Examples with FamilyChainIO io.loaders.json.FamilyChainIO used on opensource projects

Example 1 with FamilyChainIO

use of io.loaders.json.FamilyChainIO in project Smiles2Monomers by yoann-dufresne.

the class ChainLearningTests method saveTest.

/**/
@Test
public void saveTest() {
    String line = null;
    String expected = "[{\"roots\":{\"86\":\"C,0,C,1,2,-1,-1;C,0,N,1,1,0,-1;C,0,C,0,1,-1,0;C,1,O,0,0,3,-1\",\"87\":\"C,0,O,2,0,-1,-1;C,0,C,1,2,-1,0;C,0,C,0,1,-1,2;C,0,N,1,1,2,-1;C,1,O,0,0,3,-1\"},\"family\":\"D-Ser\",\"extensions\":{\"79\":[{\"idx2\":-1,\"idx1\":3,\"from\":\"85\",\"type\":\"extension\",\"ext\":\"C,0,O,0,1\"}],\"78\":[{\"idx2\":-1,\"idx1\":3,\"from\":\"84\",\"type\":\"extension\",\"ext\":\"C,0,O,0,1\"}],\"77\":[{\"num\":1,\"idx\":1,\"from\":\"84\",\"type\":\"hydrogen\"}],\"82\":[{\"idx2\":-1,\"idx1\":3,\"from\":\"87\",\"type\":\"extension\",\"ext\":\"C,0,O,0,1\"}],\"83\":[{\"idx2\":-1,\"idx1\":3,\"from\":\"86\",\"type\":\"extension\",\"ext\":\"C,0,O,0,1\"}],\"80\":[{\"idx2\":-1,\"idx1\":3,\"from\":\"81\",\"type\":\"extension\",\"ext\":\"C,0,O,0,1\"}],\"81\":[{\"num\":1,\"idx\":1,\"from\":\"87\",\"type\":\"hydrogen\"}],\"86\":[],\"87\":[],\"84\":[{\"num\":1,\"idx\":4,\"from\":\"87\",\"type\":\"hydrogen\"}],\"85\":[{\"num\":1,\"idx\":2,\"from\":\"86\",\"type\":\"hydrogen\"}]}},{\"roots\":{\"95\":\"C,0,N,2,1,-1,-1;C,0,C,1,2,-1,0;C,0,N,1,1,2,-1;C,0,C,0,1,-1,2;C,1,O,0,0,4,-1\"},\"family\":\"Dpr\",\"extensions\":{\"95\":[],\"94\":[{\"idx2\":-1,\"idx1\":4,\"from\":\"95\",\"type\":\"extension\",\"ext\":\"C,0,O,0,1\"}],\"93\":[{\"num\":1,\"idx\":3,\"from\":\"95\",\"type\":\"hydrogen\"}],\"92\":[{\"num\":1,\"idx\":1,\"from\":\"95\",\"type\":\"hydrogen\"}],\"91\":[{\"idx2\":-1,\"idx1\":4,\"from\":\"93\",\"type\":\"extension\",\"ext\":\"C,0,O,0,1\"}],\"90\":[{\"idx2\":-1,\"idx1\":4,\"from\":\"92\",\"type\":\"extension\",\"ext\":\"C,0,O,0,1\"}],\"89\":[{\"num\":1,\"idx\":1,\"from\":\"93\",\"type\":\"hydrogen\"}]}}]";
    // Computing
    ChainLearning learning = new ChainLearning(this.learningBase);
    learning.setMarkovianSize(3);
    learning.learn(this.families);
    ChainsDB db = learning.getDb();
    FamilyChainIO io = new FamilyChainIO(this.families);
    io.saveFile(db, "data_tests/chains.json");
    try {
        BufferedReader br = new BufferedReader(new FileReader(new File("data_tests/chains.json")));
        line = br.readLine();
        br.close();
    } catch (IOException e) {
        fail(e.getMessage());
    }
    Assert.assertTrue(line.equals(expected));
}

Example 2 with FamilyChainIO

use of io.loaders.json.FamilyChainIO in project Smiles2Monomers by yoann-dufresne.

the class PreComputation method main.

public static void main(String[] args) {
    // ----------------- Parameters ---------------------------
    String rulesDBName = "data/rules.json";
    String monosDBName = "data/monomers.json";
    String jsonPolymers = "data/learning.json";
    String serialFolder = "data/serials/";
    String jsonResidues = "data/residues.json";
    String jsonChains = "data/chains.json";
    int markovianSize = 3;
    // Parsing
    for (int idx = 0; idx < args.length; idx++) {
        if (args[idx].startsWith("-")) {
            switch(args[idx]) {
                case "-rul":
                    rulesDBName = args[idx + 1];
                    break;
                case "-mono":
                    monosDBName = args[idx + 1];
                    break;
                case "-poly":
                    jsonPolymers = args[idx + 1];
                    break;
                case "-res":
                    jsonResidues = args[idx + 1];
                    break;
                case "-cha":
                    jsonChains = args[idx + 1];
                    break;
                case "-serial":
                    serialFolder = args[idx + 1];
                    break;
                case "-markovian":
                    markovianSize = new Integer(args[idx + 1]);
                    break;
                default:
                    System.err.println("Wrong option " + args[idx]);
                    System.exit(1);
                    break;
            }
            idx++;
        } else {
            System.err.println("Wrong parameter " + args[idx]);
            System.exit(1);
        }
    }
    // File existence
    File file = new File(rulesDBName);
    if (!file.exists()) {
        System.err.println("No file at " + rulesDBName);
        System.exit(1);
    }
    file = new File(monosDBName);
    if (!file.exists()) {
        System.err.println("No file at " + monosDBName);
        System.exit(1);
    }
    file = new File(jsonPolymers);
    if (!file.exists()) {
        System.err.println("No file at " + jsonPolymers);
        System.exit(1);
    }
    // ------------------- Loadings ------------------------
    System.out.println("--- Loading ---");
    RulesDB rules = RulesJsonLoader.loader.loadFile(rulesDBName);
    MonomersDB monos = new MonomersJsonLoader(true).loadFile(monosDBName);
    PolymersJsonLoader pjl = new PolymersJsonLoader(monos, false);
    PolymersDB learningBase = pjl.loadFile(jsonPolymers);
    // ----------------- Serializations --------------------
    System.out.println("--- Data serialisation ---");
    File folder = new File(serialFolder);
    if (!folder.exists())
        folder.mkdir();
    MonomersSerialization ms = new MonomersSerialization();
    ms.serialize(monos, serialFolder + "monos.serial");
    // ----------------- residues --------------------------
    ResidueCreator rc = new ResidueCreator(rules);
    rc.setVerbose(false);
    System.out.println("--- Residues creation ---");
    FamilyDB families = rc.createResidues(monos);
    System.out.println("--- Saving residues ---");
    ResidueJsonLoader rjl = new ResidueJsonLoader(rules, monos);
    rjl.saveFile(families, jsonResidues);
    // ----------------- chains ----------------------------
    System.out.println("--- Learning chains ---");
    // Adapt residue structures
    for (Residue res : families.getResidues().getObjects()) res.explicitToImplicitHydrogens();
    ChainLearning learning = new ChainLearning(learningBase);
    learning.setMarkovianSize(markovianSize);
    learning.learn(families);
    // --- Save ---
    System.out.println("--- Saving chains ---");
    FamilyChainIO fcio = new FamilyChainIO(families);
    fcio.saveFile(learning.getDb(), jsonChains);
    System.out.println("--- Ended ---");
}

Example 3 with FamilyChainIO

use of io.loaders.json.FamilyChainIO in project Smiles2Monomers by yoann-dufresne.

the class ProcessPolymers method main.

public static void main(String[] args) {
    // ----------------- Parameters ---------------------------
    String monoDBname = "data/monomers.json";
    String pepDBname = "data/polymers.json";
    String rulesDBname = "data/rules.json";
    String residuesDBname = "data/residues.json";
    String chainsDBFile = "data/chains.json";
    String outfile = "results/coverages.json";
    String outfolderName = "results/";
    String imgsFoldername = "images/";
    boolean html = false;
    boolean zip = false;
    String serialFolder = "data/serials/";
    boolean lightMatch = true;
    boolean verbose = false;
    int removeDistance = 2;
    int retryCount = 2;
    int modulationDepth = 2;
    // Parsing
    loop: for (int idx = 0; idx < args.length; idx++) {
        if (args[idx].startsWith("-")) {
            switch(args[idx]) {
                case "-rul":
                    rulesDBname = args[idx + 1];
                    break;
                case "-mono":
                    monoDBname = args[idx + 1];
                    break;
                case "-poly":
                    pepDBname = args[idx + 1];
                    break;
                case "-res":
                    residuesDBname = args[idx + 1];
                    break;
                case "-cha":
                    chainsDBFile = args[idx + 1];
                    break;
                case "-serial":
                    serialFolder = args[idx + 1];
                    break;
                case "-outfile":
                    outfile = args[idx + 1];
                    break;
                case "-outfolder":
                    outfolderName = args[idx + 1];
                    break;
                case "-imgs":
                    imgsFoldername = args[idx + 1];
                    break;
                case "-strict":
                    lightMatch = false;
                    continue loop;
                case "-v":
                    verbose = true;
                    continue loop;
                case "-html":
                    html = true;
                    continue loop;
                case "-zip":
                    zip = true;
                    continue loop;
                default:
                    System.err.println("Wrong option " + args[idx]);
                    System.exit(1);
                    break;
            }
            idx++;
        } else {
            System.err.println("Wrong parameter " + args[idx]);
            System.exit(1);
        }
    }
    // ------------------- Loadings ------------------------
    System.out.println("--- Loading ---");
    // Maybe loading can be faster for the learning base, using serialized molecules instead of CDK SMILES parsing method.
    long loadingTime = System.currentTimeMillis();
    MonomersDB monoDB = new MonomersJsonLoader(false).loadFile(monoDBname);
    MonomersSerialization ms = new MonomersSerialization();
    ms.deserialize(monoDB, serialFolder + "monos.serial");
    boolean d2 = html || zip;
    PolymersJsonLoader pjl = new PolymersJsonLoader(monoDB, d2);
    PolymersDB polDB = pjl.loadFile(pepDBname);
    RulesDB rulesDB = RulesJsonLoader.loader.loadFile(rulesDBname);
    ResidueJsonLoader rjl = new ResidueJsonLoader(rulesDB, monoDB);
    // Need optimizations
    FamilyDB families = rjl.loadFile(residuesDBname);
    FamilyChainIO fcio = new FamilyChainIO(families);
    ChainsDB chains = fcio.loadFile(chainsDBFile);
    loadingTime = System.currentTimeMillis() - loadingTime;
    System.out.println("Loading time : " + (loadingTime / 1000) + "s");
    // ------------------- Spliting ------------------------
    System.out.println("--- Monomers search ---");
    long searchTime = System.currentTimeMillis();
    MonomericSpliting.setVerbose(verbose);
    MonomericSpliting split = new MonomericSpliting(families, chains, removeDistance, retryCount, modulationDepth);
    split.setAllowLightMatchs(lightMatch);
    Coverage[] covs = split.computeCoverages(polDB);
    searchTime = System.currentTimeMillis() - searchTime;
    System.out.println("Search time : " + (searchTime / 1000) + "s");
    // ------------------- Output ------------------------
    System.out.println("--- Output creations ---");
    long outputTime = System.currentTimeMillis();
    // Creation of the out directory
    File outfolder = new File(outfolderName);
    if (!outfolder.exists())
        outfolder.mkdir();
    CoveragesJsonLoader cjl = new CoveragesJsonLoader(polDB, families);
    cjl.saveFile(covs, outfile);
    // Images generation
    if (html || zip) {
        File imgsFolder = new File(imgsFoldername);
        if (!imgsFolder.exists())
            imgsFolder.mkdir();
        ImagesGeneration ig = new ImagesGeneration();
        ig.generateMonomerImages(imgsFolder, monoDB);
        Map<Coverage, ColorsMap> colors = ig.generatePeptidesImages(imgsFolder, covs);
        if (html) {
            // HTML
            Coverages2HTML c2h = new Coverages2HTML(covs, monoDB, families);
            File htmlFile = new File(outfolderName + "/s2m.html");
            c2h.createResults(htmlFile, imgsFolder, colors);
        }
        if (zip) {
            // Zip File
            OutputZiper oz = new OutputZiper(outfolderName + "/s2m.zip");
            oz.createZip(imgsFolder.getPath(), outfile, pepDBname, monoDBname, residuesDBname, colors);
        }
    }
    outputTime = System.currentTimeMillis() - outputTime;
    System.out.println("Ouputing time : " + (outputTime / 1000) + "s");
    System.out.println("--- Ended ---");
}

Example 4 with FamilyChainIO

use of io.loaders.json.FamilyChainIO in project Smiles2Monomers by yoann-dufresne.

the class ChainLearningTests method loadTest.

/**/
@Test
public void loadTest() {
    // Computing
    ChainLearning learning = new ChainLearning(this.learningBase);
    learning.setMarkovianSize(3);
    learning.learn(this.families);
    ChainsDB db = learning.getDb();
    FamilyChainIO io = new FamilyChainIO(this.families);
    io.saveFile(db, "data_tests/chains.json");
    ChainsDB loaded = io.loadFile("data_tests/chains.json");
    if (loaded.getObjects().size() != db.getObjects().size())
        fail("Not the same number of objects");
    FamilyChainsDB fc = db.getObjects().get(0);
    FamilyChainsDB fcLoaded = loaded.getObjects().get(0);
    if (fc.getRootChains().size() != fcLoaded.getRootChains().size())
        fail("Root chain number different");
    for (Residue res : fc.getFamily().getResidues()) {
        if (fc.getAdds(res).size() != fcLoaded.getAdds(res).size())
            fail("Adds of " + res.getName() + "are not correctly loaded");
    }
    Assert.assertTrue(true);
}