A free & open source python package for population receptive field (PRF) simulation. This package is mainly developed for functional magnetic resonance imaging (fMRI) experiments.
The prfsim
package can directly be installed from PyPI, in the following way:
pip install prfsim
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 |
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
)
- If you create the branch in the web interface, pull changes to your local repository (
- Change to new branch:
git checkout new_branch
- Make changes
- Commit changes to new branch (
git add .
andgit commit -m
) - Push changes to new branch (
git push origin new_branch
) - Create a pull request using the web interface
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.
Please use the github issues for questions or bug reports.
The project is licensed under GNU General Public License Version 3.
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')