'session_description', 'mouse in open exploration',...
'identifier', 'Mouse5_Day3', ...
'session_start_time', datetime(2018, 4, 25, 2, 30, 3, 'TimeZone', 'local'), ...
'timestamps_reference_time', datetime(2018, 4, 25, 3, 0, 45, 'TimeZone', 'local'), ...
'general_experimenter', 'LastName, FirstName', ... % optional
'general_session_id', 'session_1234', ... % optional
'general_institution', 'University of My Institution', ... % optional
'general_related_publications', {'DOI:10.1016/j.neuron.2016.12.011'}); % optional
optical_channel = types.core.OpticalChannel( ...
'description', 'description', ...
'emission_lambda', 500.);
device = types.core.Device();
nwb.general_devices.set('Device', device);
imaging_plane_name = 'imaging_plane';
imaging_plane = types.core.ImagingPlane( ...
'optical_channel', optical_channel, ...
'description', 'a very interesting part of the brain', ...
'device', types.untyped.SoftLink(device), ...
'excitation_lambda', 600., ...
'location', 'my favorite brain location');
nwb.general_optophysiology.set(imaging_plane_name, imaging_plane);
InternalTwoPhoton = types.core.TwoPhotonSeries( ...
'imaging_plane', types.untyped.SoftLink(imaging_plane), ...
'starting_time', 0.0, ...
'starting_time_rate', 3.0, ...
'data', ones(200, 100, 1000), ...
nwb.acquisition.set('2pInternal', InternalTwoPhoton);
% using internal data. this data will be stored inside the NWB file
InternalOnePhoton = types.core.OnePhotonSeries( ...
'data', ones(100, 100, 1000), ...
'imaging_plane', types.untyped.SoftLink(imaging_plane), ...
'starting_time_rate', 1.0, ...
'data_unit', 'normalized amplitude' ...
nwb.acquisition.set('1pInternal', InternalOnePhoton);
selection = "Create Image Mask"; % "Create Image Mask" or "Create Pixel Mask"
% generate fake image_mask data
imaging_shape = [100, 100];
image_mask = zeros(y, x, n_rois);
center = randi(90,2,n_rois);
image_mask(center(1,i):center(1,i)+10, center(2,i):center(2,i)+10, i) = 1;
if selection == "Create Pixel Mask"
[y_ind, x_ind, roi_ind] = ind2sub(size(image_mask), ind);
pixel_mask_struct = struct();
pixel_mask_struct.x = uint32(x_ind); % Add x coordinates to struct field x
pixel_mask_struct.y = uint32(y_ind); % Add y coordinates to struct field y
pixel_mask_struct.weight = single(ones(size(x_ind)));
% Create pixel mask vector data
pixel_mask = types.hdmf_common.VectorData(...
'data', struct2table(pixel_mask_struct), ...
'description', 'pixel masks');
% When creating a pixel mask, it is also necessary to specify a
% pixel_mask_index vector. See the documentation for ragged arrays linked
num_pixels_per_roi = zeros(n_rois, 1); % Column vector
num_pixels_per_roi(i_roi) = sum(roi_ind == i_roi);
pixel_mask_index = uint16(cumsum(num_pixels_per_roi)); % Note: Use an integer
% type that can accommodate the maximum value of the cumulative sum
% Create pixel_mask_index vector
pixel_mask_index = types.hdmf_common.VectorIndex(...
'description', 'Index into pixel_mask VectorData', ...
'data', pixel_mask_index, ...
'target', types.untyped.ObjectView(pixel_mask) );
plane_segmentation = types.core.PlaneSegmentation( ...
'colnames', {'pixel_mask'}, ...
'description', 'roi pixel position (x,y) and pixel weight', ...
'imaging_plane', types.untyped.SoftLink(imaging_plane), ...
'pixel_mask_index', pixel_mask_index, ...
'pixel_mask', pixel_mask ...
else % selection == "Create Image Mask"
plane_segmentation = types.core.PlaneSegmentation( ...
'colnames', {'image_mask'}, ...
'description', 'output from segmenting my favorite imaging plane', ...
'imaging_plane', types.untyped.SoftLink(imaging_plane), ...
'image_mask', types.hdmf_common.VectorData(...
'description', 'image masks') ...
roi_table_region = types.hdmf_common.DynamicTableRegion( ...
'table', types.untyped.ObjectView(plane_segmentation), ...
'description', 'all_rois', ...
roi_response_series = types.core.RoiResponseSeries( ...
'rois', roi_table_region, ...
'data', NaN(n_rois, 100), ...
'data_unit', 'lumens', ...
'starting_time_rate', 3.0, ...
fluorescence = types.core.Fluorescence();
fluorescence.roiresponseseries.set('RoiResponseSeries', roi_response_series);
ophys_module.nwbdatainterface.set('Fluorescence', fluorescence);
read_nwb.processing.get('ophys'). ...
nwbdatainterface.get('Fluorescence'). ...
roiresponseseries.get('RoiResponseSeries'). ...
data(1:5, 1:10)
NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
% read back the image/pixel masks and display the first roi
plane_segmentation = read_nwb.processing.get('ophys'). ...
nwbdatainterface.get('ImageSegmentation'). ...
planesegmentation.get('PlaneSegmentation');
if ~isempty(plane_segmentation.image_mask)
roi_mask = plane_segmentation.image_mask.data(:,:,1);
elseif ~isempty(plane_segmentation.pixel_mask)
row = plane_segmentation.getRow(1, 'columns', {'pixel_mask'});
pixel_mask = row.pixel_mask{1};
roi_mask = zeros(imaging_shape);
ind = sub2ind(imaging_shape, pixel_mask.y, pixel_mask.x);
roi_mask(ind) = pixel_mask.weight;