cleanup

Project.toml

@@ -4,19 +4,14 @@ authors = ["Andre Souza <[email protected]>"]
 version = "0.1.0"
 
 [deps]
-CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 DocumenterTools = "35a29f4d-8980-5a13-9543-d66fff28ecb8"
-GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a"
-GraphMakie = "1ecd5474-83a3-4783-bb4f-06765db800d2"
 Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 IncompleteLU = "40713840-3770-5561-ab4c-a76e7d0d7895"
-KernelAbstractions = "63c18a36-062a-441e-b654-da1e3ab1ce7c"
 KrylovKit = "0b1a1467-8014-51b9-945f-bf0ae24f4b77"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LinearSolve = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"
 MarkovChainHammer = "38c40fd0-bccb-4723-b30d-b2caea0ad8d9"
-NetworkLayout = "46757867-2c16-5918-afeb-47bfcb05e46a"
 ParallelKMeans = "42b8e9d4-006b-409a-8472-7f34b3fb58af"
 PrecompileTools = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
 ProgressBars = "49802e3a-d2f1-5c88-81d8-b72133a6f568"
@@ -27,12 +22,9 @@ SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 [compat]
 Documenter = "0.27"
 DocumenterTools = "0.1"
-GLMakie = "0.9"
-GraphMakie = "0.5"
 IncompleteLU = "0.2"
 LinearSolve = "2.22"
 MarkovChainHammer = "0.0"
-NetworkLayout = "0.4"
 ParallelKMeans = "1.1"
 PrecompileTools = "1.2"
 ProgressBars = "1.5"

examples/Project.toml

@@ -0,0 +1,7 @@
+[deps]
+GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a"
+GraphMakie = "1ecd5474-83a3-4783-bb4f-06765db800d2"
+MarkovChainHammer = "38c40fd0-bccb-4723-b30d-b2caea0ad8d9"
+NetworkLayout = "46757867-2c16-5918-afeb-47bfcb05e46a"
+ProgressBars = "49802e3a-d2f1-5c88-81d8-b72133a6f568"
+StateSpacePartitions = "5427e241-e665-444a-8288-8739a7fd2cc0"

examples/noisy_lorenz.jl

@@ -4,8 +4,8 @@ include("timestepping_utils.jl")
 include("visualization_utils.jl")
 Random.seed!(1234)
 
-dt = 0.001 
-iterations = 10^8
+dt = 0.01 
+iterations = 10^5
 
 trajectory = zeros(3, iterations)
 trajectory[:, 1] .= [14.0, 20.0, 27.0]
@@ -19,4 +19,4 @@ method = Tree(false, 0.001)
 state_space_partitions = StateSpacePartition(trajectory; method = method)
 
 length(union(state_space_partitions.partitions))
-# visualize_koopman_mode(trajectory, state_space_partitions.partitions, colormap1 = :balance, mode = 4)
+visualize_koopman_mode(trajectory, state_space_partitions.partitions, colormap1 = :balance, mode = 4)

src/StateSpacePartitions.jl

@@ -5,10 +5,8 @@ export StateSpacePartition
 using ProgressBars
 using Reexport, PrecompileTools
 
-include("Architectures.jl")
 include("Trees/Trees.jl")
 
-using .Architectures
 using .Trees
 
 # import StateSpacePartitions.Trees: unstructured_tree, UnstructuredTree, determine_partition, Tree
@@ -18,11 +16,8 @@ struct StateSpacePartition{E, P}
 end
 
 function StateSpacePartition(trajectory; 
-                             architecture = CPU(),
                              method = Tree(), 
-                             chunk_size = size(trajectory)[2],
                              cells = nothing,
-                             chunked = false,
                              override = false)
 
     @info "determine partitioning function "
@@ -34,27 +29,21 @@ function StateSpacePartition(trajectory;
         @error "cells must be an integer"
     end
 
-    embedding = determine_partition(trajectory, method; override = override, architecture)
+    embedding = determine_partition(trajectory, method; override = override)
     partitions = zeros(Int64, size(trajectory)[2])
 
-    if chunked
-        @info "computing (chunked) partition trajectory"
-        chunked_partitions = ChunkedArray(partitions, architecture; chunk_size)
-        chunked_trajectory = ChunkedArray(trajectory, architecture; chunk_size)
-        embedding(chunked_partitions, chunked_trajectory)
-        return StateSpacePartition(embedding, chunked_partitions)
-    else
-        @info "computing partition trajectory"
-        for (i, state) in ProgressBar(enumerate(eachcol(trajectory)))
-            partitions[i] = embedding(state)
-        end
-        return StateSpacePartition(embedding, partitions)
+    @info "computing partition trajectory"
+    for (i, state) in ProgressBar(enumerate(eachcol(trajectory)))
+        partitions[i] = embedding(state)
     end
-    return nothing
+
+    return StateSpacePartition(embedding, partitions)
 end
 
+# Utility Functions
+
 include("inverse_iteration.jl")
-include("coarsen.jl")
 include("markov_chain_hammer_extensions.jl")
+include("tree_extensions.jl")
 
 end # module StateSpacePartitions

src/Trees/Trees.jl

@@ -31,5 +31,6 @@ Tree(bool::Bool, args::Any) = Tree(Val(bool), args)
 
 include("unstructured.jl")
 include("binary.jl")
+include("coarsen.jl")
 
 end # module Tree

src/Trees/binary.jl

@@ -1,8 +1,4 @@
-using KernelAbstractions: @kernel, @index
-using KernelAbstractions.Extras.LoopInfo: @unroll
-
 export extract_coarse_guess
-
 struct BinaryTree{S, T}
     centers::S
     levels::T
@@ -17,35 +13,6 @@ function (embedding::BinaryTree)(current_state)
     return local_index(global_index, embedding.levels)
 end
 
-function (embedding::BinaryTree)(partitions, states)
-    worksize  = total_length(states)
-    workgroup = min(length(partitions), 256)
-
-    arch = architecture(partitions)
-    args = (partitions, states, embedding.centers, Val(embedding.levels))
-
-    launch_chunked_kernel!(arch, workgroup, worksize, _compute_binary_embedding!, args)
-
-    return nothing
-end
-
-@kernel function _compute_binary_embedding!(partitions, states, centers, ::Val{levels}) where levels
-    p = @index(Global, Linear)
-
-    @inbounds begin
-        state = states[:, p]
-
-        global_index = 1 
-
-        @unroll for _ in 1:levels
-            new_index = argmin([norm(state - center) for center in centers[global_index]])
-            global_index = child_global_index(new_index, global_index)
-        end
-
-        partitions[p] = local_index(global_index, levels)
-    end
-end
-
 # binary tree index juggling
 local_index(global_index, levels) = global_index - 2^levels + 1 # markov index from [1, 2^levels]
 child_global_index(new_index, global_parent_index) = 2 * global_parent_index + new_index - 1 

src/coarsen.jl → src/Trees/coarsen.jl

@@ -87,9 +87,3 @@ function isancestor(ancestor, child, G)
     end
     return nothing
 end
-
-# Binary Tree 
-function coarsen(ssp::StateSpacePartition{S, A}, levels) where {S <: BinaryTree, A} 
-    partitions = ssp.partitions
-    return (partitions .- 1) .÷ 2^levels .+ 1
-end