[SciPy-User] Spline Fitting on Dense and Noisy Points

[Jonathan Stickel]

On 07/22/2011 04:29 PM, scipy-user-request at scipy.org wrote:
> On Thu, Jul 21, 2011 at 5:51 AM, Chong Yang<flyzzx at gmail.com>  wrote:
>>
>> >>  Hi,
>> >>
>> >>  Recently I have been working with 1D curve fitting on dense GPS point
>> >>  cloud representing road segments. For all I know, some roads may be highly
>> >>  curved and their corresponding x value are not necessary monotonic order.
>> >>
>> >>  I tried splrep. It works very well on simple arcs but fails to give
>> >>  meaningful result on highly curved data - either because the polynomial
>> >>  order 'k' is not sufficient to fit the data well enough or the curve is
>> >>  vertically aligned. For now I get around vertical curves by flipping the
>> >>  axes and doing splrep with (y, x), but maybe a better approach is possible.
>> >>
>> >>  My guess is splprep. However I am not sure how to properly convert the
>> >>  noisy data points into their parametric form. Does anyone have an idea on
>> >>  this or faced a similar problem before? Thanks.
>> >>

I know I am replying to this thread a bit late, but you might also be 
interested in smoothing by regularization:

http://packages.python.org/scikits.datasmooth/regularsmooth.html

Scattered data are OK.  However, curves that are vertical or bend back 
over themselves will present problems, as you have found with splines.

HTH,
Jonathan