ACHPTools.py 文件源码

def smooth_curve(curve_data,N_smooth,exp_max=-1,shift_0=0,fix_first_nonzero=False,plotit=False,t='x for plot'):
    """
    smoothens the curve data for plotting as good as possible while maintaining last and first value
    curve data => np.array, 1D that should be smoothened out
    N_smooth => number of points to smooth over (float)
    exp_max => adjust exponential behavior for average (0='normal' moving average)
    shift_0 => manually fix from where on smoothing is active, e.g. up till where no smootthing is applied
    fix_first_nonzero => if set to true, then automatically determines shift_0 to be where the first nonzero entry is
    plotit => plot results
    t => x-cooordinate for plot
    """
    a=curve_data
    N=N_smooth
    v=np.exp(np.linspace(exp_max, 0., N))
    v=v/v.sum()
    a_v=np.convolve(a,v,'same')
    if fix_first_nonzero==True:
        shift_0=np.nonzero(a != 0)[0][0]
    for n in range(0,len(v)):
        if n!=0:
            v=np.exp(np.linspace(exp_max, 0., n))
            v=v/v.sum()
            a_v[n+shift_0]=np.convolve(a,v,'same')[n+shift_0]
            a_v[len(a)-n-1]=np.convolve(a,v,'same')[len(a)-n-1]
        else:
            a_v[n+shift_0]=a[n+shift_0]
            for i in range(0,n+shift_0):
                a_v[i]=a[i]
            a_v[len(a)-n-1]=a[len(a)-n-1]
    if plotit:
        try:
            np.sin(t)
        except:
            t=np.linspace(0,len(curve_data),len(curve_data))
        import pylab as plt
        plt.plot(t,a,label='original data')
        plt.plot(t,a_v,label='smoothened')
        plt.legend(loc='best',fancybox=True)
        plt.title('curve smoothing')
        plt.show()
    return a_v