prfsim 0.1.1

A free & open source python tool for population receptive field simulation of fMRI data.

# PyPRF
<img src="logo.png" width=200 align="right" />

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:

```bash
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](http://www.numpy.org/) | 1.14.0 |
| [Pandas](https://pandas.pydata.org/) | 0.22.0 |
| [SciPy](http://www.scipy.org/) | 1.0.0 |
| [Seaborn](https://seaborn.pydata.org/) | 0.8.1 |

## 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](https://github.com/arash-ash/prfsim/issues) for questions or bug reports.

## License
The project is licensed under [GNU General Public License Version 3](http://www.gnu.org/licenses/gpl.html).

## How to use
```bash
# 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 = hrf_double_gamma(t, n1, n2, lmbd1, lmbd2, t01, t02, a)
hrf_est = hrf_gen

pt = time.process_time()
stim = generateStim(radius=radius, precision=precision,
barWidth=barWidth, angles=angles,
nFrames=nFrames, length=length,
TR=TR, TRs=TRs)
elapsed_time = time.process_time() - pt
print('stimulus generated in %0.3f seconds'%elapsed_time)

pt = time.process_time()
neuronal_responses = getNeuronalResponse(stim=stim, nVoxels=nVoxels,
radius=radius, precision=precision,
duration=duration)
elapsed_time = time.process_time() - pt
print('Neuronal responses generated in %0.3f seconds'%elapsed_time)


pt = time.process_time()
bolds = generateData(neuronal_responses=neuronal_responses,
hrf=hrf_gen,
duration=duration, nVoxels=nVoxels)
elapsed_time = time.process_time() - pt
print('BOLD responses generated in %0.3f seconds'%elapsed_time)


pt = time.process_time()
print('pRF estimations started...')
results = estimateAll(bolds=bolds, stim=stim,
hrf=hrf_est, radius=radius,
precision=precision,
nVoxels=nVoxels, margin = 1)
elapsed_time = time.process_time() - pt
print('pRF estimation errors generated in %0.3f seconds'%elapsed_time)
```