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sullivan-jamessullivan-james
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Major restructure of pgmatlab for v2
- Move code into object-oriented structure, whilst keeeping loadPamguardBinaryFile, loadPamguardBinaryFolder and loadPamguardMultiFile the same. - Add a testing suite to compare the new v2 code with the 'legacy' v1 code. - Integrate the testing suite into GitHub actions for an automated testing and distribution workflow.
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‎.github/workflows/build-package.yml‎

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# This workflow will automatically create a ZIP file
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# of the distribution files, and add them to a GitHub
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# release. It only runs, on creation of the GitHub
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# release.
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name: Build Package
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on:
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release:
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types: [published]
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permissions:
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contents: write
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jobs:
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build:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout code
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uses: actions/checkout@v4
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- name: Zip code to distribute
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run: |
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zip -r "pgmatlab-${{ github.event.release.tag_name }}.zip" pgmatlab/* LICENSE README.md
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- name: Upload code to distribute
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uses: actions/upload-artifact@v4
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with:
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name: dist-files
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path: "pgmatlab-${{ github.event.release.tag_name }}.zip"
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- name: Append distribution code to release
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uses: softprops/action-gh-release@v2
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with:
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files: "pgmatlab-${{ github.event.release.tag_name }}.zip"

‎.github/workflows/matlab-testing.yml‎

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name: MATLAB Testing
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# Controls when the workflow will run
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on:
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# Triggers the workflow on push or pull request events but only for the "main" branch
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push:
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branches: [ "dev" ]
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pull_request:
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branches: [ "dev" ]
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# Allows you to run this workflow manually from the Actions tab
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workflow_dispatch:
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# A workflow run is made up of one or more jobs that can run sequentially or in parallel
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jobs:
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# This workflow contains a single job called "build"
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build:
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# The type of runner that the job will run on
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runs-on: ${{ matrix.os }}
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strategy:
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matrix:
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matlab-version: [r2020a, r2021a, r2022a, r2023a, r2025a]
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os: [ubuntu-latest, windows-latest, macos-latest]
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steps:
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- name: Check out repository
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uses: actions/checkout@v4
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- name: Set up MATLAB
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uses: matlab-actions/setup-matlab@v2.1.2
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with:
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version: ${{ matrix.matlab-version }}
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- name: Run MATLAB Tests
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uses: matlab-actions/run-tests@v2.1.2
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with:
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select-by-folder: tests

‎pgmatlab/.MATLABDriveTag‎

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‎pgmatlab/Array/.MATLABDriveTag‎

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‎pgmatlab/core/annotations/@BeamFormer/BeamFormer.m‎

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classdef BeamFormer < StandardAnnotation
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methods
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function obj = BeamFormer(); end
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function [data, selState] = read(obj, fid, data, fileInfo, anLength, anVersion);
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[data, selState] = read@StandardAnnotation(obj, fid, data, fileInfo, anLength, anVersion);
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data.hydrophones = fread(fid, 1, 'uint32');
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data.arrayType = fread(fid, 1, 'int16');
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data.localisationContent = fread( fid, 1, 'uint32');
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data.nAngles = fread(fid, 1, 'int16');
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data.angles = fread(fid, data.nAngles, 'float32');
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end
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end
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end

‎pgmatlab/core/annotations/@Bearing/Bearing.m‎

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classdef Bearing < StandardAnnotation
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methods
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function obj = Bearing(); end
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function [data, selState] = read(obj, fid, data, fileInfo, anLength, anVersion);
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[data, selState] = read@StandardAnnotation(obj, fid, data, fileInfo, anLength, anVersion);
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data.algorithmName = readJavaUTFString(fid);
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data.version = annotationVersion;
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data.hydrophones = fread(fid, 1, 'uint32');
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data.arrayType = fread(fid, 1, 'int16');
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data.localisationContent = fread( fid, 1, 'uint32');
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data.nAngles = fread(fid, 1, 'int16');
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data.angles = fread(fid, data.nAngles, 'float32');
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data.nErrors = fread(fid, 1, 'int16');
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data.errors = fread(fid, data.nErrors, 'float32');
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if (annotationVersion >= 2)
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nAng = fread(fid, 1, 'int16');
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data.refAngles = fread(fid, nAng, 'float32')
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end
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end
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end
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end

‎pgmatlab/core/annotations/@ClickClsFr/ClickClsFr.m‎

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classdef ClickClsFr < StandardAnnotation
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methods
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function obj = ClickClsFr(); end
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function [data, selState] = read(obj, fid, data, fileInfo, anLength, anVersion);
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[data, selState] = read@StandardAnnotation(obj, fid, data, fileInfo, anLength, anVersion);
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nclassifications = fread(fid, 1, 'int16');
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for i = 1:nclassifications
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data.classify_set(i) = fread(fid, 1, 'int16');
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end
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end
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end
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end
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‎pgmatlab/core/annotations/@DL/DL.m‎

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classdef DL < StandardAnnotation
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properties
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name = 'dlclassification';
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end
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methods (Access = private)
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function modeldata = readModelData(~, fid)
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modeltype = fread(fid, 1, 'char'); % bye in Java
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isbinary = fread(fid, 1, 'char'); % bye in Java
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isbinary = isbinary~=0; % boolean is stored as byte in Java
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scale = fread(fid, 1, 'float'); % the scale for prediciton results - short in Java
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nspecies = fread(fid, 1, 'int16'); % the scale for prediciton results - short in Java
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% read prediciton results
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tempdata=-1*ones(length(nspecies),1);
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for j=1:nspecies
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tempdata(j) = fread(fid, 1, 'int16')/scale;
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end
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% get the class name IDs
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nclass = fread(fid, 1, 'int16'); % the number of class names.classnames
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classnames=-1*ones(length(nclass),1);
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for j=1:nclass
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classnames(j) = fread(fid, 1, 'int16');
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end
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switch (modeltype)
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case 0 % generic deep learning annotation
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modeldata.predictions = tempdata;
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modeldata.classID = classnames;
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modeldata.isbinary = isbinary;
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modeldata.type = modeltype;
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case 1 % Sound Spot classifier.
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% Sound spot
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modeldata.predictions = tempdata;
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modeldata.classID = classnames;
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modeldata.isbinary = isbinary;
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modeldata.type = modeltype;
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case 2 % dummy result
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modeldata.predictions = [];
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modeldata.type = 'dummy';
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end
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end
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end
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methods
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function obj = DL(); end
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function [data, selState] = read(obj, fid, fileInfo, anLength, anVersion);
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nummodels = fread(fid, 1, 'int16'); % short in Java
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% disp(['Num models: ' num2str(nummodels)])
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% read the data from each model.
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% data = zeros(1, nummodels);
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for i=1:nummodels
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data(i) = obj.readModelData(fid);
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end
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end
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end
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end
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‎pgmatlab/core/annotations/@MatchCls/MatchCls.m‎

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classdef MatchCls < StandardAnnotation
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methods
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function obj = MatchCls(); end
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function [data, selState] = read(obj, fid, data, fileInfo, anLength, anVersion);
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[data, selState] = read@StandardAnnotation(obj, fid, data, fileInfo, anLength, anVersion);
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if (anVersion==1)
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%the threshold value. This is used to classify the clicks.
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threshold = fread(fid, 1, 'double');
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%the maximum correlation between the match template and the click
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matchcorr = fread(fid, 1, 'double');
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%the max correlation value between the reject template and the click.
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rejectcorr = fread(fid, 1, 'double');
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data = [threshold matchcorr rejectcorr];
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end
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% new version with multiple templates.
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if (anVersion==2)
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%read the number of templates there are
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ntemplates = fread(fid, 1, 'int16'); % short in Java
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n=1;
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data=zeros(ntemplates,3); % pre allocate the data array,
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for i=1:ntemplates
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%the threshold value. This is used to classify the clicks.
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threshold = fread(fid, 1, 'double');
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%the maximum correlation between the match template and the click
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matchcorr = fread(fid, 1, 'double');
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%the max correlation value between the reject template and the click.
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rejectcorr = fread(fid, 1, 'double');
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data(n,1) = threshold;
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data(n,2) = matchcorr;
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data(n,3) = rejectcorr;
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n=n+1;
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end
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end
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end
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end
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end
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‎pgmatlab/core/annotations/@RWUDP/RWUDP.m‎

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classdef RWUDP < StandardAnnotation
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methods
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function obj = RWUDP(); end
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function [data, selState] = read(obj, fid, data, fileInfo, anLength, anVersion);
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[data, selState] = read@StandardAnnotation(obj, fid, data, fileInfo, anLength, anVersion);
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data.label = readJavaUTFString(fid);
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data.method = readJavaUTFString(fid);
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data.score = fread(fid, 1, 'float32');
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end
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end
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end
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