def loadSeqs()

in eval/PER_src/simplePhonemLearner.py [0:0]
37 lines of code
3 McCabe index (conditional complexity)

    def loadSeqs(self):

        # Labels
        self.seqOffset = [0]
        self.phoneLabels = []
        self.phoneOffsets = [0]
        self.data = []
        self.maxSize = 0
        self.maxSizePhone = 0

        # Data

        nprocess = min(30, len(self.seqNames))

        start_time = time.time()
        to_load = [Path(self.pathDB) / x for _, x in self.seqNames]

        with Pool(nprocess) as p:
            poolData = p.map(load, to_load)

        tmpData = []
        poolData.sort()

        totSize = 0
        minSizePhone = 1000000
        for seqName, seq in poolData:
            self.phoneLabels += self.phoneLabelsDict[seqName]
            self.phoneOffsets.append(len(self.phoneLabels))
            self.maxSizePhone = max(self.maxSizePhone,
                                    len(self.phoneLabelsDict[seqName]))
            minSizePhone = min(minSizePhone, len(
                self.phoneLabelsDict[seqName]))
            sizeSeq = seq.size(1)
            self.maxSize = max(self.maxSize, sizeSeq)
            totSize += sizeSeq
            tmpData.append(seq)
            self.seqOffset.append(self.seqOffset[-1] + sizeSeq)
            del seq
        self.data = torch.cat(tmpData, dim=1)
        self.phoneLabels = torch.tensor(self.phoneLabels, dtype=torch.long)
        print(f'Loaded {len(self.phoneOffsets)} sequences '
              f'in {time.time() - start_time:.2f} seconds')
        print(f'maxSizeSeq : {self.maxSize}')
        print(f'maxSizePhone : {self.maxSizePhone}')
        print(f"minSizePhone : {minSizePhone}")
        print(f'Total size dataset {totSize / (16000 * 3600)} hours')