Metadata-Version: 1.0
Name: hrf_estimation
Version: 0.2
Summary: A module for estimating Hemodynamical Response Function from functional MRI data
Home-page: https://pypi.python.org/pypi/hrf_estimation
Author: Fabian Pedregosa
Author-email: fabian@fseoane.net
License: Simplified BSD
Description: Hemodynamic Response Function estimation from functional MRI data
        =================================================================
        
        This describes 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 (submitted)
        
        Get the code
        ------------
        
        hrf_estimation is a Python package. It can be installed through the Python Package Index (PYPI):
        
        .. code:: bash
        
           pip install -U hrf_estimation
        
        You can also download the source code from the `PYPI website <https://pypi.python.org/pypi/hrf_estimation>`_
        
        Function reference
        ------------------
        
        The principal function is rank_one
        
        .. code:: python
        
           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
            """
        
        
        Examples
        --------
        
        `This IPython notebook
        <http://nbviewer.ipython.org/url/raw.github.com/fabianp/hrf_estimation/master/doc/figures_prni2013_gallant.ipynb>`_
        contains code that reproduces the figures from the original article.
        Development
        
        The newest version can alway be grabbed from the `git repository
        <http://github.com/fabianp/hrf_estimation>`_. Feel free to submit
        patches, issues or implementations for other languages!.
        
        TODO: provide fallback for einsum
        
        Authors
        -------
        
        `Fabian Pedregosa <http://fseoane.net>`_ <fabian@fseoane.net>
        Michael Eickenberg <michael.eickenberg@nsup.org>
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
