ashra-academy / pRFsim

A free & open source python package for population receptive field (PRF) simulation

PyPRF

A free & open source python package for population receptive field (PRF) simulation. This package is mainly developed for functional magnetic resonance imaging (fMRI) experiments.

Installation

The prfsim package can directly be installed from PyPI, in the following way:

pip install prfsim

Dependencies

prfsim is implemented in [Python 3.6.4]

If you install pyprf using pip (as described above), all of the following dependencies are installed automatically - you do not have to take care of this yourself. Simply follow the above installation instructions.

pRFsim dependencies	Tested version
NumPy	1.14.0
Pandas	0.22.0
SciPy	1.0.0
Seaborn	0.8.1
Pillow	5.0.0

Contributions

For contributors, we suggest the following procedure:

• Create your own branch (in the web interface, or by git checkout -b new_branch)
  • If you create the branch in the web interface, pull changes to your local repository (git pull)
• Change to new branch: git checkout new_branch
• Make changes
• Commit changes to new branch (git add . and git commit -m)
• Push changes to new branch (git push origin new_branch)
• Create a pull request using the web interface

References

This application is based on the following work:

[1] Dumoulin, S. O. & Wandell, B. A. (2008). Population receptive field estimates in human visual cortex. NeuroImage 39, 647–660.

Support

Please use the github issues for questions or bug reports.

License

The project is licensed under GNU General Public License Version 3.

How to use

import prfsim.sim as psim
import numpy as np

# experiment parameters
radius = 10
precision = 0.1
barWidth = radius / 4
angles = [-90, 45, -180, 315, 90, 225, 0, 135]
nFrames = len(angles)*3
TR = 3.0
TRs = 5 # number of TRs for each frame
duration = nFrames*TRs

x, y = np.mgrid[-radius:radius:precision,
                -radius:radius:precision]
pos = np.dstack((x, y))
length = len(x[0])
nVoxels = 6

# parameters for double gamma distribution function hrf:
n1 = 4
lmbd1 = 2.0
t01 = 0
n2 = 7
lmbd2 = 3
t02 = 0
a = 0.3

t = np.arange(0,nFrames*TRs*TR,TR)
hrf_gen = psim.hrf_double_gamma(t, n1, n2, lmbd1, lmbd2, t01, t02, a)
hrf_est = hrf_gen

stim = psim.generateStim(radius=radius, precision=precision,
                    barWidth=barWidth, angles=angles,
                    nFrames=nFrames, length=length,
		            TR=TR, TRs=TRs, isCheckerboard=False)
print('stimulus generated')


neuronal_responses = psim.getNeuronalResponse(stim=stim, nVoxels=nVoxels,
                                        radius=radius, precision=precision,
                                        duration=duration)
print('Neuronal responses generated')


bolds = psim.generateData(neuronal_responses=neuronal_responses,
                     hrf=hrf_gen,
                     duration=duration, nVoxels=nVoxels)
print('BOLD responses generated')


print('pRF estimations started...')
results = psim.estimateAll(bolds=bolds, stim=stim,
                      hrf=hrf_est, radius=radius,
                      precision=precision,
                      nVoxels=nVoxels, margin = 1)
print('pRF estimation errors generated')