Update Julia Con poster

examples/JuliaCon_2021.jl

@@ -14,19 +14,62 @@ macro bind(def, element)
 end
 
 # ╔═╡ a565c4ee-d425-11eb-0a5c-d7d322fb95c6
-using IndexFunArrays, Plots, Colors, ImageShow, PlutoUI
+using IndexFunArrays, Plots, Colors, ImageShow, PlutoUI, MappedArrays
 
 # ╔═╡ eb26d519-61f9-49c8-bf13-fb0bd5b01be6
 md"#### IndexFunArrays.jl | Fun with indices (and functions on them)
+* _Rainer Heintzmann, Leibniz Institute of Photonic Technology in Jena, Germany_
+* _Felix Wechsler, Leibniz Institute of Photonic Technology in Jena, Germany_
 
 Main features:
 * Arrays without memory allocations
 * entries are calculated once accessed
+* feels like an arrays but is some kind of generator
 
 "
 
-# ╔═╡ 44af166f-a01f-43c2-a774-9a09f5d42712
-hello = IndexFunArray(x -> "hello", (3, 3))
+# ╔═╡ 840c51ce-1300-41b0-be75-4b298c6c3390
+md"### Motivation
+
+* Calculation with indices works efficiently with `CartesianIndices`
+* However, as you can see it can get complicated
+"
+
+# ╔═╡ 1d880f4b-71fb-4b83-8d23-4a46a1481423
+function rr_cartesian_indices(s)
+	# entry wise function
+	rr_fun(index) = √(sum(abs2.(Tuple(index) .- (s .÷ 2 .+ 1))));
+	# array of tuple indices
+	arr_indices = CartesianIndices(s)
+	# apply rr_fun elementwise
+	rr_fun.(arr_indices)
+end
+
+# ╔═╡ ab66c67d-832b-40ff-bbc2-f52761912b2d
+rr_cartesian_indices((5,6))
+
+# ╔═╡ f09a33fa-dd83-4dbd-948b-78303d3fb76d
+md"### MappedArrays
+* in principle offers similar functionality
+* but doesn't provide the explicit functions (like `rr2`, `rr`)
+"
+
+# ╔═╡ e70c0bdb-aa02-4017-ae32-ba3b9fb50906
+function rr_mapped_arrays(s)
+	# entry wise function
+	rr_fun(index) = √(sum(abs2.(Tuple(index) .- (s .÷ 2 .+ 1))));
+	# array of tuple indices
+	mappedarray(rr_fun, CartesianIndices(s))
+end
+
+# ╔═╡ bee02ceb-24a4-4d48-bfe0-c24fdfde0472
+rr_mapped_arrays((5,6))
+
+# ╔═╡ 01651a42-dce0-4015-a9d0-a92dc02cbd89
+rr((5, 6))
+
+# ╔═╡ 3faba71d-ffa3-4bad-96b8-76f488dfc600
+md"### Data Structure Behind It"
 
 # ╔═╡ da4c455d-f657-4fc8-b8d5-ac1497e29071
 struct IndexFunArray_demo{T, N, F} <: AbstractArray{T, N} where {F}
@@ -36,15 +79,51 @@ struct IndexFunArray_demo{T, N, F} <: AbstractArray{T, N} where {F}
     size::NTuple{N, Int}
 end
 
+# ╔═╡ 5b7b9e0b-9f93-43ed-aba5-56cbf9f1bea8
+Base.getindex(A::IndexFunArray{T,N}, I::Vararg{Int, N}) where {T,N} = 
+    return A.generator(I)
+
+# ╔═╡ 8604d201-c4ba-4314-bc0b-df73f8d3e24a
+md"### How To Use It"
+
+# ╔═╡ 44af166f-a01f-43c2-a774-9a09f5d42712
+hello = IndexFunArray(x -> "hello", (3, 3))
+
 # ╔═╡ 3a2ddf05-0d2a-401e-bbad-adccf7ab21bf
-hello[1, 3]
+hello[2, 3]
 
 # ╔═╡ 5d5835da-4331-4f06-a24c-e60177f38cba
 img = IndexFunArray(x -> x[1] + x[2], (3, 3))
 
 # ╔═╡ df51963f-ceea-4279-aa51-6d8c8afaa9a0
 distance_to_corner = IndexFunArray(x -> sqrt((x[1]-1)^2 + (x[2]-1)^2), (3, 3))
 
+# ╔═╡ dc58b7a5-8c64-4c3f-aa7b-8261647d2513
+md"### What is already defined?
+
+#### Window Functions
+* Gaussian 
+* Hamming
+* Hanning
+* Linear
+* ...
+
+#### Distance Functions
+* radius
+* radius squared
+* 1D ramps
+* ...
+"
+
+# ╔═╡ 455d650b-cb51-4dc3-a02f-e6c832e3e5e0
+
+
+# ╔═╡ d48829c1-c04f-47d3-aa0d-af656b6a5276
+
+
+# ╔═╡ fbfbc63b-4188-4c03-9bc3-b89b4c6c6298
+
+
 # ╔═╡ c0eb6cff-a47c-485e-8983-2bf4070d5223
 md"## Defining apertures in 1 LOC"
 
@@ -58,7 +137,10 @@ md"## Defining apertures in 1 LOC"
 ctr = [CtrFT, CtrEnd, CtrFFT][parse(Int, c_ind)];
 
 # ╔═╡ 4512c50f-e094-4ef9-a177-27a9fdc58f09
-Gray.(rr((300, 300), offset=ctr) .< radius)
+[Gray.(rr((300, 300), offset=ctr) .<= radius) Gray.(disc((300, 300), radius, offset=ctr))]
+
+# ╔═╡ d7878c97-068d-4040-9f73-2400e05636bf
+
 
 # ╔═╡ 2d23b2d8-5dac-422a-804d-e2ccd27de12e
 Gray.(0.5 .+ xx((300, 300), offset=ctr, scale=ScaNorm))
@@ -83,21 +165,55 @@ end;
 # ╔═╡ fedff1f5-5595-4ac9-b53f-a11bd864554c
 [Gray.(w_gauss) Gray.(w_linear) ]
 
+# ╔═╡ f0c86a16-f255-4b85-b32a-e0adba958504
+md"# Gauss"
+
+# ╔═╡ 5c691197-0516-4ab6-9804-0bb6d0df5a66
+md"
+$(@bind σ1 Slider(0:0.01:200, show_value=true))
+$(@bind σ2 Slider(0:0.01:200, show_value=true))
+"
+
+# ╔═╡ 7f72a169-5c33-49f4-982e-e85181425374
+w_gauss2 = gaussian((256, 256), sigma=(σ1, σ2));
+
+# ╔═╡ 0fa4b07a-5fe2-4294-8753-9adb1ed7c6f7
+Gray.(w_gauss2)
+
 # ╔═╡ Cell order:
 # ╠═a565c4ee-d425-11eb-0a5c-d7d322fb95c6
 # ╟─eb26d519-61f9-49c8-bf13-fb0bd5b01be6
-# ╠═44af166f-a01f-43c2-a774-9a09f5d42712
+# ╟─840c51ce-1300-41b0-be75-4b298c6c3390
+# ╠═1d880f4b-71fb-4b83-8d23-4a46a1481423
+# ╠═ab66c67d-832b-40ff-bbc2-f52761912b2d
+# ╟─f09a33fa-dd83-4dbd-948b-78303d3fb76d
+# ╠═e70c0bdb-aa02-4017-ae32-ba3b9fb50906
+# ╠═bee02ceb-24a4-4d48-bfe0-c24fdfde0472
+# ╠═01651a42-dce0-4015-a9d0-a92dc02cbd89
+# ╟─3faba71d-ffa3-4bad-96b8-76f488dfc600
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+# ╠═5b7b9e0b-9f93-43ed-aba5-56cbf9f1bea8
+# ╟─8604d201-c4ba-4314-bc0b-df73f8d3e24a
+# ╠═44af166f-a01f-43c2-a774-9a09f5d42712
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 # ╠═5d5835da-4331-4f06-a24c-e60177f38cba
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+# ╟─dc58b7a5-8c64-4c3f-aa7b-8261647d2513
+# ╟─455d650b-cb51-4dc3-a02f-e6c832e3e5e0
+# ╟─d48829c1-c04f-47d3-aa0d-af656b6a5276
+# ╟─fbfbc63b-4188-4c03-9bc3-b89b4c6c6298
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+# ╠═d7878c97-068d-4040-9f73-2400e05636bf
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 # ╠═fedff1f5-5595-4ac9-b53f-a11bd864554c
+# ╟─f0c86a16-f255-4b85-b32a-e0adba958504
+# ╟─5c691197-0516-4ab6-9804-0bb6d0df5a66
+# ╠═7f72a169-5c33-49f4-982e-e85181425374
+# ╠═0fa4b07a-5fe2-4294-8753-9adb1ed7c6f7

examples/Project.toml

@@ -2,5 +2,6 @@
 Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
 ImageShow = "4e3cecfd-b093-5904-9786-8bbb286a6a31"
 IndexFunArrays = "613c443e-d742-454e-bfc6-1d7f8dd76566"
+MappedArrays = "dbb5928d-eab1-5f90-85c2-b9b0edb7c900"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 Pluto = "c3e4b0f8-55cb-11ea-2926-15256bba5781"