def load_matlab(self, name):
        mht = scipy.io.loadmat(name)
        params = {}
        params['mat_version'] = mht['HT_params'][0][0][-1][0][0]
        assert params['mat_version'] >= 0.9
        self.hashbits = _bitsfor(mht['HT_params'][0][0][0][0][0])
        self.depth = mht['HT_params'][0][0][1][0][0]
        self.maxtimebits = _bitsfor(mht['HT_params'][0][0][2][0][0])
        params['hoptime'] = mht['HT_params'][0][0][3][0][0]
        params['targetsr'] = mht['HT_params'][0][0][4][0][0]
        params['nojenkins'] = mht['HT_params'][0][0][5][0][0]
        # Python doesn't support the (pointless?) jenkins hashing
        assert params['nojenkins']
        self.table = mht['HashTable'].T
        self.counts = mht['HashTableCounts'][0]
        self.names = [str(val[0]) if len(val) > 0 else []
                      for val in mht['HashTableNames'][0]]
        self.hashesperid = np.array(mht['HashTableLengths'][0]).astype(uint32)
        # Matlab uses 1-origin for the IDs in the hashes, so rather than
        # rewrite them all, we shift the corresponding decode tables
        # down one cell
        self.names.insert(0, '')
        self.hashesperid = np.append([0], self.hashesperid)
        # Otherwise unmodified database
        self.dirty = False
        return params
Try to find 'executable' in the directories listed in 'path' (a
dataset/ETHPy150Open dpwe/audfprint/hash_table.py/HashTable.load_matlab--crossover with--dataset/ETHPy150Open babble/babble/include/jython/Lib/distutils/spawn.py/find_executable