I have datapoints f(x_i) at points x_i (function f not known, only numerically) with f(0) = 0. The data show a peaklike structure at small x, to be followed by a slow shoulder-falloff at larger x that sets in half-way down from the maximum. I want to plot smoothed lines through these data points. If I use bezier then indeed f(0)=0 is ok, but the peak is significantly (by about 25%), lowered. If I use acsplines then the peak looks somewhat better, but f(0) = 0 is not maintained. How can I smooth that dataset without loosing vital info (f(0)=0) or the peak-height of the distribution?
Smoothing with acsplines
draws an approximating cubic spline, which doesn't go through your original data points.
A better approach could be to use cubic splines smooth csplines
, which go through all data points but may show overshoots for sharp peaks.
The probably best solution in your case is to use monotonic cubic splines, smooth mcsplines
, which maintain the monotonicity and convexity of the original data points (see F.N. Fritsch and R.E. Carlson, "Monotone Piecewise Cubic Interpolation", SIAM Journal on Numerical Analysis 17, pp. 238-246 (1980)).
Here is a short example which shows these differences:
The test.dat
file contains the points
0 0
0.2 1
0.4 10
0.6 80
1 30
2 20
3 13
4 7
5 2
6 1
7 0
And the script to plot them is
set xzeroaxis
set style data lines
set samples 500
plot 'test.dat' u 1:2 smooth acsplines title 'acsplines', \
'' u 1:2 smooth csplines title 'csplines', \
'' u 1:2 smooth mcsplines lw 2 title 'mcsplines',\
'' u 1:2 w p pt 7 title 'data points'
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