do rename

.travis.yml

@@ -16,4 +16,4 @@ git:
   depth: 99999
 
 after_success:
-  - julia -e 'include(Pkg.dir("ContMechTensors", "test", "coverage.jl"))'
+  - julia -e 'include(Pkg.dir("Tensors", "test", "coverage.jl"))'

LICENSE.md

@@ -1,4 +1,4 @@
-The ContMechTensors.jl package is licensed under the MIT "Expat" License:
+The Tensors.jl package is licensed under the MIT "Expat" License:
 
 > Copyright (c) 2016: Kristoffer Carlsson.
 >

README.md

@@ -1,4 +1,4 @@
-# ContMechTensors
+# Tensors
 
 *Efficient computations with symmetric and non-symmetric tensors with support for automatic differentiation.*
 
@@ -19,7 +19,7 @@ Supports Automatic Differentiation to easily compute first and second order deri
 The package is registered in `METADATA.jl` and so can be installed with `Pkg.add`.
 
 ```julia
-julia> Pkg.add("ContMechTensors")
+julia> Pkg.add("Tensors")
 ```
 
 ## Documentation
@@ -36,18 +36,18 @@ The package is tested against Julia `0.5`, and `0.6-dev` on Linux, OS X, and Win
 Contributions are very welcome, as are feature requests and suggestions. Please open an [issue][issues-url] if you encounter any problems.
 
 [docs-latest-img]: https://img.shields.io/badge/docs-latest-blue.svg
-[docs-latest-url]: https://kristofferc.github.io/ContMechTensors.jl/latest/
+[docs-latest-url]: https://kristofferc.github.io/Tensors.jl/latest/
 
 [docs-stable-img]: https://img.shields.io/badge/docs-stable-blue.svg
-[docs-stable-url]: https://kristofferc.github.io/ContMechTensors.jl/stable
+[docs-stable-url]: https://kristofferc.github.io/Tensors.jl/stable
 
-[travis-img]: https://travis-ci.org/KristofferC/ContMechTensors.jl.svg?branch=master
-[travis-url]: https://travis-ci.org/KristofferC/ContMechTensors.jl
+[travis-img]: https://travis-ci.org/KristofferC/Tensors.jl.svg?branch=master
+[travis-url]: https://travis-ci.org/KristofferC/Tensors.jl
 
 [appveyor-img]: https://ci.appveyor.com/api/projects/status/xe0ghtyas12wv555/branch/master?svg=true
-[appveyor-url]: https://ci.appveyor.com/project/KristofferC/contmechtensors-jl/branch/master
+[appveyor-url]: https://ci.appveyor.com/project/KristofferC/Tensors-jl/branch/master
 
-[issues-url]: https://github.com/KristofferC/ContMechTensors.jl/issues
+[issues-url]: https://github.com/KristofferC/Tensors.jl/issues
 
-[codecov-img]: https://codecov.io/gh/KristofferC/ContMechTensors.jl/branch/master/graph/badge.svg
-[codecov-url]: https://codecov.io/gh/KristofferC/ContMechTensors.jl
+[codecov-img]: https://codecov.io/gh/KristofferC/Tensors.jl/branch/master/graph/badge.svg
+[codecov-url]: https://codecov.io/gh/KristofferC/Tensors.jl

appveyor.yml

@@ -28,7 +28,7 @@ build_script:
 # Need to convert from shallow to complete for Pkg.clone to work
   - IF EXIST .git\shallow (git fetch --unshallow)
   - C:\projects\julia\bin\julia -e "versioninfo();
-      Pkg.clone(pwd(), \"ContMechTensors\"); Pkg.build(\"ContMechTensors\")"
+      Pkg.clone(pwd(), \"Tensors\"); Pkg.build(\"Tensors\")"
 
 test_script:
-  - C:\projects\julia\bin\julia --check-bounds=yes -e "Pkg.test(\"ContMechTensors\")"
+  - C:\projects\julia\bin\julia --check-bounds=yes -e "Pkg.test(\"Tensors\")"

benchmark/benchmark_ad.jl

@@ -1,5 +1,5 @@
-const ∇ = ContMechTensors.gradient
-const Δ = ContMechTensors.hessian
+const ∇ = Tensors.gradient
+const Δ = Tensors.hessian
 
 function Ψ(C, μ, Kb)
     detC = det(C)

benchmark/results_ad.md

@@ -0,0 +1,50 @@
+| ID | time | GC time | memory | allocations |
+|----|------|---------|--------|-------------|
+| `["gradient","dim 1 S - Float32"]` | 180.687 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 1 S - Float64"]` | 196.869 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 1 S - sym - Float32"]` | 194.498 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 1 S - sym - Float64"]` | 196.092 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 1 Ψ - Float32 - ana"]` | 111.000 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 1 Ψ - Float32"]` | 161.945 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 1 Ψ - Float64 - ana"]` | 118.000 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 1 Ψ - Float64"]` | 176.064 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 1 Ψ - sym - Float32 - ana"]` | 118.000 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 1 Ψ - sym - Float32"]` | 175.461 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 1 Ψ - sym - Float64 - ana"]` | 118.000 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 1 Ψ - sym - Float64"]` | 175.332 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 2 S - Float32"]` | 316.050 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 2 S - Float64"]` | 416.660 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 2 S - sym - Float32"]` | 315.605 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 2 S - sym - Float64"]` | 311.008 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 2 Ψ - Float32 - ana"]` | 128.000 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 2 Ψ - Float32"]` | 245.983 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 2 Ψ - Float64 - ana"]` | 127.000 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 2 Ψ - Float64"]` | 237.191 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 2 Ψ - sym - Float32 - ana"]` | 128.000 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 2 Ψ - sym - Float32"]` | 245.562 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 2 Ψ - sym - Float64 - ana"]` | 127.000 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 2 Ψ - sym - Float64"]` | 237.191 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 3 S - Float32"]` | 839.907 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 3 S - Float64"]` | 804.629 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 3 S - sym - Float32"]` | 842.720 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 3 S - sym - Float64"]` | 804.034 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 3 Ψ - Float32 - ana"]` | 141.000 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 3 Ψ - Float32"]` | 397.249 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 3 Ψ - Float64 - ana"]` | 139.000 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 3 Ψ - Float64"]` | 373.892 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 3 Ψ - sym - Float32 - ana"]` | 191.000 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 3 Ψ - sym - Float32"]` | 395.731 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 3 Ψ - sym - Float64 - ana"]` | 140.000 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["gradient","dim 3 Ψ - sym - Float64"]` | 374.348 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["hessian","dim 1 Ψ - Float32"]` | 328.355 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["hessian","dim 1 Ψ - Float64"]` | 347.819 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["hessian","dim 1 Ψ - sym - Float32"]` | 563.225 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["hessian","dim 1 Ψ - sym - Float64"]` | 347.877 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["hessian","dim 2 Ψ - Float32"]` | 695.905 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["hessian","dim 2 Ψ - Float64"]` | 902.880 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["hessian","dim 2 Ψ - sym - Float32"]` | 954.442 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["hessian","dim 2 Ψ - sym - Float64"]` | 647.164 ns (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["hessian","dim 3 Ψ - Float32"]` | 6.678 μs (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["hessian","dim 3 Ψ - Float64"]` | 7.201 μs (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["hessian","dim 3 Ψ - sym - Float32"]` | 6.676 μs (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |
+| `["hessian","dim 3 Ψ - sym - Float64"]` | 6.529 μs (5%) | 0.000 ns | 0.00 bytes (1%) | 0 |

benchmark/runbenchmarks.jl

@@ -1,4 +1,4 @@
-using ContMechTensors
+using Tensors
 using BenchmarkTools
 using JLD
 

docs/make.jl

@@ -1,9 +1,9 @@
-using Documenter, ContMechTensors
+using Documenter, Tensors
 
 makedocs(
-    modules = [ContMechTensors],
+    modules = [Tensors],
     format = :html,
-    sitename = "ContMechTensors.jl",
+    sitename = "Tensors.jl",
     doctest = true,
     strict = true,
     pages = Any[
@@ -21,7 +21,7 @@ makedocs(
 )
 
 deploydocs(
-    repo = "github.com/KristofferC/ContMechTensors.jl.git",
+    repo = "github.com/KristofferC/Tensors.jl.git",
     target = "build",
     julia = "0.5",
     deps = nothing,

docs/src/demos.md

@@ -1,6 +1,6 @@
 # Demos
 
-This section contain a few demos of applying `ContMechTensors` to continuum mechanics.
+This section contain a few demos of applying `Tensors` to continuum mechanics.
 
 ## Creating the linear elasticity tensor
 
@@ -13,7 +13,7 @@ where $\delta_{ij} = 1$ if $i = j$ otherwise $0$. It can also be computed in ter
 The code below creates the elasticity tensor for given parameters $E$ and $\nu$ and dimension $\texttt{dim}$. Note the similarity between the mathematical formula and the code.
 
 ```julia
-using ContMechTensors
+using Tensors
 E = 200e9
 ν = 0.3
 dim = 2
@@ -35,7 +35,7 @@ $\Psi(\mathbf{C}) = 1/2 \mu (\mathrm{tr}(\hat{\mathbf{C}}) - 3) + K_b(J-1)^2,$
 
 where $\hat{\mathbf{C}} = \mathrm{det}(\mathbf{C})^{-1/3} \mathbf{C}$ and $J = \det(\mathbf{F}) = \sqrt{\det(\mathbf{C})}$ and the shear and bulk modulus are given by $\mu$ and $K_b$ respectively.
 
-This free energy function can be implemented in `ContMechTensors` as:
+This free energy function can be implemented in `Tensors` as:
 
 ```julia
 function Ψ(C, μ, Kb)
@@ -69,7 +69,7 @@ For some material models it can be cumbersome to compute the analytical expressi
 ```@meta
 DocTestSetup = quote
     srand(1234)
-    using ContMechTensors
+    using Tensors
     E = 200e9
     ν = 0.3
     dim = 2
@@ -106,13 +106,13 @@ julia> F = one(Tensor{2,3}) + rand(Tensor{2,3});
 julia> C = tdot(F);
 
 julia> S_AD = 2 * gradient(C -> Ψ(C, μ, Kb), C)
-3×3 ContMechTensors.SymmetricTensor{2,3,Float64,6}:
+3×3 Tensors.SymmetricTensor{2,3,Float64,6}:
   4.30534e11  -2.30282e11  -8.52861e10
  -2.30282e11   4.38793e11  -2.64481e11
  -8.52861e10  -2.64481e11   7.85515e11
 
 julia> S(C, μ, Kb)
-3×3 ContMechTensors.SymmetricTensor{2,3,Float64,6}:
+3×3 Tensors.SymmetricTensor{2,3,Float64,6}:
   4.30534e11  -2.30282e11  -8.52861e10
  -2.30282e11   4.38793e11  -2.64481e11
  -8.52861e10  -2.64481e11   7.85515e11

docs/src/index.md

@@ -1,4 +1,4 @@
-# ContMechTensors
+# Tensors
 
 *Efficient computations with symmetric and non-symmetric tensors in Julia.*
 
@@ -12,10 +12,10 @@ Supports Automatic Differentiation to easily compute first and second order deri
 
 ## Installation
 
-`ContMechTensors` is a registered package and so can be installed via
+`Tensors` is a registered package and so can be installed via
 
 ```julia
-Pkg.add("ContMechTensors")
+Pkg.add("Tensors")
 ```
 
 ## Manual Outline

docs/src/man/automatic_differentiation.md

@@ -1,7 +1,7 @@
 ```@meta
 DocTestSetup = quote
     srand(1234)
-    using ContMechTensors
+    using Tensors
 end
 ```
 
@@ -11,13 +11,13 @@ end
 Pages = ["automatic_differentiation.md"]
 ```
 
-`ContMechTensors` supports forward mode automatic differentiation (AD) of tensorial functions to compute first order derivatives (gradients) and second order derivatives (Hessians).
+`Tensors` supports forward mode automatic differentiation (AD) of tensorial functions to compute first order derivatives (gradients) and second order derivatives (Hessians).
 It does this by exploiting the `Dual` number defined in `ForwardDiff.jl`.
 While `ForwardDiff.jl` can itself be used to differentiate tensor functions it is a bit awkward because `ForwardDiff.jl` is written to work with standard Julia `Array`s. One therefore has to send the input argument as an `Array` to `ForwardDiff.jl`, convert it to a `Tensor` and then convert the output `Array` to a `Tensor` again. This can also be inefficient since these `Array`s are allocated on the heap so one needs to preallocate which can be annoying.
 
-Instead, it is simpler to use `ContMechTensors` own AD API to do the differentiation. This does not require any conversions and everything will be stack allocated so there is no need to preallocate.
+Instead, it is simpler to use `Tensors` own AD API to do the differentiation. This does not require any conversions and everything will be stack allocated so there is no need to preallocate.
 
-The API for AD in `ContMechTensors` is `gradient(f, A)` and `hessian(f, A)` where `f` is a function and `A` is a first or second order tensor. For `gradient` the function can return a scalar, vector (in case the input is a vector) or a second order tensor. For `hessian` the function should return a scalar.
+The API for AD in `Tensors` is `gradient(f, A)` and `hessian(f, A)` where `f` is a function and `A` is a first or second order tensor. For `gradient` the function can return a scalar, vector (in case the input is a vector) or a second order tensor. For `hessian` the function should return a scalar.
 
 When evaluating the function with dual numbers, the value (value and gradient in the case of hessian) is obtained automatically, along with the gradient. To obtain the lower order results `gradient` and `hessian` accepts a third arguement, a `Symbol`. Note that the symbol is only used to dispatch to the correct function, and thus it can be any symbol. In the examples the symbol `:all` is used to obtain all the lower order derivatives and values.
 
@@ -38,12 +38,12 @@ $f(\mathbf{x}) = |\mathbf{x}| \quad \Rightarrow \quad \partial f / \partial \mat
 julia> x = rand(Vec{2});
 
 julia> gradient(norm, x)
-2-element ContMechTensors.Tensor{1,2,Float64,2}:
+2-element Tensors.Tensor{1,2,Float64,2}:
  0.61036
  0.792124
 
 julia> x / norm(x)
-2-element ContMechTensors.Tensor{1,2,Float64,2}:
+2-element Tensors.Tensor{1,2,Float64,2}:
  0.61036
  0.792124
 ```
@@ -56,12 +56,12 @@ $f(\mathbf{A}) = \det \mathbf{A} \quad \Rightarrow \quad \partial f / \partial \
 julia> A = rand(SymmetricTensor{2,2});
 
 julia> gradient(det, A)
-2×2 ContMechTensors.SymmetricTensor{2,2,Float64,3}:
+2×2 Tensors.SymmetricTensor{2,2,Float64,3}:
   0.566237  -0.766797
  -0.766797   0.590845
 
 julia> inv(A)' * det(A)
-2×2 ContMechTensors.SymmetricTensor{2,2,Float64,3}:
+2×2 Tensors.SymmetricTensor{2,2,Float64,3}:
   0.566237  -0.766797
  -0.766797   0.590845
 ```

docs/src/man/binary_operators.md

@@ -1,7 +1,7 @@
 ```@meta
 DocTestSetup = quote
     srand(1234)
-    using ContMechTensors
+    using Tensors
 end
 ```