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ytzi
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the-stack-dedup-gpt2

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1
content
stringlengths
5
1.03M
input_ids
sequencelengths
4
823k
ratio_char_token
float64
0.4
12.5
token_count
int64
4
823k
function test_circuit_codes( p::Parameters ) funcs = default_funcs( p.numinputs ) c = random_chromosome( p, funcs ) cc = circuit_code( c ) # See https://en.wikibooks.org/wiki/Introducing_Julia/Strings_and_characters#Streams iobuffer = IOBuffer() print_build_chromosome(iobuffer,c) c_str = String(take!(iobuffer)) ctc = code_to_circuit(cc,p) iobuffer = IOBuffer() print_build_chromosome(iobuffer,c) ctc_str = String(take!(iobuffer)) if c_str != ctc_str println("error c != ctc ") println("c_str: ",c_str) println("ctc_str: ",ctc_str) end end function ttt(j) for i = 1:4 println(j) r = j % 2 j ÷= 5 println("i: ",i," r: ",r," j: ",j) r = j % 2 j ÷= 2 println("i: ",i," r: ",r," j: ",j) r = j % 5 j ÷= 2 println("i: ",i," r: ",r," j: ",j) end end function intc( j::Integer, p::Parameters ) result = Int64[] for i = 1:p.numinteriors jmod = j % 2 push!( result, jmod ) println("i: ",i," j % 2: ",jmod," j: ",j) j ÷= 2 jmod = j % 2 push!( result, jmod ) println("i: ",i," j % 2: ",jmod," j: ",j) j ÷= 2 jmod = j % 5 push!( result, jmod ) println("i: ",i," j % 5: ",jmod," j: ",j) j ÷= 5 println("res: ",result) end transpose(result) end function int_to_circuit_code( c_int::Integer, p::Parameters ) c_int = Int128(c_int) c_code = zeros(Int64,3*p.numinteriors) k = 3*p.numinteriors for i = p.numinteriors:-1:1 multiplier = min(p.numlevelsback,i-1+p.numinputs) println("i: ",i," multiplier: ",multiplier) for j = p.nodearity:-1:1 c_int_mod= c_int % multiplier c_int ÷= multiplier println("i: ",i," j: ",j," c_int: ",c_int," c_int_mod: ",c_int_mod) c_code[k] = c_int_mod k -= 1 end multiplier = length(funcs) println("i: ",i," multiplier: ",multiplier) c_int_mod= c_int % multiplier c_int ÷= multiplier println("i: ",i," c_int: ",c_int," c_int_mod: ",c_int_mod) c_code[k] = c_int_mod k -= 1 end c_code end
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#=These are the initial XB Code States for the I5 code, initial_xb_code_states[1] is a 1 3 chip array which represent the shift register values initial_xb_code_states[3][4] represents the 4th shift register of the GPS Signal with PRN numver 3 =# const INITIAL_XB_CODE_STATES = [ #sat PRN number [0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0], #01 [1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1], #02 [0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], #03 [1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0], #04 [1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1], #05 [0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0], #06 [1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1], #07 [1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0], #08 [1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1], #09 [0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0], #10 [0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0], #11 [1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1], #12 [0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0], #13 [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1], #14 [0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0], #15 [0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1], #16 [0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1], #17 [1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0], #18 [1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1], #19 [0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1], #20 [0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], #21 [1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1], #22 [1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0], #23 [1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0], #24 [1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1], #25 [1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0], #26 [0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0], #27 [0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0], #28 [0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1], #29 [1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1], #30 [0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0], #31 [0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1], #32 [1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1], #33 [1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1], #34 [1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0], #35 [1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0], #36 [0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0] #37 ] """ $(SIGNATURES) Takes the status of the registers as an `integer` and returns them as an array. # Examples ```julia-repl julia> reshape(8190) [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0] ``` """ function reshape(integer) a = zeros(13) for i = 1:13 b = (integer >> (i-1)) & 1 a[14-i] = b end a end """ $(SIGNATURES) Takes the status of the registers as an Int `registers`, and an array of register `indices` to calculate and return the new register values and the register output. ```julia-repl julia> reshape(8190) [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0] julia> output, registers = shift_register(8910, [9, 10, 12, 13]) julia> reshape(registers) [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] julia> output == 1 true ``` """ function shift_register(register, indices) update = 0 for i in indices update = update ⊻ ((register >> (13 - i)) & 1) end (register & 1), (register >> 1) + 2^12 * update end """ $(SIGNATURES) Calculate the gps L5 PRN `satellite_code` for the initial XB register states `initial_xb_code_states`. ```julia-repl julia> initial_states_PRN_num_1_I = [0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0] julia> prn_code_sat_1_I_signal = gen_l5_code(initial_states_PRN_num_1_I) ``` """ function gen_l5_code(initial_xb_code_states) XA = 8191 # int with 3 leading zeros and then 13*1 XB = initial_xb_code_states' * [4096, 2048, 1024, 512, 256, 128, 64, 32, 16, 8, 4, 2, 1] satellite_code = zeros(Int8, 10230) XA_indices = [9, 10, 12, 13] XB_indices = [1, 3, 4, 6, 7, 8, 12, 13] for i = 1:10230 output_xa, XA = shift_register(XA, XA_indices) output_xb, XB = shift_register(XB, XB_indices) satellite_code[i] = 2 * (output_xa ⊻ output_xb) - 1 if (i == 8190) XA = 8191 end end return satellite_code end """ $(SIGNATURES) Generate 10 periods of the PRN L5 code, with `initial_xb_code_states`, each ⊻ with one bit of the 10bit Neuman-Hofman sequence 0000110101. """ function add_neuman_hofman_code(l5_code, neuman_hofman_code) vec(l5_code .* Vector{Int8}(neuman_hofman_code)') end function get_neuman_hofman_code() [1, 1, 1, 1, -1, -1, 1, -1, 1, -1] end const GPS_L5_CODES = mapreduce( sat -> add_neuman_hofman_code( gen_l5_code(INITIAL_XB_CODE_STATES[sat]), get_neuman_hofman_code() ), hcat, 1:37 )::Array{Int8, 2} """ $(SIGNATURES) Get codes of type GPSL5 as a Matrix where each column represents a PRN. ```julia-repl julia> get_code(GPSL5) ``` """ function get_codes(::Type{GPSL5}) GPS_L5_CODES end """ $(SIGNATURES) Get code length of GNSS system GPSL5. ```julia-repl julia> get_code_length(GPSL5) ``` """ @inline function get_code_length(::Type{GPSL5}) 10230 end """ $(SIGNATURES) Get secondary code length of GNSS system GPSL5. ```julia-repl julia> get_secondary_code_length(GPSL5) ``` """ @inline function get_secondary_code_length(::Type{GPSL5}) 10 end """ $(SIGNATURES) Get center frequency of GNSS system GPSL5. ```julia-repl julia> get_center_frequency(GPSL5) ``` """ @inline function get_center_frequency(::Type{GPSL5}) 1_176_450_000Hz end """ $(SIGNATURES) Get code frequency of GNSS system GPSL5. ```julia-repl julia> get_code_frequency(GPSL5) ``` """ @inline function get_code_frequency(::Type{GPSL5}) 10_230_000Hz end """ $(SIGNATURES) Get data frequency of GNSS system GPSL5. ```julia-repl julia> get_data_frequency(GPSL5) ``` """ @inline function get_data_frequency(::Type{GPSL5}) 100Hz end """ $(SIGNATURES) Get code of GNSS system GPSL5 at phase `phase` of PRN `prn`. The phase will not be wrapped by the code length. The phase has to smaller than the code length incl. secondary code and must be an integer. ```julia-repl julia> get_code_unsafe(GPSL5, 10, 1) ``` """ Base.@propagate_inbounds function get_code_unsafe(::Type{GPSL5}, phase::Int, prn::Int) GPS_L5_CODES[1 + phase, prn] end
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# structs and methods for basic neural accumulator / neural arithmetic # logic unit. # - Neural Accumulator - # """ A linear layer whose weights are soft constrained to be near one of {-1, 0, 1}. The weights are calculated by tanh.(W) .* σ.(M). """ struct NeuralAccumulator{R <: AbstractMatrix} W::R M::R function NeuralAccumulator{R}(w, m) where {R <: AbstractMatrix} size(w) == size(m) || error("sizes of weight matrices must match") new{R}(w, m) end end NeuralAccumulator(W::R, M::R) where {R <: AbstractMatrix} = NeuralAccumulator{R}(W, M) function NeuralAccumulator(::Type{T}, in::Integer, out::Integer, init_fn) where {T <: Number} return NeuralAccumulator(init_fn(T, out, in), init_fn(T, out, in)) end NeuralAccumulator(in::Integer, out::Integer, init_fn=randn) = NeuralAccumulator(Float64, in, out, init_fn) operator(nac::NeuralAccumulator) = (2 .* σ.(nac.W) .- 1) .* σ.(nac.M) (nac::NeuralAccumulator)(x) = operator(nac) * x # - Neural Arithmetic Logic Unit - # struct NeuralALU{R <: AbstractMatrix} nac::NeuralAccumulator{R} G::R b end NALU(in::Integer, out::Integer; initW = Flux.initn, initb = zeros) = NALU(NAC(in, out, initW=initW), param(initW(out, in)), param(initb(out))) Flux.@treelike(NALU) function (nalu::NALU)(x) # gate g = σ_stable.(nalu.G*x .+ nalu.b) # addition a = nalu.nac(x) # multiplication m = exp.(nalu.nac(log.(abs.(x) .+ eps()))) # nalu return g .* a + (1 .- g) .* m end
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643
const DEFAULT_PRIORITY = 1000 const DEFAULT_TEMPLATE_DIR = Ref{String}(joinpath(dirname(dirname(pathof(PkgTemplates))), "templates")) """ @plugin struct ... end Define a plugin subtype with keyword constructors and default values. For details on the general syntax, see [Parameters.jl](https://mauro3.github.io/Parameters.jl/stable/manual/#Types-with-default-values-and-keyword-constructors-1). There are a few extra restrictions: - Before using this macro, you must have imported `@with_kw_noshow` via `using PkgTemplates: @with_kw_noshow` - The type must be a subtype of [`Plugin`](@ref) (or one of its abstract subtypes) - The type cannot be parametric - All fields must have default values ## Example ```julia using PkgTemplates: @plugin, @with_kw_noshow, Plugin @plugin struct MyPlugin <: Plugin x::String = "hello!" y::Union{Int, Nothing} = nothing end ``` ## Implementing `@plugin` Manually If for whatever reason, you are unable to meet the criteria outlined above, you can manually implement the methods that `@plugin` would have created for you. This is only mandatory if you want to use your plugin in interactive mode. ### Keyword Constructors If possible, use `@with_kw_noshow` to create a keyword constructor for your type. Your type must be capable of being instantiated with no arguments. ### Default Values If your type's fields have sensible default values, implement `defaultkw` like so: ```julia using PkgTemplates: PkgTemplates, Plugin struct MyPlugin <: Plugin x::String end PkgTemplates.defaultkw(::Type{MyPlugin}, ::Val{:x}) = "my default" ``` Remember to add a method to the function belonging to PkgTemplates, rather than creating your own function that PkgTemplates won't see. If your plugin's fields have no sane defaults, then you'll need to implement [`prompt`](@ref) appropriately instead. """ macro plugin(ex::Expr) @assert ex.head === :struct "Expression must be a struct definition" @assert ex.args[2] isa Expr && ex.args[2].head === :<: "Type must have a supertype" T = ex.args[2].args[1] @assert T isa Symbol "@plugin does not work for parametric types" msg = "Run `using PkgTemplates: @with_kw_noshow` before using this macro" @assert isdefined(__module__, Symbol("@with_kw_noshow")) msg block = :(begin @with_kw_noshow $ex end) foreach(filter(arg -> arg isa Expr, ex.args[3].args)) do field @assert field.head === :(=) "Field must have a default value" name = QuoteNode(field.args[1].args[1]) default = field.args[2] def = :(PkgTemplates.defaultkw(::Type{$T}, ::Val{$name}) = $default) push!(block.args, def) end return esc(block) end function Base.:(==)(a::T, b::T) where T <: Plugin return all(n -> getfield(a, n) == getfield(b, n), fieldnames(T)) end struct Disabled{P<:Plugin} end Base.:(!)(P::Type{<:Plugin}) = Disabled{P}() """ Secret(name::AbstractString) Represents a GitHub repository secret. When converted to a string, yields `\${{ secrets.<name> }}`. """ struct Secret name::String end Base.print(io::IO, s::Secret) = print(io, "\${{ secrets.$(s.name) }}") """ A simple plugin that, in general, creates a single file. """ abstract type FilePlugin <: Plugin end """ default_file(paths::AbstractString...) -> String Return a path relative to the default template file directory (`PkgTemplates/templates`). """ default_file(paths::AbstractString...) = joinpath(DEFAULT_TEMPLATE_DIR[], paths...) """ view(::Plugin, ::Template, pkg::AbstractString) -> Dict{String, Any} Return the view to be passed to the text templating engine for this plugin. `pkg` is the name of the package being generated. For [`FilePlugin`](@ref)s, this is used for both the plugin badges (see [`badges`](@ref)) and the template file (see [`source`](@ref)). For other [`Plugin`](@ref)s, it is used only for badges, but you can always call it yourself as part of your [`hook`](@ref) implementation. By default, an empty `Dict` is returned. """ view(::Plugin, ::Template, ::AbstractString) = Dict{String, Any}() """ user_view(::Plugin, ::Template, pkg::AbstractString) -> Dict{String, Any} The same as [`view`](@ref), but for use by package *users* for extension. Values returned by this function will override those from [`view`](@ref) when the keys are the same. """ user_view(::Plugin, ::Template, ::AbstractString) = Dict{String, Any}() """ combined_view(::Plugin, ::Template, pkg::AbstractString) -> Dict{String, Any} This function combines [`view`](@ref) and [`user_view`](@ref) for use in text templating. If you're doing manual file creation or text templating (i.e. writing [`Plugin`](@ref)s that are not [`FilePlugin`](@ref)s), then you should use this function rather than either of the former two. !!! note Do not implement this function yourself! If you're implementing a plugin, you should implement [`view`](@ref). If you're customizing a plugin as a user, you should implement [`user_view`](@ref). """ function combined_view(p::Plugin, t::Template, pkg::AbstractString) return merge(view(p, t, pkg), user_view(p, t, pkg)) end """ tags(::Plugin) -> Tuple{String, String} Return the delimiters used for text templating. See the [`Citation`](@ref) plugin for a rare case where changing the tags is necessary. By default, the tags are `"{{"` and `"}}"`. """ tags(::Plugin) = "{{", "}}" """ priority(::Plugin, ::Union{typeof(prehook), typeof(hook), typeof(posthook)}) -> Int Determines the order in which plugins are processed (higher goes first). The default priority (`DEFAULT_PRIORITY`), is `$DEFAULT_PRIORITY`. You can implement this function per-stage (by using `::typeof(hook)`, for example), or for all stages by simply using `::Function`. """ priority(::Plugin, ::Function) = DEFAULT_PRIORITY """ gitignore(::Plugin) -> Vector{String} Return patterns that should be added to `.gitignore`. These are used by the [`Git`](@ref) plugin. By default, an empty list is returned. """ gitignore(::Plugin) = String[] """ badges(::Plugin) -> Union{Badge, Vector{Badge}} Return a list of [`Badge`](@ref)s, or just one, to be added to `README.md`. These are used by the [`Readme`](@ref) plugin to add badges to the README. By default, an empty list is returned. """ badges(::Plugin) = Badge[] """ source(::FilePlugin) -> Union{String, Nothing} Return the path to a plugin's template file, or `nothing` to indicate no file. By default, `nothing` is returned. """ source(::FilePlugin) = nothing """ destination(::FilePlugin) -> String Return the destination, relative to the package root, of a plugin's configuration file. This function **must** be implemented. """ function destination end """ Badge(hover::AbstractString, image::AbstractString, link::AbstractString) Container for Markdown badge data. Each argument can contain placeholders, which will be filled in with values from [`combined_view`](@ref). ## Arguments - `hover::AbstractString`: Text to appear when the mouse is hovered over the badge. - `image::AbstractString`: URL to the image to display. - `link::AbstractString`: URL to go to upon clicking the badge. """ struct Badge hover::String image::String link::String end Base.string(b::Badge) = "[![$(b.hover)]($(b.image))]($(b.link))" # Format a plugin's badges as a list of strings, with all substitutions applied. function badges(p::Plugin, t::Template, pkg::AbstractString) bs = badges(p) bs isa Vector || (bs = [bs]) return map(b -> render_text(string(b), combined_view(p, t, pkg)), bs) end """ validate(::Plugin, ::Template) Perform any required validation for a [`Plugin`](@ref). It is preferred to do validation here instead of in [`prehook`](@ref), because this function is called at [`Template`](@ref) construction time, whereas the prehook is only run at package generation time. """ validate(::Plugin, ::Template) = nothing """ prehook(::Plugin, ::Template, pkg_dir::AbstractString) Stage 1 of the package generation process (the "before" stage, in general). At this point, `pkg_dir` is an empty directory that will eventually contain the package, and neither the [`hook`](@ref)s nor the [`posthook`](@ref)s have run. !!! note `pkg_dir` only stays empty until the first plugin chooses to create a file. See also: [`priority`](@ref). """ prehook(::Plugin, ::Template, ::AbstractString) = nothing """ hook(::Plugin, ::Template, pkg_dir::AbstractString) Stage 2 of the package generation pipeline (the "main" stage, in general). At this point, the [`prehook`](@ref)s have run, but not the [`posthook`](@ref)s. `pkg_dir` is the directory in which the package is being generated (so `basename(pkg_dir)` is the package name). !!! note You usually shouldn't implement this function for [`FilePlugin`](@ref)s. If you do, it should probably `invoke` the generic method (otherwise, there's not much reason to subtype `FilePlugin`). """ hook(::Plugin, ::Template, ::AbstractString) = nothing """ posthook(::Plugin, ::Template, pkg_dir::AbstractString) Stage 3 of the package generation pipeline (the "after" stage, in general). At this point, both the [`prehook`](@ref)s and [`hook`](@ref)s have run. """ posthook(::Plugin, ::Template, ::AbstractString) = nothing function validate(p::T, ::Template) where T <: FilePlugin src = source(p) src === nothing && return isfile(src) || throw(ArgumentError("$(nameof(T)): The file $src does not exist")) end function hook(p::FilePlugin, t::Template, pkg_dir::AbstractString) source(p) === nothing && return pkg = basename(pkg_dir) path = joinpath(pkg_dir, destination(p)) text = render_plugin(p, t, pkg) gen_file(path, text) end function render_plugin(p::FilePlugin, t::Template, pkg::AbstractString) return render_file(source(p), combined_view(p, t, pkg), tags(p)) end """ gen_file(file::AbstractString, text::AbstractString) Create a new file containing some given text. Trailing whitespace is removed, and the file will end with a newline. """ function gen_file(file::AbstractString, text::AbstractString) mkpath(dirname(file)) text = strip(join(map(rstrip, split(text, "\n")), "\n")) * "\n" write(file, text) end """ render_file(file::AbstractString view::Dict{<:AbstractString}, tags=nothing) -> String Render a template file with the data in `view`. `tags` should be a tuple of two strings, which are the opening and closing delimiters, or `nothing` to use the default delimiters. """ function render_file(file::AbstractString, view::Dict{<:AbstractString}, tags=nothing) return render_text(read(file, String), view, tags) end """ render_text(text::AbstractString, view::Dict{<:AbstractString}, tags=nothing) -> String Render some text with the data in `view`. `tags` should be a tuple of two strings, which are the opening and closing delimiters, or `nothing` to use the default delimiters. """ function render_text(text::AbstractString, view::Dict{<:AbstractString}, tags=nothing) return tags === nothing ? render(text, view) : render(text, view; tags=tags) end """ needs_username(::Plugin) -> Bool Determine whether or not a plugin needs a Git hosting service username to function correctly. If you are implementing a plugin that uses the `user` field of a [`Template`](@ref), you should implement this function and return `true`. """ needs_username(::Plugin) = false include(joinpath("plugins", "project_file.jl")) include(joinpath("plugins", "src_dir.jl")) include(joinpath("plugins", "tests.jl")) include(joinpath("plugins", "readme.jl")) include(joinpath("plugins", "license.jl")) include(joinpath("plugins", "git.jl")) include(joinpath("plugins", "tagbot.jl")) include(joinpath("plugins", "develop.jl")) include(joinpath("plugins", "coverage.jl")) include(joinpath("plugins", "ci.jl")) include(joinpath("plugins", "compat_helper.jl")) include(joinpath("plugins", "citation.jl")) include(joinpath("plugins", "documenter.jl")) include(joinpath("plugins", "badges.jl")) include(joinpath("plugins", "benchmarks.jl"))
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3.087822
3,917
@doc raw""" """ module LotkaVolterra4d using GeometricEquations using Parameters export hamiltonian, ϑ, ϑ₁, ϑ₂, ω export lotka_volterra_4d_ode, lotka_volterra_4d_pode, lotka_volterra_4d_pdae, lotka_volterra_4d_iode, lotka_volterra_4d_idae, lotka_volterra_4d_lode, lotka_volterra_4d_ldae, lotka_volterra_4d_dg Δt = 0.1 nt = 1000 const q₀ = [2.0, 1.0, 1.0, 1.0] const p = (a₁=1.0, a₂=1.0, a₃=1.0, a₄=1.0, b₁=-1.0, b₂=-2.0, b₃=-1.0, b₄=-1.0) const reference_solution = [1.6390462434739954, 1.3764800055785835, 0.37903204434372284, 1.4399236281802124] # const q₀ = [2.0, 1.0, 2.0, 1.0] # const p = (a₁=1.0, a₂=1.0, a₃=1.0, a₄=1.0, b₁=-1.0, b₂=-2.0, b₃=-1.0, b₄=-2.0) # const q₀ = [2.0, 1.0, 2.0, 1.0] # const p = (a₁=1.0, a₂=1.0, a₃=1.0, a₄=1.0, b₁=-1.0, b₂=-4.0, b₃=-2.0, b₄=-3.0) ϑ₁(t, q) = 0.5 * ( + log(q[2]) - log(q[3]) + log(q[4]) ) / q[1] ϑ₂(t, q) = 0.5 * ( - log(q[1]) + log(q[3]) - log(q[4]) ) / q[2] ϑ₃(t, q) = 0.5 * ( + log(q[1]) - log(q[2]) + log(q[4]) ) / q[3] ϑ₄(t, q) = 0.5 * ( - log(q[1]) + log(q[2]) - log(q[3]) ) / q[4] # ϑ₁(t, q) = ( log(q[2]) + log(q[4]) ) / q[1] # ϑ₂(t, q) = ( log(q[3]) ) / q[2] # ϑ₃(t, q) = ( log(q[1]) + log(q[4]) ) / q[3] # ϑ₄(t, q) = ( log(q[2]) ) / q[4] # ϑ₁(t, q) = ( + log(q[2]) - log(q[3]) + log(q[4]) ) / q[1] / 2 + q[2] + q[3] + q[4] # ϑ₂(t, q) = ( - log(q[1]) + log(q[3]) - log(q[4]) ) / q[2] / 2 + q[1] + q[3] + q[4] # ϑ₃(t, q) = ( + log(q[1]) - log(q[2]) + log(q[4]) ) / q[3] / 2 + q[1] + q[2] + q[4] # ϑ₄(t, q) = ( - log(q[1]) + log(q[2]) - log(q[3]) ) / q[4] / 2 + q[1] + q[2] + q[3] # ϑ₁(t, q) = ( + log(q[2]) - log(q[3]) + log(q[4]) ) / q[1] # ϑ₂(t, q) = ( + log(q[3]) - log(q[4]) ) / q[2] # ϑ₃(t, q) = log(q[4]) / q[3] # ϑ₄(t, q) = zero(eltype(q)) function v₁(t, q, params) @unpack a₁, a₂, a₃, a₄, b₁, b₂, b₃, b₄ = params q[1] * (+ a₂*q[2] + a₃*q[3] + a₄*q[4] + b₂ + b₃ + b₄) end function v₂(t, q, params) @unpack a₁, a₂, a₃, a₄, b₁, b₂, b₃, b₄ = params q[2] * (- a₁*q[1] + a₃*q[3] + a₄*q[4] - b₁ + b₃ + b₄) end function v₃(t, q, params) @unpack a₁, a₂, a₃, a₄, b₁, b₂, b₃, b₄ = params q[3] * (- a₁*q[1] - a₂*q[2] + a₄*q[4] - b₁ - b₂ + b₄) end function v₄(t, q, params) @unpack a₁, a₂, a₃, a₄, b₁, b₂, b₃, b₄ = params q[4] * (- a₁*q[1] - a₂*q[2] - a₃*q[3] - b₁ - b₂ - b₃) end dϑ₁dx₁(t, q) = ( - log(q[2]) + log(q[3]) - log(q[4]) ) / q[1]^2 / 2 dϑ₁dx₂(t, q) = + 1 / (q[1] * q[2]) / 2 dϑ₁dx₃(t, q) = - 1 / (q[1] * q[3]) / 2 dϑ₁dx₄(t, q) = + 1 / (q[1] * q[4]) / 2 dϑ₂dx₁(t, q) = - 1 / (q[2] * q[1]) / 2 dϑ₂dx₂(t, q) = ( + log(q[1]) - log(q[3]) + log(q[4]) ) / q[2]^2 / 2 dϑ₂dx₃(t, q) = + 1 / (q[2] * q[3]) / 2 dϑ₂dx₄(t, q) = - 1 / (q[2] * q[4]) / 2 dϑ₃dx₁(t, q) = + 1 / (q[3] * q[1]) / 2 dϑ₃dx₂(t, q) = - 1 / (q[3] * q[2]) / 2 dϑ₃dx₃(t, q) = ( - log(q[1]) + log(q[2]) - log(q[4]) ) / q[3]^2 / 2 dϑ₃dx₄(t, q) = + 1 / (q[3] * q[4]) / 2 dϑ₄dx₁(t, q) = - 1 / (q[4] * q[1]) / 2 dϑ₄dx₂(t, q) = + 1 / (q[4] * q[2]) / 2 dϑ₄dx₃(t, q) = - 1 / (q[4] * q[3]) / 2 dϑ₄dx₄(t, q) = ( + log(q[1]) - log(q[2]) + log(q[3]) ) / q[4]^2 / 2 # dϑ₁dx₁(t, q) = - ( log(q[2]) + log(q[4]) ) / q[1]^2 # dϑ₁dx₂(t, q) = 1 / (q[1] * q[2]) # dϑ₁dx₃(t, q) = zero(eltype(q)) # dϑ₁dx₄(t, q) = 1 / (q[1] * q[4]) # dϑ₂dx₁(t, q) = zero(eltype(q)) # dϑ₂dx₂(t, q) = - ( log(q[3]) ) / q[2]^2 # dϑ₂dx₃(t, q) = 1 / (q[2] * q[3]) # dϑ₂dx₄(t, q) = zero(eltype(q)) # dϑ₃dx₁(t, q) = + 1 / (q[3] * q[1]) # dϑ₃dx₂(t, q) = zero(eltype(q)) # dϑ₃dx₃(t, q) = - ( log(q[1]) + log(q[4]) ) / q[3]^2 # dϑ₃dx₄(t, q) = + 1 / (q[3] * q[4]) # dϑ₄dx₁(t, q) = zero(eltype(q)) # dϑ₄dx₂(t, q) = 1 / (q[4] * q[2]) # dϑ₄dx₃(t, q) = zero(eltype(q)) # dϑ₄dx₄(t, q) = - ( log(q[2]) ) / q[4]^2 # dϑ₁dx₁(t, q) = ( - log(q[2]) + log(q[3]) - log(q[4]) ) / q[1]^2 / 2 # dϑ₁dx₂(t, q) = 1 + 1 / (q[1] * q[2]) / 2 # dϑ₁dx₃(t, q) = 1 - 1 / (q[1] * q[3]) / 2 # dϑ₁dx₄(t, q) = 1 + 1 / (q[1] * q[4]) / 2 # dϑ₂dx₁(t, q) = 1 - 1 / (q[2] * q[1]) / 2 # dϑ₂dx₂(t, q) = ( + log(q[1]) - log(q[3]) + log(q[4]) ) / q[2]^2 / 2 # dϑ₂dx₃(t, q) = 1 + 1 / (q[2] * q[3]) / 2 # dϑ₂dx₄(t, q) = 1 - 1 / (q[2] * q[4]) / 2 # dϑ₃dx₁(t, q) = 1 + 1 / (q[3] * q[1]) / 2 # dϑ₃dx₂(t, q) = 1 - 1 / (q[3] * q[2]) / 2 # dϑ₃dx₃(t, q) = ( - log(q[1]) + log(q[2]) - log(q[4]) ) / q[3]^2 / 2 # dϑ₃dx₄(t, q) = 1 + 1 / (q[3] * q[4]) / 2 # dϑ₄dx₁(t, q) = 1 - 1 / (q[4] * q[1]) / 2 # dϑ₄dx₂(t, q) = 1 + 1 / (q[4] * q[2]) / 2 # dϑ₄dx₃(t, q) = 1 - 1 / (q[4] * q[3]) / 2 # dϑ₄dx₄(t, q) = ( + log(q[1]) - log(q[2]) + log(q[3]) ) / q[4]^2 / 2 # dϑ₁dx₁(t, q) = ( - log(q[2]) + log(q[3]) - log(q[4]) ) / q[1]^2 # dϑ₁dx₂(t, q) = + 1 / (q[1] * q[2]) # dϑ₁dx₃(t, q) = - 1 / (q[1] * q[3]) # dϑ₁dx₄(t, q) = + 1 / (q[1] * q[4]) # dϑ₂dx₁(t, q) = zero(eltype(q)) # dϑ₂dx₂(t, q) = ( - log(q[3]) + log(q[4]) ) / q[2]^2 # dϑ₂dx₃(t, q) = + 1 / (q[2] * q[3]) # dϑ₂dx₄(t, q) = - 1 / (q[2] * q[4]) # dϑ₃dx₁(t, q) = zero(eltype(q)) # dϑ₃dx₂(t, q) = zero(eltype(q)) # dϑ₃dx₃(t, q) = ( - log(q[4]) ) / q[3]^2 # dϑ₃dx₄(t, q) = + 1 / (q[3] * q[4]) # dϑ₄dx₁(t, q) = zero(eltype(q)) # dϑ₄dx₂(t, q) = zero(eltype(q)) # dϑ₄dx₃(t, q) = zero(eltype(q)) # dϑ₄dx₄(t, q) = zero(eltype(q)) function ϑ(t::Number, q::AbstractVector, Θ::AbstractVector) Θ[1] = ϑ₁(t,q) Θ[2] = ϑ₂(t,q) Θ[3] = ϑ₃(t,q) Θ[4] = ϑ₄(t,q) nothing end function ϑ(t::Number, q::AbstractVector) Θ = zero(q) ϑ(t, q, Θ) return Θ end function ϑ(t::Number, q::AbstractVector, k::Int) if k == 1 ϑ₁(t, q) elseif k == 2 ϑ₂(t, q) elseif k == 3 ϑ₃(t, q) elseif k == 4 ϑ₄(t, q) else throw(BoundsError(ϑ,k)) end end function ω(t, q, Ω) Ω[1,1] = 0 Ω[1,2] = dϑ₁dx₂(t,q) - dϑ₂dx₁(t,q) Ω[1,3] = dϑ₁dx₃(t,q) - dϑ₃dx₁(t,q) Ω[1,4] = dϑ₁dx₄(t,q) - dϑ₄dx₁(t,q) Ω[2,1] = dϑ₂dx₁(t,q) - dϑ₁dx₂(t,q) Ω[2,2] = 0 Ω[2,3] = dϑ₂dx₃(t,q) - dϑ₃dx₂(t,q) Ω[2,4] = dϑ₂dx₄(t,q) - dϑ₄dx₂(t,q) Ω[3,1] = dϑ₃dx₁(t,q) - dϑ₁dx₃(t,q) Ω[3,2] = dϑ₃dx₂(t,q) - dϑ₂dx₃(t,q) Ω[3,3] = 0 Ω[3,4] = dϑ₃dx₄(t,q) - dϑ₄dx₃(t,q) Ω[4,1] = dϑ₄dx₁(t,q) - dϑ₁dx₄(t,q) Ω[4,2] = dϑ₄dx₂(t,q) - dϑ₂dx₄(t,q) Ω[4,3] = dϑ₄dx₃(t,q) - dϑ₃dx₄(t,q) Ω[4,4] = 0 nothing end f₁(t, q, v) = dϑ₁dx₁(t,q) * v[1] + dϑ₂dx₁(t,q) * v[2] + dϑ₃dx₁(t,q) * v[3] + dϑ₄dx₁(t,q) * v[4] f₂(t, q, v) = dϑ₁dx₂(t,q) * v[1] + dϑ₂dx₂(t,q) * v[2] + dϑ₃dx₂(t,q) * v[3] + dϑ₄dx₂(t,q) * v[4] f₃(t, q, v) = dϑ₁dx₃(t,q) * v[1] + dϑ₂dx₃(t,q) * v[2] + dϑ₃dx₃(t,q) * v[3] + dϑ₄dx₃(t,q) * v[4] f₄(t, q, v) = dϑ₁dx₄(t,q) * v[1] + dϑ₂dx₄(t,q) * v[2] + dϑ₃dx₄(t,q) * v[3] + dϑ₄dx₄(t,q) * v[4] g₁(t, q, v) = dϑ₁dx₁(t,q) * v[1] + dϑ₁dx₂(t,q) * v[2] + dϑ₁dx₃(t,q) * v[3] + dϑ₁dx₄(t,q) * v[4] g₂(t, q, v) = dϑ₂dx₁(t,q) * v[1] + dϑ₂dx₂(t,q) * v[2] + dϑ₂dx₃(t,q) * v[3] + dϑ₂dx₄(t,q) * v[4] g₃(t, q, v) = dϑ₃dx₁(t,q) * v[1] + dϑ₃dx₂(t,q) * v[2] + dϑ₃dx₃(t,q) * v[3] + dϑ₃dx₄(t,q) * v[4] g₄(t, q, v) = dϑ₄dx₁(t,q) * v[1] + dϑ₄dx₂(t,q) * v[2] + dϑ₄dx₃(t,q) * v[3] + dϑ₄dx₄(t,q) * v[4] function hamiltonian(t, q, params) @unpack a₁, a₂, a₃, a₄, b₁, b₂, b₃, b₄ = params a₁*q[1] + a₂*q[2] + a₃*q[3] + a₄*q[4] + b₁*log(q[1]) + b₂*log(q[2]) + b₃*log(q[3]) + b₄*log(q[4]) end hamiltonian_iode(t, q, v, params) = hamiltonian(t, q, params) hamiltonian_pode(t, q, p, params) = hamiltonian(t, q, params) function dHd₁(t, q, params) @unpack a₁, b₁ = params a₁ + b₁ / q[1] end function dHd₂(t, q, params) @unpack a₂, b₂ = params a₂ + b₂ / q[2] end function dHd₃(t, q, params) @unpack a₃, b₃ = params a₃ + b₃ / q[3] end function dHd₄(t, q, params) @unpack a₄, b₄ = params a₄ + b₄ / q[4] end function lotka_volterra_4d_dH(t, q, dH, params) dH[1] = dHd₁(t, q, params) dH[2] = dHd₂(t, q, params) dH[3] = dHd₃(t, q, params) dH[4] = dHd₄(t, q, params) nothing end lotka_volterra_4d_ϑ(t, q, Θ, params) = ϑ(t, q, Θ) lotka_volterra_4d_ϑ(t, q, v, Θ, params) = ϑ(t, q, Θ) lotka_volterra_4d_ω(t, q, Ω, params) = ω(t, q, Ω) function lotka_volterra_4d_v(t, q, v, params) v[1] = v₁(t, q, params) v[2] = v₂(t, q, params) v[3] = v₃(t, q, params) v[4] = v₄(t, q, params) nothing end function lotka_volterra_4d_v(t, q, p, v, params) lotka_volterra_4d_v(t, q, v, params) end function lotka_volterra_4d_v_ham(t, q, p, v, params) v .= 0 nothing end function lotka_volterra_4d_f(t::Real, q::Vector, v::Vector, f::Vector, params) f[1] = f₁(t,q,v) - dHd₁(t, q, params) f[2] = f₂(t,q,v) - dHd₂(t, q, params) f[3] = f₃(t,q,v) - dHd₃(t, q, params) f[4] = f₄(t,q,v) - dHd₄(t, q, params) nothing end function lotka_volterra_4d_f_ham(t::Real, q::Vector, f::Vector, params) f[1] = - dHd₁(t, q, params) f[2] = - dHd₂(t, q, params) f[3] = - dHd₃(t, q, params) f[4] = - dHd₄(t, q, params) nothing end function lotka_volterra_4d_f_ham(t::Real, q::Vector, v::Vector, f::Vector, params) lotka_volterra_4d_f_ham(t, q, f, params) end function lotka_volterra_4d_g(t::Real, q::Vector, v::Vector, g::Vector, params) g[1] = f₁(t,q,v) g[2] = f₂(t,q,v) g[3] = f₃(t,q,v) g[4] = f₄(t,q,v) nothing end function lotka_volterra_4d_g(t::Real, q::Vector, p::Vector, v::Vector, g::Vector, params) lotka_volterra_4d_g(t, q, v, g, params) end function lotka_volterra_4d_g̅(t::Real, q::Vector, v::Vector, g::Vector, params) g[1] = g₁(t,q,v) g[2] = g₂(t,q,v) g[3] = g₃(t,q,v) g[4] = g₄(t,q,v) nothing end function lotka_volterra_4d_g̅(t::Real, q::Vector, p::Vector, v::Vector, g::Vector, params) lotka_volterra_4d_g̅(t, q, v, g, params) end function lotka_volterra_4d_u(t, q, v, u, params) u .= v nothing end function lotka_volterra_4d_u(t, q, p, v, u, params) lotka_volterra_4d_u(t, q, v, u, params) end function lotka_volterra_4d_ϕ(t, q, p, ϕ, params) ϕ[1] = p[1] - ϑ₁(t,q) ϕ[2] = p[2] - ϑ₂(t,q) ϕ[3] = p[3] - ϑ₃(t,q) ϕ[4] = p[4] - ϑ₄(t,q) nothing end function lotka_volterra_4d_ψ(t, q, p, v, f, ψ, params) ψ[1] = f[1] - g₁(t,q,v) ψ[2] = f[2] - g₂(t,q,v) ψ[3] = f[3] - g₃(t,q,v) ψ[4] = f[4] - g₄(t,q,v) nothing end function lotka_volterra_4d_ode(q₀=q₀, params=p) ODE(lotka_volterra_4d_v, q₀; parameters=params, invariants=(h=hamiltonian,)) end function lotka_volterra_4d_pode(q₀=q₀, p₀=ϑ(0, q₀), params=p) PODE(lotka_volterra_4d_v, lotka_volterra_4d_f, q₀, p₀; parameters=params, invariants=(h=hamiltonian_pode,)) end function lotka_volterra_4d_iode(q₀=q₀, p₀=ϑ(0, q₀), params=p) IODE(lotka_volterra_4d_ϑ, lotka_volterra_4d_f, lotka_volterra_4d_g, q₀, p₀; parameters=params, invariants=(h=hamiltonian_iode,), v̄=lotka_volterra_4d_v) end function lotka_volterra_4d_lode(q₀=q₀, p₀=ϑ(0, q₀), params=p) LODE(lotka_volterra_4d_ϑ, lotka_volterra_4d_f, lotka_volterra_4d_g, q₀, p₀; parameters=params, invariants=(h=hamiltonian_iode,), v̄=lotka_volterra_4d_v, Ω=lotka_volterra_4d_ω, ∇H=lotka_volterra_4d_dH) end function lotka_volterra_4d_idae(q₀=q₀, p₀=ϑ(0, q₀), λ₀=zero(q₀), params=p) IDAE(lotka_volterra_4d_ϑ, lotka_volterra_4d_f, lotka_volterra_4d_u, lotka_volterra_4d_g, lotka_volterra_4d_ϕ, q₀, p₀, λ₀; parameters=params, invariants=(h=hamiltonian_iode,), v̄=lotka_volterra_4d_v) end function lotka_volterra_4d_pdae(q₀=q₀, p₀=ϑ(0, q₀), λ₀=zero(q₀), params=p) PDAE(lotka_volterra_4d_v_ham, lotka_volterra_4d_f_ham, lotka_volterra_4d_u, lotka_volterra_4d_g, lotka_volterra_4d_ϕ, q₀, p₀, λ₀; v̄=lotka_volterra_4d_v, f̄=lotka_volterra_4d_f, parameters=params, invariants=(h=hamiltonian_pode,)) end function lotka_volterra_4d_ldae(q₀=q₀, p₀=ϑ(0, q₀), λ₀=zero(q₀), params=p) LDAE(lotka_volterra_4d_ϑ, lotka_volterra_4d_f_ham, lotka_volterra_4d_g, lotka_volterra_4d_g̅, lotka_volterra_4d_ϕ, lotka_volterra_4d_ψ, q₀, p₀, λ₀; parameters=params, invariants=(h=hamiltonian_iode,), v̄=lotka_volterra_4d_v, f̄=lotka_volterra_4d_f,) end function lotka_volterra_4d_dg(q₀=q₀, p₀=ϑ(0, q₀), params=p) IODE(lotka_volterra_4d_ϑ, lotka_volterra_4d_f, lotka_volterra_4d_g, q₀, p₀; parameters=params, invariants=(h=hamiltonian_iode,), v̄=lotka_volterra_4d_v) end end
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__precompile__() module FastaIO using Compat using GZip export FastaReader, readentry, rewind, readfasta, FastaWriter, writeentry, writefasta import Base.start, Base.done, Base.next, Base.readall, Base.close, Base.show, Base.eof, Base.write import Compat: String const fasta_buffer_size = 4096 type FastaReader{T} # public but read-only num_parsed::Int # number of parsed entries so far # private f::IO is_eof::Bool # did we reach end of file? rbuffer::Vector{UInt8} # read buffer rbuf_sz::Int # read buffer size rbuf_pos::Int # read buffer cursor lbuffer::Vector{UInt8} # line buffer lbuf_sz::Int # line buffer size mbuffer::Vector{UInt8} # multi-line buffer mbuf_sz::Int # multi-line buffer size own_f::Bool function FastaReader(filename::AbstractString) fr = new(0, gzopen(filename), false, Array(UInt8, fasta_buffer_size), 0, 0, Array(UInt8, fasta_buffer_size), 0, Array(UInt8, fasta_buffer_size), 0, true) finalizer(fr, close) return fr end function FastaReader(io::IO) new(0, io, false, Array(UInt8, fasta_buffer_size), 0, 0, Array(UInt8, fasta_buffer_size), 0, Array(UInt8, fasta_buffer_size), 0, false) end end FastaReader(filename::AbstractString) = FastaReader{String}(filename) FastaReader(io::IO) = FastaReader{String}(io) function FastaReader(f::Function, filename::AbstractString, T::Type=String) fr = FastaReader{T}(filename) try f(fr) finally close(fr) end end close(fr::FastaReader) = fr.own_f && close(fr.f) function rewind(fr::FastaReader) seek(fr.f, 0) fr.is_eof = false fr.num_parsed = 0 fr.rbuf_sz = 0 fr.rbuf_pos = 0 fr.lbuf_sz = 0 fr.mbuf_sz = 0 return end read_chunk_ll(fr::FastaReader, s::GZipStream) = gzread(s, pointer(fr.rbuffer), fasta_buffer_size) read_chunk_ll(fr::FastaReader, s::IOStream) = ccall(:ios_readall, UInt, (Ptr{Void}, Ptr{Void}, UInt), fr.f.ios, fr.rbuffer, fasta_buffer_size) function read_chunk_ll(fr::FastaReader, s::IO) ret = 0 while !eof(fr.f) && ret < fasta_buffer_size ret += 1 fr.rbuffer[ret] = read(fr.f, UInt8) end return ret end function read_chunk(fr::FastaReader) if fr.is_eof return end ret = read_chunk_ll(fr, fr.f) ret == -1 && error("read failure") fr.rbuf_sz = ret fr.rbuf_pos = 1 if ret == 0 fr.is_eof = true end return end function readline(fr::FastaReader) fr.lbuf_sz = 0 found = false while !fr.is_eof if fr.rbuf_pos == 0 read_chunk(fr::FastaReader) end i = fr.rbuf_pos cr = false while i <= fr.rbuf_sz c = fr.rbuffer[i] if c == UInt8('\n') found = true break else cr = (c == UInt8('\r')) end i += 1 end i -= 1 + cr chunk_len = i - fr.rbuf_pos + 1 free_sbuf = length(fr.lbuffer) - fr.lbuf_sz gap = chunk_len - free_sbuf if gap > 0 resize!(fr.lbuffer, length(fr.lbuffer) + gap) end #fr.lbuffer[fr.lbuf_sz + (1:chunk_len)] = fr.rbuffer[fr.rbuf_pos:i] copy!(fr.lbuffer, fr.lbuf_sz + 1, fr.rbuffer, fr.rbuf_pos, chunk_len) fr.lbuf_sz += chunk_len i += 2 + cr if i > fr.rbuf_sz i = 0 end fr.rbuf_pos = i found && break end return end function start(fr::FastaReader) rewind(fr) readline(fr) if fr.lbuf_sz == 0 error("empty FASTA file") end return end done(fr::FastaReader, x::Void) = fr.is_eof function _next_step(fr::FastaReader) if fr.lbuffer[1] != UInt8('>') error("invalid FASTA file: description does not start with '>'") end if fr.lbuf_sz == 1 error("invalid FASTA file: empty description") end name = String(fr.lbuffer[2:fr.lbuf_sz]) isascii(name) || error("invalid non-ASCII description in FASTA file") fr.mbuf_sz = 0 while true readline(fr) if fr.lbuf_sz == 0 || fr.lbuffer[1] == UInt8('>') break end gap = fr.lbuf_sz - (length(fr.mbuffer) - fr.mbuf_sz) if gap > 0 resize!(fr.mbuffer, length(fr.mbuffer) + gap) end #fr.mbuffer[fr.mbuf_sz + (1:fr.lbuf_sz)] = fr.lbuffer[1:fr.lbuf_sz] copy!(fr.mbuffer, fr.mbuf_sz + 1, fr.lbuffer, 1, fr.lbuf_sz) fr.mbuf_sz += fr.lbuf_sz end return name end function _next(fr::FastaReader{Vector{UInt8}}) name = _next_step(fr) fr.num_parsed += 1 return (name, fr.mbuffer[1:fr.mbuf_sz]) end function _next(fr::FastaReader{String}) name = _next_step(fr) out_str = ccall(:jl_pchar_to_string, Ref{String}, (Ptr{UInt8},Int), fr.mbuffer, fr.mbuf_sz) fr.num_parsed += 1 return (name, out_str) end function _next{T}(fr::FastaReader{T}) name = _next_step(fr) fr.num_parsed += 1 return (name, T(fr.mbuffer[1:fr.mbuf_sz])) end next(fr::FastaReader, x::Void) = (_next(fr), nothing) function readall(fr::FastaReader) ret = Any[] for item in fr push!(ret, item) end return ret end function readentry(fr::FastaReader) fr.is_eof && throw(EOFError()) if fr.num_parsed == 0 readline(fr) if fr.lbuf_sz == 0 error("empty FASTA file") end end item, _ = next(fr, nothing) return item end eof(fr::FastaReader) = fr.is_eof function show{T}(io::IO, fr::FastaReader{T}) print(io, "FastaReader(input=\"$(fr.f)\", out_type=$T, num_parsed=$(fr.num_parsed), eof=$(fr.is_eof))") end function readfasta(filename::AbstractString, T::Type=String) FastaReader(filename, T) do fr readall(fr) end end readfasta(io::IO, T::Type=String) = readall(FastaReader{T}(io)) type FastaWriter f::IO in_seq::Bool entry_chars::Int desc_chars::Int parsed_nl::Bool pos::Int entry::Int own_f::Bool at_start::Bool function FastaWriter(io::IO) fw = new(io, false, 0, 0, false, 0, 1, false, true) finalizer(fw, close) return fw end function FastaWriter(filename::AbstractString, mode::AbstractString = "w") if endswith(filename, ".gz") of = gzopen else of = open end fw = new(of(filename, mode), false, 0, 0, false, 0, 1, true, true) finalizer(fw, close) return fw end end FastaWriter() = FastaWriter(STDOUT) function FastaWriter(f::Function, args...) fw = FastaWriter(args...) try f(fw) finally close(fw) end end function write(fw::FastaWriter, c) ch = convert(Char, c) isascii(ch) || error("invalid (non-ASCII) character: $c (entry $(fw.entry) of FASTA input)") if ch == '\n' && !fw.at_start fw.parsed_nl = true if !fw.in_seq fw.desc_chars == 1 && error("empty description (entry $(fw.entry) of FASTA input") write(fw.f, '\n') fw.pos = 0 fw.in_seq = true end end isspace(ch) && (fw.at_start || fw.in_seq || fw.desc_chars <= 1) && return fw.at_start && ch != '>' && error("no desctiption given (entry $(fw.entry) of FASTA input") fw.at_start = false if fw.parsed_nl @assert fw.in_seq if ch == '>' fw.entry_chars > 0 || error("description must span a single line (entry $(fw.entry) of FASTA input)") write(fw.f, '\n') fw.in_seq = false fw.pos = 0 fw.entry += 1 fw.entry_chars = 0 fw.desc_chars = 0 end elseif fw.in_seq && ch == '>' error("character '>' not allowed in sequence data (entry $(fw.entry) of FASTA input)") end if fw.pos == 80 if !fw.in_seq warn("description line longer than 80 characters (entry $(fw.entry) of FASTA input)") else write(fw.f, '\n') fw.pos = 0 end end write(fw.f, ch) fw.pos += 1 if fw.in_seq fw.entry_chars += 1 else fw.desc_chars += 1 end fw.parsed_nl = false return end function write(fw::FastaWriter, s::Vector) for c in s write(fw, c) end end function write(fw::FastaWriter, s::AbstractString) for c in s write(fw, c) end write(fw, '\n') end function writeentry(fw::FastaWriter, desc::AbstractString, seq) !fw.at_start && write(fw, '\n') desc = strip(String(desc)) isascii(desc) || error("description must be ASCCII (entry $(fw.entry+1) of FASTA input)") if search(desc, '\n') != 0 error("newlines are not allowed within description (entry $(fw.entry+1) of FASTA input)") end write(fw, '>') write(fw, desc) write(fw, '\n') #write(fw, seq) #write(fw, '\n') fw.entry_chars = writefastaseq(fw.f, seq, fw.entry, false) fw.in_seq = true fw.parsed_nl = false fw.pos = 0 fw.entry_chars > 0 || error("empty sequence data (entry $(fw.entry) of FASTA input)") return end function close(fw::FastaWriter) try write(fw.f, '\n') flush(fw.f) catch err isa(err, EOFError) || rethrow(err) end fw.pos = 0 fw.parsed_nl = true fw.own_f && close(fw.f) return end function show(io::IO, fw::FastaWriter) print(io, "FastaWriter(input=\"$(fw.f)\", entry=$(fw.entry)") end function writefastaseq(io::IO, seq, entry::Int, nl::Bool = true) i = 0 entry_chars = 0 for c in seq if i == 80 write(io, '\n') i = 0 end ch = convert(Char, c) isascii(ch) || error("invalid (non-ASCII) character: $c (entry $entry of FASTA input)") isspace(ch) && continue ch != '>' || error("character '>' not allowed in sequence data (entry $entry of FASTA input)") write(io, ch) i += 1 entry_chars += 1 end nl && write(io, '\n') return entry_chars end function writefasta(io::IO, data) entry = 0 for (desc, seq) in data entry += 1 desc = strip(String(desc)) isascii(desc) || error("description must be ASCCII (entry $entry of FASTA input)") if isempty(desc) error("empty description (entry $entry of FASTA input") end if search(desc, '\n') != 0 error("newlines are not allowed within description (entry $entry of FASTA input)") end if length(desc) > 79 warn("description line longer than 80 characters (entry $entry of FASTA input)") end println(io, ">", desc) entry_chars = writefastaseq(io, seq, entry) entry_chars > 0 || error("empty sequence data (entry $entry of FASTA input)") end end writefasta(data) = writefasta(STDOUT, data) function writefasta(filename::AbstractString, data, mode::AbstractString = "w") if endswith(filename, ".gz") of = gzopen else of = open end of(filename, mode) do f writefasta(f, data) end end end
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2.012859
5,599
module MatrixMarket using Compat.SparseArrays using Compat.LinearAlgebra export mmread, mmwrite struct ParseError error :: String end _parseint(x) = parse(Int, x) """ ### mmread(filename, infoonly::Bool=false, retcoord::Bool=false) Read the contents of the Matrix Market file 'filename' into a matrix, which will be either sparse or dense, depending on the Matrix Market format indicated by 'coordinate' (coordinate sparse storage), or 'array' (dense array storage). If infoonly is true (default: false), only information on the size and structure is returned from reading the header. The actual data for the matrix elements are not parsed. If retcoord is true (default: false), the rows, column and value vectors are returned, if it is a sparse matrix, along with the header information. """ function mmread(filename, infoonly::Bool=false, retcoord::Bool=false) open(filename,"r") do mmfile # Read first line firstline = chomp(readline(mmfile)) tokens = split(firstline) if length(tokens) != 5 throw(ParseError(string("Not enough words on first line: ", firstline))) end if tokens[1] != "%%MatrixMarket" throw(ParseError(string("Expected start of header `%%MatrixMarket`, got `$(tokens[1])`"))) end (head1, rep, field, symm) = map(lowercase, tokens[2:5]) if head1 != "matrix" throw(ParseError("Unknown MatrixMarket data type: $head1 (only \"matrix\" is supported)")) end eltype = field == "real" ? Float64 : field == "complex" ? ComplexF64 : field == "integer" ? Int64 : field == "pattern" ? Bool : throw(ParseError("Unsupported field $field (only real and complex are supported)")) symlabel = symm == "general" ? identity : symm == "symmetric" ? symmetric! : symm == "hermitian" ? hermitian! : symm == "skew-symmetric" ? skewsymmetric! : throw(ParseError("Unknown matrix symmetry: $symm (only general, symmetric, skew-symmetric and hermitian are supported)")) # Skip all comments and empty lines ll = readline(mmfile) while length(chomp(ll))==0 || (length(ll) > 0 && ll[1] == '%') ll = readline(mmfile) end # Read matrix dimensions (and number of entries) from first non-comment line dd = map(_parseint, split(ll)) if length(dd) < (rep == "coordinate" ? 3 : 2) throw(ParseError(string("Could not read in matrix dimensions from line: ", ll))) end rows = dd[1] cols = dd[2] entries = (rep == "coordinate") ? dd[3] : (rows * cols) infoonly && return (rows, cols, entries, rep, field, symm) rep == "coordinate" || return symlabel(reshape([parse(Float64, readline(mmfile)) for i in 1:entries], (rows,cols))) rr = Vector{Int}(undef, entries) cc = Vector{Int}(undef, entries) xx = Vector{eltype}(undef, entries) for i in 1:entries line = readline(mmfile) splits = find_splits(line, eltype == ComplexF64 ? 3 : (eltype == Bool ? 1 : 2)) rr[i] = _parseint(line[1:splits[1]]) cc[i] = _parseint(eltype == Bool ? line[splits[1]:end] : line[splits[1]:splits[2]]) if eltype == ComplexF64 real = parse(Float64, line[splits[2]:splits[3]]) imag = parse(Float64, line[splits[3]:length(line)]) xx[i] = ComplexF64(real, imag) elseif eltype == Bool xx[i] = true else xx[i] = parse(eltype, line[splits[2]:length(line)]) end end (retcoord ? (rr, cc, xx, rows, cols, entries, rep, field, symm) : symlabel(sparse(rr, cc, xx, rows, cols))) end end function find_splits(s::String, num) splits = Vector{Int}(undef, num) cur = 1 in_space = s[1] == '\t' || s[1] == ' ' @inbounds for i in 1:length(s) if s[i] == '\t' || s[i] == ' ' if !in_space in_space = true splits[cur] = i cur += 1 cur > num && break end else in_space = false end end splits end # Hack to represent skew-symmetric matrix as an ordinary matrix with duplicated elements function skewsymmetric!(M::AbstractMatrix) m,n = size(M) m == n || throw(DimensionMismatch()) return M .- transpose(tril(M, -1)) end function symmetric!(M::AbstractMatrix) m,n = size(M) m == n || throw(DimensionMismatch()) if eltype(M) == Bool return M .| transpose(tril(M, -1)) else return M .+ transpose(tril(M, -1)) end end function hermitian!(M::AbstractMatrix) m,n = size(M) m == n || throw(DimensionMismatch()) if eltype(M) == Bool return M .| conj(transpose(tril(M, -1))) else return M .+ conj(transpose(tril(M, -1))) end end """ ### mmwrite(filename, matrix::SparseMatrixCSC) Write a sparse matrix to file 'filename'. """ function mmwrite(filename, matrix :: SparseMatrixCSC) open(filename, "w") do file elem = eltype(matrix) <: Bool ? "pattern" : eltype(matrix) <: Integer ? "integer" : eltype(matrix) <: AbstractFloat ? "real" : eltype(matrix) <: Complex ? "complex" : error("Invalid matrix type") sym = issymmetric(matrix) ? "symmetric" : ishermitian(matrix) ? "hermitian" : "general" # write mm header write(file, "%%MatrixMarket matrix coordinate $elem $sym\n") # only use lower triangular part of symmetric and Hermitian matrices if issymmetric(matrix) || ishermitian(matrix) matrix = tril(matrix) end # write matrix size and number of nonzeros write(file, "$(size(matrix, 1)) $(size(matrix, 2)) $(nnz(matrix))\n") rows = rowvals(matrix) vals = nonzeros(matrix) for i in 1:size(matrix, 2) for j in nzrange(matrix, i) write(file, "$(rows[j]) $i") if elem == "pattern" # omit values on pattern matrices elseif elem == "complex" write(file, " $(real(vals[j])) $(imag(vals[j]))") else write(file, " $(vals[j])") end write(file, "\n") end end end end end # module
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2.147594
3,076
using SolidStateDetectors include("Plotting.jl") include("ReadGeant4Hits.jl") include("EventSimulation.jl") defaultDir = "plots/" T = Float32 function BiasVariation(configFile::String, initVoltage::Real, finalVoltage::Real, length::Integer, CCDName::String="")::AbstractDict{Real, Simulation} biasRange = range(initVoltage, stop=finalVoltage, length=length) @info "Constructing base detector" sim_base = Simulation(configFile) sim_dict = Dict{Real, Simulation}() if CCDName != "" #include(CCDName) This line needs to instead be put into the main script you are running sim_base.detector.semiconductors[1].charge_density_model = ccdm end @info "Calculating weighting potentials" for i in 1:size(sim_base.detector.contacts, 1) calculate_weighting_potential!(sim_base, i, max_refinements=2, convergence_limit=1e-3 #init_grid_spacing = T.( (2e-4, 2e-4, 2e-4) ) ) end for i in 1:size(biasRange, 1) @info "Setting bias voltage" det = deepcopy(sim_base.detector) det.contacts[1].potential = -biasRange[i] sim = Simulation(det) calculate_electric_potential!(sim, depletion_handling=true, max_refinements=2, convergence_limit=1e-3 #init_grid_spacing = T.( (2e-4, 2e-4, 2e-4) ) ) calculate_electric_field!(sim) sim.weighting_potentials = sim_base.weighting_potentials set_charge_drift_model!(sim, ADLChargeDriftModel()) calculate_drift_fields!(sim) sim_dict[biasRange[i]] = sim end return sim_dict end function GetEligibleFiles(dataDir::String, minEnergy::Real=0., maxEnergy::Real=Inf, minAngle::Real=0., maxAngle::Real=Inf)::Vector{String} fileList = readdir(dataDir, join=true) filteredList = Vector{String}() regex = r"(\d+)+" for s in fileList m = collect(eachmatch(regex, s)) E = parse(Int64, m[end-1].match) A = parse(Int64, m[end].match) if E >= minEnergy && E <= maxEnergy && A >= minAngle && A <= maxAngle push!(filteredList, s) end end return filteredList end function PlotSingleParticleDetectorResponse(sim::Simulation, fullPath::String, impactPosition::CartesianPoint=CartesianPoint{T}(0, 0, 0), prefix::String="", suffix::String="", dir::String=defaultDir, stepLimiter::Integer=1000, qe=x->1)::Vector{Event} fileName = split(fullPath, "/")[end] strippedName = split(fileName, ".")[1] suffix = "_" * strippedName * suffix gdf = GetHitInformation(fullPath) events = DriftGeant4Events(gdf, sim, impactPosition, stepLimiter=stepLimiter, qe=qe) PlotEvents(events, dir, prefix, suffix) return events end function PlotParticleDetectorResponse(sims::AbstractDict{Real, Simulation}, dataDir::String; impactPosition::CartesianPoint=CartesianPoint{T}(0, 0, 0), minEnergy::Real=0., maxEnergy::Real=Inf, minAngle::Real=0., maxAngle::Real=Inf, prefix::String="", dir::String=defaultDir, maxEvents::Integer=100, qe=x->1)::Nothing println(impactPosition) prefix = prefix * "particle_" biasRange = collect(keys(sims)) fileList = GetEligibleFiles(dataDir, minEnergy, maxEnergy, minAngle, maxAngle) @showprogress for i in 1:size(biasRange, 1) sim = sims[biasRange[i]] suffix = @sprintf "_%.1fV" biasRange[i] @showprogress for s in fileList PlotSingleParticleDetectorResponse(sim, s, impactPosition, prefix, suffix, dir, maxEvents, qe) end end end function PlotDetectorPerformance(sims::AbstractDict{Real, Simulation}; prefix::String="", dir::String=defaultDir)::Nothing biasRange = collect(keys(sims)) prefix = prefix * "detector_" PlotGeometry(sims[biasRange[1]], dir, prefix) Plot2DWeightingPotential(sims[biasRange[1]], dir, prefix) Plot1DWeightingPotential(sims[biasRange[1]], dir, prefix) PlotMaterialProperties(sims[biasRange[1]], dir, prefix) for i in 1:size(biasRange, 1) suffix = @sprintf "_%.1fV" biasRange[i] PlotElectricPotential(sims[biasRange[i]], dir, prefix, suffix) PlotElectricField(sims[biasRange[i]], dir, prefix, suffix) PlotPointType(sims[biasRange[i]], dir, prefix, suffix) end PlotCV(collect(values(sims)), dir, prefix) end
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2.443946
1,784
# demo for double loop for i = 1:2, j = 3:4 println((i, j)) end
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2.233333
30
using BHAtp, Statistics ProjDir = @__DIR__ !isdir(joinpath(ProjDir, "plots")) && mkdir(joinpath(ProjDir, "plots")) ProjName = split(ProjDir, "/")[end] bhaj = BHAtp.BHAJ(ProjName, ProjDir) bhaj.ratio = 0.5 segs = [ # Element type, Material, Length, ID, OD, fc (:bit, :steel, 0.00, 2.000, 17.500, 0.0), (:collar, :steel, 1.44, 2.000, 9.500, 0.0), (:collar, :steel, 3.00, 3.000, 9.500, 0.0), (:collar, :steel, 1.24, 2.938, 9.500, 0.0), (:stabilizer, :steel, 0.00, 2.938, 17.500, 0.0), (:collar, :steel, 4.04, 2.938, 9.500, 0.0), (:collar, :monel, 37.45, 2.875, 9.500, 0.0), (:collar, :monel, 27.09, 3.000, 9.625, 0.0), (:collar, :monel, 30.01, 2.875, 9.500, 0.0), (:collar, :steel, 1.87, 3.000, 9.500, 0.0), (:stabilizer, :steel, 0.00, 3.000, 17.500, 0.0), (:collar, :steel, 3.73, 3.000, 9.500, 0.0), (:collar, :steel, 28.73, 2.875, 9.500, 0.0), (:collar, :steel, 4.22, 3.000, 9.125, 0.0), (:collar, :steel, 27.98, 2.750, 7.875, 0.0), (:collar, :steel, 29.45, 2.875, 7.563, 0.0), (:collar, :steel, 30.87, 2.875, 7.625, 0.0), (:collar, :steel, 31.10, 2.750, 8.000, 0.0), (:collar, :steel, 32.18, 3.000, 8.000, 0.0), (:collar, :steel, 30.88, 2.750, 7.938, 0.0), (:collar, :steel, 3.50, 2.750, 8.000, 0.0) ] traj = [ #Heading, Diameter ( 60.0, 17.5) ] wobs = 30:5:40 incls = 30:5:60 # Or e.g. incls = [5 10 20 30 40 45 50] @time bhaj(segs, traj, wobs, incls) display("Fetch tp=false, wob=35, incl@bit=30 solution:") df,df_tp = show_solution(ProjDir, 35, 30, show=false, tp=false) df[:,[2; 5:6; 9:12]] |> display println() println("Fetch tp=true, wob=35, incl@bit=30 solution:") df,df_tp = show_solution(ProjDir, 35, 30, show=false); df[:,[2; 5:6; 9:12]] |> display println() tpf = create_final_tp_df(ProjDir, wobs, incls; ofu=false) tpf |> display println() name = "figure_03a" figure_03a = @pgf GroupPlot({ group_style = { group_size = "1 by 2", vertical_sep = "0pt", xticklabels_at = "edge bottom" } }, Axis({ height = "6cm", width = "8cm", ylabel = "TP [°/100ft]", title = "TP and Formatioin Correction (ΔTP)" }, Plot({ color="red", mark = "*" }, Table(incls, tpf.tp[1:7])), LegendEntry("WOB 30"), Plot({ color="blue", mark = "+" }, Table(incls, tpf.tp[8:14])), LegendEntry("WOB 35"), Plot({ color="green", mark = "o" }, Table(incls, tpf.tp[15:21])), LegendEntry("WOB 40") ), Axis({ height = "6cm", width = "8cm", ylabel = "Sideforce NBS" }, Plot({ color="red", mark = "*", title ="TP" }, Table(incls, tpf.ft[1:7])), LegendEntry("WOB 30"), Plot({ color="blue", mark = "+" }, Table(incls, tpf.ft[8:14])), LegendEntry("WOB 35"), Plot({ color="green", mark = "o" }, Table(incls, tpf.ft[15:21])), LegendEntry("WOB 40") ) ) savefig(joinpath(ProjDir, "plots", name), figure_03a, ProjDir)
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1.803115
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using Base.Test #-------------------------------------------------------------------------------------------------# include("../src/pccf.jl") @test Pccf.pccfWithConfirmation([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],2,[3,7,10]) == [1,2, 1,2,3,4,5,6,7, 1,2,3,4,5,6] # confirmed=[3,10] #-------------------------------------------------------------------------------------------------# include("../src/probs.jl") @test Probs.ifDetected(5, [3, 6, 11], 3) == true @test Probs.ifDetected(8, [3, 6, 11], 2) == true @test Probs.ifDetected(9, [3, 6, 12], 2) == false @test Probs.extractFps([1, 6, 19, 25], [5, 10, 15, 20], 3) == [1, 25] @test Probs.extractTps([1, 6, 19, 25], [5, 10, 15, 20], 3) == [6, 19] @test Probs.ifPredictedTp(2, [0.1, 0.9, 0.4], 0.41) == true @test Probs.ifPredictedTp(3, [0.1, 0.9, 0.4], 0.41) == false @test Probs.belowH(0.7, [0.1, 0.8, 0.4, 10.0, 11.0, 0.7], 4) == [1, 3] @test Probs.aboveH(0.7, [0.1, 0.2, 0.71, 0.8, 0.5], 6 ) == [3, 4] # @test Probs.whichBin(7 , [1,2,7,9]) == 3 # @test Probs.whichBin(-7, [1,2,7,9]) == 1 # @test Probs.whichBin(10, [1,2,7,9]) == 5 # @test Probs.whichBin(9 , [1,2,7,9]) == 4 #-------------------------------------------------------------------------------------------------# include("../src/sim2.jl") # fracOfRemovedFa(h, probs, delayConfirm, allErrors) @test Sim2.fracOfRemovedFa(0.1, [0.01, 0.12, 0.03, 0.04, 0.11], 5, [1, 3, 4]) == 1.0 @test Sim2.fracOfRemovedFa(0.1, [0.01, 0.12, 0.03, 0.04, 0.11], 5, [1, 3, 5]) == 2/3 @test Sim2.fracOfRemovedFa(0.1, [0.01, 0.12, 0.03, 0.04, 0.11], 3, [1, 3, 5]) == 1.0 @test Sim2.fracOfRemovedFa(0.1, [0.01, 0.12, 0.03, 0.04, 0.11], 5, [2] ) == 0.0 @test Sim2.fracOfRemovedFa(0.1, [0.01, 0.12, 0.03, 0.04, 0.11], 3, [1, 3] ) == 1.0 @test Sim2.fracOfRemovedFa(0.1, [0.01, 0.12, 0.03, 0.04, 0.11], 3, [1, 2, 5]) == 0.5 # function fracOfPredictedTp (h, probs, delayConfirm, allChanges) @test Sim2.fracOfPredictedTp(0.1, [0.11, 0.01, 0.11, 0.01, 0.11, 0.01], 6, [1, 3, 5]) == 1.0 @test Sim2.fracOfPredictedTp(0.1, [0.11, 0.01, 0.01, 0.01, 0.11, 0.01], 6, [1, 3, 5]) == 2/3 @test Sim2.fracOfPredictedTp(0.1, [0.11, 0.01, 0.01, 0.01, 0.11, 0.01], 3, [1, 3, 5]) == 0.5 @test Sim2.fracOfPredictedTp(0.1, [0.11, 0.01, 0.01, 0.01, 0.11, 0.01], 2, [1, 3, 5]) == 1.0 #-------------------------------------------------------------------------------------------------# include("../src/sim3.jl") @test Sim3.updVec([10,20,30], [2], [0]) == [10,0,30] #-------------------------------------------------------------------------------------------------# include("../src/detector.jl") @test Detector.partSig(3, [1:5;]) == collect(([1,2,3],[2,3,4],[3,4,5])) @test Detector.naiveDetector([1,2,5,2,10],4) == [3,5] @test Detector.naiveDetectorDynamic([1.0, 2.0, 3.0, 3.5, 0.4], [0.5, 2.1, 2.9, 3.4, 0.4]) == [1,3,4] #-------------------------------------------------------------------------------------------------# include("../src/roc.jl") @test Roc.tpNum([1,11] , [10,20,30,40], 3) == 1 @test Roc.tpNum([1,11,13] , [10,20,30,40], 3) == 2 @test Roc.tpNum([1,11,14] , [10,20,30,40], 3) == 1 @test Roc.tpNum([1,11,14,30], [10,20,30,40], 3) == 2 @test Roc.tpRate([1,31 ], [10,20,30,40], 3) == 1/4 @test Roc.tpRate([1,31,44], [10,20,30,40], 3) == 1/4 @test Roc.tpRate([1,31,43], [10,20,30,40], 3) == 2/4 @test Roc.faRate([1,31 ], [10,20,30,40], 3, 100) == 1/(1+(100-4*3))
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## using CSV, DataFrames, DataFramesMeta, StatsPlots, Dates, DelimitedFiles cd(@__DIR__) # download and unzip the data file, from # https://realized.oxford-man.ox.ac.uk/images/oxfordmanrealizedvolatilityindices.zip ## read the data into a DataFrame data = CSV.read("oxfordmanrealizedvolatilityindices.csv",DataFrame) ## select the desired columns data = data[:,[:Column1, :Symbol, :close_price, :rv10, :bv]] # set desired names rename!(data, :Column1 => :Date) rename!(data, :rv10 => :rv) # select desired index sp500 = @subset(data, :Symbol .== ".SPX") ## drop times from date by taking the leading 10 characters (yyyy-mm-dd) sp500.Date = [sp500.Date[i][1:10] for i = 1:size(sp500,1)] ## compute percentage returns sp500.rets = vcat(0.0,[100.0 *(log(sp500.close_price[i]) - log(sp500.close_price[i-1])) for i=2:size(sp500,1)]) # scale volatility measures to be compatible with percentage returns (log price mult. by 100) sp500.rv = 10000.0 .* sp500.rv sp500.bv = 10000.0 .* sp500.bv ## select the date range sp500 = @subset(sp500, :Date .>= "2013-12-17") # these dates give 1000 obs. sp500 = @subset(sp500, :Date .<= "2017-12-05") ## write out variables to plain text file data = sp500[:,[:Date, :rets, :rv, :bv]] #CSV.write("sp500.csv", data) # write as CSV ## # some plots plot(sp500.Date, sp500.rets, tickfontsize=5, legend=false) ## plot(sp500.Date, [sp500.rv, sp500.bv], tickfontsize=5, label=["rv" "bv"]) ## # the next plot shows that volatility is higher when # returns are in the tails of distribution, and that # the overall correlation is negative marginalkde(sp500.rets, sp500.bv)
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module ArrayIterators export EachRow, EachCol @static if VERSION < v"1.1" # added in https://github.com/JuliaLang/julia/pull/29749 # once available in Compat, we should re-export here # https://github.com/JuliaLang/Compat.jl/issues/639 error("Julia version $VERSION is not supported") end const EachRow_eachrow{A,I} = Base.Generator{I,typeof(eachrow(zeros(0,0)).f).name.wrapper{A}} const EachCol_eachcol{A,I} = Base.Generator{I,typeof(eachcol(zeros(0,0)).f).name.wrapper{A}} const EachRow_eachslice{A,I} = Base.Generator{I,typeof(eachslice(zeros(0,0),dims=1).f).name.wrapper{A,Tuple{},Tuple{Colon}}} const EachCol_eachslice{A,I} = Base.Generator{I,typeof(eachslice(zeros(0,0),dims=2).f).name.wrapper{A,Tuple{Colon},Tuple{}}} const EachRow{A,I} = Union{EachRow_eachrow{A,I}, EachRow_eachslice{A,I}} const EachCol{A,I} = Union{EachCol_eachcol{A,I}, EachCol_eachslice{A,I}} Base.parent(x::EachRow) = x.f.A Base.parent(x::EachCol) = x.f.A end # module
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@test_throws DimensionMismatch shuffleobs((X, rand(149))) @testset "typestability" begin for var in vars @test typeof(@inferred(shuffleobs(var))) <: SubArray end for tup in tuples @test typeof(@inferred(shuffleobs(tup))) <: Tuple end end @testset "Array and SubArray" begin for var in vars @test size(shuffleobs(var)) == size(var) end # tests if all obs are still present and none duplicated @test sum(shuffleobs(Y1)) == 120 end @testset "Tuple of Array and SubArray" begin for var in ((X,yv), (Xv,y), tuples...) @test_throws MethodError shuffleobs(var...) @test typeof(shuffleobs(var)) <: Tuple @test all(map(x->(typeof(x)<:SubArray), shuffleobs(var))) @test all(map(x->(numobs(x)===15), shuffleobs(var))) end # tests if all obs are still present and none duplicated # also tests that both paramter are shuffled identically x1, y1, z1 = shuffleobs((X1,Y1,X1)) @test vec(sum(x1,dims=2)) == fill(120,10) @test vec(sum(z1,dims=2)) == fill(120,10) @test sum(y1) == 120 @test all(x1' .== y1) @test all(z1' .== y1) end @testset "SparseArray" begin for var in (Xs, ys) @test typeof(shuffleobs(var)) <: SubArray @test numobs(shuffleobs(var)) == numobs(var) end # tests if all obs are still present and none duplicated @test vec(sum(getobs(shuffleobs(sparse(X1))),dims=2)) == fill(120,10) @test sum(getobs(shuffleobs(sparse(Y1)))) == 120 end @testset "Tuple of SparseArray" begin for var in ((Xs,ys), (X,ys), (Xs,y), (Xs,Xs), (XX,X,ys)) @test_throws MethodError shuffleobs(var...) @test typeof(shuffleobs(var)) <: Tuple @test numobs(shuffleobs(var)) == numobs(var) end # tests if all obs are still present and none duplicated # also tests that both paramter are shuffled identically x1, y1 = getobs(shuffleobs((sparse(X1),sparse(Y1)))) @test vec(sum(x1,dims=2)) == fill(120,10) @test sum(y1) == 120 @test all(x1' .== y1) end @testset "RNG" begin # tests reproducibility explicit_shuffle = shuffleobs(MersenneTwister(42), (X, y)) @test explicit_shuffle == shuffleobs(MersenneTwister(42), (X, y)) end
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971
using ChemistryFeaturization # Batching Utilities """ batch_graph_data(laplacians, encoded_features) Takes vectors of laplacians and encoded features and joins them into a single graph of disjoint subgraphs. The resulting graph is massive and hence the return types are sparse. Few of the layers don't work with Sparse Arrays (specifically on GPUs), so it would make sense to convert them to dense. """ struct BatchedAtomicGraph{T1,T2,S} laplacians::T1 encoded_features::T2 sizes::S end Flux.@functor BatchedAtomicGraph (laplacians, encoded_features) batch_graph_data(t::Tuple) = batch_graph_data(t[1], t[2]) function batch_graph_data(laplacians, encoded_features) # Ideally should be sparse arrays but currently doesn't work well on GPUs _sizes = map(x -> size(x, 1), laplacians) total_nodes = sum(_sizes) batched_laplacian = zeros(eltype(laplacians[1]), total_nodes, total_nodes) idx = 1 for i in 1:length(laplacians) batched_laplacian[idx:(idx + _sizes[i] - 1), idx:(idx + _sizes[i] - 1)] .= laplacians[i] idx += _sizes[i] end _sizes = vcat(0, cumsum(_sizes)) enc_feats = hcat(encoded_features...) return BatchedAtomicGraph(batched_laplacian, enc_feats, _sizes) end function BatchedAtomicGraph(batch_size::Int, atoms::Vector{FeaturizedAtoms}) return BatchedAtomicGraph(batch_size, map(x -> x.atoms.laplacian, atoms), map(x -> x.encoded_features, atoms)) end function BatchedAtomicGraph(batch_size::Int, laplacians, encoded_features) return batch_graph_data.(zip(Iterators.partition(laplacians, batch_size), Iterators.partition(encoded_features, batch_size))) end
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2.574046
655
""" selinv_from_ldlt(Fp,Fi,Fv; conj = conj) -> Bv Compute the entries `Bv` of the inverse of `A = F.L*F.D*F.Lt` contained in the sparsity pattern of the (incomplete) LDLt factorization `F`. """ function selinv_from_ldlt(Fp,Fi,Fv; conj = Base.conj) Tv = eltype(Fv) n = length(Fp)-1 @assert length(Fp) >= 1 @assert issorted(Fp) @assert length(Fi) >= Fp[end]-1 @assert length(Fv) >= Fp[end]-1 @assert all(1 .<= Fi .<= n) @inbounds begin # Return variables Bv = Vector{Tv}(undef,length(Fi)) # Workspace for a single column Fv_aj = Vector{Tv}(undef,n) Bv_aj = Vector{Tv}(undef,n) # Main algorithm for j in reverse(1:n) # Initialise column for p in Fp[j]+1:Fp[j+1]-1 i = Fi[p] Fv_aj[i] = Fv[p] Bv_aj[i] = zero(Tv) end # Pull updates into B[j+1:n,j] for p in Fp[j]+1:Fp[j+1]-1 k = Fi[p] Fv_kj = Fv_aj[k] Bv_kj = Bv_aj[k] - Bv[Fp[k]]*Fv_kj for p in Fp[k]+1:Fp[k+1]-1 i = Fi[p] Fv_ij = Fv_aj[i] Bv_ik = Bv[p] Bv_aj[i] -= Bv_ik *Fv_kj Bv_kj -= conj(Bv_ik)*Fv_ij end Bv_aj[k] = Bv_kj end # Copy temporary column into B for p in Fp[j]+1:Fp[j+1]-1 i = Fi[p] Fv_aj[i] = zero(Tv) Bv[p] = Bv_aj[i] end # Deal with diagonal d = inv(Fv[Fp[j]]) for p in Fp[j]+1:Fp[j+1]-1 d -= conj(Bv[p])*Fv[p] end Bv[Fp[j]] = d end end return Bv end
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1.521375
1,193
using AbstractLogic using Test using Suppressor @test LogicalCombo() |> nfeasible == 0 logicset = @suppress logicalparse("a,b,c,d,e in 1:2") @test logicset[1,1] == 1 @test logicset[4^3,3] == 2 @test logicset[:,:a] == logicset[:,1] logicset = @suppress logicalparse("a.1,a.2,a.3,b.1,b.2,b.3 in 1:3 || {{j}}.1 != {{j}}.2, {{j}}.3 &&& {{j}}.2 != {{j}}.3") @test nfeasible(logicset) == 36 @test @suppress nfeasible(logicalparse("a,b,c in 1:3 || {{i}}!={{>i}}")) == nfeasible(logicalparse("a,b,c in unique")) logicset = @suppress logicalparse("a,b,c in 1:3 || {{i}}!={{!i}}") @test AbstractLogic.operatoreval("a^=b",logicset) |> nfeasible == 0 @test AbstractLogic.operatoreval("a|=b",logicset) |> nfeasible == 0 @test AbstractLogic.operatoreval("a!|=b",logicset) |> nfeasible == 6 @test AbstractLogic.operatoreval("a<=b",logicset) |> nfeasible == 3 @test AbstractLogic.operatoreval("a<<b",logicset) |> nfeasible == 1 @test AbstractLogic.operatoreval("a>=b",logicset) |> nfeasible == 3 @test AbstractLogic.operatoreval("a>>b",logicset) |> nfeasible == 1 @test AbstractLogic.operatoreval("a % 3 ==0",logicset) |> nfeasible == 2 @test @suppress logicalparse("{{<3|>3}}=1", logicset) |> nfeasible == 0 @test @suppress search("{{i+4}}=1", logicset)[1] === missing @test @suppress logicalparse("a,b ∈ 0:1; a==a === b==b; a == b !=> b != a") |> nfeasible == 4 x = @suppress logicalparse("a,b in 1:5; true &&& true ||| false ==> true <== false ||| true ^^^ false") @test nfeasible(x) == 25 x = @suppress logicalparse("a,b in 1:5; true &&&& true |||| false ===> true <=== false |||| true ^^^^ false") @test nfeasible(x) == 25 logicset = @suppress logicalparse("a, b in 1; a != b") @test @suppress( search("=1", logicset)) === missing @test @suppress(checkfeasible("a=1", logicalparse("a, b in 1; a != b")) === missing) @test AbstractLogic.definelogicalset("a ∈ 1") |> nfeasible == 1 @test AbstractLogic.definelogicalset("a,b,c ∈ 1,2,3 || unique") |> nfeasible == 6 @test expand(LogicalCombo(), [:a => 1:2, :b => 1:3]) |> nfeasible == 6 logicset = @suppress logicalparse("a, b, c in 1:3; a > b") @test @suppress !dependenton("a ⊥ b", logicset) @test @suppress dependenton("a !⊥ b", logicset) @test @suppress !dependenton("a ⊥ b", logicset) @test @suppress dependenton("a !⊥ b", logicset) @test @suppress dependenton("a ⊥ c", logicset) @test @suppress !setcompare("a ⊂ b", logicset) @test @suppress setcompare("a !⊂ b", logicset) @test @suppress setcompare("c ⊃ b", logicset) @test @suppress setcompare("b !⊃ c", logicset) @test @suppress setcompare("a,b ⋂ b,c", logicset) @test @suppress !setcompare("a !⋂ c", logicset) @test logicset |> showfeasible == logicset[:,:,true] @test @suppress(logicalparse("a,b,c,d,e in 1:2; {{i}}!={{i+1}}") |> nfeasible) == 2 @test @suppress(logicalparse("a,b,c,d,e in 1:2; {{i}}!={{i-1}}") |> nfeasible) == 2 @test @suppress(logicalparse("a,b in 1:2; {{i}}!={{>i}}") |> nfeasible) == 2 @test @capture_out(help()) |> length > 500 @test @capture_out(help("")) |> length > 500 @test @capture_out(help("in")) |> length > 250 @test @capture_out(help("back")) |> length > 50 @test @capture_out(help("!=")) |> length > 50 @test @capture_out(help("!==")) |> length > 430 @test @capture_out(help("!===")) |> length > 440 @test @capture_out(help("{{i}}")) |> length > 370 @test @suppress(checkfeasible("{{i}}=1", logicalparse("a,b in 1:2")))[1] == .25 @test @capture_out(logicalparse("a,b in 1:2; error"))[(end-16):end] == "not interpreted!\n" x = @suppress logicalparse("a,b in 1:2; a==b") @test @suppress(AbstractLogic.back(x) |> nfeasible) == 4 @test @suppress nfeasible(logicalparse(["a,b in 1:2","a==b"])) == nfeasible(x) @test @suppress(logicalparse("a, b ∈ 0:1; a | b ||| a & b")) |> nfeasible == 3
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module TestEquality using Compat using Compat.Test using CategoricalArrays @testset "== and isequal() for CategoricalPool{Int} and CategoricalPool{Float64}" begin pool1 = CategoricalPool([1, 2, 3]) pool2 = CategoricalPool([2.0, 1.0, 3.0]) @test isequal(pool1, pool1) === true @test isequal(pool1, pool2) === false @test isequal(pool2, pool2) === true @test (pool1 == pool1) === true @test (pool1 == pool2) === false @test (pool2 == pool2) === true @test (pool1 === pool1) === true @test (pool1 === pool2) === false @test (pool2 === pool2) === true opool1 = CategoricalPool([1, 2, 3], true) opool2 = CategoricalPool([2.0, 1.0, 3.0], true) @test isequal(opool1, opool1) === true @test isequal(opool1, opool2) === false @test isequal(opool2, opool2) === true @test (opool1 == opool1) === true @test (opool1 == opool2) === false @test (opool2 == opool2) === true @test (opool1 === opool1) === true @test (opool1 === opool2) === false @test (opool2 === opool2) === true nv1a = CategoricalArrays.catvalue(1, pool1) nv2a = CategoricalArrays.catvalue(1, pool2) nv1b = CategoricalArrays.catvalue(2, pool1) nv2b = CategoricalArrays.catvalue(2, pool2) @test isequal(nv1a, nv1a) == true @test isequal(nv1a, nv2a) == false @test isequal(nv1a, nv1b) == false @test isequal(nv1a, nv2b) == true @test isequal(nv1b, nv1a) == false @test isequal(nv1b, nv2a) == true @test isequal(nv1b, nv1b) == true @test isequal(nv1b, nv2b) == false @test isequal(nv2a, nv1a) == false @test isequal(nv2a, nv2a) == true @test isequal(nv2a, nv1b) == true @test isequal(nv2a, nv2b) == false @test isequal(nv2b, nv1a) == true @test isequal(nv2b, nv2a) == false @test isequal(nv2b, nv1b) == false @test isequal(nv2b, nv2b) == true @test isequal(1, nv1a) == true @test isequal(1, nv2a) == false @test isequal(1, nv1b) == false @test isequal(1, nv2b) == true @test isequal(nv1a, 2) == false @test isequal(nv2a, 2) == true @test isequal(nv1b, 2) == true @test isequal(nv2b, 2) == false ov1a = CategoricalArrays.catvalue(1, opool1) ov2a = CategoricalArrays.catvalue(1, opool2) ov1b = CategoricalArrays.catvalue(2, opool1) ov2b = CategoricalArrays.catvalue(2, opool2) @test isequal(ov1a, ov1a) == true @test isequal(ov1a, ov2a) == false @test isequal(ov1a, ov1b) == false @test isequal(ov1a, ov2b) == true @test isequal(ov1b, ov1a) == false @test isequal(ov1b, ov2a) == true @test isequal(ov1b, ov1b) == true @test isequal(ov1b, ov2b) == false @test isequal(ov2a, ov1a) == false @test isequal(ov2a, ov2a) == true @test isequal(ov2a, ov1b) == true @test isequal(ov2a, ov2b) == false @test isequal(ov2b, ov1a) == true @test isequal(ov2b, ov2a) == false @test isequal(ov2b, ov1b) == false @test isequal(ov2b, ov2b) == true @test isequal(1, ov1a) == true @test isequal(1, ov2a) == false @test isequal(1, ov1b) == false @test isequal(1, ov2b) == true @test isequal(ov1a, 2) == false @test isequal(ov2a, 2) == true @test isequal(ov1b, 2) == true @test isequal(ov2b, 2) == false @test (ov1a == ov1a) == true @test (ov1a == ov2a) == false @test (ov1a == ov1b) == false @test (ov1a == ov2b) == true @test (ov1b == ov1a) == false @test (ov1b == ov2a) == true @test (ov1b == ov1b) == true @test (ov1b == ov2b) == false @test (ov2a == ov1a) == false @test (ov2a == ov2a) == true @test (ov2a == ov1b) == true @test (ov2a == ov2b) == false @test (ov2b == ov1a) == true @test (ov2b == ov2a) == false @test (ov2b == ov1b) == false @test (ov2b == ov2b) == true @testset "ordered and non-ordered values are equal" begin @test (ov1a == nv1a) === true @test (ov1a == nv2a) === false @test (ov1a == nv1b) === false @test (ov1a == nv2b) === true @test (ov1b == nv1a) === false @test (ov1b == nv2a) === true @test (ov1b == nv1b) === true @test (ov1b == nv2b) === false @test (ov2a == nv1a) === false @test (ov2a == nv2a) === true @test (ov2a == nv1b) === true @test (ov2a == nv2b) === false @test (ov2b == nv1a) === true @test (ov2b == nv2a) === false @test (ov2b == nv1b) === false @test (ov2b == nv2b) === true end @testset "non-equality with missing" begin @test ismissing(nv1a == missing) @test ismissing(ov1a == missing) @test ismissing(missing == nv1a) @test ismissing(missing == ov1a) @test isequal(nv1a, missing) == false @test isequal(ov1a, missing) == false @test isequal(missing, nv1a) == false @test isequal(missing, ov1a) == false end end @testset "in()" begin pool = CategoricalPool([5, 1, 3]) nv = CategoricalArrays.catvalue(2, pool) @test (nv in 1:3) === true @test (nv in [1, 2, 3]) === true @test (nv in 2:3) === false @test (nv in [2, 3]) === false end end
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2.128367
2,376
# # Little Group in 2D # ## Preamble using LatticeTools using Formatting using Plots function display_matrix(io::IO, matrix::AbstractMatrix; prefix::AbstractString="") width = ceil(Int, maximum(length("$item")+1 for item in matrix)/4)*4 for row in eachrow(matrix) for (icol, col) in enumerate(row) icol == 1 && print(io, prefix) printfmt(io, "{:>$(width)s}", "$col") end println(io) end end # ## Set up Lattice and Symmetry unitcell = makeunitcell([1.0 0.0; 0.0 1.0]; SiteType=String); addsite!(unitcell, "Ox", FractCoord([0,0], [0.5, 0.0])); addsite!(unitcell, "Oy", FractCoord([0,0], [0.0, 0.5])); lattice = makelattice(unitcell, [4 0; 0 4]); tsym = FiniteTranslationSymmetry(lattice); psym = project(PointSymmetryDatabase.get(13), [1 0 0; 0 1 0]); # ## Little Group lge = little_group_elements(tsym, 2, psym) lg = little_group(tsym, 2, psym) println("Little Group") println("------------") display_matrix(stdout, group_multiplication_table(lg)) lg_matrep = psym.matrix_representations[lge] println("Matrix Representations: $lg_matrep") # ## Finding Point Groups Isomorphic to the Little Group little_symmetry_candidates = Tuple{PointSymmetry, Vector{Int}}[] for i in 1:32 ps = PointSymmetryDatabase.get(i) ϕ = group_isomorphism(lg, ps.group) if !isnothing(ϕ) push!(little_symmetry_candidates, (ps, ϕ)) end end (psym2, ϕ) = first(little_symmetry_candidates) lg_matrep2 = lg_matrep[ϕ] println("Matrix Representations (Isomorphic): $lg_matrep2") # ## Multiplication Tables println("Parent Point Group") println("------------------") display_matrix(stdout, group_multiplication_table(psym2)) println("Little Group") println("------------") display_matrix(stdout, group_multiplication_table(lg_matrep)) println("Isomorphic Little Group") println("-----------------------") display_matrix(stdout, group_multiplication_table(lg_matrep2)) # ## Irreps and Little Groups println("Irreps and Little Groups") println("------------------------") for tsic in get_irrep_components(tsym) idx = tsic.irrep_index kf = tsym.fractional_momenta[idx] k = lattice.unitcell.reducedreciprocallatticevectors * kf psym_little = little_symmetry(tsym, idx, psym) println("- irrep_index: $(idx)") println(" momentum: $(k)") println(" little_point_group: { name: \"$(psym_little.hermann_mauguin)\", order: $(group_order(psym_little)) }") println(" is_psym_compatible: $(iscompatible(tsym, idx, psym))") println(" is_psym_little_compatible: $(iscompatible(tsym, idx, psym_little))") end
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2.558621
1,015
using StaticArrays const hexateron1an = [ SVector{6}([0.16666666666666666 0.16666666666666666 0.16666666666666666 0.16666666666666666 0.16666666666666666 0.16666666666666666]) ] const hexateron1wn = [ 1.0, ] #x = [0.10367258783179548] #w = [0.16666666666666667] const hexateron6an = [ SVector{6}([0.103672587831795 0.103672587831795 0.103672587831795 0.103672587831795 0.103672587831795 0.481637060841023]), SVector{6}([0.103672587831795 0.103672587831795 0.103672587831795 0.103672587831795 0.481637060841023 0.103672587831795]), SVector{6}([0.103672587831795 0.103672587831795 0.103672587831795 0.481637060841023 0.103672587831795 0.103672587831795]), SVector{6}([0.103672587831795 0.103672587831795 0.481637060841023 0.103672587831795 0.103672587831795 0.103672587831795]), SVector{6}([0.103672587831795 0.481637060841023 0.103672587831795 0.103672587831795 0.103672587831795 0.103672587831795]), SVector{6}([0.481637060841023 0.103672587831795 0.103672587831795 0.103672587831795 0.103672587831795 0.103672587831795]) ] const hexateron6wn = [ 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666 ] #x = [0.037927853919977265, 0.07427567246009045] #w = [0.007014279108054526, 0.06386095502344485] const hexateron21an = [ SVector{6}([0.0379278539199773 0.0379278539199773 0.0379278539199773 0.0379278539199773 0.0379278539199773 0.810360730400114]), SVector{6}([0.0379278539199773 0.0379278539199773 0.0379278539199773 0.0379278539199773 0.810360730400114 0.0379278539199773]), SVector{6}([0.0379278539199773 0.0379278539199773 0.0379278539199773 0.810360730400114 0.0379278539199773 0.0379278539199773]), SVector{6}([0.0379278539199773 0.0379278539199773 0.810360730400114 0.0379278539199773 0.0379278539199773 0.0379278539199773]), SVector{6}([0.0379278539199773 0.810360730400114 0.0379278539199773 0.0379278539199773 0.0379278539199773 0.0379278539199773]), SVector{6}([0.810360730400114 0.0379278539199773 0.0379278539199773 0.0379278539199773 0.0379278539199773 0.0379278539199773]), SVector{6}([0.0742756724600905 0.0742756724600905 0.0742756724600905 0.0742756724600905 0.351448655079819 0.351448655079819] ), SVector{6}([0.0742756724600905 0.0742756724600905 0.0742756724600905 0.351448655079819 0.0742756724600905 0.351448655079819] ), SVector{6}([0.0742756724600905 0.0742756724600905 0.0742756724600905 0.351448655079819 0.351448655079819 0.0742756724600905] ), SVector{6}([0.0742756724600905 0.0742756724600905 0.351448655079819 0.0742756724600905 0.0742756724600905 0.351448655079819] ), SVector{6}([0.0742756724600905 0.0742756724600905 0.351448655079819 0.0742756724600905 0.351448655079819 0.0742756724600905] ), SVector{6}([0.0742756724600905 0.0742756724600905 0.351448655079819 0.351448655079819 0.0742756724600905 0.0742756724600905] ), SVector{6}([0.0742756724600905 0.351448655079819 0.0742756724600905 0.0742756724600905 0.0742756724600905 0.351448655079819] ), SVector{6}([0.0742756724600905 0.351448655079819 0.0742756724600905 0.0742756724600905 0.351448655079819 0.0742756724600905] ), SVector{6}([0.0742756724600905 0.351448655079819 0.0742756724600905 0.351448655079819 0.0742756724600905 0.0742756724600905] ), SVector{6}([0.0742756724600905 0.351448655079819 0.351448655079819 0.0742756724600905 0.0742756724600905 0.0742756724600905] ), SVector{6}([0.351448655079819 0.0742756724600905 0.0742756724600905 0.0742756724600905 0.0742756724600905 0.351448655079819] ), SVector{6}([0.351448655079819 0.0742756724600905 0.0742756724600905 0.0742756724600905 0.351448655079819 0.0742756724600905] ), SVector{6}([0.351448655079819 0.0742756724600905 0.0742756724600905 0.351448655079819 0.0742756724600905 0.0742756724600905] ), SVector{6}([0.351448655079819 0.0742756724600905 0.351448655079819 0.0742756724600905 0.0742756724600905 0.0742756724600905] ), SVector{6}([0.351448655079819 0.351448655079819 0.0742756724600905 0.0742756724600905 0.0742756724600905 0.0742756724600905] ), ] const hexateron21wn = [ 0.007014279108054526, 0.007014279108054526, 0.007014279108054526, 0.007014279108054526, 0.007014279108054526, 0.007014279108054526, 0.06386095502344485, 0.06386095502344485, 0.06386095502344485, 0.06386095502344485, 0.06386095502344485, 0.06386095502344485, 0.06386095502344485, 0.06386095502344485, 0.06386095502344485, 0.06386095502344485, 0.06386095502344485, 0.06386095502344485, 0.06386095502344485, 0.06386095502344485, 0.06386095502344485 ] #x = [0.08368866617363178, 0.05502135047515903, 0.020512906881075616, 0.6139738022751151] #w = [ 0.032762178536868275, 0.03617369110190333, 0.0026651035580241276] const hexateron56an = [ SVector{6}([0.0836886661736318 0.0836886661736318 0.0836886661736318 0.0836886661736318 0.0836886661736318 0.581556669131841]), SVector{6}([0.0836886661736318 0.0836886661736318 0.0836886661736318 0.0836886661736318 0.581556669131841 0.0836886661736318]), SVector{6}([0.0836886661736318 0.0836886661736318 0.0836886661736318 0.581556669131841 0.0836886661736318 0.0836886661736318]), SVector{6}([0.0836886661736318 0.0836886661736318 0.581556669131841 0.0836886661736318 0.0836886661736318 0.0836886661736318]), SVector{6}([0.0836886661736318 0.581556669131841 0.0836886661736318 0.0836886661736318 0.0836886661736318 0.0836886661736318]), SVector{6}([0.581556669131841 0.0836886661736318 0.0836886661736318 0.0836886661736318 0.0836886661736318 0.0836886661736318]), SVector{6}([0.0550213504751590 0.0550213504751590 0.0550213504751590 0.278311982858174 0.278311982858174 0.278311982858174] ), SVector{6}([0.0550213504751590 0.0550213504751590 0.278311982858174 0.0550213504751590 0.278311982858174 0.278311982858174] ), SVector{6}([0.0550213504751590 0.0550213504751590 0.278311982858174 0.278311982858174 0.0550213504751590 0.278311982858174] ), SVector{6}([0.0550213504751590 0.0550213504751590 0.278311982858174 0.278311982858174 0.278311982858174 0.0550213504751590] ), SVector{6}([0.0550213504751590 0.278311982858174 0.0550213504751590 0.0550213504751590 0.278311982858174 0.278311982858174] ), SVector{6}([0.0550213504751590 0.278311982858174 0.0550213504751590 0.278311982858174 0.0550213504751590 0.278311982858174] ), SVector{6}([0.0550213504751590 0.278311982858174 0.0550213504751590 0.278311982858174 0.278311982858174 0.0550213504751590] ), SVector{6}([0.0550213504751590 0.278311982858174 0.278311982858174 0.0550213504751590 0.0550213504751590 0.278311982858174] ), SVector{6}([0.0550213504751590 0.278311982858174 0.278311982858174 0.0550213504751590 0.278311982858174 0.0550213504751590] ), SVector{6}([0.0550213504751590 0.278311982858174 0.278311982858174 0.278311982858174 0.0550213504751590 0.0550213504751590] ), SVector{6}([0.278311982858174 0.0550213504751590 0.0550213504751590 0.0550213504751590 0.278311982858174 0.278311982858174] ), SVector{6}([0.278311982858174 0.0550213504751590 0.0550213504751590 0.278311982858174 0.0550213504751590 0.278311982858174] ), SVector{6}([0.278311982858174 0.0550213504751590 0.0550213504751590 0.278311982858174 0.278311982858174 0.0550213504751590] ), SVector{6}([0.278311982858174 0.0550213504751590 0.278311982858174 0.0550213504751590 0.0550213504751590 0.278311982858174] ), SVector{6}([0.278311982858174 0.0550213504751590 0.278311982858174 0.0550213504751590 0.278311982858174 0.0550213504751590] ), SVector{6}([0.278311982858174 0.0550213504751590 0.278311982858174 0.278311982858174 0.0550213504751590 0.0550213504751590] ), SVector{6}([0.278311982858174 0.278311982858174 0.0550213504751590 0.0550213504751590 0.0550213504751590 0.278311982858174] ), SVector{6}([0.278311982858174 0.278311982858174 0.0550213504751590 0.0550213504751590 0.278311982858174 0.0550213504751590] ), SVector{6}([0.278311982858174 0.278311982858174 0.0550213504751590 0.278311982858174 0.0550213504751590 0.0550213504751590] ), SVector{6}([0.278311982858174 0.278311982858174 0.278311982858174 0.0550213504751590 0.0550213504751590 0.0550213504751590] ), SVector{6}([0.0205129068810756 0.0205129068810756 0.0205129068810756 0.0205129068810756 0.613973802275115 0.303974570200582] ), SVector{6}([0.0205129068810756 0.0205129068810756 0.0205129068810756 0.0205129068810756 0.303974570200582 0.613973802275115] ), SVector{6}([0.0205129068810756 0.0205129068810756 0.0205129068810756 0.613973802275115 0.0205129068810756 0.303974570200582] ), SVector{6}([0.0205129068810756 0.0205129068810756 0.0205129068810756 0.613973802275115 0.303974570200582 0.0205129068810756] ), SVector{6}([0.0205129068810756 0.0205129068810756 0.0205129068810756 0.303974570200582 0.0205129068810756 0.613973802275115] ), SVector{6}([0.0205129068810756 0.0205129068810756 0.0205129068810756 0.303974570200582 0.613973802275115 0.0205129068810756] ), SVector{6}([0.0205129068810756 0.0205129068810756 0.613973802275115 0.0205129068810756 0.0205129068810756 0.303974570200582] ), SVector{6}([0.0205129068810756 0.0205129068810756 0.613973802275115 0.0205129068810756 0.303974570200582 0.0205129068810756] ), SVector{6}([0.0205129068810756 0.0205129068810756 0.613973802275115 0.303974570200582 0.0205129068810756 0.0205129068810756] ), SVector{6}([0.0205129068810756 0.0205129068810756 0.303974570200582 0.0205129068810756 0.0205129068810756 0.613973802275115] ), SVector{6}([0.0205129068810756 0.0205129068810756 0.303974570200582 0.0205129068810756 0.613973802275115 0.0205129068810756] ), SVector{6}([0.0205129068810756 0.0205129068810756 0.303974570200582 0.613973802275115 0.0205129068810756 0.0205129068810756] ), SVector{6}([0.0205129068810756 0.613973802275115 0.0205129068810756 0.0205129068810756 0.0205129068810756 0.303974570200582] ), SVector{6}([0.0205129068810756 0.613973802275115 0.0205129068810756 0.0205129068810756 0.303974570200582 0.0205129068810756] ), SVector{6}([0.0205129068810756 0.613973802275115 0.0205129068810756 0.303974570200582 0.0205129068810756 0.0205129068810756] ), SVector{6}([0.0205129068810756 0.613973802275115 0.303974570200582 0.0205129068810756 0.0205129068810756 0.0205129068810756] ), SVector{6}([0.0205129068810756 0.303974570200582 0.0205129068810756 0.0205129068810756 0.0205129068810756 0.613973802275115] ), SVector{6}([0.0205129068810756 0.303974570200582 0.0205129068810756 0.0205129068810756 0.613973802275115 0.0205129068810756] ), SVector{6}([0.0205129068810756 0.303974570200582 0.0205129068810756 0.613973802275115 0.0205129068810756 0.0205129068810756] ), SVector{6}([0.0205129068810756 0.303974570200582 0.613973802275115 0.0205129068810756 0.0205129068810756 0.0205129068810756] ), SVector{6}([0.613973802275115 0.0205129068810756 0.0205129068810756 0.0205129068810756 0.0205129068810756 0.303974570200582] ), SVector{6}([0.613973802275115 0.0205129068810756 0.0205129068810756 0.0205129068810756 0.303974570200582 0.0205129068810756] ), SVector{6}([0.613973802275115 0.0205129068810756 0.0205129068810756 0.303974570200582 0.0205129068810756 0.0205129068810756] ), SVector{6}([0.613973802275115 0.0205129068810756 0.303974570200582 0.0205129068810756 0.0205129068810756 0.0205129068810756] ), SVector{6}([0.613973802275115 0.303974570200582 0.0205129068810756 0.0205129068810756 0.0205129068810756 0.0205129068810756] ), SVector{6}([0.303974570200582 0.0205129068810756 0.0205129068810756 0.0205129068810756 0.0205129068810756 0.613973802275115] ), SVector{6}([0.303974570200582 0.0205129068810756 0.0205129068810756 0.0205129068810756 0.613973802275115 0.0205129068810756] ), SVector{6}([0.303974570200582 0.0205129068810756 0.0205129068810756 0.613973802275115 0.0205129068810756 0.0205129068810756] ), SVector{6}([0.303974570200582 0.0205129068810756 0.613973802275115 0.0205129068810756 0.0205129068810756 0.0205129068810756] ), SVector{6}([0.303974570200582 0.613973802275115 0.0205129068810756 0.0205129068810756 0.0205129068810756 0.0205129068810756] ) ] const hexateron56wn = [ 0.032762178536868275, 0.032762178536868275, 0.032762178536868275, 0.032762178536868275, 0.032762178536868275, 0.032762178536868275, 0.03617369110190333, 0.03617369110190333, 0.03617369110190333, 0.03617369110190333, 0.03617369110190333, 0.03617369110190333, 0.03617369110190333, 0.03617369110190333, 0.03617369110190333, 0.03617369110190333, 0.03617369110190333, 0.03617369110190333, 0.03617369110190333, 0.03617369110190333, 0.03617369110190333, 0.03617369110190333, 0.03617369110190333, 0.03617369110190333, 0.03617369110190333, 0.03617369110190333, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276, 0.0026651035580241276 ] #x = [0.03802320588487034, 0.060931862032544144, 0.22974989389595796, 0.07972245054440287, 0.18607410108119998, 0.018946661875089935, 0.20008317880290583] #w = [0.0020951600463044906, 0.011202117898848474, 0.02763274784579167, 0.006717804212054248, 0.0033895321198490562] const hexateron126an = [ SVector{6}([0.0380232058848703 0.0380232058848703 0.0380232058848703 0.0380232058848703 0.0380232058848703 0.809883970575648]), SVector{6}([0.0380232058848703 0.0380232058848703 0.0380232058848703 0.0380232058848703 0.809883970575648 0.0380232058848703]), SVector{6}([0.0380232058848703 0.0380232058848703 0.0380232058848703 0.809883970575648 0.0380232058848703 0.0380232058848703]), SVector{6}([0.0380232058848703 0.0380232058848703 0.809883970575648 0.0380232058848703 0.0380232058848703 0.0380232058848703]), SVector{6}([0.0380232058848703 0.809883970575648 0.0380232058848703 0.0380232058848703 0.0380232058848703 0.0380232058848703]), SVector{6}([0.809883970575648 0.0380232058848703 0.0380232058848703 0.0380232058848703 0.0380232058848703 0.0380232058848703]), SVector{6}([0.0609318620325441 0.0609318620325441 0.0609318620325441 0.0609318620325441 0.378136275934912 0.378136275934912] ), SVector{6}([0.0609318620325441 0.0609318620325441 0.0609318620325441 0.378136275934912 0.0609318620325441 0.378136275934912] ), SVector{6}([0.0609318620325441 0.0609318620325441 0.0609318620325441 0.378136275934912 0.378136275934912 0.0609318620325441] ), SVector{6}([0.0609318620325441 0.0609318620325441 0.378136275934912 0.0609318620325441 0.0609318620325441 0.378136275934912] ), SVector{6}([0.0609318620325441 0.0609318620325441 0.378136275934912 0.0609318620325441 0.378136275934912 0.0609318620325441] ), SVector{6}([0.0609318620325441 0.0609318620325441 0.378136275934912 0.378136275934912 0.0609318620325441 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0.0033895321198490562, 0.0033895321198490562, 0.0033895321198490562, 0.0033895321198490562, 0.0033895321198490562, 0.0033895321198490562, 0.0033895321198490562, 0.0033895321198490562, 0.0033895321198490562, 0.0033895321198490562, 0.0033895321198490562, 0.0033895321198490562, 0.0033895321198490562, 0.0033895321198490562, 0.0033895321198490562, 0.0033895321198490562, 0.0033895321198490562, 0.0033895321198490562, 0.0033895321198490562, 0.0033895321198490562, 0.0033895321198490562, 0.0033895321198490562 ] #x = [0.017560303714676449, 0.19933481135062811, 0.10975719009185349, 0.17301439058463153, 0.048701518591342433, 0.47965829210782895, 0.057069123647292788, 0.1932661083099538, 0.12439652864574161, 0.0045303515222048316, 0.28190357541191013, 0.076906800723122254] #w = [0.00030799358595333761, 0.0035617678038822758, 0.0095468079195978103, 0.0053806629399101077, 0.00013623437539270265, 0.0024424189682134669, 0.0062373017543229771] const hexateron252an = [ SVector{6}([0.0175603037146764; 0.0175603037146764; 0.0175603037146764; 0.0175603037146764; 0.0175603037146764; 0.912198481426618] ), SVector{6}([0.0175603037146764; 0.0175603037146764; 0.0175603037146764; 0.0175603037146764; 0.912198481426618; 0.0175603037146764] ), SVector{6}([0.0175603037146764; 0.0175603037146764; 0.0175603037146764; 0.912198481426618; 0.0175603037146764; 0.0175603037146764] ), SVector{6}([0.0175603037146764; 0.0175603037146764; 0.912198481426618; 0.0175603037146764; 0.0175603037146764; 0.0175603037146764] ), SVector{6}([0.0175603037146764; 0.912198481426618; 0.0175603037146764; 0.0175603037146764; 0.0175603037146764; 0.0175603037146764] ), SVector{6}([0.912198481426618; 0.0175603037146764; 0.0175603037146764; 0.0175603037146764; 0.0175603037146764; 0.0175603037146764] ), SVector{6}([0.199334811350628; 0.199334811350628; 0.199334811350628; 0.199334811350628; 0.199334811350628; 0.00332594324685942] ), SVector{6}([0.199334811350628; 0.199334811350628; 0.199334811350628; 0.199334811350628; 0.00332594324685942; 0.199334811350628] ), SVector{6}([0.199334811350628; 0.199334811350628; 0.199334811350628; 0.00332594324685942; 0.199334811350628; 0.199334811350628] ), SVector{6}([0.199334811350628; 0.199334811350628; 0.00332594324685942; 0.199334811350628; 0.199334811350628; 0.199334811350628] ), SVector{6}([0.199334811350628; 0.00332594324685942; 0.199334811350628; 0.199334811350628; 0.199334811350628; 0.199334811350628] ), SVector{6}([0.00332594324685942; 0.199334811350628; 0.199334811350628; 0.199334811350628; 0.199334811350628; 0.199334811350628] ), SVector{6}([0.109757190091853; 0.109757190091853; 0.109757190091853; 0.109757190091853; 0.173014390584632; 0.387956849047955] ), SVector{6}([0.109757190091853; 0.109757190091853; 0.109757190091853; 0.109757190091853; 0.387956849047955; 0.173014390584632] ), SVector{6}([0.109757190091853; 0.109757190091853; 0.109757190091853; 0.173014390584632; 0.109757190091853; 0.387956849047955] ), SVector{6}([0.109757190091853; 0.109757190091853; 0.109757190091853; 0.173014390584632; 0.387956849047955; 0.109757190091853] ), SVector{6}([0.109757190091853; 0.109757190091853; 0.109757190091853; 0.387956849047955; 0.109757190091853; 0.173014390584632] ), SVector{6}([0.109757190091853; 0.109757190091853; 0.109757190091853; 0.387956849047955; 0.173014390584632; 0.109757190091853] ), SVector{6}([0.109757190091853; 0.109757190091853; 0.173014390584632; 0.109757190091853; 0.109757190091853; 0.387956849047955] ), SVector{6}([0.109757190091853; 0.109757190091853; 0.173014390584632; 0.109757190091853; 0.387956849047955; 0.109757190091853] ), SVector{6}([0.109757190091853; 0.109757190091853; 0.173014390584632; 0.387956849047955; 0.109757190091853; 0.109757190091853] ), SVector{6}([0.109757190091853; 0.109757190091853; 0.387956849047955; 0.109757190091853; 0.109757190091853; 0.173014390584632] ), SVector{6}([0.109757190091853; 0.109757190091853; 0.387956849047955; 0.109757190091853; 0.173014390584632; 0.109757190091853] ), SVector{6}([0.109757190091853; 0.109757190091853; 0.387956849047955; 0.173014390584632; 0.109757190091853; 0.109757190091853] ), SVector{6}([0.109757190091853; 0.173014390584632; 0.109757190091853; 0.109757190091853; 0.109757190091853; 0.387956849047955] ), SVector{6}([0.109757190091853; 0.173014390584632; 0.109757190091853; 0.109757190091853; 0.387956849047955; 0.109757190091853] ), SVector{6}([0.109757190091853; 0.173014390584632; 0.109757190091853; 0.387956849047955; 0.109757190091853; 0.109757190091853] ), SVector{6}([0.109757190091853; 0.173014390584632; 0.387956849047955; 0.109757190091853; 0.109757190091853; 0.109757190091853] ), SVector{6}([0.109757190091853; 0.387956849047955; 0.109757190091853; 0.109757190091853; 0.109757190091853; 0.173014390584632] ), SVector{6}([0.109757190091853; 0.387956849047955; 0.109757190091853; 0.109757190091853; 0.173014390584632; 0.109757190091853] ), SVector{6}([0.109757190091853; 0.387956849047955; 0.109757190091853; 0.173014390584632; 0.109757190091853; 0.109757190091853] ), SVector{6}([0.109757190091853; 0.387956849047955; 0.173014390584632; 0.109757190091853; 0.109757190091853; 0.109757190091853] ), SVector{6}([0.173014390584632; 0.109757190091853; 0.109757190091853; 0.109757190091853; 0.109757190091853; 0.387956849047955] ), SVector{6}([0.173014390584632; 0.109757190091853; 0.109757190091853; 0.109757190091853; 0.387956849047955; 0.109757190091853] ), SVector{6}([0.173014390584632; 0.109757190091853; 0.109757190091853; 0.387956849047955; 0.109757190091853; 0.109757190091853] ), SVector{6}([0.173014390584632; 0.109757190091853; 0.387956849047955; 0.109757190091853; 0.109757190091853; 0.109757190091853] ), SVector{6}([0.173014390584632; 0.387956849047955; 0.109757190091853; 0.109757190091853; 0.109757190091853; 0.109757190091853] ), SVector{6}([0.387956849047955; 0.109757190091853; 0.109757190091853; 0.109757190091853; 0.109757190091853; 0.173014390584632] ), SVector{6}([0.387956849047955; 0.109757190091853; 0.109757190091853; 0.109757190091853; 0.173014390584632; 0.109757190091853] ), SVector{6}([0.387956849047955; 0.109757190091853; 0.109757190091853; 0.173014390584632; 0.109757190091853; 0.109757190091853] ), SVector{6}([0.387956849047955; 0.109757190091853; 0.173014390584632; 0.109757190091853; 0.109757190091853; 0.109757190091853] ), SVector{6}([0.387956849047955; 0.173014390584632; 0.109757190091853; 0.109757190091853; 0.109757190091853; 0.109757190091853] ), SVector{6}([0.0487015185913424; 0.0487015185913424; 0.0487015185913424; 0.0487015185913424; 0.479658292107829; 0.325535633526801] ), SVector{6}([0.0487015185913424; 0.0487015185913424; 0.0487015185913424; 0.0487015185913424; 0.325535633526801; 0.479658292107829] ), SVector{6}([0.0487015185913424; 0.0487015185913424; 0.0487015185913424; 0.479658292107829; 0.0487015185913424; 0.325535633526801] ), SVector{6}([0.0487015185913424; 0.0487015185913424; 0.0487015185913424; 0.479658292107829; 0.325535633526801; 0.0487015185913424] ), SVector{6}([0.0487015185913424; 0.0487015185913424; 0.0487015185913424; 0.325535633526801; 0.0487015185913424; 0.479658292107829] ), SVector{6}([0.0487015185913424; 0.0487015185913424; 0.0487015185913424; 0.325535633526801; 0.479658292107829; 0.0487015185913424] ), SVector{6}([0.0487015185913424; 0.0487015185913424; 0.479658292107829; 0.0487015185913424; 0.0487015185913424; 0.325535633526801] ), SVector{6}([0.0487015185913424; 0.0487015185913424; 0.479658292107829; 0.0487015185913424; 0.325535633526801; 0.0487015185913424] ), SVector{6}([0.0487015185913424; 0.0487015185913424; 0.479658292107829; 0.325535633526801; 0.0487015185913424; 0.0487015185913424] ), SVector{6}([0.0487015185913424; 0.0487015185913424; 0.325535633526801; 0.0487015185913424; 0.0487015185913424; 0.479658292107829] ), SVector{6}([0.0487015185913424; 0.0487015185913424; 0.325535633526801; 0.0487015185913424; 0.479658292107829; 0.0487015185913424] ), SVector{6}([0.0487015185913424; 0.0487015185913424; 0.325535633526801; 0.479658292107829; 0.0487015185913424; 0.0487015185913424] ), SVector{6}([0.0487015185913424; 0.479658292107829; 0.0487015185913424; 0.0487015185913424; 0.0487015185913424; 0.325535633526801] ), SVector{6}([0.0487015185913424; 0.479658292107829; 0.0487015185913424; 0.0487015185913424; 0.325535633526801; 0.0487015185913424] ), SVector{6}([0.0487015185913424; 0.479658292107829; 0.0487015185913424; 0.325535633526801; 0.0487015185913424; 0.0487015185913424] ), SVector{6}([0.0487015185913424; 0.479658292107829; 0.325535633526801; 0.0487015185913424; 0.0487015185913424; 0.0487015185913424] ), SVector{6}([0.0487015185913424; 0.325535633526801; 0.0487015185913424; 0.0487015185913424; 0.0487015185913424; 0.479658292107829] ), SVector{6}([0.0487015185913424; 0.325535633526801; 0.0487015185913424; 0.0487015185913424; 0.479658292107829; 0.0487015185913424] ), SVector{6}([0.0487015185913424; 0.325535633526801; 0.0487015185913424; 0.479658292107829; 0.0487015185913424; 0.0487015185913424] ), SVector{6}([0.0487015185913424; 0.325535633526801; 0.479658292107829; 0.0487015185913424; 0.0487015185913424; 0.0487015185913424] ), SVector{6}([0.479658292107829; 0.0487015185913424; 0.0487015185913424; 0.0487015185913424; 0.0487015185913424; 0.325535633526801] ), SVector{6}([0.479658292107829; 0.0487015185913424; 0.0487015185913424; 0.0487015185913424; 0.325535633526801; 0.0487015185913424] ), SVector{6}([0.479658292107829; 0.0487015185913424; 0.0487015185913424; 0.325535633526801; 0.0487015185913424; 0.0487015185913424] ), SVector{6}([0.479658292107829; 0.0487015185913424; 0.325535633526801; 0.0487015185913424; 0.0487015185913424; 0.0487015185913424] ), SVector{6}([0.479658292107829; 0.325535633526801; 0.0487015185913424; 0.0487015185913424; 0.0487015185913424; 0.0487015185913424] ), SVector{6}([0.325535633526801; 0.0487015185913424; 0.0487015185913424; 0.0487015185913424; 0.0487015185913424; 0.479658292107829] ), SVector{6}([0.325535633526801; 0.0487015185913424; 0.0487015185913424; 0.0487015185913424; 0.479658292107829; 0.0487015185913424] ), SVector{6}([0.325535633526801; 0.0487015185913424; 0.0487015185913424; 0.479658292107829; 0.0487015185913424; 0.0487015185913424] ), SVector{6}([0.325535633526801; 0.0487015185913424; 0.479658292107829; 0.0487015185913424; 0.0487015185913424; 0.0487015185913424] ), SVector{6}([0.325535633526801; 0.479658292107829; 0.0487015185913424; 0.0487015185913424; 0.0487015185913424; 0.0487015185913424] ), SVector{6}([0.0570691236472928; 0.0570691236472928; 0.0570691236472928; 0.193266108309954; 0.193266108309954; 0.442260412438214] ), SVector{6}([0.0570691236472928; 0.0570691236472928; 0.0570691236472928; 0.193266108309954; 0.442260412438214; 0.193266108309954] ), SVector{6}([0.0570691236472928; 0.0570691236472928; 0.0570691236472928; 0.442260412438214; 0.193266108309954; 0.193266108309954] ), SVector{6}([0.0570691236472928; 0.0570691236472928; 0.193266108309954; 0.0570691236472928; 0.193266108309954; 0.442260412438214] ), SVector{6}([0.0570691236472928; 0.0570691236472928; 0.193266108309954; 0.0570691236472928; 0.442260412438214; 0.193266108309954] ), SVector{6}([0.0570691236472928; 0.0570691236472928; 0.193266108309954; 0.193266108309954; 0.0570691236472928; 0.442260412438214] ), SVector{6}([0.0570691236472928; 0.0570691236472928; 0.193266108309954; 0.193266108309954; 0.442260412438214; 0.0570691236472928] ), SVector{6}([0.0570691236472928; 0.0570691236472928; 0.193266108309954; 0.442260412438214; 0.0570691236472928; 0.193266108309954] ), SVector{6}([0.0570691236472928; 0.0570691236472928; 0.193266108309954; 0.442260412438214; 0.193266108309954; 0.0570691236472928] ), SVector{6}([0.0570691236472928; 0.0570691236472928; 0.442260412438214; 0.0570691236472928; 0.193266108309954; 0.193266108309954] ), SVector{6}([0.0570691236472928; 0.0570691236472928; 0.442260412438214; 0.193266108309954; 0.0570691236472928; 0.193266108309954] ), SVector{6}([0.0570691236472928; 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0.000475672318025089; 0.0769068007231223; 0.281903575411910; 0.0769068007231223] ), SVector{6}([0.281903575411910; 0.281903575411910; 0.000475672318025089; 0.0769068007231223; 0.0769068007231223; 0.281903575411910] ), SVector{6}([0.281903575411910; 0.0769068007231223; 0.281903575411910; 0.281903575411910; 0.0769068007231223; 0.000475672318025089] ), SVector{6}([0.281903575411910; 0.0769068007231223; 0.281903575411910; 0.281903575411910; 0.000475672318025089; 0.0769068007231223] ), SVector{6}([0.281903575411910; 0.0769068007231223; 0.281903575411910; 0.0769068007231223; 0.281903575411910; 0.000475672318025089] ), SVector{6}([0.281903575411910; 0.0769068007231223; 0.281903575411910; 0.0769068007231223; 0.000475672318025089; 0.281903575411910] ), SVector{6}([0.281903575411910; 0.0769068007231223; 0.281903575411910; 0.000475672318025089; 0.281903575411910; 0.0769068007231223] ), SVector{6}([0.281903575411910; 0.0769068007231223; 0.281903575411910; 0.000475672318025089; 0.0769068007231223; 0.281903575411910] ), SVector{6}([0.281903575411910; 0.0769068007231223; 0.0769068007231223; 0.281903575411910; 0.281903575411910; 0.000475672318025089] ), SVector{6}([0.281903575411910; 0.0769068007231223; 0.0769068007231223; 0.281903575411910; 0.000475672318025089; 0.281903575411910] ), SVector{6}([0.281903575411910; 0.0769068007231223; 0.0769068007231223; 0.000475672318025089; 0.281903575411910; 0.281903575411910] ), SVector{6}([0.281903575411910; 0.0769068007231223; 0.000475672318025089; 0.281903575411910; 0.281903575411910; 0.0769068007231223] ), SVector{6}([0.281903575411910; 0.0769068007231223; 0.000475672318025089; 0.281903575411910; 0.0769068007231223; 0.281903575411910] ), SVector{6}([0.281903575411910; 0.0769068007231223; 0.000475672318025089; 0.0769068007231223; 0.281903575411910; 0.281903575411910] ), SVector{6}([0.281903575411910; 0.000475672318025089; 0.281903575411910; 0.281903575411910; 0.0769068007231223; 0.0769068007231223] ), SVector{6}([0.281903575411910; 0.000475672318025089; 0.281903575411910; 0.0769068007231223; 0.281903575411910; 0.0769068007231223] ), SVector{6}([0.281903575411910; 0.000475672318025089; 0.281903575411910; 0.0769068007231223; 0.0769068007231223; 0.281903575411910] ), SVector{6}([0.281903575411910; 0.000475672318025089; 0.0769068007231223; 0.281903575411910; 0.281903575411910; 0.0769068007231223] ), SVector{6}([0.281903575411910; 0.000475672318025089; 0.0769068007231223; 0.281903575411910; 0.0769068007231223; 0.281903575411910] ), SVector{6}([0.281903575411910; 0.000475672318025089; 0.0769068007231223; 0.0769068007231223; 0.281903575411910; 0.281903575411910] ), SVector{6}([0.0769068007231223; 0.281903575411910; 0.281903575411910; 0.281903575411910; 0.0769068007231223; 0.000475672318025089] ), SVector{6}([0.0769068007231223; 0.281903575411910; 0.281903575411910; 0.281903575411910; 0.000475672318025089; 0.0769068007231223] ), SVector{6}([0.0769068007231223; 0.281903575411910; 0.281903575411910; 0.0769068007231223; 0.281903575411910; 0.000475672318025089] ), SVector{6}([0.0769068007231223; 0.281903575411910; 0.281903575411910; 0.0769068007231223; 0.000475672318025089; 0.281903575411910] ), SVector{6}([0.0769068007231223; 0.281903575411910; 0.281903575411910; 0.000475672318025089; 0.281903575411910; 0.0769068007231223] ), SVector{6}([0.0769068007231223; 0.281903575411910; 0.281903575411910; 0.000475672318025089; 0.0769068007231223; 0.281903575411910] ), SVector{6}([0.0769068007231223; 0.281903575411910; 0.0769068007231223; 0.281903575411910; 0.281903575411910; 0.000475672318025089] ), SVector{6}([0.0769068007231223; 0.281903575411910; 0.0769068007231223; 0.281903575411910; 0.000475672318025089; 0.281903575411910] ), SVector{6}([0.0769068007231223; 0.281903575411910; 0.0769068007231223; 0.000475672318025089; 0.281903575411910; 0.281903575411910] ), SVector{6}([0.0769068007231223; 0.281903575411910; 0.000475672318025089; 0.281903575411910; 0.281903575411910; 0.0769068007231223] ), SVector{6}([0.0769068007231223; 0.281903575411910; 0.000475672318025089; 0.281903575411910; 0.0769068007231223; 0.281903575411910] ), SVector{6}([0.0769068007231223; 0.281903575411910; 0.000475672318025089; 0.0769068007231223; 0.281903575411910; 0.281903575411910] ), SVector{6}([0.0769068007231223; 0.0769068007231223; 0.281903575411910; 0.281903575411910; 0.281903575411910; 0.000475672318025089] ), SVector{6}([0.0769068007231223; 0.0769068007231223; 0.281903575411910; 0.281903575411910; 0.000475672318025089; 0.281903575411910] ), SVector{6}([0.0769068007231223; 0.0769068007231223; 0.281903575411910; 0.000475672318025089; 0.281903575411910; 0.281903575411910] ), SVector{6}([0.0769068007231223; 0.0769068007231223; 0.000475672318025089; 0.281903575411910; 0.281903575411910; 0.281903575411910] ), SVector{6}([0.0769068007231223; 0.000475672318025089; 0.281903575411910; 0.281903575411910; 0.281903575411910; 0.0769068007231223] ), SVector{6}([0.0769068007231223; 0.000475672318025089; 0.281903575411910; 0.281903575411910; 0.0769068007231223; 0.281903575411910] ), SVector{6}([0.0769068007231223; 0.000475672318025089; 0.281903575411910; 0.0769068007231223; 0.281903575411910; 0.281903575411910] ), SVector{6}([0.0769068007231223; 0.000475672318025089; 0.0769068007231223; 0.281903575411910; 0.281903575411910; 0.281903575411910] ), SVector{6}([0.000475672318025089; 0.281903575411910; 0.281903575411910; 0.281903575411910; 0.0769068007231223; 0.0769068007231223] ), SVector{6}([0.000475672318025089; 0.281903575411910; 0.281903575411910; 0.0769068007231223; 0.281903575411910; 0.0769068007231223] ), SVector{6}([0.000475672318025089; 0.281903575411910; 0.281903575411910; 0.0769068007231223; 0.0769068007231223; 0.281903575411910] ), SVector{6}([0.000475672318025089; 0.281903575411910; 0.0769068007231223; 0.281903575411910; 0.281903575411910; 0.0769068007231223] ), SVector{6}([0.000475672318025089; 0.281903575411910; 0.0769068007231223; 0.281903575411910; 0.0769068007231223; 0.281903575411910] ), SVector{6}([0.000475672318025089; 0.281903575411910; 0.0769068007231223; 0.0769068007231223; 0.281903575411910; 0.281903575411910] ), SVector{6}([0.000475672318025089; 0.0769068007231223; 0.281903575411910; 0.281903575411910; 0.281903575411910; 0.0769068007231223] ), SVector{6}([0.000475672318025089; 0.0769068007231223; 0.281903575411910; 0.281903575411910; 0.0769068007231223; 0.281903575411910] ), SVector{6}([0.000475672318025089; 0.0769068007231223; 0.281903575411910; 0.0769068007231223; 0.281903575411910; 0.281903575411910] ), SVector{6}([0.000475672318025089; 0.0769068007231223; 0.0769068007231223; 0.281903575411910; 0.281903575411910; 0.281903575411910] ), ] const hexateron252wn = [ 0.0003079935859533376, 0.0003079935859533376, 0.0003079935859533376, 0.0003079935859533376, 0.0003079935859533376, 0.0003079935859533376, 0.003561767803882276, 0.003561767803882276, 0.003561767803882276, 0.003561767803882276, 0.003561767803882276, 0.003561767803882276, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.00954680791959781, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.005380662939910108, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.00013623437539270265, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.002442418968213467, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977, 0.006237301754322977 ] const hexateronAn = [ hexateron1an, hexateron6an, hexateron21an, hexateron56an, hexateron126an, hexateron252an ] const hexateronWn = [ hexateron1wn, hexateron6wn, hexateron21wn, hexateron56wn, hexateron126wn, hexateron252wn ]
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1.953193
39,033
using Test, StatsBase, CUDA, FixedEffects, PooledArrays, CategoricalArrays p1 = repeat(1:5, inner = 2) p2 = repeat(1:5, outer = 2) x = [ 0.5548445405298847 , 0.9444014472663531 , 0.0510866660400604 , 0.9415750229576445 , 0.697755708534771 , 0.9664962514198971 , 0.12752269572311858, 0.4633531422366297 , 0.03341608526498096, 0.1647934493047556] fes = [FixedEffect(p1), FixedEffect(p2)] r_ols = [-0.2015993617092453, 0.2015993617092464, -0.2015993617092463, 0.2015993617092462, -0.2015993617092465, 0.2015993617092467, -0.2015993617092465, 0.2015993617092470, -0.2015993617092468, 0.20159936170924628] (r, iter, conv) = solve_coefficients!(deepcopy(x), fes) (r, iter, conv) = solve_residuals!(deepcopy(x), fes) @test r ≈ r_ols # PooledArrays (r, iter, conv) = solve_residuals!(deepcopy(x), [FixedEffect(PooledArray(p1)), FixedEffect(PooledArray(p2))]) @test r ≈ r_ols # CategorialArrays (r, iter, conv) = solve_residuals!(deepcopy(x), [FixedEffect(categorical(p1)), FixedEffect(categorical(p2))]) @test r ≈ r_ols (c, iter, conv) = solve_residuals!([x x], fes) # test update_weights weights = ones(10) feM = FixedEffects.AbstractFixedEffectSolver{Float64}(fes, Weights(weights), Val{:cpu}) weights = Weights([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) FixedEffects.update_weights!(feM, weights) solve_residuals!(deepcopy(x), feM)[1] ≈ solve_residuals!(deepcopy(x), fes, weights)[1] method_s = [:cpu] if FixedEffects.has_CUDA() push!(method_s, :gpu) end for method in method_s println("$method Float32") local (r, iter, conv) = solve_residuals!(deepcopy(x),fes, method=method, double_precision = false) @test Float32.(r) ≈ Float32.(r_ols) end fe = FixedEffect([1, 2]) @test_throws "FixedEffects must have the same length as y" ỹ = solve_residuals!(ones(100), [fe])
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2.291237
776
# GridWhale module # Copyright (c) 2020 Kronosaur Productions, LLC. All Rights Reserved. # # This file provides functions for interacting with the UI. It is part of the # GridWhale module. module UI end
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3.433333
60
void main(){ String x; }
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2.230769
13
module PowerSystemsUnits import Unitful import Unitful: J, W, hr, 𝐋, 𝐌, 𝐓 using Unitful: @unit, @derived_dimension, @dimension, @refunit, @u_str, uconvert, Quantity export asqtype, fustrip, UnitfulMissing # Power Units @derived_dimension PowerHour 𝐋^2*𝐌*𝐓^-2 @unit Wh "Wh" WattHour 3600J true @derived_dimension ReactivePowerHour 𝐋^2*𝐌*𝐓^-2 @unit VARh "VARh" VARHour 3600J true # Monetary Units @dimension Money "Money" Currency @refunit USD "USD" Currency Money false # Monetary and Power Units @derived_dimension MoneyPerPowerHour Money*𝐋^-2*𝐌^-1*𝐓^2 @unit USDPerMWh "USDPerMWh" DollarPerMegaWattHour USD/(1000000*Wh) false include("utils.jl") # Some gymnastics needed to get this to work at run-time. # Sourced from https://github.com/ajkeller34/UnitfulUS.jl const localunits = Unitful.basefactors const localpromotion = Unitful.promotion function __init__() merge!(Unitful.basefactors, localunits) merge!(Unitful.promotion, localpromotion) # only if you've used @dimension Unitful.register(PowerSystemsUnits) end end
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2.653944
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function grpc_result_or_error(result::T, status::Task, f::Function) where {T<:Union{<:Proto.ProtoType,<:Channel{<:Proto.ProtoType},<:Nothing}} if istaskdone(status) s = fetch(status) if !s.success throw(TypeDBClientException(s.message, gRPCServiceCallException(s.grpc_status,fetch(status).message))) elseif result === nothing throw(TypeDBClientException("something went wrong in gRPC", gRPCServiceCallException(0, "Error not defined by gRPC"))) end end f(result) end
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using ConstrainedDynamics path = "examples/examples_files/atlas_simple.urdf" mech, shapes = Mechanism(path, floating=false, g = -.5)
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2.7
50
using SparseArrays using LinearAlgebra using QuadGK mutable struct modelParameters N c1 c2 K xf τ A1 solDir T tMax solAdj phaseSensNorm end function setParams(N,c1,c2,K,xf,τ,print=false) D = zeros(N,N) Om = zeros(N,N) Id = zeros(N,N) for i in 1:N D[i,i] = 2*damping(c1,c2,i)*i*π Om[i,i] = i*i*π*π Id[i,i] = 1 end A1 = [zeros(N,N) Id;-Om -D] A1 = sparse(A1) params = modelParameters(N,c1,c2,K,xf,τ,A1,false,false,false,false,false) if(print) println("#####################################") println("## Rijke tube Galerkin modes model ##") println("#####################################") println("\n## Parameters ##") println("N = ",params.N) println("c1 = ",params.c1) println("c2 = ",params.c2) println("K = ",params.K) println("xf = ",params.xf) println("τ = ",params.τ) println("#################\n") end return params end function damping(c1,c2,i) return (1/(2π))*(c1*i+c2*sqrt(1/i)) end function flameVel(xd,p) uf = 0 for i in 1:p.N uf = uf + xd[i]*cos(i*π*p.xf) end return uf end function RHS(u,h,p,t) uf = flameVel(h(p,t-p.τ),p) y = p.A1*u for i in 1:p.N y[N+i] = y[N+i] - i*π*p.K*(sqrt(abs(1/3+uf))-sqrt(1/3))*sin(i*π*p.xf) end return y end function pressureVelocity(modes,x,params) # Returns the pressure and velocity from the Galerkin modes at x u = zeros(length(x),) p = zeros(length(x),) for i in 1:params.N u += modes[i]*cos.(i*π*x) p -= modes[i+params.N]*sin.(i*π*x)*γ*Ma/(i*π) end return u,p end function energy(modes,params) # Calculates the non-dimensional acoustic energy per unit volume # from the Galerkin modes E = 0 for i=1:params.N E += modes[i]^2+(modes[params.N+i]/(i*π))^2 end E *= 0.5 return E end ## Linear and Adjoint routines function linHeatRelease(t,p) # Creates the time-delayed system matrix A2 from the direct solution at t. Q = zeros(p.N,p.N) z = p.solDir(t-p.τ) ufb = flameVel(z,p) for i in 1:p.N for j in 1:p.N Q[i,j]=0.5*i*π*p.K*sin(i*π*p.xf)*sign(1/3+ufb)*cos(j*π*p.xf)/sqrt(abs(1/3+ufb)) end end A2 = -[zeros(p.N,p.N) zeros(p.N,p.N);Q zeros(p.N,p.N)] return A2 end function linHeatReleaseMat(base,p) # Creates the linearised matrix A2 from the state base Q = zeros(p.N,p.N) z = base ufb = flameVel(z,p) for i in 1:p.N for j in 1:p.N Q[i,j]=0.5*i*π*p.K*sin(i*π*p.xf)*sign(1/3+ufb)*cos(j*π*p.xf)/sqrt(abs(1/3+ufb)) end end A2 = -[zeros(p.N,p.N) zeros(p.N,p.N);Q zeros(p.N,p.N)] return A2 end function adjTime(tAdj) # Allows us to solve the adjoint forwards in time return params.tMax .- tAdj end function RHSadj(u,h,p,t) A2 = linHeatRelease(adjTime(t-p.τ),p) return p.A1'*u + A2'*h(p,t-p.τ) end # Misc functions function bilinearForm(t,p) # Inner product part innerProduct = dot(p.solAdj(adjTime(t)),p.solDir(t,Val{1})) # Integral part integrand(ξ) = dot(p.solAdj(adjTime(t+ξ+p.τ)),linHeatRelease(t+ξ+p.τ,p)*p.solDir(t+ξ,Val{1})) integral,error = quadgk(integrand, -p.τ, 0., rtol=1e-4) norm = innerProduct + integral return norm, innerProduct, integral end function phaseCouplingFunction(ϕ,ωf,f,p) Tf = 2*π/ωf integrand(s) = dot(p.solAdj(adjTime(ϕ+params.T*s/Tf)),f(s)) integral,error = quadgk(integrand, 0, Tf, rtol=1e-4) Γ = integral / Tf return Γ end function phaseCouplingFunctionmn(ϕ,ωf,f,m,n,p) Tf = 2*π/ωf integrand(s) = dot(p.solAdj(adjTime(ϕ+(n/m)*params.T*s/Tf)),f(s)) integral,error = quadgk(integrand, 0, m*Tf, rtol=1e-4) Γ = integral / (m*Tf) return Γ end
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function ftriple!(f::Vector{Float64},x::Vector{Float64},beta::Vector{Float64},A0::Vector{Float64}, rho::Float64,c0::Vector{Float64},W::Vector{Float64}) # f = zeros(8); # conservation of mass f[1] = x[1] .- x[3] .- x[5] .- x[7]; # total P f[2] = (beta[1].*(sqrt.(x[2]) .- sqrt.(A0[1])) + 0.5.*rho.*(x[1]/x[2]).^2) .- (beta[2].*(sqrt.(x[4]) .- sqrt.(A0[2])) + 0.5.*rho.*(x[3]/x[4]).^2); f[3] = (beta[1].*(sqrt.(x[2]) .- sqrt.(A0[1])) + 0.5.*rho.*(x[1]/x[2]).^2) .- (beta[3].*(sqrt.(x[6]) .- sqrt.(A0[3])) + 0.5.*rho.*(x[5]/x[6]).^2); f[4] = (beta[1].*(sqrt.(x[2]) .- sqrt.(A0[1])) + 0.5.*rho.*(x[1]/x[2]).^2) .- (beta[4].*(sqrt.(x[8]) .- sqrt.(A0[4])) + 0.5.*rho.*(x[7]/x[8]).^2); # Riemann invariants f[5] = (x[1]./x[2]) .+ 4.*(sqrt.(0.5.*beta[1]./rho).*x[2].^0.25 .- c0[1]) .- W[1]; f[6] = (x[3]./x[4]) .- 4.*(sqrt.(0.5.*beta[2]./rho).*x[4].^0.25 .- c0[2]) .- W[2]; f[7] = (x[5]./x[6]) .- 4.*(sqrt.(0.5.*beta[3]./rho).*x[6].^0.25 .- c0[3]) .- W[3]; f[8] = (x[7]./x[8]) .- 4.*(sqrt.(0.5.*beta[4]./rho).*x[8].^0.25 .- c0[4]) .- W[4]; # return f end
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import Printf import Random using LinearAlgebra import DifferentialEquations import Distributions import PyPlot import StatsBase import StatsFuns import Printf import Utilities # https://gitlab.com/RoyCCWang/utilities import Calculus import AdaptiveRKHS import Statistics include("../src/misc/declarations.jl") include("../src/RKHS/RKHS.jl") include("../src/RKHS/kernel.jl") include("../src/RKHS/interpolators.jl") include("../src/warp_map/Rieszanalysis.jl") include("../src/misc/utilities.jl") include("../src/RKHS/querying.jl") PyPlot.close("all") fig_num = 1 Random.seed!(25) D = 3 σ_oracle = 10.0 ρ_oracle = 28.0 β_oracle = 8/3 params_oracle = [σ_oracle; ρ_oracle; β_oracle] # p is state vector. function lorenz!(du::Vector{T}, u, p, t) where T σ = p[1] ρ = p[2] β = p[3] du[1] = σ*(u[2]-u[1]) du[2] = u[1]*(ρ-u[3]) - u[2] du[3] = u[1]*u[2] - β*u[3] return nothing end tspan = (0.0, 100.0) N_obs = 15 #30 #15 time_stamp_range = LinRange(0.1, tspan[end], N_obs) time_stamp = collect(time_stamp_range) u0 = zeros(Float64, D) u0[1] = 1.0 prob = DifferentialEquations.ODEProblem(lorenz!, u0, tspan, params_oracle, dense = true) sol = DifferentialEquations.solve(prob, DifferentialEquations.Tsit5(), reltol = 1e-8, abstol = 1e-8) y_clean = sol.(time_stamp) import Plots Plots.plot(sol, vars = (1,2,3), show = true) @assert 1==2 N_display = 1000 t_display = LinRange(tspan[1], tspan[2], N_display) sol_x1_display = collect( sol.u[i][1] for i = 1:length(sol.u) ) sol_x2_display = collect( sol.u[i][2] for i = 1:length(sol.u) ) #y1_display = collect( y[i][d_select] for i = 1:length(y) ) t_dummy = 1.0 y_clean = y_clean #./ 100 σ = 5.0 #/100 y = collect( y_clean[i] + randn(D_state) .* σ for i = 1:length(y_clean) ) y1_clean = collect( y_clean[i][1] for i = 1:length(y_clean) ) y1_itp, dy1_itp, d2y1_itp = Utilities.setupcubicitp(y1_clean, [ time_stamp], 1.0) dy1_ND = xx->Calculus.gradient(y1_itp, xx)[1] dy1_itp_no_vec = xx->dy1_itp(xx)[1] dy2_itp_no_vec = xx->d2y1_itp(xx)[1] ### try RKHS solution since. θ_oracle = AdaptiveRKHS.GaussianKernel1DType(1.0/100) σ²_oracle = 1e-7 X_oracle = collect( [sol.t[i]] for i = 1:length(sol.t) ) u_array = Vector{Vector{Float64}}(undef,2) u_array[1] = collect( sol.u[i][1] for i = 1:length(sol.u) ) u_array[2] = collect( sol.u[i][2] for i = 1:length(sol.u) ) u_GP = fitDEsolutionGP(θ_oracle, σ²_oracle, X_oracle, u_array) u1_GP = u_GP[1] u2_GP = u_GP[2] du_GP = tt->evalpreyderivativeswrttime(u1_GP, u2_GP, p_oracle, tt) du1_GP = tt->du_GP(tt)[1] du1_GP_ND = tt->Calculus.gradient(u1_GP, tt)[1] #d2u1_GP_ND = tt->Calculus.hessian(u1_GP, tt)[1] d2u1_GP = tt->Calculus.gradient(du1_GP, tt)[1] du2_GP = tt->du_GP(tt)[1] du2_GP_ND = tt->Calculus.gradient(u2_GP, tt)[1] Nq = 500 # xq_range = LinRange(sol.t[1], sol.t[end], Nq) xq_range = LinRange(time_stamp[1], time_stamp[end], Nq) xq = collect( [ xq_range[i] ] for i = 1:length(xq_range) ) title_string = Printf.@sprintf("Predator-prey populations") PyPlot.figure(fig_num) fig_num += 1 PyPlot.plot(sol.t, sol_x1_display, label = "species 1") #PyPlot.plot(sol.t, sol_x2_display, label = "species 2") PyPlot.plot(xq, y1_itp.(xq), label = "y1 itp") PyPlot.plot(xq, u1_GP.(xq), "x", label = "u1 GP") PyPlot.title(title_string) PyPlot.legend() @assert 1==2 title_string = "derivative" PyPlot.figure(fig_num) fig_num += 1 #PyPlot.plot(xq, dy1_ND.(xq), label = "numerical itp") #PyPlot.plot(xq, dy1_itp_no_vec.(xq), "x", label = "analytical itp") PyPlot.plot(xq, du1_GP_ND.(xq), "x", label = "numerical GP") PyPlot.plot(xq, du1_GP.(xq), label = "analytical GP") PyPlot.title(title_string) PyPlot.legend() #@assert 1==2 println("norm(dy1_ND.(xq) - dy1_itp_no_vec.(xq)) = ", norm(dy1_ND.(xq) - dy1_itp_no_vec.(xq))) println() # fit RKHS. θ = AdaptiveRKHS.GaussianKernel1DType(1.0/100) σ²_RKHS_initial = sqrt(σ) #σ^2 #1e-3 X = collect( [time_stamp_vec[i]] for i = 1:length(time_stamp_vec) ) y1 = collect( y[i][1] for i = 1:length(y) ) η = AdaptiveRKHS.RKHSProblemType( zeros(Float64,length(X)), X, θ, σ²_RKHS_initial) offset_c = Statistics.mean(y1) y1_c = y1 .- offset_c AdaptiveRKHS.fitRKHS!(η, y1_c) fq = xx->sum( η.c[i]*AdaptiveRKHS.evalkernel(xx, η.X[i], η.θ) for i = 1:length(η.X) )+offset_c # adaptive kernel. θ_canonical = AdaptiveRKHS.GaussianKernel1DType(1.0/1000) #warp map. # d2f_normed = xx->norm(Calculus.hessian(f_joint,xx)) # d2f_x1 = xx->Calculus.hessian(f_joint,xx)[1,1] # d2f_x2 = xx->Calculus.hessian(f_joint,xx)[2,2] # d2f_sum = xx->sum(Calculus.hessian(f_joint,xx)) #d2y1 = xx->Calculus.hessian(fq,xx)[1] #d2y1 = xx->Calculus.hessian(y1_itp,xx)[1] d2y1 = xx->Calculus.hessian(u1_GP,xx)[1] #ϕ = d2y1 #getwarpmap(ϕ) ϕ = d2u1_GP title_string = "second derivative" PyPlot.figure(fig_num) fig_num += 1 #PyPlot.plot(xq, d2u1_GP_ND.(xq), label = "d2u1_GP_ND") PyPlot.plot(xq, d2u1_GP.(xq), "^", label = "d2u1_GP") PyPlot.title(title_string) PyPlot.legend() # adaptive RKHS. M = 30 #σ²_RKHS_array = sqrt(σ) .* ones(Float64, M) # #1.0 #1e-1 #1e-3 σ²_RKHS_array = collect( LinRange(sqrt(σ), σ^2, M) ) amp_factor_array = 100.0 .* ones(Float64, M) fq_array, c_array, θ_array = iterateGP(fq, ϕ, θ_canonical, σ²_RKHS_array, amp_factor_array, y1, X) #### visualize. fq_xq = fq.(xq) PyPlot.figure(fig_num) fig_num += 1 PyPlot.plot(time_stamp, y1, "o", label = "observed") PyPlot.plot(xq, fq_xq, label = "canonical RKHS") PyPlot.plot(xq, fq_array[1].(xq), label = "adaptive RKHS") PyPlot.plot(xq, fq_array[2].(xq), label = "adaptive RKHS 2") PyPlot.plot(xq, fq_array[3].(xq), label = "adaptive RKHS 3") PyPlot.plot(xq, fq_array[end].(xq), label = "adaptive RKHS end") PyPlot.plot(sol.t, sol_x1_display, "--", label = "species 1") #PyPlot.plot(sol.t, sol_x2_display, label = "species 2") title_string = "1-D RKHS demo" PyPlot.title(title_string) PyPlot.legend()
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""" $(SIGNATURES) Macro to set common fields in structs. See https://discourse.julialang.org/t/julia-learning-macros-metaprogramming/45753/3 # Example ``` @common_fields set1 begin x :: Int y :: Float64 end struct Foo @set1 z end ``` """ macro common_fields(name, definition) return quote macro $(esc(name))() esc($(Expr(:quote, definition))) end end end # ----------------
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using PyPlot pospart(x) = ( x > 0 ? x : zero(x) ) function K2(x, y, a, b) pt = [a, (a+b)/2, b] ℓ2 = (x-pt[1])*(x-pt[3])/((pt[2]-pt[1])*(pt[2]-pt[3])) ℓ3 = (x-pt[1])*(x-pt[2])/((pt[3]-pt[1])*(pt[3]-pt[2])) Qπ = pospart(pt[2]-y)^2 * ℓ2 / 2 + (b-y)^2 * ℓ3 / 2 return Qπ - pospart(x-y)^2/2 end a = -1.0 b = 1.0 N = 200 x = LinRange(a, b, N+1) y = LinRange(a, b, N+1) z = Float64[ K2(x[k], y[j], a, b) for j=1:N+1, k=1:N+1 ] fig = figure(1) surf(x, y, z, cstride=4, rstride=4, cmap="jet", linewidth=0.25) xlabel(L"x") ylabel(L"y") ax = gca() ax[:view_init](elev=40,azim=-100) savefig("Quadratic_PeanoK_3d.pdf") figure(2) contour(x, y, z, 12) colorbar() xlabel(L"x") ylabel(L"y") grid(true) savefig("Quadratic_PeanoK_contour.pdf")
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# reference implementation on the CPU # note that most of the code in this file serves to define a functional array type, # the actual implementation of GPUArrays-interfaces is much more limited. module JLArrays export JLArray, jl using GPUArrays using Adapt # # Device functionality # ## device properties struct JLDevice <: AbstractGPUDevice end const MAXTHREADS = 256 ## execution struct JLBackend <: AbstractGPUBackend end mutable struct JLKernelContext <: AbstractKernelContext blockdim::Int griddim::Int blockidx::Int threadidx::Int localmem_counter::Int localmems::Vector{Vector{Array}} end function JLKernelContext(threads::Int, blockdim::Int) blockcount = prod(blockdim) lmems = [Vector{Array}() for i in 1:blockcount] JLKernelContext(threads, blockdim, 1, 1, 0, lmems) end function JLKernelContext(ctx::JLKernelContext, threadidx::Int) JLKernelContext( ctx.blockdim, ctx.griddim, ctx.blockidx, threadidx, 0, ctx.localmems ) end struct Adaptor end jlconvert(arg) = adapt(Adaptor(), arg) # FIXME: add Ref to Adapt.jl (but make sure it doesn't cause ambiguities with CUDAnative's) struct JlRefValue{T} <: Ref{T} x::T end Base.getindex(r::JlRefValue) = r.x Adapt.adapt_structure(to::Adaptor, r::Base.RefValue) = JlRefValue(adapt(to, r[])) function GPUArrays.gpu_call(::JLBackend, f, args, threads::Int, blocks::Int; name::Union{String,Nothing}) ctx = JLKernelContext(threads, blocks) device_args = jlconvert.(args) tasks = Array{Task}(undef, threads) @disallowscalar for blockidx in 1:blocks ctx.blockidx = blockidx for threadidx in 1:threads thread_ctx = JLKernelContext(ctx, threadidx) tasks[threadidx] = @async f(thread_ctx, device_args...) # TODO: require 1.3 and use Base.Threads.@spawn for actual multithreading # (this would require a different synchronization mechanism) end for t in tasks fetch(t) end end return end ## executed on-device # array type struct JLDeviceArray{T, N} <: AbstractDeviceArray{T, N} data::Array{T, N} dims::Dims{N} function JLDeviceArray{T,N}(data::Array{T, N}, dims::Dims{N}) where {T,N} new(data, dims) end end Base.size(x::JLDeviceArray) = x.dims @inline Base.getindex(A::JLDeviceArray, index::Integer) = getindex(A.data, index) @inline Base.setindex!(A::JLDeviceArray, x, index::Integer) = setindex!(A.data, x, index) # indexing for f in (:blockidx, :blockdim, :threadidx, :griddim) @eval GPUArrays.$f(ctx::JLKernelContext) = ctx.$f end # memory function GPUArrays.LocalMemory(ctx::JLKernelContext, ::Type{T}, ::Val{dims}, ::Val{id}) where {T, dims, id} ctx.localmem_counter += 1 lmems = ctx.localmems[blockidx(ctx)] # first invocation in block data = if length(lmems) < ctx.localmem_counter lmem = fill(zero(T), dims) push!(lmems, lmem) lmem else lmems[ctx.localmem_counter] end N = length(dims) JLDeviceArray{T,N}(data, tuple(dims...)) end # synchronization @inline function GPUArrays.synchronize_threads(::JLKernelContext) # All threads are getting started asynchronously, so a yield will yield to the next # execution of the same function, which should call yield at the exact same point in the # program, leading to a chain of yields effectively syncing the tasks (threads). yield() return end # # Host abstractions # struct JLArray{T, N} <: AbstractGPUArray{T, N} data::Array{T, N} dims::Dims{N} function JLArray{T,N}(data::Array{T, N}, dims::Dims{N}) where {T,N} @assert isbitstype(T) "JLArray only supports bits types" new(data, dims) end end ## constructors # type and dimensionality specified, accepting dims as tuples of Ints JLArray{T,N}(::UndefInitializer, dims::Dims{N}) where {T,N} = JLArray{T,N}(Array{T, N}(undef, dims), dims) # type and dimensionality specified, accepting dims as series of Ints JLArray{T,N}(::UndefInitializer, dims::Integer...) where {T,N} = JLArray{T,N}(undef, dims) # type but not dimensionality specified JLArray{T}(::UndefInitializer, dims::Dims{N}) where {T,N} = JLArray{T,N}(undef, dims) JLArray{T}(::UndefInitializer, dims::Integer...) where {T} = JLArray{T}(undef, convert(Tuple{Vararg{Int}}, dims)) # empty vector constructor JLArray{T,1}() where {T} = JLArray{T,1}(undef, 0) Base.similar(a::JLArray{T,N}) where {T,N} = JLArray{T,N}(undef, size(a)) Base.similar(a::JLArray{T}, dims::Base.Dims{N}) where {T,N} = JLArray{T,N}(undef, dims) Base.similar(a::JLArray, ::Type{T}, dims::Base.Dims{N}) where {T,N} = JLArray{T,N}(undef, dims) Base.copy(a::JLArray{T,N}) where {T,N} = JLArray{T,N}(copy(a.data), size(a)) ## array interface Base.elsize(::Type{<:JLArray{T}}) where {T} = sizeof(T) Base.size(x::JLArray) = x.dims Base.sizeof(x::JLArray) = Base.elsize(x) * length(x) ## interop with Julia arrays JLArray{T,N}(x::AbstractArray{<:Any,N}) where {T,N} = JLArray{T,N}(convert(Array{T}, x), size(x)) # underspecified constructors JLArray{T}(xs::AbstractArray{S,N}) where {T,N,S} = JLArray{T,N}(xs) (::Type{JLArray{T,N} where T})(x::AbstractArray{S,N}) where {S,N} = JLArray{S,N}(x) JLArray(A::AbstractArray{T,N}) where {T,N} = JLArray{T,N}(A) # idempotency JLArray{T,N}(xs::JLArray{T,N}) where {T,N} = xs jl(xs) = adapt(JLArray, xs) Adapt.adapt_storage(::Type{JLArray}, xs::AbstractArray) = convert(JLArray, xs) ## conversions Base.convert(::Type{T}, x::T) where T <: JLArray = x function Base._reshape(parent::JLArray, dims::Dims) n = length(parent) prod(dims) == n || throw(DimensionMismatch("parent has $n elements, which is incompatible with size $dims")) return JLArray{eltype(parent),length(dims)}(reshape(parent.data, dims), dims) end function Base._reshape(parent::JLArray{T,1}, dims::Tuple{Int}) where T n = length(parent) prod(dims) == n || throw(DimensionMismatch("parent has $n elements, which is incompatible with size $dims")) return parent end ## broadcast using Base.Broadcast: BroadcastStyle, Broadcasted struct JLArrayStyle{N} <: AbstractGPUArrayStyle{N} end JLArrayStyle(::Val{N}) where N = JLArrayStyle{N}() JLArrayStyle{M}(::Val{N}) where {N,M} = JLArrayStyle{N}() BroadcastStyle(::Type{JLArray{T,N}}) where {T,N} = JLArrayStyle{N}() # Allocating the output container Base.similar(bc::Broadcasted{JLArrayStyle{N}}, ::Type{T}) where {N,T} = similar(JLArray{T}, axes(bc)) Base.similar(bc::Broadcasted{JLArrayStyle{N}}, ::Type{T}, dims...) where {N,T} = JLArray{T}(undef, dims...) ## math for f in (:cos, :sin, :sqrt, :log) @eval GPUArrays.$f(ctx::JLKernelContext, x) = $f(x) end ## memory operations function Base.copyto!(dest::Array{T}, d_offset::Integer, source::JLArray{T}, s_offset::Integer, amount::Integer) where T @boundscheck checkbounds(dest, d_offset+amount-1) @boundscheck checkbounds(source, s_offset+amount-1) copyto!(dest, d_offset, source.data, s_offset, amount) end function Base.copyto!(dest::JLArray{T}, d_offset::Integer, source::Array{T}, s_offset::Integer, amount::Integer) where T @boundscheck checkbounds(dest, d_offset+amount-1) @boundscheck checkbounds(source, s_offset+amount-1) copyto!(dest.data, d_offset, source, s_offset, amount) dest end function Base.copyto!(dest::JLArray{T}, d_offset::Integer, source::JLArray{T}, s_offset::Integer, amount::Integer) where T @boundscheck checkbounds(dest, d_offset+amount-1) @boundscheck checkbounds(source, s_offset+amount-1) copyto!(dest.data, d_offset, source.data, s_offset, amount) dest end ## fft using AbstractFFTs # defining our own plan type is the easiest way to pass around the plans in FFTW interface # without ambiguities struct FFTPlan{T} p::T end AbstractFFTs.plan_fft(A::JLArray; kw_args...) = FFTPlan(plan_fft(A.data; kw_args...)) AbstractFFTs.plan_fft!(A::JLArray; kw_args...) = FFTPlan(plan_fft!(A.data; kw_args...)) AbstractFFTs.plan_bfft!(A::JLArray; kw_args...) = FFTPlan(plan_bfft!(A.data; kw_args...)) AbstractFFTs.plan_bfft(A::JLArray; kw_args...) = FFTPlan(plan_bfft(A.data; kw_args...)) AbstractFFTs.plan_ifft!(A::JLArray; kw_args...) = FFTPlan(plan_ifft!(A.data; kw_args...)) AbstractFFTs.plan_ifft(A::JLArray; kw_args...) = FFTPlan(plan_ifft(A.data; kw_args...)) function Base.:(*)(plan::FFTPlan, A::JLArray) x = plan.p * A.data JLArray(x) end ## Random using Random # JLArray only supports generating random numbers with the GPUArrays RNG Random.rand!(A::JLArray) = Random.rand!(GPUArrays.default_rng(JLArray), A) Random.randn!(A::JLArray) = Random.randn!(GPUArrays.default_rng(JLArray), A) ## GPUArrays interfaces GPUArrays.device(x::JLArray) = JLDevice() GPUArrays.backend(::Type{<:JLArray}) = JLBackend() Adapt.adapt_storage(::Adaptor, x::JLArray{T,N}) where {T,N} = JLDeviceArray{T,N}(x.data, x.dims) GPUArrays.unsafe_reinterpret(::Type{T}, A::JLArray, size::Tuple) where T = reshape(reinterpret(T, A.data), size) function GPUArrays.mapreducedim!(f, op, R::JLArray, A::Union{AbstractArray,Broadcast.Broadcasted}; init=nothing) if init !== nothing fill!(R, init) end @allowscalar Base.reducedim!(op, R.data, map(f, A)) end const GLOBAL_RNG = Ref{Union{Nothing,GPUArrays.RNG}}(nothing) function GPUArrays.default_rng(::Type{<:JLArray}) if GLOBAL_RNG[] == nothing N = MAXTHREADS state = JLArray{NTuple{4, UInt32}}(undef, N) rng = GPUArrays.RNG(state) Random.seed!(rng) GLOBAL_RNG[] = rng end GLOBAL_RNG[] end ## LinearAlgebra using LinearAlgebra for TR in (UpperTriangular, LowerTriangular, UnitUpperTriangular, UnitLowerTriangular) @eval LinearAlgebra.ldiv!(x::$TR{T,<:JLArray{T,2}}, y::JLArray{T,2}) where T<:Union{Complex{Float32}, Complex{Float64}, Float32, Float64} = JLArray(LinearAlgebra.ldiv!($TR(parent(x).data),y.data)) @eval LinearAlgebra.rdiv!(x::JLArray{T,2}, y::$TR{T,<:JLArray{T,2}}) where T<:Union{Complex{Float32}, Complex{Float64}, Float32, Float64} = JLArray(LinearAlgebra.rdiv!(x.data,$TR(parent(y).data))) end end
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using MeasureBase: logdensityof, Likelihood export RadioLikelihood, logdensityof, MultiRadioLikelihood using LinearAlgebra struct RadioLikelihood{T,A} <: MB.AbstractMeasure lklhds::T ac::A end struct MultiRadioLikelihood{L} <: MB.AbstractMeasure lklhds::L end """ `MultiRadioLikelihood(lklhd1, lklhd2, ...)` Combines multiple likelihoods into one object that is useful for fitting multiple days/frequencies. ```julia lklhd1 = RadioLikelihood(dcphase1, dlcamp1) lklhd2 = RadioLikelihood(dcphase2, dlcamp2) lklhd = MultiRadioLikelihood(lklhd1, lklhd2) ``` """ MultiRadioLikelihood(lklhds::RadioLikelihood...) = MultiRadioLikelihood(lklhds) function MB.logdensityof(lklhds::MultiRadioLikelihood, m) sum(Base.Fix2(logdensityof, m), lklhds.lklhds) end """ `RadioLikelihood(data1, data2, ...)` Forms a radio likelihood from a set of data products. These data products must share the same array data/configuration. If you want to form a likelihood from multiple arrays such as when fitting different wavelengths or days, you can combine them using `MultiRadioLikelihood` ```julia lklhd1 = RadioLikelihood(dcphase1, dlcamp1) lklhd2 = RadioLikelihood(dcphase2, dlcamp2) lklhd = MultiRadioLikelihood(lklhd1, lklhd2) ``` """ function RadioLikelihood(data::EHTObservation...) ls = Tuple(map(makelikelihood, data)) acs = arrayconfig.(data) #@argcheck acs[1] == acs[2] RadioLikelihood{typeof(ls), typeof(acs[1])}(ls, acs[1]) end function RadioLikelihood(data::MT.Likelihood...) return RadioLikelihood{typeof(data), Nothing}(data, nothing) end function Base.show(io::IO, d::RadioLikelihood{T}) where {T} println(io, "RadioLikelihood{$T}") println(io, "\tNumber of data products: ", length(d.lklhds)) end """ `logclosure_amplitudes(vis, ac::ArrayConfiguration)` Compute the log-closure amplitudes for a set of visibilities and an array configuration # Notes This uses a closure design matrix for the computation. """ function logclosure_amplitudes(vis::AbstractArray{<:Complex}, ac::ArrayConfiguration) lva = log.(abs.(vis)) return ac.designmat*lva end """ `closure_phases(vis, ac::ArrayConfiguration)` Compute the closure phases for a set of visibilities and an array configuration # Notes This uses a closure design matrix for the computation. """ function closure_phases(vis::AbstractArray{<:Complex}, ac::ArrayConfiguration) ph = angle.(vis) return ac.designmat*ph end amplitudes(vis::AbstractArray{<:Complex}, ac::ArrayConfiguration) = abs.(vis) phase(vis::AbstractArray{<:Complex}) = angle.(vis) function MB.logdensityof(d::RadioLikelihood, m::ComradeBase.AbstractModel) ac = d.ac vis = visibilities(m, ac) return logdensityof(d, vis) end function MB.logdensityof(d::RadioLikelihood, vis::AbstractArray) # We use a for loop here since Zygote plays nice with this acc = logdensityof(first(d.lklhds), vis) @inbounds for l in d.lklhds[begin+1:end] acc += logdensityof(l, vis) end return acc end #function MB.logdensityof(d::RadioLikelihood, m::ComradeBase.AbstractModel) # acc = logdensityof(first(d.lklhds), m) # @inbounds for i in 2:length(d.lklhds) # acc += logdensityof(d.lklhds[i], m) # end # return acc #end function makelikelihood(data::Comrade.EHTObservation{<:Real, <:Comrade.EHTVisibilityDatum}) errinv = inv.(data[:error]) vis = StructArray{Complex{eltype(data[:visr])}}((data[:visr],data[:visi])) ℓ = Likelihood(vis) do (μ, ) ComplexNormal{(:μ, :τ)}(μ, errinv) end return ℓ end function makelikelihood(data::Comrade.EHTObservation{<:Real, <:Comrade.EHTVisibilityAmplitudeDatum}) τ = inv.(data[:error]) amp = getdata(data, :amp) ℓ = Likelihood(amp) do (μ, ) AmpNormal{(:μ, :τ)}(abs.(μ), τ) end return ℓ end function makelikelihood(data::Comrade.EHTObservation{<:Real, <:Comrade.EHTLogClosureAmplitudeDatum}) dmat = data.config.designmat τ = inv.(data[:error]) #covm = σvis*dmat*transpose(σvis) #C = Cholesky(Symmetric(covm)) #σ = C.L f = Base.Fix2(logclosure_amplitudes, data.config) amp = data[:amp] ℓ = Likelihood(amp) do (μ,) AmpNormal{(:μ, :τ)}(f(μ), τ) end #return AmpNormal{(:μ, :τ)}(amp, τ) return ℓ end function makelikelihood(data::Comrade.EHTObservation{<:Real, <:Comrade.EHTClosurePhaseDatum}) τ = inv.(getdata(data, :error)).^2 f = Base.Fix2(closure_phases, data.config) phase = data[:phase] ℓ = Likelihood(phase) do (μ,) CPVonMises{(:μ, :κ)}(f(μ), τ) end return ℓ end
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mutable struct BlackCalculator{S <: StrikedTypePayoff} payoff::S strike::Float64 forward::Float64 stdDev::Float64 discount::Float64 variance::Float64 d1::Float64 d2::Float64 alpha::Float64 beta::Float64 DalphaDd1::Float64 DbetaDd2::Float64 n_d1::Float64 cum_d1::Float64 n_d2::Float64 cum_d2::Float64 x::Float64 DxDs::Float64 DxDstrike::Float64 end function BlackCalculator(p::S, fwd::Float64, stdDev::Float64, disc::Float64) where {S <: StrikedTypePayoff} calc = BlackCalculator{S}(p, p.strike, fwd, stdDev, disc, stdDev * stdDev, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) initialize!(calc, p) return calc end function gen_addl_black_vars!(calc::BlackCalculator, optType::Call) calc.alpha = calc.cum_d1 calc.DalphaDd1 = calc.n_d1 calc.beta = -calc.cum_d2 calc.DbetaDd2 = -calc.n_d2 return calc end function gen_addl_black_vars!(calc::BlackCalculator, optType::Put) calc.alpha = -1.0 + calc.cum_d1 calc.DalphaDd1 = calc.n_d1 calc.beta = 1.0 - calc.cum_d2 calc.DbetaDd2 = -calc.n_d2 return calc end function initialize!(calc::BlackCalculator, p::StrikedTypePayoff) calc.strike >= 0.0 || error("strike $strike must be non-negative") calc.forward > 0.0 || error("forward $forward must be positive") calc.stdDev >= 0.0 || error("stdDev $stdDev must be non-negative") calc.discount > 0.0 || error("discount $discount must be positive") if calc.stdDev >= eps() if is_close(calc.strike, 0.0) calc.d1 = JQ_MAX calc.d2 = JQ_MAX calc.cum_d1 = 1.0 calc.cum_d2 = 1.0 calc.n_d1 = 0.0 calc.n_d2 = 0.0 else calc.d1 = log(calc.forward / calc.strike) / calc.stdDev + 0.5 * calc.stdDev calc.d2 = calc.d1 - calc.stdDev w = Normal() # Distributions calc.cum_d1 = cdf(w, calc.d1) calc.cum_d2 = cdf(w, calc.d2) calc.n_d1 = distribution_derivative(w, calc.d1) calc.n_d2 = distribution_derivative(w, calc.d2) end else if is_close(calc.forward, calc.strike) calc.d1 = 0.0 calc.d2 = 0.0 calc.cum_d1 = 0.5 calc.cum_d2 = 0.5 calc.n_d1 = M_SQRT_2 * M_1_SQRTPI calc.n_d2 = M_SQRT_2 * M_1_SQRTPI elseif calc.forward > calc.strike calc.d1 = JQ_MAX calc.d2 = JQ_MAX calc.cum_d1 = 1.0 calc.cum_d2 = 1.0 calc.n_d1 = 0.0 calc.n_d2 = 0.0 else calc.d1 = JQ_MIN calc.d2 = JQ_MIN calc.cum_d1 = 0.0 calc.cum_d2 = 0.0 calc.n_d1 = 0.0 calc.n_d2 = 0.0 end end calc.x = calc.strike calc.DxDstrike = 1.0 calc.DxDs = 0.0 gen_addl_black_vars!(calc, p.optionType) return calc end value(calc::BlackCalculator) = calc.discount * (calc.forward * calc.alpha + calc.x * calc.beta) itm_cash_probability(calc::BlackCalculator) = calc.cum_d2 itm_asset_probability(calc::BlackCalculator) = calc.cum_d1 function delta(calc::BlackCalculator, spot::Float64) spot > 0.0 || error("positive spot value required. $spot not allowed") DfowardDs = calc.forward / spot temp = calc.stdDev * spot DalphaDs = calc.DalphaDd1 / temp DbetaDs = calc.DbetaDd2 / temp temp2 = DalphaDs * calc.forward + calc.alpha * DfowardDs + DbetaDs * calc.x + calc.beta * calc.DxDs return calc.discount * temp2 end function delta_forward(calc::BlackCalculator) temp = calc.stdDev * calc.forward DalphaDforward = calc.DalphaDd1 / temp DbetaDforward = calc.DbetaDd2 / temp temp2 = DalphaDforward * calc.forward + calc.alpha + DbetaDforward * calc.x return calc.discount * temp2 end function elasticity(calc::BlackCalculator, spot::Float64) val = value(calc) del = delta(calc, spot) if val > eps() return del / val * spot elseif abs(del) < eps() return 0.0 elseif del > 0.0 return JQ_MAX else return JQ_MIN end end function gamma(calc::BlackCalculator, spot::Float64) spot > 0.0 || error("positive spot value required. $spot not allowed") DforwardDs = calc.forward / spot temp = calc.stdDev * spot DalphaDs = calc.DalphaDd1 / temp DbetaDs = calc.DbetaDd2 / temp D2alphaDs2 = -DalphaDs / spot * (1 + calc.d1 / calc.stdDev) D2betaDs2 = -DbetaDs / spot * (1 + calc.d2 / calc.stdDev) temp2 = D2alphaDs2 * calc.forward + 2.0 * DalphaDs * DforwardDs + D2betaDs2 * calc.x + 2.0 * DbetaDs * calc.DxDs return calc.discount * temp2 end function rho(calc::BlackCalculator, mat::Float64) mat >= 0.0 || error("negative maturity not allowed") DalphaDr = calc.DalphaDd1 / calc.stdDev DbetaDr = calc.DbetaDd2 / calc.stdDev temp = DalphaDr * calc.forward + calc.alpha * calc.forward + DbetaDr * calc.x return mat * (calc.discount * temp - value(calc)) end function dividend_rho(calc::BlackCalculator, mat::Float64) mat >= 0.0 || error("negative maturity not allowed") DalphaDq = -calc.DalphaDd1 / calc.stdDev DbetaDq = -calc.DbetaDd2 / calc.stdDev temp = DalphaDq * calc.forward - calc.alpha * calc.forward + DbetaDq * calc.x return mat * calc.discount * temp end function vega(calc::BlackCalculator, mat::Float64) temp = log(calc.strike / calc.forward) / calc.variance DalphaDsigma = calc.DalphaDd1 * (temp + 0.5) DbetaDsigma = calc.DbetaDd2 * (temp - 0.5) temp2 = DalphaDsigma * calc.forward + DbetaDsigma * calc.x return calc.discount * sqrt(mat) * temp2 end theta_per_day(calc::BlackCalculator, spot::Float64, mat::Float64) = theta(calc, spot, mat) / 365.0 function theta(calc::BlackCalculator, spot::Float64, mat::Float64) if mat < 0.0 println("WARN: Maturity is negative") return -1.0 end is_close(mat, 0.0) && return 0.0 return -( log(calc.discount) * value(calc) + log(calc.forward / spot) * spot * delta(calc, spot) + 0.5 * calc.variance * spot * spot * gamma(calc, spot)) / mat end function strike_sensitivity(calc::BlackCalculator) temp = calc.stdDev * calc.strike DalphaDstrike = -calc.DalphaDd1 / temp DbetaDstrike = -calc.DbetaDd2 / temp temp2 = DalphaDstrike * calc.forward + DbetaDstrike * calc.x + calc.beta * calc.DxDstrike return calc.discount * temp2 end
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module ModuleReplicable using Test using Hyperspecialize global A = Set{Type}([]) struct Weeble <: Real x::Int end f(::Real) = false using Qux import Qux.h Qux.h(::Weeble, ::Real) = true @testset "Module Replicable" begin # First, a test for module local widening @concretize TypicalTag [] @replicable f(::@hyperspecialize(TypicalTag)) = true @test !f(1) @test !f(1.0) @test !Qux.f(1) @test !Qux.f(1.0) @widen TypicalTag Int @widen Qux.TypicalTag Float64 @test f(1) @test !f(1.0) @test !Qux.f(1) @test Qux.f(1.0) # Do all methods corresponding to a type get widened? @test !Qux.g(1) @test Qux.g(1.0) # Can we resolve ambiguities? @test_throws MethodError h(Weeble(1), Wobble(2)) @replicable h(::Weeble, ::@hyperspecialize(Real)) = true @test h(Weeble(1), Wobble(2)) end end #module
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using IncrementalInference, KernelDensityEstimate fg = emptyFactorGraph() N=100 doors = reshape(Float64[-100.0;0.0;100.0;300.0],1,4) pd = kde!(doors,[3.0]) pd = resample(pd,N); bws = getBW(pd)[:,1] doors2 = getPoints(pd); v1 = addNode!(fg,:x0,doors,N=N) f1 = addFactor!(fg,[v1],Obsv2( doors2, reshape(bws,1,1), [1.0])) #, samplefnc=getSample tem = 2.0*randn(1,N)+getVal(v1)+50.0 v2 = addNode!(fg,:x2, tem, N=N) addFactor!(fg, [:x0; :x2], Odo(50.0*ones(1,1),2.0*ones(1,1),[1.0])) #, samplefnc=getSample # addFactor!(fg, [v1;v2], Odo(50.0*ones(1,1),[2.0]',[1.0])) #, samplefnc=getSample # monocular sighting would look something like #addFactor!(fg, Mono, [:x3,:l1], [14.0], [1.0], [1.0]) #addFactor!(fg, Mono, [:x4,:l1], [11.0], [1.0], [1.0]) v3=addNode!(fg,:x3,4.0*randn(1,N)+getVal(v2)+50.0, N=N) addFactor!(fg,[v2;v3],Odo(50.0*ones(1,1),4.0*ones(1,1),[1.0])) #, samplefnc=getSample f2 = addFactor!(fg,[v3], Obsv2(doors2, bws', [1.0])) v4=addNode!(fg,:x4,2.0*randn(1,N)+getVal(v3)+50.0, N=N) addFactor!(fg,[v3;v4],Odo(50.0*ones(1,1),2.0*ones(1,1),[1.0])) #, samplefnc=getSample l1=addNode!(fg, :l1, 0.5*randn(1,N)+getVal(v3)+64.0, N=N) addFactor!(fg, [v3,l1], Ranged([64.0],[0.5],[1.0])) #, samplefnc=getSample addFactor!(fg, [v4,l1], Ranged([16.0],[0.5],[1.0])) #, samplefnc=getSample v5=addNode!(fg,:x5,2.0*randn(1,N)+getVal(v4)+50.0, N=N) addFactor!(fg,[v4;v5],Odo(50.0*ones(1,1),2.0*ones(1,1),[1.0])) #, samplefnc=getSample v6=addNode!(fg,:x6,1.25*randn(1,N)+getVal(v5)+40.0, N=N) addFactor!(fg,[v5;v6],Odo(40.0*ones(1,1),1.20*ones(1,1),[1.0])) #, samplefnc=getSample v7=addNode!(fg,:x7,2.0*randn(1,N)+getVal(v6) +60.0, N=N) addFactor!(fg,[v6;v7],Odo(60.0*ones(1,1),2.0*ones(1,1),[1.0])) #, samplefnc=getSample f3 = addFactor!(fg,[v7], Obsv2(doors, reshape(bws,1,1), [1.0])) #, samplefnc=getSample # HMM computed ground truth, extended for 7 poses with landmark gt = Dict{Symbol, Array{Float64,2}}() gt[:x0]=reshape(Float64[0.0;1.97304 ],2,1) # -0.0342366 gt[:x2]=reshape(Float64[50.0; 2.83153 ],2,1) # 49.8797 gt[:x3]=reshape(Float64[100.0; 1.65557 ],2,1) # 99.8351 gt[:x4]=reshape(Float64[150.0; 1.64945 ],2,1) # 148.637 gt[:x5]=reshape(Float64[200.0; 1.77992 ],2,1) # 198.62 gt[:x6]=reshape(Float64[240.0; 2.20466 ],2,1) # 238.492 gt[:x7]=reshape(Float64[300.0; 2.14353 ],2,1) # 298.467 gt[:l1]=reshape(Float64[165.0; 1.17284 ],2,1) # 164.102 tree = prepBatchTree!(fg, drawpdf=false); # list vertices in fg @show xx,ll = ls(fg) # do belief propagation inference over tree once # using recursive single core approach (better stack trace for development) # inferOverTreeR!(fg, tree) inferOverTreeR!(fg, tree, N=N, dbg=true) # # test multi-processor solve (operational fast solving) inferOverTree!(fg, tree) # inferOverTree!(fg, tree, dbg=true) #
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using Photometry.Aperture: bounds @testset "Apertures" begin ap_rect = RectangularAperture(50, 40, 10, 10, 0) @test center(ap_rect) == (50, 40) @test bounds(ap_rect) == (45, 55, 35, 45) @test size(ap_rect) == (11, 11) @test size(ap_rect, 1) == 11 @test RectangularAperture([50, 40], 10, 10, 0) == ap_rect ap_ann = RectangularAnnulus(50, 40, 5, 10, 10, 0) @test bounds(ap_ann) == (45, 55, 35, 45) @test size(ap_ann) == (11, 11) @test RectangularAnnulus([50, 40], 5, 10, 10, 0) == ap_ann end @testset "Rectangle Aperture" begin ap0 = RectangularAperture(0, 0, 0, 0, 0) @test sprint(show, ap0) == "RectangularAperture(0, 0, w=0, h=0, θ=0°)" ap1 = RectangularAperture(0, 0, 1, 1, 0) @test sprint(show, ap1) == "RectangularAperture(0, 0, w=1, h=1, θ=0°)" end @testset "Rectangle Annulus" begin ap1 = RectangularAnnulus(0, 0, 1, 1, 1, 0) @test center(ap1) == (0, 0) @test sprint(show, ap1) == "RectangularAnnulus(0.0, 0.0, w_in=1.0, w_out=1.0, h_in=1.0, h_out=1.0, θ=0.0°)" end
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# TODO: have a doc for ops here. #"@knet function dot(w,x) is matrix multiplication." #"@knet function input() fetches the next network input." # ### mul2 element-wise multiplication: abstract Op # Each op must provide the following: # back_reads_y (tosave) # back_reads_x (tosave) # ninputs (netcomp1) # infersize (used by netinit) # canoverwrite (used by initforw) # ysize, loss (not used any more?) forw(l::Op, y, x...; o...) = error("$(typeof(l)) has not implemented forw") back(l::Op, dy, dx...; o...) = error("$(typeof(l)) has not implemented back") loss(l::Op, dy, y; o...) = error("$(typeof(l)) has not implemented loss") ninputs(l::Op) = error("$(typeof(l)) has not implemented ninputs") ysize(l::Op, x...) = error("$(typeof(l)) has not implemented ysize") canoverwrite(l::Op) = error("$(typeof(l)) has not implemented canoverwrite") back_reads_x(l::Op) = error("$(typeof(l)) has not implemented back_reads_x") back_reads_y(l::Op) = error("$(typeof(l)) has not implemented back_reads_y") # Base.eltype(::Op) = nothing function Base.isequal(a::Op,b::Op) typeof(a)==typeof(b) || return false for n in fieldnames(a) if isdefined(a,n) && isdefined(b,n) isequal(a.(n), b.(n)) || return false elseif isdefined(a,n) || isdefined(b,n) return false end end return true end ### DEAD CODE # Each Op implements some common methods, stubs are given below. # forw takes input x and returns output y, possibly setting some state. # back takes dy, the loss gradient wrt y, calculates loss gradient wrt # parameters and optionally returns dx, the loss gradient wrt x. # Some layers overwrite their inputs.
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2.633127
646
""" Supports conversion from PetriNets to Catalyst ReactionSystems This provides access to the parameter estimation, optimization, and sensitivity tooling provided in the Catalyst library """ module CatalystInterop using AlgebraicPetri using Catlab.CategoricalAlgebra using ...Catalyst using ...Catalyst.Symbolics: scalarize import ...Catalyst: ReactionSystem counter(a) = [count(==(i),a) for i in unique(a)] """ Convert a general PetriNet to a ReactionSystem This conversion forgets any labels or rates provided, and converts all parameters and variables into symbols. It does preserve the ordering of transitions and states though (Transition 1 has a rate of k[1], state 1 has a concentration of S[1]) """ function ReactionSystem(pn::AbstractPetriNet) @parameters t k[1:nt(pn)] @variables S[collect(1:ns(pn))](t) rxs = map(1:nt(pn)) do t inpts = pn[incident(pn, t, :it),:is] otpts = pn[incident(pn, t, :ot),:os] in_count = collect(counter(inpts)) ot_count = collect(counter(otpts)) Reaction(k[t], [S[i] for i in unique(inpts)], [S[o] for o in unique(otpts)], in_count, ot_count) end ReactionSystem(rxs, t, scalarize(S), scalarize(k)) end end
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2.704255
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# This file is part of GenericSchur.jl, released under the MIT "Expat" license # The methods in this file are derived from LAPACK's ztgsyl etc. # LAPACK is released under a BSD license, and is # Copyright: # Univ. of Tennessee # Univ. of California Berkeley # Univ. of Colorado Denver # NAG Ltd. # Note: since this is based on the routines for complex types, # several methods are hard-coded with 2x2 blocks """ trsylvester!(A,B,C,D,E,F) => R, L, σ solve the generalized Sylvester equation ``A R - L B = σ C`` ``D R - L E = σ F`` given upper triangular and square `A,B,D` and `E`. Overwrites `C` and `F` with `R` and `L`, and sets `σ` to avoid overflow. """ function trsylvester!(A::StridedMatrix{T},B::StridedMatrix{T}, C::StridedVecOrMat{T}, D::StridedMatrix{T},E::StridedMatrix{T}, F::StridedVecOrMat{T}) where {T} m = checksquare(A) n = checksquare(B) ((size(C,1) == m) && (size(C,2) == n)) || throw(DimensionMismatch( "dimensions of C $(size(C)) must match A, ($m,$m), and B, ($n,$n)")) m1 = checksquare(D) n1 = checksquare(E) (m1 == m && n1 == n) || throw(DimensionMismatch( "dimensions of D and E must match A and B")) ((size(F,1) == m) && (size(F,2) == n)) || throw(DimensionMismatch( "dimensions of F $(size(F)) must match A, ($m,$m), and B, ($n,$n)")) rtyone = one(real(T)) rtyzero = zero(real(T)) scale = rtyone scaloc = rtyone dif = rtyzero cone = one(T) rdsum = rtyone rdscale = rtyzero # ztgsyl notes: # ijob 0: solve only; 1: solve & look-ahead dif; 2: solve & 1-est dif # 3: only look-ahead dif; 4: only 1-est dif # ifunc 1: look-ahead, 2: 1-est; else 0 (reset for 2-pass cases) # ifunc => ijob in ztgsy2 # isolve 2: solve and (some) dif; else 1 # if solving: # solve for C,F # if dif: # if solving, save C,F # zero out C,F # call solver w/ ifunc # if solving, restore C,F for j=1:n for i=m:-1:1 # build 2x2 problem Z = [A[i,i] -B[j,j]; D[i,i] -E[j,j]] # TODO: replace lu with port of zgetc2 fZ = lu(Z) rhs = [C[i,j]; F[i,j]] # TODO: use safe solver, yielding scaloc; apply scaloc to C and F # scale *= scaloc x = fZ \ rhs C[i,j] = x[1] F[i,j] = x[2] # substitute R[i,j], L[i,j] into remaining eq if i > 1 α = -x[1] C[1:i-1,j] .+= α * A[1:i-1,i] F[1:i-1,j] .+= α * D[1:i-1,i] end if j < n α = x[2] C[i,j+1:n] .+= α * B[j,j+1:n] F[i,j+1:n] .+= α * E[j,j+1:n] end end end return C, F, scale end """ adjtrsylvester!(A,B,C,D,E,F) => X, σ solve a generalized adjoint Sylvester equation ``Aᴴ X + Dᴴ Y = σ C``, ``X Bᴴ + Y Eᴴ = -σ F``, for upper triangular `A, B, D,` and `E`, overwriting `C` and `F` and setting `σ` to avoid overflow. """ function adjtrsylvester!(A::StridedMatrix{T},B::StridedMatrix{T}, C::StridedVecOrMat{T}, D::StridedMatrix{T},E::StridedMatrix{T}, F::StridedVecOrMat{T}) where {T} m = checksquare(A) n = checksquare(B) ((size(C,1) == m) && (size(C,2) == n)) || throw(DimensionMismatch( "dimensions of C $(size(C)) must match A, ($m,$m), and B, ($n,$n)")) m1 = checksquare(D) n1 = checksquare(E) (m1 == m && n1 == n) || throw(DimensionMismatch( "dimensions of D and E must match A and B")) ((size(F,1) == m) && (size(F,2) == n)) || throw(DimensionMismatch( "dimensions of F $(size(F)) must match A, ($m,$m), and B, ($n,$n)")) scale = one(real(T)) scaloc = one(real(T)) dif = zero(real(T)) cone = one(T) for i=1:m for j=n:-1:1 # build 2x2 problem Z = [A[i,i]' D[i,i]'; -B[j,j]' -E[j,j]'] rhs = [C[i,j]; F[i,j]] # FIXME: use safe version and update scale fZ = lu(Z) x = fZ \ rhs C[i,j] = x[1] F[i,j] = x[2] # substitute R[i,j], L[i,j] into remaining eq for jj=1:j-1 F[i,jj] += x[1] * B[jj,j]' + x[2] * E[jj,j]' end for ii=i+1:m C[ii,j] -= x[1] * A[i,ii]' + x[2] * D[i,ii]' end end end return C, F, scale end
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1.82916
2,476
# broadcast2arg: # These functions use broadcasting to handle arrays of different sizes. # Unless otherwise specified they support: # (N,N) (N,A) (A,N) (A,A) (A,B) # where N:Number, A,B arrays of broadcast compatible sizes. broadcast2arg = [ (:.+, :dy, :dy), # extra (A,) (:.*, :(dy.*x2), :(dy.*x1)), # extra (A,) (:.-, :dy, :(-dy)), #:.% => (:dy,:(dy.*(-trunc(x1./x2)))), # BUG: WARNING: (:check_grads,(:sum,:.%),:args,([-1.6685861285973334,2.349598738753782],[0.5880954718832765,-0.0010728600840855926]),:exact,([1.0,1.0],[2.0,2190.0]),:numeric,([1.0000000000021103,-9.728600840858691],[1.9999999999997797,-4.863172375468294])), WARNING: (:check_grads,(:sum,:.%),:args,([0.20579984208295538,-0.5521335915808314],[0.14504947039368943,-5.795215813098871e-5]),:exact,([1.0,1.0],[-1.0,-9527.0]),:numeric,([0.9999999999998899,-0.15904316261985962],[-0.9999999999998899,0.5895451080050601])) (:./, :(dy./x2), :(-dy.*x1./abs2(x2))), (:.\, :(-dy.*x2./abs2(x1)), :(dy./x1)), (:.^, :(dxndx(x1,x2,dy)), :(dy.*y.*log(x1))), # domain: x1 >= 0 (unless we use complex args) #:.<< => :todo, # domain: Integers, left bit shift; operators,arraymath,broadcast #:.>> => :todo, # domain: Integers, right bit shift ] for (f,g1,g2) in broadcast2arg @eval @primitive $f(x1,x2),dy,y unbroadcast(x1,$g1) unbroadcast(x2,$g2) if f==(:.^) addtest3(f,(0,Inf)) else addtest3(f,(-Inf,Inf)) end end function dxndx(x1,x2,dy) if x2==0 dy.*0 elseif x2==1 dy elseif x2==2 2x1.*dy else dy.*x2.*x1.^(x2-1) end end broadcast2cmp = [ :.<, :.<=, :.==, :.>, :.>=, ] for f in broadcast2cmp @eval begin # To avoid conflict at broadcast.jl:414 $f(x1::AbstractArray,x2::Value)=$f(x1,x2.value) $f(x1::Value,x2::AbstractArray)=$f(x1,x2.value) @zerograd $f(x1,x2) end end # Other functions in broadcast.jl: # eval # droparg1: Not exported # longer_tuple: Not exported # longer_size: Not exported # broadcast_shape: Not exported # check_broadcast_shape: Not exported # gen_broadcast_body_cartesian: Not exported # gen_broadcast_body_iter: Not exported # bpack: Not exported # dumpbitcache: Not exported # gen_broadcast_body_cartesian_tobitarray: Not exported # gen_broadcast_body_iter_tobitarray: Not exported # gen_broadcast_function: Not exported # gen_broadcast_function_tobitarray: Not exported # broadcast! # broadcast # bitbroadcast # broadcast!_function # broadcast_function # broadcast_getindex # broadcast_getindex!: Not exported # broadcast_setindex! # .* # .% # .<< # .>> # eltype_plus: Not exported # .+ # type_minus: Not exported # .- # type_div: Not exported # ./ # .\ # type_rdiv: Not exported # .// # type_pow: Not exported # .^ # $(Expr(:$, :f)): Not a symbol # $(Expr(:$, :bitf)): Not a symbol # $(Expr(:$, :cachef)): Not a symbol # bitcache_pow: Not exported
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2.133767
1,383
# to get rid of eventually const Columns = StructVector # IndexedTable-like API """ colnames(itr) Returns the names of the "columns" in `itr`. # Examples: colnames(1:3) colnames(Columns([1,2,3], [3,4,5])) colnames(table([1,2,3], [3,4,5])) colnames(Columns(x=[1,2,3], y=[3,4,5])) colnames(table([1,2,3], [3,4,5], names=[:x,:y])) colnames(ndsparse(Columns(x=[1,2,3]), Columns(y=[3,4,5]))) colnames(ndsparse(Columns(x=[1,2,3]), [3,4,5])) colnames(ndsparse(Columns(x=[1,2,3]), [3,4,5])) colnames(ndsparse(Columns([1,2,3], [4,5,6]), Columns(x=[6,7,8]))) colnames(ndsparse(Columns(x=[1,2,3]), Columns([3,4,5],[6,7,8]))) """ function colnames end Base.@pure colnames(t::AbstractVector) = (1,) columns(v::AbstractVector) = v Base.@pure colnames(t::Columns) = fieldnames(eltype(t)) Base.@pure colnames(t::Columns{<:Pair}) = colnames(t.first) => colnames(t.second) """ columns(itr, select::Selection = All()) Select one or more columns from an iterable of rows as a tuple of vectors. `select` specifies which columns to select. Refer to the [`select`](@ref) function for the available selection options and syntax. `itr` can be `NDSparse`, `Columns`, `AbstractVector`, or their distributed counterparts. # Examples t = table(1:2, 3:4; names = [:x, :y]) columns(t) columns(t, :x) columns(t, (:x,)) columns(t, (:y, :x => -)) """ function columns end columns(c::Columns) = fieldarrays(c) columns(c::Columns{<:Tuple}) = Tuple(fieldarrays(c)) columns(c::Columns{<:Pair}) = c.first => c.second """ ncols(itr) Returns the number of columns in `itr`. # Examples ncols([1,2,3]) == 1 ncols(rows(([1,2,3],[4,5,6]))) == 2 """ function ncols end ncols(c::Columns{T, C}) where {T, C} = fieldcount(C) ncols(c::Columns{<:Pair}) = ncols(c.first) => ncols(c.second) ncols(c::AbstractArray) = 1 summary(c::Columns{D}) where {D<:Tuple} = "$(length(c))-element Columns{$D}" _sizehint!(c::Columns, n::Integer) = (foreachfield(x->_sizehint!(x,n), c); c) function _strip_pair(c::Columns{<:Pair}) f, s = map(columns, fieldarrays(c)) (f isa AbstractVector) && (f = (f,)) (s isa AbstractVector) && (s = (s,)) Columns((f..., s...)) end # fused indexing operations # these can be implemented for custom vector types like PooledVector where # you can get big speedups by doing indexing and an operation in one step. @inline copyelt!(a, i, j) = (@inbounds a[i] = a[j]) @inline copyelt!(a, i, b, j) = (@inbounds a[i] = b[j]) @inline copyelt!(a::PooledArray, i, j) = (a.refs[i] = a.refs[j]) # row operations @inline roweq(x::AbstractVector, i, j) = (@inbounds eq=isequal(x[i], x[j]); eq) @inline roweq(a::PooledArray, i, j) = (@inbounds x=a.refs[i] == a.refs[j]; x) @inline function roweq(a::StringArray{String}, i, j) weaksa = convert(StringArray{WeakRefString{UInt8}}, a) @inbounds isequal(weaksa[i], weaksa[j]) end copyrow!(I::Columns, i, src) = foreachfield(c->copyelt!(c, i, src), I) copyrow!(I::Columns, i, src::Columns, j) = foreachfield((c1,c2)->copyelt!(c1, i, c2, j), I, src) copyrow!(I::AbstractArray, i, src::AbstractArray, j) = (@inbounds I[i] = src[j]) pushrow!(to::Columns, from::Columns, i) = foreachfield((a,b)->push!(a, b[i]), to, from) pushrow!(to::AbstractArray, from::AbstractArray, i) = push!(to, from[i]) @generated function roweq(c::Columns{D,C}, i, j) where {D,C} N = fieldcount(C) ex = :(roweq(getfield(fieldarrays(c),1), i, j)) for n in 2:N ex = :(($ex) && (roweq(getfield(fieldarrays(c),$n), i, j))) end ex end # uses number of columns from `d`, assuming `c` has more or equal # dimensions, for broadcast joins. @generated function rowcmp(c::Columns, i, d::Columns{D}, j) where D N = fieldcount(D) ex = :(cmp(getfield(fieldarrays(c),$N)[i], getfield(fieldarrays(d),$N)[j])) for n in N-1:-1:1 ex = quote let k = rowcmp(getfield(fieldarrays(c),$n), i, getfield(fieldarrays(d),$n), j) (k == 0) ? ($ex) : k end end end ex end @inline function rowcmp(c::AbstractVector, i, d::AbstractVector, j) cmp(c[i], d[j]) end @inline function rowcmp(c::StringArray{String}, i, d::StringArray{String}, j) wc = convert(StringArray{WeakRefString{UInt8}}, c) wd = convert(StringArray{WeakRefString{UInt8}}, d) cmp(wc[i], wd[j]) end # test that the row on the right is "as of" the row on the left, i.e. # all columns are equal except left >= right in last column. # Could be generalized to some number of trailing columns, but I don't # know whether that has applications. @generated function row_asof(c::Columns{D,C}, i, d::Columns{D,C}, j) where {D,C} N = length(C.parameters) if N == 1 ex = :(!isless(getfield(fieldarrays(c),1)[i], getfield(fieldarrays(d),1)[j])) else ex = :(isequal(getfield(fieldarrays(c),1)[i], getfield(fieldarrays(d),1)[j])) end for n in 2:N if N == n ex = :(($ex) && !isless(getfield(fieldarrays(c),$n)[i], getfield(fieldarrays(d),$n)[j])) else ex = :(($ex) && isequal(getfield(fieldarrays(c),$n)[i], getfield(fieldarrays(d),$n)[j])) end end ex end # map """ map_rows(f, c...) Transform collection `c` by applying `f` to each element. For multiple collection arguments, apply `f` elementwise. Collect output as `Columns` if `f` returns `Tuples` or `NamedTuples` with constant fields, as `Array` otherwise. # Examples map_rows(i -> (exp = exp(i), log = log(i)), 1:5) """ function map_rows(f, iters...) collect_columns(f(i...) for i in zip(iters...)) end # 1-arg case map_rows(f, iter) = collect_columns(f(i) for i in iter) ## Special selectors to simplify column selector """ All(cols::Union{Symbol, Int}...) Select the union of the selections in `cols`. If `cols == ()`, select all columns. # Examples t = table([1,1,2,2], [1,2,1,2], [1,2,3,4], [0, 0, 0, 0], names=[:a,:b,:c,:d]) select(t, All(:a, (:b, :c))) select(t, All()) """ struct All{T} cols::T end All(args...) = All(args) """ Not(cols::Union{Symbol, Int}...) Select the complementary of the selection in `cols`. `Not` can accept several arguments, in which case it returns the complementary of the union of the selections. # Examples t = table([1,1,2,2], [1,2,1,2], [1,2,3,4], names=[:a,:b,:c], pkey = (:a, :b)) select(t, Not(:a)) select(t, Not(:a, (:a, :b))) """ struct Not{T} cols::T end Not(args...) = Not(All(args)) """ Keys() Select the primary keys. # Examples t = table([1,1,2,2], [1,2,1,2], [1,2,3,4], names=[:a,:b,:c], pkey = (:a, :b)) select(t, Keys()) """ struct Keys; end """ Between(first, last) Select the columns between `first` and `last`. # Examples t = table([1,1,2,2], [1,2,1,2], 1:4, 'a':'d', names=[:a,:b,:c,:d]) select(t, Between(:b, :d)) """ struct Between{T1 <: Union{Int, Symbol}, T2 <: Union{Int, Symbol}} first::T1 last::T2 end const SpecialSelector = Union{Not, All, Keys, Between, Function, Regex, Type} hascolumns(t, s) = true hascolumns(t, s::Symbol) = s in colnames(t) hascolumns(t, s::Int) = s in 1:length(columns(t)) hascolumns(t, s::Tuple) = all(hascolumns(t, x) for x in s) hascolumns(t, s::Not) = hascolumns(t, s.cols) hascolumns(t, s::Between) = hascolumns(t, s.first) && hascolumns(t, s.last) hascolumns(t, s::All) = all(hascolumns(t, x) for x in s.cols) hascolumns(t, s::Type) = any(x -> eltype(x) <: s, columns(t)) lowerselection(t, s) = s lowerselection(t, s::Union{Int, Symbol}) = colindex(t, s) lowerselection(t, s::Tuple) = map(x -> lowerselection(t, x), s) lowerselection(t, s::Not) = excludecols(t, lowerselection(t, s.cols)) lowerselection(t, s::Keys) = lowerselection(t, IndexedTables.pkeynames(t)) lowerselection(t, s::Between) = Tuple(colindex(t, s.first):colindex(t, s.last)) lowerselection(t, s::Function) = colindex(t, Tuple(filter(s, collect(colnames(t))))) lowerselection(t, s::Regex) = lowerselection(t, x -> occursin(s, string(x))) lowerselection(t, s::Type) = Tuple(findall(x -> eltype(x) <: s, columns(t))) function lowerselection(t, s::All) s.cols == () && return lowerselection(t, valuenames(t)) ls = (isa(i, Tuple) ? i : (i,) for i in lowerselection(t, s.cols)) ls |> Iterators.flatten |> union |> Tuple end ### Iteration API # For `columns(t, names)` and `rows(t, ...)` to work, `t` # needs to support `colnames` and `columns(t)` Base.@pure function colindex(t, col::Tuple) fns = colnames(t) map(x -> _colindex(fns, x), col) end Base.@pure function colindex(t, col) _colindex(colnames(t), col) end function colindex(t, col::SpecialSelector) colindex(t, lowerselection(t, col)) end function _colindex(fnames::Union{Tuple, AbstractArray}, col, default=nothing) if isa(col, Int) && 1 <= col <= length(fnames) return col elseif isa(col, Symbol) idx = something(findfirst(isequal(col), fnames), 0) idx > 0 && return idx elseif isa(col, Pair{<:Any, <:AbstractArray}) return 0 elseif isa(col, Tuple) return 0 elseif isa(col, Pair{Symbol, <:Pair}) # recursive pairs return _colindex(fnames, col[2]) elseif isa(col, Pair{<:Any, <:Any}) return _colindex(fnames, col[1]) elseif isa(col, AbstractArray) return 0 end default !== nothing ? default : error("column $col not found.") end # const ColPicker = Union{Int, Symbol, Pair{Symbol=>Function}, Pair{Symbol=>AbstractVector}, AbstractVector} column(c, x) = columns(c)[colindex(c, x)] # optimized method @inline function column(c::Columns, x::Union{Int, Symbol}) getfield(fieldarrays(c), x) end column(t, a::AbstractArray) = a column(t, a::Pair{Symbol, <:AbstractArray}) = column(t, a[2]) column(t, a::Pair{Symbol, <:Pair}) = rows(t, a[2]) # renaming a selection column(t, a::Pair{<:Any, <:Any}) = map(a[2], rows(t, a[1])) column(t, s::SpecialSelector) = rows(t, lowerselection(t, s)) function columns(c, sel::Union{Tuple, SpecialSelector}) which = lowerselection(c, sel) cnames = colnames(c, which) if all(x->isa(x, Symbol), cnames) tuplewrap = namedtuple(cnames...)∘tuple else tuplewrap = tuple end tuplewrap((rows(c, w) for w in which)...) end """ `columns(itr, which)` Returns a vector or a tuple of vectors from the iterator. """ columns(t, which) = column(t, which) function colnames(c, cols::Union{Tuple, AbstractArray}) map(x->colname(c, x), cols) end colnames(c, cols::SpecialSelector) = colnames(c, lowerselection(c, cols)) function colname(c, col) if isa(col, Union{Int, Symbol}) col == 0 && return 0 i = colindex(c, col) return colnames(c)[i] elseif isa(col, Pair{<:Any, <:Any}) return col[1] elseif isa(col, Tuple) #ns = map(x->colname(c, x), col) return 0 elseif isa(col, SpecialSelector) return 0 elseif isa(col, AbstractVector) return 0 end error("column named $col not found") end """ rows(itr, select = All()) Select one or more fields from an iterable of rows as a vector of their values. Refer to the [`select`](@ref) function for selection options and syntax. `itr` can be [`NDSparse`](@ref), `StructArrays.StructVector`, `AbstractVector`, or their distributed counterparts. # Examples t = table([1,2],[3,4], names=[:x,:y]) rows(t) rows(t, :x) rows(t, (:x,)) rows(t, (:y, :x => -)) """ function rows end rows(x::AbstractVector) = x rows(cols::Tup) = Columns(cols) rows(t, which...) = rows(columns(t, which...)) _cols_tuple(xs::Columns) = columns(xs) _cols_tuple(xs::AbstractArray) = (xs,) concat_cols(xs, ys) = rows(concat_tup(_cols_tuple(xs), _cols_tuple(ys))) ## Mutable Columns Dictionary mutable struct ColDict{T} pkey::Vector{Int} src::T names::Vector columns::Vector copy::Union{Nothing, Bool} end """ d = ColDict(t) Create a mutable dictionary of columns in `t`. To get the immutable iterator of the same type as `t` call `d[]` """ function ColDict(t; copy=nothing) cnames = colnames(t) if cnames isa AbstractArray cnames = Base.copy(cnames) end ColDict(Int[], t, convert(Array{Any}, collect(cnames)), Any[columns(t)...], copy) end Base.keys(d::ColDict) = d.names Base.values(d::ColDict) = d.columns function Base.getindex(d::ColDict{<:Columns}) Columns(Tuple(d.columns); names=d.names) end Base.getindex(d::ColDict, key) = rows(d[], key) Base.getindex(d::ColDict, key::AbstractArray) = key function Base.setindex!(d::ColDict, x, key::Union{Symbol, Int}) k = _colindex(d.names, key, 0) col = d[x] if k == 0 push!(d.names, key) push!(d.columns, col) elseif k in d.pkey # primary key column has been modified. # copy the table as this results in a shuffle if d.copy === nothing d.copy = true end d.columns[k] = col else d.columns[k] = col end end set!(d::ColDict, key::Union{Symbol, Int}, x) = setindex!(d, x, key) function Base.haskey(d::ColDict, key) _colindex(d.names, key, 0) != 0 end function Base.insert!(d::ColDict, index, key, col) if haskey(d, key) error("Key $key already exists. Use dict[key] = col instead of inserting.") else insert!(d.names, index, key) insert!(d.columns, index, rows(d.src, col)) for (i, pk) in enumerate(d.pkey) if pk >= index d.pkey[i] += 1 # moved right end end end end function insertafter!(d::ColDict, i, key, col) k = _colindex(d.names, i, 0) if k == 0 error("$i not found. Cannot insert column after $i") end insert!(d, k+1, key, col) end function insertbefore!(d::ColDict, i, key, col) k = _colindex(d.names, i, 0) if k == 0 error("$i not found. Cannot insert column after $i") end insert!(d, k, key, col) end function Base.pop!(d::ColDict, key::Union{Symbol, Int}=length(d.names)) k = _colindex(d.names, key, 0) local col if k == 0 error("Column $key not found") else col = d.columns[k] deleteat!(d.names, k) deleteat!(d.columns, k) idx = [pk[1] for pk in enumerate(d.pkey) if pk[2] == k] deleteat!(d.pkey, idx) for i in 1:length(d.pkey) if d.pkey[i] > k d.pkey[i] -= 1 end end if !isempty(idx) && d.copy === nothing # set copy to true d.copy = true end end col end function rename!(d::ColDict, col::Union{Symbol, Int}, newname) k = _colindex(d.names, col, 0) if k == 0 error("$col not found. Cannot rename it.") end d.names[k] = newname end Base.push!(d::ColDict, key::AbstractString, x) = push!(d, Symbol(key), x) function Base.push!(d::ColDict, key::Union{Symbol, Int}, x) push!(d.names, key) push!(d.columns, rows(d.src, x)) end for s in [:(Base.pop!), :(Base.push!), :(rename!), :(set!)] if s == :(Base.pop!) typ = :(Union{Symbol, Int}) else typ = :Pair @eval $s(t::ColDict, x::Pair) = $s(t, x.first, x.second) end @eval begin function $s(t::ColDict, args) for i in args $s(t, i) end end $s(t::ColDict, args::Vararg{$typ}) = $s(t, args) end end function _cols(expr) if expr.head == :call dict = :(dict = ColDict($(expr.args[2]))) expr.args[2] = :dict quote let $dict $expr dict[] end end |> esc else error("This form of @cols is not implemented. Use `@cols f(t,args...)` where `t` is the collection.") end end macro cols(expr) _cols(expr) end # Modifying a columns """ setcol(t::Table, col::Union{Symbol, Int}, x::Selection) Sets a `x` as the column identified by `col`. Returns a new table. setcol(t::Table, map::Pair{}...) Set many columns at a time. # Examples: t = table([1,2], [3,4], names=[:x, :y]) # change second column to [5,6] setcol(t, 2 => [5,6]) setcol(t, :y , :y => x -> x + 2) # add [5,6] as column :z setcol(t, :z => 5:6) setcol(t, :z, :y => x -> x + 2) # replacing the primary key results in a re-sorted copy t = table([0.01, 0.05], [1,2], [3,4], names=[:t, :x, :y], pkey=:t) t2 = setcol(t, :t, [0.1,0.05]) """ setcol(t, args...) = @cols set!(t, args...) """ pushcol(t, name, x) Push a column `x` to the end of the table. `name` is the name for the new column. Returns a new table. pushcol(t, map::Pair...) Push many columns at a time. # Example t = table([0.01, 0.05], [2,1], [3,4], names=[:t, :x, :y], pkey=:t) pushcol(t, :z, [1//2, 3//4]) pushcol(t, :z => [1//2, 3//4]) """ pushcol(t, args...) = @cols push!(t, args...) """ popcol(t, cols...) Remove the column(s) `cols` from the table. Returns a new table. # Example t = table([0.01, 0.05], [2,1], [3,4], names=[:t, :x, :y], pkey=:t) popcol(t, :x) """ popcol(t, args...) = @cols pop!(t, args...) """ insertcol(t, position::Integer, name, x) Insert a column `x` named `name` at `position`. Returns a new table. # Example t = table([0.01, 0.05], [2,1], [3,4], names=[:t, :x, :y], pkey=:t) insertcol(t, 2, :w, [0,1]) """ insertcol(t, i::Integer, name, x) = @cols insert!(t, i, name, x) """ insertcolafter(t, after, name, col) Insert a column `col` named `name` after `after`. Returns a new table. # Example t = table([0.01, 0.05], [2,1], [3,4], names=[:t, :x, :y], pkey=:t) insertcolafter(t, :t, :w, [0,1]) """ insertcolafter(t, after, name, x) = @cols insertafter!(t, after, name, x) """ insertcolbefore(t, before, name, col) Insert a column `col` named `name` before `before`. Returns a new table. # Example t = table([0.01, 0.05], [2,1], [3,4], names=[:t, :x, :y], pkey=:t) insertcolbefore(t, :x, :w, [0,1]) """ insertcolbefore(t, before, name, x) = @cols insertbefore!(t, before, name, x) """ renamecol(t, col, newname) Set `newname` as the new name for column `col` in `t`. Returns a new table. renamecol(t, map::Pair...) Rename multiple columns at a time. # Example t = table([0.01, 0.05], [2,1], names=[:t, :x]) renamecol(t, :t, :time) """ renamecol(t, args...) = @cols rename!(t, args...) ## Utilities for mapping and reduction with many functions / OnlineStats @inline _apply(f::OnlineStat, g, x) = (fit!(g, x); g) @inline _apply(f::Tup, y::Tup, x::Tup) = _apply(astuple(f), astuple(y), astuple(x)) @inline _apply(f::Tuple, y::Tuple, x::Tuple) = map(_apply, f, y, x) @inline _apply(f::NamedTuple, y::NamedTuple, x::NamedTuple) = map(_apply, f, y, x) @inline _apply(f, y, x) = f(y, x) @inline _apply(f::Tup, x::Tup) = _apply(astuple(f), astuple(x)) @inline _apply(f::NamedTuple, x::NamedTuple) = map(_apply, f, x) @inline _apply(f::Tuple, x::Tuple) = map(_apply, f, x) @inline _apply(f, x) = f(x) @inline init_first(f, x) = x @inline init_first(f::OnlineStat, x) = (g=copy(f); fit!(g, x); g) @inline init_first(f::Tup, x::Tup) = map(init_first, f, x) # Initialize type of output, functions to apply, input and output vectors function reduced_type(f, x, isvec, key = nothing) if key !== nothing _promote_op(f, eltype(key), typeof(x)) elseif isvec _promote_op(f, typeof(x)) else _promote_op((a,b)->_apply(f, init_first(f, a), b), eltype(x), eltype(x)) end end function init_inputs(f, x, gettype, isvec) # normal functions f, x, gettype(f, x, isvec) end nicename(f::Function) = typeof(f).name.mt.name nicename(f) = Symbol(last(split(string(f), "."))) nicename(o::OnlineStat) = Symbol(typeof(o).name.name) function mapped_type(f, x, isvec) _promote_op(f, eltype(x)) end init_funcs(f, isvec) = init_funcs((f,), isvec) function init_funcs(f::Tup, isvec) if isa(f, NamedTuple) return init_funcs((map(Pair, fieldnames(typeof(f)), f)...,), isvec) end funcmap = map(f) do g if isa(g, Pair) name = g[1] if isa(g[2], Pair) sel, fn = g[2] else sel = nothing fn = g[2] end (name, sel, fn) else (nicename(g), nothing, g) end end ns = map(x->x[1], funcmap) ss = map(x->x[2], funcmap) fs = map(map(x->x[3], funcmap)) do f f end NamedTuple{(ns...,)}((fs...,)), ss end function init_inputs(f::Tup, input, gettype, isvec) if isa(f, NamedTuple) return init_inputs((map(Pair, fieldnames(typeof(f)), f)...,), input, gettype, isvec) end fs, selectors = init_funcs(f, isvec) xs = map(s->s === nothing ? input : rows(input, s), selectors) output_eltypes = map((f,x) -> gettype(f, x, isvec), fs, xs) ns = fieldnames(typeof(fs)) NT = namedtuple(ns...) # functions, input, output_eltype NT((fs...,)), rows(NT((xs...,))), NT{Tuple{output_eltypes...}} end ### utils compact_mem(x::Columns) = Columns(map(compact_mem, columns(x)))
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### QuantumOscillator.jl # Convenience functionality for handling finite-dimensional systems, # specifically related to the (truncated) harmonic oscillator ### """ osc(n::Int[, name="Osc(n)"::QName]) Create harmonic oscillator in Fock basis with `n` levels. ### Default ops for QFactor: - 'i' : identity operator - 'n' : number operator - 'd' : lowering operator - 'u' : raising operator - 'x' : in-phase quadrature - 'y' : out-of-phase quadrature """ function osc(levels::Int, name=""::QName) if name == "" name = "Osc($(levels))" end s = QFactor(levels, name) s.ops['d'] = sparse([x == y - 1 ? sqrt(QComp(x)) : QComp(0) for x=1:levels, y=1:levels]) s.ops['u'] = s('d')' s.ops['n'] = let l=1:levels; sparse(l,l,map(QComp, 0:(levels - 1))) end s.ops['x'] = s('d') + s('u') s.ops['y'] = (s('d') - s('u')) .* im s end """ coherentvec(o::QObj, α::Number)::QKet Create a coherent state ket vector with amplitude `α`. """ function coherentvec(o::QObj, α::Number)::QKet m = length(o) nbar = abs2(α) @assert (nbar + 3*sqrt(nbar) <= m) "Mean n of $nbar too large for max n of $m." e = exp(-nbar/2) cv = map(k -> QComp(e*α^k/sqrt(gamma(k+1))), 0:(m - 1)) sparsevec(cv / norm(cv)) end """ coherent(o::QObj, α::QComp)::QKet Create a coherent state projection operator with amplitude `α`. """ coherent(o::QObj, α) = projector(coherentvec(o, α))
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# Map a rule over the grids it reads from and updating the grids it writes to. # This is broken into a setup method and an application method # to introduce a function barrier, for type stability. maprule!(data::SimData, rule) = maprule!(data, Val{ruletype(rule)}(), rule) function maprule!(data::SimData, ruletype::Val{T}, rule) where T #= keys and grids are separated instead of in a NamedTuple as `rgrids` or `wgrids` may be a single grid, not a Tuple. But we still need to know what its key is. The structure of rgrids and wgrids determines the values that are sent to the rule are in a NamedTuple or single value, and wether a tuple of single return value is expected. There may be a cleaner way of doing this. =# rkeys, rgrids = _getreadgrids(rule, data) wkeys, wgrids = _getwritegrids(rule, data) # Copy the source to dest for grids we are writing to, if needed _maybeupdatedest!(wgrids, ruletype) # Copy or zero out boundary where needed _updateboundary!(rgrids) # Combine read and write grids to a temporary simdata object. # This means that grids not specified to write to are read-only. allkeys = map(Val, keys(data)) allgrids = values(data) tempdata = _combinegrids(data, allkeys, allgrids, wkeys, wgrids) # Run the rule loop maprule!(wgrids, tempdata, proc(data), opt(data), ruletype, rule, rkeys, rgrids, wkeys) # Mask writes to dest if a mask is provided, except for # CellRule which doesn't move values into masked areas T <: CellRule || _maybemask!(wgrids) # Copy the dest status to source status if it is in use _maybecopystatus!(wgrids) # Swap the dest/source of grids that were written to readonly_wgrids = _swapsource(wgrids) |> _to_readonly # Combine the written grids with the original simdata _replacegrids(data, wkeys, readonly_wgrids) end function maprule!(simdata::SimData, ruletype::Val{<:SetGridRule}, rule) rkeys, rgrids = _getreadgrids(rule, simdata) wkeys, wgrids = _getwritegrids(rule, simdata) tempsimdata = _combinegrids(simdata, rkeys, rgrids, wkeys, wgrids) # Run the rule loop applyrule!(tempsimdata, rule) # Combine the written grids with the original simdata _replacegrids(simdata, wkeys, _to_readonly(wgrids)) end _maybeupdatedest!(ds::Tuple, ruletype) = map(d -> _maybeupdatedest!(d, ruletype), ds) _maybeupdatedest!(d::WritableGridData, ::Val{<:Rule}) = nothing function _maybeupdatedest!(d::WritableGridData, ::Val{<:SetRule}) copyto!(parent(dest(d)), parent(source(d))) end _maybemask!(wgrids::Tuple) = map(_maybemask!, wgrids) _maybemask!(wgrid::WritableGridData) = _maybemask!(wgrid, proc(wgrid), mask(wgrid)) _maybemask!(wgrid::WritableGridData, proc, mask::Nothing) = nothing function _maybemask!(wgrid::WritableGridData{Y,X}, proc::CPU, mask::AbstractArray) where {Y,X} procmap(proc, 1:X) do j for i in 1:Y dest(wgrid)[i, j] *= mask[i, j] end end end function _maybemask!(wgrid::WritableGridData{Y,X}, proc, mask::AbstractArray) where {Y,X} destview(wgrid) .*= mask end _maybecopystatus!(grids::Tuple{Vararg{<:GridData}}) = map(_maybecopystatus!, grids) _maybecopystatus!(grid::GridData) = _maybecopystatus!(sourcestatus(grid), deststatus(grid)) _maybecopystatus!(srcstatus, deststatus) = nothing function _maybecopystatus!(srcstatus::AbstractArray, deststatus::AbstractArray) copyto!(srcstatus, deststatus) end _to_readonly(data::Tuple) = map(ReadableGridData, data) _to_readonly(data::WritableGridData) = ReadableGridData(data) function maprule!( wgrids::Union{<:GridData{Y,X,R},Tuple{<:GridData{Y,X,R},Vararg}}, simdata, proc::CPU, opt, ruletype::Val, rule, rkeys, rgrids, wkeys ) where {Y,X,R} let simdata=simdata, proc=proc, opt=opt, rule=rule, rkeys=rkeys, rgrids=rgrids, wkeys=wkeys, wgrids=wgrids, ruletype=ruletype optmap(proc, opt, rgrids, Tuple{Y,X,R}) do i, j cell_kernel!(wgrids, simdata, ruletype, rule, rkeys, rgrids, wkeys, i, j) end end end function maprule!( wgrids::Union{<:GridData{Y,X,R},Tuple{<:GridData{Y,X,R},Vararg}}, simdata, proc::CPU, opt, ruletype::Val{<:NeighborhoodRule}, rule, args... ) where {Y,X,R} grid = simdata[neighborhoodkey(rule)] mapneighborhoodrule!(wgrids, simdata, grid, proc, opt, ruletype, rule, args...) return nothing end ### Rules that don't need a neighborhood buffer #################### # Run kernels with SparseOpt function optmap(f, proc, ::SparseOpt, rgrids, ::Type{Tuple{Y,X,R}}) where {Y,X,R} # Only use SparseOpt for single-grid rules with grid radii > 0 if R == 0 optmap(f, proc, NoOpt(), rgrids, Tuple{Y,X,R}) return nothing end B = 2R grid = rgrids isa Tuple ? first(rgrids) : rgrids status = sourcestatus(grid) let f=f, proc=proc, grid=grid procmap(proc, 1:_indtoblock(X, B)) do bj for bi in 1:_indtoblock(Y, B) status[bi, bj] || continue # Convert from padded block to init dimensions istart, jstart = _blocktoind(bi, B) - R, _blocktoind(bj, B) - R # Stop at the init row/column size, not the padding or block multiple istop, jstop = min(istart + B - 1, Y), min(jstart + B - 1, X) # Skip the padding istart, jstart = max(istart, 1), max(jstart, 1) for j in jstart:jstop, i in istart:istop f(i, j) end end end end return nothing end # Run kernel over the whole grid, cell by cell optmap(f, proc, ::NoOpt, g, ::Type{Tuple{Y,X,R}}) where {Y,X,R} = procmap(proc, 1:X) do j for i in 1:Y f(i, j) # Run rule for each row in column j end end # Looping over cells or blocks on CPU procmap(f, proc::SingleCPU, range) = for n in range f(n) # Run rule over each column end procmap(f, proc::ThreadedCPU, range) = Threads.@threads for n in range f(n) # Run rule over each column, threaded end @inline function cell_kernel!(wgrids, simdata, ::Val{<:Rule}, rule, rkeys, rgrids, wkeys, i, j) readval = _readgrids(rkeys, rgrids, i, j) writeval = applyrule(simdata, rule, readval, (i, j)) _writegrids!(wgrids, writeval, i, j) writeval end @inline function cell_kernel!(wgrids, simdata, ::Val{<:SetRule}, rule, rkeys, rgrids, wkeys, i, j) readval = _readgrids(rkeys, rgrids, i, j) applyrule!(simdata, rule, readval, (i, j)) nothing end ## Rules that need a Neighorhood buffer ############################################# function mapneighborhoodrule!( wgrids, simdata, grid::GridData{Y,X,R}, proc::CPU, opt, ruletype::Val, args... ) where {Y,X,R} let wgrids=wgrids, simdata=simdata, grid=grid, proc=proc, opt=opt, ruletype=ruletype, args=args B = 2R # Split the grid in 2 interleaved sets of rows, so that we never run adjacent # rows simultaneously - it could cause race conditions when setting status. procmap(proc, 1:2:_indtoblock(Y, B)) do bi row_kernel!(wgrids, simdata, grid, proc, opt, ruletype, args..., bi) end procmap(proc, 2:2:_indtoblock(Y, B)) do bi if bi <=_indtoblock(Y, B) row_kernel!(wgrids, simdata, grid, proc, opt, ruletype, args..., bi) end end end return nothing end function _maybecopystatus!(grid::GridData, opt::SparseOpt) # Copy src to dst - we don't run every block so this is necessary deststatus(grid) .= sourcestatus(grid) return nothing end _maybecopystatus!(grid, opt::NoOpt) = nothing #= Run the rule row by row. When we move along a row by one cell, we access only a single new column of data with the height of 4R, and move the existing data in the neighborhood buffers array across by one column. This saves on reads from the main array. =# function row_kernel!( wgrids, simdata::SimData, grid::GridData{Y,X,R}, proc, opt::NoOpt, ruletype::Val, rule::Rule, rkeys, rgrids, wkeys, bi ) where {Y,X,R} B = 2R i = _blocktoind(bi, B) # Loop along the block ROW. src = parent(source(grid)) buffers = _initialise_buffers(src, Val{R}(), i, 1) blocklen = min(Y, i + B - 1) - i + 1 for j = 1:X buffers = _update_buffers(buffers, src, Val{R}(), i, j) # Loop over the COLUMN of buffers covering the block for b in 1:blocklen @inbounds bufrule = _setbuffer(rule, buffers[b]) cell_kernel!(wgrids, simdata, ruletype, bufrule, rkeys, rgrids, wkeys, i + b - 1, j) end end end function row_kernel!( wgrids, simdata::SimData, grid::GridData{Y,X,R}, proc, opt::SparseOpt, ruletype::Val, rule::Rule, rkeys, rgrids, wkeys, bi ) where {Y,X,R} B = 2R S = 2R + 1 nblockcols = _indtoblock(X, B) src = parent(source(grid)) srcstatus, dststatus = sourcestatus(grid), deststatus(grid) # Blocks ignore padding! the first block contains padding. i = _blocktoind(bi, B) # Get current bloc skippedlastblock = true # Initialise block status for the start of the row # The first column always runs, it's buggy otherwise. @inbounds bs11, bs12 = true, true @inbounds bs21, bs22 = true, true # New block status newbs12 = false newbs22 = false buffers = _initialise_buffers(src, Val{R}(), i, 1) for bj = 1:nblockcols # Shuffle current buffer status bs11, bs21 = bs12, bs22 @inbounds bs12, bs22 = srcstatus[bi, bj + 1], srcstatus[bi + 1, bj + 1] # Skip this block if it and the neighboring blocks are inactive if !(bs11 | bs12 | bs21 | bs22) skippedlastblock = true # Run the rest of the chain if it exists and more than 1 grid is used if rule isa Chain && length(rule) > 1 && length(rkeys) > 1 # Loop over the grid COLUMNS inside the block jstart = _blocktoind(bj, B) jstop = min(jstart + B - 1, X) for j in jstart:jstop # Loop over the grid ROWS inside the block blocklen = min(Y, i + B - 1) - i + 1 for b in 1:blocklen cell_kernel!(wgrids, simdata, ruletype, rule, rkeys, rgrids, wkeys, i + b - 1, j) end end end continue end # Define area to loop over with the block. # It's variable because the last block may be partial jstart = _blocktoind(bj, B) jstop = min(jstart + B - 1, X) # Reinitialise neighborhood buffers if we have skipped a section of the array if skippedlastblock buffers = _initialise_buffers(src, Val{R}(), i, jstart) skippedlastblock = false end # Shuffle new buffer status newbs11 = newbs12 newbs21 = newbs22 newbs12 = newbs22 = false # Loop over the grid COLUMNS inside the block for j in jstart:jstop # Update buffers unless feshly populated buffers = _update_buffers(buffers, src, Val{R}(), i, j) # Which block column are we in, 1 or 2 curblockj = (j - jstart) ÷ R + 1 # Loop over the COLUMN of buffers covering the block blocklen = min(Y, i + B - 1) - i + 1 for b in 1:blocklen # Set rule buffer bufrule = _setbuffer(rule, buffers[b]) # Run the rule kernel for the cell writeval = cell_kernel!(wgrids, simdata, ruletype, bufrule, rkeys, rgrids, wkeys, i + b - 1, j) # Update the status for the current block cs = _cellstatus(opt, wgrids, writeval) curblocki = R == 1 ? b : (b - 1) ÷ R + 1 if curblocki == 1 curblockj == 1 ? (newbs11 |= cs) : (newbs12 |= cs) else curblockj == 1 ? (newbs21 |= cs) : (newbs22 |= cs) end end # Combine new block status with deststatus array @inbounds dststatus[bi, bj] |= newbs11 @inbounds dststatus[bi+1, bj] |= newbs21 @inbounds dststatus[bi, bj+1] |= newbs12 @inbounds dststatus[bi+1, bj+1] |= newbs22 end end return nothing end @inline _cellstatus(opt::SparseOpt, wgrids::Tuple, writeval) = _cellstatus(opt, writeval[1], writeval) @inline _cellstatus(opt::SparseOpt, wgrids, writeval) = !can_skip(opt, writeval) ## Low-level generated functiond for working with grids ###################### # Reduces array reads for single grids, when we can just use # the center of the neighborhood buffer as the cell state @inline _keys2vals(keys::Tuple) = map(Val, keys) @inline _keys2vals(key::Symbol) = Val(key) # Generate an SArray from the main array and the last SArray @generated function _update_buffers( buffers::Tuple, src::AbstractArray{T}, ::Val{R}, i, j ) where {T,R} B = 2R; S = 2R + 1; L = S^2 newvals = Expr[] for n in 0:2B-1 push!(newvals, :(@inbounds src[i + $n, j + 2R])) end newbuffers = Expr(:tuple) for b in 1:B bufvals = Expr(:tuple) for n in S+1:L push!(bufvals.args, :(@inbounds buffers[$b][$n])) end for n in b:b+B push!(bufvals.args, newvals[n]) end push!(newbuffers.args, :(SArray{Tuple{$S,$S},$T,2,$L}($bufvals))) end return quote return $newbuffers end end # Generate an SArray from the main array @generated function _initialise_buffers(src::AbstractArray{T}, ::Val{R}, i, j) where {T,R} B = 2R; S = 2R + 1; L = S^2 columns = [] zerocol = Expr[] for r in 1:2B push!(zerocol, :(zero(T))) end push!(columns, zerocol) for c in 0:S-2 newcol = Expr[] for r in 0:2B-1 push!(newcol, :(@inbounds src[i + $r, j + $c])) end push!(columns, newcol) end newbuffers = Expr(:tuple) for b in 1:B bufvals = Expr(:tuple) for c in 1:S, r in b:b+B exp = columns[c][r] push!(bufvals.args, exp) end push!(newbuffers.args, :(SArray{Tuple{$S,$S},$T,2,$L}($bufvals))) end return quote return $newbuffers end end # Read values from grid/s at index `I`. This occurs for every cell for every rule, # so has to be very fast. # Returns a single value or NamedTuple of values. function _readgrids end @generated function _readgrids(rkeys::Tuple, rgrids::Tuple, I...) expr = Expr(:tuple) for i in 1:length(rgrids.parameters) push!(expr.args, :(@inbounds rgrids[$i][I...])) end return quote keys = map(_unwrap, rkeys) vals = $expr NamedTuple{keys,typeof(vals)}(vals) end end @inline function _readgrids(rkeys::Val, rgrids::GridData, I...) @inbounds rgrids[I...] end # Write values to grid/s at index `I`. This occurs for every cell for every rule, # so has to be very fast. # Returns a single value or NamedTuple of values. function _writegrids end @generated function _writegrids!(wdata::Tuple, vals::Union{Tuple,NamedTuple}, I...) expr = Expr(:block) for i in 1:length(wdata.parameters) push!(expr.args, :(@inbounds dest(wdata[$i])[I...] = vals[$i])) end push!(expr.args, :(nothing)) return expr end @inline function _writegrids!(wdata::GridData, val, I...) @inbounds dest(wdata)[I...] = val return nothing end # Retrieves `GridData` from a `SimData` object to match the requirements of a `Rule`. # Returns a `Tuple` holding the key or `Tuple` of keys, and grid or `Tuple` of grids. @generated function _getreadgrids(::Rule{R,W}, simdata::AbstractSimData) where {R<:Tuple,W} Expr(:tuple, Expr(:tuple, (:(Val{$(QuoteNode(key))}()) for key in R.parameters)...), Expr(:tuple, (:(simdata[$(QuoteNode(key))]) for key in R.parameters)...), ) end @generated function _getreadgrids(::Rule{R,W}, simdata::AbstractSimData) where {R,W} :((Val{$(QuoteNode(R))}(), simdata[$(QuoteNode(R))])) end @generated function _getwritegrids(::Rule{R,W}, simdata::AbstractSimData) where {R,W<:Tuple} Expr(:tuple, Expr(:tuple, (:(Val{$(QuoteNode(key))}()) for key in W.parameters)...), Expr(:tuple, (:(WritableGridData(simdata[$(QuoteNode(key))])) for key in W.parameters)...), ) end @generated function _getwritegrids(::Rule{R,W}, simdata::AbstractSimData) where {R,W} :((Val{$(QuoteNode(W))}(), WritableGridData(simdata[$(QuoteNode(W))]))) end @inline _vals2syms(x::Type{<:Tuple}) = map(v -> _vals2syms(v), x.parameters) @inline _vals2syms(::Type{<:Val{X}}) where X = X # Combine grids into a new NamedTuple of grids depending # on the read and write keys required by a rule. @inline function _combinegrids(simdata::SimData, rkeys, rgrids, wkeys, wgrids) @set simdata.grids = _combinegrids(rkeys, rgrids, wkeys, wgrids) end @inline function _combinegrids(rkey, rgrids, wkey, wgrids) _combinegrids((rkey,), (rgrids,), (wkey,), (wgrids,)) end @inline function _combinegrids(rkey, rgrids, wkeys::Tuple, wgrids::Tuple) _combinegrids((rkey,), (rgrids,), wkeys, wgrids) end @inline function _combinegrids(rkeys::Tuple, rgrids::Tuple, wkey, wgrids) _combinegrids(rkeys, rgrids, (wkey,), (wgrids,)) end @generated function _combinegrids(rkeys::Tuple, rgrids::Tuple, wkeys::Tuple, wgrids::Tuple) rkeys = _vals2syms(rkeys) wkeys = _vals2syms(wkeys) keysexp = Expr(:tuple, QuoteNode.(wkeys)...) dataexp = Expr(:tuple, :(wgrids...)) for (i, key) in enumerate(rkeys) if !(key in wkeys) push!(dataexp.args, :(rgrids[$i])) push!(keysexp.args, QuoteNode(key)) end end return quote keys = $keysexp vals = $dataexp NamedTuple{keys,typeof(vals)}(vals) end end # Replace grids in a NamedTuple with new grids where required. function _replacegrids(simdata::AbstractSimData, newkeys, newgrids) @set simdata.grids = _replacegrids(grids(simdata), newkeys, newgrids) end @generated function _replacegrids(allgrids::NamedTuple, newkeys::Tuple, newgrids::Tuple) newkeys = map(_unwrap, newkeys.parameters) allkeys = allgrids.parameters[1] expr = Expr(:tuple) for key in allkeys if key in newkeys i = findfirst(k -> k == key, newkeys) push!(expr.args, :(newgrids[$i])) else push!(expr.args, :(allgrids.$key)) end end return quote vals = $expr NamedTuple{$allkeys,typeof(vals)}(vals) end end @generated function _replacegrids(allgrids::NamedTuple, newkey::Val, newgrid::GridData) newkey = _unwrap(newkey) allkeys = allgrids.parameters[1] expr = Expr(:tuple) for key in allkeys if key == newkey push!(expr.args, :(newgrid)) else push!(expr.args, :(allgrids.$key)) end end return quote vals = $expr NamedTuple{$allkeys,typeof(vals)}(vals) end end
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2.270883
8,428
using Query using FileIO """ Create a parameter `component`_`name` with the given value, and connect parameter `name` within `component` to this distinct global parameter. """ function setdistinctparameter(m::Model, component::Symbol, name::Symbol, value) globalname = Symbol(string(component, '_', name)) set_param!(m, component, name, globalname, value) end """ Load raw RV output into reformat_RV_outputs """ function load_RV(filename::String, RVname::String; output_path::String=joinpath(@__DIR__, "../../output/"), time_filter::Int=2200, region_filter::String="LatAmerica") df = DataFrame(load(joinpath(output_path, "$filename.csv"))) cols = names(df) # apply filters if necessary, currently the function supports a time filter # of a single time value and a region filter of a single region if in("time", cols) if in("region", cols) filtered_df = df |> @query(i, begin @where i.time == time_filter @where i.region == region_filter @select i end) |> DataFrame else filtered_df = df |> @query(i, begin @where i.time == time_filter @select i end) |> DataFrame end return filtered_df[!, Symbol(RVname)] # no filters applied else return df[!, Symbol(RVname)] end end
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2.349593
615
#module ArraySlices using Compat.view import Base: length, size, eltype, getindex export slices, columns, rows # Type parameters # # F : type of SubArray # D : indexed dimension of the array # A : array type # immutable SliceIterator{F, D, A<:AbstractArray} <: AbstractVector{F} array::A end # ensure compatibility between versions if VERSION < v"0.5-dev" _get_L(D, N) = D == 1 ? N : D else _get_L(D, N) = D == 1 || D == N ? true : false end """ slices(array, dim) Return a `SliceIterator` object to loop over the slices of `array` along dimension `dim`. """ @generated function slices{T, N, D}(array::AbstractArray{T, N}, ::Type{Val{D}}) # checks 1 <= D <= N || error("invalid slice dimension") # construct incomplete type of slice, then fill # example: SubArray{Float64, 1, Array{Float64,2}, Tuple{Int64, Colon}, true} F = :(SubArray{$T, $(N-1), $array}) # construct tuple of indices tupexpr = :(Tuple{}) for i = 1:N push!(tupexpr.args, :Colon) end tupexpr.args[1+D] = :Int push!(F.args, tupexpr) # add L/LD parameter push!(F.args, _get_L(D, N)) # build and return iterator :(SliceIterator{$F, $D, $array}(array)) end # ~~~ Array interface ~~~ eltype{F}(s::SliceIterator{F}) = F length{F, D}(s::SliceIterator{F, D}) = size(s.array, D) size(s::SliceIterator) = (length(s), ) # build code that produces slices with the correct indexing @generated function getindex{F, D}(s::SliceIterator{F, D}, i::Integer) # get ndims of parent array N = s.parameters[1].parameters[3].parameters[2] expr = :() expr.head = :call push!(expr.args, :view) push!(expr.args, :(getfield(s, :array))) # fill in with `Colon`s for i = 1:N push!(expr.args, Colon()) end # then replace the indexed dimension expr.args[2 + D] = :i expr end # ~~~ Convenience functions for 2D arrays ~~~ """ columns(array) Return a `SliceIterator` object to loop over the columns of `array`. """ columns(array::AbstractMatrix) = slices(array, Val{2}) """ rows(array) Return a `SliceIterator` object to loop over the rows of `array`. """ rows(array::AbstractMatrix) = slices(array, Val{1}) #end
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2.532037
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""" AbstractResponse """ abstract type AbstractResponse end """ `Response <: AbstractResponse` # Description An immutable which holds the information about response, such as the identifier of the examinee who gave the response, `examinee_id::String`, the identifier of the answered item `item_idx::String`, and the answer starting and ending times `start_time::Dates.DateTime` and `end_time::Dates.DateTime`. """ struct Response <: AbstractResponse item_idx::Int64 examinee_idx::Int64 item_id::String examinee_id::String val::Union{Missing,Float64} start_time::Dates.DateTime end_time::Dates.DateTime Response( item_idx, examinee_idx, item_id, examinee_id, val, start_time, end_time, ) = new(item_idx, examinee_idx, item_id, examinee_id, val, start_time, end_time) Response( item_idx, examinee_idx, item_id, examinee_id, val, time::Dates.DateTime, ) = new(item_idx, examinee_idx, item_id, examinee_id, val, time, time) end # Outer Constructor Methods """ ```julia get_examinees_by_item_id( item_id::String, responses::Vector{<:AbstractResponse}, examinees::Vector{<:AbstractExaminee}, ) ``` # Description It returns the examinees who answered to the item with id `item_id`. """ function get_examinees_by_item_id( item_id::String, responses::Vector{<:AbstractResponse}, examinees::Vector{<:AbstractExaminee}, ) examinees[filter(r -> r.item_id == item_id, responses).examinee_id] end """ ```julia get_items_by_examinee_id( examinee_id::String, responses::Vector{<:AbstractResponse}, items::Vector{<:AbstractItem}, ) ``` # Description It returns the items answered by the examinee with id `examinee_id`. """ function get_items_by_examinee_id( examinee_id::String, responses::Vector{<:AbstractResponse}, items::Vector{<:AbstractItem}, ) items[filter(r -> r.examinee_id == examinee_id, responses).item_id] end """ ```julia add_response!(response::AbstractResponse, responses::Vector{<:AbstractResponse}) ``` # Description Push the response in the response vector `responses`. """ function add_response!(response::AbstractResponse, responses::Vector{<:AbstractResponse}) push!(response, responses) end """ ```julia get_responses_by_examinee_id(examinee_id::String, responses::Vector{<:AbstractResponse}) ``` # Description It returns the vector of responses given by examinee with id = `id`. """ function get_responses_by_examinee_id( examinee_id::String, responses::Vector{<:AbstractResponse}, ) filter(r -> r.examinee_id == examinee_id, responses) end """ ```julia get_responses_by_item_id(item_id::String, responses::Vector{<:AbstractResponse}) ``` # Description It returns the vector of responses to item with id equal to `item_id`. """ function get_responses_by_item_id(item_id::String, responses::Vector{<:AbstractResponse}) filter(r -> r.item_id == item_id, responses) end """ ```julia get_responses_by_item_idx(item_idx::Int64, responses::Vector{<:AbstractResponse}; sorted = true) ``` # Description It returns the vector of responses to item with idx equal to `item_idx`. The vector of responses is sorted by `examinee_idx` if `sorted = true`. """ function get_responses_by_item_idx( item_idx::Int64, responses::Vector{<:AbstractResponse}; sorted = true, ) resp_item = filter(r -> r.item_idx == item_idx, responses) if sorted sort!(resp_item, by = r -> r.examinee_idx) end end """ ```julia _generate_response(latent::Latent1D, parameters::AbstractParametersBinary) ``` # Description Randomly generate a response for a 1-dimensional latent variable and custom item parameters. """ function _generate_response(latent::Latent1D, parameters::AbstractParametersBinary) Float64(rand(Distributions.Bernoulli(_probability( latent, parameters, Array{Float64,1}(undef, 0), Array{Float64,1}(undef, 0), ))))::Float64 end """ ```julia answer(examinee::AbstractExaminee, item::AbstractItem) ``` # Description Randomly generate a response by `examinee` to a dichotomous (binary) `item`. """ function answer(examinee::AbstractExaminee, item::AbstractItem) Response( item.idx, examinee.idx, item.id, examinee.id, _generate_response(examinee.latent, item.parameters), Dates.now(), Dates.now(), ) end """ ```julia answer(examinee::AbstractExaminee, items::Vector{<:AbstractItem}) ``` # Description Randomly generate a response by `examinee` to dichotomous (binary) `items`. """ function answer(examinee::AbstractExaminee, items::Vector{<:AbstractItem}) map( i -> Response( i.idx, examinee.idx, i.id, examinee.id, _generate_response(examinee.latent, i.parameters), Dates.now(), ), items, ) end """ ```julia answer(examinee_id::String, item_id::String, examinees::Vector{<:AbstractExaminee}, items::Vector{<:AbstractItem}) ``` # Description Randomly generate a response by `Examinee` with index `examinee_id` to a dichotomous (binary) `item` with index `item_id`. """ function answer( examinee_id::String, item_id::String, examinees::Vector{<:AbstractExaminee}, items::Vector{<:AbstractItem}, ) answer( get_examinee_by_id(examinee_id, examinees), get_item_by_id(item_id, items), ) end """ ```julia answer(examinees::Vector{<:AbstractExaminee}, items::Vector{<:AbstractItem}) ``` # Description Randomly generate responses by all the examinees in `examinees` to items in `items`. """ function answer(examinees::Vector{<:AbstractExaminee}, items::Vector{<:AbstractItem}) mapreduce(e -> map(i -> answer(e, i), items), vcat, examinees) end """ ```julia get_design_matrix(responses::Vector{Response}, I::Int64, N::Int64) ``` # Description Returns the ``I \times N `` design matrix. """ function get_design_matrix(responses::Vector{Response}, I::Int64, N::Int64) has_answered = map(r -> CartesianIndex(r.item_idx, r.examinee_idx), responses) design = zeros(Float64, I, N) design[has_answered] .= one(Float64) return design::Matrix{Float64} end """ ```julia get_response_matrix(responses::Vector{Response}, I::Int64, N::Int64) ``` # Description Transform vector of `Response`s in a ``I \times N`` response matrix. A non given answer has value `0.0`. """ function get_response_matrix(responses::Vector{Response}, I::Int64, N::Int64) response_matrix = Matrix{Union{Missing, Float64}}(missing .* ones(Float64, I, N)) map(r -> response_matrix[CartesianIndex(r.item_idx, r.examinee_idx)] = r.val, responses) return response_matrix::Matrix{Union{Missing, Float64}} end """ ```julia get_responses(response_matrix::Matrix{Float64}, design_matrix::Matrix{Float64}, items::Vector{<:AbstractItem}, examinees::Vector{<:AbstractExaminee}) ``` Transforms a ``I \times N`` response matrix in a vector of `Response`s given a valid `design_matrix`, a vector of `Item`s and a vector of `Examinee`s. """ function get_responses( response_matrix::Matrix{Float64}, design_matrix::Matrix{Float64}, items::Vector{<:AbstractItem}, examinees::Vector{<:AbstractExaminee}, ) mapreduce( e -> map( i -> Response( i.idx, e.idx, i.id, e.id, response_matrix[i.idx, e.idx], Dates.now(), ), items[findall(design_matrix[:, e.idx] .> 0.0)], ), vcat, examinees, ) end """ ```julia get_items_idx_answered_by_examinee( examinee::AbstractExaminee, responses::Vector{<:AbstractResponse}, ) ``` # Description Returns the idx of the items answered by examinee. """ function get_items_idx_answered_by_examinee( examinee::AbstractExaminee, responses::Vector{<:AbstractResponse}, ) resp_e = get_responses_by_examinee_id(examinee.id, responses) return map(r -> r.item_idx, resp_e) end """ ```julia get_examinees_idx_who_answered_item( item::AbstractItem, responses::Vector{<:AbstractResponse}, ) ``` # Description Returns the idx of the examinees who answered to item. """ function get_examinees_idx_who_answered_item( item::AbstractItem, responses::Vector{<:AbstractResponse}, ) resp_e = get_responses_by_item_id(item.id, responses) return map(r -> r.examinee_idx, resp_e) end
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# generate examples import Literate EXAMPLEDIR = joinpath(@__DIR__, "src", "literate") GENERATEDDIR = joinpath(@__DIR__, "src", "examples") mkpath(GENERATEDDIR) # Copy supplementary files first suplementary_fileextensions = [".inp", ".svg", ".png", ".jpg", ".gif"] for example in readdir(EXAMPLEDIR) if any(endswith.(example, suplementary_fileextensions)) cp(joinpath(EXAMPLEDIR, example), joinpath(GENERATEDDIR, example); force=true) end end for example in readdir(EXAMPLEDIR) if endswith(example, ".jl") input = abspath(joinpath(EXAMPLEDIR, example)) script = Literate.script(input, GENERATEDDIR) code = strip(read(script, String)) # remove "hidden" lines which are not shown in the markdown line_ending_symbol = occursin(code, "\r\n") ? "\r\n" : "\n" code_clean = join(filter(x->!endswith(x,"#hide"),split(code, r"\n|\r\n")), line_ending_symbol) mdpost(str) = replace(str, "@__CODE__" => code_clean) Literate.markdown(input, GENERATEDDIR, postprocess = mdpost) Literate.notebook(input, GENERATEDDIR, execute = is_ci) # Don't execute locally else if !any(endswith.(example, suplementary_fileextensions)) @warn "ignoring $example" end end end # remove any .vtu files in the generated dir (should not be deployed) cd(GENERATEDDIR) do foreach(file -> endswith(file, ".vtu") && rm(file), readdir()) foreach(file -> endswith(file, ".pvd") && rm(file), readdir()) end
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2.472906
609
macro S_str(terms) _lookup(terms) end
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2
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# Activating Julia environment using Pkg Pkg.activate(".") # Packages using JuMP, Gurobi, Random, Distributions, LinearAlgebra, Plots T = 25 S = 5 I = 6 Random.seed!(MersenneTwister(314)) # Do not change the seed. function generate_data(I::Int64, S::Int64, T::Int64) mvdist = Multinomial(I, S) # Multinomial w.r.t. server allocation raws = Array{Any}(zeros(S)) # Generating tridiagonal multivariate normals for the servers for s = 1:S randos = 1/5*randperm(S) mat = diagm(0 => 1*ones(S), 1 => randos, -1 => randos) symmat = mat * mat' raws[s] = MvNormal(symmat) end D = zeros(I, S, T); # Generating demand data for t = 1:T D[:,:, t] += 2*rand(mvdist,I)' for i = 1:I for s = 1:S D[i, s, t] += abs(rand(raws[s])[1]) end end end return D end D = generate_data(I,S,T); heatmap(1:I, 1:S, var(D, dims=3)[:,:,1], xlabel = "Resources", ylabel = "Servers", title = "Time-variance of demand") heatmap(1:I, 1:S, mean(D, dims=3)[:,:,1], xlabel = "Resources", ylabel = "Servers", title = "Time-mean of demand") # Plotting different kinds of resource costs, i ∈ [1, ... , I] V = 2*[1.5, 1.3, 0.8, 1.3, 1.2, 0.5]; # expansion costs F = [1.5, 1.3, 0.8, 1.3, 1.2, 0.9]; # fixed costs C = [0.4, 0.5, 0.6, 0.7, 0.8, 0.7]; # reallocation costs # bardata = hcat(V, F, C) # sx = repeat(["expansion", "fixed", "reallocation"], inner = 6) # nam = repeat("Resource " .* string.(1:I), outer = 3) # groupedbar(nam, bardata, group = sx, ylabel = "Costs", # title = "Costs for each resource") # OPTIMIZATION MODEL HERE. m = JuMP.Model(Gurobi.Optimizer) @variable(m, r[i=1:I, s=1:S] >= 0) @variable(m, e[i=1:I, s=1:S, t=1:T] >= 0) @variable(m, h[s=1:S, t =1:T] >= 0) @variable(m, u[i=1:I, s1=1:S, s2=1:S, t=1:T] >= 0) # Demand uncertainty is described by: # @uncertain(m, d[i=1:I, s=1:S, t =1:T]) # for s=1:S # @constraint(m, norm(d[:, s, :], 1) <= Γ) # end # Which is a budget uncertainty with ρ = 1 for the ∞ norm, and γ bounding the 1-norm. # ρ = 0 # Γ = 0 ρ = 1 Γ = sqrt(2log(1/0.05))*sqrt(I*T) @objective(m, Min, T*sum(sum(F .* r[:, s]) for s=1:S) + sum(sum(sum(V .* e[:, s, t]) for s=1:S) for t=1:T) + sum(sum(sum(sum(C .* u[:,s1,s2,t]) for s1=1:S) for s2=1:S) for t=1:T)); # Using the robust counterpart here for i = 1:I for s = 1:S for t = 1:T y = @variable(m, [j=1:I, k=1:T]) abs_y = @variable(m, [j=1:I, k=1:T]) max_diff = @variable(m) @constraint(m, abs_y .≥ y) @constraint(m, abs_y .≥ -y) for j = 1:I # The right hand side uncertainty can be tricky! for k = 1:T if j == i && k == t @constraint(m, max_diff ≥ (-1 + y[j,k])) @constraint(m, max_diff ≥ -(-1 + y[j,k])) else @constraint(m, max_diff ≥ y[j,k]) @constraint(m, max_diff ≥ -y[j,k]) end end end @constraint(m, D[i,s,t] + ρ*sum(abs_y) + Γ*max_diff + sum(u[i, s2, s, t] for s2 = 1:S) <= r[i,s] + e[i,s,t] + sum(u[i, s, s2, t] for s2 = 1:S)) end end end for s = 1:S @constraint(m, h[s, 1] == sum(e[:,s,1] ./3)) @constraint(m, h[s, 2] == sum(e[:,s,2] ./3) + sum(e[:,s,1] ./3)) @constraint(m, h[s, 1] <= 1) @constraint(m, h[s, 2] <= 1) for t = 3:T @constraint(m, h[s ,t] == sum(e[:,s,t-2] ./3) + sum(e[:,s,t-1] ./3) + sum(e[:,s,t] ./3)) @constraint(m, h[s, t] <= 1) end end optimize!(m) # Fixed capacities plot plt1 = heatmap(value.(r)', xlabel = "Resources", ylabel = "Servers", title = "Fixed capacities") # Time-mean of expansions plot plt2 = heatmap(mean(value.(e), dims=3)[:,:,1]', xlabel = "Resources", ylabel = "Servers", title = "Time-mean of expansions") # Time-variance of expansions plt3 = heatmap(var(value.(e), dims=3)[:,:,1]', xlabel = "Resources", ylabel = "Servers", title = "Time-variance of expansions") # Time-mean of job transfers out transfers_out = zeros(I,S,T); # Computing the transfers out of each server [transfers_out[i,s,t] = sum(value.(u)[i, s, :, t]) for i=1:I, s = 1:S, t = 1:T]; plt4 = heatmap(mean(transfers_out, dims=3)[:,:,1]', xlabel = "Resources", ylabel = "Servers", title = "Time-mean of job transfers out") # Time-variance of job transfers out plt5 = heatmap(var(transfers_out, dims=3)[:,:,1]', xlabel = "Resources", ylabel = "Servers", title = "Time-variance of job transfers out") # Plots of temperature temps = value.(h) plt6 = plot(1:T, temps[1,:], label=1) for s=2:S plot!(1:T, temps[s,:], label=s, title = "Server temperatures", xlabel = "Time period (t)", ylabel = "Temperature", legend = :bottomright) end # Displaying display(plt6)
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using Test for testscen in 1:2 valdir, scenario, use_permafrost, use_seaice = get_scenario(testscen) println(scenario) m = page_model() include("../src/components/CH4forcing.jl") add_comp!(m, ch4forcing, :ch4forcing) set_param!(m, :ch4forcing, :c_N2Oconcentration, readpagedata(m,"test/validationdata/$valdir/c_n2oconcentration.csv")) set_param!(m, :ch4forcing, :c_CH4concentration, readpagedata(m,"test/validationdata/$valdir/c_ch4concentration.csv")) ##running Model run(m) @test !isnan(m[:ch4forcing, :f_CH4forcing][10]) forcing=m[:ch4forcing,:f_CH4forcing] forcing_compare=readpagedata(m,"test/validationdata/$valdir/f_ch4forcing.csv") @test forcing ≈ forcing_compare rtol=1e-3 end
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using TERMIOS using Test const c_iflag = Sys.islinux() ? 0x00000500 : 0x0000000000006b02 const c_oflag = Sys.islinux() ? 0x00000005 : 0x0000000000000003 const c_cflag = Sys.islinux() ? 0x000000bf : 0x0000000000004b00 const c_lflag = Sys.islinux() ? 0x00008a3b : 0x00000000000005cf const c_cc = Sys.islinux() ? (0x03, 0x1c, 0x7f, 0x15, 0x04, 0x00, 0x01, 0x00, 0x11, 0x13, 0x1a, 0x00, 0x12, 0x0f, 0x17, 0x16, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00) : (0x04, 0xff, 0xff, 0x7f, 0x17, 0x15, 0x12, 0x00, 0x03, 0x1c, 0x1a, 0x19, 0x11, 0x13, 0x16, 0x0f, 0x01, 0x00, 0x14, 0x00) const c_ispeed = Sys.islinux() ? 0x0000000f : 0x0000000000009600 const c_ospeed = Sys.islinux() ? 0x0000000f : 0x0000000000009600 @testset "All" begin @testset "termios.jl stdout" begin term = TERMIOS.termios() TERMIOS.tcgetattr(stdout, term) @test term.c_iflag == c_iflag @test term.c_oflag == c_oflag @test term.c_cflag == c_cflag @test term.c_lflag == c_lflag @test term.c_cc.ref._c_cc == c_cc @test term.c_ispeed == c_ispeed @test term.c_ospeed == c_ospeed term = TERMIOS.termios() TERMIOS.tcgetattr(0, term) @test term.c_iflag == c_iflag @test term.c_oflag == c_oflag @test term.c_cflag == c_cflag @test term.c_lflag == c_lflag @test term.c_cc.ref._c_cc == c_cc @test term.c_ispeed == c_ispeed @test term.c_ospeed == c_ospeed term = TERMIOS.termios() TERMIOS.tcgetattr(0, term) @test TERMIOS.cfgetispeed(term) == term.c_ispeed @test TERMIOS.cfgetospeed(term) == term.c_ospeed TERMIOS.cfsetispeed(term, term.c_ispeed) @test TERMIOS.cfgetispeed(term) == term.c_ispeed TERMIOS.cfsetospeed(term, term.c_ospeed) @test TERMIOS.cfgetospeed(term) == term.c_ospeed term = TERMIOS.termios() TERMIOS.tcgetattr(0, term) TERMIOS.tcsetattr(0, TERMIOS.TCSANOW, term) @test TERMIOS.cfgetispeed(term) == term.c_ispeed @test TERMIOS.cfgetospeed(term) == term.c_ospeed end @testset "termios.jl stdin" begin term = TERMIOS.termios() TERMIOS.tcgetattr(stdin, term) @test term.c_iflag == c_iflag @test term.c_oflag == c_oflag @test term.c_cflag == c_cflag @test term.c_lflag == c_lflag @test term.c_cc.ref._c_cc == c_cc @test term.c_ispeed == c_ispeed @test term.c_ospeed == c_ospeed end end
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# # Simple multi-layer perceptron # In this example, we create a simple [multi-layer perceptron](https://en.wikipedia.org/wiki/Multilayer_perceptron) (MLP) that classifies handwritten digits # using the [MNIST dataset](http://yann.lecun.com/exdb/mnist/). A MLP consists of at least *three layers* of stacked perceptrons: Input, hidden, and output. Each neuron of an MLP has parameters # (weights and bias) and uses an [activation function](https://en.wikipedia.org/wiki/Activation_function) to compute its output. # ![mlp](../mlp_mnist/docs/mlp.svg) # Source: http://d2l.ai/chapter_multilayer-perceptrons/mlp.html # To run this example, we need the following packages: using Flux, Statistics using Flux.Data: DataLoader using Flux: onehotbatch, onecold, @epochs using Flux.Losses: logitcrossentropy using Base: @kwdef using CUDA using MLDatasets # We set default values for learning rate, batch size, epochs, and the usage of a GPU (if available) for the model: @kwdef mutable struct Args η::Float64 = 3e-4 ## learning rate batchsize::Int = 256 ## batch size epochs::Int = 10 ## number of epochs use_cuda::Bool = true ## use gpu (if cuda available) end # If a GPU is available on our local system, then Flux uses it for computing the loss and updating the weights and biases when training our model. # ## Data # We create the function `getdata` to load the MNIST train and test data from [MLDatasets](https://github.com/JuliaML/MLDatasets.jl) and reshape them so that they are in the shape that Flux expects. function getdata(args) ENV["DATADEPS_ALWAYS_ACCEPT"] = "true" ## Load dataset xtrain, ytrain = MLDatasets.MNIST(:train)[:] xtest, ytest = MLDatasets.MNIST(:test)[:] ## Reshape input data to flatten each image into a linear array xtrain = Flux.flatten(xtrain) xtest = Flux.flatten(xtest) ## One-hot-encode the labels ytrain, ytest = onehotbatch(ytrain, 0:9), onehotbatch(ytest, 0:9) ## Create two DataLoader objects (mini-batch iterators) train_loader = DataLoader((xtrain, ytrain), batchsize=args.batchsize, shuffle=true) test_loader = DataLoader((xtest, ytest), batchsize=args.batchsize) return train_loader, test_loader end # The function `getdata` performs the following tasks: # * **Loads MNIST dataset:** Loads the train and test set tensors. The shape of train data is `28x28x60000` and test data is `28X28X10000`. # * **Reshapes the train and test data:** Uses the [flatten](https://fluxml.ai/Flux.jl/stable/models/layers/#Flux.flatten) function to reshape the train data set into a `784x60000` array and test data set into a `784x10000`. Notice that we reshape the data so that we can pass these as arguments for the input layer of our model (a simple MLP expects a vector as an input). # * **One-hot encodes the train and test labels:** Creates a batch of one-hot vectors so we can pass the labels of the data as arguments for the loss function. For this example, we use the [logitcrossentropy](https://fluxml.ai/Flux.jl/stable/models/losses/#Flux.Losses.logitcrossentropy) function and it expects data to be one-hot encoded. # * **Creates mini-batches of data:** Creates two DataLoader objects (train and test) that handle data mini-batches of size `1024 ` (as defined above). We create these two objects so that we can pass the entire data set through the loss function at once when training our model. Also, it shuffles the data points during each iteration (`shuffle=true`). # ## Model # As we mentioned above, a MLP consist of *three* layers that are fully connected. For this example, we define our model with the following layers and dimensions: # * **Input:** It has `784` perceptrons (the MNIST image size is `28x28`). We flatten the train and test data so that we can pass them as arguments to this layer. # * **Hidden:** It has `32` perceptrons that use the [relu](https://fluxml.ai/Flux.jl/stable/models/nnlib/#NNlib.relu) activation function. # * **Output:** It has `10` perceptrons that output the model's prediction or probability that a digit is 0 to 9. # We define the model with the `build_model` function: function build_model(; imgsize=(28,28,1), nclasses=10) return Chain( Dense(prod(imgsize), 32, relu), Dense(32, nclasses)) end # Note that we use the functions [Dense](https://fluxml.ai/Flux.jl/stable/models/layers/#Flux.Dense) so that our model is *densely* (or fully) connected and [Chain](https://fluxml.ai/Flux.jl/stable/models/layers/#Flux.Chain) to chain the computation of the three layers. # ## Loss function # Now, we define the loss function `loss_and_accuracy`. It expects the following arguments: # * ADataLoader object. # * The `build_model` function we defined above. # * A device object (in case we have a GPU available). function loss_and_accuracy(data_loader, model, device) acc = 0 ls = 0.0f0 num = 0 for (x, y) in data_loader x, y = device(x), device(y) ŷ = model(x) ls += logitcrossentropy(ŷ, y, agg=sum) acc += sum(onecold(ŷ) .== onecold(y)) ## Decode the output of the model num += size(x)[end] end return ls / num, acc / num end # This function iterates through the `dataloader` object in mini-batches and uses the function # [logitcrossentropy](https://fluxml.ai/Flux.jl/stable/models/losses/#Flux.Losses.logitcrossentropy) to compute the difference between # the predicted and actual values (loss) and the accuracy. # ## Train function # Now, we define the `train` function that calls the functions defined above and trains the model. function train(; kws...) args = Args(; kws...) ## Collect options in a struct for convenience if CUDA.functional() && args.use_cuda @info "Training on CUDA GPU" CUDA.allowscalar(false) device = gpu else @info "Training on CPU" device = cpu end ## Create test and train dataloaders train_loader, test_loader = getdata(args) ## Construct model model = build_model() |> device ps = Flux.params(model) ## model's trainable parameters ## Optimizer opt = ADAM(args.η) ## Training for epoch in 1:args.epochs for (x, y) in train_loader x, y = device(x), device(y) ## transfer data to device gs = gradient(() -> logitcrossentropy(model(x), y), ps) ## compute gradient Flux.Optimise.update!(opt, ps, gs) ## update parameters end ## Report on train and test train_loss, train_acc = loss_and_accuracy(train_loader, model, device) test_loss, test_acc = loss_and_accuracy(test_loader, model, device) println("Epoch=$epoch") println(" train_loss = $train_loss, train_accuracy = $train_acc") println(" test_loss = $test_loss, test_accuracy = $test_acc") end end # ## Run the example # We call the `train` function: if abspath(PROGRAM_FILE) == @__FILE__ train() end # >**Note:** We can change hyperparameters by modifying train(η=0.01). # ## Resources # * [3Blue1Brown Neural networks videos](https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) # * [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/)
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# Autogenerated wrapper script for Cgl_jll for i686-linux-musl-cxx11 export libCgl using Clp_jll using Osi_jll using CoinUtils_jll using CompilerSupportLibraries_jll JLLWrappers.@generate_wrapper_header("Cgl") JLLWrappers.@declare_library_product(libCgl, "libCgl.so.1") function __init__() JLLWrappers.@generate_init_header(Clp_jll, Osi_jll, CoinUtils_jll, CompilerSupportLibraries_jll) JLLWrappers.@init_library_product( libCgl, "lib/libCgl.so", RTLD_LAZY | RTLD_DEEPBIND, ) JLLWrappers.@generate_init_footer() end # __init__()
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using QuantumStatePlots using QuantumStateBase using Test @testset "QuantumStatePlots.jl" begin ENV["GKSwstype"]="nul" @testset "plot wigner" begin x_range = -5:1.0:5 p_range = -5:1.0:5 wf = WignerFunction(x_range, p_range) state = VacuumState() ws = wf(state) file_path = "wigner.png" plot_wigner(ws, Heatmap, file_path=file_path) @test isfile(file_path) isfile(file_path) && rm(file_path) plot_wigner(ws, Contour, file_path=file_path) @test isfile(file_path) isfile(file_path) && rm(file_path) plot_wigner(ws, Surface, file_path=file_path) @test isfile(file_path) isfile(file_path) && rm(file_path) end @testset "plot ρ" begin file_path = "rho.png" plot_ρ(VacuumState(), file_path=file_path) @test isfile(file_path) isfile(file_path) && rm(file_path) plot_ρ(VacuumState(), state_n=5, file_path=file_path) @test isfile(file_path) isfile(file_path) && rm(file_path) end @testset "plot all" begin x_range = -5:1.0:5 p_range = -5:1.0:5 wf = WignerFunction(x_range, p_range) state = VacuumState() ws = wf(state) file_path = "all.png" plot_all(ws, state, file_path=file_path) @test isfile(file_path) isfile(file_path) && rm(file_path) end end
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2.138796
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using Documenter, Espresso makedocs() deploydocs( deps = Deps.pip("mkdocs", "python-markdown-math"), repo = "github.com/dfdx/Espresso.jl.git", julia = "0.6" )
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export limit_subscribers, LimitSubscribersGuard import Base: show import DataStructures: isfull """ LimitSubscribersGuard(limit::Int = 1, exclusive = true) Guard structure used in `limit_subscribers` operator. # Arguments - `limit`: number of concurrent subscribers - `exclusive`: boolean flag, which indicates whenever this guard can be shared with other observables in other `limit_subscribers` operator. If set to `true`, reusing this guard in a different `limit_subscribers` operator for other observable will result in automatic unsubscription of all present actors. # Note This structure is useful in Pluto.jl notebooks in particular, allowing for automatic subscription/unsubscription of observables. # Example ```julia # Cell 1 guard = LimitSubscribersGuard() # Cell 2 subscription = subscribe!(some_stream |> limit_subscribers(guard), logger()) ``` See also: [`limit_subscribers`](@ref), [`subscribe!`](@ref) """ struct LimitSubscribersGuard limit :: Int exclusive :: Bool handlers :: CircularBuffer{Tuple{Teardown, Any}} end LimitSubscribersGuard(limit::Int = 1, exclusive::Bool = true) = LimitSubscribersGuard(limit, exclusive, CircularBuffer{Tuple{Teardown, Any}}(limit)) Base.show(io::IO, guard::LimitSubscribersGuard) = print(io, "LimitSubscribersGuard($(getlimit(guard)), $(isexclusive(guard)))") getlimit(guard::LimitSubscribersGuard) = guard.limit isexclusive(guard::LimitSubscribersGuard) = guard.exclusive gethandlers(guard::LimitSubscribersGuard) = guard.handlers function unsubscribe_last!(guard::LimitSubscribersGuard) if !isempty(gethandlers(guard)) subscription, actor = popfirst!(gethandlers(guard)) complete!(actor) unsubscribe!(subscription) end return nothing end function remove_handler!(guard::LimitSubscribersGuard, subscription) f = filter(d -> first(d) !== subscription, gethandlers(guard)) if length(f) !== length(gethandlers(guard)) empty!(gethandlers(guard)) append!(gethandlers(guard), f) end return nothing end function add_subscription!(guard::LimitSubscribersGuard, subscription::Teardown, actor) if isfull(gethandlers(guard)) unsubscribe_last!(guard) end push!(gethandlers(guard), (subscription, actor)) return subscription end function release!(guard::LimitSubscribersGuard) foreach(gethandlers(guard)) do handler subscription, actor = handler complete!(actor) unsubscribe!(subscription) end empty!(gethandlers(guard)) return nothing end """ limit_subscribers(limit::Int = 1, exclusive::Bool = true) limit_subscribers(guard::LimitSubscribersGuard) Creates an operator that limits number of concurrent actors to the given observable. On new subscription, if limit is exceeded, oldest actor is automatically unsubscribed and receives a completion event. # Arguments - `limit`: number of concurrent subscribers - `exclusive`: boolean flag, which indicates whenever this guard can be shared with other observables in other `limit_subscribers` operator. If set to `true`, reusing this guard in a different `limit_subscribers` operator for other observable will result in automatic unsubscription of all present actors. # Note This structure is useful in Pluto.jl notebooks in particular, allowing for automatic subscription/unsubscription of observables. # Example ```julia # Cell 1 guard = LimitSubscribersGuard() # Cell 2 subscription = subscribe!(some_stream |> limit_subscribers(guard), logger()) ``` See also: [`LimitSubscribersGuard`](@ref) """ limit_subscribers(limit::Int = 1, exclusive::Bool = true) = limit_subscribers(LimitSubscribersGuard(limit, exclusive)) limit_subscribers(guard::LimitSubscribersGuard) = LimitSubscribersOperator(guard) struct LimitSubscribersOperator <: InferableOperator guard :: LimitSubscribersGuard end operator_right(::LimitSubscribersOperator, ::Type{L}) where L = L function on_call!(::Type{L}, ::Type{L}, operator::LimitSubscribersOperator, source) where L if isexclusive(operator.guard) release!(operator.guard) end return proxy(L, source, LimitSubscribersProxy(operator.guard)) end struct LimitSubscribersProxy <: SourceProxy guard :: LimitSubscribersGuard end source_proxy!(::Type{L}, proxy::LimitSubscribersProxy, source::S) where { L, S } = LimitSubscribersSource{L, S}(source, proxy.guard) struct LimitSubscribersSubscription{S} <: Teardown subscription :: S guard :: LimitSubscribersGuard end as_teardown(::Type{ <: LimitSubscribersSubscription }) = UnsubscribableTeardownLogic() function on_unsubscribe!(subscription::LimitSubscribersSubscription) remove_handler!(subscription.guard, subscription.subscription) unsubscribe!(subscription.subscription) return nothing end struct LimitSubscribersSource{L, S} <: Subscribable{L} source :: S guard :: LimitSubscribersGuard end function on_subscribe!(source::LimitSubscribersSource, actor) guard = source.guard if isfull(gethandlers(guard)) unsubscribe_last!(guard) end return LimitSubscribersSubscription(add_subscription!(guard, subscribe!(source.source, actor), actor), guard) end Base.show(io::IO, ::LimitSubscribersOperator) = print(io, "LimitSubscribersOperator()") Base.show(io::IO, ::LimitSubscribersProxy) = print(io, "LimitSubscribersProxy()") Base.show(io::IO, ::LimitSubscribersSource{L}) where L = print(io, "LimitSubscribersSource($L)") Base.show(io::IO, ::LimitSubscribersSubscription) = print(io, "LimitSubscribersSubscription()")
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3.072329
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using Random traj_folder = joinpath(dirname(pathof(TrajectoryOptimization)),"..") urdf_folder = joinpath(traj_folder, "dynamics","urdf") urdf_kuka_orig = joinpath(urdf_folder, "kuka_iiwa.urdf") urdf_kuka = joinpath(urdf_folder, "temp","kuka.urdf") function write_kuka_urdf() kuka_mesh_dir = joinpath(TrajectoryOptimization.root_dir(),"dynamics","urdf","kuka_iiwa_mesh") temp_dir = joinpath(TrajectoryOptimization.root_dir(),"dynamics","urdf","temp") if !isdir(temp_dir) mkdir(temp_dir) end open(urdf_kuka_orig,"r") do f open(urdf_kuka, "w") do fnew for ln in eachline(f) pre = findfirst("<mesh filename=",ln) post = findlast("/>",ln) if !(pre isa Nothing) && !(post isa Nothing) inds = pre[end]+2:post[1]-2 pathstr = ln[inds] file = splitdir(pathstr)[2] ln = ln[1:pre[end]+1] * joinpath(kuka_mesh_dir,file) * ln[post[1]-1:end] end println(fnew,ln) end end end end function get_kuka_ee(kuka) ee_body = findbody(kuka, "iiwa_link_ee") ee_point = Point3D(default_frame(ee_body),0.,0.,0.) return ee_body, ee_point end function get_kuka_ee_postition_fun(kuka::Mechanism,statecache=StateCache(kuka)) where {O} ee_body, ee_point = Dynamics.get_kuka_ee(kuka) world = root_frame(kuka) nn = num_positions(kuka) function ee_position(x::AbstractVector{T}) where T state = statecache[T] set_configuration!(state, x[1:nn]) RigidBodyDynamics.transform(state, ee_point, world).v end end function calc_ee_position(kuka::Mechanism,X::Trajectory) ee = zero.(X) N = length(X) state = MechanismState(kuka) world = root_frame(kuka) ee_point = get_kuka_ee(kuka)[2] nn = num_positions(kuka) for k = 1:N set_configuration!(state, X[k][1:nn]) ee[k] = RigidBodyDynamics.transform(state, ee_point, world).v end return ee end function kuka_ee_ik(kuka::Mechanism,point::Vector,ik_iterations=1000,attempts=20,tol=1e-2) state = MechanismState(kuka) world = root_frame(kuka) # Get end-effector ee_body, ee_point = get_kuka_ee(kuka) # Run IK err = Inf iter = 1 while err > tol rand!(state) goal = Point3D(world,point) ik_res = jacobian_transpose_ik!(state,ee_body,ee_point,goal,iterations=ik_iterations) point_res = RigidBodyDynamics.transform(ik_res,ee_point,world).v err = norm(point-point_res) if iter > attempts error("IK cannot get sufficiently close to the goal") end return ik_res end end function jacobian_transpose_ik!(state::MechanismState, body::RigidBody, point::Point3D, desired::Point3D; α=0.1, iterations=100) mechanism = state.mechanism world = root_frame(mechanism) # Compute the joint path from world to our target body p = path(mechanism, root_body(mechanism), body) # Allocate the point jacobian (we'll update this in-place later) Jp = point_jacobian(state, p, RigidBodyDynamics.transform(state, point, world)) q = copy(configuration(state)) for i in 1:iterations # Update the position of the point point_in_world = RigidBodyDynamics.transform(state, point, world) # Update the point's jacobian point_jacobian!(Jp, state, p, point_in_world) # Compute an update in joint coordinates using the jacobian transpose Δq = α * Array(Jp)' * (RigidBodyDynamics.transform(state, desired, world) - point_in_world).v # Apply the update q .= configuration(state) .+ Δq set_configuration!(state, q) end state end function hold_trajectory(n,m,N, mech::Mechanism, q) state = MechanismState(mech) nn = num_positions(state) set_configuration!(state, q[1:nn]) vd = zero(state.q) u0 = dynamics_bias(state) if length(q) > m throw(ArgumentError("system must be fully actuated to hold an arbitrary position ($(length(q)) should be > $m)")) end U0 = zeros(m,N) for k = 1:N U0[:,k] = u0 end return U0 end # Write new urdf file with correct absolute paths write_kuka_urdf() kuka = Model(urdf_kuka) end_effector_function = Dynamics.get_kuka_ee_postition_fun(parse_urdf(urdf_kuka,remove_fixed_tree_joints=false))
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2.076853
2,199
module Nektar using Mesh2d type NektarBndry <: Mesh2d.Bndry tag::Char elem::Int edge::Int params::Array{Float64,1} funs::Array{String, 1} NektarBndry(bt, el, ed, p, funs=Array(String,0)) = new(bt, el, ed, p, funs) end function section(flines, header) nl = length(flines) for i in 1:nl if length(search(flines[i], header)) > 0 return i end end return -1 end function element(rea, idx) if length(search(uppercase(rea[1]), "QUAD")) > 0 el = Mesh2d.Quad() else el = Mesh2d.Tri() end el.id = idx el.verts[:,1] = [parsefloat(x) for x = split(strip(rea[2]))][1:nverts(el)] el.verts[:,2] = [parsefloat(x) for x = split(strip(rea[3]))][1:nverts(el)] return el end function mesh(rea) isec = section(rea, "MESH DATA") + 1 nelem = parseint(split(strip(rea[isec]), " ")[1]) elems = Array(Mesh2d.Element2d, nelem) isec += 1 for e = 1:nelem elems[e] = element(rea[isec:(isec+2)], e) isec += 3 end return elems end function bndry_sec(rea, isec, nterms=2) s = split(strip(rea[isec])) tag = strip(s[1])[1] nums = [parsefloat(strip(ss)) for ss in s[2:end]] el = convert(Int, round(nums[1])) ed = convert(Int, round(nums[2])) p = nums[3:end] if islower(tag) funs = rea[(isec+1):(isec+nterms)] isec += nterms + 1 else funs = Array(String, 0) isec += 1 end return NektarBndry(tag, el, ed, p, funs), isec end function bndry_conds(rea, elems) isec = section(rea, "BOUNDARY CONDITIONS")+2 bndrylst = Array(Mesh2d.Bndry, 0) for el in elems ne = Mesh2d.nedges(el) for edge = 1:ne b, isec = bndry_sec(rea, isec) if b.tag == 'E' || b.tag == 'P' el.neigh[edge,1] = convert(Int, round(b.params[1])) el.neigh[edge,2] = convert(Int, round(b.params[2])) else push!(bndrylst, b) el.hasbc = true end end if ne == 3 isec += 1 end end nb = length(bndrylst) for i = 1:nb el = bndrylst[i].elem ed = bndrylst[i].edge elems[el].neigh[ed,2] = -i end return bndrylst end type NekCurve <: Mesh2d.Curve2d cname::String line::String end function curves(rea, elems) isec = section(rea, "CURVED SIDE DATA") + 1 num = convert(Int, round(parsefloat(split(strip(rea[isec]))[1]))) crv = Array(Mesh2d.Curve2d, num) if num == 0 return crv end crvdict = Dict{String,Int}() isec += 1 for i = 1:num ctype = lowercase(strip(rea[isec])) isec += 1 if ctype == "circle" s = split(strip(rea[isec])) xc = parsefloat(s[1]) yc = parsefloat(s[2]) rad = parsefloat(s[3]) inside = true if rad < 0 rad = -rad inside = false end tag = strip(s[4]) crvdict[tag] = i crv[i] = Mesh2d.Circle(xc, yc, rad, inside) elseif ctype=="str" crv[i] = Segment() tag = strip(rea[isec]) crvdict[tag] = i elseif ctype=="fil" crv[i] = NekCurve(ctype, rea[isec]) tag = strip(split(strip(rea[isec]))[2]) crvdict[tag] = i else crv[i] = NekCurve(ctype, rea[isec]) end end isec += 1 num = convert(Int, round(parsefloat(split(strip(rea[isec]))[1]))) isec += 1 for i = 1:num s = split(strip(rea[isec])) iside = parseint(strip(s[1])) iel = parseint(strip(s[2])) tag = strip(s[3]) elems[iel].curve[iside] = crvdict[tag] elems[iel].curved = true isec += 1 end return crv end function nektar(filename) fd = open(filename, "r") rea = readlines(fd) close(fd) elems = mesh(rea) crv = curves(rea, elems) bcs = bndry_conds(rea, elems) return Mesh2d.Mesh(elems, crv, bcs, rea) end end
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mutable struct EmpiricalMean{T<:Real,V<:AbstractVector{<:Real}} <: PriorMean{T} C::V opt::Optimizer end """ **EmpiricalMean** ```julia` function EmpiricalMean(c::V=1.0;opt::Optimizer=Adam(α=0.01)) where {V<:AbstractVector{<:Real}} ``` Construct a constant mean with values `c` Optionally give an optimizer `opt` (`Adam(α=0.01)` by default) """ function EmpiricalMean(c::V=1.0;opt::Optimizer=Adam(α=0.01)) where {V<:AbstractVector{<:Real}} EmpiricalMean{eltype(c),V}(c,opt) end function update!(μ::EmpiricalMean{T},grad::AbstractVector{T}) where {T<:Real} μ.C .+= update!(μ.opt,grad) end Base.:+(x::Real,y::EmpiricalMean{<:Real}) = x.+y.C Base.:+(x::AbstractVector{<:Real},y::EmpiricalMean{<:Real}) = x+y.C Base.:+(x::EmpiricalMean{<:Real},y::Real) = y.+x.C Base.:+(x::EmpiricalMean{<:Real},y::AbstractVector{<:Real}) = y+x.C Base.:+(x::EmpiricalMean{<:Real},y::EmpiricalMean{<:Real}) = EmpiricalMean(x.C+y.C) Base.:-(x::Real,y::EmpiricalMean) = x .- y.C Base.:-(x::AbstractVector{<:Real},y::EmpiricalMean) = x - y.C Base.:-(x::EmpiricalMean{<:Real},y::Real) = x.C .- y Base.:-(x::EmpiricalMean{<:Real},y::AbstractVector{<:Real}) = x.C - y Base.:-(x::EmpiricalMean{<:Real},y::EmpiricalMean{<:Real}) = EmpiricalMean(x.C-y.C) Base.:*(A::AbstractMatrix{<:Real},y::EmpiricalMean{T}) where {T<:Real} = A*y.C Base.:*(y::EmpiricalMean{T},A::AbstractMatrix{<:Real}) where {T<:Real} = transpose(y)*A Base.:convert(::T1,x::EmpiricalMean{T2}) where {T1<:Real,T2<:Real} = T1(x.C)
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2.090014
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module NEPCore using SparseArrays using LinearAlgebra # Fundamental nonlinear eigenvalue problems export NEP # export NoConvergenceException export LostOrthogonalityException export interpolate # Core interfaces export compute_Mder export compute_Mlincomb export compute_Mlincomb! export compute_MM # NEP-functions export compute_resnorm export compute_rf # Helper functions (avoid using these directly in NEP-methods) export compute_Mlincomb_from_MM export compute_Mlincomb_from_Mder export compute_Mder_from_MM export compute_Mlincomb_from_MM! import Base.size # Overload for nonlinear eigenvalue problems import SparseArrays.issparse # Overload for nonlinear eigenvalue problems #################### SHOULD WE JAVE THE LOGGER HERE? #################### export @parse_logger_param! """ @parse_logger_param!(l) If l is a number it canges l to a PrintLogger(l). """ macro parse_logger_param!(l) return esc(:( if ($l isa Number) ; $l=PrintLogger($l); end )) end ################################################################################ include("logger.jl"); ############################################ # Default NEP functions # """ abstract NEP A `NEP` object represents a nonlinear eigenvalue problem. All NEPs should implement ```julia size(nep::NEP,d) ``` and at least one of the following * M = [`compute_Mder(nep::NEP,λ::Number,i::Integer=0)`](@ref) * V = [`compute_Mlincomb(nep::NEP,λ::Number,V::AbstractVecOrMat,a::Vector)`](@ref) (or `compute_Mlincomb!`) * MM = [`compute_MM(nep::NEP,S,V)`](@ref) """ abstract type NEP end """ compute_Mder(nep::NEP,λ::Number [,i::Integer=0]) Computes the ith derivative of `nep` evaluated in `λ`. # Example This example shows that `compute_Mder(nep,λ,1)` gives the first derivative. ```julia-repl julia> nep=nep_gallery("dep0"); julia> ϵ=1e-5; λ=2.25; julia> Aminus=compute_Mder(nep,λ-ϵ); julia> Aplus=compute_Mder(nep,λ+ϵ); julia> opnorm((Aplus-Aminus)/(2ϵ)-compute_Mder(nep,λ,1)) 1.8783432885257602e-11 ``` """ function compute_Mder(nep::NEP,λ::Number,i::Integer=0) error("You need to provide an implementation of compute_Mder for this NEP.\nIf you have a compute_MM-function you may want to define: \ncompute_Mder($(typeof(nep)),λ::Number,i::Integer)=compute_Mder_from_MM(nep,λ,i)") end """ compute_Mlincomb(nep::NEP,λ::Number,V, a::Vector=ones(size(V,2)), startder=0) compute_Mlincomb!(nep::NEP,λ::Number,V, a::Vector=ones(size(V,2)), startder=0) Computes the linear combination of derivatives\\ ``Σ_i a_i M^{(i)}(λ) v_i`` starting from derivative `startder`. The function `compute_Mlincomb!` does the same but may modify the `V` matrix/array. # Example This example shows that `compute_Mder` gives a result consistent with `compute_Mlincomb`. Note that `compute_Mlincomb` is in general faster since no matrix needs to be constructed. ```julia-repl julia> nep=nep_gallery("dep0"); julia> v=ones(size(nep,1)); λ=-1+1im; julia> norm(compute_Mder(nep,λ,1)*v-compute_Mlincomb(nep,λ,hcat(v,v),[0,1])) 0.0 ``` """ compute_Mlincomb!(nep::NEP,λ::Number,V::AbstractVecOrMat,a::Vector), compute_Mlincomb(nep::NEP,λ::Number,V::AbstractVecOrMat, a::Vector) function compute_Mlincomb!(nep::NEP,λ::Number,V::AbstractVecOrMat,a::Vector) # This will manually scale the columns in V by the vector a. if (ones(eltype(a),size(a,1))==a) # No scaling necessary return compute_Mlincomb!(nep,λ,V); end if (isa(V,AbstractVector)) V[:]=V*a[1]; else D=Diagonal(a); rmul!(V,D); end return compute_Mlincomb!(nep,λ,V); end # Recommend to make a copy of V and call compute_Mlincomb! if function not available function compute_Mlincomb(nep::NEP,λ::Number,V::AbstractVecOrMat) @warn "It seems you have not implemented compute_Mlincomb(nep,λ,V) for this NEPType. If you have implemented compute_Mlincomb! you need to add \ncompute_Mlincomb(nep::$(typeof(nep)),λ::Number,V::AbstractVecOrMat)=compute_Mlincomb!(nep,λ,copy(V))" error("No compute_Mlincomb(nep,λ,V) implemented (typeof(nep)=",typeof(nep),")") end compute_Mlincomb(nep::NEP,λ::Number,V::AbstractVecOrMat, a::Vector)= compute_Mlincomb!(nep,λ,copy(V), a) # Note: The following function is commented out since default behaviour is # by to manually create a bigger a-vector (and call without startder) see below #compute_Mlincomb(nep::NEP,λ::Number,V::AbstractVecOrMat, a::Vector, startder::Integer)=compute_Mlincomb!(nep,λ,copy(V), a, startder) # Default behavior of the compute_Mlincomb! is to just call compute_Mlincomb compute_Mlincomb!(nep::NEP,λ::Number,V::AbstractVecOrMat, a::Vector, startder::Integer)=compute_Mlincomb(nep,λ,V, a, startder) # Note: The following function is commented out since, default behaviour is # by manual scaling of columns (see above), not calling compute_Mlincomb() # compute_Mlincomb!(nep::NEP,λ::Number,V::AbstractVecOrMat, a::Vector)=compute_Mlincomb(nep,λ,V, a) # This is instead achieved by compute_Mlincomb!(nep::NEP,λ::Number,V::AbstractVecOrMat)=compute_Mlincomb(nep,λ,V) """ compute_Mlincomb(nep::NEP,λ::Number,V,a::Array,startder::Integer) Computes linear combination starting with derivative startder, i.e., ``Σ_i a_i M^{(i+startder)}(λ) v_i`` The default implementation of this can be slow. Overload for specific NEP if you want efficiency, e.g., in `augnewton`, `iar`, and others. """ function compute_Mlincomb(nep::NEP,λ::Number,V::AbstractVecOrMat,a::Vector,startder::Integer) aa=[zeros(eltype(a), startder);a]; VV=[zeros(eltype(V), size(nep,1),startder) V]; # This is typically slow since copy is needed return compute_Mlincomb(nep,λ,VV,aa) end """ compute_MM(nep::NEP,S,V) Computes the sum ``Σ_i M_i V f_i(S)`` for a NEP, where ``S`` and ``V`` are matrices, and the NEP satisfies ``M(λ)=Σ_i M_i f_i(λ)``. # Example This example shows that for diagonal `S`, the result of `compute_MM` can also be computed with `compute_Mlincomb` ```julia-repl julia> nep=nep_gallery("dep0"); julia> D=diagm(0 => [1,2]) 2×2 Array{Int64,2}: 1 0 0 2 julia> V=ones(size(nep,1),2); julia> W=compute_MM(nep,D,V); julia> norm(W[:,1]-compute_Mlincomb(nep,D[1,1],V[:,1])) 0.0 julia> norm(W[:,2]-compute_Mlincomb(nep,D[2,2],V[:,2])) 4.440892098500626e-16 ``` # Reference Properties of the quantity ``Σ_i M_i V f_i(S)`` for non-polynomial nonlinear eigenvalue problems were extensively used in: * D. Kressner A block Newton method for nonlinear eigenvalue problems, Numer. Math., 114 (2) (2009), pp. 355-372 * C. Effenberger, Robust solution methods for nonlinear eigenvalue problems, PhD thesis, 2013, EPF Lausanne """ function compute_MM(nep::NEP,S,V) error("No procedure to compute MM (typeof(nep)=",typeof(nep),")") end ## Helper functions """ compute_Mlincomb_from_MM(nep::NEP,λ::Number,V,a) This function provides a `compute_Mlincomb`-function call by invoking a call to `compute_MM`. The underlying mathematical relationship is described in github issue #2 and #3. The standard usage is by the following command: ```julia compute_Mlincomb(nep::MyNEP,λ::Number,V,a)=compute_Mlincomb_from_MM(nep,λ,V,a) ``` """ compute_Mlincomb_from_MM(nep::NEP,λ::Number,V,a)=compute_Mlincomb_from_MM!(nep,λ,copy(V),copy(a)) """ compute_Mlincomb_from_MM!(nep::NEP,λ::Number,V,a) Same as [`compute_Mlincomb`](@ref), but modifies V and a. """ function compute_Mlincomb_from_MM!(nep::NEP,λ::Number,V,a::Array{<:Number,1}) # This function it is based on the evaluation of matrix function of a bidiagonal matrix # Should we document the methematical k=size(V,2); # we need to assume that the elements of a are different than zero. V[:,findall(x->x==0,a)] .= 0 a[findall(x->x==0,a)] .= 1 S=diagm(0 => λ*ones(eltype(V),k)) + diagm(-1 => (a[2:k]./a[1:k-1]).*(1:k-1)) z=compute_MM(nep,S,V)[:,1]; return a[1]*reshape(z,size(z,1)) end """ compute_Mlincomb_from_Mder(nep::NEP,λ::Number,V,a) The function computes `Mlincomb` by a call to `compute_Mder`. This function is slow since it requires the construction of the matrices. Usage normally by overloading in this way ```julia compute_Mlincomb(nep::MyNEP,λ::Number,V,a)=compute_Mlincomb_from_Mder(nep,λ,V,a) ``` """ function compute_Mlincomb_from_Mder(nep::NEP,λ::Number,V,a::Array{<:Number,1}) #println("Using poor-man's compute_Mder -> compute_Mlincomb") z=zeros(size(nep,1)) for i=1:length(a) if (a[i] != 0) z+=compute_Mder(nep,λ,i-1)*(V[:,i]*a[i]) end end return z end """ compute_Mder_from_MM(nep::NEP,λ::Number,i::Integer=0) Computes the [`compute_Mder`](@ref) via a call [`compute_MM`](@ref) using the fact that MM of a jordan block becomes derivatives. """ function compute_Mder_from_MM(nep::NEP,λ::Number,i::Integer=0) J=transpose(jordan_matrix(typeof(λ),i+1,λ)) n=size(nep,1); S=kron(J, Matrix(1.0I, n, n)) V=factorial(i) * kron(sparse(1.0I, 1, i+1)[:,end:-1:1], sparse(1.0I, n, n)) W=compute_MM(nep,S,V) return W[1:n,1:n] end """ compute_resnorm(nep::NEP,λ,v) Computes the residual norm of the `nep`, in the point `λ`, with the vector `v`, i.e., ``||M(λ)v||``. """ function compute_resnorm(nep::NEP,λ,v) return norm(compute_Mlincomb(nep,λ,reshape(v,size(nep,1),1))) end """ size(nep::NEP) size(nep::NEP,dim) Overloads the size functions for NEP. Size returns the size of the matrix defining the NEP. Note: All NEPs must implement this function. """ function size(nep::NEP) error("You need to provide an implementation of size for this NEP.") end function size(nep::NEP,dim) error("You need to provide an implementation of size for this NEP.") end """ issparse(nep::NEP) Overloads the issparse functions for NEP. Issparse returns `true` if the undelying type of the NEP is sparse, and `false` if it is dense. Default behaviour: Check sparsity of `compute_Mder(nep,0)` """ function issparse(nep::NEP) issparse(compute_Mder(nep,0.0)) end ############################################ # Misc helpers # """ struct NoConvergenceException Exeption thrown in case an iterative method does not converge\\ `λ` = current eigenvalue(s) approximation\\ `v` = current eigenvector(s) approximation\\ `errmeasure` = The error measure of the current eigenpair(s) approximation\\ `msg` """ struct NoConvergenceException <: Exception "current eigenvalue(s) approximation" λ "current eigenvector(s) approximation" v "The error measure of the current eigenpair(s) approximation" errmeasure msg end # Avoid dumping the huge eigenvectors in case of exception Base.showerror(io::IO, e::NoConvergenceException) = print(io, "No convergence: '",e.msg,"' ", "eigenvalue approx:",e.λ,", errmeasure:",e.errmeasure) """ Returns a Jordan matrix """ jordan_matrix(n::Integer,λ::Number)=jordan_matrix(ComplexF64,n,λ) function jordan_matrix(::Type{T},n::Integer,λ::Number) where T<:Number Z = T(λ) * Matrix{T}(I, n, n) + diagm(1 => ones(T, n-1)) end """ struct LostOrthogonalityException `msg` """ struct LostOrthogonalityException <: Exception msg end end # End Module
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@testset "is_schur" begin A = [0.5][:,:] @test InvariantSets.is_schur(A) end
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1.888889
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module RegistryTests using Pkg, UUIDs, LibGit2, Test using Pkg: depots1 using Pkg.REPLMode: pkgstr using Pkg.Types: PkgError include("utils.jl") function setup_test_registries(dir = pwd()) # Set up two registries with the same name, with different uuid pkg_uuids = ["c5f1542f-b8aa-45da-ab42-05303d706c66", "d7897d3a-8e65-4b65-bdc8-28ce4e859565"] reg_uuids = ["e9fceed0-5623-4384-aff0-6db4c442647a", "a8e078ad-b4bd-4e09-a52f-c464826eef9d"] for i in 1:2 regpath = joinpath(dir, "RegistryFoo$(i)") mkpath(joinpath(regpath, "Example")) write(joinpath(regpath, "Registry.toml"), """ name = "RegistryFoo" uuid = "$(reg_uuids[i])" repo = "https://github.com" [packages] $(pkg_uuids[i]) = { name = "Example$(i)", path = "Example" } """) write(joinpath(regpath, "Example", "Package.toml"), """ name = "Example$(i)" uuid = "$(pkg_uuids[i])" repo = "https://github.com/JuliaLang/Example.jl.git" """) write(joinpath(regpath, "Example", "Versions.toml"), """ ["0.5.1"] git-tree-sha1 = "8eb7b4d4ca487caade9ba3e85932e28ce6d6e1f8" """) write(joinpath(regpath, "Example", "Deps.toml"), """ ["0.5"] julia = "0.6-1.0" """) write(joinpath(regpath, "Example", "Compat.toml"), """ ["0.5"] julia = "0.6-1.0" """) LibGit2.with(LibGit2.init(regpath)) do repo LibGit2.add!(repo, "*") LibGit2.commit(repo, "initial commit"; author=TEST_SIG, committer=TEST_SIG) end end end function test_installed(registries) @test setdiff( UUID[r.uuid for r in registries], UUID[r.uuid for r in Pkg.Types.collect_registries()] ) == UUID[] end function is_pkg_available(pkg::PackageSpec) uuids = UUID[] for registry in Pkg.Types.collect_registries() reg_dict = Pkg.Types.read_registry(joinpath(registry.path, "Registry.toml")) for (uuid, pkginfo) in reg_dict["packages"] push!(uuids, UUID(uuid)) end end return in(pkg.uuid, uuids) end function with_depot2(f) Base.DEPOT_PATH[1:2] .= Base.DEPOT_PATH[2:-1:1] f() Base.DEPOT_PATH[1:2] .= Base.DEPOT_PATH[2:-1:1] end @testset "registries" begin temp_pkg_dir() do depot; mktempdir() do depot2 insert!(Base.DEPOT_PATH, 2, depot2) # set up registries regdir = mktempdir() setup_test_registries(regdir) generalurl = Pkg.Types.DEFAULT_REGISTRIES[1].url # hehe General = RegistrySpec(name = "General", uuid = "23338594-aafe-5451-b93e-139f81909106", url = generalurl) Foo1 = RegistrySpec(name = "RegistryFoo", uuid = "e9fceed0-5623-4384-aff0-6db4c442647a", url = joinpath(regdir, "RegistryFoo1")) Foo2 = RegistrySpec(name = "RegistryFoo", uuid = "a8e078ad-b4bd-4e09-a52f-c464826eef9d", url = joinpath(regdir, "RegistryFoo2")) # Packages in registries Example = PackageSpec(name = "Example", uuid = "7876af07-990d-54b4-ab0e-23690620f79a") Example1 = PackageSpec(name = "Example1", uuid = "c5f1542f-b8aa-45da-ab42-05303d706c66") Example2 = PackageSpec(name = "Example2", uuid = "d7897d3a-8e65-4b65-bdc8-28ce4e859565") # Add General registry ## Pkg REPL for reg in ("General", "23338594-aafe-5451-b93e-139f81909106", "General=23338594-aafe-5451-b93e-139f81909106") pkgstr("registry add $(reg)") test_installed([General]) pkgstr("registry up $(reg)") test_installed([General]) pkgstr("registry rm $(reg)") test_installed([]) end ## Registry API for reg in ("General", RegistrySpec("General"), RegistrySpec(name = "General"), RegistrySpec(name = "General", url = generalurl), RegistrySpec(uuid = "23338594-aafe-5451-b93e-139f81909106"), RegistrySpec(name = "General", uuid = "23338594-aafe-5451-b93e-139f81909106")) Pkg.Registry.add(reg) test_installed([General]) @test is_pkg_available(Example) Pkg.Registry.update(reg) test_installed([General]) Pkg.Registry.rm(reg) test_installed([]) @test !is_pkg_available(Example) end # Add registry from URL/local path. pkgstr("registry add $(Foo1.url)") test_installed([Foo1]) @test is_pkg_available(Example1) @test !is_pkg_available(Example2) with_depot2(() -> pkgstr("registry add $(Foo2.url)")) test_installed([Foo1, Foo2]) @test is_pkg_available(Example1) @test is_pkg_available(Example2) # reset installed registries rm.(joinpath.(Base.DEPOT_PATH[1:2], "registries"); force=true, recursive=true) Registry.add(RegistrySpec(url = Foo1.url)) test_installed([Foo1]) @test is_pkg_available(Example1) @test !is_pkg_available(Example2) with_depot2(() -> Registry.add(RegistrySpec(url = Foo2.url))) test_installed([Foo1, Foo2]) @test is_pkg_available(Example1) @test is_pkg_available(Example2) # Behaviour with conflicting registry names @test_throws PkgError pkgstr("registry up RegistryFoo") @test_throws PkgError Registry.update("RegistryFoo") @test_throws PkgError Registry.update(RegistrySpec("RegistryFoo")) @test_throws PkgError Registry.update(RegistrySpec(name = "RegistryFoo")) @test_throws PkgError pkgstr("registry remove RegistryFoo") @test_throws PkgError Registry.rm("RegistryFoo") @test_throws PkgError Registry.rm(RegistrySpec("RegistryFoo")) @test_throws PkgError Registry.rm(RegistrySpec(name = "RegistryFoo")) pkgstr("registry up $(Foo1.uuid)") pkgstr("registry update $(Foo1.name)=$(Foo1.uuid)") Registry.update(RegistrySpec(uuid = Foo1.uuid)) Registry.update(RegistrySpec(name = Foo1.name, uuid = Foo1.uuid)) test_installed([Foo1, Foo2]) pkgstr("registry rm $(Foo1.uuid)") test_installed([Foo2]) @test !is_pkg_available(Example1) @test is_pkg_available(Example2) Registry.add(RegistrySpec(url = Foo1.url)) test_installed([Foo1, Foo2]) @test is_pkg_available(Example1) @test is_pkg_available(Example2) pkgstr("registry rm $(Foo1.name)=$(Foo1.uuid)") test_installed([Foo2]) @test !is_pkg_available(Example1) @test is_pkg_available(Example2) pkgstr("registry rm $(Foo2.name)") test_installed([]) @test !is_pkg_available(Example1) @test !is_pkg_available(Example2) Registry.add(RegistrySpec(url = Foo1.url)) with_depot2(() -> Registry.add(RegistrySpec(url = Foo2.url))) test_installed([Foo1, Foo2]) @test is_pkg_available(Example1) @test is_pkg_available(Example2) Registry.rm(RegistrySpec(uuid = Foo1.uuid)) test_installed([Foo2]) @test !is_pkg_available(Example1) @test is_pkg_available(Example2) Registry.add(RegistrySpec(url = Foo1.url)) test_installed([Foo1, Foo2]) @test is_pkg_available(Example1) @test is_pkg_available(Example2) Registry.rm(RegistrySpec(name = Foo1.name, uuid = Foo1.uuid)) test_installed([Foo2]) @test !is_pkg_available(Example1) @test is_pkg_available(Example2) Registry.rm(RegistrySpec(Foo2.name)) test_installed([]) @test !is_pkg_available(Example1) @test !is_pkg_available(Example2) # multiple registries on the same time pkgstr("registry add General $(Foo1.url)") with_depot2(() -> pkgstr("registry add $(Foo2.url)")) test_installed([General, Foo1, Foo2]) @test is_pkg_available(Example) @test is_pkg_available(Example1) @test is_pkg_available(Example2) pkgstr("registry up General $(Foo1.uuid) $(Foo2.name)=$(Foo2.uuid)") pkgstr("registry rm General $(Foo1.uuid) $(Foo2.name)=$(Foo2.uuid)") test_installed([]) @test !is_pkg_available(Example) @test !is_pkg_available(Example1) @test !is_pkg_available(Example2) Registry.add([RegistrySpec("General"), RegistrySpec(url = Foo1.url)]) with_depot2(() -> Registry.add([RegistrySpec(url = Foo2.url)])) test_installed([General, Foo1, Foo2]) @test is_pkg_available(Example) @test is_pkg_available(Example1) @test is_pkg_available(Example2) Registry.update([RegistrySpec("General"), RegistrySpec(uuid = Foo1.uuid), RegistrySpec(name = Foo2.name, uuid = Foo2.uuid)]) Registry.rm([RegistrySpec("General"), RegistrySpec(uuid = Foo1.uuid), RegistrySpec(name = Foo2.name, uuid = Foo2.uuid)]) test_installed([]) @test !is_pkg_available(Example) @test !is_pkg_available(Example1) @test !is_pkg_available(Example2) # Trying to add a registry with the same name as existing one pkgstr("registry add $(Foo1.url)") @test_throws PkgError pkgstr("registry add $(Foo2.url)") @test_throws PkgError Registry.add([RegistrySpec(url = Foo2.url)]) end end # issue #711 temp_pkg_dir() do depot; mktempdir() do depot2 insert!(Base.DEPOT_PATH, 2, depot2) Registry.add("General") with_depot2(() -> Registry.add("General")) # This add should not error because depot/Example and depot2/Example have the same uuid Pkg.add("Example") @test isinstalled((name = "Example", uuid = UUID("7876af07-990d-54b4-ab0e-23690620f79a"))) end end end end # module
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2.031868
4,958
using Test, TransferEntropy x, y = rand(100), rand(100) ########################################### # Set `dim` and infer `k`, `l` and `m`. ########################################### tol = 1e-12 # Only with time series @test all(transferentropy(x, y) .>= 0 - tol) @test all(transferentropy(x, y, dim = 3) .>= 0 - tol) @test all(transferentropy(x, y, dim = 4) .>= 0 - tol) @test_throws ArgumentError transferentropy(x, y, dim = 2) # Only with time series @test transferentropy(x, y, RectangularBinning(10)) >= 0 - tol @test all(transferentropy(x, y, [RectangularBinning(x) for x in 2:4]) .>= 0 - tol) @test all(transferentropy(x, y, [RectangularBinning(x) for x in 2:4], dim = 3) .>= 0 - tol) @test all(transferentropy(x, y, [RectangularBinning(x) for x in 2:4], dim = 4) .>= 0 - tol) @test_throws ArgumentError transferentropy(x, y, [RectangularBinning(x) for x in 2:4], dim = 2) ########################################### # Infer `dim` from `k`, `l` and `m`. ########################################### # Only with time series @test all(transferentropy(x, y, 1, 1, 1) .>= 0 - tol) @test all(transferentropy(x, y, 1, 2, 1) .>= 0 - tol) @test_throws ArgumentError transferentropy(x, y, 1, 1, 0) # Only with time series @test transferentropy(x, y, RectangularBinning(10), 1, 1, 1) >= 0 - tol @test all(transferentropy(x, y, [RectangularBinning(x) for x in 2:4], 1, 1, 1) .>= 0 - tol) @test all(transferentropy(x, y, [RectangularBinning(x) for x in 2:4], 1, 2, 1) .>= 0 - tol) @test_throws ArgumentError transferentropy(x, y, [RectangularBinning(x) for x in 2:4], 1, 0, 1)
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2.576547
614
module MakieLayout using AbstractPlotting using AbstractPlotting: Rect2D import AbstractPlotting: IRect2D using AbstractPlotting.Keyboard using AbstractPlotting.Mouse using AbstractPlotting: ispressed, is_mouseinside using Observables: onany import Observables import Formatting using Match import Animations import PlotUtils using GridLayoutBase import Showoff include("types.jl") include("helpers.jl") include("mousestatemachine.jl") include("ticklocators/linear.jl") include("ticklocators/wilkinson.jl") include("defaultattributes.jl") include("lineaxis.jl") include("lobjects/laxis.jl") include("lobjects/lcolorbar.jl") include("lobjects/ltext.jl") include("lobjects/lslider.jl") include("lobjects/lbutton.jl") include("lobjects/lrect.jl") include("lobjects/ltoggle.jl") include("lobjects/llegend.jl") include("lobjects/lobject.jl") include("lobjects/lscene.jl") export LAxis export LSlider export LButton export LColorbar export LText export LRect export LToggle export LLegend export LegendEntry, MarkerElement, PolyElement, LineElement, LegendElement export LScene export linkxaxes!, linkyaxes!, linkaxes! export AxisAspect, DataAspect export autolimits! export AutoLinearTicks, ManualTicks, CustomTicks, WilkinsonTicks export layoutscene export set_close_to! # TODO: These functions are exported but are not in the docs and have no docstrings export hidexdecorations!, hideydecorations!, hidedecorations! export tight_xticklabel_spacing!, tight_yticklabel_spacing!, tight_ticklabel_spacing!, tightlimits! # from GridLayoutBase export GridLayout, GridPosition export GridLayoutSpec export BBox export LayoutObservables export Inside, Outside, Mixed export Fixed, Auto, Relative, Aspect export width, height, top, bottom, left, right export with_updates_suspended export appendcols!, appendrows!, prependcols!, prependrows!, deletecol!, deleterow!, trim! export gridnest! export AxisAspect, DataAspect export colsize!, rowsize!, colgap!, rowgap! export Left, Right, Top, Bottom, TopLeft, BottomLeft, TopRight, BottomRight export grid!, hbox!, vbox! export swap! export ncols, nrows const FPS = Node(30) const COLOR_ACCENT = Ref(RGBf0(((79, 122, 214) ./ 255)...)) const COLOR_ACCENT_DIMMED = Ref(RGBf0(((174, 192, 230) ./ 255)...)) end # module
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3.217949
702
__precompile__(true) module WinReg import Compat: @static export querykey const HKEY_CLASSES_ROOT = 0x80000000 const HKEY_CURRENT_USER = 0x80000001 const HKEY_LOCAL_MACHINE = 0x80000002 const HKEY_USERS = 0x80000003 const HKEY_PERFORMANCE_DATA = 0x80000004 const HKEY_CURRENT_CONFIG = 0x80000005 const HKEY_DYN_DATA = 0x80000006 const REG_NONE = 0 # no value type const REG_SZ = 1 # null-terminated ASCII string const REG_EXPAND_SZ = 2 # Unicode nul terminated string const REG_BINARY = 3 # Free form binary const REG_DWORD = 4 # 32-bit number const REG_DWORD_LITTLE_ENDIAN = 4 # 32-bit number (same as REG_DWORD) const REG_DWORD_BIG_ENDIAN = 5 # 32-bit number const REG_LINK = 6 # Symbolic Link (unicode) const REG_MULTI_SZ = 7 # Multiple Unicode strings const REG_RESOURCE_LIST = 8 # Resource list in the resource map const REG_FULL_RESOURCE_DESCRIPTOR = 9 # Resource list in the hardware description const REG_RESOURCE_REQUIREMENTS_LIST = 10 const REG_QWORD = 11 # 64-bit number const REG_QWORD_LITTLE_ENDIAN = 11 # 64-bit number (same as REG_QWORD) const KEY_ALL_ACCESS = 0xF003F # Combines the STANDARD_RIGHTS_REQUIRED, KEY_QUERY_VALUE, KEY_SET_VALUE, KEY_CREATE_SUB_KEY, KEY_ENUMERATE_SUB_KEYS, KEY_NOTIFY, and KEY_CREATE_LINK access rights. const KEY_CREATE_LINK = 0x00020 # Reserved for system use. const KEY_CREATE_SUB_KEY = 0x00004 # Required to create a subkey of a registry key. const KEY_ENUMERATE_SUB_KEYS = 0x00008 # Required to enumerate the subkeys of a registry key. const KEY_EXECUTE = 0x20019 # Equivalent to KEY_READ. const KEY_NOTIFY = 0x00010 # Required to request change notifications for a registry key or for subkeys of a registry key. const KEY_QUERY_VALUE = 0x00001 # Required to query the values of a registry key. const KEY_READ = 0x20019 # Combines the STANDARD_RIGHTS_READ, KEY_QUERY_VALUE, KEY_ENUMERATE_SUB_KEYS, and KEY_NOTIFY values. const KEY_SET_VALUE = 0x00002 # Required to create, delete, or set a registry value. const KEY_WOW64_32KEY = 0x00200 # Indicates that an application on 64-bit Windows should operate on the 32-bit registry view. This flag is ignored by 32-bit Windows. For more information, see Accessing an Alternate Registry View. # This flag must be combined using the OR operator with the other flags in this table that either query or access registry values. # Windows 2000: This flag is not supported. const KEY_WOW64_64KEY = 0x00100 # Indicates that an application on 64-bit Windows should operate on the 64-bit registry view. This flag is ignored by 32-bit Windows. For more information, see Accessing an Alternate Registry View. # This flag must be combined using the OR operator with the other flags in this table that either query or access registry values. # Windows 2000: This flag is not supported. const KEY_WRITE = 0x20006 # Combines the STANDARD_RIGHTS_WRITE, KEY_SET_VALUE, and KEY_CREATE_SUB_KEY access rights. function openkey(base::UInt32, path::AbstractString, accessmask::UInt32=KEY_READ) keyref = Ref{UInt32}() ret = ccall((:RegOpenKeyExW, "advapi32"), stdcall, Clong, (UInt32, Cwstring, UInt32, UInt32, Ref{UInt32}), base, path, 0, accessmask, keyref) if ret != 0 error("Could not open registry key") end keyref[] end function querykey(key::UInt32, valuename::AbstractString) dwSize = Ref{UInt32}() dwDataType = Ref{UInt32}() ret = ccall((:RegQueryValueExW, "advapi32"), stdcall, Clong, (UInt32, Cwstring, Ptr{UInt32}, Ref{UInt32}, Ptr{UInt8}, Ref{UInt32}), key, valuename, C_NULL, dwDataType, C_NULL, dwSize) if ret != 0 error("Could not find registry value name") end data = Array(UInt8, dwSize[]) ret = ccall((:RegQueryValueExW, "advapi32"), stdcall, Clong, (UInt32, Cwstring, Ptr{UInt32}, Ptr{UInt32}, Ptr{UInt8}, Ref{UInt32}), key, valuename, C_NULL, C_NULL, data, dwSize) if ret != 0 error("Could not retrieve registry data") end if dwDataType[] == REG_SZ || dwDataType[] == REG_EXPAND_SZ data_wstr = reinterpret(Cwchar_t,data) # string may or may not be null-terminated if data_wstr[end] == 0 pop!(data_wstr) end @static if isdefined(Base,:transcode) return String(transcode(UInt8,data_wstr)) else return bytestring(wstring(data_wstr)) end elseif dwDataType[] == REG_DWORD return reinterpret(Int32,data)[] elseif dwDataType[] == REG_QWORD return reinterpret(Int64,data)[] else return data end end function querykey(base::UInt32, path::AbstractString, valuename::AbstractString) key = openkey(base,path) val = querykey(key, valuename) closekey(key) val end function closekey(key::UInt32) ret = ccall((:RegCloseKey, "advapi32"), stdcall, Clong, (UInt32,), key) if ret != 0 error("Could not close key") end nothing end end # module
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220, 1366, 796, 15690, 7, 52, 5317, 23, 11, 43756, 10699, 58, 12962, 198, 220, 220, 220, 1005, 796, 269, 13345, 19510, 25, 8081, 20746, 11395, 3109, 54, 11, 366, 32225, 15042, 2624, 12340, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 14367, 13345, 11, 1012, 506, 11, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 357, 52, 5317, 2624, 11, 327, 86, 8841, 11, 350, 2213, 90, 52, 5317, 2624, 5512, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 350, 2213, 90, 52, 5317, 2624, 5512, 350, 2213, 90, 52, 5317, 23, 5512, 6524, 90, 52, 5317, 2624, 92, 828, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1994, 11, 1188, 84, 12453, 11, 327, 62, 33991, 11, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 327, 62, 33991, 11, 1366, 11, 43756, 10699, 8, 198, 220, 220, 220, 611, 1005, 14512, 657, 198, 220, 220, 220, 220, 220, 220, 220, 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220, 220, 220, 1441, 416, 9288, 1806, 7, 86, 8841, 7, 7890, 62, 86, 2536, 4008, 198, 220, 220, 220, 220, 220, 220, 220, 886, 198, 220, 220, 220, 2073, 361, 43756, 6601, 6030, 21737, 6624, 23337, 62, 42955, 12532, 198, 220, 220, 220, 220, 220, 220, 220, 1441, 302, 27381, 7, 5317, 2624, 11, 7890, 8, 21737, 198, 220, 220, 220, 2073, 361, 43756, 6601, 6030, 21737, 6624, 23337, 62, 48, 54, 12532, 198, 220, 220, 220, 220, 220, 220, 220, 1441, 302, 27381, 7, 5317, 2414, 11, 7890, 8, 21737, 198, 220, 220, 220, 2073, 198, 220, 220, 220, 220, 220, 220, 220, 1441, 1366, 198, 220, 220, 220, 886, 198, 437, 198, 198, 8818, 12405, 2539, 7, 8692, 3712, 52, 5317, 2624, 11, 3108, 3712, 23839, 10100, 11, 1188, 84, 12453, 3712, 23839, 10100, 8, 198, 220, 220, 220, 1994, 796, 1280, 2539, 7, 8692, 11, 6978, 8, 198, 220, 220, 220, 1188, 796, 12405, 2539, 7, 2539, 11, 1188, 84, 12453, 8, 198, 220, 220, 220, 1969, 2539, 7, 2539, 8, 198, 220, 220, 220, 1188, 198, 437, 198, 198, 8818, 1969, 2539, 7, 2539, 3712, 52, 5317, 2624, 8, 198, 220, 220, 220, 1005, 796, 269, 13345, 19510, 25, 8081, 26125, 9218, 11, 366, 32225, 15042, 2624, 12340, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 14367, 13345, 11, 1012, 506, 11, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 357, 52, 5317, 2624, 11, 828, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1994, 8, 198, 220, 220, 220, 611, 1005, 14512, 657, 198, 220, 220, 220, 220, 220, 220, 220, 4049, 7203, 23722, 407, 1969, 1994, 4943, 198, 220, 220, 220, 886, 198, 220, 220, 220, 2147, 198, 437, 628, 198, 437, 1303, 8265, 198 ]
2.287327
2,391
using RobustAdaptiveMetropolisSampler, Distributions, LinearAlgebra, VegaLite, DataFrames chain, accrate, S = RAM_sample( p -> logpdf(Normal(3., 2), p[1]), # log target function [0.], # Initial values 0.5, # Scaling factor 100_000 # Number of runs ) df = DataFrame(p1 = chain[:,1]) df |> @vlplot(:bar, x={:p1, bin=true}, y="count()")
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1.972851
221
using Plots, Test pgfplotsx() function create_plot(args...; kwargs...) pgfx_plot = plot(args...; kwargs...) return pgfx_plot, repr("application/x-tex", pgfx_plot) end function create_plot!(args...; kwargs...) pgfx_plot = plot!(args...; kwargs...) return pgfx_plot, repr("application/x-tex", pgfx_plot) end @testset "PGFPlotsX" begin pgfx_plot = plot(1:5) Plots._update_plot_object(pgfx_plot) @test pgfx_plot.o.the_plot isa PGFPlotsX.TikzDocument @test pgfx_plot.series_list[1].plotattributes[:quiver] === nothing axis = Plots.pgfx_axes(pgfx_plot.o)[1] @test count(x -> x isa PGFPlotsX.Plot, axis.contents) == 1 @test !haskey(axis.contents[1].options.dict, "fill") @testset "Legends" begin legends_plot = plot( rand(5,2), lab = ["1" ""] ) scatter!(legends_plot, rand(5) ) Plots._update_plot_object(legends_plot) axis_contents = Plots.pgfx_axes(legends_plot.o)[1].contents leg_entries = filter( x -> x isa PGFPlotsX.LegendEntry, axis_contents ) series = filter( x -> x isa PGFPlotsX.Plot, axis_contents ) @test length(leg_entries) == 2 @test !haskey(series[1].options.dict, "forget plot") @test haskey(series[2].options.dict, "forget plot") @test !haskey(series[3].options.dict, "forget plot") end # testset @testset "3D docs example" begin n = 100 ts = range(0, stop = 8π, length = n) x = ts .* map(cos, ts) y = (0.1ts) .* map(sin, ts) z = 1:n pl = plot( x, y, z, zcolor = reverse(z), m = (10, 0.8, :blues, Plots.stroke(0)), leg = false, cbar = true, w = 5, ) pgfx_plot = plot!(pl, zeros(n), zeros(n), 1:n, w = 10) Plots._update_plot_object(pgfx_plot) if @test_nowarn( haskey(Plots.pgfx_axes(pgfx_plot.o)[1].options.dict, "colorbar") == true ) @test Plots.pgfx_axes(pgfx_plot.o)[1]["colorbar"] === nothing end end # testset @testset "Color docs example" begin y = rand(100) plot( 0:10:100, rand(11, 4), lab = "lines", w = 3, palette = :grays, fill = 0, α = 0.6, ) pl = scatter!( y, zcolor = abs.(y .- 0.5), m = (:hot, 0.8, Plots.stroke(1, :green)), ms = 10 * abs.(y .- 0.5) .+ 4, lab = ["grad", "", "ient"], ) Plots._update_plot_object(pl) axis = Plots.pgfx_axes(pl.o)[1] @test count(x -> x isa PGFPlotsX.LegendEntry, axis.contents) == 6 @test count(x -> x isa PGFPlotsX.Plot, axis.contents) == 108 # each marker is its own plot, fillranges create 2 plot-objects marker = axis.contents[15] @test marker isa PGFPlotsX.Plot @test marker.options["mark"] == "*" @test marker.options["mark options"]["color"] == RGBA{Float64}(colorant"green", 0.8) @test marker.options["mark options"]["line width"] == 0.75 # 1px is 0.75pt end # testset @testset "Plot in pieces" begin pic = plot(rand(100) / 3, reg = true, fill = (0, :green)) scatter!(pic, rand(100), markersize = 6, c = :orange) Plots._update_plot_object(pic) axis_contents = Plots.pgfx_axes(pic.o)[1].contents leg_entries = filter( x -> x isa PGFPlotsX.LegendEntry, axis_contents ) series = filter( x -> x isa PGFPlotsX.Plot, axis_contents ) @test length(leg_entries) == 2 @test length(series) == 4 @test haskey(series[1].options.dict, "forget plot") @test !haskey(series[2].options.dict, "forget plot") @test haskey(series[3].options.dict, "forget plot") @test !haskey(series[4].options.dict, "forget plot") end # testset @testset "Marker types" begin markers = filter((m -> begin m in Plots.supported_markers() end), Plots._shape_keys) markers = reshape(markers, 1, length(markers)) n = length(markers) x = (range(0, stop = 10, length = n + 2))[2:(end - 1)] y = repeat(reshape(reverse(x), 1, :), n, 1) scatter( x, y, m = (8, :auto), lab = map(string, markers), bg = :linen, xlim = (0, 10), ylim = (0, 10), ) end # testset @testset "Layout" begin plot( Plots.fakedata(100, 10), layout = 4, palette = [:grays :blues :hot :rainbow], bg_inside = [:orange :pink :darkblue :black], ) end # testset @testset "Polar plots" begin Θ = range(0, stop = 1.5π, length = 100) r = abs.(0.1 * randn(100) + sin.(3Θ)) plot(Θ, r, proj = :polar, m = 2) end # testset @testset "Drawing shapes" begin verts = [ (-1.0, 1.0), (-1.28, 0.6), (-0.2, -1.4), (0.2, -1.4), (1.28, 0.6), (1.0, 1.0), (-1.0, 1.0), (-0.2, -0.6), (0.0, -0.2), (-0.4, 0.6), (1.28, 0.6), (0.2, -1.4), (-0.2, -1.4), (0.6, 0.2), (-0.2, 0.2), (0.0, -0.2), (0.2, 0.2), (-0.2, -0.6), ] x = 0.1:0.2:0.9 y = 0.7 * rand(5) .+ 0.15 plot( x, y, line = (3, :dash, :lightblue), marker = (Shape(verts), 30, RGBA(0, 0, 0, 0.2)), bg = :pink, fg = :darkblue, xlim = (0, 1), ylim = (0, 1), leg = false, ) end # testset @testset "Histogram 2D" begin histogram2d(randn(10000), randn(10000), nbins = 20) end # testset @testset "Heatmap-like" begin xs = [string("x", i) for i = 1:10] ys = [string("y", i) for i = 1:4] z = float((1:4) * reshape(1:10, 1, :)) pgfx_plot = heatmap(xs, ys, z, aspect_ratio = 1) Plots._update_plot_object(pgfx_plot) if @test_nowarn( haskey(Plots.pgfx_axes(pgfx_plot.o)[1].options.dict, "colorbar") == true ) @test Plots.pgfx_axes(pgfx_plot.o)[1]["colorbar"] === nothing @test Plots.pgfx_axes(pgfx_plot.o)[1]["colormap name"] == "plots1" end pgfx_plot = wireframe(xs, ys, z, aspect_ratio = 1) # TODO: clims are wrong end # testset @testset "Contours" begin x = 1:0.5:20 y = 1:0.5:10 f(x, y) = begin (3x + y^2) * abs(sin(x) + cos(y)) end X = repeat(reshape(x, 1, :), length(y), 1) Y = repeat(y, 1, length(x)) Z = map(f, X, Y) p2 = contour(x, y, Z) p1 = contour(x, y, f, fill = true) plot(p1, p2) # TODO: colorbar for filled contours end # testset @testset "Varying colors" begin t = range(0, stop = 1, length = 100) θ = (6π) .* t x = t .* cos.(θ) y = t .* sin.(θ) p1 = plot(x, y, line_z = t, linewidth = 3, legend = false) p2 = scatter( x, y, marker_z = ((x, y) -> begin x + y end), color = :bwr, legend = false, ) plot(p1, p2) end # testset @testset "Framestyles" begin scatter( fill(randn(10), 6), fill(randn(10), 6), framestyle = [:box :semi :origin :zerolines :grid :none], title = [":box" ":semi" ":origin" ":zerolines" ":grid" ":none"], color = permutedims(1:6), layout = 6, label = "", markerstrokewidth = 0, ticks = -2:2, ) # TODO: support :semi end # testset @testset "Quiver" begin x = (-2pi):0.2:(2 * pi) y = sin.(x) u = ones(length(x)) v = cos.(x) arrow_plot = plot(x, y, quiver = (u, v), arrow = true) # TODO: could adjust limits to fit arrows if too long, but how? # TODO: get latex available on CI # mktempdir() do path # @test_nowarn savefig(arrow_plot, path*"arrow.pdf") # end end # testset @testset "Annotations" begin y = rand(10) pgfx_plot = plot( y, annotations = (3, y[3], Plots.text("this is \\#3", :left)), leg = false, ) Plots._update_plot_object(pgfx_plot) axis_content = Plots.pgfx_axes(pgfx_plot.o)[1].contents nodes = filter(x -> !isa(x, PGFPlotsX.Plot), axis_content) @test length(nodes) == 1 mktempdir() do path file_path =joinpath(path,"annotations.tex") @test_nowarn savefig(pgfx_plot, file_path) open(file_path) do io lines = readlines(io) @test count(s -> occursin("node", s), lines) == 1 end end annotate!([ (5, y[5], Plots.text("this is \\#5", 16, :red, :center)), (10, y[10], Plots.text("this is \\#10", :right, 20, "courier")), ]) Plots._update_plot_object(pgfx_plot) axis_content = Plots.pgfx_axes(pgfx_plot.o)[1].contents nodes = filter(x -> !isa(x, PGFPlotsX.Plot), axis_content) @test length(nodes) == 3 mktempdir() do path file_path =joinpath(path,"annotations.tex") @test_nowarn savefig(pgfx_plot, file_path) open(file_path) do io lines = readlines(io) @test count(s -> occursin("node", s), lines) == 3 end end annotation_plot = scatter!( range(2, stop = 8, length = 6), rand(6), marker = (50, 0.2, :orange), series_annotations = [ "series", "annotations", "map", "to", "series", Plots.text("data", :green), ], ) Plots._update_plot_object(annotation_plot) axis_content = Plots.pgfx_axes(annotation_plot.o)[1].contents nodes = filter(x -> !isa(x, PGFPlotsX.Plot), axis_content) @test length(nodes) == 9 mktempdir() do path file_path =joinpath(path,"annotations.tex") @test_nowarn savefig(annotation_plot, file_path) open(file_path) do io lines = readlines(io) @test count(s -> occursin("node", s), lines) == 9 end end end # testset @testset "Ribbon" begin aa = rand(10) bb = rand(10) cc = rand(10) conf = [aa - cc bb - cc] ribbon_plot = plot(collect(1:10), fill(1, 10), ribbon = (conf[:, 1], conf[:, 2])) Plots._update_plot_object(ribbon_plot) axis = Plots.pgfx_axes(ribbon_plot.o)[1] plots = filter(x -> x isa PGFPlotsX.Plot, axis.contents) @test length(plots) == 4 @test !haskey(plots[1].options.dict, "fill") @test !haskey(plots[2].options.dict, "fill") @test !haskey(plots[3].options.dict, "fill") @test haskey(plots[4].options.dict, "fill") @test ribbon_plot.o !== nothing @test ribbon_plot.o.the_plot !== nothing # mktempdir() do path # @test_nowarn savefig(ribbon_plot, path*"ribbon.svg") # end end # testset end # testset @testset "Extra kwargs" begin pl = plot(1:5, test = "me") @test pl[1][1].plotattributes[:extra_kwargs][:test] == "me" pl = plot(1:5, test = "me", extra_kwargs = :subplot) @test pl[1].attr[:extra_kwargs][:test] == "me" pl = plot(1:5, test = "me", extra_kwargs = :plot) @test pl.attr[:extra_plot_kwargs][:test] == "me" pl = plot(1:5, extra_kwargs = Dict(:plot => Dict(:test => "me"), :series => Dict(:and => "me too"))) @test pl.attr[:extra_plot_kwargs][:test] == "me" @test pl[1][1].plotattributes[:extra_kwargs][:and] == "me too" pl = plot( plot(1:5, title="Line"), scatter(1:5, title="Scatter", extra_kwargs=Dict(:subplot=>Dict("axis line shift" => "10pt"))) ) Plots._update_plot_object(pl) axes = Plots.pgfx_axes(pl.o) @test !haskey(axes[1].options.dict, "axis line shift") @test haskey(axes[2].options.dict, "axis line shift") end # testset
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1.951613
6,014
struct Satellite <: LieGroupModel J::Diagonal{Float64,SVector{3,Float64}} end Satellite() = Satellite(Diagonal(@SVector ones(3))) RobotDynamics.control_dim(::Satellite) = 3 Base.position(::Satellite, x::SVector) = @SVector zeros(3) RobotDynamics.orientation(::Satellite, x::SVector) = UnitQuaternion(x[4], x[5], x[6], x[7]) RobotDynamics.LieState(::Satellite) = RobotDynamics.LieState(UnitQuaternion{Float64}, (3,0)) function RobotDynamics.dynamics(model::Satellite, x::SVector, u::SVector) ω = @SVector [x[1], x[2], x[3]] q = normalize(@SVector [x[4], x[5], x[6], x[7]]) J = model.J ωdot = J\(u - ω × (J*ω)) qdot = 0.5*lmult(q)*hmat()*ω return [ωdot; qdot] end model = Satellite() @test LieState(model) === RobotDynamics.QuatState(7, (4,)) @test state_dim(model) == 7 @test state_diff_size(model) == 6 x,u = rand(model) s = LieState(model) @test all(RobotDynamics.vec_states(s, x) .≈ (x[1:3],Float64[])) @test all(RobotDynamics.rot_states(s, x) .≈ (UnitQuaternion(x[4:7]),)) @test all(RobotDynamics.vec_states(model, x) .≈ (x[1:3],Float64[])) @test all(RobotDynamics.rot_states(model, x) .≈ (UnitQuaternion(x[4:7]),)) x2 = rand(s) @test norm(x[4:7]) ≈ 1 dx = RobotDynamics.state_diff(model, x, x2) @test length(dx) == 6 @test dx ≈ [x[1:3] - x2[1:3]; UnitQuaternion(x[4:7]) ⊖ UnitQuaternion(x2[4:7])]
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2.158065
620
const VecTuple{N,T}= Union{NTuple{N,T}, Vector{NTuple{N,T}}} # This is a bit inefficient. The more verbose implementation below is more efficient and # allows use of the internal expand! #expand(I::Vector{NTuple{N,T}}) where {N,T}= [map(a->a[i], I) for i in 1:N] expand(I::Vector{NTuple{N,T}}) where {N,T}= begin r= Vector{Vector{T}}(undef,N) expand!(r,I) return r end expand(I::NTuple{N,T}) where {N,T}= [I[i] for i in 1:N] expand!(r,I::Vector{NTuple{N,T}}) where {N,T}= foreach(i-> (r[i]= map(a->a[i],I)), 1:N) expand!(r,I::NTuple{N,T}) where {N,T}= foreach(i-> (r[i]= I[i]), 1:N) # NOTE this could probably be a macro # OPTIMIZE Less runtime cost? joinTuples(x,y,z...)= (x..., joinTuples(y,z...)...) joinTuples(x,y)= (x..., y...) joinTuples(x)= x cartesianIdx(iPre::VecTuple{N1,T},iPost::VecTuple{N2,T}) where {N1,N2,T<:Integer}= ( vec( map( x->CartesianIndex(joinTuples(x...)), Iterators.product(iPre,iPost) ) ) ::Vector{CartesianIndex{N1+N2}}) #using Lazy #cartesianIdx(iPre::VecTuple{N1,T},iPost::VecTuple{N2,T}) where {N1,N2,T<:Integer}= # (@>> Iterators.product(iPre,iPost) begin # map( x->CartesianIndex(joinTuples(x...)) ) # vec # end )::Vector{CartesianIndex{N1+N2}} vecTuple_2_tupleVec(i::Vector{NTuple{N,Int}}) where N= NTuple{N,Vector{Int}}(expand(i)) vecTuple_2_tupleVec(i)= i
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2.095164
641
## --- General conversions # Europium anomalies @test eustar(6.5433, 5.9037) ≈ 2.0329978601003864 @test eustar(34.7773, 6.5433, 5.9037, 0.8904) ≈ 2.0825737578695205 # Iron oxide conversions @test feoconversion(3.5, NaN, NaN, NaN) == 3.5 @test feoconversion(3.5, NaN, 7.5, NaN) == 7.5 @test feoconversion(3.5, NaN, 7.5, 10) == 7.5 @test feoconversion(3.5, 4.4, NaN, NaN) ≈ 7.45916511675966 @test feoconversion(NaN, 4.4, NaN, NaN) ≈ 3.9591651167596607 # Other oxide conversion D = elementify(["Fe" "Mg" "Ca" "P"; 10000 10000 10000 10000; 10000 10000 10000 10000], importas=:Dict) D = oxideconversion(D) @test all(D["FeOT"] .≈ (molarmass["Fe"]+molarmass["O"])/molarmass["Fe"]) @test all(D["MgO"] .≈ (molarmass["Mg"]+molarmass["O"])/molarmass["Mg"]) @test all(D["CaO"] .≈ (molarmass["Ca"]+molarmass["O"])/molarmass["Ca"]) @test all(D["P2O5"] .≈ (molarmass["P"]+2.5*molarmass["O"])/molarmass["P"]) # Weathering indices @test CIA(14.8577, 4.5611, 3.29641, 2.3992) ≈ 47.66582778067264 @test WIP(3.2964, 4.5611, 2.3992, 5.9121) ≈ 78.40320264846837 ## -- Perplex name abbreviations abbreviations = ("ak", "alm", "and", "andr", "chum", "cz", "crd", "ep", "fa", "fctd", "fcrd", "fep", "fosm", "fst", "fo", "geh", "gr", "hcrd", "tpz", "ky", "larn", "law", "merw", "mctd", "mst", "mnctd", "mncrd", "mnst", "mont", "osm1", "osm2", "phA", "pump", "py", "rnk", "sill", "spss", "sph", "spu", "teph", "ty", "vsv", "zrc", "zo", "acm", "cats", "di", "en", "fs", "hed", "jd", "mgts", "pswo", "pxmn", "rhod", "wo", "anth", "cumm", "fanth", "fgl", "ftr", "ged", "gl", "grun", "parg", "rieb", "tr", "ts", "deer", "fcar", "fspr", "mcar", "spr4", "spr7", "ann", "cel", "east", "fcel", "ma", "mnbi", "mu", "naph", "pa", "phl", "afchl", "ames", "clin", "daph", "fsud", "mnchl", "sud", "atg", "chr", "fta", "kao", "pre", "prl", "ta", "tats", "ab", "anl", "an", "coe", "crst", "heu", "abh", "kals", "lmt", "lc", "me", "mic", "ne", "q", "san", "stlb", "stv", "trd", "wrk", "bdy", "cor", "geik", "hem", "herc", "ilm", "oilm", "lime", "mft", "mt", "mang", "bunsn", "per", "pnt", "ru", "sp", "usp", "br", "dsp", "gth", "ank", "arag", "cc", "dol", "mag", "rhc", "sid", "diam", "gph", "iron", "Ni", "CO2", "CO", "H2", "CH4", "O2", "H2O", "abL", "anL", "diL", "enL", "faL", "fliq", "foL", "h2oL", "hliq", "kspL", "mliq", "qL", "silL", "H+", "Cl-", "OH-", "Na+", "K+", "Ca++", "Mg++", "Fe++", "Al+++", "CO3", "AlOH3", "AlOH4-", "KOH", "HCL", "KCL", "NaCl", "CaCl2", "CaCl+", "MgCl2", "MgCl", "FeCl2", "aqSi", "Augite(G)", "Cpx(JH)", "Cpx(l)", "Cpx(h)", "Cpx(stx)", "Cpx(stx7)", "Omph(HP)", "Cpx(HP)", "Cpx(m)", "Cpx(stx8)", "Omph(GHP)", "cAmph(G)", "Cumm", "Gl", "Tr", "GlTrTsPg", "Amph(DHP)", "Amph(DPW)", "Ca-Amph(D)", "Na-Amph(D)", "Act(M)", "GlTrTsMr", "cAmph(DP)", "melt(G)", "melt(W)", "melt(HP)", "melt(HGP)", "pMELTS(G)", "mMELTS(G)", "LIQ(NK)", "LIQ(EF)", "Chl(W)", "Chl(HP)", "Chl(LWV)", "O(JH)", "O(SG)", "O(HP)", "O(HPK)", "O(stx)", "O(stx7)", "Ol(m)", "O(stx8)", "Sp(JH)", "GaHcSp", "Sp(JR)", "Sp(GS)", "Sp(HP)", "Sp(stx)", "CrSp", "Sp(stx7)", "Sp(WPC)", "Sp(stx8)", "Pl(JH)", "Pl(h)", "Pl(stx8)", "Kf", "San", "San(TH)", "Grt(JH)", "Gt(W)", "CrGt", "Gt(MPF)", "Gt(B)", "Gt(GCT)", "Gt(HP)", "Gt(EWHP)", "Gt(WPH)", "Gt(stx)", "Gt(stx8)", "Gt(WPPH)", "ZrGt(KP)", "Maj", "Opx(JH)", "Opx(W)", "Opx(HP)", "CrOpx(HP)", "Opx(stx)", "Opx(stx8)", "Mica(W)", "Pheng(HP)", "MaPa", "Mica(CF)", "Mica(CHA1)", "Mica(CHA)", "Mica+(CHA)", "Mica(M)", "Mica(SGH)", "Ctd(W)", "Ctd(HP)", "Ctd(SGH)", "St(W)", "St(HP)", "Bi(W)", "Bio(TCC)", "Bio(WPH)", "Bio(HP)", "Crd(W)", "hCrd", "Sa(WP)", "Sapp(HP)", "Sapp(KWP)", "Sapp(TP)", "Osm(HP)", "F", "F(salt)", "COH-Fluid", "Aq_solven0", "WADDAH", "T", "Scap", "Carp", "Carp(M)", "Carp(SGH)", "Sud(Livi)", "Sud", "Sud(M)", "Anth", "o-Amph", "oAmph(DP)", "feldspar", "feldspar_B", "Pl(I1,HP)", "Fsp(C1)", "Do(HP)", "M(HP)", "Do(AE)", "Cc(AE)", "oCcM(HP)", "Carb(M)", "oCcM(EF)", "dis(EF)", "IlHm(A)", "IlGkPy", "Ilm(WPH)", "Ilm(WPH0)", "Neph(FB)", "Chum", "Atg(PN)", "B", "Pu(M)", "Stlp(M)", "Wus",) common_names = ("akermanite", "almandine", "andalusite", "andradite", "clinohumite", "clinozoisite", "cordierite", "epidote", "fayalite", "Fe-chloritoid", "Fe-cordierite", "Fe-epidote", "Fe-osumilite", "Fe-staurolite", "forsterite", "gehlenite", "grossular", "hydrous cordierite", "hydroxy-topaz", "kyanite", "larnite", "lawsonite", "merwinite", "Mg-chloritoid", "Mg-staurolite", "Mn-chloritoid", "Mn-cordierite", "Mn-staurolite", "monticellite", "osumilite(1)", "osumilite(2)", "phase A", "pumpellyite", "pyrope", "rankinite", "sillimanite", "spessartine", "sphene", "spurrite", "tephroite", "tilleyite", "vesuvianite", "zircon", "zoisite", "acmite", "Ca-tschermakite", "diopside", "enstatite", "ferrosilite", "hedenbergite", "jadeite", "Mg-tschermakite", "pseudowollastonite", "pyroxmangite", "rhodonite", "wollastonite", "anthophyllite", "cummingtonite", "Fe-anthophyllite", "Fe-glaucophane", "ferroactinolite", "gedrite", "glaucophane", "grunerite", "pargasite", "riebeckite", "tremolite", "tschermakite", "deerite", "Fe-carpholite", "Fe-sapphirine(793)", "Mg-carpholite", "sapphirine(442)", "sapphirine(793)", "annite", "celadonite", "eastonite", "Fe-celadonite", "margarite", "Mn-biotite", "muscovite", "Na-phlogopite", "paragonite", "phlogopite", "Al-free chlorite", "amesite", "clinochlore", "daphnite", "Fe-sudoite", "Mn-chlorite", "sudoite", "antigorite", "chrysotile", "Fe-talc", "kaolinite", "prehnite", "pyrophyllite", "talc", "tschermak-talc", "albite", "analcite", "anorthite", "coesite", "cristobalite", "heulandite", "highalbite", "kalsilite", "laumontite", "leucite", "meionite", "microcline", "nepheline", "quartz", "sanidine", "stilbite", "stishovite", "tridymite", "wairakite", "baddeleyite", "corundum", "geikielite", "hematite", "hercynite", "ilmenite", "ilmenite(ordered)", "lime", "magnesioferrite", "magnetite", "manganosite", "nickel oxide", "periclase", "pyrophanite", "rutile", "spinel", "ulvospinel", "brucite", "diaspore", "goethite", "ankerite", "aragonite", "calcite", "dolomite", "magnesite", "rhodochrosite", "siderite", "diamond", "graphite", "iron", "nickel", "carbon dioxide", "carbon monoxide", "hydrogen", "methane", "oxygen", "water fluid", "albite liquid", "anorthite liquid", "diopside liquid", "enstatite liquid", "fayalite liquid", "Fe-liquid (in KFMASH)", "forsterite liquid", "H2O liquid", "H2O liquid (in KFMASH)", "K-feldspar liquid", "Mg liquid (in KFMASH)", "Silica liquid", "Sillimanite liquid", "H+(aq)", "Cl(aq)", "OH(aq)", "Na+(aq)", "K+(aq)", "Ca2+(aq)", "Mg2+(aq)", "Fe2+(aq)", "Al3+(aq)", "CO3--(aq)", "Al(OH)3(aq)", "Al(OH)4----(aq)", "KOH(aq)", "HCl(aq)", "KCl(aq)", "NaCl(aq)", "CaCl(aq)", "CaCl+(aq)", "MgCl2(aq)", "MgCl+(aq)", "FeCl(aq)", "Aqueous silica", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "melt", "melt", "melt", "melt", "melt", "melt", "melt", "melt", "chlorite", "chlorite", "chlorite", "olivine", "olivine", "olivine", "olivine", "olivine", "olivine", "olivine", "olivine", "spinel", "spinel", "spinel", "spinel", "spinel", "spinel", "spinel", "spinel", "spinel", "spinel", "plagioclase", "plagioclase", "plagioclase", "k-feldspar", "k-feldspar", "k-feldspar", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "orthopyroxene", "orthopyroxene", "orthopyroxene", "orthopyroxene", "orthopyroxene", "orthopyroxene", "white mica", "white mica", "white mica", "white mica", "white mica", "white mica", "white mica", "white mica", "white mica", "chloritoid", "chloritoid", "chloritoid", "staurolite", "staurolite", "biotite", "biotite", "biotite", "biotite", "cordierite", "cordierite", "sapphirine", "sapphirine", "sapphirine", "sapphirine", "osumilite", "fluid", "fluid", "fluid", "fluid", "fluid", "talc", "scapolite", "carpholite", "carpholite", "carpholite", "sudoite", "sudoite", "sudoite", "orthoamphibole", "orthoamphibole", "orthoamphibole", "ternary feldspar", "ternary feldspar", "ternary feldspar", "ternary feldspar", "calcite", "calcite", "calcite", "calcite", "calcite", "calcite", "calcite", "calcite", "ilmenite", "ilmenite", "ilmenite", "ilmenite", "nepheline", "clinohumite", "serpentine", "brucite", "pumpellyite", "stilpnomelane", "wüstite",) @test perplex_common_name.(abbreviations) == common_names abbreviations = ("ak", "alm", "and", "andr", "chum", "cz", "crd", "ep", "fa", "fctd", "fcrd", "fep", "fosm", "fst", "fo", "geh", "gr", "hcrd", "tpz", "ky", "larn", "law", "merw", "mctd", "mst", "mnctd", "mncrd", "mnst", "mont", "osm1", "osm2", "phA", "pump", "py", "rnk", "sill", "spss", "sph", "spu", "teph", "ty", "vsv", "zrc", "zo", "acm", "cats", "di", "en", "fs", "hed", "jd", "mgts", "pswo", "pxmn", "rhod", "wo", "anth", "cumm", "fanth", "fgl", "ftr", "ged", "gl", "grun", "parg", "rieb", "tr", "ts", "deer", "fcar", "fspr", "mcar", "spr4", "spr7", "ann", "cel", "east", "fcel", "ma", "mnbi", "mu", "naph", "pa", "phl", "afchl", "ames", "clin", "daph", "fsud", "mnchl", "sud", "atg", "chr", "fta", "kao", "pre", "prl", "ta", "tats", "ab", "anl", "an", "coe", "crst", "heu", "abh", "kals", "lmt", "lc", "me", "mic", "ne", "q", "san", "stlb", "stv", "trd", "wrk", "bdy", "cor", "geik", "hem", "herc", "ilm","oilm","lime", "mft", "mt", "mang", "bunsn", "per", "pnt", "ru", "sp", "usp", "br", "dsp", "gth", "ank", "arag", "cc", "dol", "mag", "rhc", "sid", "diam", "gph", "iron", "Ni", "CO2", "CO", "H2", "CH4", "O2", "H2O", "abL", "anL", "diL", "enL", "faL", "fliq", "foL", "h2oL", "hliq", "kspL", "mliq", "qL", "silL", "H+", "Cl-", "OH-", "Na+", "K+", "Ca++", "Mg++", "Fe++", "Al+++", "CO3", "AlOH3", "AlOH4-", "KOH", "HCL", "KCL", "NaCl", "CaCl2", "CaCl+", "MgCl2", "MgCl", "FeCl2", "aqSi",) full_names = ("akermanite", "almandine", "andalusite", "andradite", "clinohumite", "clinozoisite", "cordierite", "epidote(ordered)", "fayalite", "Fe-chloritoid", "Fe-cordierite", "Fe-epidote", "Fe-osumilite", "Fe-staurolite", "forsterite", "gehlenite", "grossular", "hydrous cordierite", "hydroxy-topaz", "kyanite", "larnite-bredigite", "lawsonite", "merwinite", "Mg-chloritoid", "Mg-staurolite", "Mn-chloritoid", "Mn-cordierite", "Mn-staurolite", "monticellite", "osumilite(1)", "osumilite(2)", "phase A", "pumpellyite", "pyrope", "rankinite", "sillimanite", "spessartine", "sphene", "spurrite", "tephroite", "tilleyite", "vesuvianite", "zircon", "zoisite", "acmite", "Ca-tschermaks pyroxene", "Diopside", "enstatite", "ferrosilite", "hedenbergite", "jadeite", "mg-tschermak", "pseudowollastonite", "pyroxmangite", "rhodonite", "wollastonite", "anthophyllite", "cummingtonite", "Fe-anthophyllite", "Fe-glaucophane", "ferroactinolite", "gedrite(Na-free)", "glaucophane", "grunerite", "pargasite", "riebeckite", "tremolite", "tschermakite", "deerite", "fe-carpholite", "fe-sapphirine(793)", "mg-carpholite", "sapphirine(442)", "sapphirine(793)", "annite", "celadonite", "eastonite", "Fe-celadonite", "margarite", "Mn-biotite", "muscovite", "Na-phlogopite", "paragonite", "phlogopite", "Al-free chlorite", "amesite(14Ang)", "clinochlore(ordered)", "daphnite", "Fe-sudoite", "Mn-chlorite", "Sudoite", "antigorite", "chrysotile", "Fe-talc", "Kaolinite", "prehnite", "pyrophyllite", "talc", "tschermak-talc", "albite", "analcite", "anorthite", "coesite", "cristobalite", "heulandite", "highalbite", "kalsilite", "laumontite", "leucite", "meionite", "microcline", "nepheline", "quartz", "sanidine", "stilbite", "stishovite", "tridymite", "wairakite", "baddeleyite", "corundum", "geikielite", "hematite", "hercynite", "ilmenite", "ilmenite(ordered)","lime", "magnesioferrite", "magnetite", "manganosite", "nickel oxide", "periclase", "pyrophanite", "rutile", "spinel", "ulvospinel", "brucite", "diaspore", "goethite", "ankerite", "aragonite", "calcite", "dolomite", "magnesite", "rhodochrosite", "siderite", "diamond", "graphite", "iron", "nickel", "carbon dioxide", "carbon monoxide", "hydrogen", "methane", "oxygen", "water fluid", "albite liquid", "anorthite liquid", "diopside liquid", "enstatite liquid", "fayalite liquid", "Fe-liquid (in KFMASH)", "Forsterite liquid", "H2O liquid", "H2O liquid (in KFMASH)", "K-feldspar liquid", "Mg liquid (in KFMASH)", "Silica liquid", "Sillimanite liquid", "H+(aq)", "Cl(aq)", "OH(aq)", "Na+(aq)", "K+(aq)", "Ca2+(aq)", "Mg2+(aq)", "Fe2+(aq)", "Al3+(aq)", "CO3--(aq)", "Al(OH)3(aq)", "Al(OH)4----(aq)", "KOH(aq)", "HCl(aq)", "KCl(aq)", "NaCl(aq)", "CaCl(aq)", "CaCl+(aq)", "MgCl2(aq)", "MgCl+(aq)", "FeCl(aq)", "Aqueous silica",) @test perplex_expand_name.(abbreviations) == full_names @test perplex_abbreviate_name.(full_names) == abbreviations @test perplex_phase_is_solid.(("melt(HGP)", "q", "diL", "andr", "T(K)")) == (false, true, false, true, false) @test findall(germ_perplex_name_matches.(germ_kd["minerals"], germ_kd["minerals"])) == [3, 12, 18] ## --- Saturation models # SiO2, TiO2, Al2O3, FeOT, MnO, MgO, CaO, Na2O, K2O, P2O5 majors = [58.509, 1.022, 14.858, 4.371, 0.141, 4.561, 5.912, 3.296, 2.399, 0.279] @test tzirc(majors..., 100) ≈ 602.8489762809595 @test tzircZr(majors..., 800) ≈ 832.9689080567883 @test all(tzircM((repeat([m],2) for m in majors)...,) .≈ 2.335918319204001) @test StatGeochem.Ayers_tsphene(majors...) ≈ 637.139776663209 @test StatGeochem.Ayers_tspheneTiO2(majors..., 800) ≈ 2.3545537746637324 @test all(StatGeochem.Ayers_tspheneC.((repeat([m],2) for m in majors)...,) .≈ 2.4338232746497326) # SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O, Li2O, H2O montel_elems = [58.509, 1.022, 14.858, 4.371, 4.561, 5.912, 3.296, 2.399, 0.01, 4.0] @test StatGeochem.Montel_tmonaziteREE(montel_elems..., 750.0) ≈ 12.03834338792398 @test StatGeochem.Montel_tmonazite(montel_elems..., 100,100,100,0,0,0) ≈ 630.4499586271999 @test StatGeochem.Rusiecka_tmonaziteREE(200, 750) ≈ 0.27430570654873154 @test StatGeochem.Rusiecka_txenotimeY(200, 750) ≈ 41.9312030248943 @test StatGeochem.Harrison_tapatiteP2O5(58.509, 14.858, 5.912, 3.296, 2.399, 750.) ≈ 0.10142278764336987 @test StatGeochem.Harrison_tapatiteP(58.509, 14.858, 5.912, 3.296, 2.399, 750.) ≈ 442.6362451135793 @test StatGeochem.Harrison_tapatiteP2O5(58.509, 750.) ≈ 0.10142278764336987 @test StatGeochem.Harrison_tapatite(58.509, 0.1) ≈ 748.6127179814277 # SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O, P2O5 majors = [58.509, 1.022, 14.858, 4.371, 4.561, 5.912, 3.296, 2.399, 0.279] @test StatGeochem.Tollari_tapatite(majors...) ≈ 521.0594433599132 @test StatGeochem.Tollari_tapatiteP2O5(58.509,5.912,750.) ≈ 0.5011681927262436 ## --- End of File
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2.121643
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""" NormalCanon(η, λ) Canonical parametrisation of the Normal distribution with canonical parameters `η` and `λ`. The two *canonical parameters* of a normal distribution ``\\mathcal{N}(\\mu, \\sigma^2)`` with mean ``\\mu`` and standard deviation ``\\sigma`` are ``\\eta = \\sigma^{-2} \\mu`` and ``\\lambda = \\sigma^{-2}``. """ struct NormalCanon{T<:Real} <: ContinuousUnivariateDistribution η::T # σ^(-2) * μ λ::T # σ^(-2) μ::T # μ function NormalCanon{T}(η, λ; check_args::Bool=true) where T check_args && @check_args(NormalCanon, λ > zero(λ)) new{T}(η, λ, η / λ) end end NormalCanon(η::T, λ::T; check_args::Bool=true) where {T<:Real} = NormalCanon{typeof(η/λ)}(η, λ; check_args=check_args) NormalCanon(η::Real, λ::Real; check_args::Bool=true) = NormalCanon(promote(η, λ)...; check_args=check_args) NormalCanon(η::Integer, λ::Integer; check_args::Bool=true) = NormalCanon(float(η), float(λ); check_args=check_args) NormalCanon() = NormalCanon{Float64}(0.0, 1.0; check_args=false) @distr_support NormalCanon -Inf Inf #### Type Conversions convert(::Type{NormalCanon{T}}, η::S, λ::S) where {T <: Real, S <: Real} = NormalCanon(T(η), T(λ)) convert(::Type{NormalCanon{T}}, d::NormalCanon{S}) where {T <: Real, S <: Real} = NormalCanon(T(d.η), T(d.λ); check_args=false) ## conversion between Normal and NormalCanon convert(::Type{Normal}, d::NormalCanon) = Normal(d.μ, 1 / sqrt(d.λ)) convert(::Type{NormalCanon}, d::Normal) = (λ = 1 / d.σ^2; NormalCanon(λ * d.μ, λ)) meanform(d::NormalCanon) = convert(Normal, d) canonform(d::Normal) = convert(NormalCanon, d) #### Parameters params(d::NormalCanon) = (d.η, d.λ) @inline partype(d::NormalCanon{T}) where {T<:Real} = T #### Statistics mean(d::NormalCanon) = d.μ median(d::NormalCanon) = mean(d) mode(d::NormalCanon) = mean(d) skewness(d::NormalCanon{T}) where {T<:Real} = zero(T) kurtosis(d::NormalCanon{T}) where {T<:Real} = zero(T) var(d::NormalCanon) = 1 / d.λ std(d::NormalCanon) = sqrt(var(d)) entropy(d::NormalCanon) = (-log(d.λ) + log2π + 1) / 2 location(d::NormalCanon) = mean(d) scale(d::NormalCanon) = std(d) #### Evaluation pdf(d::NormalCanon, x::Real) = (sqrt(d.λ) / sqrt2π) * exp(-d.λ * abs2(x - d.μ)/2) logpdf(d::NormalCanon, x::Real) = (log(d.λ) - log2π - d.λ * abs2(x - d.μ))/2 zval(d::NormalCanon, x::Real) = (x - d.μ) * sqrt(d.λ) xval(d::NormalCanon, z::Real) = d.μ + z / sqrt(d.λ) cdf(d::NormalCanon, x::Real) = normcdf(zval(d,x)) ccdf(d::NormalCanon, x::Real) = normccdf(zval(d,x)) logcdf(d::NormalCanon, x::Real) = normlogcdf(zval(d,x)) logccdf(d::NormalCanon, x::Real) = normlogccdf(zval(d,x)) quantile(d::NormalCanon, p::Real) = xval(d, norminvcdf(p)) cquantile(d::NormalCanon, p::Real) = xval(d, norminvccdf(p)) invlogcdf(d::NormalCanon, lp::Real) = xval(d, norminvlogcdf(lp)) invlogccdf(d::NormalCanon, lp::Real) = xval(d, norminvlogccdf(lp)) #### Sampling rand(rng::AbstractRNG, cf::NormalCanon) = cf.μ + randn(rng) / sqrt(cf.λ) #### Affine transformations function Base.:+(d::NormalCanon, c::Real) η, λ = params(d) return NormalCanon(η + c * λ, λ) end Base.:*(c::Real, d::NormalCanon) = NormalCanon(d.η / c, d.λ / c^2)
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using PatternFolds using Documenter DocMeta.setdocmeta!(PatternFolds, :DocTestSetup, :(using PatternFolds); recursive=true) makedocs(; modules=[PatternFolds], authors="Jean-Francois Baffier", repo="https://github.com/Humans-of-Julia/PatternFolds.jl/blob/{commit}{path}#{line}", sitename="PatternFolds.jl", format=Documenter.HTML(; prettyurls=get(ENV, "CI", "false") == "true", canonical="https://Humans-of-Julia.github.io/PatternFolds.jl", assets=String[], ), pages=[ "Home" => "index.md", ], ) deploydocs(; repo="github.com/Humans-of-Julia/PatternFolds.jl", devbranch="main", )
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@block SimonDanisch ["2d"] begin @cell "Test heatmap + image overlap" [image, heatmap, transparency] begin heatmap(rand(32, 32)) image!(map(x->RGBAf0(x,0.5, 0.5, 0.8), rand(32,32))) end @cell "Interaction" [scatter, linesegment, record] begin scene = Scene() f(t, v, s) = (sin(v + t) * s, cos(v + t) * s) time_node = Node(0.0) p1 = scatter!(scene, lift(t-> f.(t, range(0, stop = 2pi, length = 50), 1), time_node))[end] p2 = scatter!(scene, lift(t-> f.(t * 2.0, range(0, stop = 2pi, length = 50), 1.5), time_node))[end] points = lift(p1[1], p2[1]) do pos1, pos2 map((a, b)-> (a, b), pos1, pos2) end linesegments!(scene, points) N = 150 record(scene, @outputfile(mp4), range(0, stop = 10, length = N)) do i push!(time_node, i) end end @cell "barplot" [barplot] begin # barplot(1:10, rand(10)) # barplot(rand(10)) barplot(rand(10), color = rand(10)) # barplot(rand(3), color = [:red, :blue, :green]) end @cell "quiver" [quiver, arrows, vectorfield, gradient] begin using ImageFiltering x = range(-2, stop = 2, length = 21) y = x z = x .* exp.(-x .^ 2 .- (y') .^ 2) scene = contour(x, y, z, levels = 10, linewidth = 3) u, v = ImageFiltering.imgradients(z, KernelFactors.ando3) arrows!(x, y, u, v, arrowsize = 0.05) end @cell "image" [image] begin AbstractPlotting.hbox( image(Makie.logo(), scale_plot = false), image(rand(100, 500), scale_plot = false), ) end @cell "scatter colormap" [scatter, colormap] begin scatter(rand(10), rand(10), color = rand(10)) end @cell "FEM polygon 2D" [fem, poly] begin coordinates = [ 0.0 0.0; 0.5 0.0; 1.0 0.0; 0.0 0.5; 0.5 0.5; 1.0 0.5; 0.0 1.0; 0.5 1.0; 1.0 1.0; ] connectivity = [ 1 2 5; 1 4 5; 2 3 6; 2 5 6; 4 5 8; 4 7 8; 5 6 9; 5 8 9; ] color = [0.0, 0.0, 0.0, 0.0, -0.375, 0.0, 0.0, 0.0, 0.0] poly(coordinates, connectivity, color = color, strokecolor = (:black, 0.6), strokewidth = 4) end @cell "FEM mesh 2D" [fem, mesh] begin coordinates = [ 0.0 0.0; 0.5 0.0; 1.0 0.0; 0.0 0.5; 0.5 0.5; 1.0 0.5; 0.0 1.0; 0.5 1.0; 1.0 1.0; ] connectivity = [ 1 2 5; 1 4 5; 2 3 6; 2 5 6; 4 5 8; 4 7 8; 5 6 9; 5 8 9; ] color = [0.0, 0.0, 0.0, 0.0, -0.375, 0.0, 0.0, 0.0, 0.0] scene = mesh(coordinates, connectivity, color = color, shading = false) wireframe!(scene[end][1], color = (:black, 0.6), linewidth = 3) end @cell "colored triangle" [mesh, polygon] begin mesh( [(0.0, 0.0), (0.5, 1.0), (1.0, 0.0)], color = [:red, :green, :blue], shading = false ) end @cell "heatmap interpolation" [heatmap, interpolate, subscene, theme] begin using AbstractPlotting: hbox, vbox data = rand(50, 100) p1 = heatmap(data, interpolate = true) p2 = heatmap(data, interpolate = false) t = Theme(align = (:left, :bottom), raw = true, camera = campixel!) title1 = text(t, "Interpolate = true") title2 = text(t, "Interpolate = false") s = vbox( hbox(p1, title1), hbox(p2, title2), ) end @cell "colored triangle" [polygon] begin poly( [(0.0, 0.0), (0.5, 1.0), (1.0, 0.0)], color = [:red, :green, :blue], strokecolor = :black, strokewidth = 2 ) end @cell "Subscenes" [image, scatter, subscene] begin img = rand(RGBAf0, 100, 100) scene = image(img, show_axis = false) subscene = Scene(scene, IRect(100, 100, 300, 300)) scatter!(subscene, rand(100) * 200, rand(100) * 200, markersize = 4) scene end @cell "Polygons" [poly, polygon, linesegments] begin using GeometryTypes scene = Scene(resolution = (500, 500)) points = decompose(Point2f0, Circle(Point2f0(50), 50f0)) pol = poly!(scene, points, color = :gray, strokewidth = 10, strokecolor = :red) # Optimized forms poly!(scene, [Circle(Point2f0(50+300), 50f0)], color = :gray, strokewidth = 10, strokecolor = :red) poly!(scene, [Circle(Point2f0(50+i, 50+i), 10f0) for i = 1:100:400], color = :red) poly!(scene, [Rectangle{Float32}(50+i, 50+i, 20, 20) for i = 1:100:400], strokewidth = 2, strokecolor = :green) linesegments!(scene, [Point2f0(50 + i, 50 + i) => Point2f0(i + 70, i + 70) for i = 1:100:400], linewidth = 8, color = :purple ) end @cell "Contour Function" [contour] begin r = range(-10, stop = 10, length = 512) z = ((x, y)-> sin(x) + cos(y)).(r, r') contour(r, r, z, levels = 5, color = :viridis, linewidth = 3) end @cell "Hbox" [lines, scatter, hbox] begin t = range(-122277.93103448274, stop=-14798.035304081845, length=29542) x = -42 .- randn(length(t)) sc1 = scatter(t, x, color=:black, markersize=sqrt(length(t)/20)) sc2 = lines(t[1:end-1], diff(x), color = :blue) hbox(sc2, sc1) end @cell "Customize Axes" [lines, axis] begin x = LinRange(0,3pi,200); y = sin.(x) lin = lines(x, y, padding = (0.0, 0.0), axis = ( names = (axisnames = ("", ""),), grid = (linewidth = (0, 0),), )) end @cell "contour" [contour] begin y = range(-0.997669, stop = 0.997669, length = 23) contour(range(-0.99, stop = 0.99, length = 23), y, rand(23, 23), levels = 10) end @cell "Heatmap" [heatmap] begin heatmap(rand(32, 32)) end @cell "Animated Scatter" [animated, scatter, updating, record] begin N = 10 r = [(rand(7, 2) .- 0.5) .* 25 for i = 1:N] scene = scatter(r[1][:, 1], r[1][:, 2], markersize = 1, limits = FRect(-25/2, -25/2, 25, 25)) s = scene[end] # last plot in scene record(scene, @outputfile(mp4), r) do m s[1] = m[:, 1] s[2] = m[:, 2] end end @cell "Text Annotation" [text, align, annotation] begin text( ". This is an annotation!", position = (300, 200), align = (:center, :center), textsize = 60, font = "Blackchancery" ) end @cell "Text rotation" [text, rotation] begin scene = Scene() pos = (500, 500) posis = Point2f0[] for r in range(0, stop = 2pi, length = 20) global pos, posis p = pos .+ (sin(r)*100.0, cos(r) * 100) push!(posis, p) t = text!( scene, "test", position = p, textsize = 50, rotation = 1.5pi - r, align = (:center, :center) ) end scatter!(scene, posis, markersize = 10) end @cell "The famous iris example" [RDatasets, DataFrames, scatter, axis] begin using DataFrames, RDatasets # do Pkg.add.(["DataFrames", "RDatasets"]) if you don't have these packages installed iris = dataset("datasets", "iris") x = iris[:SepalWidth] y = iris[:SepalLength] scene = Scene() colors = [:red, :green, :blue] i = 1 #color incrementer for sp in unique(iris[:Species]) idx = iris[:Species] .== sp sel = iris[idx, [:SepalWidth, :SepalLength]] scatter!(scene, sel[:,1], sel[:,2], color = colors[i], limits = FRect(1.5, 4.0, 3.0, 4.0)) global i = i+1 end scene axis = scene[Axis] # get axis axis[:names][:axisnames] = ("Sepal width", "Sepal length") scene end # @cell "Chess Game" [heatmap, scatter, interactive] begin # using Base.Iterators: repeated # r = 1:8 # board = isodd.(r .+ r') # scene = Scene(resolution = (1000, 1000)) # heatmap!(scene, board, scale_plot = false, show_axis = false) # white = ['♕', '♔', '♖', '♖', '♗', '♗', '♘', '♘', repeated('♙', 8)...] # wx_positions = [4, 5, 1, 8, 3, 6, 2, 7, (1:8)...] # wy_positions = [repeated(1, 8)..., repeated(2, 8)...] # w_positions = Point2.(wx_positions, wy_positions) # white_game = scatter!( # scene, w_positions, marker = white, # scale_plot = false, show_axis = false, # markersize = 0.5, marker_offset = Vec2f0(-0.7) # )[end] # black = Char.(Int.(white) .+ 6) # b_positions = Point2f0.(wx_positions, [repeated(8, 8)..., repeated(7, 8)...]) # black_game = scatter!( # scene, b_positions, marker = black, # scale_plot = false, show_axis = false, # markersize = 0.5, marker_offset = Vec2f0(-0.7) # )[end] # # function move_fig!(color, figure, target) # game = color == :white ? white_game : black_game # game[1][][figure] = target # game[1][] = game[1][] # end # display(scene) # move_fig!(:white, 9, (1, 4)) # end end
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1.887733
5,095
include("../emi.jl") using EMI using EMI.Draw, EMI.Gmsh loop = Loop([CircleArc(Point(1, 0), Point(0, 0), Point(0, 1)), CircleArc(Point(0, 1), Point(0, 0), Point(-1, 0)), CircleArc(Point(-1, 0), Point(0, 0), Point(0, -1)), CircleArc(Point(0, -1), Point(0, 0), Point(1, 0))]) canvas = Canvas() canvas = canvas + loop set_bbox!(canvas, 0.2, 0.3) gmsh_script(canvas, 0.2)
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1.990291
206
module Config struct SimulationConfig time_step_update_period::UInt8 "function defining a(t)" a end end # module
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2.8
45
########################## Auxiliary functions ############################# function eye(tpe::Type,n::S) where {S <: Integer} if tpe <: Number return Matrix{tpe}(I,n,n) end end eye(n::Integer) = eye(Float64,n::Integer) function tracem(x::Array{T,2}) where {T <: Real} # Computes the matrix-trace as defined by Gomme and Klein (2011) # We require the number of rows to be greater than the number of columns, so # that m is greater than one. trans = false if size(x,1) < size(x,2) x = Matrix(x') trans = true end n = size(x,2) m = Int(size(x,1)/n) y = zeros(m,1) # We want this to be a 2-d array for subsequent matrix multiplication @inbounds for i = 1:m @views y[i,1] = tr(x[(n*(i-1)+1):i*n,1:n]) end if trans == true y = y' end return Matrix(y) end function kron_prod_times_vector(a::AbstractArray{T,2},b::AbstractArray{T,2},v::AbstractArray{T,1}) where {T <: Real} # This function efficiently computes kron(a,b)*v where a and b are matrices # and v is a vector. (n1,n2) = size(a) (n3,n4) = size(b) n5 = length(v) if n5 != n2*n4 error("a, b, and v do not have the correct sizes") end p = vec(b*reshape(v,n4,n2)*a') return p end function vector_times_kron_prod(v::AbstractArray{T,1},a::AbstractArray{T,2},b::AbstractArray{T,2}) where {T <: Real} product = Matrix(kron_prod_times_vector(a',b',v')') return product end function kron_prod_times_matrix(a::AbstractArray{T,2},b::AbstractArray{T,2},v::AbstractArray{T,2}) where {T <: Real} # This function efficiently computes kron(a,b)*v where a, b, and v are # conformable matrices. (n1,n2) = size(a) (n3,n4) = size(b) (n5,n6) = size(v) if n5 != n2*n4 error("a, b, and v do not have the correct sizes") end p = zeros(n1*n3,n6) for i = 1:n6 @views p[:,i] = kron_prod_times_vector(a,b,v[:,i]) end return p end function matrix_times_kron_prod(v::AbstractArray{T,2},a::AbstractArray{T,2},b::AbstractArray{T,2}) where {T <: Real} product = Matrix(kron_prod_times_matrix(a',b',v')') return product end function kron_prod_times_vector(a::AbstractArray{T,2},b::AbstractArray{T,2},c::AbstractArray{T,2},v::AbstractArray{T,1}) where {T <: Real} # This function efficiently computes kron(a,kron(b,c))*v where a, b, and c # are matrices and v is a vector. Included for completeness, but not # actually used. (n1,n2) = size(a) (n3,n4) = size(b) (n5,n6) = size(c) n7 = length(v) if n7 != n2*n4*n6 error("a, b, c, and v do not have the correct sizes") end v_tilda = reshape(v,n4*n6,n2)*a' p = vec(kron_prod_times_matrix(b,c,v_tilda)) return p end function vector_times_kron_prod(v::AbstractArray{T,1},a::AbstractArray{T,2},b::AbstractArray{T,2},c::AbstractArray{T,2}) where {T <: Real} product = Matrix(kron_prod_times_vector(a',b',c',v')') return product end function kron_prod_times_matrix(a::AbstractArray{T,2},b::AbstractArray{T,2},c::AbstractArray{T,2},v::AbstractArray{T,2}) where {T <: Real} # This function efficiently computes kron(a,kron(b,c))*v where a, b, c and # v are comformable matrices. (n1,n2) = size(a) (n3,n4) = size(b) (n5,n6) = size(c) (n7,n8) = size(v) if n7 != n2*n4*n6 error("a, b, c, and v do not have the correct sizes") end p = zeros(n1*n3*n5,n8) for i = 1:n8 @views v_tilda = reshape(v[:,i],n4*n6,n2)*a' p[:,i] .= vec(kron_prod_times_matrix(b,c,v_tilda)) end return p end function matrix_times_kron_prod(v::AbstractArray{T,2},a::AbstractArray{T,2},b::AbstractArray{T,2},c::AbstractArray{T,2}) where {T <: Real} product = Matrix(kron_prod_times_matrix(a',b',c',v')') return product end function dsylvester(a::AbstractArray{T,2}, b::AbstractArray{T,2}, c::Union{AbstractArray{T,1},AbstractArray{T,2}}) where {T <: Real} #= Uses the Hessenberg-Schur method to find the bounded solution of the discrete Sylvester equation: X + A*X*B = C Based on Golub, Nash, and Van Loan (1979). =# n = size(a, 1) m = size(b, 1) x = zeros(size(c)) (s,u) = schur(Matrix(b')) (v,t) = hessenberg(a) c = v'*c*u j = m while j > 0 j1 = j if j == 1 block_size = 1 elseif isequal(s[j,j-1],0.0) == false block_size = 2 j -= 1 else; block_size = 1 end @views ajj = kron(s[j:j1,j:j1],t) + I @views rhs = vec(c[:,j:j1]) if j1 < m @views rhs2 = t*(x[:,(j+1):m]*s[j:j1,(j+1):m]') rhs -= vec(rhs2) end w = ajj\rhs @views x[:,j] = w[1:n] if block_size == 2 @views x[:,j1] = w[(n+1):2*n] end j -= 1 end x = v*x*u' return x end function trm(x::AbstractArray{T,2}) where {T <: Real} # Computes the matrix trace as defined by Binning (2013). Used for # computing the second-order terms, hss, gss. (n1,n2) = size(x) k = Int(round(sqrt(n2))) y = zeros(n1) for i = 1:k @views y += x[:,i+(i-1)*k] end return y end function trm2(x::AbstractArray{T,2}) where {T <: Real} # Computes the matrix trace as defined by Binning (2013). Used for # computing the third-order terms, hssx, gssx. (n1,n2) = size(x) k = Int(round(n2^(1//3))) y = zeros(n1,k) for j = 1:k for i = 1:k @views y[:,j] += x[:,(j-1)+i+(i-1)*k^2] end end return y end function create_omega3(n::S) where {S <: Integer} # Creates the combination matrix for a third-order perturbation as defined in Levintal (2017). # This function is a simplified version of the create_OMEGA function originally written in # Matlab by Oren Levintal for his paper "Fifth Order Perturbation Solution to DSGE Models" # published in the Journal of Economic Dynamics and Control, 2017. Permission to translate # this function into Julia and release it within the SolveDSGE module was granted by Oren # Levintal on February 5, 2020. ind = [1:n^3;] M = reshape(ind,1,n,n,n) Ix = eye(S,n^3) omega3 = (reshape(Ix[:,PermutedDimsArray(M,[1,4,2,3])],n^3,n^3) + reshape(Ix[:,PermutedDimsArray(M,[1,2,4,3])],n^3,n^3) + reshape(Ix[:,PermutedDimsArray(M,[1,2,3,4])],n^3,n^3)) return omega3 end function kron_prod_times_vector(A::Union{Array{Array{T,2},1},Array{Array{Complex{T},2},1}},x::Union{Array{T,1},Array{Complex{T},1}},n::Array{S,1},p::S) where {T <: Real, S <: Integer} # Computes y = (A[p] * A[p-1] * ... * A[1] )*x N = prod(n[1:p]) z = copy(x) for i = 1:p z = (A[i]*reshape(z,n[i],Int(N/n[i])))' end y = reshape(Matrix(z),N) return y end function kron_prod_times_matrix(A::Union{Array{Array{T,2},1},Array{Array{Complex{T},2},1}},x::Union{Array{T,2},Array{Complex{T},2}},n::Array{S,1},p::S) where {T <: Real, S <: Integer} # Computes y = (A[p] * A[p-1] * ... * A[1] )*x N = prod(n[1:p]) y = Array{Complex{T}}(undef,N,size(x,2)) for j = 1:size(x,2) @views z = x[:,j] for i = 1:p z = (A[i]*reshape(z,n[i],Int(N/n[i])))' end y[:,j] .= reshape(Matrix(z),N) end return y end function KPShiftSolve(TT::Union{Array{Array{T,2},1},Array{Array{Complex{T},2},1}},n::Array{S,1},c::Union{Array{T,1},Array{Complex{T},1}},lambda::T,alpha::Union{T,Complex{T}}) where {T <: Real, S <: Integer} p = length(n) N = prod(n) c = copy(c) TT[p] = alpha*TT[p] if p == 1 y = (TT[1] + lambda*Matrix{T}(I,n[1],n[1]))\c else y = Array{Complex{T}}(undef,N) mp = Int(N/n[p]) for i = n[p]:-1:1 idx = ((i-1)*mp+1):(i*mp) y[idx] = KPShiftSolve(TT[1:(p-1)],n[1:(p-1)],c[idx],lambda,TT[p][i,i]) z = kron_prod_times_vector(TT[1:p],y[idx],n,p-1) for j = 1:(i-1) jdx = ((j-1)*mp+1):(j*mp) c[jdx] = c[jdx] - TT[p][j,i]*z end end end return y end function martin_van_loan(a::Array{T,2},b::Array{T,2},c::Array{T,2},d::Array{T,2},k::S) where {T <: Real, S <: Integer} #= Uses a recursive Schur method to find the bounded solution of the Sylvester equation: AX + B*X*(Kron^(k)C) = D Based on Martin and Van Loan (2006). This is a simplified implementation of their algorithm, but it captures most of the gains over Golub, Nash, and van Loan (1979). =# a = copy(a) b = a\copy(b) c = copy(c) d = a\copy(d) (v,s) = hessenberg(b) # v*s*v' = b (t,q) = schur(complex(c')) # q*t*q' = c' v = Matrix(v) p = k + 2 TT = Array{typeof(t)}(undef,p) TT[1] = s for i = 2:p TT[i] = conj(t) end Q = fill(q,k+1) inv_Q = fill(Matrix(q'),k+1) n = fill(size(c,1),p) n[1] = size(b,1) N = prod(n) lambda = 1.0 e = vec(v'*kron_prod_times_matrix(inv_Q,Matrix(d'),n[2:end],k+1)') y = reshape(KPShiftSolve(TT,n,e,lambda,1.0),size(d)) x = real(Matrix(v*kron_prod_times_matrix(Q,Matrix(y'),n[2:end],k+1)')) return x end function dlyap(a::Array{T,2}, b::Array{T,2}) where {T <: Real} n = size(a,1) x = zeros(n,n) j = n (s,u) = schur(a) b = u'b*u while j > 0 j1 = j if j == 1 block = 1 elseif !isequal(s[j,j-1],0.0) block = 2 j -= 1 else block = 1 end @views lhs = kron(s[j:j1,j:j1],s) - I # I = eye(block*n) @views rhs = vec(b[:,j:j1]) if j1 < n @views rhs2 = s*(x[:,(j1+1):n]*s[j:j1,(j1+1):n]') rhs += vec(rhs2) end w = -lhs\rhs @views x[:,j] = w[1:n] if block == 2 @views x[:,j1] = w[(n+1):block*n] end j -= 1 end x = u*x*u' return x end function ind2sub(i::S,dims::Tuple{S,Vararg{S}}) where {S <: Integer} if i < 1 || i > prod(dims) error("index is out of bounds.") end subs = CartesianIndices(dims)[i] return subs end function compute_variances(soln::FirstOrderSolutionStoch) hx = soln.hx k = soln.k gx = soln.gx sigma = soln.sigma var_states = dlyap(hx,k*sigma*k') var_jumps = gx*var_states*gx' return var_states,var_jumps end function _compute_chebyshev_integrals(eps_nodes::Array{T,1},eps_weights::Array{T,1},nodes::Array{T,1},order::S,rho::T,sigma::T) where {T <: AbstractFloat, S <: Integer} # Case where shocks are AR(1) and innovations are independent if rho == 0.0 rho = eps() end selected_nodes = copy(nodes) if length(nodes) > 2 selected_number = ceil(Int,length(nodes)^(1/2)) if isodd(length(nodes)) && iseven(selected_number) selected_number -= 1 elseif iseven(length(nodes)) && isodd(selected_number) selected_number -= 1 end start_index = Int((length(nodes)-selected_number)/2) selected_nodes = nodes[start_index+1:start_index+selected_number] end terms_num = Array{T}(undef,length(eps_nodes)) integrals = Array{T,2}(undef,order+1,length(selected_nodes)) integrals2 = Array{T}(undef,order+1) for i = 1:(order+1) integrals2[i] = sum(exp.(sqrt(2)*sigma*(i-1)*eps_nodes).*eps_weights)*pi^(-1/2) for j = 1:length(selected_nodes) terms_num .= rho*nodes[j] .+ sqrt(2)*sigma*eps_nodes terms_den = rho*nodes[j] terms_num .= chebyshev_polynomial(i,terms_num)[:,i] terms_den = chebyshev_polynomial(i,terms_den)[i] integrals[i,j] = sum((terms_num/terms_den).*eps_weights)*pi^(-1/2) end end nodetoosmall = abs.(selected_nodes) .< sqrt(eps()) if sum(nodetoosmall) > 0 if length(selected_nodes) == 1 integrals[:,1] .= integrals2 else for i = 1:length(selected_nodes) if nodetoosmall[i] == 1 if i == 1 integrals[:,i] .= integrals[:,i+1] elseif i == length(selected_nodes) integrals[:,i] .= integrals[:,i-1] else integrals[:,i] .= (integrals[:,i-1]+integrals[:,i+1])/2 end end end end end return reshape(sum(integrals,dims=2)/length(selected_nodes),order+1) end function _compute_chebyshev_integrals(eps_nodes::Array{T,1},eps_weights::Array{T,1},nodes::Array{Array{T,1},1},order::Union{S,Array{S,1}},Ρ::Array{T,2},k::Array{T,2}) where {T <: AbstractFloat, S <: Integer} N = size(k,2) for i = 1:N if Ρ[i,i] == 0.0 Ρ[i,i] = eps() end end selected_nodes = similar(nodes) for i = 1:length(nodes) if length(nodes[i]) > 2 selected_number = ceil(Int,sqrt(length(nodes[i]))) if isodd(length(nodes[i])) && iseven(selected_number) selected_number -= 1 elseif iseven(length(nodes[i])) && isodd(selected_number) selected_number -= 1 end start_index = Int((length(nodes[i])-selected_number)/2) selected_nodes[i] = nodes[i][start_index+1:start_index+selected_number] end end if typeof(order) == S ord = fill(order,N) else ord = copy(order) end terms_num = Array{T}(undef,length(eps_nodes)) integrals = Array{T}(undef,((ord.+1)...,length.(selected_nodes)...)) integrals2 = Array{T}(undef,(ord.+1)...) order_prod = prod(ord.+1) nodes_prod = prod(length.(selected_nodes)) eps_prod = length(eps_nodes)^N for i = 1:order_prod ii = ind2sub(i,Tuple(ord.+1)) int2 = 0.0 for j = 1:eps_prod jj = ind2sub(j,Tuple(fill(length(eps_nodes),N))) eps_w = eps_weights[jj[1]] eps_node = eps_nodes[jj[1]] for k = 2:N eps_w *= eps_weights[jj[k]] eps_node = [eps_node;eps_nodes[jj[k]]] end int2 += exp(2^(N/2)*sqrt(det(k*k'))*collect(ii.-1)'*(k*k')*eps_node)*eps_w*pi^(-N/2) end integrals2[ii...] = int2 # exp(collect(ii.-1)'*(k*k')*collect(ii.-1)/2) # This is the analytic expression end for i = 1:order_prod ii = ind2sub(i,Tuple(ord.+1)) for m = 1:nodes_prod mm = ind2sub(m,Tuple(length.(selected_nodes))) int2 = 0.0 node = selected_nodes[1][mm[1]] for k = 2:N node = [node;selected_nodes[k][mm[k]]] end for j = 1:eps_prod jj = ind2sub(j,Tuple(fill(length(eps_nodes),N))) eps_w = eps_weights[jj[1]] eps_node = eps_nodes[jj[1]] for k = 2:N eps_w *= eps_weights[jj[k]] eps_node = [eps_node;eps_nodes[jj[k]]] end terms_num = Ρ*node + sqrt(2)*k*eps_node terms_den = Ρ*node num = chebyshev_polynomial(ii[1],terms_num[1])[ii[1]] den = chebyshev_polynomial(ii[1],terms_den[1])[ii[1]] for k = 2:N num = [num;chebyshev_polynomial(ii[k],terms_num[k])[ii[k]]] den = [den;chebyshev_polynomial(ii[k],terms_den[k])[ii[k]]] end int2 += (prod(num)/prod(den))*eps_w*pi^(-N/2) end integrals[ii...,mm...] = int2 end end for i in eachindex(integrals) ii = ind2sub(i,Tuple(size(integrals))) if abs(integrals[i]) > 2.0 || isnan(integrals[i]) if length(integrals) == 1 integrals[i] = integrals2[i] else integrals[i] = integrals2[CartesianIndex(Tuple(ii)[1:N])] end end end for i = N:-1:1 integrals = sum(integrals,dims = (N+i))/length(selected_nodes[i]) end return reshape(integrals,Tuple(ord.+1)) end function compute_chebyshev_integrals(eps_nodes::Array{T,1},eps_weights::Array{T,1},nodes::Array{Array{T,1},1},order::Union{S,Array{S,1}},Ρ::Array{T,2},k::Array{T,2}) where {T <: AbstractFloat, S <: Integer} if !isdiag(Ρ.>sqrt(eps())) error("The autoregression matrix for the shocks must be diagonal") end ns = size(k,2) if typeof(order) == S ord = fill(order,ns) else ord = order[1:ns] end if !isdiag(abs.(k).>sqrt(eps())) # Correlated innovations integrals = _compute_chebyshev_integrals(eps_nodes,eps_weights,nodes[1:ns],ord,Ρ,k) return integrals else integrals = Array{Array{T,1},1}(undef,ns) for i = 1:ns integrals[i] = _compute_chebyshev_integrals(eps_nodes,eps_weights,nodes[i],ord[i],Ρ[i,i],k[i,i]) end return integrals end end function scale_chebyshev_weights!(weights::Array{Array{T,N},1},scaled_weights::Array{Array{T,N},1},integrals::Array{Array{T,1},1},j_approx::Union{S,Array{S,1}},ns::S) where {T <: AbstractFloat, N, S <: Integer} for i = 1:length(j_approx) for j = 1:ns index = [1:ndims(weights[i]);] index[1],index[j] = index[j],index[1] scaled_weights[i] .= permutedims(integrals[j].*permutedims(weights[i],index),index) end end end function scale_chebyshev_weights!(weights::Array{Array{T,N},1},scaled_weights::Array{Array{T,N},1},integrals::Array{T,N2},j_approx::Union{S,Array{S,1}},ns::S) where {T <: AbstractFloat, N, N2, S <: Integer} for i = 1:length(j_approx) for j = 1:ns scaled_weights[i] .= integrals.*weights[i] end end end function _compute_smolyak_integrals(eps_nodes::Array{T,1},eps_weights::Array{T,1},nodes::Array{T,1},order::S,rho::T,sigma::T) where {T <: AbstractFloat, S <: Integer} # Case where shocks are AR(1) and innovations are independent if rho == 0.0 rho = eps() end terms_num = Array{T}(undef,length(eps_nodes)) integrals = Array{T,2}(undef,order+1,length(nodes)) integrals2 = Array{T}(undef,order+1) for i = 1:(order+1) integrals2[i] = sum(exp.(sqrt(2)*sigma*(i-1)*eps_nodes).*eps_weights)*pi^(-1/2) for j = 1:length(nodes) terms_num .= rho*nodes[j] .+ sqrt(2)*sigma*eps_nodes terms_den = rho*nodes[j] terms_num .= chebyshev_polynomial(i,terms_num)[:,i] terms_den = chebyshev_polynomial(i,terms_den)[i] integrals[i,j] = sum((terms_num/terms_den).*eps_weights)*pi^(-1/2) end end nodetoosmall = abs.(nodes) .< sqrt(eps()) if sum(nodetoosmall) > 0 if length(nodes) == 1 integrals[:,1] .= integrals2 else for i = 1:length(nodes) if nodetoosmall[i] == 1 if i == 1 integrals[:,i] .= integrals[:,i+1] elseif i == length(nodes) integrals[:,i] .= integrals[:,i-1] else integrals[:,i] .= (integrals[:,i-1]+integrals[:,i+1])/2 end end end end end return reshape(sum(integrals,dims=2)/length(nodes),order+1) end function compute_smolyak_integrals(eps_nodes,eps_weights,nx,order,grid,RHO,k) integrals = ones(nx,order+1) for j = 1:size(k,2) nodes = unique(grid[:,j]) integrals[j,:] .= _compute_smolyak_integrals(eps_nodes,eps_weights,nodes,order,RHO[j,j],k[j,j]) end # for j = 1:size(sigma,2) # for i = 1:(order+1) # integrals[j,i] = sum(exp.(sqrt(2)*k[j,j]*(i-1)*eps_nodes).*eps_weights)*pi^(-1/2) # end # end return integrals end function weight_scale_factors(eps_nodes,eps_weights,multi_index,nx,grid,RHO,sigma) unique_multi_index = sort(unique(multi_index)) unique_orders = SmolyakApprox.m_i(unique_multi_index).-1 # Here we construct the base integrals base_integrals = Array{Array{Float64,2}}(undef,length(unique_orders)) for i = 1:length(unique_orders) base_integrals[i] = compute_smolyak_integrals(eps_nodes,eps_weights,nx,unique_orders[i],grid,RHO,sigma) end # Compute the unique polynomial terms from the base polynomials unique_base_integrals = Array{Array{Float64,2}}(undef,length(unique_orders)) for i = length(unique_orders):-1:2 unique_base_integrals[i] = base_integrals[i][:,size(base_integrals[i-1],2)+1:end] end unique_base_integrals[1] = base_integrals[1] # Construct the first row of the interplation matrix new_integrals = unique_base_integrals[multi_index[1,1]][1,:] for i = 2:size(multi_index,2) new_integrals = kron(new_integrals,unique_base_integrals[multi_index[1,i]][i,:]) end weight_scale_factor = copy(new_integrals) # Iterate over nodes, doing the above three steps at each iteration for j = 2:size(multi_index,1) new_integrals = unique_base_integrals[multi_index[j,1]][1,:] for i = 2:size(multi_index,2) new_integrals = kron(new_integrals,unique_base_integrals[multi_index[j,i]][i,:]) end weight_scale_factor = [weight_scale_factor; new_integrals] end return weight_scale_factor end function scale_smolyak_weights(weights,weight_scale_factor) scaled_weights = weights.*weight_scale_factor end function compute_piecewise_linear_integrals(eps_nodes,eps_weights,sigma) integral = 1.0 #integral = sum(exp.(sqrt(2)*sigma*eps_nodes).*eps_weights)*pi^(-1/2) return integral end function mylen(len::T,a::Array{T,N},b::Array{T,N}) where {T <: AbstractFloat, N} for i in eachindex(a) len = max(len,abs(a[i]-b[i])) end return len end
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@testset "Diffusion Simulation" begin gx = complete_graph(5) for g in testgraphs(gx) # this makes graphs of different eltypes # Most basic @test @inferred(diffusion_rate(g, 1.0, 4)) == [1, 5, 5, 5] end for i in 1:5 add_vertex!(gx) end for g in testgraphs(gx) # this makes graphs of different eltypes ###### # Check on fully connected, prob = 1 ###### # Add disconnected for more dynamics # Basic test. Watch connected vertices @test @inferred(diffusion_rate(g, 1.0, 4, watch=collect(1:5), initial_infections=[2] )) == [1, 5, 5, 5] # Watching unconnected vertices @test @inferred(diffusion_rate(g, 1.0, 4, watch=collect(6:10), initial_infections=[2] )) == [0, 0, 0, 0] # Watch subset @test @inferred(diffusion_rate(g, 1.0, 4, watch=collect(1:2), initial_infections=[2] )) == [1, 2, 2, 2] @test @inferred(diffusion_rate(g, 1.0, 4, watch=collect(1:5), initial_infections=[10] )) == [0, 0, 0, 0] end ###### # Check along path graph ###### gx = path_graph(5) for g in testgraphs(gx) # this makes graphs of different eltypes @test @inferred(diffusion_rate(g, 1.0, 4, watch=collect(1:5), initial_infections=[1] )) == [1, 2, 3, 4] @test @inferred(diffusion_rate(g, 1.0, 4, watch=collect(1:5), initial_infections=[3] )) == [1, 3, 5, 5] end gx = path_graph(30) for g in testgraphs(gx) # Check normalize @test @inferred(diffusion_rate(g, 1.0, 6, initial_infections=[15], normalize=false )) == [1, 3, 5, 7, 9, 11] @test @inferred(diffusion_rate(g, 2.0, 6, initial_infections=[15], normalize=true) ) == [1, 3, 5, 7, 9, 11] # Test probability accurate # In a Path network, # number of nodes infected (minus 1) # is equal to number of successes of a # Burnoulli process. # So if p = 0.2, in 5 steps (seed + # 4 trials) expected value is 0.8, # with standard deviation 0.8. means = Dict(0.2 => 0.8, 0.4 => 1.6) stds = Dict(0.2 => 0.8, 0.4 => 0.98) runs = 20 for p in [0.2, 0.4] final_value = 0.0 for i in 1:20 result = @inferred(diffusion_rate(g, p, 5, initial_infections=[1])) final_value += result[5] end # Pretty loose bounds so don't get lots of failed tests. # Just want some safeguard. # Note 5 steps = 4 Bernoullis + initial infection. # False rate less than 1 in 1000 # Subtract 1 for initial infection avg = final_value / runs - 1 @test avg < means[p] + stds[p] / sqrt(runs) * 3.5 @test avg > means[p] - stds[p] / sqrt(runs) * 3.5 end end gx = path_digraph(10) for g in testdigraphs(gx) ###### # Check on digraphs ###### @test @inferred(diffusion_rate(g, 1.0, 9, initial_infections=[1] )) == collect(1:9) @test @inferred(diffusion_rate(g, 1.0, 9, initial_infections=[10] )) == ones(Int, 9) # Check probabilities. # See note in analogous tests above for undirected tests. runs = 20 means = Dict(0.2 => 2, 0.4 => 4) stds = Dict(0.2 => 1.2649110640673518, 0.4 => 1.5491933384829668) for p in [0.2, 0.4] final_value = 0.0 for i in 1:20 result = @inferred(diffusion_rate(g, p, 11, initial_infections=[1])) final_value += result[11] end # Pretty loose bounds so don't get lots of failed tests. # Just want some safeguard. # False rate less than 1 in 1000 # Subtract 1 for initial infection avg = final_value / runs - 1 @test avg < means[p] + stds[p] / sqrt(runs) * 3.5 @test avg > means[p] - stds[p] / sqrt(runs) * 3.5 end end end
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1.753394
2,725
module PhysicalConstant using Measurements, Unitful import Measurements: value, uncertainty struct Constant{sym} <: Number end function name end function ref end macro constant(sym, name, val, def, unit, unc, bigunc, reference) esym = esc(sym) qsym = esc(Expr(:quote, sym)) eunit = esc(unit) tag = Measurements.tag_counters[Base.Threads.threadid()] += 1 _bigconvert = isa(def,Symbol) ? quote function _big(::Constant{$qsym}) c = BigFloat() ccall(($(string("mpfr_const_", def)), :libmpfr), Cint, (Ref{BigFloat}, Int32), c, MPFR.ROUNDING_MODE[]) return c end end : quote _big(::Constant{$qsym}) = $(esc(def)) end quote const $esym = Constant{$qsym}() export $esym Base.float(::Constant{$qsym}) = $val * $unit Base.float(FT::DataType, ::Constant{$qsym}) = FT($val) * $eunit $_bigconvert Base.big(x::Constant{$qsym}) = _big(x) * $eunit Base.float(::Type{BigFloat}, x::Constant{$qsym}) = big(x) function Measurements.measurement(FT::DataType, ::Constant{$qsym}) vl = FT($val) newder = Measurements.empty_der2(vl) if iszero($unc) return Measurement{FT}(vl, FT($unc), UInt64(0), newder) * $unit else return Measurement{FT}(vl, FT($unc), $tag, Measurements.Derivatives(newder, (vl, $unc, $tag)=>one(FT))) * $unit end end function Measurements.measurement(::Type{BigFloat}, x::Constant{$qsym}) vl = _big(x) unc = BigFloat($bigunc) newder = Measurements.empty_der2(vl) if iszero($unc) return Measurement{BigFloat}(vl, unc, UInt64(0), newder) * $unit else return Measurement{BigFloat}(vl, unc, $tag, Measurements.Derivatives(newder, (vl, unc, $tag)=>one(BigFloat))) * $unit end end Measurements.measurement(::Constant{$qsym}) = measurement(Float64, $esym) PhysicalConstant.name(::Constant{$qsym}) = $name PhysicalConstant.ref(::Constant{$qsym}) = $reference Unitful.unit(::Constant{$qsym}) = $unit Unitful.dimension(::Constant{$qsym}) = Unitful.dimension($unit) @assert isa(ustrip(float($esym)), Float64) @assert isa(ustrip(big($esym)), BigFloat) @assert isa(ustrip(measurement($esym)), Measurement{Float64}) @assert ustrip(float(Float64, $esym)) == Float64(ustrip(big($esym))) @assert ustrip(float(Float32, $esym)) == Float32(ustrip(big($esym))) @assert Float64(value(ustrip(measurement(BigFloat, $esym)))) == value(ustrip(measurement($esym))) @assert Float64(uncertainty(ustrip(measurement(BigFloat, $esym)))) == uncertainty(ustrip(measurement($esym))) end end macro derived_constant(sym, name, val, def, unit, measure64, measurebig, reference) esym = esc(sym) qsym = esc(Expr(:quote, sym)) eunit = esc(unit) tag = Measurements.tag_counters[Base.Threads.threadid()] += 1 _bigconvert = isa(def,Symbol) ? quote function _big(::Constant{$qsym}) c = BigFloat() ccall(($(string("mpfr_const_", def)), :libmpfr), Cint, (Ref{BigFloat}, Int32), c, MPFR.ROUNDING_MODE[]) return c end end : quote _big(::Constant{$qsym}) = $(esc(def)) end quote const $esym = Constant{$qsym}() export $esym Base.float(::Constant{$qsym}) = $val * $unit Base.float(FT::DataType, ::Constant{$qsym}) = FT($val) * $eunit $_bigconvert Base.big(x::Constant{$qsym}) = _big(x) * $eunit Base.float(::Type{BigFloat}, x::Constant{$qsym}) = big(x) Measurements.measurement(::Type{Float64}, ::Constant{$qsym}) = $(esc(measure64)) Measurements.measurement(::Type{BigFloat}, ::Constant{$qsym}) = $(esc(measurebig)) Measurements.measurement(FT::DataType, x::Constant{$qsym}) = convert(Measurement{FT}, ustrip(measurement(x))) * $eunit Measurements.measurement(::Constant{$qsym}) = measurement(Float64, $esym) PhysicalConstant.name(::Constant{$qsym}) = $name PhysicalConstant.ref(::Constant{$qsym}) = $reference Unitful.unit(::Constant{$qsym}) = $unit Unitful.dimension(::Constant{$qsym}) = Unitful.dimension($unit) @assert isa(ustrip(float($esym)), Float64) @assert isa(ustrip(big($esym)), BigFloat) @assert isa(ustrip(measurement($esym)), Measurement{Float64}) @assert isa(ustrip(measurement(Float32, $esym)), Measurement{Float32}) @assert ustrip(float(Float64, $esym)) == Float64(ustrip(big($esym))) @assert ustrip(float(Float32, $esym)) == Float32(ustrip(big($esym))) @assert Float64(value(ustrip(measurement(BigFloat, $esym)))) == value(ustrip(measurement($esym))) @assert Float64(uncertainty(ustrip(measurement(BigFloat, $esym)))) == uncertainty(ustrip(measurement($esym))) @assert ustrip(big($esym)) == value(ustrip(measurement(BigFloat, $esym))) end end function Base.show(io::IO, x::Constant{sym}) where sym println(io, "$(name(x)) ($sym)") println(io, "Value = ", float(x)) println(io, "Standard uncertainty = ", iszero(uncertainty(ustrip(measurement(x)))) ? "(exact)" : uncertainty(ustrip(measurement(x))) * unit(x)) println(io, "Relative standard uncertainty = ", iszero(uncertainty(ustrip(measurement(x)))) ? "(exact)" : round(uncertainty(ustrip(measurement(x)))/value(ustrip(measurement(x))), sigdigits=2)) print(io, "Reference = ", ref(x)) end """ float(::Constant{symbol}) float(FloatType, ::Constant{symbol}) Return the physical constant as a `Quantity` with the floating type optionally specified by `FloatType`, `Float64` by default. ```jldoctest julia> using PhysicalConstant.CODATA2019 julia> Gg Newtonian constant of gravitation (Gg) Value = 6.67408e-11 m^3 kg^-1 s^-2 Standard uncertainty = 3.1e-15 m^3 kg^-1 s^-2 Relative standard uncertainty = 4.6e-5 Reference = CODATA 2019 julia> float(Gg) 6.67408e-11 m^3 kg^-1 s^-2 julia> float(Float32, G) 6.67408f-11 m^3 kg^-1 s^-2 ``` """ float(::Constant) """ measurement(::Constant{symbol}) measurement(FloatType, ::Constant{symbol}) Return the physical constant as a `Quantity` with standard uncertainty. The floating-point precision can be optionally specified with the `FloatType`, `Float64` by default. ```jldoctest julia> using PhysicalConstant.CODATA2019, Measurements julia> h Planck constant (h) Value = 6.62607004e-34 J s Standard uncertainty = 8.1e-42 J s Relative standard uncertainty = 1.2e-8 Reference = CODATA 2019 julia> measurement(h) 6.62607004e-34 ± 8.1e-42 J s julia> measurement(Float32, h) 6.62607e-34 ± 8.1e-42 J s ``` """ measurement(::Constant) include("codata2019.jl") end # module
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""" pctOnly(df::DataFrame, ColPct::String) Creates a new dataframe without rows without precipitation. # Arguments - `df::DataFrame`: The dataframe containing the data. - `ColPct::String`: The name of the column of `df` that allows to know if it has rained or not during the time division used. """ function pctOnly(df::DataFrame, ColPct::String) filter(Symbol(ColPct) => x -> x > 0, df) end """ pctOnly!(df::DataFrame, ColPct::Symbol) Deletes rows without precipitation. # Arguments - `df::DataFrame`: The dataframe containing the data. - `ColPct::String`: The name of the column of `df` that allows to know if it has rained or not during the time division used. """ function pctOnly!(df::DataFrame, ColPct::String) filter!(Symbol(ColPct) => x -> x > 0, df) end """ overflowOnly(df::DataFrame, ColOverflow::Strings) Creates a new dataframe without rows without overflow. # Arguments - `df::DataFrame`: The dataframe containing the data. - `ColOverflow::String`: The name of the column of `df` containing the binary vector of overflows. """ function overflowOnly(df::DataFrame, ColOverflow::String) filter(Symbol(ColOverflow) => x -> x == 1, df) end """ overflowOnly!(df::DataFrame, ColOverflow::String) Deletes rows without overflow. # Arguments - `df::DataFrame`: The dataframe containing the data. - `ColOverflow::String`: The name of the column of `df` containing the binary vector of overflows. """ function overflowOnly!(df::DataFrame, ColOverflow::String) filter!(Symbol(ColOverflow) => x -> x == 1, df) end
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module AttemptAtQNM include("SpectralSolver.jl") include("NewtonSolver.jl") include("SchwarszchildModes.jl") include("Interface.jl") using .Interface # Write your package code here. struct Potato Root::Float64 end print(GetModes(2,2,2,2)) export Potato export GetModes end
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# A general unary operation uses the following pipeline # 0. special rules `Identity` and `Tr`, # 1. rules reducing dimensions `Diag` and `Sum` # 2. `Permutedims`, # 3. `Repeat` and `Duplicate`, # `NT` for number of tensors abstract type EinRule{NT} end struct Tr <: EinRule{1} end struct Sum <: EinRule{1} end struct Repeat <: EinRule{1} end struct Permutedims <: EinRule{1} end struct Identity <: EinRule{1} end struct Duplicate <: EinRule{1} end struct Diag <: EinRule{1} end struct DefaultRule <: EinRule{Any} end @doc raw" match_rule(ixs, iy) match_rule(code::EinCode{ixs, iy}) match_rule(code::NestedEinCode) Returns the rule that matches, otherwise use `DefaultRule` - the slow `loop_einsum` backend. " function match_rule(@nospecialize(ixs::NTuple{Nx,NTuple} where Nx), @nospecialize(iy::Tuple)) DefaultRule() end function match_rule(@nospecialize(ixs::Tuple{NTuple{Nx,T}}), @nospecialize(iy::NTuple{Ny,T})) where {Nx, Ny, T} ix, = ixs # the first rule with the higher the priority if Ny == 0 && Nx == 2 && ix[1] == ix[2] return Tr() elseif allunique(iy) if ix == iy return Identity() elseif allunique(ix) if Nx == Ny if all(i -> i in iy, ix) return Permutedims() else # e.g. (abcd->bcde) return DefaultRule() end else if all(i -> i in ix, iy) return Sum() elseif all(i -> i in iy, ix) # e.g. ij->ijk return Repeat() else # e.g. ijkxc,ijkl return DefaultRule() end end else # ix is not unique if all(i -> i in ix, iy) && all(i -> i in iy, ix) # ijjj->ij return Diag() else return DefaultRule() end end else # iy is not unique if allunique(ix) && all(x->x∈iy, ix) if all(y->y∈ix, iy) # e.g. ij->ijjj return Duplicate() else # e.g. ij->ijjl return DefaultRule() end else return DefaultRule() end end end match_rule(code::EinCode{ixs, iy}) where {ixs, iy} = match_rule(ixs, iy) # trace # overhead ~ 0.07us # @benchmark OMEinsum.einsum(Tr(), $(('a', 'a')), $(()), x, $(Dict('a'=>1, 'b'=>1))) setup=(x=randn(1,1)) function einsum(::Tr, ix, iy, x, size_dict) @debug "Tr" size(x) asarray(tr(x), x) end # overhead ~ 0.55us # @benchmark OMEinsum.einsum(Sum(), $(('a', 'b')), $(('b',)), x, $(Dict('a'=>1, 'b'=>1))) setup=(x=randn(1,1)) function einsum(::Sum, ix, iy, x, size_dict::Dict{LT}) where LT @debug "Sum" ix => iy size(x) dims = (findall(i -> i ∉ iy, ix)...,)::NTuple{length(ix)-length(iy),Int} res = dropdims(sum(x, dims=dims), dims=dims) ix1f = filter(i -> i ∈ iy, ix)::typeof(iy) if ix1f != iy return einsum(Permutedims(), ix1f, iy, res, size_dict) else return res end end # overhead ~ 0.53us # @benchmark OMEinsum.einsum(OMEinsum.Repeat(), $(('a',)), $(('a', 'b',)), x, $(Dict('a'=>1, 'b'=>1))) setup=(x=randn(1)) function einsum(::Repeat, ix, iy, x, size_dict) @debug "Repeat" ix => iy size(x) ix1f = filter(i -> i ∈ ix, iy) res = if ix1f != ix einsum(Permutedims(), ix, ix1f, x, size_dict) else x end newshape = [l ∈ ix ? size_dict[l] : 1 for l in iy] repeat_dims = [l ∈ ix ? 1 : size_dict[l] for l in iy] repeat(reshape(res, newshape...), repeat_dims...) end # overhead ~ 0.28us # @benchmark OMEinsum.einsum(Diag(), $(('a', 'a')), $(('a',)), x, $(Dict('a'=>1, 'b'=>1))) setup=(x=randn(1,1)) function einsum(::Diag, ix, iy, x, size_dict) @debug "Diag" ix => iy size.(x) compactify!(get_output_array((x,), map(y->size_dict[y],iy); has_repeated_indices=false),x,ix, iy) end function compactify!(y, x, ix, iy) x_in_y_locs = ([findfirst(==(x), iy) for x in ix]...,) @assert size(x) == map(loc->size(y, loc), x_in_y_locs) indexer = dynamic_indexer(x_in_y_locs, size(x)) _compactify!(y, x, indexer) end function _compactify!(y, x, indexer) @inbounds for ci in CartesianIndices(y) y[ci] = x[subindex(indexer, ci.I)] end return y end function duplicate(x, ix, iy, size_dict) where {Nx,Ny,T} y = get_output_array((x,), map(y->size_dict[y],iy); has_repeated_indices=true) # compute same locs x_in_y_locs = ([findfirst(==(l), ix) for l in iy]...,) indexer = dynamic_indexer(x_in_y_locs, size(y)) _duplicate!(y, x, indexer) end @noinline function _duplicate!(y, x, indexer) map(CartesianIndices(x)) do ci @inbounds y[subindex(indexer, ci.I)] = x[ci] end return y end # e.g. 'ij'->'iij', left indices are unique, right are not # overhead ~ 0.29us # @benchmark OMEinsum.einsum(Duplicate(), $(('a', )), $(('a','a')), x, $(Dict('a'=>1, 'b'=>1))) setup=(x=randn(1)) function einsum(::Duplicate, ix, iy, x, size_dict) @debug "Duplicate" ix => iy size(x) duplicate(x, ix, iy, size_dict) end # overhead ~ 0.15us # @benchmark OMEinsum.einsum(Permutedims(), $(('a', 'b')), $(('b','a')), x, $(Dict('a'=>1, 'b'=>1))) setup=(x=randn(1,1)) function einsum(::Permutedims, ix, iy, x, size_dict) perm = map(i -> findfirst(==(i), ix), iy) @debug "Permutedims" ix => iy size(x) perm return tensorpermute(x, perm) end # overhead ~0.04us # @benchmark OMEinsum.einsum(Identity(), $(('a', 'b')), $(('a','b')), x, $(Dict('a'=>1, 'b'=>1))) setup=(x=randn(1,1)) function einsum(::Identity, ix, iy, x, size_dict) @debug "Identity" ix => iy size(x) x end # for unary operations # overhead ~ 2.3us # @benchmark OMEinsum.einsum(DefaultRule(), $(('a', 'a', 'b')), $(('c', 'b','a')), x, $(Dict('a'=>1, 'b'=>1, 'c'=>1))) setup=(x=randn(1,1,1)) function einsum(::DefaultRule, ix, iy, x::AbstractArray, size_dict::Dict{LT}) where LT @debug "DefaultRule unary" ix => iy size(x) # diag ix_ = _unique(LT, ix) x_ = length(ix_) != length(ix) ? einsum(Diag(), ix, (ix_...,), x, size_dict) : x # sum iy_b = _unique(LT, iy) iy_a = filter(i->i ∈ ix, iy_b) y_a = if length(ix_) != length(iy_a) einsum(Sum(), (ix_...,), (iy_a...,), x_, size_dict) elseif ix_ != iy_a einsum(Permutedims(), (ix_...,), (iy_a...,), x_, size_dict) else x_ end # repeat y_b = length(iy_a) != length(iy_b) ? einsum(Repeat(), (iy_a...,), (iy_b...,), y_a, size_dict) : y_a # duplicate length(iy_b) != length(iy) ? einsum(Duplicate(), (iy_b...,), iy, y_b, size_dict) : y_b end
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module FLOWVPM import Dates import GeometricTools: create_path const RealFMM = Float64 # Available Kernels const kernel_singular = (args...)->nothing const kernel_gaussian = (args...)->nothing const kernel_gaussianerf = (args...)->nothing const kernel_winckelmans = (args...)->nothing const singular = kernel_singular const gaussian = kernel_gaussian const gaussianerf = kernel_gaussianerf const winckelmans = kernel_winckelmans const rungekutta3 = (args...)->nothing const euler = (args...)->nothing mutable struct FMM # Optional user inputs p::Int32 # Multipole expansion order ncrit::Int32 # Max number of particles per leaf theta::RealFMM # Neighborhood criterion phi::RealFMM # Regularizing neighborhood criterion FMM(; p=4, ncrit=50, theta=0.4, phi=1/3) = new(p, ncrit, theta, phi) end """ `ViscousScheme{R}` Type declaring viscous scheme. Implementations must have the following properties: * `nu::R` : Kinematic viscosity. """ abstract type ViscousScheme{R} end """ Implementation of viscous diffusion scheme that gets called in the inner loop of the time integration scheme at each time step. """ function viscousdiffusion(pfield, scheme::ViscousScheme, dt; optargs...) error("Viscous diffusion scheme has not been implemented yet!") end viscousdiffusion(pfield, dt; optargs... ) = viscousdiffusion(pfield, pfield.viscous, dt; optargs...) zeta_direct(args...) = nothing zeta_fmm(args...) = nothing ##### END OF ABSTRACT VISCOUS SCHEME ########################################### ################################################################################ # INVISCID SCHEME TYPE ################################################################################ struct Inviscid{R} <: ViscousScheme{R} nu::R # Kinematic viscosity Inviscid{R}(; nu=zero(R)) where {R} = new(nu) end Inviscid() = Inviscid{RealFMM}() """ `isinviscid(scheme::ViscousScheme)` Returns true if viscous scheme is inviscid. """ isinviscid(scheme::ViscousScheme) = typeof(scheme).name == Inviscid.body.name viscousdiffusion(pfield, scheme::Inviscid, dt; optargs...) = nothing ##### END OF INVISCID SCHEME ################################################### ################################################################################ # CORE SPEADING SCHEME TYPE ################################################################################ mutable struct CoreSpreading{R} <: ViscousScheme{R} # User inputs nu::R # Kinematic viscosity sgm0::R # Core size after reset zeta::Function # Basis function evaluation method # Optional inputs beta::R # Maximum core size growth σ/σ_0 itmax::Int # Maximum number of RBF iterations tol::R # RBF interpolation tolerance iterror::Bool # Throw error if RBF didn't converge verbose::Bool # Verbose on RBF interpolation v_lvl::Int # Verbose printing tab level debug::Bool # Print verbose for debugging # Internal properties t_sgm::R # Time since last core size reset rbf::Function # RBF function rr0s::Array{R, 1} # Initial field residuals rrs::Array{R, 1} # Current field residuals prev_rrs::Array{R, 1} # Previous field residuals pAps::Array{R, 1} # pAp product alphas::Array{R, 1} # Alpha coefficients betas::Array{R, 1} # Beta coefficients flags::Array{Bool, 1} # Convergence flags CoreSpreading{R}( nu, sgm0, zeta; beta=R(1.5), itmax=R(15), tol=R(1e-3), iterror=true, verbose=false, v_lvl=2, debug=false, t_sgm=R(0.0), rbf=rbf_conjugategradient, rr0s=zeros(R, 3), rrs=zeros(R, 3), prev_rrs=zeros(R, 3), pAps=zeros(R, 3), alphas=zeros(R, 3), betas=zeros(R, 3), flags=zeros(Bool, 3) ) where {R} = new( nu, sgm0, zeta, beta, itmax, tol, iterror, verbose, v_lvl, debug, t_sgm, rbf, rr0s, rrs, prev_rrs, pAps, alphas, betas, flags ) end CoreSpreading(nu, sgm0, args...; optargs... ) = CoreSpreading{RealFMM}(RealFMM(nu), RealFMM(sgm0), args...; optargs...) """ `iscorespreading(scheme::ViscousScheme)` Returns true if viscous scheme is core spreading. """ iscorespreading(scheme::ViscousScheme ) = typeof(scheme).name == CoreSpreading.body.name mutable struct ParticleField{T, V<:ViscousScheme} # User inputs maxparticles::Int # Maximum number of particles particles # Array of particles bodies # ExaFMM array of bodies viscous::V # Viscous scheme # Internal properties np::Int # Number of particles in the field nt::Int # Current time step number t::Float64 # Current time # Solver setting kernel # Vortex particle kernel UJ::Function # Particle-to-particle calculation # Optional inputs Uinf::Function # Uniform freestream function Uinf(t) transposed::Bool # Transposed vortex stretch scheme relax::Bool # Activates relaxation scheme rlxf::Float64 # Relaxation factor (fraction of dt) integration::Function # Time integration scheme fmm::FMM # Fast-multipole settings ParticleField{T, V}( maxparticles, particles, bodies, viscous; np=0, nt=0, t=0.0, kernel=gaussianerf, UJ=UJ_fmm, Uinf=t->zeros(3), transposed=true, relax=true, rlxf=0.3, integration=rungekutta3, fmm=FMM(), ) where {T, V} = new( maxparticles, particles, bodies, viscous, np, nt, t, kernel, UJ, Uinf, transposed, relax, rlxf, integration, fmm, ) end function ParticleField(maxparticles; viscous::V=Inviscid(), optargs...) where {V<:ViscousScheme} return ParticleField{RealFMM, Inviscid{RealFMM}}(maxparticles, nothing, nothing, viscous; optargs...) end function get_np(self::ParticleField) return self.np end function add_particle(args...; optargs...) nothing end function remove_particle(args...; optargs...) nothing end function UJ_direct(args...; optargs...) nothing end function UJ_fmm(args...; optargs...) nothing end function get_particleiterator(args...; optargs...) nothing end iterator(args...; optargs...) = get_particleiterator(args...; optargs...) iterate(args...; optargs...) = get_particleiterator(args...; optargs...) get_X(self::ParticleField, i::Int) = nothing get_Gamma(self::ParticleField, i::Int) = nothing get_sigma(self::ParticleField, i::Int) = nothing get_U(self::ParticleField, i::Int) = nothing get_W(self::ParticleField, i::Int) = nothing isinviscid(self::ParticleField) = isinviscid(self.viscous) """ `run_vpm!(pfield, dt, nsteps; runtime_function=nothing, save_path=nothing, run_name="pfield", nsteps_save=1, verbose=true, prompt=true)` Solves `nsteps` of the particle field with a time step of `dt`. **Optional Arguments** * `runtime_function::Function` : Give it a function of the form `myfun(pfield, t, dt)`. On each time step it will call this function. Use this for adding particles, deleting particles, etc. * `static_particles_function::Function` : Give it a function of the form `myfun(pfield, t, dt)` to add static particles representing solid boundaries to the solver. This function is called at every time step right before solving the governing equations, and any new particles added by this function are immediately removed. * `nsteps_relax::Int` : Relaxes the particle field every this many time steps. * `save_path::String` : Give it a string for saving VTKs of the particle field. Creates the given path. * `run_name::String` : Name of output files. * `nsteps_save::Int64` : Saves vtks every this many time steps. * `prompt::Bool` : If `save_path` already exist, it will prompt the user before overwritting the folder if true; it will directly overwrite it if false. * `verbose::Bool` : Prints progress of the run to the terminal. * `verbose_nsteps::Bool`: Number of time steps between verbose. """ function run_vpm!(pfield::ParticleField, dt::Real, nsteps::Int; # RUNTIME OPTIONS runtime_function::Function=(pfield, t, dt)->false, # OUTPUT OPTIONS save_path::Union{Nothing, String}=nothing, create_savepath::Bool=true, run_name::String="pfield", save_code::String="", prompt::Bool=true, verbose::Bool=true, verbose_nsteps::Int=10, v_lvl::Int=0, optargs...) # Creates save path and save code if save_path!=nothing && create_savepath create_path(save_path, prompt) end # Save code if save_path!=nothing && save_code!="" cp(save_code, save_path*"/"; force=true) end run_id = save_path!=nothing ? joinpath(save_path, run_name) : "" if verbose time_beg = Dates.DateTime(Dates.now()) println("\t"^v_lvl*"*"^(73-8*v_lvl)*"\n"*"\t"^v_lvl*"START $run_id\t$time_beg\n"* "\t"^v_lvl*"*"^(73-8*v_lvl)) end # RUN for i in 0:nsteps if verbose && i%verbose_nsteps==0 println("\t"^(v_lvl+1)*"Time step $i out of $nsteps"* "\tParticles: $(get_np(pfield))") end # Time step if i!=0 pfield.nt +=1 pfield.t += dt end # Calls user-defined runtime function breakflag = runtime_function(pfield, pfield.t, dt) # User-indicated end of simulation if breakflag break end end if verbose time_end = Dates.DateTime(Dates.now()) hrs,mins,secs = timeformat(time_beg, time_end) println("\t"^v_lvl*"*"^(73-8*v_lvl)) println("\t"^v_lvl*"END $run_id\t$time_end") println("\t"^v_lvl*"*"^(73-8*v_lvl)) println("\t"^v_lvl*"ELAPSED TIME: $hrs hours $mins minutes $secs seconds") end return nothing end function timeformat(time_beg, time_end) time_delta = Dates.value(time_end)-Dates.value(time_beg) hrs = Int(floor(time_delta/1000/60/60)) mins = Int(floor((time_delta-hrs*60*60*1000)/1000/60)) secs = Int(floor((time_delta-hrs*60*60*1000-mins*1000*60)/1000)) return hrs,mins,secs end end # END OF MODULE
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# TODO: # - refactor, simplify branching, unify duplications # - (maybe) export latex completions into a separate package struct CompletionState offset::Int completions::Dict{String,CompletionItem} range::Range x::EXPR doc::Document server::LanguageServerInstance using_stmts::Dict{String,Any} end function add_completion_item(state::CompletionState, completion::CompletionItem) if haskey(state.completions, completion.label) && ismissing(state.completions[completion.label].data) # For the above statement: we've (1) already got a completion which (2) doesn't require adding an explicit import statement. return end state.completions[completion.label] = completion end StaticLint.getenv(state::CompletionState) = getenv(state.doc, state.server) using REPL """ is_completion_match(s::AbstractString, prefix::AbstractString, cutoff=3) Returns true if `s` starts with `prefix` or has a sufficiently high fuzzy score. """ function is_completion_match(s::AbstractString, prefix::AbstractString, cutoff=3) starter = if all(islowercase, prefix) startswith(lowercase(s), prefix) else startswith(s, prefix) end starter || REPL.fuzzyscore(prefix, s) >= cutoff end function textDocument_completion_request(params::CompletionParams, server::LanguageServerInstance, conn) state = let doc = getdocument(server, URI2(params.textDocument.uri)) offset = get_offset(doc, params.position) rng = Range(doc, offset:offset) x = get_expr(getcst(doc), offset) using_stmts = server.completion_mode == :import ? get_preexisting_using_stmts(x, doc) : Dict() CompletionState(offset, Dict{String,CompletionItem}(), rng, x, doc, server, using_stmts) end ppt, pt, t, is_at_end = get_partial_completion(state) if pt isa CSTParser.Tokens.Token && pt.kind == CSTParser.Tokenize.Tokens.BACKSLASH latex_completions(string("\\", CSTParser.Tokenize.untokenize(t)), state) elseif ppt isa CSTParser.Tokens.Token && ppt.kind == CSTParser.Tokenize.Tokens.BACKSLASH && pt isa CSTParser.Tokens.Token && pt.kind === CSTParser.Tokens.CIRCUMFLEX_ACCENT latex_completions(string("\\", CSTParser.Tokenize.untokenize(pt), CSTParser.Tokenize.untokenize(t)), state) elseif t isa CSTParser.Tokens.Token && t.kind == CSTParser.Tokenize.Tokens.COMMENT partial = is_latex_comp(t.val, state.offset - t.startbyte) !isempty(partial) && latex_completions(partial, state) elseif t isa CSTParser.Tokens.Token && (t.kind in (CSTParser.Tokenize.Tokens.STRING, CSTParser.Tokenize.Tokens.TRIPLE_STRING, CSTParser.Tokenize.Tokens.CMD, CSTParser.Tokenize.Tokens.TRIPLE_CMD)) string_completion(t, state) elseif state.x isa EXPR && is_in_import_statement(state.x) import_completions(ppt, pt, t, is_at_end, state.x, state) elseif t isa CSTParser.Tokens.Token && t.kind == CSTParser.Tokens.DOT && pt isa CSTParser.Tokens.Token && pt.kind == CSTParser.Tokens.IDENTIFIER # getfield completion, no partial px = get_expr(getcst(state.doc), state.offset - (1 + t.endbyte - t.startbyte)) _get_dot_completion(px, "", state) elseif t isa CSTParser.Tokens.Token && t.kind == CSTParser.Tokens.IDENTIFIER && pt isa CSTParser.Tokens.Token && pt.kind == CSTParser.Tokens.DOT && ppt isa CSTParser.Tokens.Token && ppt.kind == CSTParser.Tokens.IDENTIFIER # getfield completion, partial px = get_expr(getcst(state.doc), state.offset - (1 + t.endbyte - t.startbyte) - (1 + pt.endbyte - pt.startbyte)) # get offset 2 tokens back _get_dot_completion(px, t.val, state) elseif t isa CSTParser.Tokens.Token && t.kind == CSTParser.Tokens.IDENTIFIER # token completion if is_at_end && state.x !== nothing if pt isa CSTParser.Tokens.Token && pt.kind == CSTParser.Tokens.AT_SIGN spartial = string("@", t.val) else spartial = t.val end kw_completion(spartial, state) rng = Range(state.doc, state.offset:state.offset) collect_completions(state.x, spartial, state, false) end elseif t isa CSTParser.Tokens.Token && t.kind == CSTParser.Tokens.AT_SIGN # only `@` given state.x !== nothing && collect_completions(state.x, "@", state, false) elseif t isa CSTParser.Tokens.Token && Tokens.iskeyword(t.kind) && is_at_end kw_completion(CSTParser.Tokenize.untokenize(t), state) elseif t isa CSTParser.Tokens.Token && t.kind == CSTParser.Tokens.IN && is_at_end collect_completions(state.x, "in", state, false) elseif t isa CSTParser.Tokens.Token && t.kind == CSTParser.Tokens.ISA && is_at_end collect_completions(state.x, "isa", state, false) elseif t isa CSTParser.Tokens.Token && t.kind == CSTParser.Tokens.COMMA && pt isa CSTParser.Tokens.Token && pt.kind == CSTParser.Tokens.IDENTIFIER && ppt isa CSTParser.Tokens.Token && ppt.kind == CSTParser.Tokens.LPAREN && !(parentof(state.x) isa EXPR && CSTParser.iscall(parentof(state.x))) # method completion for given argument ptlen = (1 + pt.endbyte - pt.startbyte) px = get_expr(getcst(state.doc), state.offset - ptlen) method_completion(px, state, ptlen) end return CompletionList(true, unique(values(state.completions))) end function get_partial_completion(state::CompletionState) ppt, pt, t = toks = get_toks(state.doc, state.offset) is_at_end = state.offset == t.endbyte + 1 return ppt, pt, t, is_at_end end function latex_completions(partial::String, state::CompletionState) for (k, v) in REPL.REPLCompletions.latex_symbols if is_completion_match(string(k), partial) # t1 = TextEdit(Range(state.doc, (state.offset - sizeof(partial)):state.offset), v) add_completion_item(state, CompletionItem(k, 11, missing, v, v, missing, missing, missing, missing, missing, missing, texteditfor(state, partial, v), missing, missing, missing, missing)) end end end function kw_completion(partial::String, state::CompletionState) length(partial) == 0 && return for (kw, comp) in snippet_completions if startswith(kw, partial) add_completion_item(state, CompletionItem(kw, 14, missing, missing, kw, missing, missing, missing, missing, missing, InsertTextFormats.Snippet, texteditfor(state, partial, comp), missing, missing, missing, missing)) end end end const snippet_completions = Dict{String,String}( "abstract" => "abstract type \$0 end", "baremodule" => "baremodule \$1\n\t\$0\nend", "begin" => "begin\n\t\$0\nend", "break" => "break", "catch" => "catch", "const" => "const ", "continue" => "continue", "do" => "do \$1\n\t\$0\nend", "else" => "else", "elseif" => "elseif ", "end" => "end", "export" => "export ", "finally" => "finally", "for" => "for \$1 in \$2\n\t\$0\nend", "function" => "function \$1(\$2)\n\t\$0\nend", "global" => "global ", "if" => "if \$1\n\t\$0\nend", "import" => "import", "let" => "let \$1\n\t\$0\nend", "local" => "local ", "macro" => "macro \$1(\$2)\n\t\$0\nend", "module" => "module \$1\n\t\$0\nend", "mutable" => "mutable struct \$0\nend", "outer" => "outer ", "primitive" => "primitive type \$1 \$0 end", "quote" => "quote\n\t\$0\nend", "return" => "return", "struct" => "struct \$0 end", "try" => "try\n\t\$0\ncatch\nend", "using" => "using ", "while" => "while \$1\n\t\$0\nend" ) function texteditfor(state::CompletionState, partial, n) TextEdit(Range(Position(state.range.start.line, state.range.start.character - sizeof(partial)), state.range.stop), n) end function collect_completions(m::SymbolServer.ModuleStore, spartial, state::CompletionState, inclexported=false, dotcomps=false) for val in m.vals n, v = String(val[1]), val[2] (startswith(n, ".") || startswith(n, "#")) && continue !is_completion_match(n, spartial) && continue if v isa SymbolServer.VarRef v = SymbolServer._lookup(v, getsymbols(getenv(state)), true) v === nothing && return end if StaticLint.isexportedby(n, m) || inclexported add_completion_item(state, CompletionItem(n, _completion_kind(v), MarkupContent(sanitize_docstring(v.doc)), texteditfor(state, spartial, n))) elseif dotcomps push!(state.completions, CompletionItem(n, _completion_kind(v), MarkupContent(sanitize_docstring(v.doc)), texteditfor(state, spartial, string(m.name, ".", n)))) elseif length(spartial) > 3 && !variable_already_imported(m, n, state) if state.server.completion_mode === :import # These are non-exported names and require the insertion of a :using statement. # We need to insert this statement at the start of the current top-level scope (e.g. Main or a module) and tag it onto existing :using statements if possible. cmd = Command("Apply text edit", "language-julia.applytextedit", [ WorkspaceEdit(missing, [textedit_to_insert_using_stmt(m, n, state)]) ]) ci = CompletionItem(n, _completion_kind(v), missing, "This is an unexported symbol and will be explicitly imported.", MarkupContent(sanitize_docstring(v.doc)), missing, missing, missing, missing, missing, InsertTextFormats.PlainText, texteditfor(state, spartial, n), missing, missing, cmd, "import") add_completion_item(state, ci) elseif state.server.completion_mode === :qualify add_completion_item(state, CompletionItem(string(m.name, ".", n), _completion_kind(v), missing, "This is an unexported symbol and will be explicitly imported.", MarkupContent(sanitize_docstring(v.doc)), missing, missing, string(n), missing, missing, InsertTextFormats.PlainText, texteditfor(state, spartial, string(m.name, ".", n)), missing, missing, missing, missing)) end end end end function variable_already_imported(m, n, state) haskey(state.using_stmts, String(m.name.name)) && import_has_x(state.using_stmts[String(m.name.name)][1], n) end function import_has_x(expr::EXPR, x::String) if length(expr.args) == 1 && length(expr.args[1]) > 1 for i = 2:length(expr.args[1].args) arg = expr.args[1].args[i] if CSTParser.isoperator(arg.head) && length(arg.args) == 1 && CSTParser.isidentifier(arg.args[1]) && CSTParser.valof(arg.args[1]) == x return true end end end return false end function collect_completions(x::EXPR, spartial, state::CompletionState, inclexported=false, dotcomps=false) if scopeof(x) !== nothing collect_completions(scopeof(x), spartial, state, inclexported, dotcomps) if scopeof(x).modules isa Dict for m in scopeof(x).modules collect_completions(m[2], spartial, state, inclexported, dotcomps) end end end if parentof(x) !== nothing && !CSTParser.defines_module(x) return collect_completions(parentof(x), spartial, state, inclexported, dotcomps) end end function collect_completions(x::StaticLint.Scope, spartial, state::CompletionState, inclexported=false, dotcomps=false) if x.names !== nothing for n in x.names if is_completion_match(n[1], spartial) documentation = "" if n[2] isa StaticLint.Binding documentation = get_hover(n[2], documentation, state.server) sanitize_docstring(documentation) end add_completion_item(state, CompletionItem(n[1], _completion_kind(n[2]), MarkupContent(documentation), texteditfor(state, spartial, n[1]))) end end end end function is_rebinding_of_module(x) x isa EXPR && refof(x).type === StaticLint.CoreTypes.Module && # binding is a Module refof(x).val isa EXPR && CSTParser.isassignment(refof(x).val) && # binding expr is an assignment StaticLint.hasref(refof(x).val.args[2]) && refof(refof(x).val.args[2]).type === StaticLint.CoreTypes.Module && refof(refof(x).val.args[2]).val isa EXPR && CSTParser.defines_module(refof(refof(x).val.args[2]).val)# double check the rhs points to a module end function _get_dot_completion(px, spartial, state::CompletionState) end function _get_dot_completion(px::EXPR, spartial, state::CompletionState) if px !== nothing if refof(px) isa StaticLint.Binding if refof(px).val isa StaticLint.SymbolServer.ModuleStore collect_completions(refof(px).val, spartial, state, true) elseif refof(px).val isa EXPR && CSTParser.defines_module(refof(px).val) && scopeof(refof(px).val) isa StaticLint.Scope collect_completions(scopeof(refof(px).val), spartial, state, true) elseif is_rebinding_of_module(px) collect_completions(scopeof(refof(refof(px).val.args[2]).val), spartial, state, true) elseif refof(px).type isa SymbolServer.DataTypeStore for a in refof(px).type.fieldnames a = String(a) if is_completion_match(a, spartial) add_completion_item(state, CompletionItem(a, 2, MarkupContent(a), texteditfor(state, spartial, a))) end end elseif refof(px).type isa StaticLint.Binding && refof(px).type.val isa SymbolServer.DataTypeStore for a in refof(px).type.val.fieldnames a = String(a) if is_completion_match(a, spartial) add_completion_item(state, CompletionItem(a, 2, MarkupContent(a), texteditfor(state, spartial, a))) end end elseif refof(px).type isa StaticLint.Binding && refof(px).type.val isa EXPR && CSTParser.defines_struct(refof(px).type.val) && scopeof(refof(px).type.val) isa StaticLint.Scope collect_completions(scopeof(refof(px).type.val), spartial, state, true) end elseif refof(px) isa StaticLint.SymbolServer.ModuleStore collect_completions(refof(px), spartial, state, true) end end end function _completion_kind(b) if b isa StaticLint.Binding if b.type == StaticLint.CoreTypes.String return 1 elseif b.type == StaticLint.CoreTypes.Function return 2 elseif b.type == StaticLint.CoreTypes.Module return 9 elseif b.type == Int || b.type == StaticLint.CoreTypes.Float64 return 12 elseif b.type == StaticLint.CoreTypes.DataType return 22 else return 13 end elseif b isa SymbolServer.ModuleStore || b isa SymbolServer.VarRef return 9 elseif b isa SymbolServer.MethodStore return 2 elseif b isa SymbolServer.FunctionStore return 3 elseif b isa SymbolServer.DataTypeStore return 22 else return 6 end end function get_import_root(x::EXPR) if CSTParser.isoperator(headof(x.args[1])) && valof(headof(x.args[1])) == ":" return last(x.args[1].args[1].args) end end function string_completion(t, state::CompletionState) path_completion(t, state) # Need to adjust things for quotation marks if t.kind in (CSTParser.Tokenize.Tokens.STRING,CSTParser.Tokenize.Tokens.CMD) t.startbyte < state.offset <= t.endbyte || return relative_offset = state.offset - t.startbyte - 1 content = t.val[2:prevind(t.val, lastindex(t.val))] else t.startbyte < state.offset <= t.endbyte - 2 || return relative_offset = state.offset - t.startbyte - 3 content = t.val[4:prevind(t.val, lastindex(t.val), 3)] end partial = is_latex_comp(content, relative_offset) !isempty(partial) && latex_completions(partial, state) end function is_latex_comp(s, i) i0 = i while firstindex(s) <= i s[i] == '\\' && return s[i:i0] !is_latex_comp_char(s[i]) && return "" i = prevind(s, i) end return "" end is_latex_comp_char(c::Char) = UInt32(c) <= typemax(UInt8) ? is_latex_comp_char(UInt8(c)) : false function is_latex_comp_char(u) # Checks whether a Char (represented as a UInt8) is in the set of those those used to trigger # latex completions. # from: UInt8.(sort!(unique(prod([k[2:end] for (k,_) in REPL.REPLCompletions.latex_symbols])))) u === 0x28 || u === 0x29 || u === 0x2b || u === 0x2d || u === 0x2f || 0x30 <= u <= 0x39 || u === 0x3d || 0x41 <= u <= 0x5a || u === 0x5e || u === 0x5f || 0x61 <= u <= 0x7a end function path_completion(t, state::CompletionState) if t.kind == CSTParser.Tokenize.Tokens.STRING path = t.val[2:prevind(t.val, lastindex(t.val))] if startswith(path, "~") path = replace(path, '~' => homedir()) dir, partial = _splitdir(path) else dir, partial = _splitdir(path) if !startswith(dir, "/") doc_path = getpath(state.doc) isempty(doc_path) && return dir = joinpath(_dirname(doc_path), dir) end end try fs = readdir(dir) for f in fs if startswith(f, partial) try if isdir(joinpath(dir, f)) f = string(f, "/") end rng1 = Range(state.doc, state.offset - sizeof(partial):state.offset) add_completion_item(state, CompletionItem(f, 17, f, TextEdit(rng1, f))) catch err isa(err, Base.IOError) || isa(err, Base.SystemError) || rethrow() end end end catch err isa(err, Base.IOError) || isa(err, Base.SystemError) || rethrow() end end end is_in_import_statement(x::EXPR) = is_in_fexpr(x, x -> headof(x) in (:using, :import)) function import_completions(ppt, pt, t, is_at_end, x, state::CompletionState) import_statement = StaticLint.get_parent_fexpr(x, x -> headof(x) === :using || headof(x) === :import) import_root = get_import_root(import_statement) if (t.kind == CSTParser.Tokens.WHITESPACE && pt.kind ∈ (CSTParser.Tokens.USING, CSTParser.Tokens.IMPORT, CSTParser.Tokens.IMPORTALL, CSTParser.Tokens.COMMA, CSTParser.Tokens.COLON)) || (t.kind in (CSTParser.Tokens.COMMA, CSTParser.Tokens.COLON)) # no partial, no dot if import_root !== nothing && refof(import_root) isa SymbolServer.ModuleStore for (n, m) in refof(import_root).vals n = String(n) if is_completion_match(n, t.val) && !startswith(n, "#") add_completion_item(state, CompletionItem(n, _completion_kind(m), MarkupContent(m isa SymbolServer.SymStore ? sanitize_docstring(m.doc) : n), texteditfor(state, t.val, n))) end end else for (n, m) in StaticLint.getsymbols(getenv(state)) n = String(n) (startswith(n, ".") || startswith(n, "#")) && continue add_completion_item(state, CompletionItem(n, 9, MarkupContent(sanitize_docstring(m.doc)), TextEdit(state.range, n))) end end elseif t.kind == CSTParser.Tokens.DOT && pt.kind == CSTParser.Tokens.IDENTIFIER # no partial, dot if haskey(getsymbols(getenv(state)), Symbol(pt.val)) collect_completions(getsymbols(getenv(state))[Symbol(pt.val)], "", state) end elseif t.kind == CSTParser.Tokens.IDENTIFIER && is_at_end # partial if pt.kind == CSTParser.Tokens.DOT && ppt.kind == CSTParser.Tokens.IDENTIFIER if haskey(StaticLint.getsymbols(getenv(state)), Symbol(ppt.val)) rootmod = StaticLint.getsymbols(getenv(state))[Symbol(ppt.val)] for (n, m) in rootmod.vals n = String(n) if is_completion_match(n, t.val) && !startswith(n, "#") add_completion_item(state, CompletionItem(n, _completion_kind(m), MarkupContent(m isa SymbolServer.SymStore ? sanitize_docstring(m.doc) : n), texteditfor(state, t.val, n))) end end end else if import_root !== nothing && refof(import_root) isa SymbolServer.ModuleStore for (n, m) in refof(import_root).vals n = String(n) if is_completion_match(n, t.val) && !startswith(n, "#") add_completion_item(state, CompletionItem(n, _completion_kind(m), MarkupContent(m isa SymbolServer.SymStore ? sanitize_docstring(m.doc) : n), texteditfor(state, t.val, n))) end end else for (n, m) in StaticLint.getsymbols(getenv(state)) n = String(n) if is_completion_match(n, t.val) add_completion_item(state, CompletionItem(n, 9, MarkupContent(m isa SymbolServer.SymStore ? m.doc : n), texteditfor(state, t.val, n))) end end end end end end function get_preexisting_using_stmts(x::EXPR, doc::Document) using_stmts = Dict{String,Any}() tls = StaticLint.retrieve_toplevel_scope(x) file_level_arg = get_file_level_parent(x) if scopeof(getcst(doc)) == tls # check for :using stmts in current file for a in getcst(doc).args if headof(a) === :using add_using_stmt(a, using_stmts) end a == file_level_arg && break end end if tls !== nothing args = get_tls_arglist(tls) for a in args if headof(a) === :using add_using_stmt(a, using_stmts) end end end return using_stmts end function add_using_stmt(x::EXPR, using_stmts) if length(x.args) > 0 && CSTParser.is_colon(x.args[1].head) if CSTParser.is_dot(x.args[1].args[1].head) && length(x.args[1].args[1].args) == 1 using_stmts[valof(x.args[1].args[1].args[1])] = (x, get_file_loc(x)) end end end function get_file_level_parent(x::EXPR) if x.parent isa EXPR && x.parent.head === :file x else if x.parent === nothing return nothing end get_file_level_parent(x.parent) end end function textedit_to_insert_using_stmt(m::SymbolServer.ModuleStore, n::String, state::CompletionState) tls = StaticLint.retrieve_toplevel_scope(state.x) if haskey(state.using_stmts, String(m.name.name)) (using_stmt, (using_doc, using_offset)) = state.using_stmts[String(m.name.name)] l, c = get_position_at(using_doc, using_offset + using_stmt.span) TextDocumentEdit(VersionedTextDocumentIdentifier(using_doc._uri, using_doc._version), [TextEdit(Range(l, c, l, c), ", $n")]) elseif tls !== nothing if tls.expr.head === :file # Insert at the head of the file tlsdoc, offset1 = get_file_loc(tls.expr) TextDocumentEdit(VersionedTextDocumentIdentifier(tlsdoc._uri, tlsdoc._version), [TextEdit(Range(0, 0, 0, 0), "using $(m.name): $(n)\n")]) elseif tls.expr.head === :module # Insert at start of module tlsdoc, offset1 = get_file_loc(tls.expr) offset2 = tls.expr.trivia[1].fullspan + tls.expr.args[2].fullspan l, c = get_position_at(tlsdoc, offset1 + offset2) TextDocumentEdit(VersionedTextDocumentIdentifier(tlsdoc._uri, tlsdoc._version), [TextEdit(Range(l, c, l, c), "using $(m.name): $(n)\n")]) else error() end else # Fallback, add it to the start of the current file. TextDocumentEdit(VersionedTextDocumentIdentifier(state.doc._uri, state.doc._version), [TextEdit(Range(0, 0, 0, 0), "using $(m.name): $(n)\n")]) end end function get_tls_arglist(tls::StaticLint.Scope) if tls.expr.head === :file tls.expr.args elseif tls.expr.head === :module tls.expr.args[3].args else error() end end function method_completion(x, state, xlen) scope = scopeof(parentof(parentof(state.x))) x_type = refof(x).type.name if x_type isa EXPR typename = x_type.val elseif x_type isa SymbolServer.FakeTypeName typename = x_type.name.name else return end for m in scope.modules for val in m[2].vals n, v = String(val[1]), val[2] (startswith(n, ".") || startswith(n, "#") || startswith(n, "_")) && continue !(typeof(v) == SymbolServer.FunctionStore) && continue siglen_max = 0 # maximum signature length for m in v.methods isempty(m.sig) && continue !(typeof(m.sig[1][2]) == SymbolServer.FakeTypeName) && continue !(m.sig[1][2].name.name == typename) && continue siglen_max = max(siglen_max, length(m.sig)) end (siglen_max == 0) && continue prefix_edit = TextEdit(Range( Position(state.range.start.line, state.range.start.character - xlen - 2), Position(state.range.stop.line, state.range.stop.character - xlen - 2)), n) if siglen_max == 1 # need to close bracket right away additional_edits = [TextEdit(Range( Position(state.range.start.line, state.range.start.character - 1), Position(state.range.stop.line, state.range.stop.character)), ""), prefix_edit] inplace_text = "" else inplace_text = " " additional_edits = [prefix_edit] end inplace_edit = TextEdit(Range( Position(state.range.start.line, state.range.start.character), Position(state.range.stop.line, state.range.stop.character)), inplace_text) item = CompletionItem(n, 2, missing, missing, n, missing, missing, missing, missing, missing, InsertTextFormats.PlainText, inplace_edit, additional_edits, missing, missing, missing) add_completion_item(state, item) end end end
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198, 437, 198 ]
2.165435
12,452
# generateur d'Instances pour la RODD n = 10 m = 10 # Ouvrir le fichier "output.txt" dans lequel on pourra écrire fout = open("outputn10m10.txt", "w") println(fout, "title") println(fout, "n = " * string(n)) println(fout, "m = " * string(m)) for i in 1:n myLine = "" for j in 1:(m-1) myLine = myLine * string(rand(1:10)) * " " end myLine = myLine * string(rand(1:10)) println(fout, myLine) end close(fout)
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2.252632
190
#ДАНО: Робот находится в произвольной клетке ограниченного прямоугольного поля без внутренних перегородок и маркеров. #РЕЗУЛЬТАТ: Робот — в исходном положении в центре прямого креста из маркеров, расставленных вплоть до внешней рамки. function crest!(r::Robot) for side in (HorizonSide(i) for i=0:3) putmarkers!(r,side) go_by_markers(r,reverse(side)) end putmarker!(r) end function putmarkers!(r::Robot,side::HorizonSide) while isborder(r,side)==false move!(r,side) putmarker!(r) end end function go_by_markers(r::Robot,side::HorizonSide) while ismarker(r)==true move!(r,side) end end function reverse(side::HorizonSide) HorizonSide(mod(Int(side)+2, 4)) end
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function no_slip_bc(grid) v_bcs = VVelocityBoundaryConditions(grid, top = BoundaryCondition(Value, 0.0), bottom = BoundaryCondition(Value, 0.0), north = BoundaryCondition(NormalFlow, 0.0), south = BoundaryCondition(NormalFlow, 0.0)) w_bcs = WVelocityBoundaryConditions(grid, north = BoundaryCondition(Value, 0.0), south = BoundaryCondition(Value, 0.0), top = BoundaryCondition(NormalFlow, 0.0), bottom = BoundaryCondition(NormalFlow, 0.0)) return v_bcs, w_bcs end
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module FSimROS using PyCall using UnPack using FSimBase, FSimZoo using StaticArrays, ReferenceFrameRotations include("convert.jl") export state_to_msg, msg_to_state end
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@testset "Optimal tranport" begin using STMO.OptimalTransport C = [1 1 0; 0 1 1; 1 0 1] @testset "Monge" begin @test monge_brute_force(C) == ([3, 1, 2], 0) @test monge_brute_force(1.0C) == ([3, 1, 2], 0.0) end @testset "Sinkhorn" begin a, b = [1, 1, 1] / 3, [0.25, 0.6, 0.15] Ph = sinkhorn(C, a, b, λ=10, ϵ=1e-10) Pl = sinkhorn(C, a, b, λ=10, ϵ=1e-10) @test sum(Ph, dims=1)[:] ≈ b @test sum(Ph, dims=2)[:] ≈ a @test sum(Pl, dims=1)[:] ≈ b @test sum(Pl, dims=2)[:] ≈ a @test sum(Ph .* C) ≤ sum(Pl .* C) # lower cost h(p) = -sum(p .* log.(p)) @test h(Ph) ≤ h(Pl) end end
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export prunenode!, prunenode, graftnode!, delete_node!, delete_null_branches!, remove_internal_singletons, prunesubtree! """ prunenode!(node::TreeNode) Prune node `node` by detaching it from its ancestor. Return pruned `node` and the root of its ancestor. The whole tree is modified. """ function prunenode!(node::TreeNode) if node.isroot @warn "Trying to prune root: no op." return node, TreeNode() end anc = node.anc for (i,c) in enumerate(anc.child) if c == node splice!(anc.child,i) break end end if isempty(anc.child) anc.isleaf = true end node.anc = nothing node.isroot = true return node, node_findroot(anc) end """ prunenode(node::TreeNode) Prune node `node` by detaching it from its ancestor. Return pruned `node` and previous root `r`. The tree defined by `node` is copied before the operation, and therefore not modified. """ function prunenode(node::TreeNode) if node.isroot @warn "Trying to prune root: no op." node_ = deepcopy(node) return node_, node_ end node_ = deepcopy(node) r = node_findroot(node_) anc = node_.anc for (i,c) in enumerate(anc.child) if c == node_ splice!(anc.child,i) break end end node_.anc = nothing node_.isroot = true return node_, r end """ prunenode(t::Tree, label::Vararg{String}) Prune node `t.lnodes[label]` from `t` for all `label`. Return pruned copy of `t`. """ function prunenode(t::Tree, label::Vararg{String} ; propagate=true) return prunenode(t, collect(label), propagate=propagate) end """ prunenodes(tree, labels) Prune nodes corresponding to labels in `labels`. """ function prunenode(tree, labels ; propagate=true) out = deepcopy(tree) for l in labels propagate ? prunenode_!(out.lnodes[l]) : prunenode!(out.lnodes[l]) end out = node2tree(out.root) end """ """ function prunenode_!(node) if length(node.anc.child) == 1 prunenode_!(node.anc) else prunenode!(node) end end """ prunesubtree!(tree, labellist) Prune and return subtree corresponding to the MRCA of labels in `labellist`, as well as its previous direct ancestor. # Warning `TreeNode` objects contained in `tree` are modified, but `tree` is *not* re-indexed after the pruning. It is therefore necessary to call `node2tree(tree.root)` after this. """ function prunesubtree!(tree, labellist) r = lca([tree.lnodes[x] for x in labellist]) a = r.anc if !r.isroot subtree = node2tree(prunenode!(r)[1]) else @warn "Trying to prune root" end return subtree, a end """ remove_internal_singletons!(tree) Remove nodes with one child. Return a new tree. Root node is left as is. ## Warning The `TreeNode` constituting `tree` are modified in the process. This means `tree` will be be modifier as well in an uncontrolled manner. """ function remove_internal_singletons!(tree) root = tree.root for n in values(tree.nodes) if !n.isleaf && !n.isroot if length(n.child) == 1 delete_node!(n, ptau=true) end end end return node2tree(root) end """ graftnode!(r::TreeNode, n::TreeNode ; tau=n.data.tau) Graft `n` on `r`. """ function graftnode!(r::TreeNode, n::TreeNode ; tau=n.data.tau) if !n.isroot || n.anc != nothing @error "Trying to graft non-root node." end push!(r.child, n) r.isleaf = false n.anc = r n.isroot = false n.data.tau = tau end """ delete_node!(node::TreeNode; ptau=false) Delete `node` from the tree. If it is an internal node, its children are regrafted on `node.anc`. Returns the new `node.anc`. If `ptau`, branch length above `node` is added to the regrafted branch. Otherwise, the regrafted branch's length is unchanged. """ function delete_node!(node::TreeNode; ptau=false) if node.isroot @error "Cannot delete root node" error() end out = node.anc if node.isleaf prunenode!(node) else base_tau = node.data.tau child_list = [] for c in node.child push!(child_list, c) end for c in child_list nc = prunenode!(c)[1] graftnode!(node.anc, nc, tau = (base_tau*ptau + nc.data.tau)) end prunenode!(node) end return out end """ delete_null_branches!(node ; threshold = 1e-10) Delete internal node with null branch length. - If `node` needs not be deleted, call `delete_null_branches!` on its children - If need be, call `delete_null_branches!` on `node.anc.child` """ function delete_null_branches!(node; threshold = 1e-10) if !node.isleaf if !ismissing(node.data.tau) && node.data.tau < threshold && !node.isroot nr = delete_node!(node) for c in nr.child delete_null_branches!(c, threshold=threshold) end else for c in node.child delete_null_branches!(c,threshold=threshold) end end end end """ reroot!(node::TreeNode ; newroot::Union{TreeNode,Nothing}=nothing) - If `node.isroot`, - Else if `newroot == nothing`, reroot the tree defined by `node` at `node`. Call `reroot!(node.anc, node)`. - Else, call `reroot!(node.anc, node)`, then change the ancestor of `node` to be `newroot`. """ function reroot!(node::Union{TreeNode,Nothing}; newroot::Union{TreeNode, Nothing}=nothing) # Breaking cases if node.anc == nothing || node.isroot if !(node.anc == nothing && node.isroot) @warn "There was a problem with input tree: previous root node has an ancestor." elseif newroot != nothing i = findfirst(c->c.label==newroot.label, node.child) splice!(node.child, i) node.anc = newroot node.data.tau = newroot.data.tau node.isroot = false end else # Recursion if newroot == nothing if node.isleaf @warn "Rooting on a leaf node..." end node.isroot = true reroot!(node.anc, newroot=node) push!(node.child, node.anc) node.anc = nothing node.data.tau = missing else i = findfirst(c->c.label==newroot.label, node.child) splice!(node.child, i) reroot!(node.anc, newroot=node) push!(node.child, node.anc) node.anc = newroot node.data.tau = newroot.data.tau end end end
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2.542498
2,306
module StdLib using ..CxxWrap abstract type CppBasicString <: AbstractString end # These are defined in C++, but the functions need to exist to add methods function append end function cppsize end function cxxgetindex end function cxxsetindex! end function push_back end function resize end @wrapmodule(CxxWrap.libcxxwrap_julia_stl) function __init__() @initcxx end # Pass-through for fundamental types _append_dispatch(v::StdVector,a::Vector,::Type{CxxWrap.IsNormalType}) = append(v,a) # For C++ types, convert the array to an array of references, so the pointers can be read directly from a contiguous array on the C++ side _append_dispatch(v::StdVector{T}, a::Vector{<:T},::Type{CxxWrap.IsCxxType}) where {T} = append(v,CxxWrap.CxxRef.(a)) # Choose the correct append method depending on the type trait append(v::StdVector{T}, a::Vector{<:T}) where {T} = _append_dispatch(v,a,CxxWrap.cpp_trait_type(T)) Base.ncodeunits(s::CppBasicString)::Int = cppsize(s) Base.codeunit(s::StdString) = UInt8 Base.codeunit(s::StdWString) = Cwchar_t == Int32 ? UInt32 : UInt16 Base.codeunit(s::CppBasicString, i::Integer) = s[i] Base.isvalid(s::CppBasicString, i::Integer) = (0 < i <= ncodeunits(s)) function Base.iterate(s::CppBasicString, i::Integer=1) if !isvalid(s,i) return nothing end return(convert(Char,codeunit(s,i)),i+1) end function StdWString(s::String) char_arr = transcode(Cwchar_t, s) StdWString(char_arr, length(char_arr)) end function StdVector(v::Vector{T}) where {T} if (CxxWrap.cpp_trait_type(T) == CxxWrap.IsCxxType) return StdVector(CxxRef.(v)) end result = StdVector{T}() append(result, v) return result end function StdVector(v::Vector{CxxRef{T}}) where {T} result = isconcretetype(T) ? StdVector{supertype(T)}() : StdVector{T}() append(result, v) return result end function StdVector(v::Vector{Bool}) result = StdVector{CxxBool}() append(result, convert(Vector{CxxBool}, v)) return result end Base.IndexStyle(::Type{<:StdVector}) = IndexLinear() Base.size(v::StdVector) = (Int(cppsize(v)),) Base.getindex(v::StdVector, i::Int) = cxxgetindex(v,i)[] Base.setindex!(v::StdVector{T}, val, i::Int) where {T} = cxxsetindex!(v, convert(T,val), i) function Base.push!(v::StdVector, x) push_back(v, x) return v end function Base.resize!(v::StdVector, n::Integer) resize(v, n) return v end Base.empty!(v::StdVector) = Base.resize!(v, 0) function Base.append!(v::StdVector, a::Vector) append(v, a) return v end function Base.append!(v::StdVector{CxxBool}, a::Vector{Bool}) append(v, convert(Vector{CxxBool}, a)) return v end # Make sure functions taking a C++ string as argument can also take a Julia string CxxWrap.map_julia_arg_type(x::Type{<:StdString}) = AbstractString Base.convert(::Type{T}, x::String) where {T<:StdString} = StdString(x) Base.cconvert(::Type{CxxWrap.ConstCxxRef{StdString}}, x::String) = StdString(x) Base.unsafe_convert(::Type{CxxWrap.ConstCxxRef{StdString}}, x::StdString) = ConstCxxRef(x) function StdValArray(v::Vector{T}) where {T} return StdValArray{T}(v, length(v)) end Base.IndexStyle(::Type{<:StdValArray}) = IndexLinear() Base.size(v::StdValArray) = (Int(cppsize(v)),) Base.getindex(v::StdValArray, i::Int) = cxxgetindex(v,i)[] Base.setindex!(v::StdValArray{T}, val, i::Int) where {T} = cxxsetindex!(v, convert(T,val), i) end
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# # Housing data # In this example, we create a linear regression model that predicts housing data. # It replicates the housing data example from the [Knet.jl readme](https://github.com/denizyuret/Knet.jl). # Although we could have reused more of Flux (see the MNIST example), the library's abstractions are very # lightweight and don't force you into any particular strategy. # A linear model can be created as a neural network with a single layer. # The number of inputs is the same as the features that the data has. # Each input is connected to a single output with no activation function. # Then, the output of the model is a linear function that predicts unseen data. # ![singleneuron](img/singleneuron.svg) # Source: [Dive into Deep Learning](http://d2l.ai/chapter_linear-networks/linear-regression.html#from-linear-regression-to-deep-networks) # To run this example, we need the following packages: using Flux using Flux: gradient using Flux.Optimise: update! using DelimitedFiles, Statistics using Parameters: @with_kw # We set default values for the learning rate (for the training routine) and the percentage of # the data that we use when testing the model: @with_kw mutable struct Hyperparams ## Learning rate lr::Float64 = 0.1 ## Train Test split ratio, define percentage of data to be used as Test data split_ratio::Float64 = 0.1 end # ## Data # We create the function `get_processed_data` to load the housing data, normalize it, # and finally split it into train and test datasets: function get_processed_data(args) isfile("housing.data") || download("https://raw.githubusercontent.com/MikeInnes/notebooks/master/housing.data", "housing.data") rawdata = readdlm("housing.data")' ## The last feature is our target -- the price of the house. split_ratio = args.split_ratio ## For the train test split x = rawdata[1:13,:] y = rawdata[14:14,:] ## Normalise the data x = (x .- mean(x, dims = 2)) ./ std(x, dims = 2) ## Split into train and test sets split_index = floor(Int,size(x,2)*split_ratio) x_train = x[:,1:split_index] y_train = y[:,1:split_index] x_test = x[:,split_index+1:size(x,2)] y_test = y[:,split_index+1:size(x,2)] train_data = (x_train, y_train) test_data = (x_test, y_test) return train_data,test_data end # This function performs the following tasks: # 1. Downloads the housing data. The original size of the data is 505 rows and 14 columns. # 2. Loads the data as a 14x505 matrix. This is the shape that Flux expects. # 3. Splits the data into features and a target. Notice that the 14th row corresponds to the target for each example. # 4. Normalizes the data. For more information on normalizing data, see [How to Use StandardScaler and MinMaxScaler Transforms in Python](https://machinelearningmastery.com/standardscaler-and-minmaxscaler-transforms-in-python/). # 5. Splits the data into train and test datasets. # ## Model # We use a struct to define the model’s parameters. # It contains an array for holding the weights *W* and a vector for the bias term *b*: mutable struct model W::AbstractArray b::AbstractVector end # Also, we create the function `predict` to compute the model’s output: predict(x, m) = m.W*x .+ m.b # Notice that the function `predict` takes as an argument the model struct we defined above. # ## Loss function # The most commonly used loss function for Linear Regression is Mean Squared Error (MSE). # We define the MSE function as: meansquarederror(ŷ, y) = sum((ŷ .- y).^2)/size(y, 2) # **Note:** An implementation of the MSE function is also available in # [Flux](https://fluxml.ai/Flux.jl/stable/models/losses/#Flux.Losses.mse). # ## Train function # Finally, we define the `train` function so that the model learns the best parameters (*W* and *b*): function train(; kws...) ## Initialize the Hyperparamters args = Hyperparams(; kws...) ## Load the data (x_train,y_train),(x_test,y_test) = get_processed_data(args) ## The model m = model((randn(1,13)),[0.]) loss(x, y) = meansquarederror(predict(x, m), y) ## Training η = args.lr θ = params(m.W, m.b) for i = 1:500 g = gradient(() -> loss(x_train, y_train), θ) for x in θ update!(x, g[x]*η) end if i%100==0 @show loss(x_train, y_train) end end ## Predict the RMSE on the test set err = meansquarederror(predict(x_test, m),y_test) println(err) end # The function above initializes the model’s parameters *W* and *b* randomly. # Then, it sets the learning rate η and θ as a # [params object](https://fluxml.ai/Flux.jl/stable/training/training/#Flux.params) # that points to W and b. Also, it sets a # [custom training loop](https://fluxml.ai/Flux.jl/stable/training/training/#Custom-Training-loops) # which is the [Gradient descent algorithm](https://en.wikipedia.org/wiki/Gradient_descent). # Finally, it computes the MSE for the test set. # ## Run the example # We call the `train` function to run the Housing data example: cd(@__DIR__) train()
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""" PoisLik <: Likelihood Poisson likelihood ```math p(yᵢ = k | fᵢ) = θᵏ\\exp(-θ)/k! ``` for ``k ∈ N₀``, where ``θ = \\exp(f)`` and ``f`` is the latent Gaussian process. """ struct PoisLik <: Likelihood end #log of probability density function log_dens(poisson::PoisLik, f::AbstractVector, y::Vector{Int}) #where we exponentiate for positivity f = exp(fi) return y.*f - exp.(f) - lgamma.(1.0 .+ y) end #derivative of pdf wrt latent function function dlog_dens_df(poisson::PoisLik, f::AbstractVector, y::Vector{Int}) return y - exp.(f) end #mean and variance under likelihood mean_lik(poisson::PoisLik, f::AbstractVector) = exp.(f) var_lik(poisson::PoisLik, f::AbstractVector) = exp.(f) get_params(poisson::PoisLik) = [] num_params(poisson::PoisLik) = 0 function var_exp(ll::PoisLik, y::AbstractArray, m::AbstractArray, V::AbstractMatrix) tot = 0 V_diag = diag(V) for (a, b, c) in zip(y, m, V_diag) tot += a*b - exp(b + c/2) - log(factorial(convert(Int64, a))) # convert to lgamma(y+1) end return tot end function var_exp(ll::PoisLik, y::AbstractArray, m::AbstractArray, V::AbstractArray) tot = 0 for (a, b, c) in zip(y, m, V) tot += a*b - exp(b + c/2) - log(factorial(convert(Int64, a))) # convert to lgamma(y+1) end return tot end function var_exp(ll::PoisLik, y::Number, m::Number, V::Number) return y*m - exp(m + V/2) - log(factorial(convert(Int64, y))) # convert to lgamma(y+1) end function dv_var_exp(ll::PoisLik, y::Number, m::Number, V::Number) return gradient(x -> var_exp(ll, y, m, x), V)[1] end
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################################################################################ # # Roots # ################################################################################ function roots(f::Generic.Poly{T}) where T <: Union{padic, qadic, LocalFieldElem} K = base_ring(f) e = absolute_ramification_index(K) k, mk = ResidueField(K) fk = map_coefficients(mk, f) #TODO: We don't need a full Hensel factorization. lH = Hensel_factorization(f) rt = elem_type(K)[] Kx = parent(f) for (phi, g) in lH if isone(degree(phi)) if isone(degree(g)) push!(rt, -constant_coefficient(g)//leading_coefficient(g)) else #TODO: We don't need a full slope factorization. lS = slope_factorization(g) for (h, mh) in lS if isone(degree(h)) r = -constant_coefficient(h)//leading_coefficient(h) for j = 1:mh push!(rt, r) end elseif iszero(constant_coefficient(h)) || divides(numerator(e*valuation(constant_coefficient(h))), degree(h))[1] rth = _roots(h) for j = 1:mh append!(rt, rth) end end end end end end #the roots need to be refined. #rt = refine_roots(f, rt) return rt end function refine_roots(f::Generic.Poly{T}, rt::Vector{T}) where T <: Union{padic, qadic, LocalFieldElem} Rx = parent(f) x = gen(Rx) factors = typeof(f)[x-y for y in rt] push!(factors, divexact(f, prod(factors))) factors = lift_factorization(f, factors) res = Vector{T}(undef, length(rt)) for i = 1:length(rt) res[i] = -constant_coefficient(factors[i]) end return res end function refine_roots1(f::Generic.Poly{T}, rt::Vector{T}) where T <: Union{padic, qadic, LocalFieldElem} K = base_ring(f) v = numerator(absolute_ramification_index(K)*valuation(reduced_discriminant(f))) target_prec = precision(f) starting = minimum(Int[precision(x) for x in rt]) chain = [target_prec] i = target_prec while i > starting i = div(i+1, 2) pushfirst!(chain, i) end der = derivative(f) wvect = [inv(der(rt[i])) for i = 1:length(rt)] rtnew = copy(rt) for i in 1:length(chain) for j = 1:length(rtnew) wvect[j]*f(rtnew[j]) rtnew[j] = rtnew[j] - wvect[j]*f(rtnew[j]) wvect[j] = wvect[j]*(2-wvect[j]*der(rtnew[j])) end end return rtnew end function _roots(f::Generic.Poly{T}) where T <: Union{padic, qadic, LocalFieldElem} K = base_ring(f) k, mk = ResidueField(K) fk = map_coefficients(mk, f) rts = roots(fk) x = gen(parent(f)) #TODO: Is this setprecision call ok? r = setprecision(lift(rts[1], K), precision(f)) pi = uniformizer(K) g = f(pi*x+r) g = divexact(g, _content(g)) rtg = roots(g) rts = elem_type(K)[setprecision(r, precision(y)) + pi*y for y in rtg] return rts end function automorphisms(K::T) where T <: Union{LocalField, FlintQadicField} rt = roots(defining_polynomial(K), K) return morphism_type(K)[hom(K, K, x) for x in rt] end function automorphisms(K::LocalField, L::T) where T <: Union{LocalField, FlintQadicField, FlintPadicField} return _automorphisms(K, K, L) end function absolute_automorphisms(K::LocalField{qadic, S}) where S autsk = small_generating_set(automorphisms(base_field(K))) auts = morphism_type(K)[] for f in autsk fnew = map_coefficients(f, defining_polynomial(K)) rt = roots(fnew, K) for x in rt push!(auts, hom(K, K, f, x)) end end return closure(auts, *) end function absolute_automorphisms(K::LocalField) return _automorphisms(K, K, absolute_base_field(K)) end function absolute_automorphisms(K::FlintQadicField) return automorphisms(K) end function _automorphisms(K::S, F::T, L::U) where {S <: Union{LocalField, FlintQadicField, FlintPadicField}, T <: Union{LocalField, FlintQadicField, FlintPadicField}, U <: Union{LocalField, FlintQadicField, FlintPadicField}} if absolute_degree(K) < absolute_degree(L) error("The base field is not naturally a subfield!") end if K == L return morphism_type(K, F)[hom(K, F, F(gen(K)))] end autsk = _automorphisms(base_field(K), F, L) auts = morphism_type(K, F)[] for f in autsk rt = roots(map_coefficients(f, defining_polynomial(K))) for x in rt push!(auts, hom(K, F, f, x)) end end return auts end function small_generating_set(auts::Vector{T}) where T <: LocalFieldMor @assert length(auts) >= 1 @assert domain(auts[1]) == codomain(auts[1]) for i = 1:length(auts) @assert domain(auts[i]) == codomain(auts[i]) @assert domain(auts[1]) == domain(auts[i]) end if length(auts) == 1 return eltype(auts)[x for x in auts] end return small_generating_set(auts, *, id_hom(domain(auts[1]))) end ################################################################################ # # Automorphism group # ################################################################################ function automorphism_group(K::LocalField) aut = automorphisms(K) mult_table = Matrix{Int}(undef, length(aut), length(aut)) for s = 1:length(aut) for i = 1:length(aut) mult_table[s, i] = findfirst(isequal(aut[s]*aut[i]), aut) end end G = GrpGen(mult_table) return G, GrpGenToNfMorSet(G, aut, K) end @doc Markdown.doc""" automorphism_group(L::NumField, K::NumField) -> GenGrp, GrpGenToNfMorSet Given the number field extension $L$ and $K$, this function returns a group $G$ and a map from $G$ to the automorphisms of $L$ that fix $K$. """ function automorphism_group(L::LocalField, K::LocalField) aut = automorphisms(L, K) mult_table = Matrix{Int}(undef, length(aut), length(aut)) for s = 1:length(aut) for i = 1:length(aut) mult_table[s, i] = findfirst(isequal(aut[s]*aut[i]), aut) end end G = GrpGen(mult_table) return G, GrpGenToNfMorSet(G, aut, L) end @doc Markdown.doc""" absolute_automorphism_group(L::LocalField) -> GenGrp, GrpGenToNfMorSet Given the local field $L$, this function returns a group $G$ and a map from $G$ to the automorphisms of $L$ over the padics. """ function absolute_automorphism_group(L::LocalField) aut = absolute_automorphisms(L) mult_table = Matrix{Int}(undef, length(aut), length(aut)) for s = 1:length(aut) for i = 1:length(aut) mult_table[s, i] = findfirst(isequal(aut[s]*aut[i]), aut) end end G = GrpGen(mult_table) return G, GrpGenToNfMorSet(G, aut, L) end
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4129, 7, 17712, 8, 6624, 352, 198, 220, 220, 220, 1441, 1288, 4906, 7, 17712, 38381, 87, 329, 2124, 287, 1960, 82, 60, 198, 220, 886, 198, 220, 1441, 1402, 62, 8612, 803, 62, 2617, 7, 17712, 11, 1635, 11, 4686, 62, 26452, 7, 27830, 7, 17712, 58, 16, 60, 22305, 198, 437, 198, 198, 29113, 29113, 14468, 198, 2, 198, 2, 220, 220, 17406, 13425, 1042, 1448, 198, 2, 198, 29113, 29113, 14468, 628, 198, 8818, 3557, 13425, 1042, 62, 8094, 7, 42, 3712, 14565, 15878, 8, 198, 220, 1960, 796, 3557, 13425, 6583, 7, 42, 8, 198, 220, 1963, 62, 11487, 796, 24936, 90, 5317, 92, 7, 917, 891, 11, 4129, 7, 2306, 828, 4129, 7, 2306, 4008, 198, 220, 329, 264, 796, 352, 25, 13664, 7, 2306, 8, 198, 220, 220, 220, 329, 1312, 796, 352, 25, 13664, 7, 2306, 8, 198, 220, 220, 220, 220, 220, 1963, 62, 11487, 58, 82, 11, 1312, 60, 796, 1064, 11085, 7, 786, 13255, 7, 2306, 58, 82, 60, 9, 2306, 58, 72, 46570, 1960, 8, 198, 220, 220, 220, 886, 198, 220, 886, 198, 220, 402, 796, 1902, 79, 13746, 7, 16680, 62, 11487, 8, 198, 220, 1441, 402, 11, 1902, 79, 13746, 2514, 45, 69, 20044, 7248, 7, 38, 11, 1960, 11, 509, 8, 198, 437, 198, 198, 31, 15390, 2940, 2902, 13, 15390, 37811, 198, 220, 220, 220, 3557, 13425, 1042, 62, 8094, 7, 43, 3712, 33111, 15878, 11, 509, 3712, 33111, 15878, 8, 4613, 5215, 8642, 79, 11, 1902, 79, 13746, 2514, 45, 69, 20044, 7248, 198, 198, 15056, 262, 1271, 2214, 7552, 720, 43, 3, 290, 720, 42, 47113, 428, 2163, 5860, 257, 1448, 720, 38, 3, 198, 392, 257, 3975, 422, 720, 38, 3, 284, 262, 3557, 13425, 6583, 286, 720, 43, 3, 326, 4259, 720, 42, 35307, 198, 37811, 198, 8818, 3557, 13425, 1042, 62, 8094, 7, 43, 3712, 14565, 15878, 11, 509, 3712, 14565, 15878, 8, 198, 220, 1960, 796, 3557, 13425, 6583, 7, 43, 11, 509, 8, 198, 220, 1963, 62, 11487, 796, 24936, 90, 5317, 92, 7, 917, 891, 11, 4129, 7, 2306, 828, 4129, 7, 2306, 4008, 198, 220, 329, 264, 796, 352, 25, 13664, 7, 2306, 8, 198, 220, 220, 220, 329, 1312, 796, 352, 25, 13664, 7, 2306, 8, 198, 220, 220, 220, 220, 220, 1963, 62, 11487, 58, 82, 11, 1312, 60, 796, 1064, 11085, 7, 786, 13255, 7, 2306, 58, 82, 60, 9, 2306, 58, 72, 46570, 1960, 8, 198, 220, 220, 220, 886, 198, 220, 886, 198, 220, 402, 796, 1902, 79, 13746, 7, 16680, 62, 11487, 8, 198, 220, 1441, 402, 11, 1902, 79, 13746, 2514, 45, 69, 20044, 7248, 7, 38, 11, 1960, 11, 406, 8, 198, 437, 198, 198, 31, 15390, 2940, 2902, 13, 15390, 37811, 198, 220, 220, 220, 4112, 62, 2306, 25831, 1042, 62, 8094, 7, 43, 3712, 14565, 15878, 8, 4613, 5215, 8642, 79, 11, 1902, 79, 13746, 2514, 45, 69, 20044, 7248, 198, 198, 15056, 262, 1957, 2214, 720, 43, 47113, 428, 2163, 5860, 257, 1448, 720, 38, 3, 198, 392, 257, 3975, 422, 720, 38, 3, 284, 262, 3557, 13425, 6583, 286, 720, 43, 3, 625, 262, 14841, 873, 13, 198, 37811, 198, 8818, 4112, 62, 2306, 25831, 1042, 62, 8094, 7, 43, 3712, 14565, 15878, 8, 198, 220, 1960, 796, 4112, 62, 2306, 25831, 6583, 7, 43, 8, 198, 220, 1963, 62, 11487, 796, 24936, 90, 5317, 92, 7, 917, 891, 11, 4129, 7, 2306, 828, 4129, 7, 2306, 4008, 198, 220, 329, 264, 796, 352, 25, 13664, 7, 2306, 8, 198, 220, 220, 220, 329, 1312, 796, 352, 25, 13664, 7, 2306, 8, 198, 220, 220, 220, 220, 220, 1963, 62, 11487, 58, 82, 11, 1312, 60, 796, 1064, 11085, 7, 786, 13255, 7, 2306, 58, 82, 60, 9, 2306, 58, 72, 46570, 1960, 8, 198, 220, 220, 220, 886, 198, 220, 886, 198, 220, 402, 796, 1902, 79, 13746, 7, 16680, 62, 11487, 8, 198, 220, 1441, 402, 11, 1902, 79, 13746, 2514, 45, 69, 20044, 7248, 7, 38, 11, 1960, 11, 406, 8, 198, 437 ]
2.410045
2,668
@json struct Identity provider::String extern_uid::String end @json struct User id::Int username::String email::String name::String state::String avatar_url::String web_url::String created_at::DateTime is_admin::Bool bio::String location::String public_email::String skype::String linkedin::String twitter::String website_url::String organization::String last_sign_in_at::DateTime confirmed_at::DateTime theme_id::Int last_activity_on::Date color_scheme_id::Int projects_limit::Int current_sign_in_at::DateTime identities::Vector{Identity} can_create_group::Bool can_create_project::Bool two_factor_enabled::Bool external::Bool private_profile::Bool # Undocumented shared_runners_minutes_limit::Int end endpoint(::GitLabAPI, ::typeof(get_user)) = Endpoint(:GET, "/user") endpoint(::GitLabAPI, ::typeof(get_user), id::Integer) = Endpoint(:GET, "/users/$id") endpoint(::GitLabAPI, ::typeof(get_user), name::AStr) = Endpoint(:GET, "/users"; query=Dict(:username => name)) postprocessor(::GitLabAPI, ::typeof(get_user)) = DoSomething() do r v = JSON2.read(IOBuffer(r.body), Union{User, Vector{User}}) return if v isa User v else isempty(v) ? nothing : v[1] end end into(::GitLabAPI, ::typeof(get_user)) = User endpoint(::GitLabAPI, ::typeof(get_users)) = Endpoint(:GET, "/users") into(::GitLabAPI, ::typeof(get_users)) = Vector{User} endpoint(::GitLabAPI, ::typeof(update_user), id::Integer) = Endpoint(:PUT, "/users/$id") postprocessor(::GitLabAPI, ::typeof(update_user)) = DoNothing() endpoint(::GitLabAPI, ::typeof(create_user)) = Endpoint(:POST, "/users") into(::GitLabAPI, ::typeof(create_user)) = User endpoint(::GitLabAPI, ::typeof(delete_user), id::Integer) = Endpoint(:DELETE, "/users/$id") postprocessor(::GitLabAPI, ::typeof(delete_user)) = DoNothing()
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2.491003
778
using Cmdl using Base.Test # write your own tests here #@test 1 == 2
[ 3500, 327, 9132, 75, 198, 3500, 7308, 13, 14402, 198, 198, 2, 3551, 534, 898, 5254, 994, 198, 2, 31, 9288, 352, 6624, 362, 198 ]
2.8
25
### A Pluto.jl notebook ### # v0.18.0 using Markdown using InteractiveUtils # ╔═╡ 6b1ad54f-61e4-490d-9032-7a557e8dc82f md""" ## CHEME 5440/7770: Structural Analysis of the Urea Cycle Network (PS2) """ # ╔═╡ 7057c8e4-9e94-4a28-a885-07f5c96ebe39 html""" <p style="font-size:20px;">Student name, Student name, Student name ... Student name</br> Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca NY 14850</p> """ # ╔═╡ 87a183bc-3857-4189-8103-18c46ff3245d md""" #### Build the stoichiometric array """ # ╔═╡ 5338451e-3c4b-4030-bbbb-42eaf4209a89 begin # fill me in # ... end # ╔═╡ 6970dab5-16bd-4898-b88d-723cb1b3d89e md""" #### Convex analysis: extreme pathways """ # ╔═╡ 97b0763d-dcab-4afa-b660-52e18b3d523f begin # fill me in # ... end # ╔═╡ b473b17e-3bf5-4b6c-af24-fe57b5a7e7e9 md""" #### Metabolite connectivity array (MCA) """ # ╔═╡ 999ae1fd-5341-4f66-9db2-dec53fa0cd49 # ╔═╡ b7e5d1a6-57ed-4d09-a039-a4bd12386367 md""" #### Reaction connectivity array (RCA) """ # ╔═╡ 4520fc6e-7305-487e-924d-af22406e6d45 begin # fill me in ... end # ╔═╡ 267865de-1b5c-4579-861b-c6c46beb4739 function ingredients(path::String) # this is from the Julia source code (evalfile in base/loading.jl) # but with the modification that it returns the module instead of the last object name = Symbol("lib") m = Module(name) Core.eval(m, Expr(:toplevel, :(eval(x) = $(Expr(:core, :eval))($name, x)), :(include(x) = $(Expr(:top, :include))($name, x)), :(include(mapexpr::Function, x) = $(Expr(:top, :include))(mapexpr, $name, x)), :(include($path)))) m end # ╔═╡ 67f5db98-88d0-11ec-27ac-b57538a166f4 begin # import some packages - using PlutoUI using PrettyTables using LinearAlgebra # setup paths - const _PATH_TO_NOTEBOOK = pwd() const _PATH_TO_SRC = joinpath(_PATH_TO_NOTEBOOK,"src") # load the PS2 code lib - lib = ingredients(joinpath(_PATH_TO_SRC, "Include.jl")); # return - nothing end # ╔═╡ ab2bcfd5-3ba7-4388-8a3c-2cb95fba989a html""" <style> main { max-width: 900px; width: 75%; margin: auto; font-family: "Roboto, monospace"; } a { color: blue; text-decoration: none; } </style>""" # ╔═╡ 00000000-0000-0000-0000-000000000001 PLUTO_PROJECT_TOML_CONTENTS = """ [deps] LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8" PrettyTables = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d" [compat] PlutoUI = "~0.7.34" PrettyTables = "~1.3.1" """ # ╔═╡ 00000000-0000-0000-0000-000000000002 PLUTO_MANIFEST_TOML_CONTENTS = """ # This file is machine-generated - editing it directly is not advised julia_version = "1.7.1" manifest_format = "2.0" [[deps.AbstractPlutoDingetjes]] deps = ["Pkg"] git-tree-sha1 = "8eaf9f1b4921132a4cff3f36a1d9ba923b14a481" uuid = "6e696c72-6542-2067-7265-42206c756150" version = "1.1.4" [[deps.ArgTools]] uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f" [[deps.Artifacts]] uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33" [[deps.Base64]] uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f" [[deps.ColorTypes]] deps = ["FixedPointNumbers", "Random"] git-tree-sha1 = "024fe24d83e4a5bf5fc80501a314ce0d1aa35597" uuid = "3da002f7-5984-5a60-b8a6-cbb66c0b333f" version = "0.11.0" [[deps.CompilerSupportLibraries_jll]] deps = ["Artifacts", "Libdl"] uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae" [[deps.Crayons]] git-tree-sha1 = "249fe38abf76d48563e2f4556bebd215aa317e15" uuid = "a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f" version = "4.1.1" [[deps.DataAPI]] git-tree-sha1 = "cc70b17275652eb47bc9e5f81635981f13cea5c8" uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a" version = "1.9.0" [[deps.DataValueInterfaces]] git-tree-sha1 = "bfc1187b79289637fa0ef6d4436ebdfe6905cbd6" uuid = "e2d170a0-9d28-54be-80f0-106bbe20a464" version = "1.0.0" [[deps.Dates]] deps = ["Printf"] uuid = "ade2ca70-3891-5945-98fb-dc099432e06a" [[deps.Downloads]] deps = ["ArgTools", "LibCURL", "NetworkOptions"] uuid = "f43a241f-c20a-4ad4-852c-f6b1247861c6" [[deps.FixedPointNumbers]] deps = ["Statistics"] git-tree-sha1 = "335bfdceacc84c5cdf16aadc768aa5ddfc5383cc" uuid = "53c48c17-4a7d-5ca2-90c5-79b7896eea93" version = "0.8.4" [[deps.Formatting]] deps = ["Printf"] git-tree-sha1 = "8339d61043228fdd3eb658d86c926cb282ae72a8" uuid = "59287772-0a20-5a39-b81b-1366585eb4c0" version = "0.4.2" [[deps.Hyperscript]] deps = ["Test"] git-tree-sha1 = "8d511d5b81240fc8e6802386302675bdf47737b9" uuid = "47d2ed2b-36de-50cf-bf87-49c2cf4b8b91" version = "0.0.4" [[deps.HypertextLiteral]] git-tree-sha1 = "2b078b5a615c6c0396c77810d92ee8c6f470d238" uuid = "ac1192a8-f4b3-4bfe-ba22-af5b92cd3ab2" version = "0.9.3" [[deps.IOCapture]] deps = ["Logging", "Random"] git-tree-sha1 = "f7be53659ab06ddc986428d3a9dcc95f6fa6705a" uuid = "b5f81e59-6552-4d32-b1f0-c071b021bf89" version = "0.2.2" [[deps.InteractiveUtils]] deps = ["Markdown"] uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240" [[deps.IteratorInterfaceExtensions]] git-tree-sha1 = "a3f24677c21f5bbe9d2a714f95dcd58337fb2856" uuid = "82899510-4779-5014-852e-03e436cf321d" version = "1.0.0" [[deps.JSON]] deps = ["Dates", "Mmap", "Parsers", "Unicode"] git-tree-sha1 = "8076680b162ada2a031f707ac7b4953e30667a37" uuid = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" version = "0.21.2" [[deps.LibCURL]] deps = ["LibCURL_jll", "MozillaCACerts_jll"] uuid = "b27032c2-a3e7-50c8-80cd-2d36dbcbfd21" [[deps.LibCURL_jll]] deps = ["Artifacts", "LibSSH2_jll", "Libdl", "MbedTLS_jll", "Zlib_jll", "nghttp2_jll"] uuid = "deac9b47-8bc7-5906-a0fe-35ac56dc84c0" [[deps.LibGit2]] deps = ["Base64", "NetworkOptions", "Printf", "SHA"] uuid = "76f85450-5226-5b5a-8eaa-529ad045b433" [[deps.LibSSH2_jll]] deps = ["Artifacts", "Libdl", "MbedTLS_jll"] uuid = "29816b5a-b9ab-546f-933c-edad1886dfa8" [[deps.Libdl]] uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb" [[deps.LinearAlgebra]] deps = ["Libdl", "libblastrampoline_jll"] uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" [[deps.Logging]] uuid = "56ddb016-857b-54e1-b83d-db4d58db5568" [[deps.Markdown]] deps = ["Base64"] uuid = "d6f4376e-aef5-505a-96c1-9c027394607a" [[deps.MbedTLS_jll]] deps = ["Artifacts", "Libdl"] uuid = "c8ffd9c3-330d-5841-b78e-0817d7145fa1" [[deps.Mmap]] uuid = "a63ad114-7e13-5084-954f-fe012c677804" [[deps.MozillaCACerts_jll]] uuid = "14a3606d-f60d-562e-9121-12d972cd8159" [[deps.NetworkOptions]] uuid = "ca575930-c2e3-43a9-ace4-1e988b2c1908" [[deps.OpenBLAS_jll]] deps = ["Artifacts", "CompilerSupportLibraries_jll", "Libdl"] uuid = "4536629a-c528-5b80-bd46-f80d51c5b363" [[deps.Parsers]] deps = ["Dates"] git-tree-sha1 = "0b5cfbb704034b5b4c1869e36634438a047df065" uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0" version = "2.2.1" [[deps.Pkg]] deps = ["Artifacts", "Dates", "Downloads", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"] uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f" [[deps.PlutoUI]] deps = ["AbstractPlutoDingetjes", "Base64", "ColorTypes", "Dates", "Hyperscript", "HypertextLiteral", "IOCapture", "InteractiveUtils", "JSON", "Logging", "Markdown", "Random", "Reexport", "UUIDs"] git-tree-sha1 = "8979e9802b4ac3d58c503a20f2824ad67f9074dd" uuid = "7f904dfe-b85e-4ff6-b463-dae2292396a8" version = "0.7.34" [[deps.PrettyTables]] deps = ["Crayons", "Formatting", "Markdown", "Reexport", "Tables"] git-tree-sha1 = "dfb54c4e414caa595a1f2ed759b160f5a3ddcba5" uuid = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d" version = "1.3.1" [[deps.Printf]] deps = ["Unicode"] uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7" [[deps.REPL]] deps = ["InteractiveUtils", "Markdown", "Sockets", "Unicode"] uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb" [[deps.Random]] deps = ["SHA", "Serialization"] uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" [[deps.Reexport]] git-tree-sha1 = "45e428421666073eab6f2da5c9d310d99bb12f9b" uuid = "189a3867-3050-52da-a836-e630ba90ab69" version = "1.2.2" [[deps.SHA]] uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce" [[deps.Serialization]] uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b" [[deps.Sockets]] uuid = "6462fe0b-24de-5631-8697-dd941f90decc" [[deps.SparseArrays]] deps = ["LinearAlgebra", "Random"] uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" [[deps.Statistics]] deps = ["LinearAlgebra", "SparseArrays"] uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" [[deps.TOML]] deps = ["Dates"] uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76" [[deps.TableTraits]] deps = ["IteratorInterfaceExtensions"] git-tree-sha1 = "c06b2f539df1c6efa794486abfb6ed2022561a39" uuid = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c" version = "1.0.1" [[deps.Tables]] deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "TableTraits", "Test"] git-tree-sha1 = "bb1064c9a84c52e277f1096cf41434b675cd368b" uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c" version = "1.6.1" [[deps.Tar]] deps = ["ArgTools", "SHA"] uuid = "a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e" [[deps.Test]] deps = ["InteractiveUtils", "Logging", "Random", "Serialization"] uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40" [[deps.UUIDs]] deps = ["Random", "SHA"] uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4" [[deps.Unicode]] uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5" [[deps.Zlib_jll]] deps = ["Libdl"] uuid = "83775a58-1f1d-513f-b197-d71354ab007a" [[deps.libblastrampoline_jll]] deps = ["Artifacts", "Libdl", "OpenBLAS_jll"] uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" [[deps.nghttp2_jll]] deps = ["Artifacts", "Libdl"] uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" [[deps.p7zip_jll]] deps = ["Artifacts", "Libdl"] uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" """ # ╔═╡ Cell order: # ╟─6b1ad54f-61e4-490d-9032-7a557e8dc82f # ╟─7057c8e4-9e94-4a28-a885-07f5c96ebe39 # ╟─87a183bc-3857-4189-8103-18c46ff3245d # ╠═5338451e-3c4b-4030-bbbb-42eaf4209a89 # ╠═6970dab5-16bd-4898-b88d-723cb1b3d89e # ╠═97b0763d-dcab-4afa-b660-52e18b3d523f # ╟─b473b17e-3bf5-4b6c-af24-fe57b5a7e7e9 # ╠═999ae1fd-5341-4f66-9db2-dec53fa0cd49 # ╟─b7e5d1a6-57ed-4d09-a039-a4bd12386367 # ╠═4520fc6e-7305-487e-924d-af22406e6d45 # ╠═67f5db98-88d0-11ec-27ac-b57538a166f4 # ╠═267865de-1b5c-4579-861b-c6c46beb4739 # ╟─ab2bcfd5-3ba7-4388-8a3c-2cb95fba989a # ╟─00000000-0000-0000-0000-000000000001 # ╟─00000000-0000-0000-0000-000000000002
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module ShapML using Distributed using DataFrames using Random include("shap_sample.jl") # Load _shap_sample(). include("aggregate.jl") # Load _aggregate(). include("predict.jl") # Load _predict(). export shap """ shap(explain::DataFrame, reference::Union{DataFrame, Nothing} = nothing, model, predict_function::Function, target_features::Union{Vector, Nothing} = nothing, sample_size::Integer = 60, parallel::Symbol = [:none, :samples, :features, :both], seed::Integer = 1, precision::Union{Integer, Nothing} = nothing, chunk::Bool = true, reconcile_instance::Bool = false ) Compute stochastic feature-level Shapley values for any ML model. # Arguments - `explain::DataFrame`: A DataFrame of model features with 1 or more instances to be explained using Shapley values. - `reference`: Optional. A DataFrame with the same format as `explain` which serves as a reference group against which the Shapley value deviations from `explain` are compared (i.e., the model intercept). - `model`: A trained ML model that is passed into `predict_function`. - `predict_function`: A wrapper function that takes 2 required positional arguments–(1) the trained model from `model` and (2) a DataFrame of instances with the same format as `explain`. The function should return a 1-column DataFrame of model predictions; the column name does not matter. - `target_features`: Optional. An `Array{String, 1}` of model features that is a subset of feature names in `explain` for which Shapley values will be computed. For high-dimensional models, selecting a subset of features may dramatically speed up computation time. The default behavior is to return Shapley values for all instances and features in `explain`. - `sample_size::Integer`: The number of Monte Carlo samples used to compute the stochastic Shapley values for each feature. - `parallel::Union{Symbol, Nothing}`: One of [:none, :samples, :features, :both]. Whether to perform the calculation serially (:none) or in parallel over Monte Carlo samples (:samples) with `pmap()` and/or multi-threaded over target features (:features) with @threads or :both. - `seed::Integer`: A number passed to `Random.seed!()` to get reproducible results. - `precision::Union{Integer, Nothing}`: The number of digits to `round()` results in the ouput (to reduce the size of the returned DataFrame). - `chunk::Bool`: Default `true`. Increases speed on data with many instances and/or features. Calls the `predict()` function once per sample in `sample_size` instead of once per call to `ShapML.shap()`. - `reconcile_instance`: EXPERIMENTAL. For each instance in `explain`, the stochastic feature-level Shapley values are adjusted so that their sum equals the model prediction. The adjustments are based on feature-level sampling variances and are typically small compared to the model prediction. # Return - A `size(explain, 1)` * `length(target_features)` row by 6 column DataFrame. + `index`: An instance in `explain`. + `feature_name`: Model feature. + `feature_value`: Feature value. + `shap_effect`: The average Shapley value across Monte Carlo samples. + `shap_effect_sd`: The standard deviation of Shapley values across Monte Carlo samples. + `intercept`: The average model prediction from `explain` or `reference`. """ function shap(;explain::DataFrame, reference::Union{DataFrame, Nothing} = nothing, model, predict_function::Function, target_features::Union{Vector, Nothing} = nothing, sample_size::Integer = 60, parallel::Union{Symbol, Nothing} = nothing, seed::Integer = 1, precision::Union{Integer, Nothing} = nothing, chunk::Bool = true, reconcile_instance::Bool = false ) feature_names = String.(names(explain)) feature_names_symbol = Symbol.(feature_names) if (target_features === nothing) target_features = copy(feature_names) # Default is to explain with all features. else if !all(isa.(target_features, String)) error(""""target_features" should be an array of feature names of type "String".""") end if !all(map(x -> any(x .== target_features), target_features)) error("""One or more "target_features" is not in String.(names(explain)).""") end end #---------------------------------------------------------------------------- n_instances_explain = size(explain, 1) n_features = size(explain, 2) n_target_features = length(target_features) #---------------------------------------------------------------------------- if (reference === nothing) # Default is to explain all instances in 'explain' without a specific reference group. reference = copy(explain) else if names(explain) != names(reference) error(""""explain" and "reference" should have the same model features and no outcome column.""") end end n_instances = size(reference, 1) #-------------------------------------------------------------------------- # Parallel computation setup; the type of parallelization, if any, depends on # the 'parallel' argument. if (parallel === nothing) parallel = :none end if !any(parallel .== [:none, :samples, :features, :both]) error(""""parallel" should be one of [:none, :samples, :features, :both].""") end #-------------------------------------------------------------------------- # Create a vector of random seeds to get reproducible results with both # serial and parallel computations. This is not the perfect solution because there # could potentially be correlation between the seeds, but the effect on randomness, # if any, will be small. To-do: Pass in seed generator objects. Random.seed!(seed) seeds = abs.(rand(Int, sample_size)) #-------------------------------------------------------------------------- # Main Shapley value computation from _shap_sample(). This code is either # run serially or in parallel. if any(parallel .== [:none, :features]) data_predict = _shap_sample(explain, reference, n_instances, n_instances_explain, n_features, n_target_features, target_features, feature_names, feature_names_symbol, sample_size, parallel, seeds, chunk, model, # chunk = true. predict_function, # chunk = true. precision # chunk = true. ) elseif any(parallel .== [:samples, :both]) data_predict = pmap(_i -> _shap_sample(explain, reference, n_instances, n_instances_explain, n_features, n_target_features, target_features, feature_names, feature_names_symbol, sample_size, parallel, seeds[_i], chunk, model, # chunk = true. predict_function, # chunk = true. precision # chunk = true. ), 1:sample_size) end # End Shapley value Monte Carlo calculation. #-------------------------------------------------------------------------- # Put all Frankenstein instances from all instances passed in 'explain' into # a single DataFrame for the user-defined predict() function. data_predict = vcat(data_predict...) if any(parallel .== [:samples, :both]) data_predict = vcat(data_predict...) data_predict.sample = repeat(1:sample_size, inner = n_instances_explain * n_target_features * 2) end data_shap = _predict(reference = reference, # input arg. data_predict = data_predict, # Calculated. model = model, # input arg. predict_function = predict_function, # input arg. n_features = n_features, # Calculated. n_target_features = n_target_features, # Calculated. n_instances_explain = n_instances_explain, # Calculated. sample_size = sample_size, # input arg. precision = precision, # input arg. chunk = chunk, # input arg. reconcile_instance = reconcile_instance, # input arg. explain = explain # input arg; needed if reconcile_instance = true. ) #-------------------------------------------------------------------------- # Melt the input 'explain' DataFrame for merging the model features to the Shapley values. data_merge = DataFrames.stack(explain, feature_names_symbol) rename!(data_merge, Dict(:variable => "feature_name", :value => "feature_value")) data_merge.feature_name = String.(data_merge.feature_name) # Coerce for merging. data_merge.index = repeat(1:n_instances_explain, n_features) # The merge index for each instance. # Each instance in explain has one Shapley value per instance in a long DataFrame format. data_shap = leftjoin(data_shap, data_merge, on = [:index, :feature_name]) # Re-order columns for easier reading. DataFrames.select!(data_shap, [:index, :feature_name, :feature_value, :shap_effect, :shap_effect_sd, :intercept]) return data_shap end # End shap(). end # End module.
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# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # using FMI using Flux using DifferentialEquations: Tsit5 import Random Random.seed!(5678); t_start = 0.0 t_step = 0.1 t_stop = 3.0 tData = t_start:t_step:t_stop # generate training data realFMU = fmiLoad("BouncingBall1D", ENV["EXPORTINGTOOL"], ENV["EXPORTINGVERSION"]) realSimData = fmiSimulate(realFMU, t_start, t_stop; recordValues=["mass_s", "mass_v"], saveat=tData) x0 = collect(realSimData.values.saveval[1]) @test x0 == [1.0, 0.0] # setup traing data posData = fmi2GetSolutionValue(realSimData, "mass_s") velData = fmi2GetSolutionValue(realSimData, "mass_v") # loss function for training function losssum() global problem, x0, posData solution = problem(x0) posNet = fmi2GetSolutionState(solution, 1; isIndex=true) velNet = fmi2GetSolutionState(solution, 2; isIndex=true) Flux.Losses.mse(posNet, posData) + Flux.Losses.mse(velNet, velData) end # callback function for training global iterCB = 0 global lastLoss = 0.0 function callb() global iterCB += 1 global lastLoss if iterCB % 1 == 0 loss = losssum() @info "Loss: $loss" # This test condition is weak, because when the FMU passes an event, the error might increase. # ToDo: More intelligent testing condition. @test (loss < lastLoss*2.0) && (loss != lastLoss) lastLoss = loss end end vr = fmi2StringToValueReference(realFMU, "mass_m") numStates = fmiGetNumberOfStates(realFMU) # some NeuralFMU setups nets = [] # 1. default ME-NeuralFMU (learn dynamics and states, almost-neutral setup, parameter count << 100) net = Chain(Dense( [1.0 0.0; 0.0 1.0] + rand(numStates,numStates)*0.01, zeros(numStates), identity), states -> fmiEvaluateME(realFMU, states), Dense( [1.0 0.0; 0.0 1.0] + rand(numStates,numStates)*0.01, zeros(numStates), identity)) push!(nets, net) # 2. default ME-NeuralFMU (learn dynamics) net = Chain(states -> fmiEvaluateME(realFMU, states), Dense(numStates, 16, tanh), Dense(16, 16, tanh), Dense(16, numStates)) push!(nets, net) # 3. default ME-NeuralFMU (learn states) net = Chain(Dense(numStates, 16, identity), Dense(16, 16, identity), Dense(16, numStates), states -> fmiEvaluateME(realFMU, states)) push!(nets, net) # 4. default ME-NeuralFMU (learn dynamics and states) net = Chain(Dense(numStates, 16, leakyrelu), Dense(16, 16, leakyrelu), Dense(16, numStates), states -> fmiEvaluateME(realFMU, states), Dense(numStates, 16, tanh), Dense(16, 16, tanh), Dense(16, numStates)) push!(nets, net) # 5. NeuralFMU with hard setting time to 0.0 net = Chain(states -> fmiEvaluateME(realFMU, states), # not supported by this FMU: states -> fmiEvaluateME(realFMU, states, 0.0), Dense(numStates, 8, tanh), Dense(8, 16, tanh), Dense(16, numStates)) push!(nets, net) # 6. NeuralFMU with additional getter getVRs = [fmi2StringToValueReference(realFMU, "mass_m")] numGetVRs = length(getVRs) net = Chain(states -> fmiEvaluateME(realFMU, states, realFMU.components[end].t, fmi2ValueReference[], Real[], getVRs), Dense(numStates+numGetVRs, 8, tanh), Dense(8, 16, tanh), Dense(16, numStates)) push!(nets, net) # 7. NeuralFMU with additional setter setVRs = [fmi2StringToValueReference(realFMU, "mass_m")] numSetVRs = length(setVRs) net = Chain(states -> fmiEvaluateME(realFMU, states, realFMU.components[end].t, setVRs, [1.1]), Dense(numStates, 8, tanh), Dense(8, 16, tanh), Dense(16, numStates)) push!(nets, net) # 8. NeuralFMU with additional setter and getter net = Chain(states -> fmiEvaluateME(realFMU, states, realFMU.components[end].t, setVRs, [1.1], getVRs), Dense(numStates+numGetVRs, 8, tanh), Dense(8, 16, tanh), Dense(16, numStates)) push!(nets, net) # 9. Empty NeuralFMU net = Chain(states -> fmiEvaluateME(realFMU, states), Dense(ones(numStates, numStates), false, identity)) push!(nets, net) optim = ADAM(1e-4) for i in 1:length(nets) @testset "Net setup #$i" begin global nets, problem, lastLoss, iterCB net = nets[i] problem = ME_NeuralFMU(realFMU, net, (t_start, t_stop), Tsit5(); saveat=tData) @test problem !== nothing solutionBefore = problem(x0) if solutionBefore.success @test length(solutionBefore.states.t) == length(tData) @test solutionBefore.states.t[1] == t_start @test solutionBefore.states.t[end] == t_stop end # train it ... p_net = Flux.params(problem) iterCB = 0 lastLoss = losssum() @info "Start-Loss for net #$i: $lastLoss" # check results solutionAfter = problem(x0) if solutionAfter.success @test length(solutionAfter.states.t) == length(tData) @test solutionAfter.states.t[1] == t_start @test solutionAfter.states.t[end] == t_stop end end end fmiUnload(realFMU)
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struct OrdinalFactor{T<:Unsigned} <: AbstractFactor{T} name::String levels::AbstractVector{<:AbstractString} basefactor::AbstractFactor{T} newindex::Vector{T} end Base.length(factor::OrdinalFactor{T}) where {T<:Unsigned} = length(factor.basefactor) function OrdinalFactor(name::String, basefactor::AbstractFactor{T}, islessfun::Function) where {T<:Unsigned} baselevels = getlevels(basefactor) levelcount = length(baselevels) perm = sortperm(baselevels, lt = islessfun) sortlevels = baselevels[perm] newindex = Vector{T}(levelcount + 1) newindex[1] = 0 for i in 1:levelcount newindex[i + 1] = perm[i] end OrdinalFactor{T}(name, sortlevels, basefactor, newindex) end function OrdinalFactor(factor::AbstractFactor{T}) where {T<:Unsigned} OrdinalFactor{T}(getname(factor), getlevels(factor), factor, Vector{T}()) end function slice(factor::OrdinalFactor{T}, fromobs::Integer, toobs::Integer, slicelength::Integer) where {T<:Unsigned} if length(factor.newindex) == 0 slice(factor.basefactor, fromobs, toobs, slicelength) else newindex = factor.newindex f = (i::T -> newindex[i + 1]) slicelength = verifyslicelength(fromobs, toobs, slicelength) slices = slice(factor.basefactor, fromobs, toobs, slicelength) mapslice(f, slices, slicelength, T) end end function isordinal(factor::OrdinalFactor{T}) where {T<:Unsigned} true end
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