function [sFile, ChannelMat] = in_fopen_tdt(DataFile)
% IN_FOPEN_TDT Open recordings saved in the Tucker Davis Technologies format.
%
% The importer needs the folder that the files are in. K Nasiotis selected one type
% of files to work as the "raw file" - (.Tbk)
 
% @=============================================================================
% This function is part of the Brainstorm software:
% https://neuroimage.usc.edu/brainstorm
% 
% Copyright (c) University of Southern California & McGill University
% This software is distributed under the terms of the GNU General Public License
% as published by the Free Software Foundation. Further details on the GPLv3
% license can be found at http://www.gnu.org/copyleft/gpl.html.
% 
% FOR RESEARCH PURPOSES ONLY. THE SOFTWARE IS PROVIDED "AS IS," AND THE
% UNIVERSITY OF SOUTHERN CALIFORNIA AND ITS COLLABORATORS DO NOT MAKE ANY
% WARRANTY, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO WARRANTIES OF
% MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, NOR DO THEY ASSUME ANY
% LIABILITY OR RESPONSIBILITY FOR THE USE OF THIS SOFTWARE.
%
% For more information type "brainstorm license" at command prompt.
% =============================================================================@
%
% Author: Konstantinos Nasiotis 2019-2020


% Not available in the compiled version
if bst_iscompiled()
    error('Reading TDT files is not available in the compiled version of Brainstorm.');
end
% Install/load TDT-SDK library
[isInstalled, errMsg] = bst_plugin('Install', 'tdt-sdk');
if ~isInstalled
    error(errMsg);
end


 %% ===== GET FILES =====
% Get base dataset folder
[DataFolder, FileName] = bst_fileparts(DataFile);

hdr.BaseFolder = DataFolder;

 %% ===== FILE COMMENT =====
% Comment: BaseFolder
Comment = DataFolder;

%% ===== READ DATA HEADERS =====
bst_progress('text', 'TDT: Reading headers...');

% Load one second segment to see what type of signals exist in this dataset
% Use as general sampling rate the rate of the HIGHEST sampled signal
% The signals that have a lower sampling rate will be interpolated to match
% the general sampling rate

headers = TDTbin2mat(DataFolder, 'HEADERS', 1);

data = TDTbin2mat(DataFolder, 'T1', 0, 'T2', 1); % 1 second segment
all_streams = fieldnames(data.streams);


%% Get rid of streams that are pNe and SynC

all_streams = all_streams(~ismember(all_streams,{'pNe1','pNe2','pNe3','pNe4','SynC'}));

% The sampling rates present are the weirdest numbers I have ever seen:
% e.g. Fs = 3051.7578125 Hz !!!
% Those numbers create problems when loading segments of data.
% The segment loading is in TimeBounds, not SampleBounds that makes it even
% worse with those sampling rates
stream_info = struct;   

LFP_label_exists = 0;

ii = 1;
for iStream = 1:length(all_streams)
    stream_info(iStream).label          = all_streams{iStream};
    stream_info(iStream).fs             = data.streams.(all_streams{iStream}).fs;
    stream_info(iStream).total_channels = size(data.streams.(all_streams{iStream}).data,1);
    stream_info(iStream).channelIndices = ii:ii+size(data.streams.(all_streams{iStream}).data,1)-1;
    
    ii = ii + size(data.streams.(all_streams{iStream}).data,1);

    
    % Brainstorm needs a single sampling rate. I assign the one on the LFPs
    
    theLFPlabel = 'LFP';
    if strfind(all_streams{iStream},theLFPlabel)
        data_new = TDTbin2mat(DataFolder, 'STORE', all_streams{iStream},'T1', 0, 'T2', 1); % 1 second segment       
        
        general_sampling_rate = data_new.streams.(all_streams{iStream}).fs;
        LFP_label_exists = 1;
    end
    popup_labels{iStream} = [all_streams{iStream} ' - ' num2str(stream_info(iStream).fs) ' Hz'];
end

if ~LFP_label_exists    
    [indx,tf] = listdlg('PromptString',{'Select the label of the electrophysiological signal that is present in this dataset',...
    'If more streams are present, they will be resampled to match the Fs of this stream.',''},...
    'SelectionMode','single','ListString', popup_labels);
    
    data_new = TDTbin2mat(DataFolder, 'STORE', all_streams{indx},'T1', 0, 'T2', 1); % 1 second segment       
        
    general_sampling_rate = data_new.streams.(all_streams{indx}).fs;
    LFP_label_exists = 1;
    
    if isempty(data_new.streams)
        error('The selected stream is empty');
    end
    
    if isempty(indx)
        error('No stream was selected');
    end
end
    
nChannels = sum([stream_info.total_channels]);

 %% ===== CREATE BRAINSTORM SFILE STRUCTURE =====
% Initialize returned file structure
sFile = db_template('sfile');

% Add information read from header
sFile.prop.sfreq   =  general_sampling_rate;
sFile.byteorder    = 'l';
sFile.filename     = DataFolder;
sFile.format       = 'EEG-TDT';
sFile.device       = 'Tucker Davis Technologies';
sFile.header.tdt   = headers;
sFile.comment      = FileName;
sFile.prop.times   = [0, headers.stopTime-headers.startTime];
sFile.prop.nAvg    = 1;
% No info on bad channels
sFile.channelflag  = ones(nChannels, 1);

sFile.header.stream_info = stream_info;

%% ===== CREATE EMPTY CHANNEL FILE =====
ChannelMat = db_template('channelmat');
ChannelMat.Comment = 'TDT channels';
ChannelMat.Channel = repmat(db_template('channeldesc'), [1, nChannels]);

ii = 0;
for iStream = 1:length(all_streams)
     for iChannel = 1:stream_info(iStream).total_channels
         ii = ii+1;
         if ~(stream_info(iStream).total_channels==1)
            ChannelMat.Channel(ii).Name = [all_streams{iStream} '_' num2str(iChannel)];
         else
             ChannelMat.Channel(ii).Name= [all_streams{iStream}];
         end
         ChannelMat.Channel(ii).Loc     = [0;0;0];

         ChannelMat.Channel(ii).Group   = all_streams{iStream};
         
         if strfind(stream_info(iStream).label, theLFPlabel)
            ChannelMat.Channel(ii).Type = 'EEG'; % Not all are EEGs - NOT SURE WHAT TO PUT HERE - AS A STARTING POINT, I PUT WHATEVER IS ONLY ONE CHANNEL SET IT AS Misc
         else
            ChannelMat.Channel(ii).Type = all_streams{iStream};
         end
         ChannelMat.Channel(ii).Orient  = [];
         ChannelMat.Channel(ii).Weight  = 1;
         ChannelMat.Channel(ii).Comment = [];
     end
end


%% Check for acquisition events

bst_progress('text', 'TDT: Collecting acquisition events...');

disp('Getting Acquisition System events')
NO_data = TDTbin2mat(DataFolder, 'TYPE', 2); % Just load epocs / events

are_there_events = ~isempty(NO_data.epocs);

if are_there_events
    
    all_event_Labels = fieldnames(NO_data.epocs);
    
    iindex = 0;

    for iEvent = 1:length(all_event_Labels)
        
        if sum(ismember({'Tick','Swep','Swe+'},NO_data.epocs.(all_event_Labels{iEvent}).name))~=0
            iindex = iindex + 1;
            
            events(iindex).label      = NO_data.epocs.(all_event_Labels{iEvent}).name;
            events(iindex).color      = rand(1,3);
            events(iindex).epochs     = ones(1,length(NO_data.epocs.(all_event_Labels{iEvent}).onset))  ;
            events(iindex).times      = NO_data.epocs.(all_event_Labels{iEvent}).onset';
            events(iindex).reactTimes = [];
            events(iindex).select     = 1;
            events(iindex).channels   = [];
            events(iindex).notes      = [];
        else
            conditions_in_event = unique(NO_data.epocs.(all_event_Labels{iEvent}).data);
            
            for iCondition = 1:length(conditions_in_event)
                
                selected_Events_for_condition = find(NO_data.epocs.(all_event_Labels{iEvent}).data == conditions_in_event(iCondition));
                
                iindex = iindex+1;
                
                
                if strcmp(all_event_Labels{iEvent}, 'Note')
                    events(iindex).label  = NO_data.epocs.Note.notes{iCondition};
                else
                    events(iindex).label  = [NO_data.epocs.(all_event_Labels{iEvent}).name num2str(conditions_in_event(iCondition))];
                end
                events(iindex).color      = rand(1,3);
                events(iindex).epochs     = ones(1,length(selected_Events_for_condition))  ;
                events(iindex).times      = NO_data.epocs.(all_event_Labels{iEvent}).onset(selected_Events_for_condition)';
                events(iindex).reactTimes = [];
                events(iindex).select     = 1;
                events(iindex).channels   = [];
                events(iindex).notes      = [];
            end
        end
    end
end
    
    
%% Check for spike events

check_for_spikes = 1;

if check_for_spikes
    bst_progress('text', 'TDT: Collecting spiking events...');
    NO_data = TDTbin2mat(DataFolder, 'TYPE', 3); % Just load spikes
    are_there_spikes = ~isempty(NO_data.snips);
else
    are_there_spikes = 0;
end


%%%%%%%%
disp('***************************************************')
disp('CHECK THE SPIKES. THEY ARE ONLY ASSIGNED ON RIG TWO')
disp('***************************************************')
%%%%%%%%


if are_there_spikes
    
    if  ~exist ('events','var')
        events = struct;
        last_event_index = 0;
    else
        last_event_index = length(events);
    end
    
    all_spike_event_Labels = fieldnames(NO_data.snips);

    for iSpikeDetectedField = 1:length(all_spike_event_Labels)
%         channels_are_EEG = find(strcmp({ChannelMat.Channel.Type}, 'EEG'));
        
        try
            channels_are_EEG_on_selected_RIG = find(strcmp({ChannelMat.Channel.Type}, 'EEG') & strcmp({ChannelMat.Channel.Group}, ['LFP' num2str(iSpikeDetectedField)]));
        catch
            error('There is an assumption here that LFP1 corresponds to eNe1, LFP2 to eNe2 and so on. If that''s not the case on your system please contact the forum') 
        end

        for iChannel = 1:length(channels_are_EEG_on_selected_RIG)
            
            NeuronIDs = unique(NO_data.snips.(all_spike_event_Labels{iSpikeDetectedField}).sortcode(find(NO_data.snips.(all_spike_event_Labels{iSpikeDetectedField}).chan == iChannel)));
            
            if length(NeuronIDs)>1 && sum(ismember(NeuronIDs,0))~=0
                warning('There are Sorted AND Unsorted Spikes in this Dataset - Probably the selection for Online sorting was made mid-Recording - THE UNSORTED SPIKES WILL BE IGNORED')
                NeuronIDs = NeuronIDs(~ismember(NeuronIDs,[0,31]));
            else
                NeuronIDs = NeuronIDs(~ismember(NeuronIDs, 31));
            end
           
            for iNeuron = 1:length(NeuronIDs)
                
                if NeuronIDs(iNeuron) ~= 31 && (length(NeuronIDs) > 1 && NeuronIDs(iNeuron) ~= 0) % MARYSE MENTIONED THAT .DATA = 31 INDICATES ARTIFACTS - NOISE
                
                    last_event_index = last_event_index + 1;

                    if length(NeuronIDs) == 1
                        SpikesOfThatNeuronOnChannel_Indices = find(NO_data.snips.(all_spike_event_Labels{iSpikeDetectedField}).chan == iChannel & NO_data.snips.(all_spike_event_Labels{iSpikeDetectedField}).sortcode == 0); % Unsorted
                        events(last_event_index).label = ['Spikes Channel ' ChannelMat.Channel(channels_are_EEG_on_selected_RIG(iChannel)).Name];
                    else
                        SpikesOfThatNeuronOnChannel_Indices = find(NO_data.snips.(all_spike_event_Labels{iSpikeDetectedField}).chan == iChannel & NO_data.snips.(all_spike_event_Labels{iSpikeDetectedField}).sortcode == NeuronIDs(iNeuron)); % Sorted
                        events(last_event_index).label = ['Spikes Channel ' ChannelMat.Channel(channels_are_EEG_on_selected_RIG(iChannel)).Name ' |' num2str(NeuronIDs(iNeuron)) '|'];                    
                    end

                    events(last_event_index).color      = rand(1,3);
                    events(last_event_index).epochs     = ones(1,length(SpikesOfThatNeuronOnChannel_Indices));
                    events(last_event_index).times      = NO_data.snips.(all_spike_event_Labels{iSpikeDetectedField}).ts(SpikesOfThatNeuronOnChannel_Indices)';
                    events(last_event_index).reactTimes = [];
                    events(last_event_index).select     = 1;
                    events(last_event_index).channels   = repmat({{ChannelMat.Channel(channels_are_EEG_on_selected_RIG(iChannel)).Name}}, 1, size(events(last_event_index).times, 2));
                    events(last_event_index).notes      = [];
                end
            end
        end
    end
end

% Import this list
sFile = import_events(sFile, [], events);

end
