Add MATLAB support

[lcnbeapp]

I am attempting to write a wrapper function for this, but I'm having some trouble. I am trying to use this command: py.bycycle.features.compute_features() (inspired by the fooof wrapper, where py.fooof.FOOOF does the trick), but I'm getting the "Undefined variable "py" or class "py.bycycle.features.compute_features" error. Any insight? Thanks!

[srcole]

Thanks for working on it!

This was a conversation on the lab slack. See the screenshot below for Tom's advice.

[lcnbeapp]

Fixed it! I had the wrong python loaded in. Thank you, this was helpful.

[mgm248]

I am working on integrating bycycle with BEAPP (https://github.com/lcnbeapp/beapp), so I did some work on this issue. It's a work in progress, and some of the variables might be confusing, but I wanted to share what I have in case it's helpful:

`src_dir = find_input_dir('bycycle',grp_proc_info_in.beapp_toggle_mods); max_n_chans = 256; for curr_file=1:length(grp_proc_info_in.beapp_fname_all)

cd(src_dir{1});

  if exist(strcat(src_dir{1},filesep,grp_proc_info_in.beapp_fname_all{curr_file}),'file')
     tic;
     % load eeg if module takes continuous input
    load(grp_proc_info_in.beapp_fname_all{curr_file});
    curr_eeg = eeg_w{1,1};
    if size(curr_eeg,3) > (grp_proc_info_in.bycyc_num_segs-1)
        segments_torun = randsample(size(curr_eeg,3),grp_proc_info_in.bycyc_num_segs);
        results_table = NaN(22,max_n_chans);
        results_table_burst = NaN(22,max_n_chans);
        results_table_nburst = NaN(22,max_n_chans);
        for chan = 1:max_n_chans
            curr_chan_results = NaN(500,22,size(segments_torun,1));
            if ismember(chan,file_proc_info.beapp_indx{1,1})
                for seg = 1:size(segments_torun,1) 
                    curr_seg = segments_torun(seg,1); 
                    signal = curr_eeg(chan,:,curr_seg); 
                    signal = py.numpy.array(signal);
                    f_range = py.list([12,20]);
                    fs = size(curr_eeg,2) / 10; %where 10 = segment length
                    fs = py.float(fs);
                    signal = py.bycycle.filt.lowpass_filter(signal, fs, py.float(35));
                    burst_kwargs = py.dict(pyargs('amplitude_fraction_threshold',.3,'amplitude_consistency_threshold',.4,'period_consistency_threshold',.5, 'monotonicity_threshold',.8,'N_cycles_min',3));
                    bycyc = py.bycycle.features.compute_features(signal, fs, f_range, py.str('P'), py.str('cycles'),burst_kwargs);
                    %py.bycycle.burst.plot_burst_detect_params(signal, fs, bycyc, burst_kwargs)
                    df = bycyc.to_dict;
                    %results_table = NaN(double(py.len(df{'sample_peak'})),double(py.len(keys(df))));
                    for row = 1:(double(py.len(df{'sample_peak'}))) %length sems to be 1 more than it actually is ...  maybe it counts the title?
                        curr_chan_results(row,1,seg) = df{'sample_peak'}{row-1};
                        curr_chan_results(row,2,seg) = df{'sample_zerox_decay'}{row-1};
                        curr_chan_results(row,3,seg)= df{'sample_zerox_rise'}{row-1};
                        curr_chan_results(row,4,seg)= df{'sample_last_trough'}{row-1};
                        curr_chan_results(row,5,seg) = df{'sample_next_trough'}{row-1};
                        curr_chan_results(row,6,seg) = df{'period'}{row-1};
                        curr_chan_results(row,7,seg) = df{'time_peak'}{row-1};
                        curr_chan_results(row,8,seg) = df{'time_trough'}{row-1};
                        curr_chan_results(row,9,seg) = df{'volt_peak'}{row-1};
                        curr_chan_results(row,10,seg) = df{'volt_trough'}{row-1};
                        curr_chan_results(row,11,seg) = df{'time_rise'}{row-1};
                        curr_chan_results(row,12,seg) = df{'volt_decay'}{row-1};
                        curr_chan_results(row,13,seg) = df{'volt_rise'}{row-1};
                        curr_chan_results(row,14,seg) = df{'volt_amp'}{row-1};
                        curr_chan_results(row,15,seg) = df{'time_rdsym'}{row-1};
                        curr_chan_results(row,16,seg) = df{'time_ptsym'}{row-1};
                        curr_chan_results(row,17,seg) = df{'band_amp'}{row-1};
                        curr_chan_results(row,18,seg) = df{'amp_fraction'}{row-1};
                        curr_chan_results(row,19,seg) = df{'amp_consistency'}{row-1};
                        curr_chan_results(row,20,seg) = df{'period_consistency'}{row-1};
                        curr_chan_results(row,21,seg) = df{'monotonicity'}{row-1};
                        curr_chan_results(row,22,seg) = df{'is_burst'}{row-1};
                    end
                   % avg_cyc_chan_results = squeeze(nanmean(curr_chan_results,1)); %avg over cycles
                end
                curr_chan_results_burst = NaN(size(curr_chan_results));
                curr_chan_results_nburst = NaN(size(curr_chan_results));
                for seg = 1:size(curr_chan_results,3)
                    for i=1:size(curr_chan_results,1)
                        if curr_chan_results(i,22,seg) == 1
                            curr_chan_results_burst(i,:,seg) = curr_chan_results(i,:,seg);
                        else
                            curr_chan_results_nburst(i,:,seg) = curr_chan_results(i,:,seg);
                        end
                    end  
                end

                results_table(:,chan) = squeeze(nanmean(nanmean(curr_chan_results,1),3)); %avg over cycles, then avg over segments
                results_table_burst(:,chan) = squeeze(nanmean(nanmean(curr_chan_results_burst,1),3));
                results_table_nburst(:,chan) = squeeze(nanmean(nanmean(curr_chan_results_nburst,1),3));
            end
        end

        %% save and update file history
        cd(grp_proc_info_in.beapp_toggle_mods{'bycycle','Module_Dir'}{1});
        filename = erase(file_proc_info.beapp_fname{1},'.mat');
        save(strcat(filename,'_bycycle_results','.mat'),'results_table','results_table_burst','results_table_nburst');
    end
  end
  clearvars -except grp_proc_info_in src_dir curr_file max_n_chans

end`

[mgm248]

Also, re getting Matlab to find python, see this https://www.mathworks.com/matlabcentral/answers/427187-problem-with-python-numpy

TLDR: Matlab doesn't support anaconda python

[StefanoBuccelli]

Hi guys, inspired by mgm248's code I created a small repo. Take a look if you like :) and thanks for your great work with bycycle !

Stefano

[srcole]

Awesome. Thanks @mgm248 and @StefanoBuccelli!

[irenevigueguix]

Hey! I've got problems as well to launch Python into MATLAB, and I've successfully made it work (after spending the whole afternoon) by following your instructions together with other info that I found on the internet regarding the same problem with other Python packages. Yay! Also, I've created this tutorial (https://irenevigueguix.wordpress.com/2020/03/25/loading-python-into-matlab/) for those who might have the same issues in the future and probably end up on this very page. I hope you find the tutorial useful and clearly explained!