2 + alpha * ||u - u_0||^2
Parameters
----------
X : array-like, sparse matrix or LinearOperator, shape (n, p)
The design matrix
Y : array-lime, shape (n, k)
Time-series vector. Several time-series vectors can be given at once,
however for large system becomes unstable. We do not recommend
using more than k > 100.
size_u : integer
Must be divisor of p
u0 : array
Z : array, sparse matrix or LinearOperator, shape (n, q)
Represents the drift vectors.
rtol : float
Relative tolerance
maxiter : int
maximum number of iterations
verbose : boolean
Returns
-------
U : array, shape (size_u, k)
V : array, shape (p / size_u, k)
W : coefficients associated to the drift vectors
"""This IPython notebook contains code that reproduces the figures from the original article. Development
The newest version can alway be grabbed from the git repository. Feel free to submit issues or patches.
Fabian Pedregosa <f@bianp.net>
Michael Eickenberg <michael.eickenberg@nsup.org>