neuralynx.py 文件源码

def _get_slice_(self, t_start, t_stop):

        x_beg = numpy.int64(t_start // self.SAMPLES_PER_RECORD)
        r_beg = numpy.mod(t_start, self.SAMPLES_PER_RECORD)
        x_end = numpy.int64(t_stop // self.SAMPLES_PER_RECORD)
        r_end = numpy.mod(t_stop, self.SAMPLES_PER_RECORD)

        data_slice  = []

        if x_beg == x_end:
            g_offset = x_beg * self.SAMPLES_PER_RECORD + self.OFFSET_PER_BLOCK[0]*(x_beg + 1) + self.OFFSET_PER_BLOCK[1]*x_beg
            data_slice = numpy.arange(g_offset + r_beg, g_offset + r_end, dtype=numpy.int64)
        else:
            for count, nb_blocks in enumerate(numpy.arange(x_beg, x_end + 1, dtype=numpy.int64)):
                g_offset = nb_blocks * self.SAMPLES_PER_RECORD + self.OFFSET_PER_BLOCK[0]*(nb_blocks + 1) + self.OFFSET_PER_BLOCK[1]*nb_blocks
                if count == 0:
                    data_slice += numpy.arange(g_offset + r_beg, g_offset + self.SAMPLES_PER_RECORD, dtype=numpy.int64).tolist()
                elif (count == (x_end - x_beg)):
                    data_slice += numpy.arange(g_offset, g_offset + r_end, dtype=numpy.int64).tolist()
                else:
                    data_slice += numpy.arange(g_offset, g_offset + self.SAMPLES_PER_RECORD, dtype=numpy.int64).tolist()
        return data_slice