From 6b73fcd87a6f57b21d70053e14639fe38a9cdd34 Mon Sep 17 00:00:00 2001 From: Mats Date: Sat, 10 Dec 2022 07:40:14 +0000 Subject: [PATCH] add examples for using reproducescript (adapted from preprint) (#615) --- .../filedir_example1_analysis.jpg | Bin 0 -> 13970 bytes .../filedir_example1_reproducescript.jpg | Bin 0 -> 40890 bytes .../filedir_example2_analysis.jpg | Bin 0 -> 50724 bytes .../filedir_example2_reproducescript.jpg | Bin 0 -> 138308 bytes .../reproducescript/fnins-12-00261-g002.jpg | Bin 0 -> 416498 bytes .../reproducescript/fnins-12-00261-t002.jpg | Bin 0 -> 151233 bytes example.md | 3 + example/reproducescript.md | 178 ++++ example/reproducescript_andersen.md | 116 +++ example/reproducescript_group.md | 768 ++++++++++++++++++ 10 files changed, 1065 insertions(+) create mode 100755 assets/img/example/reproducescript/filedir_example1_analysis.jpg create mode 100755 assets/img/example/reproducescript/filedir_example1_reproducescript.jpg create mode 100755 assets/img/example/reproducescript/filedir_example2_analysis.jpg create mode 100755 assets/img/example/reproducescript/filedir_example2_reproducescript.jpg create mode 100755 assets/img/example/reproducescript/fnins-12-00261-g002.jpg create mode 100755 assets/img/example/reproducescript/fnins-12-00261-t002.jpg create mode 100644 example/reproducescript.md create mode 100644 example/reproducescript_andersen.md create mode 100644 example/reproducescript_group.md diff --git a/assets/img/example/reproducescript/filedir_example1_analysis.jpg b/assets/img/example/reproducescript/filedir_example1_analysis.jpg new file mode 100755 index 0000000000000000000000000000000000000000..76c2f37f22188bc0c4923eca9ef3cb4df91b8902 GIT binary patch literal 13970 zcmc(_c{E#H+%_E5mKv(m&>GvK+A7)_Yf4p7b)%>$K{`-F8mXFsNLn=yRTMXhyHpiL z4KYhYvr7?_OCqXIFPm@9@az*!aZnNj!->_h)`#acOyly0yKt zOWULGAN-4p1Hkzou>K3#f5Ron#&wK62gkYo#l>+fkR6{{r^^;KBp=I62sp$0-N^ z0uC3)nZEf5sPnL2zoG5O8b&GDB+49R~PLDNq1~hX^RGZ7M6P35a?K9X7VP 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Be mindful of where you want to save it! + + clear + close all + + global ft_default + ft_default = []; + ft_default.checksize = inf; + + % enable reproducescript + ft_default.reproducescript = 'reproduce/'; + + % the original source code from listing 1 goes here. + + % disable reproducescript + ft_default.reproducescript = []; + + + +When applied to the original source code it generates a *reproduce* folder containing input and output data files with unique file identifiers, a MATLAB script, and a *hashes* data file: + +{% include image src="/assets/img/example/reproducescript/filedir_example1_reproducescript.jpg" width="500" %} + +*reproducescript* traces the steps to each FieldTrip function call, and recreates human-readable read-eval-print loops (REPL) code from scratch (script.m). For this example, script.m looks like the code below. +*reproducescript* copies the input data to a function and the output data it generates, and gives them a unique identified (i.e. filename). Pointers to these identifiers end up in the standardized script as cfg.inputfile and cfg.outputfile. Note that this means that no input or output data structures as they normally appear in the MATLAB workspace appear in the standardized script; these are all handled using data on disk corresponding with cfg.inputfile and cfg.outputfile. Similarly, if the function’s output is a figure (e.g. in ft_topoplotER) the figure is also directly saved to disk, in .png (bitmap) and .fig (MATLAB figure) formats. + + + %% + + cfg = []; + cfg.dataset = '../rawdata/Subject01.ds'; + cfg.trialfun = 'ft_trialfun_general'; + cfg.trialdef.eventtype = 'backpanel trigger'; + cfg.trialdef.eventvalue = 3; + cfg.trialdef.prestim = 1; + cfg.trialdef.poststim = 2; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg = ft_definetrial(cfg); + + %% + + cfg = []; + cfg.dataset = '../rawdata/Subject01.ds'; + cfg.trialfun = 'ft_trialfun_general'; + cfg.trialdef.eventtype = 'backpanel trigger'; + cfg.trialdef.eventvalue = 3; + cfg.trialdef.prestim = 1; + cfg.trialdef.poststim = 2; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.datafile = '../rawdata/Subject01.ds/Subject01.meg4'; + cfg.headerfile = '../rawdata/Subject01.ds/Subject01.res4'; + cfg.dataformat = 'ctf_meg4'; + cfg.headerformat = 'ctf_res4'; + cfg.representation = 'numeric'; + cfg.trl = 'reproduce/20210112T113326_ft_preprocessing_largecfginput_trl.mat'; + cfg.outputfile = { 'reproduce/20210112T113326_ft_preprocessing_output_data.mat' }; + cfg.channel = {'MEG', 'EOG'}; + cfg.continuous = 'yes'; + ft_preprocessing(cfg); + + %% + + cfg = []; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { 'reproduce/20210112T113326_ft_preprocessing_output_data.mat' }; + cfg.outputfile = { 'reproduce/20210112T113332_ft_timelockanalysis_output_timelock.mat' }; + ft_timelockanalysis(cfg); + + %% + + % a new input variable is entering the pipeline here: 20210112T113333_ft_topoplotER_input_varargin_1.mat + + cfg = []; + cfg.xlim = [0.3 0.5]; + cfg.layout = 'CTF151_helmet.mat'; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { + 'reproduce/20210112T113333_ft_topoplotER_input_varargin_1.mat' }; + cfg.outputfile = 'reproduce/20210112T113338_ft_topoplotER_output'; + figure; + ft_topoplotER(cfg); + +Note that the fields from the cfg input to `ft_definetrial` are repeated as input to `ft_preprocessing` because the configuration in the original script was not emptied. There are also additional fields created by `ft_definetrial`. If these fields exceed a certain printed size, which would make them unwieldy to include inline in a script (e.g. `cfg.trl`, which normally consists of a [Ntrials x 3] matrix specifying the relevant sections of the data on disk), these too are saved on disk instead of being printed in the standardized script. One last thing that should stand out is the comment “a new input variable is entering the pipeline here: ...”. This points to the mat-file subsequently specified in cfg.inputfile to `ft_topoplotER`. The data structure in this file was not originally created by a FieldTrip function but comes from another source: in this case it consists of the data in which originates from the T to fT unit conversion step (see the original code at the top of the page). Thus, this comment puts an emphasis on the fact that a data structure with unknown provenance enters the pipeline. See the section below on [Note on using functions outside of the FieldTrip ecosystem](https://www.fieldtriptoolbox.org/example/reproducescript/#note-on-using-functions-outside-of-the-fieldtrip-ecosystem) for a note on how to work with *reproducescript* and non-FieldTrip code. + +Finally, the reproduce folder contains a file named `hashes.mat`. This is a file containing MD5 hashes for bookkeeping all input and output files. It allows reproducescript to match the output files of any one step to the input files of any subsequent step. For example, the output from `ft_preprocessing` is used as input to ft_timelockanalysis, which means that the data structure only needs to be stored once and `…_ft_timelockanalysis_input_timelock.mat` does not have to be additionally saved to disk. If the output data from one function and the input data to the next function are slightly different, they are both saved under different file names. This happens when the researcher modified the data using custom code (as in the example when converting channel units). The hashes.mat file furthermore allows any researcher to check the integrity of all the intermediate and final result files of the pipeline. + +### Note on using functions outside of the FieldTrip ecosystem + +All analysis steps that do not use FieldTrip functions will create such comments and save the data structure. Importantly, the pipeline thus remains reproducible without relying on external code. However, this does mean that it will be important to annotate script.m after it's created and note where the data with unknown provenance comes from. Even if the pipeline exclusively uses FieldTrip functions, some FieldTrip functions evaluate custom-written code. For example, a user can specify custom code to select trials in ft_definetrial (i.e. cfg.trialfun). If this code were not shared, this particular analysis step could not be re-executed, but since intermediate results are stored as well (in the example of cfg.trialfun, cfg.trl is stored), it is always possible to skip a particular step and continue with the rest of the pipeline. + +If a researcher wishes that *every* analysis step can be re-exceuted, including non-FieldTrip code, a user can "FieldTrip-ify" their non-FieldTrip functions by writing a wrapper around it (see [ft_examplefunction](https://github.com/fieldtrip/fieldtrip/blob/release/ft_examplefunction.m)) and [Implementing a new high-level function](https://www.fieldtriptoolbox.org/development/guideline/code/#implementing-a-new-high-level-function), such that there are no unknowns in the data provenance. Under the hood, this wrapper function uses low level FieldTrip bookkeeping functions (see [Toolbox architecture and organization of the source code](https://www.fieldtriptoolbox.org/development/architecture/#toolbox-architecture-and-organization-of-the-source-code) for more information). + + +## Conclusion +FieldTrip provides researchers with a tool to easily share complete analysis pipelines that use the FieldTrip toolbox. This is espcially aimed at researchers with limited coding experience, that nevertheless want to share their analysis code and/or data with the confidence that their code is reproducible. + +Here we applied *reproducescript* to the simplest analysis pipeline, which is example 1 in [Reducing the efforts to create reproducible analysis code with FieldTrip](https://doi.org/10.1101/2021.02.05.429886). For more complicated analysis pipelines, have a look at example 2 and 3 in the links in [Suggested further reading](https://www.fieldtriptoolbox.org/example/reproducescript/#suggested-further-reading). + +Note that there are other strategies for improving shareability and reproducibility, and we don't assume that *reproducescript* is the best way in every scenario. Rather, it is one of many tools that can aid the researcher to improve the community's standard in methodological transparency and robustness of results. For other strategies, we refer the reader to the pre-print in which we first described *reproducescript*. + + +## Suggested further reading +- [Reducing the efforts to create reproducible analysis code with FieldTrip](https://doi.org/10.1101/2021.02.05.429886 ) +- [*reproducescript* group analysis](/example/reproducescript_group) +- [*reproducescript* on a full study](/example/reproducescript_andersen) diff --git a/example/reproducescript_andersen.md b/example/reproducescript_andersen.md new file mode 100644 index 000000000..e0bc21078 --- /dev/null +++ b/example/reproducescript_andersen.md @@ -0,0 +1,116 @@ +--- +title: reproducescript on a full study +tags: [example, reproducescript, reproducibility] +--- + +#*reproducescript* on a full study + +This example script will introduce you to the a FieldTrip tool that was designed to aid in making your analysis pipeline - including code, data and results - easily reproducible and shareable. It is based on [Reducing the efforts to create reproducible analysis code with FieldTrip](https://doi.org/10.1101/2021.02.05.429886) by Mats van Es, Eelke Spaak, Jan-Mathijs Schoffelen, and Robert Oostenveld. We assume the reader has already had a look at [Making your analysis pipeline reprodudible using *reproducescript*](/example/reproducescript) and [*reproducescript* group analysis](/example/reproducescript_group). + +##Example 3 +Example 1 and 2 were fairly simple analysis pipelines that didn't particularly benefit from *reproducescript* because the original scripts and data organisation were already clear. Those examples are intended to show how *reproducescript* works and how it's used. In this example, we apply *reproducescript* to a published analysis pipeline of MEG data by [Andersen (2018)](https://doi.org/10.3389/fnins.2018.00261). We hope that this will help you to set up your own analysis pipeline using *reproducescript*. + +### Original analysis +The analysis pipeline in Andersen (2018) is well-documented and itself a good demonstration of a reproducible analysis pipeline in the FieldTrip ecosystem. Nevertheless, it consists of a complex set of 10 analysis scripts and 46 functions, which, without the extensive documentation that has been provided by the author, would be challenging to reuse and reproduce the results. This makes it particularly suited to demonstrate the effectiveness and simplicity of reproducescript. +Andersen describes an analysis pipeline from raw single-subject MEG data to group-level statistics in source space. Each of the custom-written scripts has a specific purpose: + +{% include image src="/assets/img/example/reproducescript/fnins-12-00261-t002.jpg" width="500" %} +*Figure copied from [Andersen (2018)](https://doi.org/10.3389/fnins.2018.00261) with permission from the author* + +Still, multiple analysis steps in separate functions are required for the purpose of one script (see figure 6 for the full analysis pipeline), creating a complex hierarchy of scripts and functions: + +{% include image src="/assets/img/example/reproducescript/fnins-12-00261-g002.jpg" width="500" %} +*Figure copied from [Andersen (2018)](https://doi.org/10.3389/fnins.2018.00261) with permission from the author* + + +To keep the computational time and storage requirements low, we applied the full analysis pipeline to two subjects only. Both the original source code from Andersen and the standardized scripts generated by reproducescript are available on [GitHub](https://github.com/matsvanes/reproducescript). Using the documentation in Andersen (2018), we wrote the master script `run_all.m` , which calls all relevant functions in Andersen's analysis pipeline in the correct order. Here we added the *reproducescript* field to *ft_default* the way we did it in [*reproducescript* group analysis](/example/reproducescript_group), i.e. by initialising *reproducescript* seperately on each subject, and then once for the group analysis (see run_all.m). + +####[run_all.m](https://github.com/matsvanes/reproducescript/blob/master/example3_andersen/omission_frontiers/FieldTrip/run_all.m) + + clear + close all + + global ft_default + ft_default = []; + ft_default.checksize = inf; + + %% Single subject analysis + datainfo; + for do_subject = 1:numel(all_subjects) + reproduce_dir = [home_dir, sprintf('reproduce%02d/', do_subject)]; + + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % enable reproducescript + ft_default.reproducescript = reproduce_dir; + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + % Create all relevant directories where all data and all figures will be + % saved + create_MEG_BIDS_data_structure + + % Go from raw MEG data to a time-frequency representation + sensor_space_analysis + + % Go from raw MRI data to a volume conductor and a forward model + mr_preprocessing + + % Extract fourier transforms and do beamformer source reconstructions + source_space_analysis + + %%%%%%%%%%%% + % plotting % + %%%%%%%%%%%% + % Plot all steps in the sensor space analysis + plot_sensor_space + + % Plot all steps in the MR processing + plot_processed_mr + + % Plot all steps in the source space analysis + plot_source_space + + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % disable reproducescript + ft_default.reproducescript = []; + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + end + + %% Group analysis + datainfo; + reproduce_dir = [home_dir, 'reproduce_group']; + + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % enable reproducescript + ft_default.reproducescript = reproduce_dir; + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + % Do grand averages across subjects for both sensor and source spaces + grand_averages + + % Do statistics on time-frequency representations and beamformer source + % reconstructions + statistics + + % Plot grand averages in both the sensor and source spaces, with and + % without statistical masking + plot_grand_averages + + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % disable reproducescript + ft_default.reproducescript = []; + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +We refer the reader to [GitHub](https://github.com/matsvanes/reproducescript/tree/master/example3_andersen) to see what the reproduced `script.m` look like for the two subjects and the group level. + + +## Conclusion +In three examples we have shown how FieldTrip's *reproducescript* functionality can be applied to any analysis pipeline that is based on the FieldTrip ecosystem. This functionality can be applied without much effort on the researcher's side, and it generates all code, original and intermediate data, as well as final results in a format in which it's readily shareable and reproducible. + +Note that there are other strategies for improving shareability and reproducibility, and we don't assume that *reproducescript* is the best way in every scenario. Rather, it is one of many tools that can aid the researcher to improve the community's standard in methodological transparency and robustness of results. For other strategies, we refer the reader to the pre-print in which we first described *reproducescript*. + + +## Suggested further reading +- [Reducing the efforts to create reproducible analysis code with FieldTrip](https://doi.org/10.1101/2021.02.05.429886 ) +- [Andersen. (2018). Group Analysis in FieldTrip of Time-Frequency Responses: A Pipeline for Reproducibility at Every Step of Processing, Going From Individual Sensor Space Representations to an Across-Group Source Space Representation](https://doi.org/10.3389/fnins.2018.00261) + + diff --git a/example/reproducescript_group.md b/example/reproducescript_group.md new file mode 100644 index 000000000..0902d4166 --- /dev/null +++ b/example/reproducescript_group.md @@ -0,0 +1,768 @@ +--- +title: reproducescript group analysis +tags: [example, reproducescript, reproducibility] +--- + +#*reproducescript* group analysis + + +This example script will introduce you to the a FieldTrip tool that was designed to aid in making your analysis pipeline - including code, data and results - easily reproducible and shareable. It is based on [Reducing the efforts to create reproducible analysis code with FieldTrip](https://doi.org/10.1101/2021.02.05.429886) by Mats van Es, Eelke Spaak, Jan-Mathijs Schoffelen, and Robert Oostenveld. We assume the reader has already had a look at [Making your analysis pipeline reprodudible using *reproducescript*](/example/reproducescript). + +##Example 2 +The first example contained only a few analysis steps in a single subject. More realistic data analysis pipelines consist of many more steps in which often the same (or similar) pipelines are used for multiple subjects. In this section, we will show how the reproducescript functionality applies in such a case. + +### Original analysis +The analysis example follows the strategy outlined in [Knuth: Literate Programming](https://www-cs-faculty.stanford.edu/~knuth/lp.html)(31) and starts with a single subject analysis pipeline that is repeated for four subjects. The directory is structured as depicted below. + +{% include image src="/assets/img/example/reproducescript/filedir_example2_analysis.jpg" width="500" %} + +After the single-subject analysis, all single-subject results are used in a group analysis. The single-subject and group analyses are executed from the master script `analyze.m`: + +####**analyze.m** + + + clear + close all + + subjlist = { + 'Subject01' + 'Subject02' + 'Subject03' + 'Subject04' + }; + + %% Loop single-subject analysis over subjects + for i=1:numel(subjlist) + subj = subjlist{i}; + doSingleSubjectAnalysis(subj); + end + + %% Group analysis + doGroupAnalysis(subjlist); + + + This is the control script from which the relevant analysis scripts and functions are called. The master script relies on two functions: `doSingleSubjectAnalysis` and `doGroupAnalysis`, which are each stored in separate m-files. The original source code for these scripts can be found below. + +####**doSingleSubjectAnalysis.m** + + function doSingleSubjectAnalysis(subj) + + % the details of each subject are in separate files + % details_Subject01.m + % details_Subject02.m + % details_Subject03.m + % details_Subject04.m + + fprintf('evaluating single subject analysis for %s\n', subj); + eval(['details_' subj]); + + % this is for artifact detection + interactive = false; + + %% + + cfg = []; + cfg.dataset = fullfile(datadir, filename); + cfg.trialfun = 'ft_trialfun_general'; + cfg.trialdef.eventtype = 'backpanel trigger'; + cfg.trialdef.eventvalue = [triggerFIC triggerIC triggerFC]; + cfg.trialdef.prestim = 1; + cfg.trialdef.poststim = 2; + cfg = ft_definetrial(cfg); + + % the EOG channel has a different name in the different datasets + cfg.channel = {'MEG' eogchannel}; + cfg.continuous = 'yes'; + data = ft_preprocessing(cfg); + + %% + + if interactive + % visually identify the artifacts + cfg = []; + cfg.channel = eogchannel; + cfg.method = 'channel'; + dummy1 = ft_rejectvisual(cfg, data); + + cfg = []; + cfg.channel = 'MEG'; + cfg.method = 'summary'; + dummy2 = ft_rejectvisual(cfg, data); + + % combine the artifacts that have been detected + artifact = [ + dummy1.cfg.artfctdef.channel.artifact + dummy2.cfg.artfctdef.summary.artifact + ]; + + % print them and copy them to the subject details file + disp(artifact); + + % use the MATLAB debugger to wait on this line + disp('please copy these artifacts to the subject details file'); + keyboard + end + + % remove the artifacts that were previously detected + cfg = []; + cfg.artfctdef.visual.artifact = artifact; + data_clean = ft_rejectartifact(cfg, data); + + %% + + cfg = []; + cfg.trials = data_clean.trialinfo==triggerFIC; + avgFIC = ft_timelockanalysis(cfg, data_clean); + + cfg.trials = data_clean.trialinfo==triggerFC; + avgFC = ft_timelockanalysis(cfg, data_clean); + + cfg.trials = data_clean.trialinfo==triggerIC; + avgIC = ft_timelockanalysis(cfg, data_clean); + + %% + + cfg = []; + cfg.showlabels = 'no'; + cfg.fontsize = 6; + cfg.layout = 'CTF151_helmet.mat'; + cfg.baseline = [-0.2 0]; + cfg.xlim = [-0.2 1.0]; + cfg.ylim = [-3e-13 3e-13]; + ft_multiplotER(cfg, avgFC, avgIC, avgFIC); + + %% + + cfg = []; + cfg.feedback = 'yes'; + cfg.method = 'template'; + cfg.neighbours = ft_prepare_neighbours(cfg, avgFIC); + cfg.planarmethod = 'sincos'; + avgFICplanar = ft_megplanar(cfg, avgFIC); + avgFCplanar = ft_megplanar(cfg, avgFC); + avgICplanar = ft_megplanar(cfg, avgIC); + + %% + + cfg = []; + avgFICplanarComb = ft_combineplanar(cfg, avgFICplanar); + avgFCplanarComb = ft_combineplanar(cfg, avgFCplanar); + avgICplanarComb = ft_combineplanar(cfg, avgICplanar); + + %% + + cfg = []; + cfg.xlim = [0.3 0.5]; + cfg.zlim = 'maxmin'; + cfg.colorbar = 'yes'; + cfg.layout = 'CTF151_helmet.mat'; + subplot(2,3,1); ft_topoplotER(cfg, avgFIC) + subplot(2,3,2); ft_topoplotER(cfg, avgFC) + subplot(2,3,3); ft_topoplotER(cfg, avgIC) + + cfg.zlim = 'maxabs'; + cfg.layout = 'CTF151_helmet.mat'; + subplot(2,3,4); ft_topoplotER(cfg, avgFICplanarComb) + subplot(2,3,5); ft_topoplotER(cfg, avgFCplanarComb) + subplot(2,3,6); ft_topoplotER(cfg, avgICplanarComb) + + %% + + % save the results to disk + outputdir = ['result_' subj]; + mkdir(outputdir) + save(fullfile(outputdir, 'avgFIC'), 'avgFIC'); + save(fullfile(outputdir, 'avgFC'), 'avgFC'); + save(fullfile(outputdir, 'avgIC'), 'avgIC'); + save(fullfile(outputdir, 'avgFICplanarComb'), 'avgFICplanarComb'); + save(fullfile(outputdir, 'avgFCplanarComb'), 'avgFCplanarComb'); + save(fullfile(outputdir, 'avgICplanarComb'), 'avgICplanarComb'); + +####**doGroupAnalysis.m** + + function doGroupAnalysis(allsubj) + + avgFIC = cell(size(allsubj)); + avgFC = cell(size(allsubj)); + avgIC = cell(size(allsubj)); + + % load the results from disk + for i=1:numel(allsubj) + subj = allsubj{i}; + fprintf('loading data for subject %s\n', subj); + + inputdir = ['result_' subj]; + tmp = load(fullfile(inputdir, 'avgFIC')); avgFIC{i} = tmp.avgFIC; + tmp = load(fullfile(inputdir, 'avgFC')); avgFC{i} = tmp.avgFC; + tmp = load(fullfile(inputdir, 'avgIC')); avgIC{i} = tmp.avgIC; + clear tmp + end + + %% + + cfg = []; + cfg.showlabels = 'no'; + cfg.fontsize = 6; + cfg.layout = 'CTF151_helmet.mat'; + cfg.baseline = [-0.2 0]; + cfg.xlim = [-0.2 1.0]; + cfg.ylim = [-3e-13 3e-13]; + figure + ft_multiplotER(cfg, avgFIC{:}); + title('Fully incongruent condition'); + + figure + ft_multiplotER(cfg, avgFC{:}); + title('Fully congruent condition'); + + figure + ft_multiplotER(cfg, avgIC{:}); + title('Initially congruent condition'); + + %% + + avgFICvsFC = cell(size(allsubj)); + for i=1:numel(allsubj) + cfg = []; + cfg.parameter = 'avg'; + cfg.operation = 'x1-x2'; + avgFICvsFC{i} = ft_math(cfg, avgFIC{i}, avgFC{i}); + end + + cfg = []; + cfg.showlabels = 'no'; + cfg.fontsize = 6; + cfg.layout = 'CTF151_helmet.mat'; + cfg.baseline = [-0.2 0]; + cfg.xlim = [-0.2 1.0]; + cfg.ylim = [-3e-13 3e-13]; + ft_multiplotER(cfg, avgFICvsFC{:}); + title('FIC minus FC'); + + %% + + % let's make a manual change to the data that is not caputured in the provenance + for i=1:numel(allsubj) + avgFIC{i}.avg = avgFIC{i}.avg * 1e15; % convert from T to fT + avgFC{i}.avg = avgFC{i}.avg * 1e15; % convert from T to fT + avgIC{i}.avg = avgIC{i}.avg * 1e15; % convert from T to fT + end + + %% + + cfg = []; + grandavgFIC = ft_timelockgrandaverage(cfg, avgFIC{:}); + grandavgFC = ft_timelockgrandaverage(cfg, avgFC{:}); + grandavgIC = ft_timelockgrandaverage(cfg, avgIC{:}); + + %% + + % save the results to disk + outputdir = 'result_Group'; + mkdir(outputdir) + save(fullfile(outputdir, 'grandavgFIC'), 'grandavgFIC'); + save(fullfile(outputdir, 'grandavgFC'), 'grandavgFC'); + save(fullfile(outputdir, 'grandavgIC'), 'grandavgIC'); + +###Initialization of reproducescript +To create a standard script from the analysis pipeline, the `ft_default` variable is initialized at the top of `analyze.m`. Note that we do not immediately initiate reproducescript, this is done in the loop just before `doSingleSubjectAnalysis`, and just before `doGroupAnalysis` by specifying unique directories for each subject and foir the group. In fact, reproducescript can be stopped and restarted between different subjects, or even in between analysis steps, which is especially convenient in pipelines that require a lot of compute resources and that the researcher rather splits up to allow for parallel execution on a compute cluster. + +####**analyze.m** with reproducescript initialisation + + clear + close all + + % initialize ft_default variable + global ft_default + ft_default = []; + ft_default.checksize = inf; + + subjlist = { + 'Subject01' + 'Subject02' + 'Subject03' + 'Subject04' + }; + + %% Loop single-subject analysis over subjects + for i=1:numel(subjlist) + subj = subjlist{i}; + % initiate reproducescript + ft_default.reproducescript = ['reproduce_' subj]; + doSingleSubjectAnalysis(subj); + ft_default.reproducescript = []; % disable + end + + %% Group analysis + % initiate reproducescript + ft_default.reproducescript = 'reproduce_Group'; + doGroupAnalysis(subjlist); + ft_default.reproducescript = []; % disable + +### Reproduced analysis + +{% include image src="/assets/img/example/reproducescript/filedir_example2_reproducescript.jpg" width="500" %} + +We devoted a specific folder to the reproducescript content of each subject, and one for the group analysis. Thus, upon execution of the master script in `analyze.m`, folders are created for each of the subjects, and for the group analysis. These all contain the intermediate data, a standardized script, and a `hashes.mat` file for the bookkeeping. The reproducescript standardized scripts for the single-subject analysis and group analysis can be found below. Note that these are quite lenghty, but they unambiguously list all analysis steps. + +####Single subject reproduced analysis +**project/example2/reproduce_Subject01/script.m** + %% + + cfg = []; + cfg.dataset = '../rawdata/Subject01.ds'; + cfg.trialfun = 'ft_trialfun_general'; + cfg.trialdef.eventtype = 'backpanel trigger'; + cfg.trialdef.eventvalue = [3 5 9]; + cfg.trialdef.prestim = 1; + cfg.trialdef.poststim = 2; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg = ft_definetrial(cfg); + + %% + + cfg = []; + cfg.dataset = '../rawdata/Subject01.ds'; + cfg.trialfun = 'ft_trialfun_general'; + cfg.trialdef.eventtype = 'backpanel trigger'; + cfg.trialdef.eventvalue = [3 5 9]; + cfg.trialdef.prestim = 1; + cfg.trialdef.poststim = 2; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.datafile = '../rawdata/Subject01.ds/Subject01.meg4'; + cfg.headerfile = '../rawdata/Subject01.ds/Subject01.res4'; + cfg.dataformat = 'ctf_meg4'; + cfg.headerformat = 'ctf_res4'; + cfg.representation = 'numeric'; + cfg.trl = 'reproduce_Subject01/20210112T113604_ft_preprocessing_largecfginput_trl.mat'; + cfg.outputfile = { 'reproduce_Subject01/20210112T113604_ft_preprocessing_output_data.mat' }; + cfg.channel = {'MEG', 'EOG'}; + cfg.continuous = 'yes'; + ft_preprocessing(cfg); + + %% + + cfg = []; + cfg.artfctdef.visual.artifact = [8101 9000; + 68401 69300; + 99001 99900; + ... + 228601 229500]; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { 'reproduce_Subject01/20210112T113604_ft_preprocessing_output_data.mat' }; + cfg.outputfile = { 'reproduce_Subject01/20210112T113611_ft_rejectartifact_output_data.mat' }; + ft_rejectartifact(cfg); + + %% + + cfg = []; + cfg.trials = logical([true false ... false]); + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { 'reproduce_Subject01/20210112T113611_ft_rejectartifact_output_data.mat' }; + cfg.outputfile = { 'reproduce_Subject01/20210112T113616_ft_timelockanalysis_output_timelock.mat' }; + ft_timelockanalysis(cfg); + + %% + + cfg = []; + cfg.trials = logical([false false ... false]); + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { 'reproduce_Subject01/20210112T113611_ft_rejectartifact_output_data.mat' }; + cfg.outputfile = { 'reproduce_Subject01/20210112T113621_ft_timelockanalysis_output_timelock.mat' }; + ft_timelockanalysis(cfg); + + %% + + cfg = []; + cfg.trials = logical([false true ... true]); + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { 'reproduce_Subject01/20210112T113611_ft_rejectartifact_output_data.mat' }; + cfg.outputfile = { 'reproduce_Subject01/20210112T113625_ft_timelockanalysis_output_timelock.mat' }; + ft_timelockanalysis(cfg); + + %% + + cfg = []; + cfg.showlabels = 'no'; + cfg.fontsize = 6; + cfg.layout = 'CTF151_helmet.mat'; + cfg.baseline = [-0.2 0]; + cfg.xlim = [-0.2 1]; + cfg.ylim = [-3e-13 3e-13]; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { + 'reproduce_Subject01/20210112T113621_ft_timelockanalysis_output_timelock.mat', 'reproduce_Subject01/20210112T113625_ft_timelockanalysis_output_timelock.mat', 'reproduce_Subject01/20210112T113616_ft_timelockanalysis_output_timelock.mat' + }; + cfg.outputfile = 'reproduce_Subject01/20210112T113634_ft_multiplotER_output'; + figure; + ft_multiplotER(cfg); + + %% + + cfg = []; + cfg.feedback = 'yes'; + cfg.method = 'template'; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { 'reproduce_Subject01/20210112T113616_ft_timelockanalysis_output_timelock.mat' }; + cfg.outputfile = { 'reproduce_Subject01/20210112T113643_ft_prepare_neighbours_output_neighbours.mat' }; + ft_prepare_neighbours(cfg); + + %% + + cfg = []; + cfg.feedback = 'yes'; + cfg.method = 'template'; + cfg.neighbours = 'reproduce_Subject01/20210112T113643_ft_megplanar_largecfginput_neighbours.mat'; + cfg.planarmethod = 'sincos'; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { 'reproduce_Subject01/20210112T113616_ft_timelockanalysis_output_timelock.mat' }; + cfg.outputfile = { 'reproduce_Subject01/20210112T113647_ft_megplanar_output_data.mat' }; + ft_megplanar(cfg); + + %% + + cfg = []; + cfg.feedback = 'yes'; + cfg.method = 'template'; + cfg.neighbours = 'reproduce_Subject01/20210112T113643_ft_megplanar_largecfginput_neighbours.mat'; + cfg.planarmethod = 'sincos'; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { 'reproduce_Subject01/20210112T113621_ft_timelockanalysis_output_timelock.mat' }; + cfg.outputfile = { 'reproduce_Subject01/20210112T113650_ft_megplanar_output_data.mat' }; + ft_megplanar(cfg); + + %% + + cfg = []; + cfg.feedback = 'yes'; + cfg.method = 'template'; + cfg.neighbours = 'reproduce_Subject01/20210112T113643_ft_megplanar_largecfginput_neighbours.mat'; + cfg.planarmethod = 'sincos'; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { 'reproduce_Subject01/20210112T113625_ft_timelockanalysis_output_timelock.mat' }; + cfg.outputfile = { 'reproduce_Subject01/20210112T113653_ft_megplanar_output_data.mat' }; + ft_megplanar(cfg); + + %% + + cfg = []; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { 'reproduce_Subject01/20210112T113647_ft_megplanar_output_data.mat' }; + cfg.outputfile = { 'reproduce_Subject01/20210112T113657_ft_combineplanar_output_data.mat' }; + ft_combineplanar(cfg); + + %% + + cfg = []; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { 'reproduce_Subject01/20210112T113650_ft_megplanar_output_data.mat' }; + cfg.outputfile = { 'reproduce_Subject01/20210112T113701_ft_combineplanar_output_data.mat' }; + ft_combineplanar(cfg); + + %% + + cfg = []; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { 'reproduce_Subject01/20210112T113653_ft_megplanar_output_data.mat' }; + cfg.outputfile = { 'reproduce_Subject01/20210112T113704_ft_combineplanar_output_data.mat' }; + ft_combineplanar(cfg); + + %% + + cfg = []; + cfg.xlim = [0.3 0.5]; + cfg.zlim = 'maxmin'; + cfg.colorbar = 'yes'; + cfg.layout = 'CTF151_helmet.mat'; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { 'reproduce_Subject01/20210112T113616_ft_timelockanalysis_output_timelock.mat' }; + cfg.outputfile = 'reproduce_Subject01/20210112T113708_ft_topoplotER_output'; + figure; + ft_topoplotER(cfg); + + %% + + cfg = []; + cfg.xlim = [0.3 0.5]; + cfg.zlim = 'maxmin'; + cfg.colorbar = 'yes'; + cfg.layout = 'CTF151_helmet.mat'; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { 'reproduce_Subject01/20210112T113621_ft_timelockanalysis_output_timelock.mat' }; + cfg.outputfile = 'reproduce_Subject01/20210112T113712_ft_topoplotER_output'; + figure; + ft_topoplotER(cfg); + + %% + + cfg = []; + cfg.xlim = [0.3 0.5]; + cfg.zlim = 'maxmin'; + cfg.colorbar = 'yes'; + cfg.layout = 'CTF151_helmet.mat'; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { 'reproduce_Subject01/20210112T113625_ft_timelockanalysis_output_timelock.mat' }; + cfg.outputfile = 'reproduce_Subject01/20210112T113716_ft_topoplotER_output'; + figure; + ft_topoplotER(cfg); + + %% + + cfg = []; + cfg.xlim = [0.3 0.5]; + cfg.zlim = 'maxabs'; + cfg.colorbar = 'yes'; + cfg.layout = 'CTF151_helmet.mat'; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { 'reproduce_Subject01/20210112T113657_ft_combineplanar_output_data.mat' }; + cfg.outputfile = 'reproduce_Subject01/20210112T113721_ft_topoplotER_output'; + figure; + ft_topoplotER(cfg); + + %% + + cfg = []; + cfg.xlim = [0.3 0.5]; + cfg.zlim = 'maxabs'; + cfg.colorbar = 'yes'; + cfg.layout = 'CTF151_helmet.mat'; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { 'reproduce_Subject01/20210112T113701_ft_combineplanar_output_data.mat' }; + cfg.outputfile = 'reproduce_Subject01/20210112T113726_ft_topoplotER_output'; + figure; + ft_topoplotER(cfg); + + %% + + cfg = []; + cfg.xlim = [0.3 0.5]; + cfg.zlim = 'maxabs'; + cfg.colorbar = 'yes'; + cfg.layout = 'CTF151_helmet.mat'; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { 'reproduce_Subject01/20210112T113704_ft_combineplanar_output_data.mat' }; + cfg.outputfile = 'reproduce_Subject01/20210112T113731_ft_topoplotER_output'; + figure; + ft_topoplotER(cfg); + + +####Group reproduced analysis +**project/example2/reproduce_Group/script.m** + + %% + + % a new input variable is entering the pipeline here: 20210112T114236_ft_multiplotER_input_varargin_1.mat + % a new input variable is entering the pipeline here: 20210112T114236_ft_multiplotER_input_varargin_2.mat + % a new input variable is entering the pipeline here: 20210112T114236_ft_multiplotER_input_varargin_3.mat + % a new input variable is entering the pipeline here: 20210112T114236_ft_multiplotER_input_varargin_4.mat + + cfg = []; + cfg.showlabels = 'no'; + cfg.fontsize = 6; + cfg.layout = 'CTF151_helmet.mat'; + cfg.baseline = [-0.2 0]; + cfg.xlim = [-0.2 1]; + cfg.ylim = [-3e-13 3e-13]; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { + 'reproduce_Group/20210112T114236_ft_multiplotER_input_varargin_1.mat', 'reproduce_Group/20210112T114236_ft_multiplotER_input_varargin_2.mat', 'reproduce_Group/20210112T114236_ft_multiplotER_input_varargin_3.mat', 'reproduce_Group/20210112T114236_ft_multiplotER_input_varargin_4.mat' + }; + cfg.outputfile = 'reproduce_Group/20210112T114246_ft_multiplotER_output'; + figure; + ft_multiplotER(cfg); + + %% + + % a new input variable is entering the pipeline here: 20210112T114253_ft_multiplotER_input_varargin_1.mat + % a new input variable is entering the pipeline here: 20210112T114253_ft_multiplotER_input_varargin_2.mat + % a new input variable is entering the pipeline here: 20210112T114253_ft_multiplotER_input_varargin_3.mat + % a new input variable is entering the pipeline here: 20210112T114253_ft_multiplotER_input_varargin_4.mat + + cfg = []; + cfg.showlabels = 'no'; + cfg.fontsize = 6; + cfg.layout = 'CTF151_helmet.mat'; + cfg.baseline = [-0.2 0]; + cfg.xlim = [-0.2 1]; + cfg.ylim = [-3e-13 3e-13]; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { + 'reproduce_Group/20210112T114253_ft_multiplotER_input_varargin_1.mat', 'reproduce_Group/20210112T114253_ft_multiplotER_input_varargin_2.mat', 'reproduce_Group/20210112T114253_ft_multiplotER_input_varargin_3.mat', 'reproduce_Group/20210112T114253_ft_multiplotER_input_varargin_4.mat' + }; + cfg.outputfile = 'reproduce_Group/20210112T114303_ft_multiplotER_output'; + figure; + ft_multiplotER(cfg); + + %% + + % a new input variable is entering the pipeline here: 20210112T114310_ft_multiplotER_input_varargin_1.mat + % a new input variable is entering the pipeline here: 20210112T114310_ft_multiplotER_input_varargin_2.mat + % a new input variable is entering the pipeline here: 20210112T114310_ft_multiplotER_input_varargin_3.mat + % a new input variable is entering the pipeline here: 20210112T114310_ft_multiplotER_input_varargin_4.mat + + cfg = []; + cfg.showlabels = 'no'; + cfg.fontsize = 6; + cfg.layout = 'CTF151_helmet.mat'; + cfg.baseline = [-0.2 0]; + cfg.xlim = [-0.2 1]; + cfg.ylim = [-3e-13 3e-13]; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { + 'reproduce_Group/20210112T114310_ft_multiplotER_input_varargin_1.mat', 'reproduce_Group/20210112T114310_ft_multiplotER_input_varargin_2.mat', 'reproduce_Group/20210112T114310_ft_multiplotER_input_varargin_3.mat', 'reproduce_Group/20210112T114310_ft_multiplotER_input_varargin_4.mat' + }; + cfg.outputfile = 'reproduce_Group/20210112T114321_ft_multiplotER_output'; + figure; + ft_multiplotER(cfg); + + %% + + cfg = []; + cfg.parameter = 'avg'; + cfg.operation = 'x1-x2'; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { + 'reproduce_Group/20210112T114236_ft_multiplotER_input_varargin_1.mat', 'reproduce_Group/20210112T114253_ft_multiplotER_input_varargin_1.mat' + }; + cfg.outputfile = { 'reproduce_Group/20210112T114330_ft_math_output_data.mat' }; + ft_math(cfg); + + %% + + cfg = []; + cfg.parameter = 'avg'; + cfg.operation = 'x1-x2'; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { + 'reproduce_Group/20210112T114236_ft_multiplotER_input_varargin_2.mat', 'reproduce_Group/20210112T114253_ft_multiplotER_input_varargin_2.mat' + }; + cfg.outputfile = { 'reproduce_Group/20210112T114333_ft_math_output_data.mat' }; + ft_math(cfg); + + %% + + cfg = []; + cfg.parameter = 'avg'; + cfg.operation = 'x1-x2'; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { + 'reproduce_Group/20210112T114236_ft_multiplotER_input_varargin_3.mat', 'reproduce_Group/20210112T114253_ft_multiplotER_input_varargin_3.mat' + }; + cfg.outputfile = { 'reproduce_Group/20210112T114337_ft_math_output_data.mat' }; + ft_math(cfg); + + %% + + cfg = []; + cfg.parameter = 'avg'; + cfg.operation = 'x1-x2'; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { + 'reproduce_Group/20210112T114236_ft_multiplotER_input_varargin_4.mat', 'reproduce_Group/20210112T114253_ft_multiplotER_input_varargin_4.mat' + }; + cfg.outputfile = { 'reproduce_Group/20210112T114340_ft_math_output_data.mat' }; + ft_math(cfg); + + %% + + cfg = []; + cfg.showlabels = 'no'; + cfg.fontsize = 6; + cfg.layout = 'CTF151_helmet.mat'; + cfg.baseline = [-0.2 0]; + cfg.xlim = [-0.2 1]; + cfg.ylim = [-3e-13 3e-13]; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { + 'reproduce_Group/20210112T114330_ft_math_output_data.mat', 'reproduce_Group/20210112T114333_ft_math_output_data.mat', 'reproduce_Group/20210112T114337_ft_math_output_data.mat', 'reproduce_Group/20210112T114340_ft_math_output_data.mat' + }; + cfg.outputfile = 'reproduce_Group/20210112T114351_ft_multiplotER_output'; + figure; + ft_multiplotER(cfg); + + %% + + % a new input variable is entering the pipeline here: 20210112T114358_ft_timelockgrandaverage_input_varargin_1.mat + % a new input variable is entering the pipeline here: 20210112T114358_ft_timelockgrandaverage_input_varargin_2.mat + % a new input variable is entering the pipeline here: 20210112T114358_ft_timelockgrandaverage_input_varargin_3.mat + % a new input variable is entering the pipeline here: 20210112T114358_ft_timelockgrandaverage_input_varargin_4.mat + + cfg = []; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { + 'reproduce_Group/20210112T114358_ft_timelockgrandaverage_input_varargin_1.mat', 'reproduce_Group/20210112T114358_ft_timelockgrandaverage_input_varargin_2.mat', 'reproduce_Group/20210112T114358_ft_timelockgrandaverage_input_varargin_3.mat', 'reproduce_Group/20210112T114358_ft_timelockgrandaverage_input_varargin_4.mat' + }; + cfg.outputfile = { 'reproduce_Group/20210112T114403_ft_timelockgrandaverage_output_grandavg.mat' }; + ft_timelockgrandaverage(cfg); + + %% + + % a new input variable is entering the pipeline here: 20210112T114404_ft_timelockgrandaverage_input_varargin_1.mat + % a new input variable is entering the pipeline here: 20210112T114404_ft_timelockgrandaverage_input_varargin_2.mat + % a new input variable is entering the pipeline here: 20210112T114404_ft_timelockgrandaverage_input_varargin_3.mat + % a new input variable is entering the pipeline here: 20210112T114404_ft_timelockgrandaverage_input_varargin_4.mat + + cfg = []; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { + 'reproduce_Group/20210112T114404_ft_timelockgrandaverage_input_varargin_1.mat', 'reproduce_Group/20210112T114404_ft_timelockgrandaverage_input_varargin_2.mat', 'reproduce_Group/20210112T114404_ft_timelockgrandaverage_input_varargin_3.mat', 'reproduce_Group/20210112T114404_ft_timelockgrandaverage_input_varargin_4.mat' + }; + cfg.outputfile = { 'reproduce_Group/20210112T114409_ft_timelockgrandaverage_output_grandavg.mat' }; + ft_timelockgrandaverage(cfg); + + %% + + % a new input variable is entering the pipeline here: 20210112T114411_ft_timelockgrandaverage_input_varargin_1.mat + % a new input variable is entering the pipeline here: 20210112T114411_ft_timelockgrandaverage_input_varargin_2.mat + % a new input variable is entering the pipeline here: 20210112T114411_ft_timelockgrandaverage_input_varargin_3.mat + % a new input variable is entering the pipeline here: 20210112T114411_ft_timelockgrandaverage_input_varargin_4.mat + + cfg = []; + cfg.tracktimeinfo = 'yes'; + cfg.trackmeminfo = 'yes'; + cfg.inputfile = { + 'reproduce_Group/20210112T114411_ft_timelockgrandaverage_input_varargin_1.mat', 'reproduce_Group/20210112T114411_ft_timelockgrandaverage_input_varargin_2.mat', 'reproduce_Group/20210112T114411_ft_timelockgrandaverage_input_varargin_3.mat', 'reproduce_Group/20210112T114411_ft_timelockgrandaverage_input_varargin_4.mat' + }; + cfg.outputfile = { 'reproduce_Group/20210112T114415_ft_timelockgrandaverage_output_grandavg.mat' }; + ft_timelockgrandaverage(cfg); + + + +## Conclusion +We show how *reproducescript* can be applied to a group analysis by enabling it seperately for each subject, and then once for the group analysis. It would be possible as well to enable reproducescript only once at the top of the master script, but we found the current solution to have a more clear organisation. There is one more example in which we apply reproducescript to full study published by Andersen et al. This shows how *reproducescript* would be applied in a more complex scenario. + +Note that there are other strategies for improving shareability and reproducibility, and we don't assume that *reproducescript* is the best way in every scenario. Rather, it is one of many tools that can aid the researcher to improve the community's standard in methodological transparency and robustness of results. For other strategies, we refer the reader to the pre-print in which we first described *reproducescript*. + +## Suggested further reading +- [Reducing the efforts to create reproducible analysis code with FieldTrip](https://doi.org/10.1101/2021.02.05.429886 ) +- [Reproducescript on a full study](/example/reproducescript_andersen)