[SciPy-User] UnivariateSpline returning NaN

[nicky van foreest]

Hi,

I also had this problem, but this was due to setting s = 0 in
UnivariateSpline. In my case, setting s = 0.0001 helped remove the
nans. I am not a specialist on splines, but settting s = 0 requires
the splines to go through each and every point. That may be, perhaps,
a too strong requirement.

Nicky

On 19 August 2012 15:44, Vagabond_Aero <vagabondaero at gmail.com> wrote:
> Looking at your data, about 3/4 of the way down you have 2 x values exactly
> the same.  Leaving those in, I could recreate your problem, but removing one
> of them made the problem go away.
>
> On Fri, Aug 17, 2012 at 2:34 PM, Kevin Gullikson <kevin.gullikson at gmail.com>
> wrote:
>>
>> Hi all,
>>
>> I am having a weird issue with UnivariateSpline return nan when trying to
>> interpolate some data. I have had this happen before a couple times, but it
>> was always because a nan was in the input arrays. Here, they are all real
>> numbers. Here is some minimal code that recreates my problem:
>>
>> import numpy
>> x,y = numpy.loadtxt("test.out", usecols=(0,1), unpack=True)
>> from scipy.interpolate import UnivariateSpline
>> fcn = UnivariateSpline(x,y,s=0)
>> print fcn(numpy.median(x))
>>
>> (the result of the print is nan)
>>
>>
>> Here is the data that I used:
>>
>> 960.38174     2218580.3     2205540
>> 960.3904     2209433.1     2205465.9
>> 960.39906     2202205.2     2205391.9
>> 960.40773     2209385.9     2205317.8
>> 960.41639     2218550.3     2205243.7
>> 960.42505     2220656.8     2205169.7
>> 960.43372     2220779.4     2205095.6
>> 960.44238     2220771.8     2205021.5
>> 960.45104     2220764.3     2204947.5
>> 960.67545     2220409.5     2203030.8
>> 960.68725     2220387.5     2202930.1
>> 960.69905     2220365.5     2202829.5
>> 960.71085     2220343.4     2202728.8
>> 960.72265     2220321.4     2202628.2
>> 960.73445     2220299.3     2202527.6
>> 960.74625     2220277.3     2202427
>> 960.75805     2220222.8     2202326.4
>> 960.76985     2220200.7     2202225.7
>> 960.78165     2220178.7     2202125.2
>> 960.79345     2220156.6     2202024.6
>> 960.80525     2220134.6     2201924
>> 960.81705     2220080.1     2201823.4
>> 961.16055     2219013.1     2198897.2
>> 961.16699     2218983.4     2198842.3
>> 961.17344     2218953.6     2198787.4
>> 961.17988     2218923.8     2198732.5
>> 961.18788     2218886.9     2198664.3
>> 961.19587     2218882.5     2198596.2
>> 961.20386     2218910.5     2198528.1
>> 961.21186     2218906.1     2198459.9
>> 961.21963     2218837.8     2198393.7
>> 961.2274     2218769.4     2198327.4
>> 961.23517     2218701.1     2198261.2
>> 961.24294     2218697.7     2198194.9
>> 961.25071     2218661.8     2198128.7
>> 961.25695     2218633     2198075.6
>> 961.26317     2218604.3     2198022.5
>> 961.27485     2218550.4     2197922.9
>> 961.28653     2218496.5     2197823.4
>> 961.2982     2218475     2197723.8
>> 961.30988     2218421.1     2197624.3
>> 961.32156     2218367.3     2197524.8
>> 961.75259     2216379.3     2193857.4
>> 961.75259     2225525.5     2193857.4
>> 962.04986     2222983.5     2191338.5
>> 962.05175     2222942.3     2191322.5
>> 962.69908     2217396.7     2185841.9
>> 962.71837     2217210.7     2185678.5
>> 962.73766     2217024.8     2185515.2
>> 962.75695     2214605.6     2185352
>> 962.77624     2180663.5     2185188.7
>> 962.79552     2080767.6     2185025.6
>> 962.81481     2027222.1     2184862.4
>> 962.83411     2080471.4     2184699.3
>> 962.8534     2180022.7     2184536.2
>> 962.87269     2213620.2     2184373.1
>>
>>
>>
>> Thanks,
>>
>> Kevin Gullikson
>>
>>
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>
>
>
> --
> Co-discoverer of KBO:
>
> IH-X-694190
>
>
>
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