Comments (6)
dlfivefifty
commented on June 29, 2024
1
PS You should update to Julia v1.3 as this package will be 1.3+ only once we switch to FastTransforms.jl v0.8
from multivariateorthogonalpolynomials.jl.
dlfivefifty
commented on June 29, 2024
Yes. This can be done once everything is up to date with FastTransforms.jl v0.8
from multivariateorthogonalpolynomials.jl.
TSGut
commented on June 29, 2024
Seems like the workaround for the triangle at the bottom of Triangle.jl still attempts to import old structures: https://github.com/JuliaApproximation/MultivariateOrthogonalPolynomials.jl/blob/2b4244fac5201c6adba4acf9913b7c8eddebbab0/src/Triangle/Triangle.jl
I think this might be all that is left, though.
from multivariateorthogonalpolynomials.jl.
dlfivefifty
commented on June 29, 2024
If you click on the line number on the left and choose "Copy permalink" that will show which line you are referring to
from multivariateorthogonalpolynomials.jl.
TSGut
commented on June 29, 2024
Useful. It's right at the bottom:
from multivariateorthogonalpolynomials.jl.
dlfivefifty
commented on June 29, 2024
Changing it to ApproxFunBase or BlockBandedMatrices should fix it
from multivariateorthogonalpolynomials.jl.
Related Issues (20)
- Weak Laplacians on disks?
- Make evaluation of triangle functions type stable
- Weighted ∂ₓ on triangles errs
- Generalized eigenvalue problems on a disc HOT 3
- Fix plotting HOT 12
- How to make rotationally invariant solves fast? HOT 5
- Missing triangle functionality (Laplacian, restriction) HOT 2
- JacobiTriangle() expansions failing in strange way HOT 2
- Lowering bug? HOT 6
- Jacobi matrices for RectPolynomial HOT 1
- Triangle examples in examples/triangleexamples.jl fail HOT 5
- Dependency with HOPs reversed? HOT 3
- Laplacian vs. Laplace? HOT 1
- New Operator: AngularMomentum HOT 2
- Better syntax for x,y = first.(xy),last.(xy) HOT 2
- Are these actually the adjoint Jacobi matrices? I.e. multiplication operators? HOT 1
- Add ZeroTo (somewhere)? HOT 1
- Which notation is best? HOT 3
- Either: PartialDerivative{k}(<:Basis) or Inclusion(::Basis) should err (or both) HOT 5
- Ridge polynomials (on the disk)? HOT 2
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from multivariateorthogonalpolynomials.jl.