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MSendenMSenden
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changed naming of pRF parameters in pRF tool: X -> mu_x, Y -> mu_y, Sigma -> sigma, R -> corr_fit
1 parent 0c1a533 commit ed27902

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3 files changed

+25
-24
lines changed

3 files changed

+25
-24
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‎Computational Neuroimaging Toolbox.mltbx

4.66 KB
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‎Computational Neuroimaging Toolbox.prj

Lines changed: 7 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -3,17 +3,17 @@
33
<param.appname>Computational Neuroimaging Toolbox</param.appname>
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<param.authnamewatermark>Mario Senden</param.authnamewatermark>
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<param.email>mario.senden@maastrichtuniversity.nl</param.email>
6-
<param.company />
6+
<param.company>Maastricht University</param.company>
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<param.summary />
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<param.description>Toolbox for estimating input-referred models containing 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.</param.description>
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<param.screenshot>${PROJECT_ROOT}/icon.png</param.screenshot>
10-
<param.version>1.01</param.version>
10+
<param.version>1.1</param.version>
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<param.output>${PROJECT_ROOT}/Computational Neuroimaging Toolbox.mltbx</param.output>
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<param.products.name />
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<param.products.id />
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<param.products.version />
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<param.platforms />
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<param.guid>64d4d129-6dae-4028-ada3-d89a5c3cc8b3</param.guid>
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<param.guid>45386ee3-9879-4212-9d25-81d0d56faf46</param.guid>
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<param.exclude.filters />
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<param.exclude.pcodedmfiles>true</param.exclude.pcodedmfiles>
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<param.examples />
@@ -42,6 +42,8 @@
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<param.additional.sw.mac.url />
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<param.additional.sw.linux.url />
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<unset>
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<param.authnamewatermark />
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<param.email />
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<param.company />
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<param.summary />
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<param.output />
@@ -63,8 +65,6 @@
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<param.required.addons />
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<param.matlab.project.id />
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<param.matlab.project.name />
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<param.release.start />
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<param.release.end />
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<param.release.current.only />
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<param.compatiblity.windows />
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<param.compatiblity.macos />
@@ -85,6 +85,7 @@
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<file>${PROJECT_ROOT}/code/PEA.m</file>
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<file>${PROJECT_ROOT}/code/RRT.m</file>
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<file>${PROJECT_ROOT}/code/pRF.m</file>
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<file>${PROJECT_ROOT}/code/pRF.m~</file>
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</fileset.rootfiles>
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<fileset.depfun.included />
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<fileset.depfun.excluded />
@@ -137,7 +138,7 @@
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<vista>false</vista>
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<linux>true</linux>
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<solaris>false</solaris>
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<osver>4.15.0-52-generic</osver>
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<osver>4.15.0-88-generic</osver>
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<os32>false</os32>
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<os64>true</os64>
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<arch>glnxa64</arch>

‎code/pRF.m

Lines changed: 18 additions & 18 deletions
Original file line numberDiff line numberDiff line change
@@ -338,10 +338,10 @@ function create_timecourses(self,varargin)
338338
data = zscore(data);
339339
mag_d = sqrt(sum(data.^2));
340340

341-
results.R = zeros(self.n_total,1);
342-
results.X = zeros(self.n_total,1);
343-
results.Y = zeros(self.n_total,1);
344-
results.Sigma = zeros(self.n_total,1);
341+
results.corr_fit = zeros(self.n_total,1);
342+
results.mu_x = zeros(self.n_total,1);
343+
results.mu_y = zeros(self.n_total,1);
344+
results.sigma = zeros(self.n_total,1);
345345

346346
if size(self.hrf,2)==1
347347
hrf_fft = fft(repmat([self.hrf;...
@@ -355,12 +355,12 @@ function create_timecourses(self,varargin)
355355
(mag_tc*mag_d(v));
356356
id = isinf(CS) | isnan(CS);
357357
CS(id) = 0;
358-
[results.R(v),j] = max(CS);
359-
results.X(v) = cos(self.pa(self.idx(j,1))) * ...
358+
[results.corr_fit(v),j] = max(CS);
359+
results.mu_x(v) = cos(self.pa(self.idx(j,1))) * ...
360360
self.ecc(self.idx(j,2));
361-
results.Y(v) = sin(self.pa(self.idx(j,1))) * ...
361+
results.mu_y(v) = sin(self.pa(self.idx(j,1))) * ...
362362
self.ecc(self.idx(j,2));
363-
results.Sigma(v) = self.ecc(self.idx(j,2)) * ...
363+
results.sigma(v) = self.ecc(self.idx(j,2)) * ...
364364
self.slope(self.idx(j,3));
365365
end
366366
waitbar(v/self.n_total,wb)
@@ -379,23 +379,23 @@ function create_timecourses(self,varargin)
379379
(mag_tc*mag_d(v));
380380
id = isinf(CS) | isnan(CS);
381381
CS(id) = 0;
382-
[results.R(v),j] = max(CS);
383-
results.X(v) = cos(self.pa(self.idx(j,1))) * ...
382+
[results.corr_fit(v),j] = max(CS);
383+
results.mu_x(v) = cos(self.pa(self.idx(j,1))) * ...
384384
self.ecc(self.idx(j,2));
385-
results.Y(v) = sin(self.pa(self.idx(j,1))) * ...
385+
results.mu_y(v) = sin(self.pa(self.idx(j,1))) * ...
386386
self.ecc(self.idx(j,2));
387-
results.Sigma(v) = self.ecc(self.idx(j,2)) * ...
387+
results.sigma(v) = self.ecc(self.idx(j,2)) * ...
388388
self.slope(self.idx(j,3));
389389
end
390390
waitbar(v/self.n_total,wb)
391391
end
392392
end
393-
results.R = reshape(results.R,self.n_rows,self.n_cols,self.n_slices);
394-
results.X = reshape(results.X,self.n_rows,self.n_cols,self.n_slices);
395-
results.Y = reshape(results.Y,self.n_rows,self.n_cols,self.n_slices);
396-
results.Sigma = reshape(results.Sigma,self.n_rows,self.n_cols,self.n_slices);
397-
results.Eccentricity = abs(results.X+results.Y*1i);
398-
results.Polar_Angle = angle(results.X+results.Y*1i);
393+
results.corr_fit = reshape(results.corr_fit,self.n_rows,self.n_cols,self.n_slices);
394+
results.mu_x = reshape(results.mu_x,self.n_rows,self.n_cols,self.n_slices);
395+
results.mu_y = reshape(results.mu_y,self.n_rows,self.n_cols,self.n_slices);
396+
results.sigma = reshape(results.sigma,self.n_rows,self.n_cols,self.n_slices);
397+
results.Eccentricity = abs(results.mu_x+results.mu_y*1i);
398+
results.Polar_Angle = angle(results.mu_x+results.mu_y*1i);
399399
close(wb)
400400
fprintf('done\n');
401401
end

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