Please consider the following distribution :
rs={{400, 0.00929}, {410, 0.0348}, {420, 0.0966}, {430, 0.2}, {440, 0.328}, {450, 0.455},
{460, 0.567}, {470, 0.676}, {480, 0.793}, {490, 0.904}, {500, 0.982}, {510, 0.开发者_JAVA技巧997},
{520,0.935}, {530, 0.811}, {540, 0.65}, {550, 0.481}, {560, 0.329}, {570,0.208},
{580, 0.121}, {590, 0.0655}, {600, 0.0332}, {610, 0.0159}, {620, 0.00737},
{630, 0.00334}, {640, 0.0015}, {650,0.000677}, {660, 0.000313}, {670, 0.000148},
{680, 0.0000715}, {690,0.0000353}, {700, 0.0000178}}
How could I interpolate this distribution to obtain value for points at any location on the X Axis ?
Just use the standard Interpolation
function:
rsInterpolation = Interpolation@rs;
Plot[rsInterpolation@x, {x, 400, 700}]
If you want to fit a specific class of functions (such as a normal distribution), instead use FindFit
.
If you need nice derivatives, you may do something like:
interp = Interpolation[rs, InterpolationOrder -> 3, Method -> "Spline"]
Show[Plot[{interp[x], 10 interp'[x]}, {x, Min[First /@ rs], Max[First /@ rs]},
PlotRange -> Full],
ListPlot@rs]
Look at the difference in the derivative's behavior when you use the "Spline" Method:
interp = Interpolation[rs, InterpolationOrder -> 3, Method -> "Spline"]
interp1 = Interpolation[rs, InterpolationOrder -> 3]
Show[Plot[{interp1'[x], interp'[x] - .005},
{x, Min[First /@ rs], Max[First /@ rs]}, PlotRange -> Full]]
If it is a distribution, I think you should use SmoothKernelDistribution instead.
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