Merge pull request #6 from ccnmaastricht/version2

Version2

Author: MSenden

.gitignore

@@ -5,3 +5,5 @@ model/
 VF2Cort.m
 *pipeline*
 *workspace*
+*cache*
+*pyc

Computational Neuroimaging Toolbox.mltbx

@@ -1,178 +1,47 @@
 # Computational Neuroimaging Toolbox
 
-![](https://img.shields.io/badge/release-v1.2.0-blueviolet.svg?style=flat)
 ![](https://img.shields.io/github/license/ccnmaastricht/CNI_toolbox)
 ![](https://img.shields.io/github/issues/ccnmaastricht/CNI_toolbox)
 ![](https://img.shields.io/github/forks/ccnmaastricht/CNI_toolbox)
 ![](https://img.shields.io/github/stars/ccnmaastricht/CNI_toolbox)
 
-The Computational Neuroimaging Toolbox is a MATLAB toolbox for estimating input-referred models. Specifically, the toolbox contains tools for Fourier analyses of phase-encoded stimuli, population receptive field mapping, estimating parameters of generic (user-defined) input-referred models as well as performing ridge regression.
+The Computational Neuroimaging Toolbox contains tools for estimating input-referred models. Specifically, it includes tools for Fourier analyses of phase-encoded stimuli, population receptive field (pRF) mapping, fast (real-time) pRF mapping and for estimating parameters of generic (user-defined) input-referred models.
 
 This code is hosted at https://github.com/ccnmaastricht/CNI_toolbox
 The latest version may always be found here.
 
+## Matlab implementation
+![](https://img.shields.io/badge/release-v2.0.0-blueviolet.svg?style=flat)
+
 This software was developed with MATLAB R2017a and access to the full suite of MATLAB add-on packages. Some of these packages may be required to run the software.
 
-## Installation
-There are two options for installing the toolbox. Either download the toolbox file [Computational Neuroimaging Toolbox.mltbx](https://github.com/ccnmaastricht/CNI_toolbox/raw/master/Computational%20Neuroimaging%20Toolbox.mltbx), navigate to the downloaded file within MATLAB and then execute the following command:
+### Installation
+Download the toolbox file [Computational Neuroimaging Toolbox.mltbx](https://github.com/ccnmaastricht/CNI_toolbox/raw/master/Computational%20Neuroimaging%20Toolbox.mltbx), navigate to the downloaded file within MATLAB and then execute the following command:
 
 ```MATLAB
 matlab.addons.toolbox.installToolbox('Computational Neuroimaging Toolbox.mltbx');
 ```
+### Usage
 
-Alternatively, download the compressed toolbox [Computational Neuroimaging Toolbox.zip](https://github.com/ccnmaastricht/CNI_toolbox/raw/master/Computational%20Neuroimaging%20Toolbox.zip) and extract it into Documents/MATLAB/Add-Ons/Toolboxes.
-
-## Files
-This repository contains four files.
-1. `PEA.m` - MATLAB class implementation of Fourier analysis of phase-encoded stimuli.
-2. `pRF.m` - MATLAB class implementation of population receptive field mapping.
-3. `IRM.m` - MATLAB class implementation of input-referred model estimation.
-4. `RRT.m` - MATLAB class implementation of voxel-wise ridge regression.
-
-
-### Phase-encoding analysis tool.
-`pea = PEA(parameters)` creates an instance of the PEA class.
-parameters is a structure with 7 required fields
-- f_sampling: sampling frequency (1/TR)
-- f_stim    : stimulation frequency
-- n_samples : number of samples (volumes)
-- n_rows    : number of rows (in-plane resolution)
-- n_cols    : number of columns (in-plance resolution)
-- n_slices  : number of slices
+* [Data Handling](https://github.com/ccnmaastricht/CNI_toolbox/wiki/Data-Handling-(Matlab))
+* [Phase Encoding Analysis](https://github.com/ccnmaastricht/CNI_toolbox/wiki/Phase-Encoding-Analysis-(Matlab))
+* [Population Receptive Field Mapping](https://github.com/ccnmaastricht/CNI_toolbox/wiki/Population-Receptive-Field-Mapping-(Matlab))
+* [Input Referred Models](https://github.com/ccnmaastricht/CNI_toolbox/wiki/Input-Referred-Models-(Matlab))
+* [Fast & real-time pRF mapping](https://github.com/ccnmaastricht/CNI_toolbox/wiki/Fast-&-real-time-pRF-mapping-(Matlab))
 
-This class has the following functions
+## Python implementation
+![](https://img.shields.io/badge/release-v1.0.0-blueviolet.svg?style=flat)
 
-- `delay = PEA.get_delay();`
-- `direction = PEA.get_direction();`
-- `PEA.set_delay(delay);`
-- `PEA.set_direction(direction);`
-- `results = PEA.fitting(data);`
+### Installation
 
-Use `help PEA.function` to get more detailed help on any specific
-function (e.g. `help PEA.fitting`)
-
-typical workflow:
-```Matlab
-pea = PEA(parameters);
-pea.set_delay(delay);
-pea.set_direction(direction);
-results = pea.fitting(data);
+```Python
+pip install cni-tlbx
 ```
 
-### Population receptive field (pRF) mapping tool.
-`prf = pRF(parameters)` creates an instance of the pRF class.
-parameters is a structure with 7 required fields
-  - f_sampling: sampling frequency (1/TR)
-  - n_samples : number of samples (volumes)
-  - n_rows    : number of rows (in-plane resolution)
-  - n_cols    : number of columns (in-plance resolution)
-  - n_slices  : number of slices
-  - w_stimulus: width of stimulus images in pixels
-  - h_stimulus: height of stimulus images in pixels
-
-optional inputs are
-  - hrf       : either a column vector containing a single hemodynamic
-                response used for every voxel;
-                or a matrix with a unique hemodynamic response along
-                its columns for each voxel.
-                By default the canonical two-gamma hemodynamic response
-                function is generated internally based on the scan parameters.
-
-This class has the following functions
-
-  - `hrf = pRF.get_hrf();`
-  - `stimulus = pRF.get_stimulus();`
-  - `tc = pRF.get_timecourses();`
-  - `pRF.set_hrf(hrf);`
-  - `pRF.set_stimulus(stimulus);`
-  - `pRF.import_stimulus();`
-  - `pRF.create_timecourses();`
-  - `results = pRF.mapping(data);`
-
-Use `help pRF.function` to get more detailed help on any specific function
-(e.g. `help pRF.mapping`)
-
-typical workflow:
-```Matlab
-prf = pRF(parameters);
-prf.import_stimulus();
-prf.create_timecourses();
-results = prf.mapping(data);
-```
-
-### Input-referred model (IRM) mapping tool.
-
-`irm = IRM(parameters)` creates an instance of the IRM class.
-parameters is a structure with 5 required fields
-  - f_sampling: sampling frequency (1/TR)
-  - n_samples : number of samples (volumes)
-  - n_rows    : number of rows (in-plane resolution)
-  - n_cols    : number of columns (in-plance resolution)
-  - n_slices  : number of slices
-
-optional inputs are
-  - hrf       : either a column vector containing a single hemodynamic
-                response used for every voxel;
-                or a matrix with a unique hemodynamic response along
-                its columns for each voxel.
-                By default the canonical two-gamma hemodynamic response
-                function is generated internally based on the scan parameters.
-
-This class has the following functions
-
-  - `hrf = IRM.get_hrf();`
-  - `stimulus = IRM.get_stimulus();`
-  - `tc = IRM.get_timecourses();`
-  - `IRM.set_hrf(hrf);`
-  - `IRM.set_stimulus(stimulus);`
-  - `IRM.create_timecourses();`
-  - `results = IRM.mapping(data);`
-
-Use `help IRM.function` to get more detailed help on any specific function
-(e.g. `help IRM.mapping`)
-
-typical workflow:
-```Matlab
-irm = IRM(parameters);
-irm.set_stimulus();
-irm.create_timecourse(FUN,xdata);
-results = irm.mapping(data);
-```
-
-### Ridge-based analysis tool.
-
-`rrt = RRT(parameters)` creates an instance of the RRT class.
-parameters is a structure with 5 required fields
-  - f_sampling: sampling frequency (1/TR)
-  - n_samples : number of samples (volumes)
-  - n_rows    : number of rows (in-plane resolution)
-  - n_cols    : number of columns (in-plance resolution)
-  - n_slices  : number of slices
-
-optional inputs are
-  - hrf       : either a column vector containing a single hemodynamic
-                response used for every voxel;
-                or a matrix with a unique hemodynamic response along
-                its columns for each voxel.
-                By default the canonical two-gamma hemodynamic response
-                function is generated internally based on the scan parameters.
-
-This class has the following functions
-
-  - `hrf = RRT.get_hrf();`
-  - `X = RRT.get_design();`
-  - `RRT.set_hrf(hrf);`
-  - `RRT.set_design(X);`
-  - `RRT.optimize_lambda(data,range);`
-  - `results = RRT.perform_ridge(data);`
-
-Use `help RRT.function` to get more detailed help on any specific
-function (e.g. `help RRT.perform_ridge`)
+### Usage
 
-typical workflow:
-```Matlab
-rrt = RRT(parameters);
-rrt.set_design(X);
-rrt.optimize_lambda(data,range);
-results = rrt.perform_ridge(data);
-```
+* [Data Handling](https://github.com/ccnmaastricht/CNI_toolbox/wiki/Data-Handling-(Python))
+* [Phase Encoding Analysis](https://github.com/ccnmaastricht/CNI_toolbox/wiki/Phase-Encoding-Analysis-(Python))
+* [Population Receptive Field Mapping](https://github.com/ccnmaastricht/CNI_toolbox/wiki/Population-Receptive-Field-Mapping-(Python))
+* [Input Referred Models](https://github.com/ccnmaastricht/CNI_toolbox/wiki/Input-Referred-Models-(Python))
+* [Fast & real-time pRF mapping](https://github.com/ccnmaastricht/CNI_toolbox/wiki/Fast-&-real-time-pRF-mapping-(Python))