base

Base-classes for poulation encoding models and fits.

PopulationFit(model, data, grids, bounds, ...) Base class for all pRF model fits.
PopulationModel(stimulus, hrf_model[, nuissance]) Base class for all pRF models.
StimulusModel(stim_arr[, dtype, tr_length])
set_verbose(verbose) A convenience function for setting the verbosity of a popeye model+fit.

PopulationFit

class popeye.base.PopulationFit(model, data, grids, bounds, Ns, voxel_index, auto_fit, verbose)

Bases: object

Base class for all pRF model fits.

__init__(model, data, grids, bounds, Ns, voxel_index, auto_fit, verbose)

A class containing tools for fitting pRF models.

The PopulationFit class houses all the fitting tool that are associated with estimatinga pRF model. The PopulationFit takes a PoulationModel instance model and a time-series data. In addition, extent and sampling-rate of a brute-force grid-search is set with grids and Ns. Use bounds to set limits on the search space for each parameter.

model : AuditoryModel class instance
An object representing the 1D Gaussian model.
data : ndarray
An array containing the measured BOLD signal of a single voxel.
grids : tuple

A tuple indicating the search space for the brute-force grid-search. The tuple contains pairs of upper and lower bounds for exploring a given dimension. For example grids=((-10,10),(0,5),) will search the first dimension from -10 to 10 and the second from 0 to 5. These values cannot be None.

For more information, see scipy.optimize.brute.

bounds : tuple
A tuple containing the upper and lower bounds for each parameter in parameters. If a parameter is not bounded, simply use None. For example, fit_bounds=((0,None),(-10,10),) would bound the first parameter to be any positive number while the second parameter would be bounded between -10 and 10.
Ns : int

Number of samples per stimulus dimension to sample during the ballpark search.

For more information, see scipy.optimize.brute.

voxel_index : tuple
A tuple containing the index of the voxel being modeled. The fitting procedure does not require a voxel index, but collating the results across many voxels will does require voxel indices. With voxel indices, the brain volume can be reconstructed using the newly computed model estimates.
auto_fit : bool
A flag for automatically running the fitting procedures once the GaussianFit object is instantiated.
verbose : int
0 = silent 1 = print the final solution of an error-minimization 2 = print each error-minimization step
Jout()
allvecs()
ballpark()
brute_force()
direc()
estimate()
fopt()
funcalls()
fval()
gradient_descent()
grid()
iter()
msg()
overloaded_estimate()
prediction()
rsquared()
rss()

PopulationModel

class popeye.base.PopulationModel(stimulus, hrf_model, nuissance=None)

Bases: object

Base class for all pRF models.

__init__(stimulus, hrf_model, nuissance=None)

Base class for all pRF models.

stimulus : StimulusModel class instance
An instance of the StimulusModel class containing the stim_arr and various stimulus parameters, and can represent various stimulus modalities, including visual, auditory, etc.
hrf_model : callable
A function that generates an HRF model given an HRF delay. For more information, see popeye.utilties.double_gamma_hrf_hrf and `popeye.utilities.spm_hrf
nuissance : ndarray
A nuissance regressor for removing effects of non-interest. You can regress out any nuissance effects from you data prior to fitting the model of interest. The nuissance model is a statsmodels.OLS compatible design matrix, and the user is expected to have already added any constants.
generate_ballpark_prediction()
generate_prediction()

StimulusModel

class popeye.base.StimulusModel(stim_arr, dtype=<class 'ctypes.c_short'>, tr_length=1.0)

Bases: object

__init__(stim_arr, dtype=<class 'ctypes.c_short'>, tr_length=1.0)

A base class for all encoding stimuli.

This class houses the basic and common features of the encoding stimulus, which along with a PopulationModel constitutes what is commonly referred to as the pRF model.

stim_arr : ndarray
An array containing the stimulus. The dimensionality of the data is arbitrary but must be consistent with the pRF model, as specified in PopulationModel and PopluationFit class instances.
dtype : string
Sets the data type the stimulus array is cast into.
dtype : string
Sets the data type the stimulus array is cast into.
tr_length : float
The repetition time (TR) in seconds.

auto_attr

popeye.base.auto_attr(func)

Decorator to create OneTimeProperty attributes.

func : method
The method that will be called the first time to compute a value. Afterwards, the method’s name will be a standard attribute holding the value of this computation.
>>> class MagicProp(object):
...     @auto_attr
...     def a(self):
...         return 99
...
>>> x = MagicProp()
>>> 'a' in x.__dict__
False
>>> x.a
99
>>> 'a' in x.__dict__
True

set_verbose

popeye.base.set_verbose(verbose)

A convenience function for setting the verbosity of a popeye model+fit.

verbose : int
0 = silent 1 = print the final solution of an error-minimization 2 = print each error-minimization step