Python scipy.signal.cspline1d() Examples

def test_complex(self):
        #  create some smoothly varying complex signal to interpolate
        x = np.arange(2)
        y = np.zeros(x.shape, dtype=np.complex64)
        T = 10.0
        f = 1.0 / T
        y = np.exp(2.0J * np.pi * f * x)

        # get the cspline transform
        cy = signal.cspline1d(y)

        # determine new test x value and interpolate
        xnew = np.array([0.5])
        ynew = signal.cspline1d_eval(cy, xnew)

        assert_equal(ynew.dtype, y.dtype) 

def test_basic(self):
        y = array([1,2,3,4,3,2,1,2,3.0])
        x = arange(len(y))
        dx = x[1]-x[0]
        cj = signal.cspline1d(y)

        x2 = arange(len(y)*10.0)/10.0
        y2 = signal.cspline1d_eval(cj, x2, dx=dx,x0=x[0])

        # make sure interpolated values are on knot points
        assert_array_almost_equal(y2[::10], y, decimal=5) 

def test_basic(self):
        y = array([1, 2, 3, 4, 3, 2, 1, 2, 3.0])
        x = arange(len(y))
        dx = x[1] - x[0]
        cj = signal.cspline1d(y)

        x2 = arange(len(y) * 10.0) / 10.0
        y2 = signal.cspline1d_eval(cj, x2, dx=dx, x0=x[0])

        # make sure interpolated values are on knot points
        assert_array_almost_equal(y2[::10], y, decimal=5) 

def wiggle(self, values):
        """
        Plot a trace in VAWT(Variable Area Wiggle Trace)
        """
        if self.zmax is None:
            self.zmax = values.size

        # Rescale so that values ranges from -1 to 1
        if self.rescale:
            values = values.astype(np.float)
            values -= values.min()
            values /= values.ptp()
            values *= 2
            values -= 1

        # Interpolate at resampleRatio x the previous density
        resample_z = np.linspace(0, values.size, values.size * self.resampleRatio)
        # cubic spline interpolation
        cj = cspline1d(values)
        resample_v = cspline1d_eval(cj, resample_z)
        print(resample_v)
        newz = resample_z
        if self.origin is None:
            self.origin = resample_v.mean()

        # Plot
        if self.posFill is not None:
            self.ax.fill_betweenx(newz, resample_v, self.origin,
                                  where=resample_v > self.origin,
                                  facecolor=self.posFill)
        if self.negFill is not None:
            self.ax.fill_betweenx(newz, resample_v, self.origin,
                                  where=resample_v < self.origin,
                                  facecolor=self.negFill)
        if self.lineColor is not None:
            self.ax.plot(resample_v, newz, color=self.lineColor, linewidth=.1) 

def doresample(orig_x, orig_y, new_x, method='cubic', padlen=0, antialias=False):
    """
    Resample data from one spacing to another.  By default, does not apply any antialiasing filter.

    Parameters
    ----------
    orig_x
    orig_y
    new_x
    method
    padlen

    Returns
    -------

    """
    pad_y = tide_filt.padvec(orig_y, padlen=padlen)
    tstep = orig_x[1] - orig_x[0]
    if padlen > 0:
        pad_x = np.concatenate((np.arange(orig_x[0] - padlen * tstep, orig_x[0], tstep),
                                orig_x,
                                np.arange(orig_x[-1] + tstep, orig_x[-1] + tstep * (padlen + 1), tstep)))
    else:
        pad_x = orig_x
    if padlen > 0:
        print('padlen=', padlen)
        print('tstep=', tstep)
        print(pad_x)

    # antialias and ringstop filter
    init_freq = len(pad_x) / (pad_x[-1] - pad_x[0])
    final_freq = len(new_x) / (new_x[-1] - new_x[0])
    if antialias and (init_freq > final_freq):
        aafilterfreq = final_freq / 2.0
        aafilter = tide_filt.noncausalfilter(filtertype='arb', usebutterworth=False)
        aafilter.setfreqs(0.0, 0.0, 0.95 * aafilterfreq, aafilterfreq)
        pad_y = aafilter.apply(init_freq, pad_y)

    if method == 'cubic':
        cj = signal.cspline1d(pad_y)
        return tide_filt.unpadvec(
            np.float64(signal.cspline1d_eval(cj, new_x, dx=(orig_x[1] - orig_x[0]), x0=orig_x[0])), padlen=padlen)
    elif method == 'quadratic':
        qj = signal.qspline1d(pad_y)
        return tide_filt.unpadvec(
            np.float64(signal.qspline1d_eval(qj, new_x, dx=(orig_x[1] - orig_x[0]), x0=orig_x[0])), padlen=padlen)
    elif method == 'univariate':
        interpolator = sp.interpolate.UnivariateSpline(pad_x, pad_y, k=3, s=0)  # s=0 interpolates
        return tide_filt.unpadvec(np.float64(interpolator(new_x)), padlen=padlen)
    else:
        print('invalid interpolation method')
        return None 

def fitSpline(self,degree=2):
        """
        **SUMMARY**

        A function to generate a spline curve fitting over the points in LineScan with
        order of precision given by the parameter degree

        **PARAMETERS**

        * *degree* - the precision of the generated spline 

        **RETURNS**

        The spline as a LineScan fitting over the initial values of LineScan

        **EXAMPLE**

        >>> import matplotlib.pyplot as plt
        >>> img = Image("lenna")
        >>> ls = img.getLineScan(pt1=(10,10)),pt2=(20,20)).normalize()
        >>> spline = ls.fitSpline()
        >>> plt.plot(ls)
        >>> plt.show()
        >>> plt.plot(spline)
        >>> plt.show()
        
        **NOTES**

        Implementation taken from http://www.scipy.org/Cookbook/Interpolation  

        """
        if degree > 4:
            degree = 4  # No significant improvement with respect to time usage
        if degree < 1:
            warnings.warn('LineScan.fitSpline - degree needs to be >= 1')
            return None
        retVal = None
        y = np.array(self)
        x = np.arange(0,len(y),1)
        dx = 1
        newx = np.arange(0,len(y)-1,pow(0.1,degree))
        cj = sps.cspline1d(y)
        retVal = sps.cspline1d_eval(cj,newx,dx=dx,x0=x[0])
        return retVal