v0.2.4

Author: mcabbott

Project.toml

@@ -1,7 +1,7 @@
 name = "SliceMap"
 uuid = "82cb661a-3f19-5665-9e27-df437c7e54c8"
 authors = ["Michael Abbott"]
-version = "0.2.3"
+version = "0.2.4"
 
 [deps]
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
@@ -13,9 +13,9 @@ ZygoteRules = "700de1a5-db45-46bc-99cf-38207098b444"
 [compat]
 ForwardDiff = "0.8, 0.9, 0.10"
 JuliennedArrays = "0.0.3, 0.2"
-StaticArrays = "0.10, 0.11, 0.12"
+StaticArrays = "0.10, 0.11, 0.12, 1.0"
 Tracker = "0.2"
-Zygote = "0.4"
+Zygote = "0.4, 0.5, 0.6"
 ZygoteRules = "0.2"
 julia = "1"
 

docs/intro.md

@@ -1,6 +1,7 @@
 # SliceMap.jl
 
-Some examples & benchmarks. 
+Some examples & benchmarks. Times shown were on Julia 1.2 I think, 
+and some have improved quite a bit since. 
 
 ## Simple example
 
@@ -66,12 +67,10 @@ Zygote.gradient(m -> sum(sin, jumap(fun, m)), mat)[1]
 @btime Zygote.gradient(m -> sum(jumap1(fun, m)), $mat1k) #    412.904 μs, really?
 ```
 
-It used to do the same thing for the slice/glue functions in 
-[TensorCast](https://github.com/mcabbott/TensorCast.jl),
-but but that should soon be part of that package:
+Similar gradients also moved to [TensorCast](https://github.com/mcabbott/TensorCast.jl):
 
 ```julia
-using TensorCast#two
+using TensorCast
 @cast [i,j] := fun(mat[:,j])[i]                   # same as mapcols
 
 tcm(mat) = @cast out[i,j] := fun(mat[:,j])[i]