ApproxFun.jl
ApproxFun.jl copied to clipboard
Julia package for function approximation
Integration in the default (Chebyshev between [-1,1], maybe?) and Fourier spaces gives incorrect results in the following code. Using Chebyshev with explicit bounds gives the correct result. ```julia Sθ=PeriodicSegment(-π,π); fn...
Hello! I'm trying to use ApproxFun for some Functional Data Analysis, as it has a lot of quite nice features for the work I'm doing. From other issues, I see...
Hi, When trying to make Fourier approximations work with BigFloat coefficients instead of Float64 ones, eg in the following minimal example: ``` using ApproxFun # FS = Fourier(0..2π) FS =...
Hi, This is not really an issue... I was wondering if you can improve the [tutorial](https://rveltz.github.io/BifurcationKit.jl/dev/tutorials1b/#Temperature-model-with-ApproxFun-(intermediate)-1) by using more idiomatic ApproxFun. On a side note, if you execute the code,...
I tried adapting Nonlinear Boundary Value problems example from the README to a BVP I was interested in and his this error: ```julia using ApproxFun, Plots @time let rmin =...
Hi Devs, I was wondering if there is / will be support 3D operations? My problem is quite simple except that it's 3D. Given knowledge of `f(x, y, z)`, solve...
When trying to use ApproxFun to interpolate a function on Chebyshev-points and compute derivatives, I came across the following behaviour: Define two spaces of Chebyshev and ultraspherical polynomials: ```julia using...
I am somewhat puzzled about what exactly is happening when computing derivatives and conversions on 2d Chebyshev product spaces. Some enlightenment would be greatly appreciated. This is also related to...
Hi, I am trying to solve a two-point bvp with ApproxFun but receive the following error: ERROR: AssertionError: abs(last(g)) < tol I had a hard time searching what this error...
`transform` and `itransform` behave strangely when used on a `TensorSpace` made of two `Fourier` spaces: ```julia using ApproxFun S = Fourier()^2 itransform(S, transform(S, [1.,2.,3.,4.])) # gives [2.5, 3.0, 2.5, 2.0,...