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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def compute_from_data(self, data, sample_rate):
 """ Compute MFCCs for the given audio data. The audio data must be a 1D :class:`numpy.ndarray`, that is, it must represent a monoaural (single channel) array of ``float64`` values in ``[-1.0, 1.0]``. :param data: the audio data :type data: :class:`numpy.ndarray` (1D) :param int sample_rate: the sample rate of the audio data, in samples/s (Hz) :raises: ValueError: if the data is not a 1D :class:`numpy.ndarray` (i.e., not mono), or if the data is empty :raises: ValueError: if the upper frequency defined in the ``rconf`` is larger than the Nyquist frequenct (i.e., half of ``sample_rate``) """

        def _process_frame(self, frame):
            """
            Process each frame, returning the log(power()) of it.
            """
            # apply Hamming window
            frame *= self.hamming_window
            # compute RFFT
            fft = numpy.fft.rfft(frame, self.fft_order)
            # equivalent to power = fft.real * fft.real + fft.imag * fft.imag
            power = numpy.square(numpy.absolute(fft))
            #
            # return the log(power()) of the transformed vector
            # v1
            # COMMENTED logspec = numpy.log(numpy.dot(power, self.filters).clip(self.CUTOFF, numpy.inf))
            # COMMENTED return numpy.dot(logspec, self.s2dct) / self.filter_bank_size
            # v2
            return numpy.log(numpy.dot(power, self.filters).clip(self.CUTOFF, numpy.inf))

        if len(data.shape) != 1:
            self.log_exc(u"The audio data must be a 1D numpy array (mono).", None, True, ValueError)
        if len(data) < 1:
            self.log_exc(u"The audio data must not be empty.", None, True, ValueError)

        self.data = data
        self.sample_rate = sample_rate

        # number of samples in the audio
        data_length = len(self.data)

        # frame length in number of samples
        frame_length = int(self.window_length * self.sample_rate)

        # frame length must be at least equal to the FFT order
        frame_length_padded = max(frame_length, self.fft_order)

        # frame shift in number of samples
        frame_shift = int(self.window_shift * self.sample_rate)

        # number of MFCC vectors (one for each frame)
        # this number includes the last shift,
        # where the data will be padded with zeros
        # if the remaining samples are less than frame_length_padded
        number_of_frames = int((1.0 * data_length) / frame_shift)

        # create Hamming window
        self.hamming_window = numpy.hamming(frame_length_padded)

        # build Mel filter bank
        self._create_mel_filter_bank()

        # pre-emphasize the entire audio data
        self._pre_emphasis()

        # allocate the MFCCs matrix
        # v1
        # COMMENTED mfcc = numpy.zeros((number_of_frames, self.mfcc_size), 'float64')
        # v2
        mfcc = numpy.zeros((number_of_frames, self.filter_bank_size), 'float64')

        # compute MFCCs one frame at a time
        for frame_index in range(number_of_frames):
            # COMMENTED print("Computing frame %d / %d" % (frame_index, number_of_frames))

            # get the start and end indices for this frame,
            # do not overrun the data length
            frame_start = frame_index * frame_shift
            frame_end = min(frame_start + frame_length_padded, data_length)

            # frame is zero-padded if the remaining samples
            # are less than its length
            frame = numpy.zeros(frame_length_padded)
            frame[0:(frame_end - frame_start)] = self.data[frame_start:frame_end]

            # process the frame
            mfcc[frame_index] = _process_frame(self, frame)

        # v1
        # COMMENTED return mfcc
        # v2
        # return the dot product with the DCT matrix
        return numpy.dot(mfcc, self.s2dct) / self.filter_bank_size