Closed sathvikbhagavan closed 3 months ago
Fixes: #234
Now we get the same outputs as scipy
julia> CubicS.(v) 20-element Vector{Float64}: 318.6243760456969 334.40200181048806 347.13345942996295 356.2838248343795 362.79265929502935 367.66363214151 371.88318244762684 376.04145340397156 380.33229231792177 384.9323162410631 390.002925359128 395.35553194322165 400.451560349822 404.7372180695537 407.7255576184023 410.46706709565404 415.5496701838955 425.6281355910744 443.20840335017215 467.3733539699478
https://en.wikiversity.org/wiki/Cubic_Spline_Interpolation
The first entry of the third column and last entry of first column in the tridiagonal matrix should have been zero.
For the tests, the polynomial mentioned in https://github.com/SciML/DataInterpolations.jl/blob/master/test/interpolation_tests.jl#L501 is not correct for natural boundary condition. Found the right ones in https://tools.timodenk.com/cubic-spline-interpolation
Fixes: #234
Now we get the same outputs as scipy
https://en.wikiversity.org/wiki/Cubic_Spline_Interpolation
The first entry of the third column and last entry of first column in the tridiagonal matrix should have been zero.
For the tests, the polynomial mentioned in https://github.com/SciML/DataInterpolations.jl/blob/master/test/interpolation_tests.jl#L501 is not correct for natural boundary condition. Found the right ones in https://tools.timodenk.com/cubic-spline-interpolation