bump version

Project.toml

@@ -1,7 +1,7 @@
 name = "TaylorTest"
 uuid = "967b12e5-70be-421a-a124-e33167727e0a"
 authors = ["Gaspard Jankowiak <gaspard.jankowiak@uni-graz.at>"]
-version = "0.2.0"
+version = "0.2.1"
 
 [deps]
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"

README.md

@@ -5,15 +5,23 @@ Simple package to check derivatives
 # Usage
 
 ```
-  check(f, Jf, x[, constant_components]; f_kwargs...)
+  check(f, Jf, x[, constant_components]; taylortestplot=false, taylortestdirection=nothing, f_kwargs...)
 ```
 
-Returns true if `Jf` approximates the derivative/gradient/Jacobian of `f` at point `x` (along a random direction).
+Returns true if `Jf` approximates the derivative/gradient/Jacobian of `f` at point `x` (along a random direction unless specified using `taylortestdirection`).
 `f_kwargs` are keywords arguments to be passed to `f` and `Jf`.
 `constant_components` is an optional `Vector{Int}` corresponding to components of the direction which should be set to zero,
 effectively ignoring the dependency of `f` on these components.
+If `taylortestplot` is `true`, a log-log plot of the error against the perturbation size will be shown.
 
-## Examples
+
+```
+  check!(f!, Jf!, x, size_f_x, size_Jf_x, [, constant_components]; taylortestplot=false, taylortestdirection=nothing, f_kwargs...)
+```
+
+Like `check` but handling non-allocating functions. Output size for both `f!` and the Jacobian `Jf!` must be provided (as `Tuple`s) via `size_f_x` and `size_Jf_x`.
+
+## Examples (see `test` directory for more)
 ```julia
 import TaylorTest
 

src/TaylorTest.jl

@@ -7,12 +7,17 @@ import TensorOperations: @tensor
 import UnicodePlots
 
 """
-  `check(f, Jf, x[, constant_components]; f_kwargs...)`
+```
+  check(f, Jf, x[, constant_components]; taylortestplot=false, taylortestdirection=nothing, f_kwargs...)
+```
 
-Returns true if `Jf` approximates the derivative/gradient/Jacobian of `f` at point `x` (along a random direction).
+Returns true if `Jf` approximates the derivative/gradient/Jacobian of `f` at point `x` (along a random direction unless specified using `taylortestdirection`).
 `f_kwargs` are keywords arguments to be passed to `f` and `Jf`.
 `constant_components` is an optional `Vector{Int}` corresponding to components of the direction which should be set to zero,
 effectively ignoring the dependency of `f` on these components.
+If `taylortestplot` is `true`, a log-log plot of the error against the perturbation size will be shown.
+
+See also: `check!`
 
 # Examples
 ```julia-repl
@@ -70,7 +75,16 @@ function check(f, Jf, x, constant_components::Vector{Int}=Int[]; taylortestplot:
   return isapprox(order, 1; atol=0.5)
 end
 
-function check!(f!, Jf!, x, size_f_x, size_Jf_x, constant_components::Vector{Int}=Int[];
+"""
+```
+  check!(f!, Jf!, x, size_f_x, size_Jf_x, [, constant_components]; taylortestplot=false, taylortestdirection=nothing, f_kwargs...)
+```
+
+Like `check` but handling non-allocating functions. Output size for both `f!` and the Jacobian `Jf!` must be provided (as `Tuple`s) via `size_f_x` and `size_Jf_x`.
+
+See also: `check`
+"""
+function check!(f!, Jf!, x, size_f_x::Tuple, size_Jf_x::Tuple, constant_components::Vector{Int}=Int[];
   taylortestdirection=nothing, taylortestplot::Bool=false, f_kwargs...)
 
   f = x -> begin