def simpleRepeatTest():
print("Hello!\n");
MACROSTATES = enum("E-DHF-NADPH", "E-NADPH", "E-OPEN", "E-THF", "E-THF-NADPX", "TS");
RESIDUES = enum('A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y');
ensembleSizes = numpy.array([50]);
backrubTemps = numpy.array([0.3, 0.6, 0.9, 1.2, 1.5, 1.8]);
boltzmannTemps = numpy.array([0, -1, 1, 5.0]);
steepnessRange = numpy.array([0.5, 5]);
minWeights = numpy.array([0, 0, 0, 0, 0, 1]);
maxWeights = numpy.array([0, 0, 0, 0, 0, 1]);
print("Initializing objects\n");
targetFreqs = "C:\\Users\\Candy\\SkyDrive\\Documents\\rotation 2\\ecDHFR_openseq_bacterial_representative_final_align_trim.fasta";
targetFreqsAlt = "C:\\Users\\Candy_000\\SkyDrive\\Documents\\rotation 2\\ecDHFR_openseq_bacterial_representative_final_align_trim.fasta";
data = "C:\\Users\\Candy\\SkyDrive\\Documents\\rotation 2\\DHFR_MSD_M20loop\\DHFR_MSD_M20loop_repeat1.tsv";
dataAlt = "C:\\Users\\Candy_000\\SkyDrive\\Documents\\rotation 2\\DHFR_MSD_M20loop\\DHFR_MSD_M20loop_repeat1.tsv";
dataMicro = "C:\\Users\\Candy\\SkyDrive\\Documents\\rotation 2\\20160120_M20_enumeration_scores\\20160120_M20_enumeration_scores.tsv";
dataMicroAlt = "C:\\Users\\Candy_000\\SkyDrive\\Documents\\rotation 2\\20160120_M20_enumeration_scores\\20160120_M20_enumeration_scores.tsv";
optimizer = Optimizer(MACROSTATES);
# slightly different paths on my two computers
try:
optimizer.readTargetFrequencies(targetFreqs);
optimizer.readData(data);
except:
optimizer.readTargetFrequencies(targetFreqsAlt);
optimizer.readData(dataAlt);
print("Files read in");
search = CuckooSearch(optimizer.models, JensenShannonDistance(optimizer.targetFrequencies), False, 1, 1, 0.25);
search.setMaxIterations(1);
search.setParamBounds(ensembleSizes, backrubTemps, boltzmannTemps, steepnessRange, minWeights, maxWeights);
#search.setSearchParameters(False, True, True, True, numpy.array([True, True, False, True, True, True]));
search.setAllSearchToFalse();
search.suppressOutputs = True;
optimizer.useAlgorithm(search);
print("\nJS Dist");
for i in range(64):
optimizer.optimize();
params = optimizer.getBestParameters();
m = search.population[0];
#print(search.similarityMeasure.getSimilarityMeasure(m.getFrequencies()));
# TODO: getModelByParams doesn't always return the same object.
m1 = Model.constructFromExisting(optimizer.getModelByParams(m.backrubTemp, m.ensembleSize, m.boltzmannTemp), m.ensembleSize, m.backrubTemp, m.boltzmannTemp, m.getWeights(), m.steepness);
#print(search.similarityMeasure.getSimilarityMeasure(m1.getFrequencies()));
if not m.equalTo(m1):
print("\t{:s}".format(Optimizer.calcParamsID(m.backrubTemp, m.ensembleSize, m.boltzmannTemp)));
print("\t{:s}".format(Optimizer.calcParamsID(m1.backrubTemp, m1.ensembleSize, m1.boltzmannTemp)));
#m2 = Model.constructFromExisting(m, m.ensembleSize, m.backrubTemp, m.boltzmannTemp, m.getWeights(), m.steepness);
#print(search.similarityMeasure.getSimilarityMeasure(m2.getFrequencies()));
#print(m.equalTo(m2));
#print(m2.backrubTemp);
#print(m2.boltzmannTemp);
#print(m2.ensembleSize);
#print(m2.steepness);
#print(m2.weights);
#print(search.similarityMeasure.getSimilarityMeasure(m2.getFrequencies()));
#print(numpy.sum(numpy.sum(numpy.abs( m.getFrequencies() - m2.getFrequencies()))));
return None;
