コード例 #1
0
def test_sparseconv():
    # time vector for stimulus (long)
    maxT = .5  # seconds
    nt = 100000
    t = np.linspace(0, maxT, nt)

    # stimulus (10 Hz anondic and cathodic pulse train)
    stim = np.zeros(nt)
    stim[0:nt:10000] = 1
    stim[100:nt:1000] = -1

    # time vector for impulse response (shorter)
    tt = t[t < .1]

    # impulse reponse (kernel)
    G = np.exp(-tt / .005)

    # make sure sparseconv returns the same result as np.convolve
    # for all modes
    modes = ["full", "valid", "same"]
    for mode in modes:
        # np.convolve
        conv = np.convolve(stim, G, mode=mode)

        # utils.sparseconv
        sparse_conv = utils.sparseconv(stim, G, mode=mode, use_jit=False)

        npt.assert_equal(conv.shape, sparse_conv.shape)
        npt.assert_almost_equal(conv, sparse_conv)

    with pytest.raises(ValueError):
        utils.sparseconv(G, stim, mode='invalid')
コード例 #2
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def test_sparseconv():
    # time vector for stimulus (long)
    maxT = .5  # seconds
    nt = 100000
    t = np.linspace(0, maxT, nt)

    # stimulus (10 Hz anondic and cathodic pulse train)
    stim = np.zeros(nt)
    stim[0:nt:10000] = 1
    stim[100:nt:1000] = -1

    # time vector for impulse response (shorter)
    tt = t[t < .1]

    # impulse reponse (kernel)
    G = np.exp(-tt / .005)

    # make sure sparseconv returns the same result as np.convolve
    # for all modes
    modes = ["full", "valid", "same"]
    for mode in modes:
        # np.convolve
        conv = np.convolve(stim, G, mode=mode)

        # utils.sparseconv
        sparse_conv = utils.sparseconv(G, stim, mode=mode, dojit=False)

        npt.assert_equal(conv.shape, sparse_conv.shape)
        npt.assert_almost_equal(conv, sparse_conv)
コード例 #3
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    def fast_response(self, b1, gamma, dojit=True, usefft=False):
        """Fast response function (Box 2) for the bipolar layer

        Convolve a stimulus `b1` with a temporal low-pass filter (1-stage
        gamma) with time constant `self.tau_inl` ~ 14ms representing bipolars.

        Parameters
        ----------
        b1 : array
           Temporal signal to process, b1(r,t) in Nanduri et al. (2012).
        dojit : bool, optional
           If True (default), use numba just-in-time compilation.
        usefft : bool, optional
           If False (default), use sparseconv, else fftconvolve.

        Returns
        -------
        b2 : array
           Fast response, b2(r,t) in Nanduri et al. (2012).

        Notes
        -----
        The function utils.sparseconv can be much faster than np.convolve and
        scipy.signals.fftconvolve if `b1` is sparse and much longer than the
        convolution kernel.

        The output is not converted to a TimeSeries object for speedup.
        """
        if usefft:  # In Krishnan model, b1 is no longer sparse (run FFT)
            conv = self.tsample * fftconvolve(b1, gamma, mode='full')
        else:
            conv = self.tsample * utils.sparseconv(gamma, b1,
                                                   mode='full', dojit=dojit)
            # Cut off the tail of the convolution to make the output signal
            # match the dimensions of the input signal.
        return conv[:b1.shape[-1]]