Initial commit

- update of the README file

LICENSE

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+Copyright (c) 2013, Matthieu Berjon and others.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+    * Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in the
+      documentation and/or other materials provided with the distribution.
+    * Neither the name of the <organization> nor the
+      names of its contributors may be used to endorse or promote products
+      derived from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL <COPYRIGHT HOLDER> BE LIABLE FOR ANY
+DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

README.md

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+# MbOct
+
+This project has been developed within the Action and Perception group of the school of
+Psychology at Cardiff University. A new audiovisual lab has been created and needed a bit
+of code in order to be analyse the data provided by the measurements and simulations. 
+[Octave][1] has been chosen for several reasons:
+
+* Open source software
+* Good compatibility with Matlab (used inside the Lab)
+* Good toolboxes for signal processing
+
+Please, download it, study it, improve it and share it!
+
+## Installation
+
+Copy the files in your home folder ~/ and edit the ~/Octave file by adding the following line:
+
+~~~.{bash}
+addpath('<directory/where/are/stored/the/files')
+~~~
+
+## License
+
+Please refer to the LICENSE file at the root of the project.
+
+## Contributing
+
+We’re really happy to accept contributions from the community, that’s the main reason why 
+we open-sourced it! There are many ways to contribute, even if you’re not a technical person.
+
+We’re using the infamous [simplified Github workflow][2] to accept modifications (even internally), 
+basically you’ll have to:
+
+* create an issue related to the problem you want to fix (good for traceability and cross-reference)
+* fork the repository
+* create a branch (optionally with the reference to the issue in the name)
+* hack hack hack
+* commit incrementally with readable and detailed commit messages
+* submit a pull-request against the master branch of this repository
+
+We’ll take care of tagging your issue with the appropriated labels and answer within a week 
+(hopefully less!) to the problem you encounter.
+
+If you’re not familiar with open-source workflows or our set of technologies, do not hesitate to ask 
+for help! We can mentor you or propose good first bugs (as labeled in our issues). Also welcome to 
+add your name to Credits section of this document.
+
+
+### Submitting bugs
+
+You can report issues the issue tracker of the [project][4], that would be a really useful contribution given that we lack 
+some user testing on the project. Please document as much as possible the steps to reproduce your problem 
+(even better with screenshots).
+
+
+## Credits
+
+* [Matthieu Berjon][1]
+* All the contributors of Pure Data
+
+
+
+[1]: https://www.gnu.org/software/octave/
+[2]: http://scottchacon.com/2011/08/31/github-flow.html
+[3]: https://berjon.net/matt
+[4]: https://berjon.net/projects/mboct

VERSION

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+1.0.0

analysis/ComputePsychFunc.m

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+function [param good_ans_perc] = ComputePsychFunc(data, answer_comp)
+% function [param good_ans_perc] = ComputePsychFunc(data, answer_comp)
+% compute the psychometric function for one parameter based on a 2AFC. The
+% parameters has to be the first the first column, the answer on the second
+% column.
+
+  [b, id] = sort(data(:, 1));
+  data_sort = data(id, :);
+
+  % for each speed, we count the number of trials
+  param = unique(data_sort(:, 1));
+  for a = 1:length(param)
+    nb_trials_per_param = length(find(data_sort(:, 1) == param(a)));
+    ids = find(data_sort(:, 1) == param(a));
+    good_ans_perc(a) = length(find(data_sort(ids, 2) == answer_comp)) ...
+      * 100 / nb_trials_per_param;
+  end
+
+%  plot(speeds, right_left_ans_perc)
+end % end function

analysis/LauraPlot.m

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+function [fighandle p50] = LauraPlot (filename)
+% function [fighandle p50] = LauraPlot (filename)
+%
+% Plot the psychometric function for the intensity experiment
+%
+% Input Arguments
+%
+% **  filename [string]
+%     filename of the .dat file required for the analysis. In order to get 
+%     everything working with this function the filename must follow this
+%     rule: 
+%       * participant initials (2 letters), 
+%       * condition (2 numbers),
+%       * session (2 letters).
+%
+%     example: matthieu berjon, 55dB, session 1
+%               mb_55_s1.dat
+%     example: matthieu berjon, 40, pilot session
+%               mb_40_ps.dat
+%
+%
+% Output arguments
+%
+%     p50 [scalar]
+%     Point of subjective equality.
+%
+%     fighandle [scalar]
+%     Handle to the figure
+%   
+%
+% More information
+%
+% The file contains on each line the following data
+% test speed; standard intensity; direction; presentation; answer
+%
+% test speed: degree/sec
+% standard intensity: decibels
+% direction: 0 = left to right; 1 = right to left
+% presentation: 0 = standard then test; 1 = test then standard
+% answer: 0 = first goes faster; 1 = second goes faster
+%
+% We want to display on x (the speed) and on y (the test appears faster)
+%
+%
+%
+% KNOWN BUGS
+% 
+% No known bugs
+%
+%
+% TODO
+%
+% No todo
+%
+%
+% modified: 08 Feb 2013
+% author: matthieu berjon <matthieu.berjon@wavefield.fr>
+% licence: BSD
+
+
+% depending on the Operating system the path where are stored the data will be
+% different
+if IsOs('WIN')
+  pathname = 'D:\phd\data\intensity\orig';
+else
+  pathname = '/home/mattberjon/phd/data/intensity/orig';
+end
+
+% we get information according to the name file
+participant = filename(1:2);
+intensity = filename(4:5);
+session = filename(7:8);
+
+% data loading
+data = load ([pathname '/' filename]);
+
+% store the data in a new variable
+data_cur = data;
+
+% speed and answers ID related to the current data format
+SPD = 1;
+RSP = 5;
+
+% speeds and answers in new variables
+speeds_cur = data_cur(:, SPD);
+answers_cur = data_cur(:, RSP);
+
+% extraction of the range of speeds used
+speeds_u = unique (speeds_cur);
+
+% some trials have the presentation inversed we need to inverse the answer in 
+% order to get all trials as standard then test presentation and inverse the 
+% related answers
+pres_id = find (data_cur(:, 4) == 1);
+answers_cur(pres_id) = ~answers_cur(pres_id);
+
+% Now we compute the percentage of good answers for each test conditions
+for a = 1:length (speeds_u)
+  % we look for trials corresponding to the given test value
+  id = find (speeds_cur == speeds_u(a));
+  % we sum the answers (because it only ones or zeros
+  r = sum (answers_cur(id));
+  % counting of the number of trials for that condition
+  n(a) = length (id);
+  % percentage of the given condition
+  pc(a) = r / n(a) * 100;
+end
+
+% Fitting of the points through a Probit analysis
+[fit_signal prob_perc p50] = ProbitFit (speeds_u', pc, n);
+
+% some parameters for plotting such as color, marker for the points and the
+% size of the labels
+color = [27 158 119] / 255;
+marker = ['o'];
+label_size = 18;
+text_size = 14;
+% to place some information correctly we need to convert some units in 
+% percentages
+speeds_range = max (speeds_u) - min (speeds_u);
+margin = 2 * speeds_range / 100;
+% plot infos
+infos = {['participant: ' participant]; ...
+        ['session: ' session]; ...
+        ['condition: ' intensity]; ...
+        ['P50: ' num2str(p50)]};
+
+% creation of new figure
+fighandle = figure();
+% points representing the percentages
+plot (speeds_u, pc, marker, 'MarkerFaceColor', color, 'MarkerEdgeColor', color)
+% hold on let us to draw several things on the same figure (otherwise it will 
+% erase what we drawn before and plot the new result
+hold on
+% psychometric fit
+plot (fit_signal, prob_perc, 'Color', color, 'LineWidth', 2)
+% line representing the p50 along the X axis
+plot ([min(speeds_u) max(speeds_u)], [50 50], 'k', 'LineStyle', '-.')
+% line representing the p50 along the Y axis
+plot ([p50 p50], [0 50], 'Color', color, 'LineStyle', '--')
+% information about the participant, session and p50
+text ((min(speeds_u)+margin), 80, infos, 'FontSize', text_size)
+% label on X
+xlabel ('test speeds (deg/s)', 'FontSize', label_size)
+% label on Y
+axis ([min(speeds_u) max(speeds_u) 0 100])
+ylabel ('test perceived as fastest (%)', 'FontSize', label_size)
+set (gca, 'YTick', [0 50 100])
+end