ogb

Classes and functions for fitting Gaussian population encoding models

GaussianFit(model, data, grids, bounds, Ns) A class containing tools for fitting the 2D Gaussian pRF model.
GaussianModel(stimulus, hrf_model)

GaussianFit

class popeye.ogb.GaussianFit(model, data, grids, bounds, Ns, voxel_index=(1, 2, 3), auto_fit=True, verbose=0)

Bases: popeye.base.PopulationFit

A class containing tools for fitting the 2D Gaussian pRF model.

__init__(model, data, grids, bounds, Ns, voxel_index=(1, 2, 3), auto_fit=True, verbose=0)

A class containing tools for fitting the 2D Gaussian pRF model.

The GaussianFit class houses all the fitting tool that are associated with estimatinga pRF model. The GaussianFit takes a GaussianModel 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 : GaussianModel class instance
An object representing the 2D 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
baseline()
baseline0()
beta()
beta0()
hemodynamic_response()
hrf0()
hrf_delay()
overloaded_estimate()
receptive_field()
rho()
s0()
sigma()
theta()
x()
x0()
y()
y0()

GaussianModel

class popeye.ogb.GaussianModel(stimulus, hrf_model)

Bases: popeye.base.PopulationModel

__init__(stimulus, hrf_model)

A 2D Gaussian population receptive field model [1].

stimulus : VisualStimulus class object
A class instantiation of the VisualStimulus class containing a representation of the visual stimulus.
hrf_model : callable
A function that generates an HRF model given an HRF delay. For more information, see popeye.utilties.double_gamma_hrf_hrf
[1]Dumoulin SO, Wandell BA. (2008) Population receptive field estimates in human visual cortex. NeuroImage 39, 647-660
generate_ballpark_prediction(x, y, sigma, beta, baseline, hrf_delay)
generate_prediction(x, y, sigma, beta, baseline, hrf_delay)

PopulationFit

class popeye.ogb.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.ogb.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()

auto_attr

popeye.ogb.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

fftconvolve

popeye.ogb.fftconvolve(in1, in2, mode='full')

Convolve two N-dimensional arrays using FFT.

Convolve in1 and in2 using the fast Fourier transform method, with the output size determined by the mode argument.

This is generally much faster than convolve for large arrays (n > ~500), but can be slower when only a few output values are needed, and can only output float arrays (int or object array inputs will be cast to float).

in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as in1; if sizes of in1 and in2 are not equal then in1 has to be the larger array.
mode : str {‘full’, ‘valid’, ‘same’}, optional

A string indicating the size of the output:

full
The output is the full discrete linear convolution of the inputs. (Default)
valid
The output consists only of those elements that do not rely on the zero-padding.
same
The output is the same size as in1, centered with respect to the ‘full’ output.
out : array
An N-dimensional array containing a subset of the discrete linear convolution of in1 with in2.

generate_og_receptive_field

popeye.ogb.generate_og_receptive_field()

Generate a Gaussian.

x : float
The x coordinate of the center of the Gaussian (degrees)
y : float
The y coordinate of the center of the Gaussian (degrees)
s : float
The dispersion of the Gaussian (degrees)
beta : float
The amplitude of the Gaussian
deg_x : 2D array
The coordinate matrix along the horizontal dimension of the display (degrees)
deg_y : 2D array
The coordinate matrix along the vertical dimension of the display (degrees)

Returns

stim : ndarray
The 1D array containing the stimulus energies given the Gaussian coordinates

generate_rf_timeseries_nomask

popeye.ogb.generate_rf_timeseries_nomask()