function [nh_split,masks]=cosmo_neighborhood_split(nh, varargin)
% partitions a neighborhood in a cell with (smaller) neigborhoods
%
% cosmo_neighborhood_split(nh, ...)
%
% Inputs:
% nh neighborhood struct with fields .neighbors and
% .origin
% 'divisions', d Number of divisions along each feature dimension.
% For example, if d=4 for an fMRI dataset (with
% three feature dimensions corresponding to the three
% spatial dimensions), then the output will have
% at most 4^3=64 neighborhood structs and masks.
%
% Output:
% nh_split Cell with neighborhood structs. When all elements
% in nh_split are combined they should contain the
% same information in .neighbors and .fa as in the
% input nh, but with a re-indexing of the feature ids
% corresponding to the masks.
% masks Cell with mask datasets, corresponding to the
% features returned in neighborhoods in nh_split.
% When the dataset derived from the input
% neighborhood nh is masked by the k-th mask, then
% the indices in the resulting dataset match the
% indices in .neighbors
%
%
% Example:
% % (This example cannot be documentation tested using Octave,
% % since Octave does not allow for-loops with evalc)
% cosmo_skip_test_if_no_external('matlab');
% %
% % the following example shows how a neighborhood can be split in
% % parts, a searchlight run on each part, and the different outputs
% % joined together. This gives identical results as running the
% % searchlight on the entire dataset
% %
% % generate a tiny dataset with 6 voxels
% ds=cosmo_synthetic_dataset();
% %
% % define tiny neighborhoods with radii of 1 voxel
% % (more typical values are ~3 voxels)
% nh=cosmo_spherical_neighborhood(ds,'radius',1,'progress',false);
% %
% % Use a simple 'measure' that just averages data along the feature
% % dimension. (Any other measure can be used as well)
% averager=@(x,opt)cosmo_structjoin('samples',mean(x.samples,2));
% %
% % disable progress reporting for the searchlight
% opt=struct();
% opt.progress=false;
% %
% % run the searchlight
% result=cosmo_searchlight(ds,nh,averager,opt);
% %
% % show output from searchlight
% cosmo_disp({result.samples; result.fa});
% %|| { [ 0.768 0.368 -1 1.45 0.0342 -0.32
% %|| 0.526 1.77 0.937 1.08 1.07 1.49
% %|| 0.46 -1.25 -0.152 0.0899 0.795 -0.522
% %|| 1.23 0.685 0.954 0.606 1.19 0.335
% %|| 0.914 -0.0442 0.0295 0.664 0.274 -0.221
% %|| 0.633 1.24 0.797 0.861 1.54 1.02 ]
% %|| .nvoxels
% %|| [ 3 4 3 3 4 3 ]
% %|| .radius
% %|| [ 1 1 1 1 1 1 ]
% %|| .center_ids
% %|| [ 1 2 3 4 5 6 ]
% %|| .i
% %|| [ 1 2 3 1 2 3 ]
% %|| .j
% %|| [ 1 1 1 2 2 2 ]
% %|| .k
% %|| [ 1 1 1 1 1 1 ] }
% %
% [nh_splits,masks_split]=cosmo_neighborhood_split(nh);
% n_split=numel(nh_splits);
% %
% % Allocate space for searchlight output from each part
% res2_cell=cell(1,n_split);
% %
% for k=1:n_split
% % get the k-th neighborhood
% nh_sel=nh_splits{k};
% %
% % slice the dataset to select only the features indexed by
% % the nh_sel neighborhood.
% %
% % (when using fmri datasets, it is also possible to use
% % cosmo_fmri_dataset with a filename and the mask from masks,
% % as in:
% %
% % ds_sel=cosmo_fmri_dataset('data.nii','mask',masks_split{k});
% %
% % Such an approach may result in significant reduction of memory
% % usage, if the file format supports loading partial files
% % (currently unzipped NIFTI (.nii), ANALYZE (.hdr) and AFNI)))
% ds_sel=cosmo_slice(ds,masks_split{k}.samples,2);
% %
% % run the searchlight for this split, and store the result
% res2_cell{k}=cosmo_searchlight(ds_sel,nh_sel,averager,opt);
% end;
% %
% % join the results along the second (feature) dimension
% result2=cosmo_stack(res2_cell,2);
% %
% % show the results; they are identical to the original output, modulo
% % a possible permutation of the features.
% cosmo_disp({result2.samples; result2.fa});
% %|| { [ 0.768 0.368 1.45 0.0342 -1 -0.32
% %|| 0.526 1.77 1.08 1.07 0.937 1.49
% %|| 0.46 -1.25 0.0899 0.795 -0.152 -0.522
% %|| 1.23 0.685 0.606 1.19 0.954 0.335
% %|| 0.914 -0.0442 0.664 0.274 0.0295 -0.221
% %|| 0.633 1.24 0.861 1.54 0.797 1.02 ]
% %|| .center_ids
% %|| [ 1 2 3 4 1 2 ]
% %|| .i
% %|| [ 1 2 1 2 3 3 ]
% %|| .j
% %|| [ 1 1 2 2 1 2 ]
% %|| .k
% %|| [ 1 1 1 1 1 1 ]
% %|| .nvoxels
% %|| [ 3 4 3 4 3 3 ]
% %|| .radius
% %|| [ 1 1 1 1 1 1 ] }
%
%
% Notes:
% - typical use cases of this function are:
% * searchlights on fMRI dataset with many samples and features
% (i.e. too many to load all of them in memory). In this case, and
% if the file format supports it (NIFTI, ANALYZE, AFNI), parts
% of the data can be loaded with cosmo_fmri_datset, where the masks
% are based on the second output from this function
% * parallel searchlights on a cluster using multiple computing nodes.
%
% See also: cosmo_searchlight, cosmo_fmri_dataset
%
% # For CoSMoMVPA's copyright information and license terms, #
% # see the COPYING file distributed with CoSMoMVPA. #
defaults=struct();
defaults.divisions=4;
opt=cosmo_structjoin(defaults,varargin);
validate_input(nh, opt);
% see how big each block is along each dimension
[first,last,nfeatures]=get_dim_range(nh);
min_first=min(first,[],2);
max_last=max(last,[],2);
max_size=max(last-first,[],2)+1;
extent=max_last-min_first+1;
% Examples of how max_size, step and extent are related:
% max_size=1, step=5, extent=25, 5 div: 1-5, 6-10, 11-15, 16-20, 21-25
% max_size=1, step=4, extent=20, 5 div: 1-4, 5-8, 9-12, 13-16, 17-20
% max_size=2, step=5, extent=26, 5 div: 1-6, 6-11, 11-16, 16-21, 21-26
% max_size=2, step=4, extent=21, 5 div: 1-5, 5-9, 9-13, 13-17, 17-21
% max_size=3, step=4, extent=22, 5 div: 1-6, 5-10, 9-14, 13-18, 17-22
% first element: step+max_size-1 elements
% all other (div-1) elements: step elements
step=ceil((extent+1-max_size)/opt.divisions);
assert(all(step>=1));
% for each element in nh.neighbors, assign it to a block
% indexed by ndim integers (where ndim is the number of dimensions)
dim_pos=floor(bsxfun(@rdivide,first-1,step))+1;
% get the indices for each block.
% idxs{k}=[a_k1, ..., a_kN] means that nh.neighbors{a_k1}, ...,
% nh.neighbors{a_kN} all refer to neighboring features
%
[idxs,unq]=cosmo_index_unique(dim_pos');
nsplit=size(unq,1);
nh_split=cell(nsplit,1);
masks=cell(nsplit,1);
keep_split_mask=false(nsplit,1);
for split_id=1:nsplit
msk=false(1,nfeatures);
idx=idxs{split_id};
n_nbrs=numel(idx);
for j=1:n_nbrs
nb=nh.neighbors{idx(j)};
msk(1,nb)=true;
end
if ~any(msk)
% skip empty neighbors
continue;
end
keep_split_mask(split_id)=true;
m_k=struct();
m_k.samples=msk;
m_k.a=nh.origin.a;
m_k.fa=nh.origin.fa;
nh_k=struct();
nh_k.origin.a=m_k.a;
nh_k.origin.fa=cosmo_slice(m_k.fa,msk,2,'struct');
nh_k.a=nh.a;
nh_k.fa=cosmo_slice(nh.fa, idx, 2, 'struct');
all2some=zeros(1,nfeatures);
all2some(msk)=1:sum(msk);
neighbors=cell(n_nbrs,1);
for j=1:n_nbrs
some=all2some(nh.neighbors{idx(j)});
assert(all(some~=0));
neighbors{j}=some;
end
nh_k.neighbors=neighbors;
nh_split{split_id}=nh_k;
masks{split_id}=m_k;
end
% only keep non-empty masks
nh_split=nh_split(keep_split_mask);
masks=masks(keep_split_mask);
function [first,last,nfeatures]=get_dim_range(nh)
% for each feature dimension, find the minimum and maximum value
% in each neighborhood
% If nh has N neighbors and M dimensions, then the output of first and
% last in M x N.
fdim=nh.origin.a.fdim;
labels=fdim.labels;
n_labels=numel(labels);
n_neighbors=numel(nh.neighbors);
fa=nh.origin.fa;
% pre-store values for each feature attribute
fa_idxs_cell=cell(n_labels,1);
for k=1:n_labels
label=labels{k};
fa_idxs=fa.(label);
fa_idxs_cell{k}=fa_idxs;
end
first=zeros(n_labels,n_neighbors);
last=zeros(n_labels,n_neighbors);
nfeatures=0;
for j=1:n_neighbors
nb=nh.neighbors{j};
mx_nb=max(nb);
if mx_nb>nfeatures
nfeatures=mx_nb;
end
for k=1:n_labels
% (this is a timing-critical part of the loop)
nb_values=fa_idxs_cell{k}(nb);
first(k,j)=min(nb_values);
last(k,j)=max(nb_values);
end
end
function opt=validate_input(nh, opt)
% some checks on the input
cosmo_check_neighborhood(nh);
allowed_fieldnames={'divisions'};
delta=setdiff(fieldnames(opt), allowed_fieldnames);
if ~isempty(delta)
error('illegal field %s', delta{1});
end
divisions=opt.divisions;
if ~(isnumeric(divisions) && ...
isscalar(divisions) && ...
isfinite(divisions) && ...
divisions > 1)
error('count must be finite scalar >1');
end
origin_labels={'origin.fa',...
'origin.a.fdim.labels',...
'origin.a.fdim.values'};
if ~all(cosmo_isfield(nh,origin_labels))
error('origin has missing labels');
end
is_numeric_vector=@(x)isnumeric(x) && isvector(x);
if ~all(cellfun(is_numeric_vector, nh.a.fdim.values))
error('elements in .a.fdim.values must be numeric vectors');
end
