Fix typos found by codespell

bf_inverse_ebb.m

@@ -21,8 +21,8 @@
     corr.name       = 'Correlated & homologous sources';
     corr.help       = {['Prior matrix is modified so to account for power in a location '...
         'and its correlated partner, allowing for correlated sources normally suppressed '...
-        'by beamformers to be reconstructed. Correlated pairs can be definied in a matrix'...
-        '(see below) or automatically guessed by looking for the mirror along the saggital plane.']};
+        'by beamformers to be reconstructed. Correlated pairs can be defined in a matrix'...
+        '(see below) or automatically guessed by looking for the mirror along the sagittal plane.']};
     corr.labels     = {'yes','no'};
     corr.values     = {true, false};
     corr.val        = {false};
@@ -51,7 +51,7 @@
     mixmethod.name   = 'Prior combination method';
     mixmethod.help   = {['How should we combine the correlated and uncorrelated '...
         'priors (assuming we have both) +SUM: simple addition of the two '...
-        'priors to make one matrix. +REML: Two seperate priors, where the ReML'...
+        'priors to make one matrix. +REML: Two separate priors, where the ReML'...
         'optimisation will scale them automatically. (Default: sum)']};
     mixmethod.labels = {'sum','reml'};
     mixmethod.values = {'sum','reml'};
@@ -70,7 +70,7 @@
     iid             = cfg_menu;
     iid.tag         = 'iid';
     iid.name        = 'Identity source covariance';
-    iid.help        = {['Assumes sources are indepedent and identically distributed, equivalent to a Bayesian '...
+    iid.help        = {['Assumes sources are independent and identically distributed, equivalent to a Bayesian '...
         'minimum norm estimation. This option bypasses correlated source mode']};
     iid.labels      = {'yes','no'};
     iid.values      = {true, false};
@@ -135,7 +135,7 @@
     S.corr = true;
 end
 
-% Inital setup
+% Initial setup
 %----------------------------------------------------------------
 
 C       = BF.features.(S.modality).C;
@@ -160,7 +160,7 @@
 % Check to see if tdcov has been used, warn model evidence scores may not
 % behave as expected
 ntrials = size(BF.data.D,3);
-if Nn > ntrials*16 % maxmium possible samples using tdcov
+if Nn > ntrials*16 % maximum possible samples using tdcov
     warning(['covariance matrix not generated using tdcov - '...
         'model evidence values may not scale as expected!'])
     Nn = 1;
@@ -269,7 +269,7 @@
                 'file is the one you want me to use, please only have one in there!']);
             eval(['pairs = X.' flds{1} ';']);
             % Check it has the correct size
-            assert(length(pairs)==nvert,'size of pairs mat doesnt correspond to number of sources');
+            assert(length(pairs)==nvert,'size of pairs mat doesn''t correspond to number of sources');
             %             % need to check if its symmetric - and make this fail if so.
             %             % (except for the condition of an indetity matrix)
             %             tmp = pairs - pairs';
@@ -295,9 +295,9 @@
             if ~isnan(L{i})
 
                 if isempty(S.pairs)
-                    % We are looking for sources in the mirror of the saggital
+                    % We are looking for sources in the mirror of the sagittal
                     % plane, which means flipping the position in the x-axis and
-                    % looking for the source which is the closest (Which isnt
+                    % looking for the source which is the closest (Which isn't
                     % itself!)
                     target  = [-pos(i,1) pos(i,2) pos(i,3)];
                     del     = bsxfun(@minus,pos,target);
@@ -307,8 +307,8 @@
                 else
                     % If using the pairs matrix, there are three possible
                     % outcomes:
-                    % 1) Nothing to be found to corellate to
-                    % 2) It finds itself (useful if we need a contolled mix
+                    % 1) Nothing to be found to correlate to
+                    % 2) It finds itself (useful if we need a controlled mix
                     % of correlated and uncorrelated sources).
                     % 3) Finds other source(s) to correlate with.
                     id = find(pairs(i,:));
@@ -336,7 +336,7 @@
 
                     % allocate to corresponding locations in array
                     % count how many times a point comes up (should be twice but
-                    % could be less/more depeding on asymmetry in source space).
+                    % could be less/more depending on asymmetry in source space).
                     if ondiag
                         pow_dual(i,i) = pow_dual(i,i) + pow2_tmp;
                         count(i,i) = count(i,i) + 1;
@@ -364,7 +364,7 @@
         end
 
         % correct power for the fact that a) having two sets of lead fields
-        % doubles the power and b) some locations may have been visted more
+        % doubles the power and b) some locations may have been visited more
         % than once,stops this skewing the covariance too much in the
         % correlated sources favour.
         idx = find(count);
@@ -421,8 +421,8 @@
 hP(1) = -5;		% assumes IID noise is 1/100th of signal.
 hC(1) = 1e-64;
 
-% Step 2: optional room noise: WARNING this hasnt been tested for
-% compatibilty with multiple source priors yet!
+% Step 2: optional room noise: WARNING this hasn't been tested for
+% compatibility with multiple source priors yet!
 if ~isempty(S.noise)
 
     if iscell(S.noise)
@@ -456,7 +456,7 @@
 
 % ReML to optimise the weighted combination of covariances to
 % best match the original sensor covariance. Comes in two flavours,
-% STRICT: where hyperpriors and precisions have been predfined earlier
+% STRICT: where hyperpriors and precisions have been predefined earlier
 % LOOSE: hyperpriors have free reign, similar to SPM's inversions
 %------------------------------------------------------------------
 switch lower(S.reml)
@@ -466,7 +466,7 @@
     case 'loose'
         fprintf('Using ReML: Loose hyperprior settings\n');
         % Need to add a final extra term here to allow ReML to not run into
-        % trouble, a fixed (co)variance componenent which is ~1/100 the
+        % trouble, a fixed (co)variance component which is ~1/100 the
         % magnitude of the sensor covariance.
         Q0          = exp(-5)*trace(C)*Qe{1};
         [Cy,h,~,F,Fa,Fc]= bf_reml_sc(C,[],[Qe LQpL],Nn,-4,16,Q0);

bf_output_PLI.m

@@ -138,7 +138,7 @@
         if isfield(BF.sources, 'voi')
             montage.labelnew = BF.sources.voi.label;
         elseif isfield(S, 'vois')
-            % collect labels from voi and maks definitions
+            % collect labels from voi and mask definitions
             if isfield(S.vois{1}, 'voidef')
                 montage.labelnew{1} = S.vois{v}.voidef.label;
             elseif isfield(S.vois{1}, 'maskdef')

bf_output_image_cfGLM.m

@@ -567,7 +567,7 @@
 B2=squeeze(mean(mean(Beta(:,:,:,2,:),2),1));
 B3=squeeze(mean(mean(Beta(:,:,:,3,:),2),1));
 
-% seond level stats
+% second level stats
 for i=1:nvert
 
     V=[];
@@ -639,4 +639,4 @@
 % end
 
 
-res = image;
+res = image;

bf_output_image_mv.m

@@ -228,7 +228,7 @@
     end
 
     if min(num_trials)~= max(num_trials)
-        warning ('Number of trials are not the same accross conditions- throwing away');
+        warning ('Number of trials are not the same across conditions- throwing away');
         num_trials
     end
     nt = min(num_trials); % ANNA -throw away the extra trials

bf_output_montage.m

@@ -91,7 +91,7 @@
         if isfield(BF.sources, 'voi')
             montage.labelnew = BF.sources.voi.label;
         elseif isfield(S, 'vois')
-            % collect labels from voi and maks definitions
+            % collect labels from voi and mask definitions
             for v = 1:numel(S.vois)
                 if isfield(S.vois{v}, 'voidef')
                     montage.labelnew{v} = S.vois{v}.voidef.label;

bf_sources.m

@@ -54,7 +54,7 @@
 visualise = cfg_menu;
 visualise.tag = 'visualise';
 visualise.name = 'Visualise head model and sources';
-visualise.help = {'Visualise head model and sourses to verify that everythin was done correctly'};
+visualise.help = {'Visualise head model and sources to verify that everything was done correctly'};
 visualise.labels = {'yes', 'no'};
 visualise.values = {true, false};
 visualise.val = {true};

private/GALA_find_localmin.m

@@ -63,7 +63,7 @@
         % add boundary vertices to appropriate cluster
 
         A0=Aip-eye(length(Aip));    % direct neighbors matrix
-        for i=1:length(bound)   % loop by all boudary vertices
+        for i=1:length(bound)   % loop by all boundary vertices
 
             neib = find(A0(:,bound(i)));    % direct neighbors of current verex
 

private/GALA_invert.m

@@ -180,7 +180,7 @@
     % different subjects, so correlated between subjects part of covarince
     % prior is overlapping but not identical patches
 
-    % pay attention - it's to exclude disconected vertices from Jcov
+    % pay attention - it's to exclude disconnected vertices from Jcov
     % it seems ssQ1 = spones(sQ1) works better than simple sQ1
     % may be because in Jcov.*sQ1 there is double attenuation of tails
 

private/bf_reml_sc.m

@@ -100,12 +100,12 @@
 try, hE = hE(:);        catch, hE = -32;   end
 try, hP = spm_inv(hC);  catch, hP = 1/256; end
 
-% check sise
+% check size
 %--------------------------------------------------------------------------
 if length(hE) < m, hE = hE(1)*ones(m,1);   end
 if length(hP) < m, hP = hP(1)*speye(m,m);  end
 
-% intialise h: so that sum(exp(h)) = 1
+% initialise h: so that sum(exp(h)) = 1
 %--------------------------------------------------------------------------
 if any(diag(hP) > exp(16))
     h = hE;

private/champagne_aug2015.m

@@ -25,7 +25,7 @@
 % Two parameters in code that can be adjusted are:
 % eps1 = Default is 1e-8 For numerical stability while inverting model covariance matrix.
 % eps1z = Default is 1e-8 For numerical stability while calculating
-%         voxel variance and auxilliary variable z 
+%         voxel variance and auxiliary variable z 
 
 
 function [gamma,x,w,sigu,like]=champagne(y,f,sigu,nem,nd,vcs,nupd,gamma0,retx, fig);

private/get_components.m

@@ -4,9 +4,9 @@
 %   [comps,comp_sizes] = get_components(adj);
 %
 %   Returns the components of an undirected graph specified by the binary and 
-%   undirected adjacency matrix adj. Components and their constitutent nodes are 
+%   undirected adjacency matrix adj. Components and their constituent nodes are 
 %   assigned the same index and stored in the vector, comps. The vector, comp_sizes,
-%   contains the number of nodes beloning to each component.
+%   contains the number of nodes belonging to each component.
 %
 %   Inputs:         adj,    binary and undirected adjacency matrix
 %

private/get_data_features.m

@@ -2,12 +2,12 @@
 function [trialfeatures,vedata]=get_data_features(flatpepdata,Nbins,Ntrials,weights,Tfull,datatype,featureind,regressout)
 
 %%% put data in form for mv test
-%% options are datatye
+%% options are datatype
 %% {'peakabs','peakreal','peakimag','peaksin','peakcos','sumpower'};
 %% datatype=pwr power (re^2+comp^2)
-%% datatpe=pwrsincos: power+sin and cos terms
+%% datatype=pwrsincos: power+sin and cos terms
 %% datatype=4:
-%% if weights==-1 just set up static variables depnding on datatype
+%% if weights==-1 just set up static variables depending on datatype
 %% 
 flagnames={'peakabs','peakreal','peakimag','peaksin','peakcos','sumpower'};
 

private/nut_dSPM.m

@@ -1,7 +1,7 @@
 function [weight]=nut_dSPM(Lp,data, flags) %---------------------------------------------------------
 % [weight,eta]=nut_dSPM(Lp,data,flags)
 % Lp : lead field
-% inputs for regularization contant:
+% inputs for regularization constant:
 % [1] data.Ryy = sample covariance, for data-dependent regularization
 % [2] flags.gamma = user defined regularization constant, or 'auto' for
 %     leadfield-based regularization

private/nut_sLORETA.m

@@ -1,6 +1,6 @@
 function [weight]=nut_sLORETA(Lp,data,flags)
 % weight=nut_sLORETA(Lp,data,flags)
-% inputs for regularization contant:
+% inputs for regularization constant:
 % [1] data.Ryy = sample covariance, for data-dependent regularization
 % [2] flags.gamma = user defined regularization constant, or 'auto' for
 %     leadfield-based regularization