Using the scipy.interpolate.splrep Function

Question

I am trying to put a cubic spline in a given set of points. My points are not ordered. I CANNOT sort or reorder points, as I need this information.

But since the scipy.interpolate.splrep function only works on non-public and monotonically increasing points, I defined a function that maps x-coordinates to monotonously increasing space.

My old points:

xpoints=[4913.0, 4912.0, 4914.0, 4913.0, 4913.0, 4913.0, 4914.0, 4915.0, 4918.0, 4921.0, 4925.0, 4932.0, 4938.0, 4945.0, 4950.0, 4954.0, 4955.0, 4957.0, 4956.0, 4953.0, 4949.0, 4943.0, 4933.0, 4921.0, 4911.0, 4898.0, 4886.0, 4874.0, 4865.0, 4858.0, 4853.0, 4849.0, 4848.0, 4849.0, 4851.0, 4858.0, 4864.0, 4869.0, 4877.0, 4884.0, 4893.0, 4903.0, 4913.0, 4923.0, 4935.0, 4947.0, 4959.0, 4970.0, 4981.0, 4991.0, 5000.0, 5005.0, 5010.0, 5015.0, 5019.0, 5020.0, 5021.0, 5023.0, 5025.0, 5027.0, 5027.0, 5028.0, 5028.0, 5030.0, 5031.0, 5033.0, 5035.0, 5037.0, 5040.0, 5043.0] ypoints=[10557.0, 10563.0, 10567.0, 10571.0, 10575.0, 10577.0, 10578.0, 10581.0, 10582.0, 10582.0, 10582.0, 10581.0, 10578.0, 10576.0, 10572.0, 10567.0, 10560.0, 10550.0, 10541.0, 10531.0, 10520.0, 10511.0, 10503.0, 10496.0, 10490.0, 10487.0, 10488.0, 10488.0, 10490.0, 10495.0, 10504.0, 10513.0, 10523.0, 10533.0, 10542.0, 10550.0, 10556.0, 10559.0, 10560.0, 10559.0, 10555.0, 10550.0, 10543.0, 10533.0, 10522.0, 10514.0, 10505.0, 10496.0, 10490.0, 10486.0, 10482.0, 10481.0, 10482.0, 10486.0, 10491.0, 10497.0, 10506.0, 10516.0, 10524.0, 10534.0, 10544.0, 10552.0, 10558.0, 10564.0, 10569.0, 10573.0, 10576.0, 10578.0, 10581.0, 10582.0] 

Plots:

Code for display and interpolation function:

 xnew=[] ynew=ypoints for c3,i in enumerate(xpoints): if np.isfinite(np.log(i*pow(2,c3))): xnew.append(np.log(i*pow(2,c3))) else: if c==0: xnew.append(np.random.random_sample()) else: xnew.append(xnew[c3-1]+np.random.random_sample()) xnew=np.asarray(xnew) ynew=np.asarray(ynew) constant1=10.0 nknots=len(xnew)/constant1 idx_knots = (np.arange(1,len(xnew)-1,(len(xnew)-2)/np.double(nknots))).astype('int') knots = [xnew[i] for i in idx_knots] knots = np.asarray(knots) int_range=np.linspace(min(xnew),max(xnew),len(xnew)) tck = interpolate.splrep(xnew,ynew,k=3,task=-1,t=knots) y1= interpolate.splev(int_range,tck,der=0) 

The code throws an error in the interpolate.splrep () function for some set of points similar to the one above.

Error: File "/home/neeraj/Desktop/koustav/res/BOS5/fit_spline3.py", line 58, in the file save_spline_f tck = interpolate.splrep (xnew, ynew, k = 3, task = -1, t = nodes ) File "/usr/lib/python2.7/dist-packages/scipy/interpolate/fitpack.py", line 465, in splrep raise _iermess ier (_iermess [ier] [0]) ValueError: input error

But for a different set of points, it works great. For example, for the next set of points.

 xpoints=[1629.0, 1629.0, 1629.0, 1629.0, 1629.0, 1629.0, 1629.0, 1629.0, 1629.0, 1629.0, 1629.0, 1629.0, 1629.0, 1629.0, 1629.0, 1629.0, 1630.0, 1630.0, 1630.0, 1631.0, 1631.0, 1631.0, 1631.0, 1630.0, 1629.0, 1629.0, 1629.0, 1628.0, 1627.0, 1627.0, 1625.0, 1624.0, 1624.0, 1623.0, 1620.0, 1618.0, 1617.0, 1616.0, 1615.0, 1614.0, 1614.0, 1612.0, 1612.0, 1612.0, 1611.0, 1610.0, 1609.0, 1608.0, 1607.0, 1607.0, 1603.0, 1602.0, 1602.0, 1601.0, 1601.0, 1600.0, 1599.0, 1598.0] ypoints=[10570.0, 10572.0, 10572.0, 10573.0, 10572.0, 10572.0, 10571.0, 10570.0, 10569.0, 10565.0, 10564.0, 10563.0, 10562.0, 10560.0, 10558.0, 10556.0, 10554.0, 10551.0, 10548.0, 10547.0, 10544.0, 10542.0, 10541.0, 10538.0, 10534.0, 10532.0, 10531.0, 10528.0, 10525.0, 10522.0, 10519.0, 10517.0, 10516.0, 10512.0, 10509.0, 10509.0, 10507.0, 10504.0, 10502.0, 10500.0, 10501.0, 10499.0, 10498.0, 10496.0, 10491.0, 10492.0, 10488.0, 10488.0, 10488.0, 10486.0, 10486.0, 10485.0, 10485.0, 10486.0, 10483.0, 10483.0, 10482.0, 10480.0] 

Plots: Can anyone suggest what is happening? Thanks in advance...

Answer 1

In fact, you do not need to define a new function yourself. This is so trajectory interpolation very much: scipy: Interpolating trajectory ( scipy: Interpolating trajectory )

And the answer is good for me, I hope it can help you.

 from scipy import interpolate as itp mytck,myu=itp.splprep([xpoints,ypoints]) xnew,ynew= itp.splev(np.linspace(0,1,1000),mytck) plot(xnew,ynew) 

Answer 2

I believe the purpose of the used splrep( ) function is to match the y coordinate as a function x coordinate: y = f (x) .

For splrep() to work, as expected, your function should be unique. That is, you should be able to draw a vertical line on the chart anywhere and cross the curve exactly once.

Instead, perhaps you want to fit x and y separately to the third parameter t , which is monotonically increasing.

x = f (t)
y = g (t)

There are two simple choices for t . The first is just the index of the point (0 for the first point, 1 for the second point, etc.). The second choice is a little more complicated, the accumulated rectilinear distance traveled from point to point. You will then call slrep() separately for the x and y coordinates.

 t = [0] for i in range(1, len(x)): t[i] = t[i-1]+np.hypot(x[i]-x[i-1], y[i]-y[i-1]) 

Maybe you need a bezier spline?

Answer 3

As mentioned in the documentation , it is very important that the X values ​​are unique in order to produce tangible results. What is broken in your case, so some data correction may help.