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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 coherence(self, other, fftlength=None, overlap=None, window='hann', **kwargs):
 """Calculate the frequency-coherence between this `TimeSeries` and another. Parameters other : `TimeSeries` `TimeSeries` signal to calculate coherence with fftlength : `float`, optional number of seconds in single FFT, defaults to a single FFT covering the full duration overlap : `float`, optional number of seconds of overlap between FFTs, defaults to the recommended overlap for the given window (if given), or 0 window : `str`, `numpy.ndarray`, optional window function to apply to timeseries prior to FFT, see :func:`scipy.signal.get_window` for details on acceptable formats **kwargs any other keyword arguments accepted by :func:`matplotlib.mlab.cohere` except ``NFFT``, ``window``, and ``noverlap`` which are superceded by the above keyword arguments Returns ------- coherence : `~gwpy.frequencyseries.FrequencySeries` the coherence `FrequencySeries` of this `TimeSeries` with the other Notes ----- If `self` and `other` have difference :attr:`TimeSeries.sample_rate` values, the higher sampled `TimeSeries` will be down-sampled to match the lower. See Also -------- :func:`matplotlib.mlab.cohere` for details of the coherence calculator """

        from matplotlib import mlab
        from ..frequencyseries import FrequencySeries
        # check sampling rates
        if self.sample_rate.to('Hertz') != other.sample_rate.to('Hertz'):
            sampling = min(self.sample_rate.value, other.sample_rate.value)
            # resample higher rate series
            if self.sample_rate.value == sampling:
                other = other.resample(sampling)
                self_ = self
            else:
                self_ = self.resample(sampling)
        else:
            sampling = self.sample_rate.value
            self_ = self
        # check fft lengths
        if overlap is None:
            overlap = 0
        else:
            overlap = int((overlap * self_.sample_rate).decompose().value)
        if fftlength is None:
            fftlength = int(self_.size/2. + overlap/2.)
        else:
            fftlength = int((fftlength * self_.sample_rate).decompose().value)
        if window is not None:
            kwargs['window'] = signal.get_window(window, fftlength)
        coh, freqs = mlab.cohere(self_.value, other.value, NFFT=fftlength,
                                 Fs=sampling, noverlap=overlap, **kwargs)
        out = coh.view(FrequencySeries)
        out.xindex = freqs
        out.epoch = self.epoch
        out.name = 'Coherence between %s and %s' % (self.name, other.name)
        out.unit = 'coherence'
        return out