[SciPy-User] Interpolate: Derivatives of parametric splines

Zachary Pincus zachary.pincus@yale....
Thu Nov 12 07:19:58 CST 2009


Without thinking deeply about this at all, aren't the derivatives of a  
parametric spline [x(p), y(p)] given as dx/dp and dy/dp, not the dx/dy  
that you are perhaps expecting?


On Nov 12, 2009, at 6:35 AM, anderse@gmx.de wrote:

> Hi,
>
> I'd like to get the derivatives of parametric splines.
> Looking at the tutorial (http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html 
> )
> I get a spline like this:
>
>>>> x = np.arange(0, 2*np.pi + np.pi / 4, 2 * np.pi / 8)
>>>> y = np.sin(x)
>>>> tck = interpolate.splrep(x, y, s = 0, k = 5)
>>>> xnew = np.arange(0, 2 * np.pi, np.pi / 50)
>>>> ynew = interpolate.splev(xnew, tck, der = 0)
>
> now, the derivatives can be determined like this:
>
>>>> yder = interpolate.splev(xnew, tck, der = 1)
>>>> yder2 = interpolate.splev(xnew, tck, der = 2)
>
>>>> plt.plot(x, y, 'x', xnew, ynew, xnew, yder, xnew, yder2)
>
> The first derivative is about null at pi / 2,
> the second one at pi, as they should be:
>
>>>> interpolate.spalde(np.pi, tck)
> array([  0.00000000e+00,  -1.00064770e+00,  -1.73418916e-17,
>         1.00726743e+00,  -2.65046223e-16,  -1.01680119e+00])
>
>>>> interpolate.spalde(np.pi / 2, tck)
> array([ 1.        , -0.00199181, -0.99629386,  0.02365328,   
> 0.90756527,
>       -0.1387468 ])
>
> Of course, the x-range is the same, no matter of der=#.
>
> Now the parametric version:
>
>>>> tckp, u = interpolate.splprep([x, y], s=0, k=5)
>>>> u
> array([ 0.        ,  0.13941767,  0.25      ,  0.36058233,   
> 0.5       ,
>        0.63941767,  0.75      ,  0.86058233,  1.        ])
>
> so pi is at 0.5, pi/2 is at 0.25.
>
> And this is what I get at these 'x' values:
>
>>>> interpolate.spalde(0.5, tckp)
> [array([  3.14159265e+00,   5.14754151e+00,   1.10395807e-13,
>         1.69542498e+02,  -4.03851332e-11,  -2.01255417e+04]),
> array([  7.73894012e-16,  -5.38240284e+00,  -1.31811639e-13,
>         7.74093936e+01,   5.58012792e-11,   1.89849315e+04])]
>
>>>> interpolate.spalde(0.25, tckp)
> [array([  1.57079633e+00,   7.44935679e+00,  -7.65674781e-02,
>        -1.85343925e+02,   7.51370411e+01,   2.46939899e+04]),
> array([  1.00000000e+00,  -3.47491248e-01,  -5.16420728e+01,
>         2.05418849e+02,   3.66866738e+03,  -5.71113127e+04])]
>
> The first array states the x-values, the second one the y-values,  
> respectively, AFAIK.
> This makes sense without derivatives, and I get a plot using
>
>>>> unew = np.arange(0, 1.01, 0.01)
>>>> out = interpolate.splev(unew, tckp, der = 0)
>>>> plt.plot(out[0], out[1])
>
> which looks like the one above, but what about the derivatives?
>
>>>> der1 = interpolate.splev(unew, tckp, der = 1)
>>>> der2 = interpolate.splev(unew, tckp, der = 2)
>>>> plt.plot(der1[0], der1[1], der2[0], der2[1])
>
> dont make sense to me at all.
>
> Thank you in advance for your help.
>
> Raimund
>
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