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push!(LOAD_PATH,"../../src/") import UNSflow alpha_init = 10. *pi/180 alphadot_init = 0. h_init = 0. hdot_init = 0. u = 0.467 udot = 0 kinem = UNSflow.KinemPar2DOF(alpha_init, h_init, alphadot_init, hdot_init, u) x_alpha = 0.05 r_alpha = 0.5 kappa = 0.05 w_alpha = 1. w_h = 1. w_alphadot = 0. w_hdot = 0. cubic_h_1 = 1. cubic_h_3 = 0. cubic_alpha_1 = 1. cubic_alpha_3 = 0. strpar = UNSflow.TwoDOFPar(x_alpha, r_alpha, kappa, w_alpha, w_h, w_alphadot, w_hdot, cubic_h_1, cubic_h_3, cubic_alpha_1, cubic_alpha_3) #Dummy kinematic definitions to initialise surface alphadef = UNSflow.ConstDef(alpha_init) hdef = UNSflow.ConstDef(h_init) udef = UNSflow.ConstDef(1.) #This is relative to uref startkinem = UNSflow.KinemDef(alphadef, hdef, udef) lespcrit = [0.11;] pvt = 0.35 c = 1. surf = UNSflow.TwoDSurf("FlatPlate", pvt, startkinem, lespcrit, c=c, uref=u) curfield = UNSflow.TwoDFlowField() dtstar = 0.015 nsteps = 50000 t_tot = nsteps * dtstar / u startflag = 0 writeflag = 0 writeInterval = dtstar * nsteps/20. writeInterval = t_tot/20. delvort = UNSflow.delSpalart(500, 12, 1e-5) mat, surf, curfield = UNSflow.ldvm2DOF(surf, curfield, strpar, kinem, nsteps, dtstar,startflag, writeflag, writeInterval, delvort) UNSflow.makeForcePlots2D() #cleanWrite()
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struct DoFHandler{N,T} DoF::Vector{NTuple{N,T}} nDofs::T end Base.getindex(a::DoFHandler, I::Int64) = a.DoF[I] Base.getindex(a::DoFHandler, I::Int32) = a.DoF[I] Base.getindex(a::DoFHandler, I::Int8) = a.DoF[I] function DoFs_Thermal(EL2NOD, nnodel) DoF = [ntuple(j->EL2NOD[j,i], nnodel) for i in axes(EL2NOD,2)] nDofs = maximum(view(EL2NOD,1:nnodel,:)) DoFHandler(DoF,nDofs) end function DoFs_Pressure(e2nP) DoF = [ntuple(j->e2nP[j,i],3) for i in axes(e2nP,2)] nDofs = maximum(e2nP) DoFHandler(DoF, nDofs) end function DoFs_Velocity(EL2NOD) dummy = Matrix{Int64}(undef, size(EL2NOD, 2), 12) @views dummy[:, 1:2:end] .= (@. 2*(EL2NOD-1)+1)' @views dummy[:, 2:2:end] .= (@. 2*(EL2NOD))' DoF = [ntuple(j->dummy[i,j], 12) for i in axes(EL2NOD,2)] nDofs = maximum(EL2NOD)*2 DoFHandler(DoF,nDofs) end
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using AbstractTrees using Test @testset "Array" begin tree = Any[1,Any[2,3]] @test collect(Leaves(tree)) == [1,2,3] @test collect(Leaves(tree)) isa Vector{Int} @test collect(PostOrderDFS(tree)) == Any[1,2,3,Any[2,3],Any[1,Any[2,3]]] @test collect(StatelessBFS(tree)) == Any[Any[1,Any[2,3]],1,Any[2,3],2,3] @test treesize(tree) == 5 @test treebreadth(tree) == 3 @test treeheight(tree) == 2 @test ischild(1, tree) @test !ischild(2, tree) @test ischild(tree[2], tree) @test !ischild(copy(tree[2]), tree) # Should work on identity, not equality @test isdescendant(1, tree) @test isdescendant(2, tree) @test !isdescendant(4, tree) @test isdescendant(tree[2], tree) @test !isdescendant(copy(tree[2]), tree) @test !isdescendant(tree, tree) @test intree(1, tree) @test intree(2, tree) @test !intree(4, tree) @test intree(tree[2], tree) @test !intree(copy(tree[2]), tree) @test intree(tree, tree) tree2 = Any[Any[1,2],Any[3,'4']] @test collect(PreOrderDFS(tree2)) == Any[tree2,Any[1,2],1,2,Any[3,'4'],3,'4'] @test treesize(tree2) == 7 @test treebreadth(tree2) == 4 @test treeheight(tree2) == 2 tree3 = [] for itr in [Leaves, PreOrderDFS, PostOrderDFS] @test collect(itr(tree3)) == [tree3] end @test treesize(tree3) == 1 @test treebreadth(tree3) == 1 @test treeheight(tree3) == 0 @test collect(PostOrderDFS([])) == Any[[]] end @testset "Pair" begin tree = 1=>(3=>4) @test collect(PreOrderDFS(tree)) == Any[tree, tree.second, 4] end @testset "Expr" begin expr = :(foo(x^2 + 3)) @test children(expr) == expr.args @test collect(Leaves(expr)) == [:foo, :+, :^, :x, 2, 3] end @testset "Array-Dict" begin t = [1, 2, Dict("a"=>3, "b"=>[4,5])] @test Set(Leaves(t)) == Set(1:5) # don't want to guarantee ordering because of dict t = [1, Dict("a"=>2)] @test collect(Leaves(t)) == [1, 2] @test collect(PreOrderDFS(t)) == [t, 1, t[2], "a"=>2, 2] @test collect(PostOrderDFS(t)) == [1, 2, "a"=>2, t[2], t] end @testset "treemap" begin a = [1,[2,[3]]] f = n -> n isa AbstractArray ? (nothing, children(n)) : (n+1, children(n)) b = treemap(f, a) @test collect(nodevalues(PreOrderDFS(b))) == [nothing, 2, nothing, 3, nothing, 4] g = n -> isempty(children(n)) ? (nodevalue(n), ()) : (nothing, [0; children(n)]) b = treemap(g, a) @test nodevalue.(PostOrderDFS(b)) == [0, 1, 0, 2, 0, 3, nothing, nothing, nothing] end
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const QUOTE_CACHE = Cache{String,Float64}(Minute(1)) finnhub_token() = get(ENV, "FINNHUB_TOKEN", "") function commander(c::Client, m::Message, ::Val{:ig}) @debug "ig_commander called" command = extract_command("ig", m.content) args = split(command) @debug "parse result" command args if length(args) == 0 || args[1] ∉ ["start-game", "abandon-game", "quote", "chart", "buy", "sell", "rank", "view", "abandon-game-really"] help_commander(c, m, :ig) return end user = @discord retrieve(c, User, m.author.id) try ig_execute(c, m, user, Val(Symbol(args[1])), args[2:end]) catch ex if ex isa IgUserError discord_reply(c, m, ig_hey(user.username, ex.message)) else discord_reply(c, m, ig_hey(user.username, "sorry, looks like you've hit a bug. Please report the problem.")) @error "Internal error" ex Base.showerror(stdout, ex, catch_backtrace()) end end end function help_commander(c::Client, m::Message, ::Val{:ig}) discord_reply(c, m, """ Play the investment game (ig). US market only for now. ``` ig start-game ig abandon-game ``` Research stocks: ``` ig quote <symbol> - get current price quote ig chart <symbol> [period] - historical price chart Period is optional. Examples are: 200d, 36m, or 10y for 200 days, 36 months, or 10 years respectively. ``` Manage portfolio: ``` ig view - view holdings and current market values ig buy <n> <symbol> - buy <n> shares of a stock ig sell <n> <symbol> - sell <n> shares of a stock ``` How are you doing? ``` ig rank [n] - display top <n> portfolios, defaults to 5. ``` """) end """ ig_execute This function executes any of the following commands. # start-game Start a new game by giving the player \$1MM USD. # abandon-game Abandon current game by wiping out the player's record. # quote <symbol> Fetch current market price of a stock. # chart <symbol> <lookback> The `lookback` argument is optional. If it is not specified, then it is defaulted to 1 year. Otherwise, it takes the format of a number followed by `y`, `m`, or `d` for number of years, months, or days respectively. # buy <n> <symbol> Buy `n` shares of a stock at the current market price. Player must have enough cash to settle the trade. # sell <n> <symbol> Sell `n` shares of a stock at the current market price. Player must have that many shares in the portfolio. # view Display detailed information about the portfolio, its holdings, and total market value. # rank Display top portfolios with highest market value. """ function ig_execute end function ig_execute(c::Client, m::Message, user::User, ::Val{Symbol("start-game")}, args) ig_affirm_non_player(user.id) pf = ig_start_new_game(user.id) discord_reply(c, m, ig_hey(user.username, "you have \$" * format_amount(pf.cash) * " in your shiny new portfolio now! Good luck!")) end function ig_execute(c::Client, m::Message, user::User, ::Val{Symbol("abandon-game")}, args) ig_affirm_player(user.id) discord_reply(c, m, ig_hey(user.username, "do you REALLY want to abandon the game and wipe out all of your data? " * "If so, type `ig abandon-game-really`.")) end function ig_execute(c::Client, m::Message, user::User, ::Val{Symbol("abandon-game-really")}, args) ig_affirm_player(user.id) ig_remove_game(user.id) discord_reply(c, m, ig_hey(user.username, "your investment game is now over. Play again soon!")) end function ig_execute(c::Client, m::Message, user::User, ::Val{:buy}, args) ig_affirm_player(user.id) length(args) == 2 || throw(IgUserError("Invalid command. Try `ig buy 100 aapl` to buy 100 shares of Apple Inc.")) symbol = strip(uppercase(args[2])) shares = tryparse(Int, args[1]) shares !== nothing || throw(IgUserError("please enter number of shares as a number: `$shares`")) purchase_price = ig_buy(user.id, symbol, shares) discord_reply(c, m, ig_hey(user.username, "you have bought $shares shares of $symbol at \$" * format_amount(purchase_price))) end function ig_execute(c::Client, m::Message, user::User, ::Val{:sell}, args) ig_affirm_player(user.id) length(args) == 2 || throw(IgUserError("Invalid command. Try `ig sell 100 aapl` to sell 100 shares of Apple Inc.")) symbol = strip(uppercase(args[2])) shares = tryparse(Int, args[1]) shares !== nothing || throw(IgUserError("please enter number of shares as a number: `$shares`")) current_price = ig_sell(user.id, symbol, shares) discord_reply(c, m, ig_hey(user.username, "you have sold $shares shares of $symbol at \$" * format_amount(current_price))) end function ig_execute(c::Client, m::Message, user::User, ::Val{:view}, args) ig_affirm_player(user.id) df = ig_mark_to_market_portfolio(user.id) ig_reformat_view!(df) view = length(args) == 1 && args[1] == "simple" ? SimpleView() : PrettyView() table = ig_view_table(view, df) total_str = format_amount(round(Int, sum(df.amount))) discord_reply(c, m, ig_hey(user.username, """ here is your portfolio: ``` $table ``` Total portfolio Value: $total_str """)) end # Shorten colummn headings for better display in Discord function ig_reformat_view!(df::AbstractDataFrame) select!(df, Not(:purchase_price)) rename!(df, "current_price" => "price", "market_value" => "amount") end function ig_execute(c::Client, m::Message, user::User, ::Val{:quote}, args) length(args) == 1 || throw(IgUserError("Invalid command. Try `ig quote aapl` to fetch the current price of Apple Inc.")) symbol = strip(uppercase(args[1])) price = ig_get_real_time_quote(symbol) discord_reply(c, m, ig_hey(user.username, "the current price of $symbol is " * format_amount(price))) end function ig_execute(c::Client, m::Message, user::User, ::Val{:chart}, args) 1 <= length(args) <= 2 || throw(IgUserError("Invalid command. Try `ig chart aapl` to see a chart for Apple Inc.")) ch = discord_channel(c, m.channel_id) symbol = strip(uppercase(args[1])) lookback = length(args) == 1 ? Year(1) : date_period(lowercase(args[2])) from_date, to_date = recent_date_range(lookback) df = ig_historical_prices(symbol, from_date, to_date) filename = ig_chart(symbol, df.Date, df."Adj Close") discord_upload_file(c, ch, filename; content = ig_hey(user.username, "here is the chart for $symbol for the past $lookback. " * "To plot a chart with different time horizon, " * "try something like `ig chart $symbol 90d` or `ig chart $symbol 10y`.")) end function ig_execute(c::Client, m::Message, user::User, ::Val{:rank}, args) length(args) <= 1 || throw(IgUserError("Invalid command. Try `ig rank` or `ig rank 10`")) n = length(args) == 0 ? 5 : tryparse(Int, args[1]) n !== nothing || throw(IgUserError("invalid rank argument `$(args[1])`. " * "Try `ig rank` or `ig rank 10`")) rt = ig_ranking_table(c) rt = rt[1:min(n, nrow(rt)), :] # get top N results rt_str = ig_view_table(PrettyView(), rt) discord_reply(c, m, ig_hey(user.username, """here's the current ranking: ``` $rt_str ``` """)) end function ig_ranking_table(c::Client) valuations = ig_value_all_portfolios() if length(valuations) > 0 users_dict = retrieve_users(c, [v.id for v in valuations]) @debug "ig_ranking_table" valuations @debug "ig_ranking_table" users_dict df = DataFrame( rank = 1:length(valuations), player = [users_dict[v.id].username for v in valuations], portfolio_value = [v.total for v in valuations], ) else return DataFrame(player = String[], portfolio_value = Float64[]) end end @mockable function retrieve_users(c::Client, ids::Vector{UInt64}) futures = retrieve.(Ref(c), User, ids) responses = fetch.(futures) unknown = User(; id = UInt64(0), username = "Unknown") return Dict(k => res.val === nothing ? unknown : res.val for (k, res) in zip(ids, responses)) end function ig_value_all_portfolios() pfs = ig_load_all_portfolios() valuations = [] for (id, pf) in pfs @debug "Evaluating portfolio" id pf df = ig_mark_to_market!(ig_holdings_data_frame(pf)) mv = nrow(df) > 0 ? sum(df.market_value) : 0.0 cash = pf.cash total = mv + cash push!(valuations, (; id, mv, cash, total)) end sort!(valuations; lt = (x,y) -> x.total < y.total, rev = true) # @info "ig_value_all_portfolios result" valuations valuations end "Format money amount" format_amount(x::Real) = format(x, commas = true, precision = 2) format_amount(x::Integer) = format(x, commas = true) "Pretty table formatters" decimal_formatter(v, i, j) = v isa Real ? format_amount(v) : v integer_formatter(v, i, j) = v isa Real ? format_amount(round(Int, v)) : v "Hey someone" ig_hey(username::AbstractString, message::AbstractString) = "Hey, " * username * ", " * message "File location of the game file for a user" ig_file_path(user_id::UInt64) = joinpath(ig_data_directory(), "$user_id.json") "Directory of the investment game data files" ig_data_directory() = joinpath("data", "ig") "Returns true if user already has a game in progress." ig_is_player(user_id::UInt64) = isfile(ig_file_path(user_id)) "Start a new game file" function ig_start_new_game(user_id::UInt64) pf = IgPortfolio(1_000_000.00, IgHolding[]) ig_save_portfolio(user_id, pf) return pf end "Destroy the existing game file for a user." ig_remove_game(user_id::UInt64) = rm(ig_file_path(user_id)) "Affirms the user has a game or throw an exception." function ig_affirm_player(user_id::UInt64) ig_is_player(user_id) || throw(IgUserError("you don't have a game yet.")) return nothing end "Affirms the user does not have game or throw an exception." function ig_affirm_non_player(user_id::UInt64) !ig_is_player(user_id) || throw(IgUserError( "you already have a game running. Use `ig view` to see your current portfolio." )) return nothing end "Save a user portfolio in the data directory." function ig_save_portfolio(user_id::UInt64, pf::IgPortfolio) @debug "Saving portfolio" user_id path = ig_file_path(user_id) mkpath(dirname(path)) # ensure directory is there write(path, JSON3.write(pf)) return nothing end "Load the portfolio for a single user" function ig_load_portfolio(user_id::UInt64) @debug "Loading portfolio" user_id path = ig_file_path(user_id) return ig_load_portfolio(path) end "Load a single portfolio from game file" function ig_load_portfolio(path::AbstractString) mkpath(dirname(path)) # ensure directory is there bytes = read(path) return JSON3.read(bytes, IgPortfolio) end "Extract user id from the portfolio data file" function ig_user_id_from_path(path::AbstractString) filename = basename(path) filename_without_extension = replace(filename, r"\.json$" => "") return parse(UInt64, filename_without_extension) end "Load all game files" function ig_load_all_portfolios() dir = ig_data_directory() files = readdir(dir) user_ids = ig_user_id_from_path.(files) return Dict(user_id => ig_load_portfolio(joinpath(dir, file)) for (user_id, file) in zip(user_ids, files)) end "Fetch the current quote of a stock" @mockable function ig_get_real_time_quote(symbol::AbstractString) @info "$(now()) real time quote: $symbol" token = finnhub_token() length(token) > 0 || throw(IgSystemError("No market price provider. Please report to admin.")) symbol = HTTP.escapeuri(symbol) response = HTTP.get("https://finnhub.io/api/v1/quote?symbol=$symbol&token=$token") data = JSON3.read(response.body) current_price = data.c current_price > 0 || throw(IgUserError("there is no price for $symbol. Is it a valid stock symbol?")) current_price end "Fetch quote of a stock, but possibly with a time delay." function ig_get_quote(symbol::AbstractString) return get!(QUOTE_CACHE, symbol) do ig_get_real_time_quote(symbol) end end "Buy stock for a specific user at a specific price." function ig_buy( user_id::UInt64, symbol::AbstractString, shares::Real, current_price::Real = ig_get_real_time_quote(symbol) ) @debug "Buying stock" user_id symbol shares pf = ig_load_portfolio(user_id) cost = shares * current_price if pf.cash >= cost pf.cash -= cost push!(pf.holdings, IgHolding(symbol, shares, current_date(), current_price)) ig_save_portfolio(user_id, pf) return current_price end throw(IgUserError("you don't have enough cash. " * "Buying $shares shares of $symbol will cost you $(format_amount(cost)) " * "but you only have $(format_amount(pf.cash))")) end "Sell stock for a specific user. Returns executed price." function ig_sell( user_id::UInt64, symbol::AbstractString, shares::Real, current_price::Real = ig_get_real_time_quote(symbol) ) @debug "Selling stock" user_id symbol shares pf = ig_load_portfolio(user_id) pf_new = ig_sell_fifo(pf, symbol, shares, current_price) ig_save_portfolio(user_id, pf_new) return current_price end "Sell stock based upon FIFO accounting scheme. Returns the resulting `IgPortfolio` object." function ig_sell_fifo(pf::IgPortfolio, symbol::AbstractString, shares::Real, current_price::Real) existing_shares = ig_count_shares(pf, symbol) if existing_shares == 0 throw(IgUserError("you do not have $symbol in your portfolio")) elseif shares > existing_shares existing_shares_str = format_amount(round(Int, existing_shares)) throw(IgUserError("you cannot sell more than what you own ($existing_shares_str shares)")) end proceeds = shares * current_price # Construct a new IgPortfolio object that contains the resulting portfolio after # selling the stock. The following logic does it incrementally but just for documentation # purpose an alternative algorithm would be to make a copy and then relief the sold lots. holdings = IgHolding[] pf_new = IgPortfolio(pf.cash + proceeds, holdings) remaining = shares # keep track of how much to sell for h in pf.holdings if h.symbol != symbol || remaining == 0 push!(holdings, h) else if h.shares > remaining # relief lot partially revised_lot = IgHolding(symbol, h.shares - remaining, h.date, h.purchase_price) push!(holdings, revised_lot) remaining = 0 else # relief this lot completely and continue remaining -= h.shares end end end return pf_new end """ Returns a data frame for the portfolio holdings. Note that: 1. It does not include cash portion of the portfolio 2. Multiple lots of the same stock will be in different rows See also: `ig_grouped_holdings`(@ref) """ function ig_holdings_data_frame(pf::IgPortfolio) return DataFrame( symbol = [h.symbol for h in pf.holdings], shares = [h.shares for h in pf.holdings], purchase_price = [h.purchase_price for h in pf.holdings], purchase_date = [h.date for h in pf.holdings], ) end "Returns grouped holdings by symbol with average purchase price" function ig_grouped_holdings(df::AbstractDataFrame) df = combine(groupby(df, :symbol)) do sdf shares = sum(sdf.shares) weights = sdf.shares / shares purchase_price = sum(weights .* sdf.purchase_price) (; shares, purchase_price) end return sort!(df, :symbol) end "Return a data frame with the user's portfolio marked to market." function ig_mark_to_market_portfolio(user_id::UInt64) pf = ig_load_portfolio(user_id) df = ig_grouped_holdings(ig_holdings_data_frame(pf)) cash_entry = ig_cash_entry(pf) if nrow(df) > 0 ig_mark_to_market!(df) push!(df, cash_entry) else df = DataFrame([cash_entry]) end return df end "Return the portoflio cash as named tuple that can be appended to the portfolio data frame." function ig_cash_entry(pf::IgPortfolio) return (symbol = "CASH:USD", shares = pf.cash, purchase_price = 1.0, current_price = 1.0, market_value = pf.cash) end "Add columns with current price and market value" function ig_mark_to_market!(df::AbstractDataFrame) df.current_price = [ig_get_quote(s) for s in df.symbol] df.market_value = df.shares .* df.current_price return df end "Format data frame using pretty table" function ig_view_table(::PrettyView, df::AbstractDataFrame) return pretty_table(String, df; formatters = integer_formatter, header = names(df)) end "Return portfolio view as string in a simple format" function ig_view_table(::SimpleView, df::AbstractDataFrame) io = IOBuffer() # @show "ig_view_table" df for (i, r) in enumerate(eachrow(df)) if !startswith(r.symbol, "CASH:") println(io, i, ". ", r.symbol, ": ", round(Int, r.shares), " x \$", format_amount(r.price), " = \$", format_amount(round(Int, r.amount))) else println(io, i, ". ", r.symbol, " = ", format_amount(round(Int, r.amount))) end end return String(take!(io)) end "Returns a tuple of two dates by looking back from today's date" function recent_date_range(lookback::DatePeriod) T = current_date() to_date = T from_date = T - lookback return from_date, to_date end @mockable current_date() = today() """ Return a data frame with historical prices data. Columns include: - `Date` - `Open` - `High` - `Low` - `Close` - `Adj Close` - `Volume` """ function ig_historical_prices(symbol::AbstractString, from_date::Date, to_date::Date) from_sec = seconds_since_1970(from_date) to_sec = seconds_since_1970(to_date + Day(1)) # apparently, Yahoo is exclusive on this end symbol = HTTP.escapeuri(symbol) url = "https://query1.finance.yahoo.com/v7/finance/download/$symbol?" * "period1=$from_sec&period2=$to_sec&interval=1d&events=history&includeAdjustedClose=true" try return DataFrame(CSV.File(Downloads.download(url))) catch ex if ex isa Downloads.RequestError && ex.response.status == 404 throw(IgUserError("there is no historical prices for $symbol. Is it a valid stock symbol?")) else rethrow() end end end "Plot a simple price chart" function ig_chart(symbol::AbstractString, dates::Vector{Date}, values::Vector{<:Real}) theme(:dark) height = 720 width = height * 16 ÷ 9 from_date, to_date = extrema(dates) last_price_str = format_amount(last(values)) p = plot(dates, values, title = "$symbol Historical Prices ($from_date to $to_date)\nLast price: $last_price_str", linewidth = 2, size = (width, height), legend = nothing, tickfontsize = 12, ) filename = tempname() * ".png" savefig(p, filename) return filename end "Find lots for a specific stock in the portfolio. Sort by purchase date." function ig_find_lots(pf::IgPortfolio, symbol::AbstractString) lots = IgHolding[x for x in pf.holdings if x.symbol == symbol] return sort(lots, lt = (x,y) -> x.date < y.date) end "Return total number of shares for a specific stock in the portfolio." function ig_count_shares(pf::IgPortfolio, symbol::AbstractString) lots = ig_find_lots(pf, symbol) return length(lots) > 0 ? round(sum(lot.shares for lot in lots); digits = 0) : 0 # note that we store shares as Float64 but uses it as Int (for now) end seconds_since_1970(d::Date) = (d - Day(719163)).instant.periods.value * 24 * 60 * 60 function date_period(s::AbstractString) m = match(r"^(\d+)([ymd])$", s) m !== nothing || throw(IgUserError("invalid date period: $s. Try `5y` or `30m`.")) num = parse(Int, m.captures[1]) dct = Dict("y" => Year, "m" => Month, "d" => Day) return dct[m.captures[2]](num) end
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2.522702
8,127
function p53(data::Dict) # Setup basic dimensions of arrays # Parse & check FEdict data if :struc_el in keys(data) struc_el = data[:struc_el] else println("No fin_el type specified.") return end ndim = struc_el.ndim nst = struc_el.nst fin_el = struc_el.fin_el @assert typeof(fin_el) <: FiniteElement if typeof(fin_el) == Line (nels, nn) = mesh_size(fin_el, struc_el.nxe) elseif typeof(fin_el) == Triangle || typeof(fin_el) == Quadrilateral (nels, nn) = mesh_size(fin_el, struc_el.nxe, struc_el.nye) elseif typeof(fin_el) == Hexahedron (nels, nn) = mesh_size(fin_el, struc_el.nxe, struc_el.nye, struc_el.nze) else println("$(typeof(fin_el)) is not a known finite element.") return end nodof = fin_el.nodof # Degrees of freedom per node ndof = fin_el.nod * nodof # Degrees of freedom per fin_el # Update penalty if specified in FEdict penalty = 1e20 if :penalty in keys(data) penalty = data[:penalty] end # Allocate all arrays # Start with arrays to be initialized from FEdict if :properties in keys(data) prop = zeros(size(data[:properties], 1), size(data[:properties], 2)) for i in 1:size(data[:properties], 1) prop[i, :] = data[:properties][i, :] end else println("No :properties key found in FEdict") end nf = ones(Int, nodof, nn) if :support in keys(data) for i in 1:size(data[:support], 1) nf[:, data[:support][i][1]] = data[:support][i][2] end end x_coords = zeros(nn) if :x_coords in keys(data) x_coords = data[:x_coords] end y_coords = zeros(nn) if :y_coords in keys(data) y_coords = data[:y_coords] end z_coords = zeros(nn) if :z_coords in keys(data) z_coords = data[:z_coords] end etype = ones(Int, nels) if :etype in keys(data) etype = data[:etype] end # All other arrays points = zeros(struc_el.nip, ndim) g = zeros(Int, ndof) g_coord = zeros(ndim,nn) fun = zeros(fin_el.nod) coord = zeros(fin_el.nod, ndim) gamma = zeros(nels) jac = zeros(ndim, ndim) g_num = zeros(Int, fin_el.nod, nels) der = zeros(ndim, fin_el.nod) deriv = zeros(ndim, fin_el.nod) bee = zeros(nst,ndof) km = zeros(ndof, ndof) mm = zeros(ndof, ndof) gm = zeros(ndof, ndof) kg = zeros(ndof, ndof) eld = zeros(ndof) weights = zeros(struc_el.nip) g_g = zeros(Int, ndof, nels) num = zeros(Int, fin_el.nod) actions = zeros(ndof, nels) displacements = zeros(size(nf, 1), ndim) gc = ones(ndim) dee = zeros(nst,nst) sigma = zeros(nst) axial = zeros(nels) formnf!(nodof, nn, nf) neq = maximum(nf) println("There are $(neq) equations.\n") # Find global array sizes for iel in 1:nels hexahedron_xz!(iel, x_coords, y_coords, z_coords, coord, num) num_to_g!(num, nf, g) g_num[:, iel] = num g_coord[:, num] = coord' g_g[:, iel] = g end sample!(fin_el, points, weights) gsm = spzeros(neq, neq) for iel in 1:nels deemat!(dee, prop[etype[iel], 1], prop[etype[iel], 2]) num = g_num[:, iel] coord = g_coord[:, num]' # Transpose g = g_g[:, iel] km = zeros(ndof, ndof) for i in 1:struc_el.nip shape_der!(der, points, i) jac = der*coord detm = det(jac) jac = inv(jac) deriv = jac*der beemat!(bee, deriv) km += (bee')*dee*bee*detm*weights[i] end fsparm!(gsm, g, km) end loads = OffsetArray(zeros(neq + 1), 0:neq) if :loaded_nodes in keys(data) for i in 1:size(data[:loaded_nodes], 1) loads[nf[:, data[:loaded_nodes][i][1]]] = data[:loaded_nodes][i][2] end end fixed_freedoms = 0 if :fixed_freedoms in keys(data) fixed_freedoms = size(data[:fixed_freedoms], 1) end no = zeros(Int, fixed_freedoms) node = zeros(Int, fixed_freedoms) sense = zeros(Int, fixed_freedoms) value = zeros(Float64, fixed_freedoms) if :fixed_freedoms in keys(data) && fixed_freedoms > 0 for i in 1:fixed_freedoms no[i] = nf[data[:fixed_freedoms][i][2], data[:fixed_freedoms][i][1]] value[i] = data[:fixed_freedoms][i][3] gsm[no[i], no[i]] += penalty loads[no[i]] = gsm[no[i], no[i]] * value[i] end end cfgsm = cholesky(gsm) loads[1:neq] = cfgsm \ loads[1:neq] displacements = zeros(size(nf)) for i in 1:size(displacements, 1) for j in 1:size(displacements, 2) if nf[i, j] > 0 displacements[i,j] = loads[nf[i, j]] end end end displacements = displacements' dis_df = DataFrame( r_disp = displacements[:, 1], y_disp = displacements[:, 2], z_disp = displacements[:, 3] ) struc_el.nip = 1 points = zeros(struc_el.nip, ndim) weights = zeros(struc_el.nip) sample!(fin_el, points, weights) gc1 = Vector{Float64}() gc2 = Vector{Float64}() s1 = Vector{Float64}() s2 = Vector{Float64}() s3 = Vector{Float64}() t1 = Vector{Float64}() t2 = Vector{Float64}() t3 = Vector{Float64}() for iel in 1:nels deemat!(dee, prop[etype[iel], 1], prop[etype[iel], 2]) num = g_num[:, iel] coord = g_coord[:, num]' g = g_g[:, iel] eld = loads[g] for i in 1:struc_el.nip shape_fun!(fun, points, i) shape_der!(der, points, i) gc = fun'*coord jac = inv(der*coord) deriv = jac*der beemat!(bee, deriv) sigma = dee*(bee*eld) gc1 = append!(gc1, gc[1]) gc2 = append!(gc2, gc[2]) s1 = append!(s1, sigma[1]) s2 = append!(s2, sigma[2]) s3 = append!(s3, sigma[3]) t1 = append!(t1, sigma[4]) t2 = append!(t2, sigma[5]) t3 = append!(t3, sigma[6]) end end sigma_df = DataFrame( r_coord = gc1, y_coord = gc2, sig_x = s1, sig_y = s2, sig_z = s3, tau_xy = t1, tau_yz = t2, tau_zx = t3 ) fem = PtFEM.jFEM(struc_el, fin_el, ndim, nels, nst, ndof, nn, nodof, neq, penalty, etype, g, g_g, g_num, nf, no, node, num, sense, actions, bee, coord, gamma, dee, der, deriv, displacements, eld, fun, gc, g_coord, jac, km, mm, kg, cfgsm, loads, points, prop, sigma, value, weights, x_coords, y_coords, z_coords, axial) (fem, dis_df, sigma_df) end
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include("../src/read/read_datasets.jl") include("../src/utils.jl") include("../src/pq/PQ.jl"); include("../src/lsq_sparse/LSQ_SPGL1.jl"); include("../src/linscan/Linscan.jl"); function demo_lsq_sparse( dataset_name="SIFT1M", nread::Integer=Int(1e4)) # === Hyperparams === m = 7 # In LSQ we use m-1 codebooks h = 256 verbose = true nquery = Int(1e4) knn = Int(1e3) # Compute recall up to b = Int( log2(h) * m ) niter = 10 # === PQ initialization === x_train = read_dataset(dataset_name, nread ) d, _ = size( x_train ) C, B, train_error = train_pq(x_train, m, h, verbose) @printf("Error after PQ is %e\n", train_error) # === LSQ sparse train === ilsiter = 8 icmiter = 4 randord = true npert = 4 randord = true S = d*h # SLSQ1 in the paper. Use S = d*h + (d.^2) for SLSQ2 tau = 0.7 # 0.7 for SLSQ1. Use 0.9 for SLSQ2 # Multiply tau times the l1 norm of the PQ solution taus = zeros( d ) subdims = splitarray( 1:d, m ) for i = 1:m taus[ subdims[i] ] += sum( abs(C[i]), 2 ) .* tau end tau = sum( taus ) spgl1_path = joinpath( pwd(), "matlab/spgl1") C, B, R, train_error, cbnorms, objs = train_lsq_sparse(x_train, m, h, niter, ilsiter, icmiter, randord, npert, S, tau, B, C, eye(Float32, d), spgl1_path) cbnorms = vec( cbnorms[:] ) # === Encode the base set === nread_base = Int(1e6) x_base = read_dataset(dataset_name * "_base", nread_base ) B_base = randinit(nread_base, m, h) # initialize B at random ilsiter_base = 16 # LSQ-16 in the paper for i = 1:ilsiter_base @printf("Iteration %02d / %02d\n", i, ilsiter_base) @time B_base = encoding_icm( x_base, B_base, C, icmiter, randord, npert, verbose ) end base_error = qerror( x_base, B_base, C ) @printf("Error in base is %e\n", base_error) # Compute and quantize the database norms B_base_norms = quantize_norms( B_base, C, cbnorms ) db_norms = vec( cbnorms[ B_base_norms ] ) # === Compute recall === x_query = read_dataset( dataset_name * "_query", nquery, verbose ) gt = read_dataset( dataset_name * "_groundtruth", nquery, verbose ) if dataset_name == "SIFT1M" || dataset_name == "GIST1M" gt = gt + 1; end gt = convert( Vector{UInt32}, gt[1,1:nquery] ) B_base = convert( Matrix{UInt8}, B_base-1 ) B_base_norms = convert( Vector{UInt8}, B_base_norms-1 ) print("Querying m=$m ... ") @time dists, idx = linscan_lsq( B_base, x_query, C, db_norms, eye(Float32, d), knn ) println("done") idx = convert( Matrix{UInt32}, idx ); rec = eval_recall( gt, idx, knn ) end # train demo_lsq_sparse()
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""" Lasso(; alpha = 0.1, tol = 1e-4, mi = 1e+8) Lasso Regression structure. eEach parameters are as follows: - `alpha` : leaarning rate. - `tol` : Allowable error. - `mi` : Maximum number of learning. # Example ```jldoctest regression julia> model = Lasso() Lasso(Float64[], 0.1, 0.0001, 100000000) julia> fit!(model, x, t) 3-element Vector{Float64}: 0.0 0.5022766549841176 154.43624186616267 julia> model(x) 20-element Vector{Float64}: 188.64541774549468 187.30088075704523 181.5191726873298 184.15748544986084 185.72158909964372 176.33798923891868 185.80014722707335 184.71565381947883 189.20524796663838 189.67502263476888 189.50409373058318 188.39535519538825 188.481083670683 188.88872347085172 182.8477136378307 188.64156231429416 181.43996475587224 188.9400571253936 179.39836073711297 185.6065850765288 ``` """ mutable struct Lasso w::Array{Float64, 1} α::Float64 tol::Float64 mi::Int64 Lasso(; alpha = 0.1, tol = 1e-4, mi = 1e+8) = new(Array{Float64}(undef, 0), alpha, tol, mi) end sfvf(x, y) = sign(x) * max(abs(x) - y, 0) function fit!(model::Lasso, x, t) function update!(x, t, w, α) n, d = size(x) w[1] = mean(t - x' * w[2:end]) wvec = fill!(Array{Float64}(undef, d), w[1]) for k in 1 : n ww = w[2:end] ww[k] = 0 q = (t - wvec - x' * ww) ⋅ x[k, :] r = x[k, :] ⋅ x[k, :] w[k+1] = sfvf(q / r, α) end end α, tol, mi = model.α, model.tol, model.mi check_size(x, t) if ndims(x) == 1 x = x[:, :] end w = zeros(size(x, 1) + 1) e = 0.0 for _ in 1 : mi eb = e update!(x, t, w, α) e = sum(abs.(w)) / length(w) abs(e - eb) <= tol && break end model.w = w[end:-1:1] end (model::Lasso)(x) = expand(x)' * model.w
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# script to run inference for the OU SDEMEM model using Pkg using LinearAlgebra using DataFrames # TODO # what to do w the adaptive tuning? # updated resources for sbatch scripts println("start run script") # load functions include(pwd()*"/src/SDEMEM OU process/ou_sdemem.jl") include(pwd()*"/src/SDEMEM OU process/mcmc.jl") N_time = 200 M_subjects = 40 nbr_particles = parse(Int,ARGS[1]) ρ = parse(Float64,ARGS[2]) seed = parse(Int,ARGS[3]) y,x,t_vec,dt,η,σ_ϵ,ϕ,prior_parameters_η,prior_parameters_σ_ϵ = set_up(M=M_subjects,N=N_time,seed=seed) # run MH-Gibbs # These are now adjusted R = 60000 burn_in = 10000 job = string(M_subjects)*"_"*string(N_time)*"_"*string(nbr_particles)*"_"*string(ρ) # hard coded start values #startval_ϕ = ϕ #startval_σ_ϵ = σ_ϵ startval_ϕ = ones(M_subjects,3) for j = 1:3 μ_0_j, M_0_j, α_j, β_j = prior_parameters_η[j,:] startval_ϕ[:,j,1] = μ_0_j*ones(M_subjects) end startval_σ_ϵ = 0.2 # Set correct start values startval_ϕ = ϕ startval_σ_ϵ = σ_ϵ startval_η = η Σ_i_σ_ϵ = 0.02^2 Σ_i_ϕ = Matrix{Float64}[] for i in 1:M_subjects; push!(Σ_i_ϕ, [0.06 0 0;0 0.1 0;0 0 0.06]); end γ_ϕ_0 = 1. γ_σ_ϵ_0 = 1. μ_i_ϕ = startval_ϕ μ_i_σ_ϵ = startval_σ_ϵ α_star_ϕ = 0.25 α_star_σ_ϵ = 0.25 log_λ_i_ϕ = log.(2.4/sqrt(3)*ones(M_subjects)) log_λ_i_σ_ϵ = log(2.4) update_interval = 1 α_power = 0.7 start_update = 100 # estimate parameters using exact Gibbs sampling # set random numbers Random.seed!(seed) run_time_cpmmh = @elapsed chain_ϕ_cpmmh, chain_σ_ϵ_cpmmh, chain_η_cpmmh, accept_vec_cpmmh = gibbs_cpmmh(R, y, dt, Σ_i_σ_ϵ, Σ_i_ϕ, γ_ϕ_0, γ_σ_ϵ_0, μ_i_ϕ, μ_i_σ_ϵ, α_star_ϕ, α_star_σ_ϵ, log_λ_i_ϕ, log_λ_i_σ_ϵ, update_interval, start_update, α_power, startval_ϕ, startval_σ_ϵ, startval_η, prior_parameters_η, prior_parameters_σ_ϵ, nbr_particles, ρ); println(run_time_cpmmh) println(sum(accept_vec_cpmmh[1,:])/(M_subjects*R)) println(sum(accept_vec_cpmmh[2,:])/R) println(sum(accept_vec_cpmmh[3,:])/(3*R)) sim_data = zeros(4,1) sim_data[1] = run_time_cpmmh sim_data[2] = sum(accept_vec_cpmmh[1,:])/(M_subjects*R) sim_data[3] = sum(accept_vec_cpmmh[2,:])/R sim_data[4] = sum(accept_vec_cpmmh[3,:])/(3*R) # Save results chain_ϕ_export = zeros(M_subjects*3, R) idx = 0 for m = 1:M_subjects for j = 1:3 global idx = idx + 1 chain_ϕ_export[idx,:] = chain_ϕ_cpmmh[m,j,:] end end CSV.write("data/SDEMEM OU/cpmmh for plot mess vs N/sim_data_"*string(seed)*"_"*job*".csv", DataFrame(sim_data)) CSV.write("data/SDEMEM OU/cpmmh for plot mess vs N/chain_sigma_epsilon_"*string(seed)*"_"*job*".csv", DataFrame(chain_σ_ϵ_cpmmh')) CSV.write("data/SDEMEM OU/cpmmh for plot mess vs N/chain_eta_"*string(seed)*"_"*job*".csv", DataFrame(chain_η_cpmmh')) CSV.write("data/SDEMEM OU/cpmmh for plot mess vs N/chain_phi_"*string(seed)*"_"*job*".csv", DataFrame(chain_ϕ_export'))
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# Tests for Big Data Radial DEA Models @testset "BigData RadialDEAModel" begin ## Test Radial DEA Models with FLS Book data X = [5 13; 16 12; 16 26; 17 15; 18 14; 23 6; 25 10; 27 22; 37 14; 42 25; 5 17] Y = [12; 14; 25; 26; 8; 9; 27; 30; 31; 26; 12] # Input oriented CRS deaio = deabigdata(X, Y, orient = :Input, rts = :CRS) @test typeof(deaio) == RadialDEAModel @test nobs(deaio) == 11 @test ninputs(deaio) == 2 @test noutputs(deaio) == 1 @test efficiency(deaio) ≈ [1.0000000000; 0.6222896791; 0.8198562444; 1.0000000000; 0.3103709311; 0.5555555556; 1.0000000000; 0.7576690896; 0.8201058201; 0.4905660377; 1.0000000000] @test convert(Matrix, peers(deaio)) ≈ [1.000000000 0 0 0.0000000000 0 0 0.00000000000 0 0 0 0; 0.000000000 0 0 0.4249783174 0 0 0.10928013877 0 0 0 0; 1.134321653 0 0 0.4380053908 0 0 0.00000000000 0 0 0 0; 0.000000000 0 0 1.0000000000 0 0 0.00000000000 0 0 0 0; 0.000000000 0 0 0.2573807721 0 0 0.04844814534 0 0 0 0; 0.000000000 0 0 0.0000000000 0 0 0.33333333333 0 0 0 0; 0.000000000 0 0 0.0000000000 0 0 1.00000000000 0 0 0 0; 0.000000000 0 0 1.0348650979 0 0 0.11457435013 0 0 0 0; 0.000000000 0 0 0.0000000000 0 0 1.14814814815 0 0 0 0; 0.000000000 0 0 0.4905660377 0 0 0.49056603774 0 0 0 0; 0.000000000 0 0 0.0000000000 0 0 0.00000000000 0 0 0 1.000000000] atol = 1e-8 @test slacks(deaio, :X) ≈ [0.000000000 0; 0.000000000 0; 0.000000000 0; 0.000000000 0; 0.000000000 0; 4.444444444 0; 0.000000000 0; 0.000000000 0; 1.640211640 0; 0.000000000 0; 0.000000000 4] @test slacks(deaio, :Y) ≈ zeros(11) @test efficiency(deabigdata(targets(deaio, :X), targets(deaio, :Y), orient = :Input, rts = :CRS, slack = false)) ≈ ones(11) @test efficiency(deaadd(targets(deaio, :X), targets(deaio, :Y))) ≈ zeros(11) atol=1e-14 # Otuput oriented CRS deaoo = deabigdata(X, Y, orient = :Output, rts = :CRS) @test nobs(deaoo) == 11 @test ninputs(deaoo) == 2 @test noutputs(deaoo) == 1 @test efficiency(deaoo) ≈ [1.0000000000; 1.606968641; 1.219726027; 1.0000000000; 3.221951220; 1.800000000; 1.0000000000; 1.319837398; 1.219354839; 2.038461538; 1.0000000000] @test convert(Matrix, peers(deaoo)) ≈ [1.000000000 0 0 0.0000000000 0 0 0.00000000000 0 0 0 0; 0.000000000 0 0 0.6829268293 0 0 0.1756097561 0 0 0 0; 1.383561644 0 0 0.5342465753 0 0 0.00000000000 0 0 0 0; 0.000000000 0 0 1.0000000000 0 0 0.00000000000 0 0 0 0; 0.000000000 0 0 0.8292682927 0 0 0.1560975610 0 0 0 0; 0.000000000 0 0 0.0000000000 0 0 0.6000000000 0 0 0 0; 0.000000000 0 0 0.0000000000 0 0 1.00000000000 0 0 0 0; 0.000000000 0 0 1.3658536585 0 0 0.1512195122 0 0 0 0; 0.000000000 0 0 0.0000000000 0 0 1.4000000000 0 0 0 0; 0.000000000 0 0 1.0000000000 0 0 1.0000000000 0 0 0 0; 1.000000000 0 0 0.0000000000 0 0 0.00000000000 0 0 0 0] @test slacks(deaoo, :X) ≈ [0.000000000 0; 0.000000000 0; 0.000000000 0; 0.000000000 0; 0.000000000 0; 8 0; 0.000000000 0; 0.000000000 0; 2 0; 0.000000000 0; 0.000000000 4] @test slacks(deaoo, :Y) ≈ zeros(11) @test efficiency(deabigdata(targets(deaoo, :X), targets(deaoo, :Y), orient = :Output, rts = :CRS, slack = false)) ≈ ones(11) @test efficiency(deaadd(targets(deaoo, :X), targets(deaoo, :Y))) ≈ zeros(11) atol=1e-10 # Input oriented VRS deaiovrs = deabigdata(X, Y, orient = :Input, rts = :VRS) @test nobs(deaiovrs) == 11 @test ninputs(deaiovrs) == 2 @test noutputs(deaiovrs) == 1 @test efficiency(deaiovrs) ≈ [1.0000000000; 0.8699861687; 1.0000000000; 1.0000000000; 0.7116402116; 1.0000000000; 1.0000000000; 1.0000000000; 1.0000000000; 0.4931209269; 1.0000000000] @test convert(Matrix, peers(deaiovrs)) ≈ [1.000000000 0 0 0.0000000000 0 0.00000000000 0.00000000000 0 0 0 0; 0.52558782849 0 0 0.0000000000 0 0.2842323651 0.1901798064 0 0 0 0; 0.000000000 0 1 0.0000000000 0 0.00000000000 0.00000000000 0 0 0 0; 0.000000000 0 0 1.0000000000 0 0.00000000000 0.00000000000 0 0 0 0; 0.56613756614 0 0 0.0000000000 0 0.4338624339 0.00000000000 0 0 0 0; 0.000000000 0 0 0.0000000000 0 1.00000000000 0.00000000000 0 0 0 0; 0.000000000 0 0 0.0000000000 0 0.00000000000 1.00000000000 0 0 0 0; 0.000000000 0 0 0.0000000000 0 0.00000000000 0.00000000000 1 0 0 0; 0.000000000 0 0 0.0000000000 0 0.00000000000 0.00000000000 0 1 0 0; 0.03711078928 0 0 0.4433381608 0 0.00000000000 0.5195510500 0 0 0 0; 0.000000000 0 0 0.0000000000 0 0.00000000000 0.00000000000 0 0 0 1.000000000] @test slacks(deaiovrs, :X) ≈ [0.000000000 0; 0.000000000 0; 0.000000000 0; 0.000000000 0; 0.000000000 0; 0 0; 0.000000000 0; 0.000000000 0; 0 0; 0.000000000 0; 0.000000000 4] @test slacks(deaiovrs, :Y) ≈ [0.000000000; 0.000000000; 0.000000000; 0.000000000; 2.698412698; 0.000000000; 0.000000000; 0.000000000; 0.000000000; 0.000000000; 0.000000000] @test efficiency(deabigdata(targets(deaiovrs, :X), targets(deaiovrs, :Y), orient = :Input, rts = :VRS, slack = false)) ≈ ones(11) @test efficiency(deaadd(targets(deaiovrs, :X), targets(deaiovrs, :Y))) ≈ zeros(11) atol=1e-12 # Output oriented VRS deaoovrs = deabigdata(X, Y, orient = :Output, rts = :VRS) @test nobs(deaoovrs) == 11 @test ninputs(deaoovrs) == 2 @test noutputs(deaoovrs) == 1 @test efficiency(deaoovrs) ≈ [1.0000000000; 1.507518797; 1.0000000000; 1.0000000000; 3.203947368; 1.000000000; 1.0000000000; 1.000000000; 1.000000000; 1.192307692; 1.0000000000] @test convert(Matrix, peers(deaoovrs)) ≈ [1.000000000 0 0 0.0000000000 0 0 0.00000000000 0 0 0 0; 0.38157894737 0 0 0.1710526316 0 0 0.4473684211 0 0 0 0; 0.000000000 0 1 0.0000000000 0 0 0.00000000000 0 0 0 0; 0.000000000 0 0 1.0000000000 0 0 0.00000000000 0 0 0 0; 0.03947368421 0 0 0.7763157895 0 0 0.1842105263 0 0 0 0; 0.000000000 0 0 0.0000000000 0 1 0.00000000000 0 0 0 0; 0.000000000 0 0 0.0000000000 0 0 1.00000000000 0 0 0 0; 0.000000000 0 0 0.0000000000 0 0 0.00000000000 1 0 0 0; 0.000000000 0 0 0.0000000000 0 0 0.00000000000 0 1 0 0; 0.000000000 0 0 0.0000000000 0 0 0.00000000000 0 1 0 0; 1.000000000 0 0 0.0000000000 0 0 0.00000000000 0 0 0 0] @test slacks(deaoovrs, :X) ≈ [0.000000000 0; 0.000000000 0; 0.000000000 0; 0.000000000 0; 0.000000000 0; 0.000000000 0; 0.000000000 0; 0.000000000 0; 0.000000000 0; 5 11; 0.000000000 4] @test slacks(deaoovrs, :Y) ≈ zeros(11) atol=1e-10 @test efficiency(deabigdata(targets(deaoovrs, :X), targets(deaoovrs, :Y), orient = :Output, rts = :VRS, slack = false)) ≈ ones(11) @test efficiency(deaadd(targets(deaoovrs, :X), targets(deaoovrs, :Y))) ≈ zeros(11) atol=1e-12 # Test no slacks deaionoslack = deabigdata(X, Y, slack = false) @test efficiency(deaionoslack) == efficiency(deaio) @test isempty(slacks(deaionoslack, :X)) == 1 @test isempty(slacks(deaionoslack, :Y)) == 1 @test efficiency(deabigdata(targets(deaionoslack, :X), targets(deaionoslack, :Y), slack = false)) ≈ ones(11) @test efficiency(deaadd(targets(deaionoslack, :X), targets(deaionoslack, :Y))) != zeros(11) # Different as there is no slacks in first model # Print show(IOBuffer(), deaio) show(IOBuffer(), deaionoslack) # Test errors     @test_throws DimensionMismatch deabigdata([1; 2 ; 3], [4 ; 5]) #  Different number of observations   @test_throws ArgumentError deabigdata([1; 2; 3], [4; 5; 6], orient = :Error) # Invalid orientation @test_throws ArgumentError deabigdata([1; 2; 3], [4; 5; 6], rts = :Error) # Invalid returns to scale @test_throws ErrorException deabigdata(X, Y, atol = 0.0, optimizer = DEAOptimizer(:NLP)) # ------------------ # Test if no exteriors # ------------------ Xnoext = [1 1; 1.5 1; 2 1] Ynoext = [2 2; 1.5 1.5; 1 0.5] deanoext = dea(Xnoext, Ynoext, orient = :Input) deabignoext = deabigdata(Xnoext, Ynoext, orient = :Input) @test efficiency(deanoext) ≈ efficiency(deabignoext) @test slacks(deanoext, :X) ≈ slacks(deabignoext, :X) @test slacks(deanoext, :X) ≈ slacks(deabignoext, :X) @test targets(deanoext, :X) ≈ targets(deabignoext, :X) @test targets(deanoext, :Y) ≈ targets(deabignoext, :Y) @test peersmatrix(deanoext) ≈ peersmatrix(deabignoext) # ------------------ # Test with random data # ------------------ rng = StableRNG(1234567) X = rand(Uniform(10, 20), 500, 6) Y = rand(Uniform(10, 20), 500, 4) rdea = dea(X, Y, progress = false) rdeabig = deabigdata(X, Y, progress = false) @test efficiency(rdeabig) ≈ efficiency(rdea) @test slacks(rdeabig, :X) ≈ slacks(rdea, :X) @test slacks(rdeabig, :Y) ≈ slacks(rdea, :Y) @test targets(rdeabig, :X) ≈ targets(rdea, :X) @test targets(rdeabig, :Y) ≈ targets(rdea, :Y) @test peersmatrix(rdeabig) ≈ peersmatrix(rdea) # ------------------ # Test Vector and Matrix inputs and outputs # ------------------ # Tests against results in R # Inputs is Matrix, Outputs is Vector X = [2 2; 1 4; 4 1; 4 3; 5 5; 6 1; 2 5; 1.6 8] Y = [1; 1; 1; 1; 1; 1; 1; 1] @test efficiency(deabigdata(X, Y, orient = :Input)) ≈ [1; 1; 1; 0.6; 0.4; 1; 0.6666666667; 0.625] # Inputs is Vector, Output is Matrix X = [1; 1; 1; 1; 1; 1; 1; 1] Y = [7 7; 4 8; 8 4; 3 5; 3 3; 8 2; 6 4; 1.5 5] @test efficiency(deabigdata(X, Y, orient = :Output)) ≈ [1; 1; 1; 1.555555556; 2.333333333; 1; 1.272727273; 1.6] # Inputs is Vector, Output is Vector X = [2; 4; 8; 12; 6; 14; 14; 9.412] Y = [1; 5; 8; 9; 3; 7; 9; 2.353] @test efficiency(deabigdata(X, Y, orient = :Input)) ≈ [0.4; 1; 0.8; 0.6; 0.4; 0.4; 0.5142857143; 0.2] end
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using IterativeSolvers using FactCheck using Base.Test # Type used in SOL test type Wrapper m::Int n::Int end Base.size(op::Wrapper, dim::Integer) = (dim == 1) ? op.m : (dim == 2) ? op.n : 1 Base.size(op::Wrapper) = (op.m, op.n) Base.eltype(op::Wrapper) = Int function Base.A_mul_B!(α, A::Wrapper, x, β, y) m, n = size(A) scale!(y, β) y[1] = y[1] + α * x[1] for i = 2:n y[i] = y[i] + i * α * x[i] + (i-1) * α * x[i-1] end for i = n+1:m y[i] = y[i] end return y end function Base.Ac_mul_B!(α, A::Wrapper, x, β, y) m, n = size(A) mn = min(m, n) scale!(y, β) for i = 1:mn-1 y[i] = y[i] + α * i* (x[i]+x[i+1]) end y[mn] = y[mn] + α * mn * x[mn] for i = m+1:n y[i] = y[i] end return y end # Type used in Dampenedtest # solve (A'A + diag(v).^2 ) x = b # using LSMR in the augmented space A' = [A ; diag(v)] b' = [b; zeros(size(A, 2)] type DampenedVector{Ty, Tx} y::Ty x::Tx end Base.eltype(a::DampenedVector) = promote_type(eltype(a.y), eltype(a.x)) function Base.norm(a::DampenedVector) return sqrt(norm(a.y)^2 + norm(a.x)^2) end function Base.copy!{Ty, Tx}(a::DampenedVector{Ty, Tx}, b::DampenedVector{Ty, Tx}) copy!(a.y, b.y) copy!(a.x, b.x) return a end function Base.fill!(a::DampenedVector, α::Number) fill!(a.y, α) fill!(a.x, α) return a end function Base.scale!(a::DampenedVector, α::Number) scale!(a.y, α) scale!(a.x, α) return a end function Base.similar(a::DampenedVector, T) return DampenedVector(similar(a.y, T), similar(a.x, T)) end function Base.length(a::DampenedVector) length(a.y) + length(a.x) end type DampenedMatrix{TA, Tx} A::TA diagonal::Tx end Base.eltype(A::DampenedMatrix) = promote_type(eltype(A.A), eltype(A.diagonal)) function Base.size(A::DampenedMatrix, dim::Integer) m, n = size(A.A) l = length(A.diagonal) dim == 1 ? (m + l) : dim == 2 ? n : 1 end function Base.A_mul_B!{TA, Tx, Ty}(α::Number, mw::DampenedMatrix{TA, Tx}, a::Tx, β::Number, b::DampenedVector{Ty, Tx}) if β != 1. if β == 0. fill!(b, 0.) else scale!(b, β) end end A_mul_B!(α, mw.A, a, 1.0, b.y) map!((z, x, y)-> z + α * x * y, b.x, b.x, a, mw.diagonal) return b end function Base.Ac_mul_B!{TA, Tx, Ty}(α::Number, mw::DampenedMatrix{TA, Tx}, a::DampenedVector{Ty, Tx}, β::Number, b::Tx) if β != 1. if β == 0. fill!(b, 0.) else scale!(b, β) end end Ac_mul_B!(α, mw.A, a.y, 1.0, b) map!((z, x, y)-> z + α * x * y, b, b, a.x, mw.diagonal) return b end facts(string("lsmr")) do context("Small dense matrix") do A = rand(10, 5) b = rand(10) x, = lsmr(A, b) @fact norm(x - A\b) --> less_than(√eps()) end context("SOL test") do # Test adapted from the BSD-licensed Matlab implementation at # http://www.stanford.edu/group/SOL/software/lsqr.html # Michael Saunders, Systems Optimization Laboratory, # Dept of MS&E, Stanford University. #----------------------------------------------------------------------- # This is a simple example for testing LSMR. # It uses the leading m*n submatrix from # A = [ 1 # 1 2 # 2 3 # 3 4 # ... # n ] # suitably padded by zeros. # # 11 Apr 1996: First version for distribution with lsqr.m. # Michael Saunders, Dept of EESOR, Stanford University. function SOLtest(m, n, damp) A = Wrapper(m, n) xtrue = n:-1:1 b = Array(Float64, m) b = float(A_mul_B!(1.0, A, xtrue, 0.0, b)) x, = lsmr(A, b, atol = 1e-7, btol = 1e-7, conlim = 1e10, maxiter = 10n) r = A_mul_B!(-1, A, x, 1, b) @fact norm(r) --> less_than_or_equal(1e-4) end SOLtest(10, 10, 0) SOLtest(20, 10, 0) SOLtest(20, 10, 0.1) end context("dampened test") do # Test used to make sure A, b can be generic matrix / vector srand(1234) function DampenedTest(m, n) b = rand(m) A = rand(m, n) v = rand(n) Adampened = DampenedMatrix(A, v) bdampened = DampenedVector(b, zeros(n)) x, ch = lsmr(Adampened, bdampened) @fact norm((A'A + diagm(v).^2)x - A'b) --> less_than(1e-3) end DampenedTest(10, 10) DampenedTest(20, 10) end end
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include("module.jl") using Logging Logging.global_logger(Logging.ConsoleLogger(Logging.Info)) F5_DETECTED_USELESS_NF = 0 F5_USELESS_NF = 0 F5_NF = 0 F5_BASIS_SIZE = 0 function zerof5() global F5_DETECTED_USELESS_NF global F5_USELESS_NF global F5_NF global F5_BASIS_SIZE F5_DETECTED_USELESS_NF = 0 F5_USELESS_NF = 0 F5_NF = 0 F5_BASIS_SIZE = 0 end function katsuran(n; ground=QQ) R, x = PolynomialRing(ground, ["x$i" for i in 0:n]) return [ (sum(x[abs(l)+1]*x[abs(m-l)+1] for l=-n:n if abs(m-l)<=n) - x[m+1] for m=0:n-1)..., x[1] + 2sum(x[i+1] for i=1:n) - 1 ] end #----------------------------------------------------------------------------- function homogenize(F) base = parent(F[1]) s = copy(symbols(base)) push!(s, :h) R, xsu = PolynomialRing(base_ring(base), s, ordering=ordering(base)) xs = xsu[1:end-1] u = xsu[end] homF = Vector{eltype(F)}(undef, 0) for f in F tdeg = total_degree(leading_monomial(f)) f = evaluate(f, xs) homf = zero(R) for t in terms(f) homf += t * u^(tdeg - total_degree(t)) end push!(homF, homf) end homF end function dehomogenize(homF) R = parent(homF[1]) xs = collect(gens(R)) xs[end] = one(R) F = Vector{eltype(homF)}(undef, 0) for homf in homF f = evaluate(homf, xs) push!(F, f) end F end #----------------------------------------------------------------------------- # Regular reductions # try to reduce f with g in a regular way and return result function regular_reduction_step(f::ModuleElement, g::ModuleElement) evalf = f.ev evalg = g.ev # @info "reducing $(f) with $(g)" evalf evalg for t in terms(evalf) # if divides success, u = divides(t, leading_term(evalg)) if success u = divexact(t, leading_term(evalg)) @info "$(leading_term(evalg)) | $t" # if reduction is regular (and does not change signature) # not really correct! if f > u*g newf = f - u*g # @info "regular! $(f) - $(u)*$(g) --> $newf" return true, newf end end end return false, f end # // same as above, but in terms of set G // # try to reduce f with G in a regular way and return result function regular_reduction_step(f::ModuleElement, G) evalf = f.ev for g in G success, newf = regular_reduction_step(f, g) if success return true, newf end end return false, f end # regular normal form of f w.r.t. G function regular_normal_form(f::ModuleElement, G) # @info "computing reg normalform of $f w.r.t $G.." success = true newf = copy(f) while success success, newf = regular_reduction_step(newf, G) # @info "reduction $success" end newf end #----------------------------------------------------------------------------- # Singular reductions # if f is singurarly top reducible by G function issingularlytopreducible(f::ModuleElement, G) leadevalf = leading_monomial(f.ev) for g in G evalg = g.ev success, u = divides(leadevalf, leading_monomial(evalg)) if success if (g*u).sgn == f.sgn return true end end end return false end # if f is singurarly top reducible by G function syzygy_criterion(f::ModuleElement, syzygies) for syz in syzygies success, u = divides(f.sgn.monom, syz.sgn.monom) if success && f.sgn.index == syz.sgn.index # @warn "DISCARDED $f by $syz" global F5_DETECTED_USELESS_NF F5_DETECTED_USELESS_NF += 1 return true end end return false end #----------------------------------------------------------------------------- # S-polynomial # returns u, v, such that # u*a - v*b is spoly(a, b) function mults(a::ModuleElement, b::ModuleElement) u = lcm(leading_monomial(a.ev), leading_monomial(b.ev)) u = divexact(u, leading_term(a.ev)) v = lcm(leading_monomial(a.ev), leading_monomial(b.ev)) v = divexact(v, leading_term(b.ev)) u, v end # S-polynomial of a and b function spoly(a::ModuleElement, b::ModuleElement) u, v = mults(a, b) u*a - v*b end #----------------------------------------------------------------------------- # Groebner basis things function construct_module(F) [ ModuleElement(f, Signature(i, one(f))) for (i, f) in enumerate(F) ] end function signature_groebner_basis(F) sort!(F, by=leading_monomial, lt=f5_total_degree_lead_cmp) F = map(f -> map_coefficients(c -> c // leading_coefficient(f), f), F) G = construct_module(F) syzygies = [] for i in 1:length(G) for j in i+1:length(G) s1 = (G[i]*leading_monomial(G[j].ev)).sgn s2 = (G[j]*leading_monomial(G[i].ev)).sgn push!(syzygies, ModuleElement( zero(F[1]), max(s1, s2) ) ) end end P = [] for i in 1:length(G) for j in i+1:length(G) if syzygy_criterion(spoly(G[i], G[j]), syzygies) continue end push!(P, spoly(G[i], G[j])) end end # @info "generated initial G and P:" # @info "F = $F" # @info "G = $G" # @info "P = $P" # @info "syz = $syzygies" while !isempty(P) sort!(P, rev=false) f = popfirst!(P) @info "selected $f" if syzygy_criterion(f, syzygies) continue end # global F5_NF # F5_NF += 1 fNF = regular_normal_form(f, G) global F5_NF F5_NF += 1 if issyzygy(fNF) # @warn "Reduction to zero!" # global F5_REDUCED # F5_REDUCED += 1 push!(syzygies, fNF) global F5_USELESS_NF F5_USELESS_NF += 1 elseif !issingularlytopreducible(fNF, G) # update P for fj in G if syzygy_criterion(spoly(fNF, fj), syzygies) continue end push!(P, spoly(fNF, fj)) # @info "$fNF $fj" # @info "SPOLY $(last(P))" end # update G fNFnormalzed = fNF * inv(leading_coefficient(fNF.ev)) push!(G, fNFnormalzed) else global F5_USELESS_NF F5_USELESS_NF += 1 end # @info "updated G and P" # @info "G = $G" # @info "P = $P" # @info "syz = $syzygies" end # global F5_SIZE # F5_SIZE = length(G) - length(F) # global F5_REDUCED # global F5_DISCARDED global F5_BASIS_SIZE F5_BASIS_SIZE = length(G) G end #----------------------------------------------------------------------------- using Logging Logging.global_logger(ConsoleLogger(Logging.Warn)) R, (x,y,z) = PolynomialRing(QQ, ["x","y", "z"], ordering=:degrevlex) F = [x*z + 1, y*z + 1] # evaluation G = signature_groebner_basis(F) Gev = [g.ev for g in G] println("############") println(G) println(Gev)
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@testset "rand" begin # Some arbitrary alignment x = iterdates() expected_times = _eval_fast(x).times @testset "scalar" begin n = rand(x) # calling multiple times should give different nodes, since we use a different rng. @test n != rand(x) # If we specify an explicit rng, then we should get identical nodes back. rng = MersenneTwister() n = rand(rng, x) @test n === rand(rng, x) @test value_type(n) == Float64 block = _eval(n) @test block.times == expected_times @test all(0 .<= block.values .< 1) n = rand(rng, x, Int32) @test n === rand(rng, x, Int32) @test value_type(n) == Int32 block = _eval(n) @test block.times == expected_times @test all(typemin(Int32) .<= block.values .<= typemax(Int32)) end @testset "array" begin n = rand(x, ()) @test value_type(n) == Array{Float64,0} # If we specify an explicit rng, then we should get identical nodes back. rng = MersenneTwister() n = rand(rng, x, (2,)) @test n === rand(rng, x, (2,)) @test n != rand(rng, x, (3,)) @test n != rand(rng, x, (2, 3)) @test rand(rng, x, (2,)) === rand(rng, x, 2) @test rand(rng, x, Float64, (2,)) === rand(rng, x, 2) @test rand(rng, x, (2,)) === rand(rng, x, Float64, 2) @test rand(rng, x, (2, 3)) === rand(rng, x, 2, 3) @test rand(rng, x, Float64, (2, 3)) === rand(rng, x, 2, 3) @test rand(rng, x, (2, 3)) === rand(rng, x, Float64, 2, 3) @test value_type(n) == Vector{Float64} block = _eval(n) @test block.times == expected_times @test all(map(value -> all(0 .<= value .< 1), block.values)) end end
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### Run Simulation ### function initialize_sim_and_run(;m::MapData , routes_path::OrderedDict{Symbol, OrderedDict{Int64,Int64}} , routes_distances::OrderedDict{Symbol, OrderedDict{Int64,Float64}} , routes_types_both_dir::Dict{Symbol,Symbol} , orig_map_nodes_num::Int , when_to_run_people::Float64 = sim_params.when_to_run_people , when_to_run_wagons::Float64 = sim_params.when_to_run_wagons , TTC_freq::Int = sim_params.TTC_car_freq , max_load_subway::Int = sim_params.max_load_subway , max_load_streetcar::Int = sim_params.max_load_streetcar , max_load_gov_restr::Float64 = sim_params.max_load_gov_restr , N_agents::Int = sim_params.N_agents , sim_run_time_in_secs::Int = sim_params.sim_run_time_in_secs , serialize_finished_sim_object::Bool = false , p0::Float64 = sim_params.p0 , parset_id::Int64 = 1) """ Description: Initialization of the simulation with a given parameters. Next the simulation will be running for the specified number of seconds. Returns: Simulation objects with the results. """ println("By default parameters from `sim_params` object will be used") println("--------------------") simul = Simulation(m , orig_map_nodes_num , routes_path , routes_distances , routes_types_both_dir , N_agents , sim_run_time_in_secs , TTC_freq , max_load_subway , max_load_streetcar , max_load_gov_restr , p0) println("when_to_run_people: ",when_to_run_people) println("when_to_run_wagons: ",when_to_run_wagons) enqueue!.(Ref(simul.events), simul.agents, tuple.(when_to_run_people,rand(length(simul.agents)))) enqueue!.(Ref(simul.events), simul.wagons, tuple.(when_to_run_wagons,rand(length(simul.wagons)))) enqueue!(simul.events, simul.artificial_agent_stats, tuple(when_to_run_people, rand())) println("--------------------") println("Final simul object before run: \n", simul) println("--------------------") current_time = 0 while sim_run_time_in_secs > current_time && length(simul.events) > 0 event, t = dequeue_pair!(simul.events) step!(simul, t, event, orig_map_nodes_num) current_time = deepcopy(t[1]) if simul.infected_agents_count[end] >= 0.95*N_agents println("Simulation has ended ealier, because 95% of agents are already infected. Current time: ",current_time) break end end if serialize_finished_sim_object == true path_simul = "./data_output/simulation_finished_parset_id_$parset_id.bin" serialize(path_simul, simul) println("Simulation object with results was saved in: ", path_simul) end println("Simulation has already ended. You can check the results.") return simul end ### Run Simulation N times### function initialize_sim_and_run_N_times_and_gather_results(;m::MapData , routes_path::OrderedDict{Symbol, OrderedDict{Int64,Int64}} , routes_distances::OrderedDict{Symbol, OrderedDict{Int64,Float64}} , routes_types_both_dir::Dict{Symbol,Symbol} , orig_map_nodes_num::Int , when_to_run_people::Float64 = sim_params.when_to_run_people , when_to_run_wagons::Float64 = sim_params.when_to_run_wagons , TTC_freq::Int = sim_params.TTC_car_freq , max_load_subway::Int = sim_params.max_load_subway , max_load_streetcar::Int = sim_params.max_load_streetcar , max_load_gov_restr::Float64 = sim_params.max_load_gov_restr , N_agents::Int = sim_params.N_agents , sim_run_time_in_secs::Int = sim_params.sim_run_time_in_secs , serialize_finished_sim_object::Bool = false , parset_id::Int64 = 1 , p0::Float64 = sim_params.p0 , N_times_to_run::Int64 = 30) """ Description: Run the simulation with specified parameters N times and then gather related results into dict. Returns: Dict object with mean results of N runs. """ res_gathering = Dict{String,Any}() total_cnt = Vector{Int}[] ttc_cnt = Vector{Int}[] street_cnt = Vector{Int}[] prct_ag_used_TTC = Float64[] mean_time_spent_in_TTC = Float64[] max_load_achieved_streetcars = Float64[] max_load_achieved_subway = Float64[] all_TTC_trips_count_streetcars = Float64[] all_TTC_trips_count_subway = Float64[] max_pass_per_wagon_subway = Vector{Float64}[] max_pass_per_wagon_streetcars = Vector{Float64}[] println("Your simulation will be run ", N_times_to_run, " times") for i in 1:N_times_to_run println("Is is a run number $i for parameters set number $parset_id.") @time s = initialize_sim_and_run(m = m , routes_path = routes_path , routes_distances = routes_distances , routes_types_both_dir = routes_types_both_dir , orig_map_nodes_num = orig_map_nodes_num , when_to_run_people = when_to_run_people , when_to_run_wagons = when_to_run_wagons , TTC_freq = TTC_freq , max_load_subway = max_load_subway , max_load_streetcar = max_load_streetcar , max_load_gov_restr = max_load_gov_restr , N_agents = N_agents , sim_run_time_in_secs = sim_run_time_in_secs , serialize_finished_sim_object =serialize_finished_sim_object , p0 = p0 , parset_id = parset_id); push!(total_cnt, s.infected_agents_count) push!(ttc_cnt, s.infected_agents_wagon) push!(street_cnt, s.infected_agents_street) time_spend_in_TTC, TTC_types_used, time_spend_in_TTC_comp_to_total_path_time = TTC_usage_by_agents(s,orig_map_nodes_num); push!(prct_ag_used_TTC, round((sum([1 for i in time_spend_in_TTC if i > 0]) / N_agents)*100)) push!(mean_time_spent_in_TTC, mean([i for i in time_spend_in_TTC if i > 0])) loop_max_load_achieved_streetcar = [1 for (k,v) in s.max_passengers_per_TTC_car if (routes_types_both_dir[k[2]] == :streetcar) & (v >= max_load_streetcar)] loop_max_load_achieved_subway = [1 for (k,v) in s.max_passengers_per_TTC_car if (routes_types_both_dir[k[2]] == :subway) & (v >= max_load_subway)] push!(max_load_achieved_streetcars,sum(loop_max_load_achieved_streetcar)) push!(max_load_achieved_subway,sum(loop_max_load_achieved_subway)) loop_all_TTC_trips_count_streetcars = [1 for (k,v) in s.max_passengers_per_TTC_car if (routes_types_both_dir[k[2]] == :streetcar)] loop_all_TTC_trips_count_subway = [1 for (k,v) in s.max_passengers_per_TTC_car if (routes_types_both_dir[k[2]] == :subway)] push!(all_TTC_trips_count_streetcars,sum(loop_all_TTC_trips_count_streetcars)) push!(all_TTC_trips_count_subway,sum(loop_all_TTC_trips_count_subway)) loop_max_pass_per_wagon_streetcars = [v for (k,v) in s.max_passengers_per_TTC_car if (routes_types_both_dir[k[2]] == :streetcar)] loop_max_pass_per_wagon_subway = [v for (k,v) in s.max_passengers_per_TTC_car if (routes_types_both_dir[k[2]] == :subway)] push!(max_pass_per_wagon_streetcars, [mean(loop_max_pass_per_wagon_streetcars),std(loop_max_pass_per_wagon_streetcars),maximum(loop_max_pass_per_wagon_streetcars)]) push!(max_pass_per_wagon_subway, [mean(loop_max_pass_per_wagon_subway),std(loop_max_pass_per_wagon_subway),maximum(loop_max_pass_per_wagon_subway)]) end res_gathering["total_infected"] = (floor.(mean(total_cnt)), floor.(std(total_cnt))) res_gathering["TTC_infected"] = (floor.(mean(ttc_cnt)), floor.(std(ttc_cnt))) res_gathering["street_infected"] = (floor.(mean(street_cnt)), floor.(std(street_cnt))) res_gathering["prct_of_agents_used_TTC"] = (floor.(mean(prct_ag_used_TTC)), floor.(std(prct_ag_used_TTC))) res_gathering["mean_sec_spent_in_TTC_by_those_who_used"] = (floor.(mean(mean_time_spent_in_TTC)), floor.(std(mean_time_spent_in_TTC))) res_gathering["times_max_load_achieved_streetcars"] = (floor.(mean(max_load_achieved_streetcars)), floor.(std(max_load_achieved_streetcars))) res_gathering["times_max_load_achieved_subway"] = (floor.(mean(max_load_achieved_subway)), floor.(std(max_load_achieved_subway))) res_gathering["all_TTC_trips_count_streetcars"] = (floor.(mean(all_TTC_trips_count_streetcars)),floor.(std(all_TTC_trips_count_streetcars))) res_gathering["all_TTC_trips_count_subway"] = (floor.(mean(all_TTC_trips_count_subway)),floor.(std(all_TTC_trips_count_subway))) res_gathering["max_pass_per_wagon_subway"] = (floor.(mean(max_pass_per_wagon_subway)),floor.(maximum(max_pass_per_wagon_subway))) res_gathering["max_pass_per_wagon_streetcars"] = (floor.(mean(max_pass_per_wagon_streetcars)),floor.(maximum(max_pass_per_wagon_subway))) println("All runs completed!") return res_gathering end ### Create parameters grid ### function create_df_with_sim_params(;TTC_car_freq::Union{Vector{Int64},Int64} , max_load_gov_restr::Union{Vector{Float64},Float64} , when_to_run_people::Union{Vector{Float64},Float64} , when_to_run_wagons::Union{Vector{Float64},Float64} , sim_run_time_in_secs::Union{Vector{Int64},Int64} , N_agents::Union{Vector{Int64},Int64} , agents_speed_in_sec_per_m::Union{Vector{Float64},Float64} , max_load_subway::Union{Vector{Int64},Int64} , max_load_streetcar::Union{Vector{Int64},Int64} , p0::Union{Vector{Float64},Float64}) """ Description: Make all possible combinations of given values. Returns: DataFrame with all possible combinations of given parameters where one row means one set of parameters for Simulation. """ df = DataFrame(Iterators.product(TTC_car_freq , max_load_gov_restr , when_to_run_people , when_to_run_wagons , sim_run_time_in_secs , N_agents , agents_speed_in_sec_per_m , max_load_subway , max_load_streetcar , p0)) colnames = ["TTC_car_freq", "max_load_gov_restr", "when_to_run_people", "when_to_run_wagons", "sim_run_time_in_secs", "N_agents", "agents_speed_in_sec_per_m", "max_load_subway", "max_load_streetcar", "p0"] rename!(df,Symbol.(colnames)) df[!,"parset_id"] = [i for i in 1:nrow(df)] df = df[:,["parset_id","TTC_car_freq", "max_load_gov_restr", "when_to_run_people", "when_to_run_wagons", "sim_run_time_in_secs", "N_agents", "agents_speed_in_sec_per_m", "max_load_subway", "max_load_streetcar", "p0"]] return df end println("Run sim functions were read")
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32604, 7, 9806, 62, 6603, 62, 525, 62, 41127, 62, 7266, 1014, 36911, 28300, 12195, 47033, 7, 9806, 62, 6603, 62, 525, 62, 41127, 62, 7266, 1014, 22305, 198, 197, 411, 62, 70, 25545, 14692, 9806, 62, 6603, 62, 525, 62, 41127, 62, 25662, 37993, 8973, 796, 357, 28300, 12195, 32604, 7, 9806, 62, 6603, 62, 525, 62, 41127, 62, 25662, 37993, 36911, 28300, 12195, 47033, 7, 9806, 62, 6603, 62, 525, 62, 41127, 62, 7266, 1014, 22305, 198, 197, 198, 197, 35235, 7203, 3237, 4539, 5668, 2474, 8, 198, 197, 7783, 581, 62, 70, 25545, 198, 437, 628, 198, 21017, 13610, 10007, 10706, 44386, 198, 8818, 2251, 62, 7568, 62, 4480, 62, 14323, 62, 37266, 7, 26, 51, 4825, 62, 7718, 62, 19503, 80, 3712, 38176, 90, 38469, 90, 5317, 2414, 5512, 5317, 2414, 92, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 837, 3509, 62, 2220, 62, 9567, 62, 2118, 81, 3712, 38176, 90, 38469, 90, 43879, 2414, 5512, 43879, 2414, 92, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 837, 618, 62, 1462, 62, 5143, 62, 15332, 3712, 38176, 90, 38469, 90, 43879, 2414, 5512, 43879, 2414, 92, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 837, 618, 62, 1462, 62, 5143, 62, 86, 34765, 3712, 38176, 90, 38469, 90, 43879, 2414, 5512, 43879, 2414, 92, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 837, 985, 62, 5143, 62, 2435, 62, 259, 62, 2363, 82, 3712, 38176, 90, 38469, 90, 5317, 2414, 5512, 5317, 2414, 92, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 837, 399, 62, 49638, 3712, 38176, 90, 38469, 90, 5317, 2414, 5512, 5317, 2414, 92, 198, 197, 197, 197, 197, 197, 197, 197, 197, 197, 11, 6554, 62, 12287, 62, 259, 62, 2363, 62, 525, 62, 76, 3712, 38176, 90, 38469, 90, 43879, 2414, 5512, 43879, 2414, 92, 198, 197, 197, 197, 197, 197, 197, 197, 197, 197, 11, 3509, 62, 2220, 62, 7266, 1014, 3712, 38176, 90, 38469, 90, 5317, 2414, 5512, 5317, 2414, 92, 198, 197, 197, 197, 197, 197, 197, 197, 197, 197, 11, 3509, 62, 2220, 62, 25662, 7718, 3712, 38176, 90, 38469, 90, 5317, 2414, 5512, 5317, 2414, 92, 198, 197, 197, 197, 197, 197, 197, 197, 197, 197, 11, 279, 15, 3712, 38176, 90, 38469, 90, 43879, 2414, 5512, 43879, 2414, 30072, 198, 197, 37811, 198, 197, 11828, 25, 198, 197, 197, 12050, 477, 1744, 17790, 286, 1813, 3815, 13, 198, 197, 35561, 25, 198, 197, 197, 6601, 19778, 351, 477, 1744, 17790, 286, 1813, 10007, 810, 530, 5752, 1724, 530, 900, 286, 10007, 329, 41798, 13, 198, 197, 37811, 197, 198, 220, 220, 220, 47764, 796, 6060, 19778, 7, 29993, 2024, 13, 11167, 7, 51, 4825, 62, 7718, 62, 19503, 80, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 837, 3509, 62, 2220, 62, 9567, 62, 2118, 81, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 837, 618, 62, 1462, 62, 5143, 62, 15332, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 837, 618, 62, 1462, 62, 5143, 62, 86, 34765, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 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35235, 7203, 10987, 985, 5499, 547, 1100, 4943, 198 ]
2.252166
4,616
devices = Dict{Symbol, DeviceModel}(:Generators => DeviceModel(PSY.ThermalStandard, ThermalDispatch), :Loads => DeviceModel(PSY.PowerLoad, StaticPowerLoad)) branches = Dict{Symbol, DeviceModel}(:L => DeviceModel(PSY.Line, StaticLineUnbounded)) services = Dict{Symbol, ServiceModel}() @testset "Operation set ref models" begin model_ref = ModelReference(CopperPlatePowerModel, devices, branches, services); op_model = OperationModel(TestOptModel, model_ref, c_sys5) set_transmission_ref!(op_model, DCPLLPowerModel) @test op_model.model_ref.transmission == DCPLLPowerModel model_ref = ModelReference(CopperPlatePowerModel, devices, branches, services); op_model = OperationModel(TestOptModel, model_ref, c_sys5) new_devices = Dict{Symbol, DeviceModel}(:Generators => DeviceModel(PSY.ThermalStandard, ThermalBasicUnitCommitment), :Loads => DeviceModel(PSY.PowerLoad, StaticPowerLoad)) set_devices_ref!(op_model, new_devices) @test op_model.model_ref.devices[:Generators].formulation == ThermalBasicUnitCommitment jump_model = op_model.canonical.JuMPmodel @test ((JuMP.VariableRef, MOI.ZeroOne) in JuMP.list_of_constraint_types(jump_model)) == true model_ref = ModelReference(DCPPowerModel, devices, branches, services); op_model = OperationModel(TestOptModel, model_ref, c_sys5) new_branches = Dict{Symbol, DeviceModel}(:L => DeviceModel(PSY.Line, StaticLine)) set_branches_ref!(op_model, new_branches) @test op_model.model_ref.branches[:L].formulation == StaticLine end @testset "Operation set models" begin model_ref = ModelReference(CopperPlatePowerModel, devices, branches, services); op_model = OperationModel(TestOptModel, model_ref, c_sys5) set_device_model!(op_model, :Generators, DeviceModel(PSY.ThermalStandard, ThermalBasicUnitCommitment)) @test op_model.model_ref.devices[:Generators].formulation == ThermalBasicUnitCommitment jump_model = op_model.canonical.JuMPmodel @test ((JuMP.VariableRef, MOI.ZeroOne) in JuMP.list_of_constraint_types(jump_model)) == true model_ref = ModelReference(DCPPowerModel, devices, branches, services); op_model = OperationModel(TestOptModel, model_ref, c_sys5) set_branch_model!(op_model, :L, DeviceModel(PSY.Line, StaticLine)) @test op_model.model_ref.branches[:L].formulation == StaticLine end
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""" ZeroKernel() Create a kernel that always returning zero ``` κ(x,y) = 0.0 ``` The output type depends of `x` and `y` """ struct ZeroKernel <: SimpleKernel end kappa(κ::ZeroKernel, d::T) where {T<:Real} = zero(T) metric(::ZeroKernel) = Delta() Base.show(io::IO, ::ZeroKernel) = print(io, "Zero Kernel") """ WhiteKernel() ``` κ(x,y) = δ(x,y) ``` Kernel function working as an equivalent to add white noise. Can also be called via `EyeKernel()` """ struct WhiteKernel <: SimpleKernel end """ EyeKernel() See [WhiteKernel](@ref) """ const EyeKernel = WhiteKernel kappa(κ::WhiteKernel, δₓₓ::Real) = δₓₓ metric(::WhiteKernel) = Delta() Base.show(io::IO, ::WhiteKernel) = print(io, "White Kernel") """ ConstantKernel(; c=1.0) Kernel function always returning a constant value `c` ``` κ(x,y) = c ``` """ struct ConstantKernel{Tc<:Real} <: SimpleKernel c::Vector{Tc} function ConstantKernel(;c::T=1.0) where {T<:Real} new{T}([c]) end end kappa(κ::ConstantKernel,x::Real) = first(κ.c)*one(x) metric(::ConstantKernel) = Delta() Base.show(io::IO, κ::ConstantKernel) = print(io, "Constant Kernel (c = ", first(κ.c), ")")
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module SpikeTrains export SpikeTrain, draw_uncorrelated_spikes, draw_correlated_spikes, draw_coincident_spikes, length, iterate, convert, vcat, merge, make_exponentialShift, correlation_code, coincidence_code, plot_spike_trains using Distributions, Plots#, PlotRecipes struct SpikeTrain times::Vector{Float64} SpikeTrain(times=Float64[]) = new(sort(times)) end Base.length(s::SpikeTrain) = length(s.times) Base.iterate(s::SpikeTrain, args...) = iterate(s.times, args...) Base.convert(SpikeTrain, v) = SpikeTrain(v) Base.vcat(s::SpikeTrain...) = SpikeTrain(vcat(getfield.(s,:times)...)) Base.merge(s::Array{SpikeTrain}; dims=Base.OneTo(ndims(s))) = dropdims(mapslices(x->vcat(x...), s, dims=dims), dims=dims) function has_min_distance(times, min_distance) keep = ones(Bool, length(times)) last_time = -Inf for (i,t) ∈ enumerate(times) if t < last_time keep[i] = false else last_time = t+min_distance end end return keep end ensure_min_distance(times, min_distance) = times[has_min_distance(times, min_distance)] """ draw_uncorrelated_spikes(trange, rates; sorted=true) Draw uncorrelated spike trains in the interval `trange` with the respective `rates`. """ function draw_uncorrelated_spikes(trange, rates) duration = trange[2]-trange[1] source = Vector{SpikeTrain}(undef, length(rates)) # draw uncorrelated spike trains with high rate for (i,r) ∈ enumerate(rates) num_spikes = rand(Poisson(duration*r)) spikes = rand(num_spikes).*duration.+trange[1] sort!(spikes) source[i] = SpikeTrain(spikes) end return source end make_exponentialShift(τ) = t -> t+rand(Exponential(τ)) """ draw_correlated_spikes(trange, rates, c, [shift]; min_master_distance=0) Draws spike trains with given `rates` within the given time interval `trange` and pairwise correlation coefficients c. A function to `shift` the drawn spikes can be included. A minimum distance between master spikes can be required. The algorithm implemented is the offline version of the mixture process (4.6.1) by Brette 2008 [1]. [1](http://romainbrette.fr/WordPress3/wp-content/uploads/2014/06/Brette2008NC.pdf) E[wt] = E[n_good]*min_master_distance P(n_good|E[wt]) = Poisson(n_good|rate*(1-E[n_good]*min_master_distance)) E[n_good] = rate*1-rate*E[n_good]*min_master_distance E[n_good] = rate/(1+rate*min_master_distance) = 1/(1/rate+min_master_distance) rate_desired =!= E[n_good] = 1/(1/rate+min_master_distance) => rate := rate_desired/(1-rate_desired*min_master_distance) """ function draw_correlated_spikes(trange, rates, c, shift = make_exponentialShift(1.0); min_master_distance=-0) # correct the rates to account for the expected loss due to pruning overlapping spikes frac_overlap = sum(rates) # draw the uncorrelated source spiketrains source = if c≈0.0 @assert all(0 .<= rates*min_master_distance .< 1) "Cannot draw $(maximum(rates)) master spikes per unit-time with $(min_master_distance) minimum distance (rates must sum to < $(1/min_master_distance))." # correct sampling rate for expected loss of master spikes due to enforced minimum distance rates .= rates./(1 .- rates.*min_master_distance) draw_uncorrelated_spikes(trange, rates) else # determine master spike sampling rate v = 1/c * mean(rates.^2)/sum(rates) @assert 0<=length(rates)*v*min_master_distance<1 "Cannot draw ~$(length(rates)*v) master spikes per unit-time with $(min_master_distance) minimum distance (rates must sum to < $(1/min_master_distance))." # correct sampling rate for expected loss of master spikes due to enforced minimum distance v /= (1-length(rates)*v*min_master_distance) # determine subsampling probability p = (c/mean(rates.^2)) .* (rates * rates') @assert all(0 .<= p .<= 1) "P not all valid probabilities." draw_uncorrelated_spikes(trange, fill(v, length(rates))) end # ensure minimum distance between all source spike trains if min_master_distance > 0 n = sum(length, source) times = Vector{Float64}(undef, n) sources = Vector{Int}(undef, n) i = 1 for (s,spikes) ∈ enumerate(source) inew = i+length(spikes) times[i:inew-1] = spikes.times sources[i:inew-1] .= s i=inew end # sort times and corresponding sources idx=sortperm(times) times=times[idx] sources=sources[idx] # determine which spikes to keep ... keep=has_min_distance(times, min_master_distance) # ... and keep only those spikes for the respective source for (s,spikes) ∈ enumerate(source) source[s] = SpikeTrain(times[(sources.==s) .& keep]) end end if c≈0.0 return source else target = Vector{SpikeTrain}(undef, length(rates)) # draw correlated spike trains for i ∈ eachindex(target) t = target[i] = SpikeTrain() for (k,s) ∈ enumerate(source) num_spikes = rand(Binomial(length(s.times), p[i,k])) append!(t.times, sample(s.times, num_spikes; replace=false)) end # shift each value randomly t.times .= shift.(t.times) sort!(t.times) end return target end end """ draw_coincident_spikes(trange, num_spikes, p=ones(Float64,1), shift = make_exponentialShift(1.0); sorted=true) Draws `num_spikes` Poisson spikes in the interval `trange`, each of which appears in each spike-train `i` with the respective probability `p[i]`. """ function draw_coincident_spikes(trange, num_spikes, p=ones(Float64,1), shift = make_exponentialShift(1.0); sorted=true) times = rand(num_spikes).*(trange[2]-trange[1]).+trange[1] if sorted sort!(times) end target = Vector{SpikeTrain}(undef, length(p)) for i ∈ eachindex(target) n = rand(Binomial(num_spikes, p[i])) target[i] = SpikeTrain(sample(times, n; replace=false)) end return target end """ correlation_code(trange, stimulus, rates, correlations; kwargs...) Each slice `stimulus[:,...,:,i]` contains an array indicating the class that the spike-train with corresponding index represents in the time interval `trange[i:i+1]`. Each class `c` results in spike-trains with rate `rates[c]` and mutual pairwise correlation `correlations[c]`. """ function correlation_code(trange, stimulus, rates, correlations; kwargs...) @assert length(trange)-1 == size(stimulus)[end] "trange must include n+1 steps for stimuli with last dimension n" spiketrains = Array{SpikeTrain}(undef, size(stimulus)) classes = unique(stimulus) for c ∈ classes for i ∈ Base.OneTo(length(trange)-1) idx = findall(s->c==s, view(stimulus, fill(Colon(), ndims(stimulus)-1)...,i)) spiketrains[idx,i] = draw_correlated_spikes(trange[i:i+1], fill(rates[c], length(idx)), correlations[c]; kwargs...) end end return spiketrains end """ coincidence_code(trange, stimulus, background_rate, p; kwargs...) Each slice `stimulus[:,...,:,i]` contains a boolean array corresponding to whether or not a spike train with corresponding index participates or not in the coincident spiking during the time interval `trange[i:i+1]`. A single spike is drawn within each interval, that appears with probability `p` in each of the spike trains for which the corresponding entry in `stimulus` is `true`. In addition to the signal spike for each interval, uncorrelated poisson spike-trains, drawn with a given `background_rate`, are added as background noise. """ function coincidence_code(trange, stimulus, background_rate, p; kwargs...) @assert length(trange)-1 == size(stimulus)[end] "trange must include n+1 steps for stimuli with last dimension n" spiketrains = reshape(draw_uncorrelated_spikes((trange[1],trange[end]), fill(background_rate, length(stimulus))), size(stimulus)) for i ∈ Base.OneTo(length(trange)-1) idx = findall(view(stimulus, fill(Colon(), ndims(stimulus)-1)...,i)) nd = ndims(idx)+1 spiketrains[idx,i] = merge(cat(spiketrains[idx,i], draw_coincident_spikes(trange[i:i+1], 1, fill(p, length(idx)); kwargs...), dims=nd),dims=nd) end return spiketrains end """ plot_spike_trains(spiketrains::Array{SpikeTrain}, colors=fill(:auto, length(spiketrains)), plt=plot(); kwargs...) Draw an array of `spiketrains` with given `colors` within a single given or new plot `plt`. """ function plot_spike_trains(spiketrains::Array{SpikeTrain}, colors=fill(:auto, length(spiketrains)), plt=plot(); kwargs...) for (i,(spiketrain, color)) ∈ enumerate(zip(spiketrains, colors)) plot!(plt, ([spiketrain.times spiketrain.times fill(NaN, length(spiketrain.times))])'[:], i .- 0.5 .+ ([zeros(length(spiketrain.times)) ones(length(spiketrain.times)) fill(NaN, length(spiketrain.times))])'[:], lc=color; kwargs...) end return plt end end
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2.48197
3,716
# DBL_EPSILON - jest to dokładność dla liczb zmiennoprzecinkowych # dla Float64 jest to 2.2204460492503131e-16 DBL_EPSILON = 2.2204460492503131e-16 function DIFFSIGN(x, y) if (x <=0 && y >= 0) || (x >= 0 && y <= 0) return true else return false end end function fun(x) return 1 / (x - 3) - 6 end function between(x, a, b) if b > a return (x >= a && x <= b) else return (x >= b && x <= a) end end function lfun(b, a, fb, fa) if fb != fa return b - fb*(b - a) / (fb - fa) # metoda siecznych elseif fa != 0 return Inf else return b end end function hfun(b, c) if c > b return b + abs(b*DBL_EPSILON) else return b - abs(b*DBL_EPSILON) end end function mfun(b, c) return 0.5*(b + c) # metoda bisekcji end function wfun(l, b, c) h = hfun(b, c) m = mfun(b, c) if between(l, h, m) == true return l elseif (fun(abs(l - b)) <= abs(b*DBL_EPSILON)) && (between(l, b, m) == false) return h else return m end end function ffun(a, b, fa, fb) return (fa - fb) / (a - b) end function rfun(b, a, d, fb, fa, fd) alpha = ffun(b, d, fb, fd)*fa beta = ffun(a, d, fa, fd)*fb if beta != alpha return b - beta*(b - a) / (beta - alpha) elseif alpha != 0 return Inf else return 0 # beta == alpha == 0 end end function DekkerM(x0, x1, Eps) """ b - ostatnie przyblizenie wyniku c - kontrapunkt b, punkt w którym funkcja f ma przeciwny znak niż w punkcie b a – poprzednia wartość punktu a, używana do wyliczania następnego punktu metodą siecznych Metoda bisekcji z punktów b i c tworzy punkt m pomiędzy nimi na środku przedziału. Wyliczany jest ciąg xi, którego ostatni element oznaczany jest przez x, a poprzedni przez xp. xk - ostatni punkt w ciągu, który ma różny znak niż x. Punkt x wyliczany jest dwoma metodami: - siecznych - bisekcji i wybierany jest ten obliczony z metody siecznych jeśli leży pomiędzy punktem b (ze względów dokładnościowych z pewną poprawką) a punktem m wyliczonym z bisekcji. Jeżeli f(x) czy f(xk) leży bliżej zera i jeśli f(x) leży bliżej zera wtedy b ma wartość x, c ma wartość xk, w przeciwnym razie zamiana. """ d = NaN fd = NaN fxp = fun(x0) fx = fun(x1) if x0 == x1 return fx end if DIFFSIGN(fx, fxp) == false return 0 end if abs(fx) <= abs(fxp) b = x1 a = c = x0 fa = fxp fb = fx else b = x0 a = c = x1 fa = fx fb = fxp end xk = xp = x0 fxk = fxp x = x1 iter = 0 age = 0 bp = b cp = c ap = a while abs(b - c) > 2*Eps iter = iter + 1 age = age + 1 if abs(b - c) <= (0.5 + 2 * DBL_EPSILON)*(abs(bp - cp) + abs(b*DBL_EPSILON)) age = 1 end xp = x if iter == 2 lambda = lfun(b, a, fb, fa) if abs(lambda - b) < abs(b*DBL_EPSILON) break end x = wfun(lambda, b, c) elseif iter >= 3 && age <= 3 rho = rfun(b, a, d, fb, fa, fd) if abs(rho - b) < abs(b*DBL_EPSILON) break end x = wfun(rho, b, c) elseif iter >=3 && age == 4 rho = rfun(b, a, d, fb, fa, fd) if abs(rho - b) < abs(b*DBL_EPSILON) break end x = wfun(2 * rho - b, b, c) else x = mfun(b, c) end fxp = fx fx = fun(x) if DIFFSIGN(fxp, fx) == true xk = xp fxk = fxp end bp = b fbp = fb ap = a fap = fa cp = c if abs(fx) <= abs(fxk) a = b fa = fb b = x fb = fx c = xk else b = xk fb = fxk a = c = x fa = fx end if b == x || b == bp d = ap fd = fap else d = ap fd = fdp end end println("Number of iterations: ", iter) return b end println("f(x) = 1/(x-3)-6\n") result = DekkerM(3.01, 4, 1e-12) println("x0 = ", result)
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7, 81, 8873, 532, 275, 8, 1279, 2352, 7, 65, 9, 35, 9148, 62, 36, 3705, 4146, 1340, 8, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2270, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 886, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2124, 796, 266, 12543, 7, 81, 8873, 11, 275, 11, 269, 8, 198, 220, 220, 220, 220, 220, 220, 220, 2073, 361, 11629, 18189, 18, 11405, 2479, 6624, 604, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 374, 8873, 796, 374, 12543, 7, 65, 11, 257, 11, 288, 11, 277, 65, 11, 24685, 11, 277, 67, 8, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 611, 2352, 7, 81, 8873, 532, 275, 8, 1279, 2352, 7, 65, 9, 35, 9148, 62, 36, 3705, 4146, 1340, 8, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2270, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 886, 198, 220, 220, 220, 220, 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1.624325
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using BenchmarkTools, Random, Statistics, WaveFD _nthreads = [2^i for i in 0:floor(Int,log2(Sys.CPU_THREADS))] if Sys.CPU_THREADS ∉ _nthreads push!(_nthreads, Sys.CPU_THREADS) end const SUITE = BenchmarkGroup() z0,y0,x0,dz,dy,dx,nt,dt = 0.0,0.0,0.0,10.0,10.0,10.0,3000,0.001 n_2D = (z=parse(Int,get(ENV,"2D_NZ","501")), x=parse(Int,get(ENV,"2D_NX","1001"))) n_3D = (z=parse(Int,get(ENV,"3D_NZ","101")), y=parse(Int,get(ENV,"3D_NY","201")), x=parse(Int,get(ENV,"3D_NX","301"))) nb_2D = (z=parse(Int,get(ENV,"2D_NBZ","$(n_2D.z)")), x=parse(Int,get(ENV,"2D_NBX","8"))) nb_3D = (z=parse(Int,get(ENV,"3D_NBZ","$(n_3D.z)")), y=parse(Int,get(ENV,"3D_NBY","8")), x=parse(Int,get(ENV,"3D_NBX","8"))) @info "size 2D: $n_2D, use ENV[\"2D_NZ\"], ENV[\"2D_NX\"] to customize" @info "size 3D: $n_3D, use ENV[\"3D_NZ\"], ENV[\"3D_NY\"], ENV[\"3D_NX\"] to customize" @info "cache block size 2D: $nb_2D, use ENV[\"2D_NBZ\"], ENV[\"2D_NBX\"] to customize" @info "cache block size 3D: $nb_3D, use ENV[\"3D_NBZ\"], ENV[\"3D_NBY\"], ENV[\"3D_NBX\"] to customize" rz,rx = dz*ones(n_2D.x),dx*[0:n_2D.x-1;] iz,ix,c = WaveFD.hickscoeffs(dz, dx, z0, x0, n_2D.z, n_2D.x, rz, rx) blocks = WaveFD.source_blocking(n_2D.z, n_2D.x, 4, 4, iz, ix, c) field = zeros(n_2D.z, n_2D.x) data = rand(nt, n_2D.x) it = 1 SUITE["2D Utils"] = BenchmarkGroup() SUITE["2D Utils"]["source_blocking"] = @benchmarkable WaveFD.source_blocking($(n_2D.z), $(n_2D.x), $(nb_2D.z), $(nb_2D).x, $iz, $ix, $c); SUITE["2D Utils"]["injectdata!"] = @benchmarkable WaveFD.injectdata!($field, $blocks, $data, $it) dtmod = 0.0002 ntmod = nt * round(Int,dt / dtmod) h = WaveFD.interpfilters(dtmod, dt, 0, WaveFD.LangC(), Sys.CPU_THREADS) data_mod = rand(ntmod, n_2D.x) SUITE["2D Utils"]["interpadjoint!"] = @benchmarkable WaveFD.interpadjoint!($h, $data_mod, $data) SUITE["2D Utils"]["interpforward!"] = @benchmarkable WaveFD.interpforward!($h, $data, $data_mod) compressor = WaveFD.Compressor(Float32, Float32, UInt32, (n_2D.z,n_2D.x), (32,32), 1e-2, 1024, false) field2 = rand(Float32,n_2D.z,n_2D.x) SUITE["2D compression"] = BenchmarkGroup() SUITE["2D compression"]["write"] = @benchmarkable WaveFD.compressedwrite(io, $compressor, 1, $field2) setup=(open($compressor); io=open(tempname(),"w")) teardown=(close($compressor); close(io)) SUITE["2D compression"]["read"] = @benchmarkable WaveFD.compressedread!(io, $compressor, 1, $field2) setup=(open($compressor); tfile=tempname(); _io=open(tfile,"w"); WaveFD.compressedwrite(_io, $compressor, 1, $field2); close(_io); io=open(tfile)) teardown=(close($compressor); close(io)) rng2 = (10:n_2D.z-10,10:n_2D.x) _field2 = view(field2, rng2...) _compressor = WaveFD.Compressor(Float32, Float32, UInt32, size(_field2), (32,32), 1e-2, 1024, true) SUITE["2D compression, interior"] = BenchmarkGroup() SUITE["2D compression, interior"]["write"] = @benchmarkable WaveFD.compressedwrite(io, $_compressor, 1, $field2, $rng2) setup=(open($_compressor); io=open(tempname(),"w")) teardown=(close($_compressor); close(io)) SUITE["2D compression, interior"]["read"] = @benchmarkable WaveFD.compressedread!(io, $_compressor, 1, $field2, $rng2) setup=(open($_compressor); tfile=tempname(); _io=open(tfile,"w"); WaveFD.compressedwrite(_io, $_compressor, 1, $field2, rng2); close(_io); io=open(tfile)) teardown=(close($_compressor); close(io)) SUITE["2DAcoIsoDenQ_DEO2_FDTD"] = BenchmarkGroup([Dict("ncells"=>n_2D.z*n_2D.x,"nbz"=>nb_2D.z,"nbx"=>nb_2D.x,"nthreads"=>_nthreads)]) function p2diso(nthreads,nz,nx,nbz,nbx) p = WaveFD.Prop2DAcoIsoDenQ_DEO2_FDTD(freesurface=false, nz=nz, nx=nx, nbz=nbz, nbx=nbx, dz=10.0, dx=10.0, dt=0.001, nthreads=nthreads) v,b,pcur,pold = WaveFD.V(p),WaveFD.B(p),WaveFD.PCur(p),WaveFD.POld(p) v .= 1500 b .= 1 rand!(pcur) rand!(pold) p end for nthreads in _nthreads SUITE["2DAcoIsoDenQ_DEO2_FDTD"]["$nthreads threads"] = @benchmarkable WaveFD.propagateforward!(p) setup=(p=p2diso($nthreads,$(n_2D.z),$(n_2D.x),$(nb_2D.z),$(nb_2D.x))) teardown=(free(p)) end function fields2diso(p::WaveFD.Prop2DAcoIsoDenQ_DEO2_FDTD) nz,nx=size(p) δm = Dict("v"=>rand(Float32,nz,nx)) fields = Dict("pspace"=>rand(Float32,nz,nx)) δm,fields end SUITE["2DAcoIsoDenQ_DEO2_FDTD"]["imaging condition, standard"] = @benchmarkable WaveFD.adjointBornAccumulation!(p,WaveFD.Prop2DAcoIsoDenQ_DEO2_FDTD_Model_V(),ic,δm,fields) setup=(p=p2diso(Sys.CPU_THREADS,$(n_2D.z),$(n_2D.x),$(nb_2D.z),$(nb_2D.x)); ic=WaveFD.ImagingConditionStandard(); (δm,fields)=fields2diso(p)) teardown=(free(p)) SUITE["2DAcoIsoDenQ_DEO2_FDTD"]["imaging condition, wave field separation FWI"] = @benchmarkable WaveFD.adjointBornAccumulation!(p,WaveFD.Prop2DAcoIsoDenQ_DEO2_FDTD_Model_V(),ic,δm,fields) setup=(p=p2diso(Sys.CPU_THREADS,$(n_2D.z),$(n_2D.x),$(nb_2D.z),$(nb_2D.x)); ic=WaveFD.ImagingConditionWaveFieldSeparationFWI(); (δm,fields)=fields2diso(p)) teardown=(free(p)) SUITE["2DAcoIsoDenQ_DEO2_FDTD"]["imaging condition, wave field separation RTM"] = @benchmarkable WaveFD.adjointBornAccumulation!(p,WaveFD.Prop2DAcoIsoDenQ_DEO2_FDTD_Model_V(),ic,δm,fields) setup=(p=p2diso(Sys.CPU_THREADS,$(n_2D.z),$(n_2D.x),$(nb_2D.z),$(nb_2D.x)); ic=WaveFD.ImagingConditionWaveFieldSeparationRTM(); (δm,fields)=fields2diso(p)) teardown=(free(p)) SUITE["2DAcoVTIDenQ_DEO2_FDTD"] = BenchmarkGroup([Dict("ncells"=>n_2D.z*n_2D.x,"nbz"=>nb_2D.z,"nbx"=>nb_2D.x,"nthreads"=>_nthreads)]) function p2dvti(nthreads,nz,nx,nbz,nbx) p = WaveFD.Prop2DAcoVTIDenQ_DEO2_FDTD(freesurface=false, nz=nz, nx=nx, nbz=nbz, nbx=nbx, dz=10.0, dx=10.0, dt=0.001, nthreads=nthreads) v,b,ϵ,η,f,pcur,pold = WaveFD.V(p),WaveFD.B(p),WaveFD.Eps(p),WaveFD.Eta(p),WaveFD.F(p),WaveFD.PCur(p),WaveFD.POld(p) rand!(pcur) rand!(pold) v .= 1500 b .= 1 ϵ .= 0.2 η .= 0.0 f .= 0.85 p end for nthreads in _nthreads SUITE["2DAcoVTIDenQ_DEO2_FDTD"]["$nthreads threads"] = @benchmarkable WaveFD.propagateforward!(p) setup=(p=p2dvti($nthreads,$(n_2D.z),$(n_2D.x),$(nb_2D.z),$(nb_2D.x))) teardown=(free(p)) end function fields2dvti(p::WaveFD.Prop2DAcoVTIDenQ_DEO2_FDTD) nz,nx=size(p) δm = Dict("v"=>rand(Float32,nz,nx)) fields = Dict("pspace"=>rand(Float32,nz,nx),"mspace"=>rand(Float32,nz,nx)) δm,fields end SUITE["2DAcoVTIDenQ_DEO2_FDTD"]["imaging condition, standard"] = @benchmarkable WaveFD.adjointBornAccumulation!(p,WaveFD.Prop2DAcoVTIDenQ_DEO2_FDTD_Model_V(),ic,δm,fields) setup=(p=p2dvti(Sys.CPU_THREADS,$(n_2D.z),$(n_2D.x),$(nb_2D.z),$(nb_2D.x)); ic=WaveFD.ImagingConditionStandard(); (δm,fields)=fields2dvti(p)) teardown=(free(p)) SUITE["2DAcoVTIDenQ_DEO2_FDTD"]["imaging condition, wave field separation FWI"] = @benchmarkable WaveFD.adjointBornAccumulation!(p,WaveFD.Prop2DAcoVTIDenQ_DEO2_FDTD_Model_V(),ic,δm,fields) setup=(p=p2dvti(Sys.CPU_THREADS,$(n_2D.z),$(n_2D.x),$(nb_2D.z),$(nb_2D.x)); ic=WaveFD.ImagingConditionWaveFieldSeparationFWI(); (δm,fields)=fields2dvti(p)) teardown=(free(p)) SUITE["2DAcoVTIDenQ_DEO2_FDTD"]["imaging condition, wave field separation RTM"] = @benchmarkable WaveFD.adjointBornAccumulation!(p,WaveFD.Prop2DAcoVTIDenQ_DEO2_FDTD_Model_V(),ic,δm,fields) setup=(p=p2dvti(Sys.CPU_THREADS,$(n_2D.z),$(n_2D.x),$(nb_2D.z),$(nb_2D.x)); ic=WaveFD.ImagingConditionWaveFieldSeparationRTM(); (δm,fields)=fields2dvti(p)) teardown=(free(p)) SUITE["2DAcoTTIDenQ_DEO2_FDTD"] = BenchmarkGroup([Dict("ncells"=>n_2D.z*n_2D.x,"nbz"=>nb_2D.z,"nbx"=>nb_2D.x,"nthreads"=>_nthreads)]) function p2dtti(nthreads,nz,nx,nbz,nbx) p = WaveFD.Prop2DAcoTTIDenQ_DEO2_FDTD(freesurface=false, nz=nz, nx=nx, nbz=nbz, nbx=nbx, dz=10.0, dx=10.0, dt=0.001, nthreads=nthreads) v,b,ϵ,η,f,sinθ,cosθ,pcur,pold = WaveFD.V(p),WaveFD.B(p),WaveFD.Eps(p),WaveFD.Eta(p),WaveFD.F(p),WaveFD.SinTheta(p),WaveFD.CosTheta(p),WaveFD.PCur(p),WaveFD.POld(p) v .= 1500 b .= 1 ϵ .= 0.2 η .= 0.0 f .= 0.85 cosθ .= cos(pi/4) sinθ .= sin(pi/4) rand!(pcur) rand!(pold) p end for nthreads in _nthreads SUITE["2DAcoTTIDenQ_DEO2_FDTD"]["$nthreads threads"] = @benchmarkable WaveFD.propagateforward!(p) setup=(p=p2dtti($nthreads,$(n_2D.z),$(n_2D.x),$(nb_2D.z),$(nb_2D.x))) teardown=(free(p)) end function fields2dtti(p::WaveFD.Prop2DAcoTTIDenQ_DEO2_FDTD) nz,nx = size(p) δm = Dict("v"=>rand(Float32,nz,nx)) fields = Dict("pspace"=>rand(Float32,nz,nx),"mspace"=>rand(Float32,nz,nx)) δm,fields end SUITE["2DAcoTTIDenQ_DEO2_FDTD"]["imaging condition, standard"] = @benchmarkable WaveFD.adjointBornAccumulation!(p,WaveFD.Prop2DAcoTTIDenQ_DEO2_FDTD_Model_V(),ic,δm,fields) setup=(p=p2dtti(Sys.CPU_THREADS,$(n_2D.z),$(n_2D.x),$(nb_2D.z),$(nb_2D.x)); ic=WaveFD.ImagingConditionStandard(); (δm,fields)=fields2dtti(p)) teardown=(free(p)) SUITE["2DAcoTTIDenQ_DEO2_FDTD"]["imaging condition, wave field separation FWI"] = @benchmarkable WaveFD.adjointBornAccumulation!(p,WaveFD.Prop2DAcoTTIDenQ_DEO2_FDTD_Model_V(),ic,δm,fields) setup=(p=p2dtti(Sys.CPU_THREADS,$(n_2D.z),$(n_2D.x),$(nb_2D.z),$(nb_2D.x)); ic=WaveFD.ImagingConditionWaveFieldSeparationFWI(); (δm,fields)=fields2dtti(p)) teardown=(free(p)) SUITE["2DAcoTTIDenQ_DEO2_FDTD"]["imaging condition, wave field separation RTM"] = @benchmarkable WaveFD.adjointBornAccumulation!(p,WaveFD.Prop2DAcoTTIDenQ_DEO2_FDTD_Model_V(),ic,δm,fields) setup=(p=p2dtti(Sys.CPU_THREADS,$(n_2D.z),$(n_2D.x),$(nb_2D.z),$(nb_2D.x)); ic=WaveFD.ImagingConditionWaveFieldSeparationRTM(); (δm,fields)=fields2dtti(p)) teardown=(free(p)) nz,ny,nx = 501,301,1001 rz = [dz for iy = 1:ny, ix = 1:nx][:] ry = [(iy-1)*dy for iy = 1:ny, ix = 1:nx][:] rx = [(ix-1)*dx for iy = 1:ny, ix = 1:nx][:] iz,iy,ix,c = WaveFD.hickscoeffs(dz, dy, dx, z0, y0, x0, nz, ny, nx, rz, ry, rx) blocks = WaveFD.source_blocking(nz, ny, nx, 4, 4, 4, iz, iy, ix, c) field = zeros(nz, ny, nx) data = rand(nt, nx*ny) it = 1 SUITE["3D Utils"] = BenchmarkGroup() SUITE["3D Utils"]["source_blocking"] = @benchmarkable WaveFD.source_blocking($nz, $ny, $nx, 256, 8, 8, $iz, $iy, $ix, $c); SUITE["3D Utils"]["injectdata!"] = @benchmarkable WaveFD.injectdata!($field, $blocks, $data, $it) compressor = WaveFD.Compressor(Float32, Float32, UInt32, (n_3D.z,n_3D.y,n_3D.x), (32,32,32), 1e-2, 1024, false) field3 = rand(Float32,n_3D.z,n_3D.y,n_3D.x) SUITE["3D compression"] = BenchmarkGroup() SUITE["3D compression"]["write"] = @benchmarkable WaveFD.compressedwrite(io, $compressor, 1, $field3) setup=(open($compressor); io=open(tempname(), "w")) teardown=(close($compressor); close(io)) SUITE["3D compression"]["read"] = @benchmarkable WaveFD.compressedread!(io, $compressor, 1, $field3) setup=(open($compressor); tfile=tempname(); _io=open(tfile,"w"); WaveFD.compressedwrite(_io, $compressor, 1, $field3); close(_io); io=open(tfile)) teardown=(close($compressor); close(io)) rng3 = (10:n_3D.z-10,10:n_3D.y,10:n_3D.x) _field3 = view(field3, rng3...) _compressor = WaveFD.Compressor(Float32, Float32, UInt32, size(_field3), (32,32,32), 1e-2, 1024, true) SUITE["3D compression, interior"] = BenchmarkGroup() SUITE["3D compression, interior"]["write"] = @benchmarkable WaveFD.compressedwrite(io, $_compressor, 1, $field3, $rng3) setup=(open($_compressor); io=open(tempname(),"w")) teardown=(close($_compressor); close(io)) SUITE["3D compression, interior"]["read"] = @benchmarkable WaveFD.compressedread!(io, $_compressor, 1, $field3, $rng3) setup=(open($_compressor); tfile=tempname(); _io=open(tfile,"w"); WaveFD.compressedwrite(_io, $_compressor, 1, $field3, rng3); close(_io); io=open(tfile)) teardown=(close($_compressor); close(io)) SUITE["3DAcoIsoDenQ_DEO2_FDTD"] = BenchmarkGroup([Dict("ncells"=>n_3D.z*n_3D.y*n_3D.x,"nbz"=>nb_3D.z,"nby"=>nb_3D.y,"nbx"=>nb_3D.x,"nthreads"=>_nthreads)]) function p3diso(nthreads,nz,ny,nx,nbz,nby,nbx) p = WaveFD.Prop3DAcoIsoDenQ_DEO2_FDTD(freesurface=false, nz=nz, ny=ny, nx=nx, nbz=nbz, nby=nby, nbx=nbx, dz=10.0, dy=10.0, dx=10.0, dt=0.001, nthreads=nthreads) v,b,pcur,pold = WaveFD.V(p), WaveFD.B(p),WaveFD.PCur(p),WaveFD.POld(p) v .= 1500 b .= 1 rand!(pcur) rand!(pold) p end for nthreads in _nthreads SUITE["3DAcoIsoDenQ_DEO2_FDTD"]["$nthreads threads"] = @benchmarkable WaveFD.propagateforward!(p) setup=(p=p3diso($nthreads,$(n_3D.z),$(n_3D.y),$(n_3D.x),$(nb_3D.z),$(nb_3D.y),$(nb_3D.x))) teardown=(free(p)) seconds=15 end function fields3diso(p::WaveFD.Prop3DAcoIsoDenQ_DEO2_FDTD) nz,ny,nx=size(p) δm = Dict("v"=>rand(Float32,nz,ny,nx)) fields = Dict("pspace"=>rand(Float32,nz,ny,nx)) δm,fields end SUITE["3DAcoIsoDenQ_DEO2_FDTD"]["imaging condition, standard"] = @benchmarkable WaveFD.adjointBornAccumulation!(p,WaveFD.Prop3DAcoIsoDenQ_DEO2_FDTD_Model_V(),ic,δm,fields) setup=(p=p3diso(Sys.CPU_THREADS,$(n_3D.z),$(n_3D.y),$(n_3D.x),$(nb_3D.z),$(nb_3D.y),$(nb_3D.x)); ic=WaveFD.ImagingConditionStandard(); (δm,fields)=fields3diso(p)) teardown=(free(p)) SUITE["3DAcoIsoDenQ_DEO2_FDTD"]["imaging condition, wave field separation FWI"] = @benchmarkable WaveFD.adjointBornAccumulation!(p,WaveFD.Prop3DAcoIsoDenQ_DEO2_FDTD_Model_V(),ic,δm,fields) setup=(p=p3diso(Sys.CPU_THREADS,$(n_3D.z),$(n_3D.y),$(n_3D.x),$(nb_3D.z),$(nb_3D.y),$(nb_3D.x)); ic=WaveFD.ImagingConditionWaveFieldSeparationFWI(); x=zeros(Float32,$(n_3D.z),$(n_3D.y),$(n_3D.x)); (δm,fields)=fields3diso(p)) teardown=(free(p)) SUITE["3DAcoIsoDenQ_DEO2_FDTD"]["imaging condition, wave field separation RTM"] = @benchmarkable WaveFD.adjointBornAccumulation!(p,WaveFD.Prop3DAcoIsoDenQ_DEO2_FDTD_Model_V(),ic,δm,fields) setup=(p=p3diso(Sys.CPU_THREADS,$(n_3D.z),$(n_3D.y),$(n_3D.x),$(nb_3D.z),$(nb_3D.y),$(nb_3D.x)); ic=WaveFD.ImagingConditionWaveFieldSeparationRTM(); x=zeros(Float32,$(n_3D.z),$(n_3D.y),$(n_3D.x)); (δm,fields)=fields3diso(p)) teardown=(free(p)) SUITE["3DAcoVTIDenQ_DEO2_FDTD"] = BenchmarkGroup([Dict("ncells"=>n_3D.z*n_3D.y*n_3D.x,"nbz"=>nb_3D.z,"nby"=>nb_3D.y,"nbx"=>nb_3D.x,"nthreads"=>_nthreads)]) function p3dvti(nthreads,nz,ny,nx,nbz,nby,nbx) p = WaveFD.Prop3DAcoVTIDenQ_DEO2_FDTD(freesurface=false, nz=nz, ny=ny, nx=nx, nbz=nbz, nby=nby, nbx=nbx, dz=10.0, dy=10.0, dx=10.0, dt=0.001, nthreads=nthreads) v,b,ϵ,η,f,pcur,pold = WaveFD.V(p),WaveFD.B(p),WaveFD.Eps(p),WaveFD.Eta(p),WaveFD.F(p),WaveFD.PCur(p),WaveFD.POld(p) rand!(pcur) rand!(pold) v .= 1500 b .= 1 ϵ .= 0.2 η .= 0.0 f .= 0.85 p end for nthreads in _nthreads SUITE["3DAcoVTIDenQ_DEO2_FDTD"]["$nthreads threads"] = @benchmarkable WaveFD.propagateforward!(p) setup=(p=p3dvti($nthreads,$(n_3D.z),$(n_3D.y),$(n_3D.x),$(nb_3D.z),$(nb_3D.y),$(nb_3D.x))) teardown=(free(p)) seconds=15 end function fields3dvti(p::WaveFD.Prop3DAcoVTIDenQ_DEO2_FDTD) nz,ny,nx=size(p) δm = Dict("v"=>rand(Float32,nz,ny,nx)) fields = Dict("pspace"=>rand(Float32,nz,ny,nx),"mspace"=>rand(Float32,nz,ny,nx)) δm,fields end SUITE["3DAcoVTIDenQ_DEO2_FDTD"]["imaging condition, standard"] = @benchmarkable WaveFD.adjointBornAccumulation!(p,WaveFD.Prop3DAcoVTIDenQ_DEO2_FDTD_Model_V(),ic,δm,fields) setup=(p=p3dvti(Sys.CPU_THREADS,$(n_3D.z),$(n_3D.y),$(n_3D.x),$(nb_3D.z),$(nb_3D.y),$(nb_3D.x)); ic=WaveFD.ImagingConditionStandard(); (δm,fields)=fields3dvti(p)) teardown=(free(p)) SUITE["3DAcoVTIDenQ_DEO2_FDTD"]["imaging condition, wave field separation FWI"] = @benchmarkable WaveFD.adjointBornAccumulation!(p,WaveFD.Prop3DAcoVTIDenQ_DEO2_FDTD_Model_V(),ic,δm,fields) setup=(p=p3dvti(Sys.CPU_THREADS,$(n_3D.z),$(n_3D.y),$(n_3D.x),$(nb_3D.z),$(nb_3D.y),$(nb_3D.x)); ic=WaveFD.ImagingConditionWaveFieldSeparationFWI(); (δm,fields)=fields3dvti(p)) teardown=(free(p)) SUITE["3DAcoVTIDenQ_DEO2_FDTD"]["imaging condition, wave field separation RTM"] = @benchmarkable WaveFD.adjointBornAccumulation!(p,WaveFD.Prop3DAcoVTIDenQ_DEO2_FDTD_Model_V(),ic,δm,fields) setup=(p=p3dvti(Sys.CPU_THREADS,$(n_3D.z),$(n_3D.y),$(n_3D.x),$(nb_3D.z),$(nb_3D.y),$(nb_3D.x)); ic=WaveFD.ImagingConditionWaveFieldSeparationRTM(); (δm,fields)=fields3dvti(p)) teardown=(free(p)) SUITE["3DAcoTTIDenQ_DEO2_FDTD"] = BenchmarkGroup([Dict("ncells"=>n_3D.z*n_3D.y*n_3D.x,"nbz"=>nb_3D.z,"nby"=>nb_3D.y,"nbx"=>nb_3D.x,"nthreads"=>_nthreads)]) function p3dtti(nthreads,nz,ny,nx,nbz,nby,nbx) p = WaveFD.Prop3DAcoTTIDenQ_DEO2_FDTD(freesurface=false, nz=nz, ny=ny, nx=nx, nbz=nbz, nby=nby, nbx=nbx, dz=10.0, dx=10.0, dt=0.001, nthreads=nthreads) v,b,ϵ,η,f,sinθ,cosθ,sinϕ,cosϕ,pcur,pold = WaveFD.V(p),WaveFD.B(p),WaveFD.Eps(p),WaveFD.Eta(p),WaveFD.F(p),WaveFD.SinTheta(p),WaveFD.CosTheta(p),WaveFD.SinPhi(p),WaveFD.CosPhi(p),WaveFD.PCur(p),WaveFD.POld(p) v .= 1500 b .= 1 ϵ .= 0.2 η .= 0.0 f .= 0.85 cosθ .= cos(pi/4) sinθ .= sin(pi/4) cosϕ .= cos(pi/8) sinϕ .= sin(pi/8) rand!(pcur) rand!(pold) p end for nthreads in _nthreads SUITE["3DAcoTTIDenQ_DEO2_FDTD"]["$nthreads threads"] = @benchmarkable WaveFD.propagateforward!(p) setup=(p=p3dtti($nthreads,$(n_3D.z),$(n_3D.y),$(n_3D.x),$(nb_3D.z),$(nb_3D.y),$(nb_3D.x))) teardown=(free(p)) seconds=15 end function fields3dtti(p::WaveFD.Prop3DAcoTTIDenQ_DEO2_FDTD) nz,ny,nx = size(p) δm = Dict("v"=>rand(Float32,nz,ny,nx)) fields = Dict("pspace"=>rand(Float32,nz,ny,nx),"mspace"=>rand(Float32,nz,ny,nx)) δm,fields end SUITE["3DAcoTTIDenQ_DEO2_FDTD"]["imaging condition, standard"] = @benchmarkable WaveFD.adjointBornAccumulation!(p,WaveFD.Prop3DAcoTTIDenQ_DEO2_FDTD_Model_V(),ic,δm,fields) setup=(p=p3dtti(Sys.CPU_THREADS,$(n_3D.z),$(n_3D.y),$(n_3D.x),$(nb_3D.z),$(nb_3D.y),$(nb_3D.x)); ic=WaveFD.ImagingConditionStandard(); (δm,fields)=fields3dtti(p)) teardown=(free(p)) SUITE["3DAcoTTIDenQ_DEO2_FDTD"]["imaging condition, wave field separation FWI"] = @benchmarkable WaveFD.adjointBornAccumulation!(p,WaveFD.Prop3DAcoTTIDenQ_DEO2_FDTD_Model_V(),ic,δm,fields) setup=(p=p3dtti(Sys.CPU_THREADS,$(n_3D.z),$(n_3D.y),$(n_3D.x),$(nb_3D.z),$(nb_3D.y),$(nb_3D.x)); ic=WaveFD.ImagingConditionWaveFieldSeparationFWI(); (δm,fields)=fields3dtti(p)) teardown=(free(p)) SUITE["3DAcoTTIDenQ_DEO2_FDTD"]["imaging condition, wave field separation RTM"] = @benchmarkable WaveFD.adjointBornAccumulation!(p,WaveFD.Prop3DAcoTTIDenQ_DEO2_FDTD_Model_V(),ic,δm,fields) setup=(p=p3dtti(Sys.CPU_THREADS,$(n_3D.z),$(n_3D.y),$(n_3D.x),$(nb_3D.z),$(nb_3D.y),$(nb_3D.x)); ic=WaveFD.ImagingConditionWaveFieldSeparationRTM(); (δm,fields)=fields3dtti(p)) teardown=(free(p)) include(joinpath(pkgdir(WaveFD), "benchmark", "mcells_per_second.jl")) SUITE
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1.962705
9,331
const Theme = Attributes Base.broadcastable(x::AbstractScene) = Ref(x) Base.broadcastable(x::AbstractPlot) = Ref(x) Base.broadcastable(x::Attributes) = Ref(x) # The rules that we use to convert values to a Observable in Attributes value_convert(x::Observables.AbstractObservable) = Observables.observe(x) value_convert(@nospecialize(x)) = x # We transform a tuple of observables into a Observable(tuple(values...)) function value_convert(x::NTuple{N, Union{Any, Observables.AbstractObservable}}) where N result = Observable(to_value.(x)) onany((args...)-> args, x...) return result end value_convert(x::NamedTuple) = Attributes(x) # Version of `convert(Observable{Any}, obj)` that doesn't require runtime dispatch node_any(@nospecialize(obj)) = isa(obj, Observable{Any}) ? obj : isa(obj, Observable) ? convert(Observable{Any}, obj) : Observable{Any}(obj) node_pairs(pair::Union{Pair, Tuple{Any, Any}}) = (pair[1] => node_any(value_convert(pair[2]))) node_pairs(pairs) = (node_pairs(pair) for pair in pairs) Attributes(; kw_args...) = Attributes(Dict{Symbol, Observable}(node_pairs(kw_args))) Attributes(pairs::Pair...) = Attributes(Dict{Symbol, Observable}(node_pairs(pairs))) Attributes(pairs::AbstractVector) = Attributes(Dict{Symbol, Observable}(node_pairs.(pairs))) Attributes(pairs::Iterators.Pairs) = Attributes(collect(pairs)) Attributes(nt::NamedTuple) = Attributes(; nt...) attributes(x::Attributes) = getfield(x, :attributes) Base.keys(x::Attributes) = keys(x.attributes) Base.values(x::Attributes) = values(x.attributes) function Base.iterate(x::Attributes, state...) s = iterate(keys(x), state...) s === nothing && return nothing return (s[1] => x[s[1]], s[2]) end function Base.copy(attributes::Attributes) result = Attributes() for (k, v) in attributes # We need to create a new Signal to have a real copy result[k] = copy(v) end return result end function Base.deepcopy(obs::Observable) return Observable{Any}(to_value(obs)) end function Base.deepcopy(attributes::Attributes) result = Attributes() for (k, v) in attributes # We need to create a new Signal to have a real copy result[k] = deepcopy(v) end return result end Base.filter(f, x::Attributes) = Attributes(filter(f, attributes(x))) Base.empty!(x::Attributes) = (empty!(attributes(x)); x) Base.length(x::Attributes) = length(attributes(x)) function Base.merge!(target::Attributes, args::Attributes...) for elem in args merge_attributes!(target, elem) end return target end Base.merge(target::Attributes, args::Attributes...) = merge!(copy(target), args...) @generated hasfield(x::T, ::Val{key}) where {T, key} = :($(key in fieldnames(T))) @inline function Base.getproperty(x::T, key::Symbol) where T <: Union{Attributes, Transformable} if hasfield(x, Val(key)) getfield(x, key) else getindex(x, key) end end @inline function Base.setproperty!(x::T, key::Symbol, value) where T <: Union{Attributes, Transformable} if hasfield(x, Val(key)) setfield!(x, key, value) else setindex!(x, value, key) end end function Base.getindex(x::Attributes, key::Symbol) x = attributes(x)[key] # We unpack Attributes, even though, for consistency, we store them as nodes # this makes it easier to create nested attributes return x[] isa Attributes ? x[] : x end function Base.setindex!(x::Attributes, value, key::Symbol) if haskey(x, key) x.attributes[key][] = value else x.attributes[key] = node_any(value) end end function Base.setindex!(x::Attributes, value::Observable, key::Symbol) if haskey(x, key) # error("You're trying to update an attribute Observable with a new Observable. This is not supported right now. # You can do this manually like this: # lift(val-> attributes[$key] = val, Observable::$(typeof(value))) # ") return x.attributes[key] = node_any(value) else #TODO make this error. Attributes should be sort of immutable return x.attributes[key] = node_any(value) end return x end _indent_attrs(s, n) = join(split(s, '\n'), "\n" * " "^n) function Base.show(io::IO,::MIME"text/plain", attr::Attributes) io = IOContext(io, :compact => true) d = Dict() print(io, """Attributes with $(length(attr)) $(length(attr) != 1 ? "entries" : "entry")""") if length(attr) < 1 return end print(io, ":") ks = sort(collect(keys(attr)), by = lowercase ∘ String) maxlength = maximum(length ∘ String, ks) for k in ks print(io, "\n ") print(io, k) print(io, " => ") v = to_value(attr[k]) if v isa Attributes print(io, _indent_attrs(repr(v), 2)) else print(io, to_value(attr[k])) end end end Base.show(io::IO, attr::Attributes) = show(io, MIME"text/plain"(), attr) theme(x::AbstractPlot) = x.attributes isvisible(x) = haskey(x, :visible) && to_value(x[:visible]) #dict interface const AttributeOrPlot = Union{AbstractPlot, Attributes} Base.pop!(x::AttributeOrPlot, args...) = pop!(x.attributes, args...) Base.haskey(x::AttributeOrPlot, key) = haskey(x.attributes, key) Base.delete!(x::AttributeOrPlot, key) = delete!(x.attributes, key) function Base.get!(f::Function, x::AttributeOrPlot, key::Symbol) if haskey(x, key) return x[key] else val = f() x[key] = val return x[key] end end Base.get!(x::AttributeOrPlot, key::Symbol, default) = get!(()-> default, x, key) Base.get(f::Function, x::AttributeOrPlot, key::Symbol) = haskey(x, key) ? x[key] : f() Base.get(x::AttributeOrPlot, key::Symbol, default) = get(()-> default, x, key) # This is a bit confusing, since for a plot it returns the attribute from the arguments # and not a plot for integer indexing. But, we want to treat plots as "atomic" # so from an interface point of view, one should assume that a plot doesn't contain subplots # Combined plots break this assumption in some way, but the way to look at it is, # that the plots contained in a Combined plot are not subplots, but _are_ actually # the plot itself. Base.getindex(plot::AbstractPlot, idx::Integer) = plot.converted[idx] Base.getindex(plot::AbstractPlot, idx::UnitRange{<:Integer}) = plot.converted[idx] Base.setindex!(plot::AbstractPlot, value, idx::Integer) = (plot.input_args[idx][] = value) Base.length(plot::AbstractPlot) = length(plot.converted) function Base.getindex(x::AbstractPlot, key::Symbol) argnames = argument_names(typeof(x), length(x.converted)) idx = findfirst(isequal(key), argnames) if idx === nothing return x.attributes[key] else x.converted[idx] end end function Base.getindex(x::AttributeOrPlot, key::Symbol, key2::Symbol, rest::Symbol...) dict = to_value(x[key]) dict isa Attributes || error("Trying to access $(typeof(dict)) with multiple keys: $key, $key2, $(rest)") dict[key2, rest...] end function Base.setindex!(x::AttributeOrPlot, value, key::Symbol, key2::Symbol, rest::Symbol...) dict = to_value(x[key]) dict isa Attributes || error("Trying to access $(typeof(dict)) with multiple keys: $key, $key2, $(rest)") dict[key2, rest...] = value end function Base.setindex!(x::AbstractPlot, value, key::Symbol) argnames = argument_names(typeof(x), length(x.converted)) idx = findfirst(isequal(key), argnames) if idx === nothing && haskey(x.attributes, key) return x.attributes[key][] = value elseif !haskey(x.attributes, key) x.attributes[key] = convert(Observable, value) else return setindex!(x.converted[idx], value) end end function Base.setindex!(x::AbstractPlot, value::Observable, key::Symbol) argnames = argument_names(typeof(x), length(x.converted)) idx = findfirst(isequal(key), argnames) if idx === nothing if haskey(x, key) # error("You're trying to update an attribute Observable with a new Observable. This is not supported right now. # You can do this manually like this: # lift(val-> attributes[$key] = val, Observable::$(typeof(value))) # ") return x.attributes[key] = value else return x.attributes[key] = value end else return setindex!(x.converted[idx], value) end end # a few shortcut functions to make attribute conversion easier function get_attribute(dict, key) convert_attribute(to_value(dict[key]), Key{key}()) end function merge_attributes!(input::Attributes, theme::Attributes) for (key, value) in theme if !haskey(input, key) input[key] = copy(value) else current_value = input[key] if value isa Attributes && current_value isa Attributes # if nested attribute, we merge recursively merge_attributes!(current_value, value) elseif value isa Attributes || current_value isa Attributes error(""" Type missmatch while merging plot attributes with theme for key: $(key). Found $(value) in theme, while attributes contains: $(current_value) """) else # we're good! input already has a value, can ignore theme end end end return input end
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2.542419
3,713
using NNLS using Test # import NonNegLeastSquares using PyCall using ECOS using JuMP using Random using LinearAlgebra import Libdl const pyopt = pyimport_conda("scipy.optimize", "scipy") macro wrappedallocs(expr) argnames = [gensym() for a in expr.args] quote function g($(argnames...)) @allocated $(Expr(expr.head, argnames...)) end $(Expr(:call, :g, [esc(a) for a in expr.args]...)) end end include("nnls.jl") include("qp.jl")
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2.323671
207
# This file was generated by the Julia Swagger Code Generator # Do not modify this file directly. Modify the swagger specification instead. mutable struct DeploymentExtendedFilter <: SwaggerModel provisioningState::Any # spec type: Union{ Nothing, String } # spec name: provisioningState function DeploymentExtendedFilter(;provisioningState=nothing) o = new() validate_property(DeploymentExtendedFilter, Symbol("provisioningState"), provisioningState) setfield!(o, Symbol("provisioningState"), provisioningState) o end end # type DeploymentExtendedFilter const _property_map_DeploymentExtendedFilter = Dict{Symbol,Symbol}(Symbol("provisioningState")=>Symbol("provisioningState")) const _property_types_DeploymentExtendedFilter = Dict{Symbol,String}(Symbol("provisioningState")=>"String") Base.propertynames(::Type{ DeploymentExtendedFilter }) = collect(keys(_property_map_DeploymentExtendedFilter)) Swagger.property_type(::Type{ DeploymentExtendedFilter }, name::Symbol) = Union{Nothing,eval(Meta.parse(_property_types_DeploymentExtendedFilter[name]))} Swagger.field_name(::Type{ DeploymentExtendedFilter }, property_name::Symbol) = _property_map_DeploymentExtendedFilter[property_name] function check_required(o::DeploymentExtendedFilter) true end function validate_property(::Type{ DeploymentExtendedFilter }, name::Symbol, val) end
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3.311905
420
import Base: readline, readuntil export readline, readuntil #== Timeout versions of tbe base functions ==# """ readline(s::IO, timeout::T; keep::Bool=false) where {T<:Real} Like Base.readline, except times-out after `timeout` seconds. """ function readline(s::IO, timeout::T; keep::Bool=false) where {T<:Real} line = readuntil(s, 0x0a, timeout, keep=true) i = length(line) if keep || i == 0 || line[i] != 0x0a return String(line) elseif i < 2 || line[i-1] != 0x0d return String(resize!(line,i-1)) else return String(resize!(line,i-2)) end end """ readuntil(s::IO, delim::AbstractChar, timeout::T; keep::Bool=false) where {T<:Real} readuntil(s::IO, delim::T, timeout::U; keep::Bool=false) where {T, U<:Real} Like Base.readuntil, except times-out after `timeout` seconds. """ function readuntil(s::IO, delim::AbstractChar, timeout::T; keep::Bool=false) where {T<:Real} if delim ≤ '\x7f' return Base.readuntil_string(s, delim % UInt8, keep) end out = IOBuffer() t = Timer(timeout) while !eof(s) && isopen(t) bytesavailable(s) == 0 && continue c = read(s, Char) if c == delim keep && write(out, c) break elseif c != 0x00 write(out, c) end yield() end return String(take!(out)) end function readuntil(s::IO, delim::T, timeout::U; keep::Bool=false) where {T, U<:Real} out = (T === UInt8 ? Base.StringVector(0) : Vector{T}()) t = Timer(timeout) while !eof(s) && isopen(t) bytesavailable(s) == 0 && continue c = read(s, T) if c == delim keep && push!(out, c) break elseif c != 0x00 push!(out, c) end yield() end return out end
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2.160096
837
# ********************************************************************************* # REopt, Copyright (c) 2019-2020, Alliance for Sustainable Energy, LLC. # All rights reserved. # # Redistribution and use in source and binary forms, with or without modification, # are permitted provided that the following conditions are met: # # Redistributions of source code must retain the above copyright notice, this list # of conditions and the following disclaimer. # # Redistributions in binary form must reproduce the above copyright notice, this # list of conditions and the following disclaimer in the documentation and/or other # materials provided with the distribution. # # Neither the name of the copyright holder nor the names of its contributors may be # used to endorse or promote products derived from this software without specific # prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. # IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, # INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, # BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF # LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE # OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED # OF THE POSSIBILITY OF SUCH DAMAGE. # ********************************************************************************* function add_chp_fuel_burn_constraints(m, p; _n="") # Fuel burn slope and intercept fuel_burn_full_load = 1.0 / p.s.chp.elec_effic_full_load # [kWt/kWe] fuel_burn_half_load = 0.5 / p.s.chp.elec_effic_half_load # [kWt/kWe] fuel_burn_slope = (fuel_burn_full_load - fuel_burn_half_load) / (1.0 - 0.5) # [kWt/kWe] fuel_burn_intercept = fuel_burn_full_load - fuel_burn_slope * 1.0 # [kWt/kWe_rated] # Fuel cost fuel_cost_per_kwh = p.s.chp.fuel_cost_per_mmbtu / MMBTU_TO_KWH fuel_cost_series = per_hour_value_to_time_series(fuel_cost_per_kwh, p.s.settings.time_steps_per_hour, "CHP.fuel_cost_per_mmbtu") m[:TotalCHPFuelCosts] = @expression(m, p.pwf_fuel["CHP"] * sum(m[:dvFuelUsage]["CHP", ts] * fuel_cost_series[ts] for ts in p.time_steps) ) # Conditionally add dvFuelBurnYIntercept if coefficient p.FuelBurnYIntRate is greater than ~zero if fuel_burn_intercept > 1.0E-7 dv = "dvFuelBurnYIntercept"*_n m[Symbol(dv)] = @variable(m, [p.techs.chp, p.time_steps], base_name=dv, lower_bound=0) #Constraint (1c1): Total Fuel burn for CHP **with** y-intercept fuel burn and supplementary firing @constraint(m, CHPFuelBurnCon[t in p.techs.chp, ts in p.time_steps], m[Symbol("dvFuelUsage"*_n)][t,ts] == p.hours_per_timestep * ( m[Symbol("dvFuelBurnYIntercept"*_n)][t,ts] + p.production_factor[t,ts] * fuel_burn_slope * m[Symbol("dvRatedProduction"*_n)][t,ts] + m[Symbol("dvSupplementaryThermalProduction"*_n)][t,ts] / p.s.chp.supplementary_firing_efficiency ) ) #Constraint (1d): Y-intercept fuel burn for CHP @constraint(m, CHPFuelBurnYIntCon[t in p.techs.chp, ts in p.time_steps], fuel_burn_intercept * m[Symbol("dvSize"*_n)][t] - p.s.chp.max_kw * (1-m[Symbol("binCHPIsOnInTS"*_n)][t,ts]) <= m[Symbol("dvFuelBurnYIntercept"*_n)][t,ts] ) else #Constraint (1c2): Total Fuel burn for CHP **without** y-intercept fuel burn @constraint(m, CHPFuelBurnConLinear[t in p.techs.chp, ts in p.time_steps], m[Symbol("dvFuelUsage"*_n)][t,ts] == p.hours_per_timestep * ( p.production_factor[t,ts] * fuel_burn_slope * m[Symbol("dvRatedProduction"*_n)][t,ts] + m[Symbol("dvSupplementaryThermalProduction"*_n)][t,ts] / p.s.chp.supplementary_firing_efficiency ) ) end end function add_chp_thermal_production_constraints(m, p; _n="") # Thermal production slope and intercept thermal_prod_full_load = 1.0 / p.s.chp.elec_effic_full_load * p.s.chp.thermal_effic_full_load # [kWt/kWe] thermal_prod_half_load = 0.5 / p.s.chp.elec_effic_half_load * p.s.chp.thermal_effic_half_load # [kWt/kWe] thermal_prod_slope = (thermal_prod_full_load - thermal_prod_half_load) / (1.0 - 0.5) # [kWt/kWe] thermal_prod_intercept = thermal_prod_full_load - thermal_prod_slope * 1.0 # [kWt/kWe_rated dv = "dvProductionToWaste"*_n m[Symbol(dv)] = @variable(m, [p.techs.chp, p.time_steps], base_name=dv, lower_bound=0) # Conditionally add dvThermalProductionYIntercept if coefficient p.s.chpThermalProdIntercept is greater than ~zero if thermal_prod_intercept > 1.0E-7 dv = "dvThermalProductionYIntercept"*_n m[Symbol(dv)] = @variable(m, [p.techs.chp, p.time_steps], base_name=dv, lower_bound=0) #Constraint (2a-1): Upper Bounds on Thermal Production Y-Intercept @constraint(m, CHPYInt2a1Con[t in p.techs.chp, ts in p.time_steps], m[Symbol("dvThermalProductionYIntercept"*_n)][t,ts] <= thermal_prod_intercept * m[Symbol("dvSize"*_n)][t] ) # Constraint (2a-2): Upper Bounds on Thermal Production Y-Intercept @constraint(m, CHPYInt2a2Con[t in p.techs.chp, ts in p.time_steps], m[Symbol("dvThermalProductionYIntercept"*_n)][t,ts] <= thermal_prod_intercept * p.s.chp.max_kw * m[Symbol("binCHPIsOnInTS"*_n)][t,ts] ) #Constraint (2b): Lower Bounds on Thermal Production Y-Intercept @constraint(m, CHPYInt2bCon[t in p.techs.chp, ts in p.time_steps], m[Symbol("dvThermalProductionYIntercept"*_n)][t,ts] >= thermal_prod_intercept * m[Symbol("dvSize"*_n)][t] - thermal_prod_intercept * p.s.chp.max_kw * (1 - m[Symbol("binCHPIsOnInTS"*_n)][t,ts]) ) # Constraint (2c): Thermal Production of CHP # Note: p.HotWaterAmbientFactor[t,ts] * p.HotWaterThermalFactor[t,ts] removed from this but present in math @constraint(m, CHPThermalProductionCon[t in p.techs.chp, ts in p.time_steps], m[Symbol("dvThermalProduction"*_n)][t,ts] == thermal_prod_slope * p.production_factor[t,ts] * m[Symbol("dvRatedProduction"*_n)][t,ts] + m[Symbol("dvThermalProductionYIntercept"*_n)][t,ts] + m[Symbol("dvSupplementaryThermalProduction"*_n)][t,ts] ) else @constraint(m, CHPThermalProductionConLinear[t in p.techs.chp, ts in p.time_steps], m[Symbol("dvThermalProduction"*_n)][t,ts] == thermal_prod_slope * p.production_factor[t,ts] * m[Symbol("dvRatedProduction"*_n)][t,ts] + m[Symbol("dvSupplementaryThermalProduction"*_n)][t,ts] ) end end """ add_chp_supplementary_firing_constraints(m, p; _n="") Used by add_chp_constraints to add supplementary firing constraints if p.s.chp.supplementary_firing_max_steam_ratio > 1.0 to add CHP supplementary firing operating constraints. Else, the supplementary firing dispatch and size decision variables are set to zero. """ function add_chp_supplementary_firing_constraints(m, p; _n="") thermal_prod_full_load = 1.0 / p.s.chp.elec_effic_full_load * p.s.chp.thermal_effic_full_load # [kWt/kWe] thermal_prod_half_load = 0.5 / p.s.chp.elec_effic_half_load * p.s.chp.thermal_effic_half_load # [kWt/kWe] thermal_prod_slope = (thermal_prod_full_load - thermal_prod_half_load) / (1.0 - 0.5) # [kWt/kWe] # Constrain upper limit of dvSupplementaryThermalProduction, using auxiliary variable for (size * useSupplementaryFiring) @constraint(m, CHPSupplementaryFireCon[t in p.techs.chp, ts in p.time_steps], m[Symbol("dvSupplementaryThermalProduction"*_n)][t,ts] <= (p.s.chp.supplementary_firing_max_steam_ratio - 1.0) * p.production_factor[t,ts] * (thermal_prod_slope * m[Symbol("dvSupplementaryFiringSize"*_n)][t] + m[Symbol("dvThermalProductionYIntercept"*_n)][t,ts]) ) # Constrain lower limit of 0 if CHP tech is off @constraint(m, NoCHPSupplementaryFireOffCon[t in p.techs.chp, ts in p.time_steps], !m[Symbol("binCHPIsOnInTS"*_n)][t,ts] => {m[Symbol("dvSupplementaryThermalProduction"*_n)][t,ts] <= 0.0} ) end function add_binCHPIsOnInTS_constraints(m, p; _n="") # Note, min_turn_down_pct for CHP is only enforced in p.time_steps_with_grid @constraint(m, [t in p.techs.chp, ts in p.time_steps_with_grid], m[Symbol("dvRatedProduction"*_n)][t, ts] <= p.s.chp.max_kw * m[Symbol("binCHPIsOnInTS"*_n)][t, ts] ) @constraint(m, [t in p.techs.chp, ts in p.time_steps_with_grid], p.s.chp.min_turn_down_pct * m[Symbol("dvSize"*_n)][t] - m[Symbol("dvRatedProduction"*_n)][t, ts] <= p.s.chp.max_kw * (1 - m[Symbol("binCHPIsOnInTS"*_n)][t, ts]) ) end function add_chp_rated_prod_constraint(m, p; _n="") @constraint(m, [t in p.techs.chp, ts in p.time_steps], m[Symbol("dvSize"*_n)][t] >= m[Symbol("dvRatedProduction"*_n)][t, ts] ) end """ add_chp_hourly_om_charges(m, p; _n="") - add decision variable "dvOMByHourBySizeCHP"*_n for the hourly CHP operations and maintenance costs - add the cost to TotalPerUnitHourOMCosts """ function add_chp_hourly_om_charges(m, p; _n="") dv = "dvOMByHourBySizeCHP"*_n m[Symbol(dv)] = @variable(m, [p.techs.chp, p.time_steps], base_name=dv, lower_bound=0) #Constraint CHP-hourly-om-a: om per hour, per time step >= per_unit_size_cost * size for when on, >= zero when off @constraint(m, CHPHourlyOMBySizeA[t in p.techs.chp, ts in p.time_steps], p.s.chp.om_cost_per_hr_per_kw_rated * m[Symbol("dvSize"*_n)][t] - p.max_size[t] * p.s.chp.om_cost_per_hr_per_kw_rated * (1-m[Symbol("binCHPIsOnInTS"*_n)][t,ts]) <= m[Symbol("dvOMByHourBySizeCHP"*_n)][t, ts] ) #Constraint CHP-hourly-om-b: om per hour, per time step <= per_unit_size_cost * size for each hour @constraint(m, CHPHourlyOMBySizeB[t in p.techs.chp, ts in p.time_steps], p.s.chp.om_cost_per_hr_per_kw_rated * m[Symbol("dvSize"*_n)][t] >= m[Symbol("dvOMByHourBySizeCHP"*_n)][t, ts] ) #Constraint CHP-hourly-om-c: om per hour, per time step <= zero when off, <= per_unit_size_cost*max_size @constraint(m, CHPHourlyOMBySizeC[t in p.techs.chp, ts in p.time_steps], p.max_size[t] * p.s.chp.om_cost_per_hr_per_kw_rated * m[Symbol("binCHPIsOnInTS"*_n)][t,ts] >= m[Symbol("dvOMByHourBySizeCHP"*_n)][t, ts] ) m[:TotalHourlyCHPOMCosts] = @expression(m, p.third_party_factor * p.pwf_om * sum(m[Symbol(dv)][t, ts] * p.hours_per_timestep for t in p.techs.chp, ts in p.time_steps)) nothing end """ add_chp_constraints(m, p; _n="") Used in src/reopt.jl to add_chp_constraints if !isempty(p.techs.chp) to add CHP operating constraints and cost expressions. """ function add_chp_constraints(m, p; _n="") # TODO if chp.min_turn_down_pct is 0.0, and there is no fuel burn or thermal y-intercept, we don't need the binary below @warn """Adding binary variable to model CHP. Some solvers are very slow with integer variables""" @variables m begin binCHPIsOnInTS[p.techs.chp, p.time_steps], Bin # 1 If technology t is operating in time step; 0 otherwise end m[:TotalHourlyCHPOMCosts] = 0 m[:TotalCHPFuelCosts] = 0 m[:TotalCHPPerUnitProdOMCosts] = @expression(m, p.third_party_factor * p.pwf_om * sum(p.s.chp.om_cost_per_kwh * p.hours_per_timestep * m[:dvRatedProduction][t, ts] for t in p.techs.chp, ts in p.time_steps) ) if p.s.chp.om_cost_per_hr_per_kw_rated > 1.0E-7 add_chp_hourly_om_charges(m, p) end add_chp_fuel_burn_constraints(m, p; _n=_n) add_chp_thermal_production_constraints(m, p; _n=_n) add_binCHPIsOnInTS_constraints(m, p; _n=_n) add_chp_rated_prod_constraint(m, p; _n=_n) if p.s.chp.supplementary_firing_max_steam_ratio > 1.0 add_chp_supplementary_firing_constraints(m,p; _n=_n) else for t in p.techs.chp fix(m[Symbol("dvSupplementaryFiringSize"*_n)][t], 0.0, force=true) for ts in p.time_steps fix(m[Symbol("dvSupplementaryThermalProduction"*_n)][t,ts], 0.0, force=true) end end end end
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module ENN export TimedAutomata, TimePetriNets, Neurons include(joinpath("TimedAutomata/","TimedAutomata.jl")) include(joinpath("TimePetriNets/","TimePetriNets.jl")) include(joinpath("Neurons/","Neurons.jl")) end
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using RecipesBase, Measures export InvMcmcSampler, McmcOutput struct InvMcmcSampler{T<:InvMcmcMove} move::T desired_samples::Int burn_in::Int lag::Int K::Int init::McmcInitialiser pointers::InteractionSequence{Int} function InvMcmcSampler( move::T; desired_samples::Int=1000, burn_in::Int=0, lag::Int=1, K::Int=100, init=InitMode() ) where {T<:InvMcmcMove} pointers = [Int[] for i in 1:(2K)] new{T}(move, desired_samples, burn_in, lag, K, init, pointers) end end Base.show(io::IO, x::InvMcmcSampler{T}) where {T} = print(io, typeof(x)) struct McmcOutput{T<:Union{SIS,SIM}} sample::Vector{Vector{Path{Int}}} # The sample model::T end Base.show(io::IO, x::McmcOutput) = print(io, typeof(x)) @recipe function f(output::McmcOutput) model = output.model sample = output.sample x = map(x -> model.dist(model.mode, x), sample) xguide --> "Sample" yguide --> "Distance from Mode" legend --> false size --> (800, 300) margin --> 5mm x end acceptance_prob(mcmc::InvMcmcSampler) = acceptance_prob(mcmc.move) function eval_accept_prob( S_curr::InteractionSequence{Int}, S_prop::InteractionSequence{Int}, model::T, log_ratio::Float64 ) where {T<:Union{SIS,SIM}} mode, γ, dist = (model.mode, model.γ, model.dist) # @show S_curr, S_prop log_lik_ratio = -γ * ( dist(mode, S_prop)-dist(mode, S_curr) ) if typeof(model)==SIM log_multinom_term = log_multinomial_ratio(S_curr, S_prop) return log_lik_ratio + log_ratio + log_multinom_term else return log_lik_ratio + log_ratio end end function accept_reject!( S_curr::InteractionSequence{Int}, S_prop::InteractionSequence{Int}, pointers::InteractionSequence{Int}, move::InvMcmcMove, model::T ) where {T<:Union{SIS,SIM}} move.counts[2] += 1 log_ratio = prop_sample!(S_curr, S_prop, move, pointers, model.V) # Catch out of bounds proposals (reject them, i.e. 0 acc prob) if any(!(1 ≤ length(x) ≤ model.K_inner.u) for x in S_prop) log_α = -Inf elseif !(1 ≤ length(S_prop) ≤ model.K_outer.u) log_α = -Inf else log_α = eval_accept_prob(S_curr, S_prop, model, log_ratio) end # @show log_α if log(rand()) < log_α # We accept! move.counts[1] += 1 enact_accept!(S_curr, S_prop, pointers, move) else # We reject! enact_reject!(S_curr, S_prop, pointers, move) end end function intialise_states!( pointers::InteractionSequence{Int}, init::InteractionSequence{Int} ) S_curr = InteractionSequence{Int}() S_prop = InteractionSequence{Int}() for init_path in init tmp = pop!(pointers) copy!(tmp, init_path) push!(S_curr, tmp) tmp = pop!(pointers) copy!(tmp, init_path) push!(S_prop, tmp) end return S_curr, S_prop end function return_states!( S_curr::InteractionSequence{Int}, S_prop::InteractionSequence{Int}, pointers::InteractionSequence{Int} ) for i in eachindex(S_curr) tmp = pop!(S_curr) pushfirst!(pointers, tmp) tmp = pop!(S_prop) pushfirst!(pointers, tmp) end end function draw_sample!( sample_out::Union{InteractionSequenceSample{Int}, SubArray}, mcmc::InvMcmcSampler, model::T; burn_in::Int=mcmc.burn_in, lag::Int=mcmc.lag, init::InteractionSequence{Int}=get_init(mcmc.init, model) ) where {T<:Union{SIS,SIM}} pointers = mcmc.pointers S_curr, S_prop = intialise_states!(pointers, init) sample_count = 1 # Keeps which sample to be stored we are working to get i = 0 # Keeps track all samples (included lags and burn_ins) reset_counts!(mcmc.move) # Reset counts for mcmc move (tracks acceptances) while sample_count ≤ length(sample_out) i += 1 # Store value if (i > burn_in) & (((i-1) % lag)==0) @inbounds sample_out[sample_count] = deepcopy(S_curr) sample_count += 1 end accept_reject!( S_curr, S_prop, pointers, mcmc.move, model ) # println(S_prop) end return_states!(S_curr, S_prop, pointers) end function draw_sample( mcmc::InvMcmcSampler, model::T; desired_samples::Int=mcmc.desired_samples, burn_in::Int=mcmc.burn_in, lag::Int=mcmc.lag, init::InteractionSequence{Int}=get_init(mcmc.init, model) ) where {T<:Union{SIS,SIM}} sample_out = InteractionSequenceSample{Int}(undef, desired_samples) draw_sample!(sample_out, mcmc, model, burn_in=burn_in, lag=lag, init=init) return sample_out end function (mcmc::InvMcmcSampler)( model::T; desired_samples::Int=mcmc.desired_samples, burn_in::Int=mcmc.burn_in, lag::Int=mcmc.lag, init::InteractionSequence{Int}=get_init(mcmc.init, model) ) where {T<:Union{SIS,SIM}} sample_out = draw_sample(mcmc, model, desired_samples=desired_samples, burn_in=burn_in, lag=lag, init=init) return McmcOutput(sample_out, model) end
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2.12832
2,447
using PFKernels using PFKernels.PowerSystem using PFKernels.ForwardDiff using LinearAlgebra AT = PFKernels.AT function mynorm(x) t1s{N} = ForwardDiff.Dual{Nothing,Float64, N} where N n = length(x) FT = t1s{n} V = Vector F = zeros(n) adF = V{FT}(undef, n) adx = V{FT}(undef, n) # ForwardDiff.seed!(adx, x, one(ForwardDiff.Partials{n,Float64})) ForwardDiff.seed!(adx, x, ForwardDiff.construct_seeds(ForwardDiff.Partials{n,Float64})) dF = AT(adF) dx = AT(adx) PFKernels.kernel!(PFKernels.backend, dF, dx) nrm2 = t1s{n}(0.0) for v in dF nrm2 += v end @show typeof(nrm2) @show nrm2 return ForwardDiff.value(nrm2), ForwardDiff.partials(nrm2) end # Inject GPU kernels PFKernels.include(joinpath(dirname(@__FILE__), "kernels.jl")) x = rand(10) nrm2 = x -> norm(x)^2 res = nrm2(x) dres = ForwardDiff.gradient(nrm2, x) @show x F, dF = mynorm(x) @assert(F ≈ res) @assert(dF ≈ dres)
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2.085153
458
# Copyright 2016, Iain Dunning, Joey Huchette, Miles Lubin, and contributors # This Source Code Form is subject to the terms of the Mozilla Public # License, v. 2.0. If a copy of the MPL was not distributed with this # file, You can obtain one at http://mozilla.org/MPL/2.0/. ############################################################################# # JuMP # An algebraic modeling language for Julia # See http://github.com/JuliaOpt/JuMP.jl ############################################################################# # print.jl # All "pretty printers" for JuMP types. # - Delegates to appropriate handler methods for REPL or IJulia. # - These handler methods then pass the correct symbols to use into a # generic string builder. The IJulia handlers will also wrap in MathJax # start/close tags. # - To find printing code for a type in this file, search for `## TypeName` # - Code here does not need to be fast, in fact simplicity trumps speed # within reason as this code is thorny enough as it is. # - Corresponding tests are in test/print.jl, although test/operator.jl # is also testing the constraint/expression code extensively as well. # - Base.print and Base.string both delegate to Base.show, if they are not # separately defined. ############################################################################# # Used for dispatching abstract PrintMode abstract REPLMode <: PrintMode abstract IJuliaMode <: PrintMode # Whether something is zero or not for the purposes of printing it const PRINT_ZERO_TOL = 1e-10 # List of indices available for variable printing const DIMS = ["i","j","k","l","m","n"] # Helper function that rounds carefully for the purposes of printing # e.g. 5.3 => 5.3 # 1.0 => 1 function str_round(f::Float64) abs(f) == 0.0 && return "0" # strip sign off zero str = string(f) length(str) >= 2 && str[end-1:end] == ".0" ? str[1:end-2] : str end # TODO: get rid of this! This is only a helper, and should be Base.values # (and probably live there, as well) _values(x::Array) = x _values(x) = Base.values(x) # REPL-specific symbols # Anything here: https://en.wikipedia.org/wiki/Windows-1252 # should probably work fine on Windows const repl = Dict{Symbol,UTF8String}( :leq => (is_windows() ? "<=" : "≤"), :geq => (is_windows() ? ">=" : "≥"), :eq => (is_windows() ? "==" : "="), :times => "*", :sq => "²", :ind_open => "[", :ind_close => "]", :for_all => (is_windows() ? "for all" : "∀"), :in => (is_windows() ? "in" : "∈"), :open_set => "{", :dots => (is_windows() ? ".." : "…"), :close_set => "}", :union => (is_windows() ? "or" : "∪"), :infty => (is_windows() ? "Inf" : "∞"), :open_rng => "[", :close_rng => "]", :integer => "integer", :succeq0 => " is semidefinite", :Vert => (is_windows() ? "||" : "‖"), :sub2 => (is_windows() ? "_2" : "₂")) # IJulia-specific symbols const ijulia = Dict{Symbol,UTF8String}( :leq => "\\leq", :geq => "\\geq", :eq => "=", :times => "\\times ", :sq => "^2", :ind_open => "_{", :ind_close => "}", :for_all => "\\quad\\forall", :in => "\\in", :open_set => "\\{", :dots => "\\dots", :close_set => "\\}", :union => "\\cup", :infty => "\\infty", :open_rng => "\\[", :close_rng => "\\]", :integer => "\\in \\mathbb{Z}", :succeq0 => "\\succeq 0", :Vert => "\\Vert", :sub2 => "_2") typealias PrintSymbols Dict{Symbol,UTF8String} # If not already mathmode, then wrap in MathJax start/close tags math(s,mathmode) = mathmode ? s : "\$\$ $s \$\$" # helper to look up corresponding JuMPContainerData printdata(v::JuMPContainer) = getmeta(v, :model).varData[v] function printdata(v::Array{Variable}) if isempty(v) error("Cannot locate printing data for an empty array") end m = first(v).m m.varData[v] end #------------------------------------------------------------------------ ## Model #------------------------------------------------------------------------ function Base.print(io::IO, m::Model; ignore_print_hook=(m.printhook==nothing)) ignore_print_hook || return m.printhook(io, m) print(io, model_str(REPLMode,m)) end function Base.show(io::IO, m::Model) plural(n) = (n==1 ? "" : "s") print(io, m.objSense == :Max ? "Maximization" : ((m.objSense == :Min && (!isempty(m.obj) || (m.nlpdata !== nothing && isa(m.nlpdata.nlobj, NonlinearExprData)))) ? "Minimization" : "Feasibility")) println(io, " problem with:") nlin = length(m.linconstr) println(io, " * $(nlin) linear constraint$(plural(nlin))") nquad = length(m.quadconstr) if nquad > 0 println(io, " * $(nquad) quadratic constraint$(plural(nquad))") end nsos = length(m.sosconstr) if nsos > 0 println(io, " * $(nsos) SOS constraint$(plural(nsos))") end nsoc = length(m.socconstr) if nsoc > 0 println(io, " * $(nsoc) SOC constraint$(plural(nsoc))") end nsdp = length(m.sdpconstr) if nsdp > 0 println(io, " * $(nsdp) semidefinite constraint$(plural(nsdp))") end nlp = m.nlpdata if nlp !== nothing && length(nlp.nlconstr) > 0 println(io, " * $(length(nlp.nlconstr)) nonlinear constraint$(plural(length(nlp.nlconstr)))") end print(io, " * $(m.numCols) variable$(plural(m.numCols))") nbin = sum(m.colCat .== :Bin) nint = sum(m.colCat .== :Int) nsc = sum(m.colCat .== :SemiCont) nsi = sum(m.colCat .== :SemiInt) varstr = Any[] nbin == 0 || push!(varstr, "$nbin binary") nint == 0 || push!(varstr, "$nint integer") nsc == 0 || push!(varstr, "$nsc semicontinuous") nsi == 0 || push!(varstr, "$nsi semi-integer") if isempty(varstr) println(io,) else println(io, ": $(join(varstr, ", "))") end print(io, "Solver is ") if isa(m.solver, UnsetSolver) print(io, "default solver") else print(io, split(split(string(m.solver), "Solver")[1], ".")[2]) end end @compat Base.show(io::IO, ::MIME"text/latex", m::Model) = print(io, model_str(IJuliaMode,m)) function model_str(mode, m::Model, sym::PrintSymbols) ijl = mode == IJuliaMode sep = ijl ? " & " : " " eol = ijl ? "\\\\\n" : "\n" nlp = m.nlpdata # Objective qobj_str = quad_str(mode, m.obj) obj_sense = ijl ? (m.objSense == :Max ? "\\max" : "\\min")*"\\quad" : (m.objSense == :Max ? "Max" : "Min") str = obj_sense * sep if nlp !== nothing && nlp.nlobj !== nothing str *= (qobj_str=="0"?"":"$qobj_str + ") * "(nonlinear expression)" else str *= qobj_str end str *= eol # Constraints str *= ijl ? "\\text{Subject to} \\quad" : "Subject to" * eol for c in m.linconstr str *= sep * con_str(mode,c,mathmode=true) * eol end for c in m.quadconstr str *= sep * con_str(mode,c,mathmode=true) * eol end for c in m.sosconstr str *= sep * con_str(mode,c,mathmode=true) * eol end for c in m.socconstr str *= sep * con_str(mode,c,mathmode=true) * eol end if nlp !== nothing && length(nlp.nlconstr) > 0 num = length(nlp.nlconstr) str *= sep * string("$num nonlinear constraint", num>1?"s":"") * eol end # Display indexed variables in_dictlist = falses(m.numCols) for d in m.dictList # make sure that you haven't changed a variable type in the collection firstval = first(_values(d)) cat = getcategory(firstval) lb, ub = getlowerbound(firstval), getupperbound(firstval) allsame = true for v in _values(d) if !(getcategory(v) == cat && getlowerbound(v) == lb && getupperbound(v) == ub) allsame = false break elseif v in m.customNames allsame = false break end end if allsame for it in _values(d) # Mark variables in JuMPContainer as printed in_dictlist[it.col] = true end str *= sep * cont_str(mode,d,mathmode=true) * eol end end # Display non-indexed variables for i in 1:m.numCols in_dictlist[i] && continue var_name = var_str(mode,m,i) var_lb, var_ub = m.colLower[i], m.colUpper[i] str_lb = var_lb == -Inf ? "-"*sym[:infty] : str_round(var_lb) str_ub = var_ub == +Inf ? sym[:infty] : str_round(var_ub) var_cat = m.colCat[i] if var_cat == :Bin # x binary str *= string(sep, var_name, " ", sym[:in], " ", sym[:open_set], "0,1", sym[:close_set]) elseif var_cat == :SemiInt # x in union of 0 and {lb,...,ub} str *= string(sep, var_name, " ", sym[:in], " ", sym[:open_set], str_lb, ",", sym[:dots], ",", str_ub, sym[:close_set], " ", sym[:union], " ", sym[:open_set], "0", sym[:close_set]) elseif var_cat == :SemiCont # x in union of 0 and [lb,ub] str *= string(sep, var_name, " ", sym[:in], " ", sym[:open_rng], str_lb, ",", str_ub, sym[:close_rng], " ", sym[:union], " ", sym[:open_set], "0", sym[:close_set]) elseif var_cat == :Fixed str *= string(sep, var_name, " = ", str_lb) elseif var_lb == -Inf && var_ub == +Inf # Free variable str *= string(sep, var_name, " free") elseif var_lb == -Inf # No lower bound str *= string(sep, var_name, " ", sym[:leq], " ", str_ub) elseif var_ub == +Inf # No upper bound str *= string(sep, var_name, " ", sym[:geq], " ", str_lb) else str *= string(sep, str_lb, " ", sym[:leq], " ", var_name, " ", sym[:leq], " ", str_ub) end if var_cat == :Int str *= string(", ", sym[:integer]) end str *= eol end ijl ? "\$\$ \\begin{alignat*}{1}"*str*"\\end{alignat*}\n \$\$" : str end # Handlers to use correct symbols model_str(::Type{REPLMode}, m::Model) = model_str(REPLMode, m, repl) model_str(::Type{IJuliaMode}, m::Model; mathmode=true) = math(model_str(IJuliaMode, m, ijulia), mathmode) #------------------------------------------------------------------------ ## Variable #------------------------------------------------------------------------ Base.show(io::IO, v::Variable) = print(io, var_str(REPLMode,v)) @compat Base.show(io::IO, ::MIME"text/latex", v::Variable) = print(io, var_str(IJuliaMode,v,mathmode=false)) function var_str(mode, m::Model, col::Int; mathmode=true) colNames = mode == REPLMode ? m.colNames : m.colNamesIJulia if colNames[col] === EMPTYSTRING for cont in m.dictList fill_var_names(mode, colNames, cont) end end return math(colNames[col] == "" ? "col_$col" : colNames[col], mathmode) end function fill_var_names{N}(mode, colNames, v::JuMPArray{Variable,N}) data = printdata(v) idxsets = data.indexsets lengths = map(length, idxsets) name = data.name cprod = cumprod([lengths...]) for (ind,var) in enumerate(v.innerArray) idx_strs = [string( idxsets[1][mod1(ind,lengths[1])] )] for i = 2:N push!(idx_strs, string(idxsets[i][Int(ceil(mod1(ind,cprod[i]) / cprod[i-1]))])) end if mode == IJuliaMode colNames[var.col] = string(name, "_{", join(idx_strs,",") , "}") else colNames[var.col] = string(name, "[", join(idx_strs,",") , "]") end end end function fill_var_names(mode, colNames, v::JuMPDict{Variable}) name = printdata(v).name for (ind,var) in zip(keys(v),values(v)) if mode == IJuliaMode colNames[var.col] = string(name, "_{", join([string(i) for i in ind],","), "}") else colNames[var.col] = string(name, "[", join([string(i) for i in ind],","), "]") end end end function fill_var_names(mode, colNames, v::Array{Variable}) isempty(v) && return sizes = size(v) m = first(v).m if !haskey(m.varData, v) return end name = m.varData[v].name for (ii,var) in enumerate(v) @assert var.m === m ind = ind2sub(sizes, ii) colNames[var.col] = if mode === IJuliaMode string(name, "_{", join(ind, ","), "}") else string(name, "[", join(ind, ","), "]") end end return end # Handlers to use correct symbols var_str(::Type{REPLMode}, v::Variable) = var_str(REPLMode, v.m, v.col) var_str(::Type{IJuliaMode}, v::Variable; mathmode=true) = var_str(IJuliaMode, v.m, v.col, mathmode=mathmode) #------------------------------------------------------------------------ ## Norm #------------------------------------------------------------------------ Base.show(io::IO, j::Norm) = print(io, norm_str(REPLMode,j)) @compat Base.show(io::IO, ::MIME"text/latex", j::Norm) = print(io, norm_str(IJuliaMode,j)) function norm_str(mode, n::Norm, sym::PrintSymbols) string(sym[:Vert], "[", join(map(t->aff_str(mode,t),n.terms),","), "]", sym[:Vert], sym[:sub2]) end # Handlers to use correct symbols norm_str(::Type{REPLMode}, n::Norm) = norm_str(REPLMode, n, repl) norm_str(::Type{IJuliaMode}, n::Norm; mathmode=true) = math(norm_str(IJuliaMode, n, ijulia), mathmode) exprstr(n::Norm) = norm_str(REPLMode, n) #------------------------------------------------------------------------ ## JuMPContainer{Variable} #------------------------------------------------------------------------ Base.show(io::IO, j::Union{JuMPContainer{Variable}, Array{Variable}}) = print(io, cont_str(REPLMode,j)) @compat Base.show(io::IO, ::MIME"text/latex", j::Union{JuMPContainer{Variable},Array{Variable}}) = print(io, cont_str(IJuliaMode,j,mathmode=false)) # Generic string converter, called by mode-specific handlers # Assumes that !isempty(j) _getmodel(j::Array{Variable}) = first(j).m _getmodel(j::JuMPContainer) = getmeta(j, :model) function cont_str(mode, j, sym::PrintSymbols) # Check if anything in the container if isempty(j) name = isa(j, JuMPContainer) ? printdata(j).name : "Empty Array{Variable}" return "$name (no indices)" end m = _getmodel(j) # If this looks like a user-created Array, then defer to base printing if !haskey(m.varData, j) @assert isa(j, Array{Variable}) if ndims(j) == 1 return sprint((io,v) -> Base.show_vector(io, v, "[", "]"), j) else return sprint((io,X) -> Base.showarray(io, X), j) end end data = printdata(j) # 1. construct the part with variable name and indexing locvars = map(data.indexexprs) do tmp var = tmp.idxvar if var == nothing return "" else return string(var) end end num_dims = length(data.indexsets) idxvars = Array(UTF8String, num_dims) dimidx = 1 for i in 1:num_dims if data.indexexprs[i].idxvar == nothing while DIMS[dimidx] in locvars dimidx += 1 end if dimidx > length(DIMS) error("Unexpectedly ran out of indices") end idxvars[i] = DIMS[dimidx] dimidx += 1 else idxvars[i] = locvars[i] end end name_idx = string(data.name, sym[:ind_open], join(idxvars,","), sym[:ind_close]) # 2. construct part with what we index over idx_sets = sym[:for_all]*" "*join(map(dim->string(idxvars[dim], " ", sym[:in], " ", sym[:open_set], cont_str_set(data.indexsets[dim],sym[:dots]), sym[:close_set]), 1:num_dims), ", ") # 3. Handle any conditions if isa(j, JuMPDict) && data.condition != :() idx_sets *= string(" s.t. ",join(parse_conditions(data.condition), " and ")) end # 4. Bounds and category, if possible, and return final string a_var = first(_values(j)) model = a_var.m var_cat = model.colCat[a_var.col] var_lb = model.colLower[a_var.col] var_ub = model.colUpper[a_var.col] # Variables may have different bounds, so we can't really print nicely # at this time (possibly ever, as they could have been changed post # creation, which we'd never be able to handle. all_same_lb = true all_same_ub = true for var in _values(j) all_same_lb &= model.colLower[var.col] == var_lb all_same_ub &= model.colUpper[var.col] == var_ub end str_lb = var_lb == -Inf ? "-"*sym[:infty] : str_round(var_lb) str_ub = var_ub == +Inf ? sym[:infty] : str_round(var_ub) # Special case bounds printing based on the category if var_cat == :Bin # x in {0,1} return "$name_idx $(sym[:in]) $(sym[:open_set])0,1$(sym[:close_set]) $idx_sets" elseif var_cat == :SemiInt # x in union of 0 and {lb,...,ub} si_lb = all_same_lb ? str_lb : sym[:dots] si_ub = all_same_ub ? str_ub : sym[:dots] return "$name_idx $(sym[:in]) $(sym[:open_set])$si_lb,$(sym[:dots]),$si_ub$(sym[:close_set]) $(sym[:union]) $(sym[:open_set])0$(sym[:close_set]) $idx_sets" elseif var_cat == :SemiCont # x in union of 0 and [lb,ub] si_lb = all_same_lb ? str_lb : sym[:dots] si_ub = all_same_ub ? str_ub : sym[:dots] return "$name_idx $(sym[:in]) $(sym[:open_rng])$si_lb,$si_ub$(sym[:close_rng]) $(sym[:union]) $(sym[:open_set])0$(sym[:close_set]) $idx_sets" elseif var_cat == :Fixed si_bnd = all_same_lb ? str_lb : sym[:dots] return "$name_idx = $si_bnd $idx_sets" end # Continuous and Integer idx_sets = var_cat == :Int ? ", $(sym[:integer]), $idx_sets" : " $idx_sets" if all_same_lb && all_same_ub # Free variable var_lb == -Inf && var_ub == +Inf && return "$name_idx free$idx_sets" # No lower bound var_lb == -Inf && return "$name_idx $(sym[:leq]) $str_ub$idx_sets" # No upper bound var_ub == +Inf && return "$name_idx $(sym[:geq]) $str_lb$idx_sets" # Range return "$str_lb $(sym[:leq]) $name_idx $(sym[:leq]) $str_ub$idx_sets" end if all_same_lb && !all_same_ub var_lb == -Inf && return "$name_idx $(sym[:leq]) $(sym[:dots])$idx_sets" return "$str_lb $(sym[:leq]) $name_idx $(sym[:leq]) $(sym[:dots])$idx_sets" end if !all_same_lb && all_same_ub var_ub == +Inf && return "$name_idx $(sym[:geq]) $(sym[:dots])$idx_sets" return "$(sym[:dots]) $(sym[:leq]) $name_idx $(sym[:leq]) $str_ub$idx_sets" end return "$(sym[:dots]) $(sym[:leq]) $name_idx $(sym[:leq]) $(sym[:dots])$idx_sets" end # UTILITY FUNCTIONS FOR cont_str function cont_str_set(idxset::Union{Range,Array}, dots) # 2:2:20 -> {2,4..18,20} length(idxset) == 0 && return dots length(idxset) == 1 && return string(idxset[1]) length(idxset) == 2 && return string(idxset[1],",",idxset[2]) length(idxset) == 3 && return string(idxset[1],",",idxset[2],",",idxset[3]) length(idxset) == 4 && return string(idxset[1],",",idxset[2],",",idxset[3],",",idxset[4]) return string(idxset[1],",",idxset[2],",",dots,",",idxset[end-1],",",idxset[end]) end cont_str_set(idxset, dots) = return dots # Fallback # parse_conditions # Not exported. Traverses an expression and constructs an array with entries # corresponding to each condition. More specifically, if the condition is # a && (b || c) && (d && e), it returns [a, b || c, d, e]. parse_conditions(not_an_expr) = not_an_expr function parse_conditions(expr::Expr) ret = Any[] if expr.head != :&& return push!(ret, expr) end recurse = map(parse_conditions, expr.args) vcat(ret, recurse...) end # Handlers to use correct symbols cont_str(::Type{REPLMode}, j; mathmode=false) = cont_str(REPLMode, j, repl) cont_str(::Type{IJuliaMode}, j; mathmode=true) = math(cont_str(IJuliaMode, j, ijulia), mathmode) #------------------------------------------------------------------------ ## JuMPContainer{Float64} #------------------------------------------------------------------------ Base.show(io::IO, j::JuMPContainer{Float64}) = print(io, val_str(REPLMode,j)) function val_str{N}(mode, j::JuMPArray{Float64,N}) m = _getmodel(j) data = printdata(j) out_str = "$(data.name): $N dimensions:\n" if isempty(j) return out_str * " (no entries)" end function val_str_rec(depth, parent_index::Vector{Any}, parent_str::AbstractString) # Turn index set into strings indexset = data.indexsets[depth] index_strs = map(string, indexset) # Determine longest index so we can align columns max_index_len = 0 for index_str in index_strs max_index_len = max(max_index_len, strwidth(index_str)) end # If have recursed, we need to prepend the parent's index strings # accumulated, as well as white space so the alignment works. for i = 1:length(index_strs) index_strs[i] = parent_str * lpad(index_strs[i],max_index_len," ") end # Create a string for the number of spaces we need to indent indent = " "^(2*(depth-1)) # Determine the need to recurse if depth == N # Deepest level for i = 1:length(indexset) value = length(parent_index) == 0 ? j[indexset[i]] : j[parent_index...,indexset[i]] out_str *= indent * "[" * index_strs[i] * "] = $value\n" end else # At least one more layer to go for i = 1:length(indexset) index = indexset[i] # Print the ":" version of indices we will recurse over out_str *= indent * "[" * index_strs[i] * ",:"^(N-depth) * "]\n" val_str_rec(depth+1, length(parent_index) == 0 ? Any[index] : Any[parent_index...,index], index_strs[i] * ",") end end end val_str_rec(1,Any[],"") return out_str end # support types that don't have built-in comparison function _isless(t1::Tuple, t2::Tuple) n1, n2 = length(t1), length(t2) for i = 1:min(n1, n2) a, b = t1[i], t2[i] if !isequal(a, b) return applicable(isless,a,b) ? isless(a, b) : isless(hash(a),hash(b)) end end return n1 < n2 end function val_str(mode, dict::JuMPDict{Float64}) nelem = length(dict.tupledict) isempty(dict) && return "" m = _getmodel(dict) data = printdata(dict) out_str = "$(data.name): $(length(data.indexsets)) dimensions, $nelem " out_str *= nelem == 1 ? "entry" : "entries" out_str *= ":" sortedkeys = sort(collect(keys(dict.tupledict)), lt = _isless) ndim = length(first(keys(dict.tupledict))) key_strs = Array(AbstractString, length(dict), ndim) for (i, key) in enumerate(sortedkeys) for j in 1:ndim key_strs[i,j] = string(key[j]) end end max_dim_lens = map(1:ndim) do i maximum(map(length,key_strs[:,i])) end key_str = map(1:length(dict)) do i join(map(1:ndim) do j lpad(key_strs[i,j], max_dim_lens[j]) end, ",") end max_key_len = maximum(map(length,key_str)) for (i,key) in enumerate(sortedkeys) val = dict[key...] out_str *= "\n" * lpad("[$(key_str[i])]", max_key_len+3) out_str *= " = $val" end return out_str end #------------------------------------------------------------------------ ## AffExpr (not GenericAffExpr) #------------------------------------------------------------------------ Base.show(io::IO, a::AffExpr) = print(io, aff_str(REPLMode,a)) @compat Base.show(io::IO, ::MIME"text/latex", a::AffExpr) = print(io, math(aff_str(IJuliaMode,a),false)) # Generic string converter, called by mode-specific handlers function aff_str(mode, a::AffExpr, show_constant=true) # If the expression is empty, return the constant (or 0) if length(a.vars) == 0 return show_constant ? str_round(a.constant) : "0" end # Get reference to models included in this expression moddict = Dict{Model,IndexedVector{Float64}}() for var in a.vars if !haskey(moddict, var.m) moddict[var.m] = IndexedVector(Float64,var.m.numCols) end end # Collect like terms for ind in 1:length(a.vars) addelt!(moddict[a.vars[ind].m], a.vars[ind].col, a.coeffs[ind]) end elm = 1 term_str = Array(UTF8String, 2*length(a.vars)) # For each model for m in keys(moddict) indvec = moddict[m] # For each non-zero for this model for i in 1:indvec.nnz idx = indvec.nzidx[i] elt = indvec.elts[idx] abs(elt) < PRINT_ZERO_TOL && continue # e.g. x - x pre = abs(abs(elt)-1) < PRINT_ZERO_TOL ? "" : str_round(abs(elt)) * " " var = var_str(mode,m,idx) term_str[2*elm-1] = elt < 0 ? " - " : " + " term_str[2*elm ] = "$pre$var" elm += 1 end end if elm == 1 # Will happen with cancellation of all terms # We should just return the constant, if its desired return show_constant ? str_round(a.constant) : "0" else # Correction for very first term - don't want a " + "/" - " term_str[1] = (term_str[1] == " - ") ? "-" : "" ret = join(term_str[1:2*(elm-1)]) if abs(a.constant) >= PRINT_ZERO_TOL && show_constant ret = string(ret, a.constant < 0 ? " - " : " + ", str_round(abs(a.constant))) end return ret end end # Precompile for faster boot times Base.precompile(aff_str, (Type{JuMP.REPLMode}, AffExpr, Bool)) Base.precompile(aff_str, (Type{JuMP.IJuliaMode}, AffExpr, Bool)) Base.precompile(aff_str, (Type{JuMP.REPLMode}, AffExpr)) Base.precompile(aff_str, (Type{JuMP.IJuliaMode}, AffExpr)) #------------------------------------------------------------------------ ## GenericQuadExpr #------------------------------------------------------------------------ Base.show(io::IO, q::GenericQuadExpr) = print(io, quad_str(REPLMode,q)) @compat Base.show(io::IO, ::MIME"text/latex", q::GenericQuadExpr) = print(io, quad_str(IJuliaMode,q,mathmode=false)) # Generic string converter, called by mode-specific handlers function quad_str(mode, q::GenericQuadExpr, sym) length(q.qvars1) == 0 && return aff_str(mode,q.aff) # Canonicalize x_i * x_j so i <= j for ind in 1:length(q.qvars1) if q.qvars2[ind].col < q.qvars1[ind].col q.qvars1[ind],q.qvars2[ind] = q.qvars2[ind],q.qvars1[ind] end end # Merge duplicates Q = sparse([v.col for v in q.qvars1], [v.col for v in q.qvars2], q.qcoeffs) I,J,V = findnz(Q) Qnnz = length(V) # Odd terms are +/i, even terms are the variables/coeffs term_str = Array(UTF8String, 2*Qnnz) if Qnnz > 0 for ind in 1:Qnnz val = abs(V[ind]) pre = (val == 1.0 ? "" : str_round(val)*" ") x = var_str(mode,q.qvars1[ind].m,I[ind]) y = var_str(mode,q.qvars1[ind].m,J[ind]) term_str[2*ind-1] = V[ind] < 0 ? " - " : " + " term_str[2*ind ] = "$pre$x" * (x == y ? sym[:sq] : "$(sym[:times])$y") end # Correction for first term as there is no space # between - and variable coefficient/name term_str[1] = V[1] < 0 ? "-" : "" end ret = join(term_str) if q.aff.constant == 0 && length(q.aff.vars) == 0 return ret else aff = aff_str(mode,q.aff) if aff[1] == '-' return string(ret, " - ", aff[2:end]) else return string(ret, " + ", aff) end end end # Handlers to use correct symbols quad_str(::Type{REPLMode}, q::GenericQuadExpr) = quad_str(REPLMode, q, repl) quad_str(::Type{IJuliaMode}, q::GenericQuadExpr; mathmode=true) = math(quad_str(IJuliaMode, q, ijulia), mathmode) #------------------------------------------------------------------------ ## SOCExpr #------------------------------------------------------------------------ Base.show(io::IO, c::SOCExpr) = print(io, expr_str(REPLMode, c)) @compat Base.show(io::IO, ::MIME"text/latex", c::SOCExpr) = print(io, expr_str(IJuliaMode, c)) function expr_str(mode, c::SOCExpr) coeff = (c.coeff == 1) ? "" : string(c.coeff, " ") aff = aff_str(mode, c.aff) if aff[1] == '-' chain = " - " aff = aff[2:end] elseif aff == "0" aff = "" chain = "" else # positive chain = " + " end string(coeff, norm_str(mode, c.norm), chain, aff) end #------------------------------------------------------------------------ ## GenericRangeConstraint #------------------------------------------------------------------------ Base.show(io::IO, c::GenericRangeConstraint) = print(io, con_str(REPLMode,c)) @compat Base.show(io::IO, ::MIME"text/latex", c::GenericRangeConstraint) = print(io, con_str(IJuliaMode,c,mathmode=false)) # Generic string converter, called by mode-specific handlers function con_str(mode, c::GenericRangeConstraint, sym) s = sense(c) a = aff_str(mode,c.terms,false) if s == :range out_str = "$(str_round(c.lb)) $(sym[:leq]) $a $(sym[:leq]) $(str_round(c.ub))" else rel = s == :<= ? sym[:leq] : (s == :>= ? sym[:geq] : sym[:eq]) out_str = string(a," ",rel," ",str_round(rhs(c))) end out_str end # Handlers to use correct symbols con_str(::Type{REPLMode}, c::GenericRangeConstraint; args...) = con_str(REPLMode, c, repl) con_str(::Type{IJuliaMode}, c::GenericRangeConstraint; mathmode=true) = math(con_str(IJuliaMode, c, ijulia), mathmode) #------------------------------------------------------------------------ ## QuadConstraint #------------------------------------------------------------------------ Base.show(io::IO, c::QuadConstraint) = print(io, con_str(REPLMode,c)) @compat Base.show(io::IO, ::MIME"text/latex", c::QuadConstraint) = print(io, con_str(IJuliaMode,c,mathmode=false)) # Generic string converter, called by mode-specific handlers function con_str(mode, c::QuadConstraint, sym) s = c.sense r = (s == :<=) ? sym[:leq] : (s == :>= ? sym[:geq] : sym[:eq]) "$(quad_str(mode,c.terms)) $r 0" end # Handlers to use correct symbols con_str(::Type{REPLMode}, c::QuadConstraint; args...) = con_str(REPLMode, c, repl) con_str(::Type{IJuliaMode}, c::QuadConstraint; mathmode=true) = math(con_str(IJuliaMode, c, ijulia), mathmode) #------------------------------------------------------------------------ ## SOCConstraint #------------------------------------------------------------------------ Base.show(io::IO, c::SOCConstraint) = print(io, con_str(REPLMode,c)) @compat Base.show(io::IO, ::MIME"text/latex", c::SOCConstraint) = print(io, con_str(IJuliaMode,c)) function con_str(mode, c::SOCConstraint, sym::PrintSymbols) ne = c.normexpr coeff = ne.coeff == 1 ? "" : string(ne.coeff, " ") nrm = norm_str(mode, ne.norm) aff = aff_str(mode, -ne.aff) string(coeff, nrm, " $(repl[:leq]) ", aff) end # Handlers to use correct symbols con_str(::Type{REPLMode}, c::SOCConstraint; args...) = con_str(REPLMode, c, repl) con_str(::Type{IJuliaMode}, c::SOCConstraint; mathmode=true) = math(con_str(IJuliaMode, c, ijulia), mathmode) #------------------------------------------------------------------------ ## SOSConstraint #------------------------------------------------------------------------ Base.show(io::IO, c::SOSConstraint) = print(io, con_str(REPLMode,c)) @compat Base.show(io::IO, ::MIME"text/latex", c::SOSConstraint) = print(io, con_str(IJuliaMode,c,mathmode=false)) # Generic string converter, called by mode-specific handlers function con_str(mode, c::SOSConstraint, sym::PrintSymbols) term_str = [string(str_round(c.weights[i]), " ", c.terms[i]) for i in 1:length(c.terms)] "$(c.sostype): $(sym[:open_set])$(join(term_str,", "))$(sym[:close_set])" end # Handlers to use correct symbols con_str(::Type{REPLMode}, c::SOSConstraint; args...) = con_str(REPLMode, c, repl) con_str(::Type{IJuliaMode}, c::SOSConstraint; mathmode=true) = math(con_str(IJuliaMode, c, ijulia), mathmode) #------------------------------------------------------------------------ ## SDConstraint #------------------------------------------------------------------------ Base.show(io::IO, c::SDConstraint) = print(io, con_str(REPLMode,c)) @compat Base.show(io::IO, ::MIME"text/latex", c::SDConstraint) = print(io, con_str(IJuliaMode,c,mathmode=false)) # Generic string converter, called by mode-specific handlers function con_str(mode, c::SDConstraint, succeq0) t = c.terms str = sprint(print, t) splitted = split(str, "\n")[2:end] center = ceil(Int, length(splitted)/2) splitted[center] *= succeq0 join(splitted, "\n") end # Handlers to use correct symbols con_str(::Type{REPLMode}, c::SDConstraint; args...) = con_str(REPLMode, c, repl[:succeq0]) con_str(::Type{IJuliaMode}, c::SDConstraint; mathmode=true) = math(con_str(IJuliaMode, c, ijulia[:succeq0], mathmode)) #------------------------------------------------------------------------ ## ConstraintRef #------------------------------------------------------------------------ Base.show(io::IO, c::ConstraintRef{Model,LinearConstraint}) = print(io, con_str(REPLMode,c.m.linconstr[c.idx])) Base.show(io::IO, c::ConstraintRef{Model,QuadConstraint}) = print(io, con_str(REPLMode,c.m.quadconstr[c.idx])) Base.show(io::IO, c::ConstraintRef{Model,SOSConstraint}) = print(io, con_str(REPLMode,c.m.sosconstr[c.idx])) Base.show(io::IO, c::ConstraintRef{Model,SOCConstraint}) = print(io, con_str(REPLMode,c.m.socconstr[c.idx])) Base.show(io::IO, c::ConstraintRef{Model,SDConstraint}) = print(io, con_str(REPLMode,c.m.sdpconstr[c.idx])) function Base.show(io::IO, c::ConstraintRef{Model,NonlinearConstraint}) print(io, "Reference to nonlinear constraint #$(linearindex(c))") end @compat Base.show(io::IO, ::MIME"text/latex", c::ConstraintRef{Model,LinearConstraint}) = print(io, con_str(IJuliaMode,c.m.linconstr[c.idx],mathmode=false)) @compat Base.show(io::IO, ::MIME"text/latex", c::ConstraintRef{Model,QuadConstraint}) = print(io, con_str(IJuliaMode,c.m.quadconstr[c.idx],mathmode=false)) @compat Base.show(io::IO, ::MIME"text/latex", c::ConstraintRef{Model,SOSConstraint}) = print(io, con_str(IJuliaMode,c.m.sosconstr[c.idx],mathmode=false))
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2.234091
15,793
############################################################ ## joMatrix - extra functions # elements(jo) #elements(A::joAbstractDAparallelToggleOperator) = throw(joAbstractDAparallelToggleOperatorException("elements: pointless operation for joDAdistribute/joDAgather operations")) # hasinverse(jo) # issquare(jo) # istall(jo) # iswide(jo) # iscomplex(jo) # islinear(jo) # isadjoint(jo)
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3.11811
127
matmul_sizes(s::Integer) = (s,s,s) matmul_sizes(m_k_n::Tuple{Vararg{<:Integer, 3}}) = m_k_n """ runbench(backend, TA, TB, TC; kwargs...) """ function runbench(backend::Symbol, ::Type{TA}, ::Type{TB}, ::Type{TC}; kwargs...) where {TA, TB, TC} return runbench(Val(backend), TA, TB, TC; kwargs...) end """ runbench(::Val{:CUDA}, TA, TB, TC; kwargs...) """ function runbench(::Val{:CUDA}, ::Type{TA}, ::Type{TB}, ::Type{TC}; estimator::F = Base.minimum, libs = all_libs(), sizes = [2^n for n in 7:14]) where {TA, TB, TC, F} sizes_vec = collect(sizes) _sizes_column = Vector{Int}(undef, 0) _libraries_column = Vector{Symbol}(undef, 0) _tflops_column = Vector{Float64}(undef, 0) _times_column = Vector{Float64}(undef, 0) p = ProgressMeter.Progress(length(sizes_vec)) warmed_up = falses(length(sizes_vec)) for (j, sz) ∈ enumerate(sizes_vec) showvalues = [] push!(showvalues, (:Size, sz)) GC.gc() CUDA.reclaim() M = sz K = sz N = sz for lib in libs benchfunc! = getbenchfunc(lib) if !(warmed_up[j]) A_h = rand(TA, (M, K)) / sqrt(TA(K)); B_h = rand(TB, (K, N)) / sqrt(TB(K)); C_h = rand(TC, (M, N)); A = CUDA.CuArray(A_h); B = CUDA.CuArray(B_h); C = CUDA.CuArray(C_h); benchfunc!(C, A, B) # warmup warmed_up[j] = true end A_h = rand(TA, (M, K)) / sqrt(TA(K)); B_h = rand(TB, (K, N)) / sqrt(TB(K)); C_h = rand(TC, (M, N)); A = CUDA.CuArray(A_h); B = CUDA.CuArray(B_h); C = CUDA.CuArray(C_h); trial = benchfunc!(C, A, B) point_estimate = estimator(trial) t_ns = Base.time(point_estimate) # time in nanoseconds t_ps = 1_000 * t_ns # time in picoseconds flops_factor = 2 tflops = flops_factor * M * K * N / t_ps # teraflops push!(showvalues, (lib, "$(round(tflops; digits = 2)) TFLOPS")) push!(_sizes_column, sz) push!(_libraries_column, lib) push!(_tflops_column, tflops) push!(_times_column, t_ns) end ProgressMeter.next!(p; showvalues = showvalues) GC.gc() CUDA.reclaim() end df = DataFrames.DataFrame( Size = _sizes_column, Library = _libraries_column, TFLOPS = _tflops_column, Time = _times_column, ) benchmark_result = BenchmarkResult{TA, TB, TC}(df) return benchmark_result end
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# SPDX-License-Identifier: X11 # 2020-08-29 # Five Variables (100pt) println(findfirst(x -> x == 0, parse.(Int, split(readline(), " "))))
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using Revise using ReversibleSeismic nx = 1000 ny = 1000 param = AcousticPropagatorParams(nx=nx, ny=ny, Rcoef=0.2, dx=20.0, dy=20.0, dt=0.05, nstep=100) c = 1000*ones(param.NX+2, param.NY+2) src = (param.NX÷2, param.NY÷2) srcv = Ricker(param, 100.0, 500.0) tu = solve(param, src, srcv, c) loss = sum(tu .^ 2) @assert loss ≈ 10.931466822080788 using PyPlot function viz(param, tu) close("all") plot(srcv) savefig("_srcv.png") for i = 1:200:param.NSTEP+1 close("all") pcolormesh(tu[:,:,i]) colorbar() savefig("_f$i.png") end end viz(param, tu)
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#= JoiceTypeConstructorFunctions: - Julia version: 1.6 - Author: connorforsythe - Date: 2021-11-05 =# export ConstructModelInfo function checkModelInfo(Data::DataFrame, ChoiceColumn::Symbol, Parameters::Vector{Symbol}, ChoiceSetIDColumn::Symbol, RandomParameters::RandColsType=nothing, PanelIDColumn::ColsType=nothing, WeightColumn::ColsType=nothing) dataCols = Symbol.(names(Data)) missingCols = [] if !(ChoiceColumn in dataCols) push!(missingCols, ChoiceColumn) end if !(ChoiceSetIDColumn in dataCols) push!(missingCols, ChoiceSetIDColumn) end if !(PanelIDColumn in dataCols) && (PanelIDColumn!=nothing) push!(missingCols, PanelIDColumn) end for tempSymbol in Parameters if !(tempSymbol in dataCols) push!(missingCols, tempSymbol) end end if RandomParameters!=nothing for tempSymbol in keys(RandomParameters) if !(tempSymbol in dataCols) push!(missingCols, tempSymbol) end end end if length(missingCols)>0 error("You have specified the following cols that aren't in the provided dataset: $missingCols") end end function convertParameterColumns(x::Vector{String}) return Symbol.(x) end function convertParameterColumns(x::Vector{Symbol}) return x end function convertParameterColumns(x::Symbol) return [x] end function convertParameterColumns(x::String) return convertParameterColumns(Symbol(x)) end function convertParameterColumns(x::Nothing) return x end function convertParameterColumn(x::String) return Symbol(x) end function convertParameterColumn(x::Symbol) return x end function convertParameterColumn(x::Nothing) return x end function convertRandomParameterColumns(x::Dict{Symbol, String}) return x end function convertRandomParameterColumns(x::Dict{String, String}) return Dict(zip(convertParameterColumns([keys(x)...]), values(x))) end function convertRandomParameterColumns(x::Nothing) return x end function ConstructModelInfo(Data::DataFrame, ChoiceColumn::ColType, Parameters::ColsType, ChoiceSetIDColumn::ColType; RandomParameters::RandColsType=nothing, PanelIDColumn::ColType=nothing, Space::String="preference", Name::NameType=nothing, InterDrawCount::Int=2048, WeightColumn::ColType=nothing) TimeCreated = Dates.now() ChoiceColumn = convertParameterColumn(ChoiceColumn) Parameters = convertParameterColumns(Parameters) ChoiceSetIDColumn = convertParameterColumn(ChoiceSetIDColumn) WeightColumn = convertParameterColumn(WeightColumn) PanelIDColumn = convertParameterColumn(PanelIDColumn) RandomParameters = convertRandomParameterColumns(RandomParameters) RandomDraws = getDraws(RandomParameters, InterDrawCount) typedSpace::JoiceModelSpace = Preference() colsToKeep = copy(Parameters) appendParamNames!(colsToKeep, RandomParameters) push!(colsToKeep, ChoiceColumn) push!(colsToKeep, ChoiceSetIDColumn) if(lowercase(Space)=="wtp") typedSpace = WTP() end typedDataStructure::JoiceDataStructure = Panel() ObservationColumn::Symbol = ChoiceSetIDColumn if(PanelIDColumn==nothing) typedDataStructure = Cross() else push!(colsToKeep, PanelIDColumn) ObservationColumn = PanelIDColumn end if(WeightColumn!=nothing) push!(colsToKeep, WeightColumn) end checkModelInfo(Data, ChoiceColumn, Parameters, ChoiceSetIDColumn, RandomParameters, PanelIDColumn, WeightColumn) PreferenceSearchBounds = [-1, 1] println("Model being constructed with the following properties:") if(PanelIDColumn!=nothing) println("Panel Variable: $PanelIDColumn") end println("Choice Set Variable: $ChoiceSetIDColumn") println("Choice Indicator Variable: $ChoiceColumn") println("Fixed Parameters: $Parameters") if(RandomParameters!=nothing) println("Random Parameters: $RandomParameters") end print("Data has ") print(size(Data)[1]) println(" rows") cleanDataFrame = cleanData(Data, colsToKeep, ChoiceSetIDColumn, PanelIDColumn) r = ModelInfo(cleanDataFrame, ChoiceColumn, Parameters, :choiceSetID, RandomParameters, RandomDraws, PanelIDColumn, ObservationColumn, typedDataStructure, typedSpace, Name, InterDrawCount, WeightColumn, TimeCreated, PreferenceSearchBounds) return r end function cleanData(data::DataFrame, colsToKeep::Vector{Symbol}, choiceSetIDColumn::Symbol, panelIDColumn::Symbol) data = data[:, colsToKeep] #Construct new id to allow for repeated choice set ids given maxChoiceSetID = max(data[:, choiceSetIDColumn]...) panelIDMultiplier = 10^(ceil(log10(maxChoiceSetID))) data[:, :choiceSetID] .= (data[:, panelIDColumn].*panelIDMultiplier).+data[:, choiceSetIDColumn] return data end function cleanData(data::DataFrame, colsToKeep::Vector{Symbol}, choiceSetIDColumn::Symbol, panelIDColumn::Nothing) data = data[:, colsToKeep] data[:, :choiceSetID] .= data[:, choiceSetIDColumn] return data end function appendParamNames!(currentVect::Vector{Symbol}, randomParameters::Nothing) end function appendParamNames!(currentVect::Vector{Symbol}, randomParameters::Dict{Symbol, String}) for tempCol in keys(randomParameters) push!(currentVect, tempCol) end end
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2.946154
1,820
function BigFraction(arg0::BigInteger) return BigFraction((BigInteger,), arg0) end function BigFraction(arg0::BigInteger, arg1::BigInteger) return BigFraction((BigInteger, BigInteger), arg0, arg1) end function BigFraction(arg0::jdouble) return BigFraction((jdouble,), arg0) end function BigFraction(arg0::jdouble, arg1::jdouble, arg2::jint) return BigFraction((jdouble, jdouble, jint), arg0, arg1, arg2) end function BigFraction(arg0::jdouble, arg1::jint) return BigFraction((jdouble, jint), arg0, arg1) end function BigFraction(arg0::jint) return BigFraction((jint,), arg0) end function BigFraction(arg0::jint, arg1::jint) return BigFraction((jint, jint), arg0, arg1) end function BigFraction(arg0::jlong) return BigFraction((jlong,), arg0) end function BigFraction(arg0::jlong, arg1::jlong) return BigFraction((jlong, jlong), arg0, arg1) end function abs(obj::BigFraction) return jcall(obj, "abs", BigFraction, ()) end function add(obj::BigFraction, arg0::BigFraction) return jcall(obj, "add", BigFraction, (BigFraction,), arg0) end function add(obj::BigFraction, arg0::BigInteger) return jcall(obj, "add", BigFraction, (BigInteger,), arg0) end function add(obj::BigFraction, arg0::jint) return jcall(obj, "add", BigFraction, (jint,), arg0) end function add(obj::BigFraction, arg0::jlong) return jcall(obj, "add", BigFraction, (jlong,), arg0) end function big_decimal_value(obj::BigFraction) return jcall(obj, "bigDecimalValue", BigDecimal, ()) end function big_decimal_value(obj::BigFraction, arg0::jint) return jcall(obj, "bigDecimalValue", BigDecimal, (jint,), arg0) end function big_decimal_value(obj::BigFraction, arg0::jint, arg1::jint) return jcall(obj, "bigDecimalValue", BigDecimal, (jint, jint), arg0, arg1) end function byte_value(obj::Number) return jcall(obj, "byteValue", jbyte, ()) end function compare_to(obj::BigFraction, arg0::BigFraction) return jcall(obj, "compareTo", jint, (BigFraction,), arg0) end function divide(obj::BigFraction, arg0::BigFraction) return jcall(obj, "divide", BigFraction, (BigFraction,), arg0) end function divide(obj::BigFraction, arg0::BigInteger) return jcall(obj, "divide", BigFraction, (BigInteger,), arg0) end function divide(obj::BigFraction, arg0::jint) return jcall(obj, "divide", BigFraction, (jint,), arg0) end function divide(obj::BigFraction, arg0::jlong) return jcall(obj, "divide", BigFraction, (jlong,), arg0) end function double_value(obj::BigFraction) return jcall(obj, "doubleValue", jdouble, ()) end function equals(obj::BigFraction, arg0::Object) return jcall(obj, "equals", jboolean, (Object,), arg0) end function float_value(obj::BigFraction) return jcall(obj, "floatValue", jfloat, ()) end function get_denominator(obj::BigFraction) return jcall(obj, "getDenominator", BigInteger, ()) end function get_denominator_as_int(obj::BigFraction) return jcall(obj, "getDenominatorAsInt", jint, ()) end function get_denominator_as_long(obj::BigFraction) return jcall(obj, "getDenominatorAsLong", jlong, ()) end function get_field(obj::BigFraction) return jcall(obj, "getField", BigFractionField, ()) end function get_numerator(obj::BigFraction) return jcall(obj, "getNumerator", BigInteger, ()) end function get_numerator_as_int(obj::BigFraction) return jcall(obj, "getNumeratorAsInt", jint, ()) end function get_numerator_as_long(obj::BigFraction) return jcall(obj, "getNumeratorAsLong", jlong, ()) end function get_reduced_fraction(::Type{BigFraction}, arg0::jint, arg1::jint) return jcall(BigFraction, "getReducedFraction", BigFraction, (jint, jint), arg0, arg1) end function hash_code(obj::BigFraction) return jcall(obj, "hashCode", jint, ()) end function int_value(obj::BigFraction) return jcall(obj, "intValue", jint, ()) end function long_value(obj::BigFraction) return jcall(obj, "longValue", jlong, ()) end function multiply(obj::BigFraction, arg0::BigFraction) return jcall(obj, "multiply", BigFraction, (BigFraction,), arg0) end function multiply(obj::BigFraction, arg0::BigInteger) return jcall(obj, "multiply", BigFraction, (BigInteger,), arg0) end function multiply(obj::BigFraction, arg0::jint) return jcall(obj, "multiply", BigFraction, (jint,), arg0) end function multiply(obj::BigFraction, arg0::jlong) return jcall(obj, "multiply", BigFraction, (jlong,), arg0) end function negate(obj::BigFraction) return jcall(obj, "negate", BigFraction, ()) end function percentage_value(obj::BigFraction) return jcall(obj, "percentageValue", jdouble, ()) end function pow(obj::BigFraction, arg0::BigInteger) return jcall(obj, "pow", BigFraction, (BigInteger,), arg0) end function pow(obj::BigFraction, arg0::jdouble) return jcall(obj, "pow", jdouble, (jdouble,), arg0) end function pow(obj::BigFraction, arg0::jint) return jcall(obj, "pow", BigFraction, (jint,), arg0) end function pow(obj::BigFraction, arg0::jlong) return jcall(obj, "pow", BigFraction, (jlong,), arg0) end function reciprocal(obj::BigFraction) return jcall(obj, "reciprocal", BigFraction, ()) end function reduce(obj::BigFraction) return jcall(obj, "reduce", BigFraction, ()) end function short_value(obj::Number) return jcall(obj, "shortValue", jshort, ()) end function signum(obj::BigFraction) return jcall(obj, "signum", jint, ()) end function subtract(obj::BigFraction, arg0::BigFraction) return jcall(obj, "subtract", BigFraction, (BigFraction,), arg0) end function subtract(obj::BigFraction, arg0::BigInteger) return jcall(obj, "subtract", BigFraction, (BigInteger,), arg0) end function subtract(obj::BigFraction, arg0::jint) return jcall(obj, "subtract", BigFraction, (jint,), arg0) end function subtract(obj::BigFraction, arg0::jlong) return jcall(obj, "subtract", BigFraction, (jlong,), arg0) end function to_string(obj::BigFraction) return jcall(obj, "toString", JString, ()) end
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2.605206
2,305
module QJuliaRegisters # Plain baseline registers const half2 = NTuple{2, VecElement{Float16}} const half4 = NTuple{4, VecElement{Float16}} # const float2 = NTuple{2, VecElement{Float32}} const float4 = NTuple{4, VecElement{Float32}} const float8 = NTuple{8, VecElement{Float32}} const float16 = NTuple{16, VecElement{Float32}} # const double2 = NTuple{2, VecElement{Float64}} const double4 = NTuple{4, VecElement{Float64}} const double8 = NTuple{8, VecElement{Float64}} # Helper functions for plain registers function register_type(T) if T == Float16 || T == ComplexF16 || T == half2 || T == half4 return Float16 elseif T == Float32 || T == ComplexF32 || T == float2 || T == float4 || T == float8 || T == float16 return Float32 elseif T == Float64 || T == ComplexF64 || T == double2 || T == double4 || T == double8 return Float64 elseif T == BigFloat || T == Complex{BigFloat} return BigFloat else error("Cannot deduce a type.") end return nothing end function register_size(T) if T == Float16 || T == Float32 || T == Float64 || T == BigFloat return 1 elseif T == ComplexF16 || T == ComplexF32 || T == ComplexF64 || T == Complex{BigFloat} || T == half2 || T == float2 || T == double2 return 2 elseif T == double4 || T == float4 || T == half4 return 4 elseif T == double8 || T == float8 return 8 elseif T == float16 return 16 else error("Cannot deduce the register size (type is not supported).") end return nothing end # More generic registers abstract type GenericRegister{T<:Real,N} end mutable struct FloatN{T<:AbstractFloat,N} <: GenericRegister{T,N} val::NTuple{N, VecElement{T}} FloatN{T,N}() where {T} where {N} = new( NTuple{N, VecElement{T}}(ntuple(i->0, N)) ) FloatN{T,N}(src::FloatN) where {T} where {N} = new( src.val ) FloatN{T,N}(reg::NTuple{N, VecElement{T}}) where {T} where {N} = new( reg ) end # re-implement plain complex type for compatibility mutable struct Complex2{T<:AbstractFloat, N} <: GenericRegister{T,N} val::Complex{T} Complex2{T,N}() where {T} where {N} = new( Complex{T}(0.0, 0.0) ) Complex2{T,N}(src::Complex2) where {T} where {N} = new( src.val ) Complex2{T,N}(reg::Complex{T}) where {T} where {N} = new( reg ) Complex2{T,N}(rea::T, img::T) where {T} where {N} = new( Complex{T}(rea, img) ) Complex2{T,N}(tpl::NTuple{2, VecElement{T}}) where {T} where {N} = new( Complex{T}(tpl[1], tpl[2]) ) end mutable struct IntN{T<:Integer,N} <: GenericRegister{T,N} val::NTuple{N, VecElement{T}} IntN{T,N}() where {T} where {N} = new( NTuple{N, VecElement{T}}(ntuple(i->0, N)) ) IntN{T,N}(src::IntN) where {T} where {N} = new( src.val ) IntN{T,N}(reg::NTuple{N, VecElement{T}}) where {T} where {N} = new( reg ) end # Floating point registers Half2 = FloatN{Float16, 2} Half4 = FloatN{Float16, 4} Half8 = FloatN{Float16, 8} Half16 = FloatN{Float16, 16} Single2 = FloatN{Float32, 2} Single4 = FloatN{Float32, 4} Single8 = FloatN{Float32, 8} Single16 = FloatN{Float32, 16} Double2 = FloatN{Float64, 2} Double4 = FloatN{Float64, 4} Double8 = FloatN{Float64, 8} # Big float type BigDouble2 = FloatN{BigFloat, 2} # Additional complex types (are they compatible with Half2, Single2, Double2?) ComplexH = Complex2{Float16,2} ComplexS = Complex2{Float32,2} ComplexD = Complex2{Float64,2} ComplexB = Complex2{BigFloat,2} # Signed: BigInt Int128 Int16 Int32 Int64 Int8 # Unsigned: UInt128 UInt16 UInt32 UInt64 UInt8 Int2 = IntN{Int32, 2} Int4 = IntN{Int32, 4} UInt2 = IntN{UInt32, 2} UInt4 = IntN{UInt32, 4} LongInt2 = IntN{Int64, 2} LongInt4 = IntN{Int64, 4} # Helper methods function register_type(reg::GenericRegister{T,N}) where T where N; return T; end function register_size(reg::GenericRegister{T,N}) where T where N; return N; end end #QJuliaRegisters
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2.518229
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module TestParameterTypes using Mimi using Test import Mimi: external_params, external_param, TimestepMatrix, TimestepVector, ArrayModelParameter, ScalarModelParameter, FixedTimestep, build, import_params! # # Test that parameter type mismatches are caught # expr = :( @defcomp BadComp1 begin a = Parameter(index=[time, regions], default=[10, 11, 12]) # should be 2D default function run_timestep(p, v, d, t) end end ) @test_throws LoadError eval(expr) expr = :( @defcomp BadComp2 begin a = Parameter(default=[10, 11, 12]) # should be scalar default function run_timestep(p, v, d, t) end end ) @test_throws LoadError eval(expr) # # Test that the old type parameterization syntax errors # expr = :( @defcomp BadComp3 begin a::Int = Parameter() function run_timestep(p, v, d, t) end end ) eval(expr) # Just a deprecation warning for v0.10, then will change to error in v1.0 # @test_throws LoadError eval(expr) @defcomp MyComp begin a = Parameter(index=[time, regions], default=ones(101,3)) b = Parameter(index=[time], default=1:101) c = Parameter(index=[regions]) d = Parameter() e = Parameter(index=[four]) f = Parameter{Array{Float64, 2}}() g = Parameter{Int}(default=10.0) # value should be Int despite Float64 default h = Parameter(default=10) # should be "numtype", despite Int default j = Parameter{Int}(index = [regions]) function run_timestep(p, v, d, t) end end # Check that explicit number type for model works as expected numtype = Float32 arrtype = Union{Missing, numtype} m = Model(numtype) set_dimension!(m, :time, 2000:2100) set_dimension!(m, :regions, 3) set_dimension!(m, :four, 4) add_comp!(m, MyComp) set_param!(m, :MyComp, :c, [4,5,6]) set_param!(m, :MyComp, :d, 0.5) # 32-bit float constant set_param!(m, :MyComp, :e, [1,2,3,4]) set_param!(m, :MyComp, :f, reshape(1:16, 4, 4)) set_param!(m, :MyComp, :j, [1,2,3]) build(m) # applies defaults, creating external params extpars = external_params(m) # TBD: These are not (yet) external params. Defaults are applied at build time. @test isa(extpars[:a], ArrayModelParameter) @test isa(extpars[:b], ArrayModelParameter) @test isa(extpars[:c], ArrayModelParameter) @test isa(extpars[:d], ScalarModelParameter) @test isa(extpars[:e], ArrayModelParameter) @test isa(extpars[:f], ScalarModelParameter) # note that :f is stored as a scalar parameter even though its values are an array @test typeof(extpars[:a].values) == TimestepMatrix{FixedTimestep{2000, 1}, arrtype, 1} @test typeof(extpars[:b].values) == TimestepVector{FixedTimestep{2000, 1}, arrtype} @test typeof(extpars[:c].values) == Array{arrtype, 1} @test typeof(extpars[:d].value) == numtype @test typeof(extpars[:e].values) == Array{arrtype, 1} @test typeof(extpars[:f].value) == Array{Float64, 2} @test typeof(extpars[:g].value) <: Int @test typeof(extpars[:h].value) == numtype # test updating parameters @test_throws ErrorException update_param!(m, :a, 5) # expects an array @test_throws ErrorException update_param!(m, :a, ones(101)) # wrong size @test_throws ErrorException update_param!(m, :a, fill("hi", 101, 3)) # wrong type update_param!(m, :a, Array{Int,2}(zeros(101, 3))) # should be able to convert from Int to Float @test_throws ErrorException update_param!(m, :d, ones(5)) # wrong type; should be scalar update_param!(m, :d, 5) # should work, will convert to float @test extpars[:d].value == 5 @test_throws ErrorException update_param!(m, :e, 5) # wrong type; should be array @test_throws ErrorException update_param!(m, :e, ones(10)) # wrong size update_param!(m, :e, [4,5,6,7]) @test length(extpars) == 9 @test typeof(extpars[:a].values) == TimestepMatrix{FixedTimestep{2000, 1}, arrtype, 1} @test typeof(extpars[:d].value) == numtype @test typeof(extpars[:e].values) == Array{arrtype, 1} #------------------------------------------------------------------------------ # Test updating TimestepArrays with update_param! #------------------------------------------------------------------------------ @defcomp MyComp2 begin x=Parameter(index=[time]) y=Variable(index=[time]) function run_timestep(p,v,d,t) v.y[t]=p.x[t] end end # 1. Test with Fixed Timesteps m = Model() set_dimension!(m, :time, 2000:2002) add_comp!(m, MyComp2) # ; first=2000, last=2002) set_param!(m, :MyComp2, :x, [1, 2, 3]) # N.B. `first` and `last` are now disabled. # Can't move last beyond last for a component # @test_throws ErrorException set_dimension!(m, :time, 2001:2003) set_dimension!(m, :time, 2001:2002) update_param!(m, :x, [4, 5, 6], update_timesteps = false) x = external_param(m.md, :x) @test x.values isa Mimi.TimestepArray{Mimi.FixedTimestep{2000, 1, LAST} where LAST, Union{Missing,Float64}, 1} @test x.values.data == [4., 5., 6.] # TBD: this fails, but I'm not sure how it's supposed to behave. It says: # (ERROR: BoundsError: attempt to access 3-element Array{Float64,1} at index [4]) # run(m) # @test m[:MyComp2, :y][1] == 5 # 2001 # @test m[:MyComp2, :y][2] == 6 # 2002 update_param!(m, :x, [2, 3], update_timesteps = true) x = external_param(m.md, :x) @test x.values isa Mimi.TimestepArray{Mimi.FixedTimestep{2001, 1, LAST} where LAST, Union{Missing,Float64}, 1} @test x.values.data == [2., 3.] run(m) @test m[:MyComp2, :y][1] == 2 # 2001 @test m[:MyComp2, :y][2] == 3 # 2002 # 2. Test with Variable Timesteps m = Model() set_dimension!(m, :time, [2000, 2005, 2020]) add_comp!(m, MyComp2) set_param!(m, :MyComp2, :x, [1, 2, 3]) set_dimension!(m, :time, [2005, 2020, 2050]) update_param!(m, :x, [4, 5, 6], update_timesteps = false) x = external_param(m.md, :x) @test x.values isa Mimi.TimestepArray{Mimi.VariableTimestep{(2000, 2005, 2020)}, Union{Missing,Float64}, 1} @test x.values.data == [4., 5., 6.] #run(m) #@test m[:MyComp2, :y][1] == 5 # 2005 #@test m[:MyComp2, :y][2] == 6 # 2020 update_param!(m, :x, [2, 3, 4], update_timesteps = true) x = external_param(m.md, :x) @test x.values isa Mimi.TimestepArray{Mimi.VariableTimestep{(2005, 2020, 2050)}, Union{Missing,Float64}, 1} @test x.values.data == [2., 3., 4.] run(m) @test m[:MyComp2, :y][1] == 2 # 2005 @test m[:MyComp2, :y][2] == 3 # 2020 # 3. Test updating from a dictionary m = Model() set_dimension!(m, :time, [2000, 2005, 2020]) add_comp!(m, MyComp2) set_param!(m, :MyComp2, :x, [1, 2, 3]) set_dimension!(m, :time, [2005, 2020, 2050]) update_params!(m, Dict(:x=>[2, 3, 4]), update_timesteps = true) x = external_param(m.md, :x) @test x.values isa Mimi.TimestepArray{Mimi.VariableTimestep{(2005, 2020, 2050)}, Union{Missing,Float64}, 1} @test x.values.data == [2., 3., 4.] run(m) @test m[:MyComp2, :y][1] == 2 # 2005 @test m[:MyComp2, :y][2] == 3 # 2020 @test m[:MyComp2, :y][3] == 4 # 2050 # 4. Test updating the time index to a different length m = Model() set_dimension!(m, :time, 2000:2002) # length 3 add_comp!(m, MyComp2) set_param!(m, :MyComp2, :x, [1, 2, 3]) set_dimension!(m, :time, 1999:2003) # length 5 @test_throws ErrorException update_param!(m, :x, [2, 3, 4, 5, 6], update_timesteps = false) update_param!(m, :x, [2, 3, 4, 5, 6], update_timesteps = true) x = external_param(m.md, :x) @test x.values isa Mimi.TimestepArray{Mimi.FixedTimestep{1999, 1, LAST} where LAST, Union{Missing,Float64}, 1} @test x.values.data == [2., 3., 4., 5., 6.] run(m) @test m[:MyComp2, :y] == [2., 3., 4., 5., 6.] # 5. Test all the warning and error cases @defcomp MyComp3 begin regions=Index() x=Parameter(index=[time]) # One timestep array parameter y=Parameter(index=[regions]) # One non-timestep array parameter z=Parameter() # One scalar parameter end m = Model() # Build the model set_dimension!(m, :time, 2000:2002) # Set the time dimension set_dimension!(m, :regions, [:A, :B]) add_comp!(m, MyComp3) set_param!(m, :MyComp3, :x, [1, 2, 3]) set_param!(m, :MyComp3, :y, [10, 20]) set_param!(m, :MyComp3, :z, 0) @test_throws ErrorException update_param!(m, :x, [1, 2, 3, 4]) # Will throw an error because size @test_throws ErrorException update_param!(m, :y, [10, 15], update_timesteps=true) # Not a timestep array update_param!(m, :y, [10, 15]) @test external_param(m.md, :y).values == [10., 15.] @test_throws ErrorException update_param!(m, :z, 1, update_timesteps=true) # Scalar parameter update_param!(m, :z, 1) @test external_param(m.md, :z).value == 1 # Reset the time dimensions set_dimension!(m, :time, 2005:2007) update_params!(m, Dict(:x=>[3,4,5], :y=>[10,20], :z=>0), update_timesteps=true) # Won't error when updating from a dictionary @test external_param(m.md, :x).values isa Mimi.TimestepArray{Mimi.FixedTimestep{2005,1},Union{Missing,Float64},1} @test external_param(m.md, :x).values.data == [3.,4.,5.] @test external_param(m.md, :y).values == [10.,20.] @test external_param(m.md, :z).value == 0 #------------------------------------------------------------------------------ # Test the three different set_param! methods for a Symbol type parameter #------------------------------------------------------------------------------ @defcomp A begin p1 = Parameter{Symbol}() end function _get_model() m = Model() set_dimension!(m, :time, 10) add_comp!(m, A) return m end # Test the 3-argument version of set_param! m = _get_model() @test_throws MethodError set_param!(m, :p1, 3) # Can't set it with an Int set_param!(m, :p1, :foo) # Set it with a Symbol run(m) @test m[:A, :p1] == :foo # Test the 4-argument version of set_param! m = _get_model() @test_throws MethodError set_param!(m, :A, :p1, 3) set_param!(m, :A, :p1, :foo) run(m) @test m[:A, :p1] == :foo # Test the 5-argument version of set_param! m = _get_model() @test_throws MethodError set_param!(m, :A, :p1, :A_p1, 3) set_param!(m, :A, :p1, :A_p1, :foo) run(m) @test m[:A, :p1] == :foo #------------------------------------------------------------------------------ # Test that if set_param! errors in the connection step, # the created param doesn't remain in the model's list of params #------------------------------------------------------------------------------ @defcomp A begin p1 = Parameter(index = [time]) end @defcomp B begin p1 = Parameter(index = [time]) end m = Model() set_dimension!(m, :time, 10) add_comp!(m, A) add_comp!(m, B) @test_throws ErrorException set_param!(m, :p1, 1:5) # this will error because the provided data is the wrong size @test isempty(m.md.external_params) # But it should not be added to the model's dictionary end #module
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# ThomasFermi.jl # - Thomas Fermi Density Functional Theory # Marder is: "Condensed Mattery Physics", Michael Marder, 2000. # Also very useful is Andrei Postnikov's first DFT lecture, on the Thomas-Fermi method # http://www.home.uni-osnabrueck.de/apostnik/Lectures/DFT-1.pdf """ ThomasFermi_T(n) Thomas Fermi kinetic energy (T). Following eqn. 9.69 Marder p/ 217 """ function ThomasFermi_T(n) T= (hbar^2)/(2*me) * 3/5 * (3*pi^2)^(2/3) * n^(5/3) T end " ThomasFermi_T_fnderiv(n) Thomas Fermi kinetic energy (T) as a functional derivative. Following eqn. 9.76 Marder p/ 217 " function ThomasFermi_T_fnderiv(n) # @printf("ThomasFermi_T_fnderiv(%g,%g)\n",n,V) # to locate NaN error T= (hbar^2)/(2*me) * 3/5 * (3*pi^2)^(2/3) * n^(2/3) T end " ThomasFermi_Exc(n) Thomas Fermi exchange and correlation energy; takes electron density, returns energy. Following eqn. 9.73 Marder p/ 217 " function ThomasFermi_Exc(n) Exc= - 3/4 * (3/pi)^(1/3) * q^2 * n^(5/3) Exc end " UAtomic(Z,r) Potential due to Atomic charge; simple bare Coulomb form. " function UAtomic(Z,r) U = -k_e * Z * q^2/r end function ThomasFermi_CoulombPotential(density,i,gridspacing,N) # Coulomb integral. # Nb: as spherical atom, probably need some horrible weighting parameters for the area of the spherical shell in the range [i] or [j] # i.e. currently it's some 1D pipe full of electrons with a nuclear charge at the end Potential=0.0 for j in 1:N if j==i continue end Potential+= k_e * q^2*density[j]/(gridspacing*(i-j)) end Potential end " AtomicTest(Z,N=100) Evaluate Thomas Fermi energy for a spherical atom, atomic charge Z. N: number of grid points in 1D density repr. c.f. Marder, p.219, Eqn (9.76): 'The energy of an atomic of nuclear charge Z is approximately -1.5375 Z^(7/3) Ry' " function AtomicTest(Z; N=10,verbose::Bool=false) println("AtomicTest, atomic charge: ",Z) const radius=25Å density=zeros(N).+Z/N # I know, a bit dirty... Fills density with flat electron density as initial guess. gridspacing=N/radius # Do some DFT here... V = 4/3*pi*radius^3 # Volume of total sphere of charge density considered. # Nb: Not sure if corect; Kinetic energy T is proport. to V. Defo volume and not potential? sumE=0.0 for n in 1:10 if verbose; @printf("SCF Loop: %d\n",n); end; sumE=0.0 # Central equation as (9.76) in Marder, dropping Dirac term for i in 1:N # mu being the chemical potential; this pulls together (9.76) # Mu is the Lagrange multiplier to enforce the constraint that the density is conserved; # helpfully this is also identical to the chemical potential mu=ThomasFermi_T_fnderiv(density[i]) + UAtomic(Z,i*gridspacing) + ThomasFermi_CoulombPotential(density,i,gridspacing,N) # So I gues we could use the fact that the chem potential is constant, to start moving electron density around? # From Postnikov 1.2; mu drops to zero at r=infinity, therefore mu is zero everywhere # TODO: Insert self-consistency here... if verbose @printf("\t i: %d density: %g T: %g \n\t\tmu %g = T_fnderiv %g + UAtomic: %g + Coulomb %g\n", i,density[i], ThomasFermi_T(density[i]), mu,ThomasFermi_T_fnderiv(density[i]),UAtomic(Z,i*gridspacing),ThomasFermi_CoulombPotential(density,i,gridspacing,N)) end # OK; calculate total energy E=ThomasFermi_T(density[i]) + density[i]*UAtomic(Z,i*gridspacing) + density[i]*ThomasFermi_CoulombPotential(density,i,gridspacing,N) if verbose @printf("\t\tE %g = T %g + U %g + Coulomb %g\nTotal E: %g J = %g eV\n", E,ThomasFermi_T(density[i]), density[i]*UAtomic(Z,i*gridspacing), density[i]*ThomasFermi_CoulombPotential(density,i,gridspacing,N), sumE,sumE/q) end sumE+=E # Nb: horrid hack :^) density[i]-=mu*10E35 # vary density based on how much chemical potential mu exceeds 0 if density[i]<0.0; density[i]=0.0; end end # Impose constraint sum. density = Z if verbose @printf("Sum of density pre normalisation: %f\n",sum(density)) end density=density.*Z/sum(density) end # TODO: calculate total Thomas-Fermi energy here, from density... println("Density: ",density) @printf("Total E: %g J = %g Ry = %g eV\n",sumE,sumE/Ry,sumE/q) println("Marder analytic reference: E ~= -1.5375.Z^(7/3) = ",-1.5375*Z^(7/3), " Ry") # Nb: do some unit conversions end
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using LowRankIntegrators, SparseArrays @testset "Burgers equation" begin n = 1000 # spatial discretization l = π # length of spatial domain Δx = l/n # step size x_range = Δx/2:Δx:l-Δx/2 # uniform grid # boundary conditions left(i) = i > 1 ? i - 1 : n right(i) = i < n ? i + 1 : 1 # discretized diff operators # laplacian (times viscosity) Δ = spzeros(n, n) ν = 0.005 for i in 1:n Δ[i,left(i)] = ν/Δx^2 Δ[i,i] = -2ν/Δx^2 Δ[i,right(i)] = ν/Δx^2 end # gradient ∇ = spzeros(n, n) for i in 1:n ∇[i,left(i)] = -1/2/Δx ∇[i,right(i)] = 1/2/Δx end function burgers(ρ, (∇,Δ), t) return Δ*ρ - (∇*ρ) .* ρ end # uncertainty range m = 20 # parameter realizations scale as m^2 σ = [0.5,0.5] ξ_range = [(ξ_1,ξ_2) for ξ_1 in range(-1,1,length=m), ξ_2 in range(-1,1,length=m)]; #initial condition ub(x) = 0.5*(exp.(cos.(x)) .- 1.5).*sin.(x .+ 2π*0.37) # deterministic initial condition uprime(x,ξ,σ) = σ[1]*ξ[1]*sin.(2π*x) .+ σ[2]*ξ[2]*sin.(3π*x) # stochastic fluctuation ρ0_mat = hcat([ub(x_range) + uprime(x_range, ξ, σ) for ξ in ξ_range]...) # full rank initial condition r = 5 # approximation rank lr_ρ0 = truncated_svd(ρ0_mat, r); # intial condition dt = 1e-2 # time step lr_prob = MatrixDEProblem((ρ,t) -> burgers(ρ, (∇,Δ), t), lr_ρ0, (0.0, 1.0)) # defines the matrix differential equation problem solvers = [UnconventionalAlgorithm(), ProjectorSplitting(PrimalLieTrotter()), ProjectorSplitting(DualLieTrotter()), ProjectorSplitting(Strang()), RankAdaptiveUnconventionalAlgorithm(1e-4, rmax=10)] for solver in solvers lr_sol = LowRankIntegrators.solve(lr_prob, solver, dt) # solves the low rank approximation end end
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@defcomp climatedynamics begin # Total radiative forcing radforc = Parameter(index=[time]) # Average global temperature temp = Variable(index=[time]) # lifetempconst lifetempconst = Parameter() # lifetemplin lifetemplin = Parameter() # lifetempqd lifetempqd = Parameter() # Climate sensitivity climatesensitivity = Parameter(default = 2.999999803762826) function run_timestep(p, v, d, t) if is_first(t) v.temp[t] = 0.20 else LifeTemp = max(p.lifetempconst + p.lifetemplin * p.climatesensitivity + p.lifetempqd * p.climatesensitivity^2.0, 1.0) delaytemp = 1.0 / LifeTemp temps = p.climatesensitivity / 5.35 / log(2.0) # Calculate temperature dtemp = delaytemp * temps * p.radforc[t] - delaytemp * v.temp[t - 1] v.temp[t] = v.temp[t - 1] + dtemp end end end
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module SeekerBarrier using ..Ahorn, Maple const placements = Ahorn.PlacementDict( "Seeker Barrier" => Ahorn.EntityPlacement( Maple.SeekerBarrier, "rectangle" ), ) Ahorn.minimumSize(entity::Maple.SeekerBarrier) = 8, 8 Ahorn.resizable(entity::Maple.SeekerBarrier) = true, true function Ahorn.selection(entity::Maple.SeekerBarrier) x, y = Ahorn.position(entity) width = Int(get(entity.data, "width", 8)) height = Int(get(entity.data, "height", 8)) return Ahorn.Rectangle(x, y, width, height) end function Ahorn.render(ctx::Ahorn.Cairo.CairoContext, entity::Maple.SeekerBarrier, room::Maple.Room) width = Int(get(entity.data, "width", 32)) height = Int(get(entity.data, "height", 32)) Ahorn.drawRectangle(ctx, 0, 0, width, height, (0.25, 0.25, 0.25, 0.8), (0.0, 0.0, 0.0, 0.0)) end end
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using DataFrames using LinearAlgebra """ UniqueTypes A subtype of AbstractTypes where all individuals are completely distinct. This type is the simplest AbstractTypes subtype, which identifies all individuals as unique and completely distinct from each other. """ struct UniqueTypes <: Diversity.API.AbstractTypes num::Int64 names::Vector{String} function UniqueTypes(num::Integer) num > 0 || error("Too few species") new(num, map(x -> "$x", 1:num)) end function UniqueTypes(names::Vector{String}) num = length(names) num > 0 || error("Too few species") new(num, names) end end import Diversity.API._hassimilarity _hassimilarity(::UniqueTypes) = false import Diversity.API._counttypes function _counttypes(ut::UniqueTypes, ::Bool) return ut.num end import Diversity.API._gettypenames function _gettypenames(ut::UniqueTypes, ::Bool) return ut.names end import Diversity.API._calcsimilarity function _calcsimilarity(ut::UniqueTypes, ::Real) return I end import Diversity.API._calcordinariness function _calcordinariness(::UniqueTypes, abundances::AbstractArray, ::Real) return abundances end import Diversity.API._getdiversityname _getdiversityname(::UniqueTypes) = "Unique" """ Species A subtype of AbstractTypes where all species are completely distinct. This type is the simplest AbstractTypes subtype, which identifies all species as unique and completely distinct from each other. """ const Species = UniqueTypes """ Taxonomy A subtype of AbstractTypes with similarity between related taxa, creating taxonomic similarity matrices. """ struct Taxonomy{FP <: AbstractFloat} <: Diversity.API.AbstractTypes speciesinfo::DataFrame taxa::Dict{Symbol, FP} typelabel::Symbol function Taxonomy{FP}(speciesinfo::DataFrame, taxa::Dict{Symbol, FP}, typelabel::Symbol) where FP <: AbstractFloat sort(describe(speciesinfo)[!,:variable]) == sort([keys(taxa)...]) || error("Taxon labels do not match similarity values") typelabel ∈ describe(speciesinfo)[!,:variable] || error("$typelabel not found in DataFrame column names") new{FP}(speciesinfo, taxa, typelabel) end end function Taxonomy(speciesinfo::DataFrame, taxa::Dict, typelabel::Symbol = :Species) Taxonomy{valtype(taxa)}(speciesinfo, taxa, typelabel) end import Diversity.API.floattypes function floattypes(::Taxonomy{FP}) where FP <: AbstractFloat return Set([FP]) end function _counttypes(tax::Taxonomy, ::Bool) return nrow(tax.speciesinfo) end function _gettypenames(tax::Taxonomy, ::Bool) return tax.speciesinfo[!,tax.typelabel] end function _calcsimilarity(::Taxonomy, ::Real) error("Can't generate a taxonomic similarity matrix yet") end _getdiversityname(::Taxonomy) = "Taxonomy" """ GeneralTypes{FP, M} An AbstractTypes subtype with a general similarity matrix. This subtype simply holds a matrix with similarities between individuals. # Members: - `z` A two-dimensional matrix representing similarity between individuals. """ struct GeneralTypes{FP <: AbstractFloat, M <: AbstractMatrix{FP}} <: Diversity.API.AbstractTypes """ z A two-dimensional matrix representing similarity between individuals. """ z::M """ names Optional vector of type names. """ names::Vector{String} """ # Constructor for GeneralTypes Creates an instance of the GeneralTypes class, with an arbitrary similarity matrix. """ function GeneralTypes(zmatrix::M) where {FP <: AbstractFloat, M <: AbstractMatrix{FP}} size(zmatrix, 1) == size(zmatrix, 2) || throw(DimensionMismatch("Similarity matrix is not square")) minimum(zmatrix) ≥ 0 || throw(DomainError(minimum(zmatrix), "Similarities must be ≥ 0")) maximum(zmatrix) ≤ 1 || @warn "Similarity matrix has values above 1" new{FP, M}(zmatrix, map(x -> "$x", 1:size(zmatrix, 1))) end function GeneralTypes(zmatrix::M, names::Vector{String}) where {FP <: AbstractFloat, M <: AbstractMatrix{FP}} size(zmatrix, 1) == size(zmatrix, 2) || throw(DimensionMismatch("Similarity matrix is not square")) minimum(zmatrix) ≥ 0 || throw(DomainError(minimum(zmatrix), "Similarities must be ≥ 0")) maximum(zmatrix) ≤ 1 || @warn "Similarity matrix has values above 1" length(names) == size(zmatrix, 1) || error("Species name vector does not match similarity matrix") new{FP, M}(zmatrix, names) end end function floattypes(::GeneralTypes{FP, M}) where {FP <: AbstractFloat, M <: AbstractMatrix{FP}} return Set([FP]) end function _counttypes(gt::GeneralTypes, ::Bool) return size(gt.z, 1) end function _gettypenames(gt::GeneralTypes, ::Bool) return gt.names end function _calcsimilarity(gt::GeneralTypes, ::Real) return gt.z end _getdiversityname(::GeneralTypes) = "Arbitrary Z"
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2.622
2,000
using BRIKHEAD using Documenter makedocs(; modules=[BRIKHEAD], authors="Zaki A <zaki@live.ca> and contributors", repo="https://github.com/notZaki/BRIKHEAD.jl/blob/{commit}{path}#L{line}", sitename="BRIKHEAD.jl", format=Documenter.HTML(; prettyurls=get(ENV, "CI", "false") == "true", canonical="https://notZaki.github.io/BRIKHEAD.jl", assets=String[], ), pages=[ "Home" => "index.md", ], ) deploydocs(; repo="github.com/notZaki/BRIKHEAD.jl", )
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2.102459
244
using IterativeSolvers using Base.Test @testset "SVD Lanczos" begin srand(1234567) #Thick restart methods @testset "Thick restart with method=$method" for method in (:ritz, :harmonic) for T in (Float32, Float64) @testset "Diagonal Matrix{$T}" begin n = 30 ns = 5 tol = 1e-5 A = full(Diagonal(T[1.0 : n;])) q = ones(T, n) / √n σ, L, history = svdl(A, nsv=ns, v0=q, tol=tol, reltol=tol, maxiter=n, method=method, vecs=:none, log=true) @test isa(history, ConvergenceHistory) @test norm(σ - [n : -1.0 : n - 4;]) < 5^2 * 1e-5 @test_throws ArgumentError svdl(A, nsv=ns, v0=q, tol=tol, reltol=tol, maxiter=n, method=:fakemethod, vecs=:none) #Check the singular vectors also Σ, L = svdl(A, nsv=ns, v0=q, tol=tol, reltol=tol, maxiter=n, method=method, vecs=:both) #The vectors should have the structure # [ 0 0 ... 0 ] # [ ... ] # [ 0 0 ... ±1 ] # [ 0 ... ] # [ 0 ±1 ... 0 ] # [±1 0 ... 0 ] # and furthermore the signs should be aligned across Σ[:U] and Σ[:V] for i = 1 : 5 Σ[:U][end + 1 - i, i] -= sign(Σ[:U][end + 1 - i, i]) end @test vecnorm(Σ[:U]) < σ[1] * √tol for i = 1 : 5 Σ[:Vt][i, end + 1 - i] -= sign(Σ[:Vt][i, end + 1 - i]) end @test vecnorm(Σ[:U]) < σ[1] * √tol @test norm(σ - Σ[:S]) < 2max(tol * ns * σ[1], tol) #Issue #55 let σ1, _ = svdl(A, nsv=1, tol=tol, reltol=tol) @test abs(σ[1] - σ1[1]) < 2max(tol * σ[1], tol) end end @testset "Rectangular Matrix{$T}" begin srand(1) m = 300 n = 200 k = 5 l = 10 A = randn(T, m, n) q = randn(T, n) |> x -> x / norm(x) σ, L = svdl(A, nsv=k, k=l, v0=q, tol=1e-5, maxiter=30, method=method) @test norm(σ - svdvals(A)[1 : k]) < k^2 * 1e-5 end end end end #svdl @testset "BrokenArrowBidiagonal" begin B = IterativeSolvers.BrokenArrowBidiagonal([1, 2, 3], [1, 2], Int[]) @test full(B) == [1 0 1; 0 2 2; 0 0 3] @test B[3,3] == 3 @test B[2,3] == 2 @test B[3,2] == 0 @test B[1,3] == 1 @test size(B) == (3,3) @test_throws ArgumentError size(B,3) @test_throws BoundsError B[1,5] end
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# --- # title: 127. Word Ladder # id: problem127 # author: AquaIndigo # date: 2020-11-07 # difficulty: Medium # categories: Breadth-first Search # link: <https://leetcode.com/problems/word-ladder/description/> # hidden: true # --- # # Given two words ( _beginWord_ and _endWord_ ), and a dictionary's word list, # find the length of shortest transformation sequence from _beginWord_ to # _endWord_ , such that: # # 1. Only one letter can be changed at a time. # 2. Each transformed word must exist in the word list. # # **Note:** # # * Return 0 if there is no such transformation sequence. # * All words have the same length. # * All words contain only lowercase alphabetic characters. # * You may assume no duplicates in the word list. # * You may assume _beginWord_ and _endWord_ are non-empty and are not the same. # # **Example 1:** # # # # Input: # beginWord = "hit", # endWord = "cog", # wordList = ["hot","dot","dog","lot","log","cog"] # # Output: 5 # # Explanation: As one shortest transformation is "hit" -> "hot" -> "dot" -> "dog" -> "cog", # return its length 5. # # # **Example 2:** # # # # Input: # beginWord = "hit" # endWord = "cog" # wordList = ["hot","dot","dog","lot","log"] # # Output: 0 # # Explanation: The endWord "cog" is not in wordList, therefore no possible ** ** transformation. # # # ## @lc code=start using LeetCode using DataStructures function ladder_length(begin_word::String, end_word::String, word_list::Vector{String})::Int function isadj(s1, s2) flg = false for i in 1:length(s1) if s1[i] != s2[i] if flg return false else flg = true end end end return true end if !(end_word in word_list) return 0 end !(begin_word in word_list) && push!(word_list, begin_word) s, t = findall(x -> x == begin_word, word_list)[1], findall(x -> x == end_word, word_list)[1] ## println(s, ", ", t) ### wl = collect.(word_list) len = length(word_list) edges = [Set{Int}() for i in 1:len] ### construct adj list for i in 1:len for j in (i + 1):len if isadj(word_list[i], word_list[j]) push!(edges[i], j) push!(edges[j], i) end end end ## BFS qs, qt = Queue{Int}(), Queue{Int}() dists = [0 for i in 1:len] visited = [0 for i in 1:len] enqueue!(qs, s) enqueue!(qt, t) visited[s] = 1 visited[t] = 2 dists[s] = dists[t] = 1 while !isempty(qs) && !isempty(qt) rt1, rt2 = dequeue!(qs), dequeue!(qt) dis1, dis2 = dists[rt1] + 1, dists[rt2] + 1 ## println(rt1, " ", dis1) for neib in edges[rt1] if visited[neib] == 2 return dis1 + dists[neib] - 1 elseif visited[neib] == 0 visited[neib] = 1 enqueue!(qs, neib) dists[neib] = dis1 end end for neib in edges[rt2] if visited[neib] == 1 return dis2 + dists[neib] - 1 elseif visited[neib] == 0 visited[neib] = 2 enqueue!(qt, neib) dists[neib] = dis2 end end end return 0 end ## @lc code=end
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if basename(pwd()) == "aoc" cd("2019/6") end struct Planet name parent children end Planet(name) = Planet(name, nothing, Planet[]) function Base.getindex(planet::Planet, name::AbstractString) if planet.name == name planet else for child in planet.children result = child[name] if !isnothing(result) return result end end end end Base.haskey(planet::Planet, name::AbstractString) = !isnothing(planet[name]) function loadsystem(filename::AbstractString) system = Planet("COM") lines = readlines(filename) while length(lines) > 0 line = popfirst!(lines) parent, child = split(line, ')') if haskey(system, parent) push!(system[parent].children, Planet(child, system[parent], Planet[])) else push!(lines, line) end end system end function orbits(planet::Planet) depth(planet) * length(planet.children) + sum(orbits.(planet.children), init = 0) end depth(::Nothing) = 0 depth(planet::Planet) = 1 + depth(planet.parent) function parents(planet::Planet) if isnothing(planet.parent) [planet] else [planet, parents(planet.parent)...] end end part1(filename::AbstractString) = orbits(loadsystem(filename)) @assert part1("example.txt") == 42 function part2(filename::AbstractString) system = loadsystem(filename) you = system["YOU"].parent santa = system["SAN"].parent common = first(intersect(parents(you), parents(santa))) depth(you) + depth(santa) - 2 * depth(common) end @assert part2("example2.txt") == 4 part2("input.txt")
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2.491779
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# Core types and definitions if VERSION < v"0.5.0-dev" macro pure(ex) esc(ex) end else using Base: @pure end const Symbols = Tuple{Symbol,Vararg{Symbol}} @doc """ Type-stable axis-specific indexing and identification with a parametric type. ### Type parameters ```julia immutable Axis{name,T} ``` * `name` : the name of the axis, a Symbol * `T` : the type of the axis ### Constructors ```julia Axis{name}(I) ``` ### Arguments * `name` : the axis name Symbol or integer dimension * `I` : the indexer, any indexing type that the axis supports ### Examples Here is an example with a Dimensional axis representing a time sequence along rows and a Categorical axis of Symbols for column headers. ```julia A = AxisArray(reshape(1:60, 12, 5), .1:.1:1.2, [:a, :b, :c, :d, :e]) A[Axis{:col}(2)] # grabs the second column A[Axis{:col}(:b)] # Same as above, grabs column :b (the second column) A[Axis{:row}(2)] # grabs the second row A[Axis{2}(2:5)] # grabs the second through 5th columns ``` """ -> immutable Axis{name,T} val::T end # Constructed exclusively through Axis{:symbol}(...) or Axis{1}(...) (::Type{Axis{name}}){name,T}(I::T=()) = Axis{name,T}(I) Base.:(==){name}(A::Axis{name}, B::Axis{name}) = A.val == B.val Base.hash{name}(A::Axis{name}, hx::UInt) = hash(A.val, hash(name, hx)) axistype{name,T}(::Axis{name,T}) = T axistype{name,T}(::Type{Axis{name,T}}) = T # Pass indexing and related functions straight through to the wrapped value # TODO: should Axis be an AbstractArray? AbstractArray{T,0} for scalar T? Base.getindex(A::Axis, i...) = A.val[i...] Base.eltype{_,T}(::Type{Axis{_,T}}) = eltype(T) Base.size(A::Axis) = size(A.val) Base.endof(A::Axis) = length(A) Base.indices(A::Axis) = indices(A.val) Base.indices(A::Axis, d) = indices(A.val, d) Base.length(A::Axis) = length(A.val) (A::Axis{name}){name}(i) = Axis{name}(i) Base.convert{name,T}(::Type{Axis{name,T}}, ax::Axis{name,T}) = ax Base.convert{name,T}(::Type{Axis{name,T}}, ax::Axis{name}) = Axis{name}(convert(T, ax.val)) @doc """ An AxisArray is an AbstractArray that wraps another AbstractArray and adds axis names and values to each array dimension. AxisArrays can be indexed by using the named axes as an alternative to positional indexing by dimension. Other advanced indexing along axis values are also provided. ### Type parameters The AxisArray contains several type parameters: ```julia immutable AxisArray{T,N,D,Ax} <: AbstractArray{T,N} ``` * `T` : the elemental type of the AbstractArray * `N` : the number of dimensions * `D` : the type of the wrapped AbstractArray * `Ax` : the names and types of the axes, as a (specialized) NTuple{N, Axis} ### Constructors ```julia AxisArray(A::AbstractArray, axes::Axis...) AxisArray(A::AbstractArray, names::Symbol...) AxisArray(A::AbstractArray, vectors::AbstractVector...) AxisArray(A::AbstractArray, (names...,), (steps...,), [(offsets...,)]) ``` ### Arguments * `A::AbstractArray` : the wrapped array data * `axes` or `names` or `vectors` : dimensional information for the wrapped array The dimensional information may be passed in one of three ways and is entirely optional. When the axis name or value is missing for a dimension, a default is substituted. The default axis names for dimensions `(1, 2, 3, 4, 5, ...)` are `(:row, :col, :page, :dim_4, :dim_5, ...)`. The default axis values are `indices(A, d)` for each missing dimension `d`. ### Indexing Indexing returns a view into the original data. The returned view is a new AxisArray that wraps a SubArray. Indexing should be type stable. Use `Axis{axisname}(idx)` to index based on a specific axis. `axisname` is a Symbol specifying the axis to index/slice, and `idx` is a normal indexing object (`Int`, `Array{Int,1}`, etc.) or a custom indexing type for that particular type of axis. Two main types of axes supported by default include: * Categorical axis -- These are vectors of labels, normally Symbols or strings. Elements or slices can be indexed by elements or vectors of elements. * Dimensional axis -- These are sorted vectors or iterators that can be indexed by `ClosedInterval()`. These are commonly used for sequences of times or date-times. For regular sample rates, ranges can be used. User-defined axis types can be added along with custom indexing behaviors. To add add a custom type as a Categorical or Dimensional axis, add a trait using `AxisArrays.axistrait`. Here is the example of adding a custom Dimensional axis: ```julia AxisArrays.axistrait(v::MyCustomAxis) = AxisArrays.Dimensional ``` For more advanced indexing, you can define custom methods for `AxisArrays.axisindexes`. ### Examples Here is an example with a Dimensional axis representing a time sequence along rows (it's a FloatRange) and a Categorical axis of Symbols for column headers. ```julia A = AxisArray(reshape(1:15, 5, 3), Axis{:time}(.1:.1:0.5), Axis{:col}([:a, :b, :c])) A[Axis{:time}(1:3)] # equivalent to A[1:3,:] A[Axis{:time}(ClosedInterval(.2,.4))] # restrict the AxisArray along the time axis A[ClosedInterval(0.,.3), [:a, :c]] # select an interval and two columns ``` """ -> immutable AxisArray{T,N,D,Ax} <: AbstractArray{T,N} data::D # D <:AbstractArray, enforced in constructor to avoid dispatch bugs (https://github.com/JuliaLang/julia/issues/6383) axes::Ax # Ax<:NTuple{N, Axis}, but with specialized Axis{...} types (::Type{AxisArray{T,N,D,Ax}}){T,N,D,Ax}(data::AbstractArray{T,N}, axs::Tuple{Vararg{Axis,N}}) = new{T,N,D,Ax}(data, axs) end # Helper functions: Default axis names (if not provided) _defaultdimname(i) = i == 1 ? (:row) : i == 2 ? (:col) : i == 3 ? (:page) : Symbol(:dim_, i) # Why doesn't @pure work here? @generated function _nextaxistype{M}(axs::NTuple{M,Axis}) name = _defaultdimname(M+1) :(Axis{$(Expr(:quote, name))}) end """ default_axes(A::AbstractArray) default_axes(A::AbstractArray, axs) Return a tuple of Axis objects that appropriately index into the array A. The optional second argument can take a tuple of vectors or axes, which will be wrapped with the appropriate axis name, and it will ensure no axis goes beyond the dimensionality of the array A. """ @inline default_axes(A::AbstractArray, args=indices(A)) = _default_axes(A, args, ()) _default_axes{T,N}(A::AbstractArray{T,N}, args::Tuple{}, axs::NTuple{N,Axis}) = axs _default_axes{T,N}(A::AbstractArray{T,N}, args::Tuple{Any, Vararg{Any}}, axs::NTuple{N,Axis}) = throw(ArgumentError("too many axes provided")) _default_axes{T,N}(A::AbstractArray{T,N}, args::Tuple{Axis, Vararg{Any}}, axs::NTuple{N,Axis}) = throw(ArgumentError("too many axes provided")) @inline _default_axes{T,N}(A::AbstractArray{T,N}, args::Tuple{}, axs::Tuple) = _default_axes(A, args, (axs..., _nextaxistype(axs)(indices(A, length(axs)+1)))) @inline _default_axes{T,N}(A::AbstractArray{T,N}, args::Tuple{Any, Vararg{Any}}, axs::Tuple) = _default_axes(A, Base.tail(args), (axs..., _nextaxistype(axs)(args[1]))) @inline _default_axes{T,N}(A::AbstractArray{T,N}, args::Tuple{Axis, Vararg{Any}}, axs::Tuple) = _default_axes(A, Base.tail(args), (axs..., args[1])) # Axis consistency checks — ensure sizes match and the names are unique @inline checksizes(axs, sz) = (length(axs[1]) == sz[1]) & checksizes(tail(axs), tail(sz)) checksizes(::Tuple{}, sz) = true @inline function checknames(name::Symbol, names...) matches = false for n in names matches |= name == n end matches && throw(ArgumentError("axis name :$name is used more than once")) checknames(names...) end checknames(name, names...) = throw(ArgumentError("the Axis names must be Symbols")) checknames() = () # The primary AxisArray constructors — specify an array to wrap and the axes AxisArray(A::AbstractArray, vects::Union{AbstractVector, Axis}...) = AxisArray(A, vects) AxisArray(A::AbstractArray, vects::Tuple{Vararg{Union{AbstractVector, Axis}}}) = AxisArray(A, default_axes(A, vects)) function AxisArray{T,N}(A::AbstractArray{T,N}, axs::NTuple{N,Axis}) checksizes(axs, _size(A)) || throw(ArgumentError("the length of each axis must match the corresponding size of data")) checknames(axisnames(axs...)...) AxisArray{T,N,typeof(A),typeof(axs)}(A, axs) end # Simple non-type-stable constructors to specify names as symbols AxisArray(A::AbstractArray) = AxisArray(A, ()) # Disambiguation AxisArray(A::AbstractArray, names::Symbol...) = (inds = indices(A); AxisArray(A, ntuple(i->Axis{names[i]}(inds[i]), length(names)))) function AxisArray{T,N}(A::AbstractArray{T,N}, names::NTuple{N,Symbol}, steps::NTuple{N,Number}, offsets::NTuple{N,Number}=map(zero, steps)) axs = ntuple(i->Axis{names[i]}(range(offsets[i], steps[i], size(A,i))), N) AxisArray(A, axs...) end # Traits immutable HasAxes{B} end HasAxes{A<:AxisArray}(::Type{A}) = HasAxes{true}() HasAxes{A<:AbstractArray}(::Type{A}) = HasAxes{false}() HasAxes(A::AbstractArray) = HasAxes(typeof(A)) # Axis definitions @doc """ axisdim(::AxisArray, ::Axis) -> Int axisdim(::AxisArray, ::Type{Axis}) -> Int Given an AxisArray and an Axis, return the integer dimension of the Axis within the array. """ -> axisdim(A::AxisArray, ax::Axis) = axisdim(A, typeof(ax)) @generated function axisdim{T<:Axis}(A::AxisArray, ax::Type{T}) dim = axisdim(A, T) :($dim) end # The actual computation is done in the type domain, which is a little tricky # due to type invariance. axisdim{T,N,D,Ax,name,S}(A::Type{AxisArray{T,N,D,Ax}}, ::Type{Axis{name,S}}) = axisdim(A, Axis{name}) function axisdim{T,N,D,Ax,name}(::Type{AxisArray{T,N,D,Ax}}, ::Type{Axis{name}}) isa(name, Int) && return name <= N ? name : error("axis $name greater than array dimensionality $N") names = axisnames(Ax) idx = findfirst(names, name) idx == 0 && error("axis $name not found in array axes $names") idx end # Base definitions that aren't provided by AbstractArray @inline Base.size(A::AxisArray) = size(A.data) @inline Base.size(A::AxisArray, Ax::Axis) = size(A.data, axisdim(A, Ax)) @inline Base.size{Ax<:Axis}(A::AxisArray, ::Type{Ax}) = size(A.data, axisdim(A, Ax)) @inline Base.indices(A::AxisArray) = indices(A.data) @inline Base.indices(A::AxisArray, Ax::Axis) = indices(A.data, axisdim(A, Ax)) @inline Base.indices{Ax<:Axis}(A::AxisArray, ::Type{Ax}) = indices(A.data, axisdim(A, Ax)) Base.convert{T,N}(::Type{Array{T,N}}, A::AxisArray{T,N}) = convert(Array{T,N}, A.data) Base.parent(A::AxisArray) = A.data # Similar is tricky. If we're just changing the element type, it can stay as an # AxisArray. But if we're changing dimensions, there's no way it can know how # to keep track of the axes, so just punt and return a regular old Array. # TODO: would it feel more consistent to return an AxisArray without any axes? Base.similar{S}(A::AxisArray, ::Type{S}) = (d = similar(A.data, S); AxisArray(d, A.axes)) Base.similar{S,N}(A::AxisArray, ::Type{S}, dims::Dims{N}) = similar(A.data, S, dims) # If, however, we pass Axis objects containing the new axis for that dimension, # we can return a similar AxisArray with an appropriately modified size Base.similar{T}(A::AxisArray{T}, ax1::Axis, axs::Axis...) = similar(A, T, (ax1, axs...)) Base.similar{S}(A::AxisArray, ::Type{S}, ax1::Axis, axs::Axis...) = similar(A, S, (ax1, axs...)) @generated function Base.similar{T,S,N}(A::AxisArray{T,N}, ::Type{S}, axs::Tuple{Axis,Vararg{Axis}}) inds = Expr(:tuple) ax = Expr(:tuple) for d=1:N push!(inds.args, :(indices(A, Axis{$d}))) push!(ax.args, :(axes(A, Axis{$d}))) end to_delete = Int[] for i=1:length(axs.parameters) a = axs.parameters[i] d = axisdim(A, a) axistype(a) <: Tuple{} && push!(to_delete, d) inds.args[d] = :(indices(axs[$i].val, 1)) ax.args[d] = :(axs[$i]) end sort!(to_delete) deleteat!(inds.args, to_delete) deleteat!(ax.args, to_delete) quote d = similar(A.data, S, $inds) AxisArray(d, $ax) end end # These methods allow us to preserve the AxisArray under reductions # Note that we only extend the following two methods, and then have it # dispatch to package-local `reduced_indices` and `reduced_indices0` # methods. This avoids a whole slew of ambiguities. if VERSION == v"0.5.0" Base.reduced_dims(A::AxisArray, region) = reduced_indices(axes(A), region) Base.reduced_dims0(A::AxisArray, region) = reduced_indices0(axes(A), region) else Base.reduced_indices(A::AxisArray, region) = reduced_indices(axes(A), region) Base.reduced_indices0(A::AxisArray, region) = reduced_indices0(axes(A), region) end reduced_indices{N}(axs::Tuple{Vararg{Axis,N}}, ::Tuple{}) = axs reduced_indices0{N}(axs::Tuple{Vararg{Axis,N}}, ::Tuple{}) = axs reduced_indices{N}(axs::Tuple{Vararg{Axis,N}}, region::Integer) = reduced_indices(axs, (region,)) reduced_indices0{N}(axs::Tuple{Vararg{Axis,N}}, region::Integer) = reduced_indices0(axs, (region,)) reduced_indices{N}(axs::Tuple{Vararg{Axis,N}}, region::Dims) = map((ax,d)->d∈region ? reduced_axis(ax) : ax, axs, ntuple(identity, Val{N})) reduced_indices0{N}(axs::Tuple{Vararg{Axis,N}}, region::Dims) = map((ax,d)->d∈region ? reduced_axis0(ax) : ax, axs, ntuple(identity, Val{N})) @inline reduced_indices{Ax<:Axis}(axs::Tuple{Vararg{Axis}}, region::Type{Ax}) = _reduced_indices(reduced_axis, (), region, axs...) @inline reduced_indices0{Ax<:Axis}(axs::Tuple{Vararg{Axis}}, region::Type{Ax}) = _reduced_indices(reduced_axis0, (), region, axs...) @inline reduced_indices(axs::Tuple{Vararg{Axis}}, region::Axis) = _reduced_indices(reduced_axis, (), region, axs...) @inline reduced_indices0(axs::Tuple{Vararg{Axis}}, region::Axis) = _reduced_indices(reduced_axis0, (), region, axs...) reduced_indices(axs::Tuple{Vararg{Axis}}, region::Tuple) = reduced_indices(reduced_indices(axs, region[1]), tail(region)) reduced_indices(axs::Tuple{Vararg{Axis}}, region::Tuple{Vararg{Axis}}) = reduced_indices(reduced_indices(axs, region[1]), tail(region)) reduced_indices0(axs::Tuple{Vararg{Axis}}, region::Tuple) = reduced_indices0(reduced_indices0(axs, region[1]), tail(region)) reduced_indices0(axs::Tuple{Vararg{Axis}}, region::Tuple{Vararg{Axis}}) = reduced_indices0(reduced_indices0(axs, region[1]), tail(region)) @pure samesym{n1,n2}(::Type{Axis{n1}}, ::Type{Axis{n2}}) = Val{n1==n2}() samesym{n1,n2,T1,T2}(::Type{Axis{n1,T1}}, ::Type{Axis{n2,T2}}) = samesym(Axis{n1},Axis{n2}) samesym{n1,n2}(::Type{Axis{n1}}, ::Axis{n2}) = samesym(Axis{n1}, Axis{n2}) samesym{n1,n2}(::Axis{n1}, ::Type{Axis{n2}}) = samesym(Axis{n1}, Axis{n2}) samesym{n1,n2}(::Axis{n1}, ::Axis{n2}) = samesym(Axis{n1}, Axis{n2}) @inline _reduced_indices{Ax<:Axis}(f, out, chosen::Type{Ax}, ax::Axis, axs...) = __reduced_indices(f, out, samesym(chosen, ax), chosen, ax, axs) @inline _reduced_indices(f, out, chosen::Axis, ax::Axis, axs...) = __reduced_indices(f, out, samesym(chosen, ax), chosen, ax, axs) _reduced_indices(f, out, chosen) = out @inline __reduced_indices(f, out, ::Val{true}, chosen, ax, axs) = _reduced_indices(f, (out..., f(ax)), chosen, axs...) @inline __reduced_indices(f, out, ::Val{false}, chosen, ax, axs) = _reduced_indices(f, (out..., ax), chosen, axs...) reduced_axis(ax) = ax(oftype(ax.val, Base.OneTo(1))) reduced_axis0(ax) = ax(oftype(ax.val, length(ax.val) == 0 ? Base.OneTo(0) : Base.OneTo(1))) function Base.permutedims(A::AxisArray, perm) p = permutation(perm, axisnames(A)) AxisArray(permutedims(A.data, p), axes(A)[[p...]]) end Base.transpose{T}(A::AxisArray{T,2}) = AxisArray(transpose(A.data), A.axes[2], A.axes[1]) Base.ctranspose{T}(A::AxisArray{T,2}) = AxisArray(ctranspose(A.data), A.axes[2], A.axes[1]) Base.transpose{T}(A::AxisArray{T,1}) = AxisArray(transpose(A.data), Axis{:transpose}(Base.OneTo(1)), A.axes[1]) Base.ctranspose{T}(A::AxisArray{T,1}) = AxisArray(ctranspose(A.data), Axis{:transpose}(Base.OneTo(1)), A.axes[1]) Base.map!{F}(f::F, A::AxisArray) = (map!(f, A.data); A) Base.map(f, A::AxisArray) = AxisArray(map(f, A.data), A.axes...) function Base.map!{F,T,N,D,Ax<:Tuple{Vararg{Axis}}}(f::F, dest::AxisArray{T,N,D,Ax}, As::AxisArray{T,N,D,Ax}...) matchingdims((dest, As...)) || error("All axes must be identically-valued") data = map(a -> a.data, As) map!(f, dest.data, data...) return dest end function Base.map{T,N,D,Ax<:Tuple{Vararg{Axis}}}(f, As::AxisArray{T,N,D,Ax}...) matchingdims(As) || error("All axes must be identically-valued") data = map(a -> a.data, As) return AxisArray(map(f, data...), As[1].axes...) end permutation(to::Union{AbstractVector{Int},Tuple{Int,Vararg{Int}}}, from::Symbols) = to """ permutation(to, from) -> p Calculate the permutation of labels in `from` to produce the order in `to`. Any entries in `to` that are missing in `from` will receive an index of 0. Any entries in `from` that are missing in `to` will have their indices appended to the end of the permutation. Consequently, the length of `p` is equal to the longer of `to` and `from`. """ function permutation(to::Symbols, from::Symbols) n = length(to) nf = length(from) li = linearindices(from) d = Dict(from[i]=>i for i in li) covered = similar(dims->falses(length(li)), li) ind = Array{Int}(max(n, nf)) for (i,toi) in enumerate(to) j = get(d, toi, 0) ind[i] = j if j != 0 covered[j] = true end end k = n for i in li if !covered[i] d[from[i]] != i && throw(ArgumentError("$(from[i]) is a duplicated argument")) k += 1 k > nf && throw(ArgumentError("no incomplete containment allowed in $to and $from")) ind[k] = i end end ind end function Base.squeeze(A::AxisArray, dims::Dims) keepdims = setdiff(1:ndims(A), dims) AxisArray(squeeze(A.data, dims), axes(A)[keepdims]) end # This version is type-stable function Base.squeeze{Ax<:Axis}(A::AxisArray, ::Type{Ax}) dim = axisdim(A, Ax) AxisArray(squeeze(A.data, dim), dropax(Ax, axes(A)...)) end @inline dropax(ax, ax1, axs...) = (ax1, dropax(ax, axs...)...) @inline dropax{name}(ax::Axis{name}, ax1::Axis{name}, axs...) = dropax(ax, axs...) @inline dropax{name}(ax::Type{Axis{name}}, ax1::Axis{name}, axs...) = dropax(ax, axs...) @inline dropax{name,T}(ax::Type{Axis{name,T}}, ax1::Axis{name}, axs...) = dropax(ax, axs...) dropax(ax) = () # A simple display method to include axis information. It might be nice to # eventually display the axis labels alongside the data array, but that is # much more difficult. function summaryio(io::IO, A::AxisArray) _summary(io, A) for (name, val) in zip(axisnames(A), axisvalues(A)) print(io, " :$name, ") show(IOContext(io, :limit=>true), val) println(io) end print(io, "And data, a ", summary(A.data)) end _summary{T,N}(io, A::AxisArray{T,N}) = println(io, "$N-dimensional AxisArray{$T,$N,...} with axes:") function Base.summary(A::AxisArray) io = IOBuffer() summaryio(io, A) String(io) end # Custom methods specific to AxisArrays @doc """ axisnames(A::AxisArray) -> (Symbol...) axisnames(::Type{AxisArray{...}}) -> (Symbol...) axisnames(ax::Axis...) -> (Symbol...) axisnames(::Type{Axis{...}}...) -> (Symbol...) Returns the axis names of an AxisArray or list of Axises as a tuple of Symbols. """ -> axisnames{T,N,D,Ax}(::AxisArray{T,N,D,Ax}) = _axisnames(Ax) axisnames{T,N,D,Ax}(::Type{AxisArray{T,N,D,Ax}}) = _axisnames(Ax) axisnames{Ax<:Tuple{Vararg{Axis}}}(::Type{Ax}) = _axisnames(Ax) @pure _axisnames(Ax) = axisnames(Ax.parameters...) axisnames() = () @inline axisnames{name }(::Axis{name}, B::Axis...) = tuple(name, axisnames(B...)...) @inline axisnames{name }(::Type{Axis{name}}, B::Type...) = tuple(name, axisnames(B...)...) @inline axisnames{name,T}(::Type{Axis{name,T}}, B::Type...) = tuple(name, axisnames(B...)...) axisname{name,T}(::Type{Axis{name,T}}) = name axisname{name }(::Type{Axis{name }}) = name axisname(ax::Axis) = axisname(typeof(ax)) @doc """ axisvalues(A::AxisArray) -> (AbstractVector...) axisvalues(ax::Axis...) -> (AbstractVector...) Returns the axis values of an AxisArray or list of Axises as a tuple of vectors. """ -> axisvalues(A::AxisArray) = axisvalues(A.axes...) axisvalues() = () axisvalues(ax::Axis, axs::Axis...) = tuple(ax.val, axisvalues(axs...)...) @doc """ axes(A::AxisArray) -> (Axis...) axes(A::AxisArray, ax::Axis) -> Axis axes(A::AxisArray, dim::Int) -> Axis Returns the tuple of axis vectors for an AxisArray. If an specific `Axis` is specified, then only that axis vector is returned. Note that when extracting a single axis vector, `axes(A, Axis{1})`) is type-stable and will perform better than `axes(A)[1]`. For an AbstractArray without `Axis` information, `axes` returns the default axes, i.e., those that would be produced by `AxisArray(A)`. """ -> axes(A::AxisArray) = A.axes axes(A::AxisArray, dim::Int) = A.axes[dim] axes(A::AxisArray, ax::Axis) = axes(A, typeof(ax)) @generated function axes{T<:Axis}(A::AxisArray, ax::Type{T}) dim = axisdim(A, T) :(A.axes[$dim]) end axes(A::AbstractArray) = default_axes(A) axes(A::AbstractArray, dim::Int) = default_axes(A)[dim] ### Axis traits ### @compat abstract type AxisTrait end immutable Dimensional <: AxisTrait end immutable Categorical <: AxisTrait end immutable Unsupported <: AxisTrait end axistrait(::Any) = Unsupported axistrait(ax::Axis) = axistrait(ax.val) axistrait{T<:Union{Number, Dates.AbstractTime}}(::AbstractVector{T}) = Dimensional axistrait{T<:Union{Symbol, AbstractString}}(::AbstractVector{T}) = Categorical checkaxis(ax::Axis) = checkaxis(ax.val) checkaxis(ax) = checkaxis(axistrait(ax), ax) checkaxis(::Type{Unsupported}, ax) = nothing # TODO: warn or error? # Dimensional axes must be monotonically increasing checkaxis(::Type{Dimensional}, ax) = issorted(ax) || throw(ArgumentError("Dimensional axes must be monotonically increasing")) # Categorical axes must simply be unique function checkaxis(::Type{Categorical}, ax) seen = Set{eltype(ax)}() for elt in ax if elt in seen throw(ArgumentError("Categorical axes must be unique")) end push!(seen, elt) end end _length(A::AbstractArray) = length(linearindices(A)) _length(A) = length(A) _size(A::AbstractArray) = map(length, indices(A)) _size(A) = size(A) _size(A::AbstractArray, d) = length(indices(A, d)) _size(A, d) = size(A, d)
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# This file is a part of BAT.jl, licensed under the MIT License (MIT). using Test Test.@testset "Package BAT" begin include("utils/test_utils.jl") include("rngs/test_rngs.jl") include("distributions/test_distributions.jl") include("parameters/test_parameters.jl") include("statistics/test_statistics.jl") include("densities/test_densities.jl") include("samplers/test_samplers.jl") include("io/test_io.jl") include("plotting/test_plotting.jl") end
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# --- # title: 23. Merge k Sorted Lists # id: problem23 # author: Indigo # date: 2021-04-14 # difficulty: Hard # categories: Linked List, Divide and Conquer, Heap # link: <https://leetcode.com/problems/merge-k-sorted-lists/description/> # hidden: true # --- # # You are given an array of `k` linked-lists `lists`, each linked-list is sorted # in ascending order. # # _Merge all the linked-lists into one sorted linked-list and return it._ # # # # **Example 1:** # # # # Input: lists = [[1,4,5],[1,3,4],[2,6]] # Output: [1,1,2,3,4,4,5,6] # Explanation: The linked-lists are: # [ # 1->4->5, # 1->3->4, # 2->6 # ] # merging them into one sorted list: # 1->1->2->3->4->4->5->6 # # # **Example 2:** # # # # Input: lists = [] # Output: [] # # # **Example 3:** # # # # Input: lists = [[]] # Output: [] # # # # # **Constraints:** # # * `k == lists.length` # * `0 <= k <= 10^4` # * `0 <= lists[i].length <= 500` # * `-10^4 <= lists[i][j] <= 10^4` # * `lists[i]` is sorted in **ascending order**. # * The sum of `lists[i].length` won't exceed `10^4`. # # ## @lc code=start using LeetCode Base.isless(l1::ListNode, l2::ListNode) = l1.val < l2.val function merge_k_lists(lists::Vector{T}) where {T<:Union{ListNode{Int},Nothing}} pq = BinaryMinHeap{ListNode{Int}}() for l in lists isnothing(l) || push!(pq, l) end res = ListNode{Int}() p = res while !isempty(pq) tp_list = pop!(pq) p = p.next = tp_list tp_list = tp_list.next isnothing(tp_list) || push!(pq, tp_list) end return res.next end ## @lc code=end
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using Plots using FFTW tan_taylor(x) = x+( x^3/3 )+( x^5*2/15.0 )+( x^7*17/315.0 )+( x^9*62/2835.0 )+( x^11*1382/155925.0 )+( x^13*21844/6081075.0 )+( x^15*929569/638512875.0 ); x = LinRange(-π/2+0.1, π/2-0.1, 1001); tanx_taylor = tan_taylor.(x); tanx_native = tan.(x); delta = tanx_native-tanx_taylor # Time domain comparison. plot( x, tanx_native, label = "Julia native tan", size = (1024, 720),) plot!( x, tanx_taylor, label = "8-term Taylor series",) savefig("figures/tan/time_domain")
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module Separators using Genie, Stipple, StippleUI, StippleUI.API import Genie.Renderer.Html: HTMLString, normal_element, template export separator function __init__() Genie.Renderer.Html.register_normal_element("q__separator", context = Genie.Renderer.Html) end function separator(args...; wrap::Function = StippleUI.DEFAULT_WRAPPER, kwargs...) wrap() do Genie.Renderer.Html.q__separator(args...; kwargs...) end end end
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module PostgreSQLLoader using Octo.Repo: ExecuteResult # https://github.com/invenia/LibPQ.jl using LibPQ # v0.9.1 using .LibPQ.Tables const current = Dict{Symbol, Any}( :conn => nothing, ) current_conn() = current[:conn] # db_connect function db_connect(; kwargs...) if !isempty(kwargs) str = join(map(kv->join(kv, '='), collect(kwargs)), ' ') conn = LibPQ.Connection(str) current[:conn] = conn end end # db_disconnect function db_disconnect() conn = current_conn() close(conn) current[:conn] = nothing end # query function query(sql::String) conn = current_conn() stmt = LibPQ.prepare(conn, sql) result = LibPQ.execute(stmt) df = Tables.rowtable(result) LibPQ.close(result) df end function query(prepared::String, vals::Vector) conn = current_conn() stmt = LibPQ.prepare(conn, prepared) result = LibPQ.execute(stmt, vals) df = Tables.rowtable(result) LibPQ.close(result) df end # execute function execute(sql::String)::ExecuteResult conn = current_conn() LibPQ.execute(conn, sql) ExecuteResult() end function execute(prepared::String, vals::Vector)::ExecuteResult conn = current_conn() stmt = LibPQ.prepare(conn, prepared) LibPQ.execute(stmt, vals) ExecuteResult() end function execute(prepared::String, nts::Vector{<:NamedTuple})::ExecuteResult conn = current_conn() stmt = LibPQ.prepare(conn, prepared) for tup in nts LibPQ.execute(stmt, collect(tup)) end ExecuteResult() end end # module Octo.Backends.PostgreSQLLoader
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struct Clibrary handle::Ptr{Cvoid} Clibrary(libName::Union{AbstractString, Nothing} = nothing, flags = Libdl.RTLD_LAZY | Libdl.RTLD_DEEPBIND | Libdl.RTLD_LOCAL) = new(Libdl.dlopen(libName === nothing ? _NullCString() : libName, flags)) end Base.close(lib::Clibrary) = Libdl.dlclose(lib.handle) # NOTE: risky hack to trick Julia dlopen into ccalling the C dlopen with NULL (meaning to dlopen current process rather than a library) struct _NullCString <: AbstractString end Base.cconvert(::Type{Cstring}, ::_NullCString) = Cstring(C_NULL)
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""" LinearRegression() Classic regression model. This struct has no parameter. If you want to use polynomial model, use `Regression.make_design_matrix()`. see also: [`make_design_matrix`](@ref) # Example ```jldoctest regression julia> x = [ 16.862463771320925 68.10823385851712 15.382965696961577 65.4313485700859 8.916228406218375 53.92034559524475 10.560285659132695 59.17305391117168 12.142253214135884 62.28708207525656 5.362107221163482 43.604947901567414 13.893239446341777 62.44348617377496 11.871357065173395 60.28433066289655 29.83792267802442 69.22281924803998 21.327107214235483 70.15810991597944 23.852372696012498 69.81780163668844 26.269031430914108 67.61037566099782 22.78907104644012 67.78105545358633 26.73342178134947 68.59263965946904 9.107259141706415 56.565383817343495 29.38551885863976 68.1005579469209 7.935966787763017 53.76264777936664 29.01677894379809 68.69484161138638 6.839609488194577 49.69794758177567 13.95215840314148 62.058116579899085]; #These data are also used to explanations of other functions. julia> t = [169.80980778351542, 167.9081124078835, 152.30845618985222, 160.3110300206261, 161.96826472170756, 136.02842285615077, 163.98131131382686, 160.117817321485, 172.22758529098235, 172.21342437006865, 171.8939175591617, 169.83018083884602, 171.3878062674257, 170.52487535026015, 156.40282783981309, 170.6488327896672, 151.69267899906185, 172.32478221316322, 145.14365314788827, 163.79383292080666]; julia> model = LinearRegression() LinearRegression(Float64[]) julia> fit!(model, x, t) 3-element Vector{Float64}: -0.04772448076255398 1.395963968616736 76.7817095600793 julia> model(x) 20-element Vector{Float64}: 171.05359766482795 167.38737053144575 151.62704681535598 158.88117658330424 163.15274911747872 137.3967419011542 163.28751869479999 160.3699086857777 171.99027166957023 173.70207799107243 173.10650291105486 169.9096820022986 170.31402414534642 171.25872436348817 155.31030802635905 170.44522606721017 151.45368882321284 171.29242257091374 145.83183688699864 162.74674475052848 ``` """ mutable struct LinearRegression w::Array LinearRegression() = new(Array{Float64}(undef, 0)) end """ fit!(model, x, t) `x` must be the number of features in the first dimension and the second dimension must be the number of data. """ function fit!(model::LinearRegression, x, t) check_size(x, t) x = expand(x) model.w = inv(x * x') * x * t end (model::LinearRegression)(x) = expand(x)' * model.w
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# beefblup # Julia package for performing single-variate BLUP to find beef cattle # breeding values # (C) 2021 Thomas A. Christensen II # Licensed under BSD-3-Clause License # cSpell:includeRegExp #.* # cSpell:includeRegExp ("""|''')[^\1]*\1 module BeefBLUP # Import the required packages using CSV using DataFrames using LinearAlgebra using Dates using Gtk # Main entry-level function - acts just like the script function beefblup() # Ask for an input spreadsheet path = open_dialog_native( "Select a beefblup worksheet", GtkNullContainer(), ("*.csv", GtkFileFilter("*.csv", name="beefblup worksheet")) ) # Ask for an output text filename savepath = save_dialog_native( "Save your beefblup results", GtkNullContainer(), (GtkFileFilter("*.txt", name="Results file"), "*.txt") ) # Ask for heritability print("What is the heritability for this trait?> ") h2 = parse(Float64, readline(stdin)) beefblup(path, savepath, h2) end function beefblup(datafile::String, h2::Float64) # Assume the data is named the same as the file without the trailing extension dataname = join(split(datafile, ".")[1:end - 1]) # Create a new results name resultsfile = string(dataname, "_results.txt") # Pass this info on to the worker beefblup(datafile, resultsfile, h2) end # Main worker function, can perform all the work if given all the user input function beefblup(path::String, savepath::String, h2::Float64) # Import data from a suitable spreadsheet data = DataFrame(CSV.File(path)) # Make sure the data is in the proper format renamecolstospec!(data) # Sort the array by date sort!(data, :birthdate) # Define fields to hold id values for animals and their parents numanimals = length(data.id) # Calculate the relationship matrix A = additiverelationshipmatrix(data.id, data.dam, data.sire) # Extract all of the fixed effects fixedeffectdata = data[:,5:end-1] (X, fixedeffects) = fixedeffectmatrix(fixedeffectdata) # Extract the observed data Y = convert(Array{Float64}, data[:,end]) # The random effects matrix Z = Matrix{Int}(I, numanimals, numanimals) # Remove items where there is no data nullobs = findall(isnothing, Y) Z[nullobs, nullobs] .= 0 # Calculate heritability λ = (1 - h2) / h2 # Use the mixed-model equations MME = [X' * X X' * Z; Z' * X (Z' * Z) + (inv(A) .* λ)] MMY = [X' * Y; Z' * Y] solutions = MME \ MMY # Find the accuracies diaginv = diag(inv(MME)) reliability = ones(Float64, length(diaginv)) .- diaginv .* λ # Find how many traits we found BLUE for numgroups = length(reliability) - numanimals # Split the BLUP and BLUE results β = solutions[1:numgroups] μ = solutions[numgroups+1:end] μ_reliability = reliability[numgroups+1:end] # Extract the names of the traits traitname = names(data)[end] # Start printing results to output fileID = open(savepath, "w") write(fileID, "beefblup Results Report\n") write(fileID, "Produced using beefblup (") write(fileID, "https://github.com/millironx/beefblup") write(fileID, ")\n\n") write(fileID, "Input:\t") write(fileID, path) write(fileID, "\nAnalysis performed:\t") write(fileID, string(Dates.today())) write(fileID, "\nTrait examined:\t") write(fileID, traitname) write(fileID, "\n\n") # Print base population stats write(fileID, "Base Population:\n") for i in 1:length(fixedeffects) effect = fixedeffects[i] write(fileID, "\t") write(fileID, string(effect.name)) write(fileID, ":\t") write(fileID, string(effect.basetrait)) write(fileID, "\n") end write(fileID, "\tMean ") write(fileID, traitname) write(fileID, ":\t") write(fileID, string(solutions[1])) write(fileID, "\n\n") # Contemporary group adjustments counter = 2 write(fileID, "Contemporary Group Effects:\n") for i in 1:length(fixedeffects) effect = fixedeffects[i] write(fileID, "\t") write(fileID, effect.name) write(fileID, "\tEffect\n") for j in 1:length(effect.alltraits) types = effect.alltraits[j] write(fileID, "\t") write(fileID, string(types)) write(fileID, "\t") write(fileID, string(β[counter])) write(fileID, "\n") counter = counter + 1 end write(fileID, "\n") end write(fileID, "\n") # Expected breeding values write(fileID, "Expected Breeding Values:\n") write(fileID, "\tID\tEBV\tReliability\n") for i in 1:numanimals write(fileID, "\t") write(fileID, string(data.id[i])) write(fileID, "\t") write(fileID, string(μ[i])) write(fileID, "\t") write(fileID, string(μ_reliability[i])) write(fileID, "\n") end write(fileID, "\n - END REPORT -") close(fileID) end """ fixedeffectmatrix(fixedeffectdata::DataFrame) Creates contemporary groupings and the fixed-effect incidence matrix based on the fixed effects listed in `fixedeffectdata`. Returns a tuple `(X::Matrix{Int}, fixedeffects::Array{FixedEffect})` in which `X` is the actual matrix, and `fixedeffects` is the contemporary groupings. """ function fixedeffectmatrix(fixedeffectdata::DataFrame) # Declare an empty return matrix fixedeffects = FixedEffect[] # Add each trait to the array for i in 1:size(fixedeffectdata)[2] name = names(fixedeffectdata)[i] traits = eachcol(fixedeffectdata)[i] if length(unique(traits)) > 1 push!(fixedeffects, FixedEffect(name, traits)) else @warn string("column '", name, "' does not have any unique animals and will be dropped from analysis") pname = propertynames(fixedeffectdata)[i] DataFrames.select!(fixedeffectdata, Not(pname)) end end X = ones(Int64, (size(fixedeffectdata)[1], 1)) for i in 1:length(fixedeffects) trait = fixedeffects[i] for phenotype in trait.alltraits X = cat(X, Int64.(fixedeffectdata[:,i] .== phenotype), dims=2) end end return X, fixedeffects end """ additiverelationshipmatrix(id, dam, sire) Returns the additive numerator relationship matrix based on the pedigree provided in `dam` and `sire` for animals in `id`. """ function additiverelationshipmatrix(id::AbstractVector, damid::AbstractVector, sireid::AbstractVector) # Sanity-check for valid pedigree if !(length(id) == length(damid) && length(damid) == length(sireid)) throw(ArgumentError("id, dam, and sire must be of the same length")) end # Convert to positions dam = indexin(damid, id) sire = indexin(sireid, id) # Calculate loop iterations numanimals = length(dam) # Create an empty matrix for the additive relationship matrix A = zeros(numanimals, numanimals) # Create the additive relationship matrix by the FORTRAN method presented by # Henderson for i in 1:numanimals if !isnothing(dam[i]) && !isnothing(sire[i]) for j in 1:(i - 1) A[j,i] = 0.5 * (A[j,sire[i]] + A[j,dam[i]]) A[i,j] = A[j,i] end A[i,i] = 1 + 0.5 * A[sire[i], dam[i]] elseif !isnothing(dam[i]) && isnothing(sire[i]) for j in 1:(i - 1) A[j,i] = 0.5 * A[j,dam[i]] A[i,j] = A[j,i] end A[i,i] = 1 elseif isnothing(dam[i]) && !isnothing(sire[i]) for j in 1:(i - 1) A[j,i] = 0.5 * A[j,sire[i]] A[i,j] = A[j,i] end A[i,i] = 1 else for j in 1:(i - 1) A[j,i] = 0 A[i,j] = 0 end A[i,i] = 1 end end return A end """ renamecolstospec(::DataFrame) Renames the first four columns of the beefblup data sheet so that they can be referred to by name instead of by column index, regardless of user input. """ function renamecolstospec!(df::DataFrame) # Pull out the fixed-effect and observation name othernames = propertynames(df)[5:end] # Put specification column names and user-defined names together allnames = cat([:id, :birthdate, :dam, :sire], othernames, dims=1) # Rename in the DataFrame rename!(df, allnames, makeunique=true) return df end struct FixedEffect name::String basetrait::Any alltraits::AbstractArray{Any} end function FixedEffect(name::String, incidences) basetrait = last(unique(incidences)) types = unique(incidences)[1:end-1] return FixedEffect(name, basetrait, types) end end
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""" Execution state that encapsulates synchronization primitives and resources bound to a command submission on the GPU. Resources bound to the related execution can be either freed or released (dereferenced) once the execution completes. The execution is assumed to be completed when the execution state has been waited on (see [`wait`](@ref)), or when an inherited execution state has been completed execution. Execution state can be inherited across submissions, where resource dependencies are transfered over to a new execution state bound to a command that **must** include the inherited `ExecutionState`'s semaphore as a wait semaphore. """ struct ExecutionState queue::Queue semaphore::Optional{Vk.SemaphoreSubmitInfoKHR} fence::Optional{Vk.Fence} free_after_completion::Vector{Ref} release_after_completion::Vector{Ref} end function ExecutionState(queue; semaphore = nothing, fence = nothing, free_after_completion = [], release_after_completion = []) ExecutionState(queue, semaphore, fence, free_after_completion, release_after_completion) end function finalize!(exec::ExecutionState) for resource in exec.free_after_completion finalize(resource[]) end empty!(exec.free_after_completion) empty!(exec.release_after_completion) true end function _wait(fence::Vk.Fence, timeout) ret = unwrap(Vk.wait_for_fences(Vk.device(fence), [fence], true, timeout)) ret == Vk.SUCCESS end function _wait(fences, timeout) isempty(fences) && return true ret = unwrap(Vk.wait_for_fences(Vk.device(first(fence)), fences, true, timeout)) ret == Vk.SUCCESS end function Base.wait(exec::ExecutionState, timeout = typemax(UInt32)) fence = exec.fence::Vk.Fence _wait(fence, timeout) && finalize!(exec) end function Base.wait(execs::AbstractVector{ExecutionState}, timeout = typemax(UInt32)) fences = map(Base.Fix2(getproperty, :fence), execs)::Vector{Fence} _wait(fences, timeout) && all(finalize!, execs) end function submit(dispatch::QueueDispatch, queue_family_index, submit_infos; signal_fence = false, semaphore = nothing, free_after_completion = [], release_after_completion = [], inherit = nothing, check_inheritance = true, ) q = queue(dispatch, queue_family_index) fence = signal_fence ? Vk.Fence(Vk.device(q)) : nothing if inherit isa ExecutionState append!(free_after_completion, inherit.free_after_completion) append!(release_after_completion, inherit.release_after_completion) empty!(inherit.free_after_completion) empty!(inherit.release_after_completion) if check_inheritance && !any(Base.Fix1(in, inherit.semaphore), submit_infos.wait_semaphores) error("No wait semaphore has been registered that matches the inherited state.") end end for submit_info in submit_infos for command_buffer_info in submit_info.command_buffer_infos end_recording(command_buffer_info.command_buffer) end end unwrap(Vk.queue_submit_2_khr(q, submit_infos; fence = something(fence, C_NULL))) ExecutionState(q; semaphore, fence, free_after_completion, release_after_completion) end function submit(dispatch::QueueDispatch, queue_family_index, submit_info::Vk.SubmitInfo2KHR; signal_fence = false, semaphore = nothing, free_after_completion = [], release_after_completion = [], inherit = nothing, check_inheritance = true, ) if !isnothing(semaphore) semaphore in submit_info.signal_semaphore_infos || error("The provided semaphore was not included in the submission structure") end submit(dispatch, queue_family_index, [submit_info]; signal_fence, semaphore, free_after_completion, release_after_completion, inherit, check_inheritance) end function Base.show(io::IO, exec::ExecutionState) print(io, ExecutionState, "($(exec.queue)") if !isnothing(exec.fence) is_complete = _wait(exec.fence, 0) if is_complete print(io, ", completed execution") else print(io, ", in progress") end end print(io, ')') end
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function setupCommBuffers!(domain::Domain, edgeNodes::Int) function cache_align_real(n::Int64) n = Int32(n) (n + CACHE_COHERENCE_PAD_REAL%Int32 - one(Int32)) & ~(CACHE_COHERENCE_PAD_REAL%Int32 - one(Int32)) end @unpack_Domain domain # allocate a buffer large enough for nodal ghost data maxEdgeSize = max(domain.sizeX, max(domain.sizeY, domain.sizeZ))+1 m_maxPlaneSize = cache_align_real(maxEdgeSize*maxEdgeSize) m_maxEdgeSize = cache_align_real(maxEdgeSize) maxPlaneSize = m_maxPlaneSize maxEdgeSize = m_maxEdgeSize # assume communication to 6 neighbors by default m_rowMin = (m_rowLoc == 0) ? 0 : 1 m_rowMax = (m_rowLoc == m_tp-1) ? 0 : 1 m_colMin = (m_colLoc == 0) ? 0 : 1 m_colMax = (m_colLoc == m_tp-1) ? 0 : 1 m_planeMin = (m_planeLoc == 0) ? 0 : 1 m_planeMax = (m_planeLoc == m_tp-1) ? 0 : 1 # account for face communication comBufSize =( (m_rowMin + m_rowMax + m_colMin + m_colMax + m_planeMin + m_planeMax) * m_maxPlaneSize * MAX_FIELDS_PER_MPI_COMM ) # account for edge communication comBufSize += ( ((m_rowMin & m_colMin) + (m_rowMin & m_planeMin) + (m_colMin & m_planeMin) + (m_rowMax & m_colMax) + (m_rowMax & m_planeMax) + (m_colMax & m_planeMax) + (m_rowMax & m_colMin) + (m_rowMin & m_planeMax) + (m_colMin & m_planeMax) + (m_rowMin & m_colMax) + (m_rowMax & m_planeMin) + (m_colMax & m_planeMin)) * m_maxEdgeSize * MAX_FIELDS_PER_MPI_COMM ) # account for corner communication # factor of 16 is so each buffer has its own cache line comBufSize += (((m_rowMin & m_colMin & m_planeMin) + (m_rowMin & m_colMin & m_planeMax) + (m_rowMin & m_colMax & m_planeMin) + (m_rowMin & m_colMax & m_planeMax) + (m_rowMax & m_colMin & m_planeMin) + (m_rowMax & m_colMin & m_planeMax) + (m_rowMax & m_colMax & m_planeMin) + (m_rowMax & m_colMax & m_planeMax)) * CACHE_COHERENCE_PAD_REAL ) commDataSend = Vector{Float64}(undef, comBufSize) commDataRecv = Vector{Float64}(undef, comBufSize) # prevent floating point exceptions fill!(commDataSend, 0) fill!(commDataRecv, 0) # Boundary nodesets if (m_colLoc == 0) resize!(symmX, edgeNodes*edgeNodes) end if (m_rowLoc == 0) resize!(symmY, edgeNodes*edgeNodes) end if (m_planeLoc == 0) resize!(symmZ, edgeNodes*edgeNodes) end @pack_Domain! domain end getMyRank(comm::MPI.Comm) = MPI.Comm_rank(comm) getMyRank(::Nothing) = 0 getNumRanks(comm::MPI.Comm) = MPI.Comm_size(comm) getNumRanks(::Nothing) = 1 getWtime(::MPI.Comm) = MPI.Wtime() getWtime(::Nothing) = time() comm_max(data::Float64, comm::MPI.Comm) = MPI.Allreduce(data, MPI.MAX, comm) comm_max(data::Float64, ::Nothing) = data comm_min(data::Float64, comm::MPI.Comm) = MPI.Allreduce(data, MPI.MIN, comm) comm_min(data::Float64, ::Nothing) = data comm_barrier(comm::MPI.Comm) = MPI.Barrier(comm) comm_barrier(::Nothing) = nothing
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using ParallelAccelerator #ParallelAccelerator.ParallelIR.set_debug_level(3) @acc function find_chg(k,m,W,Wp) W_tmp = [abs(W[i,j] - Wp[i,j]) for i in 1:m, j in 1:k] s = [sum(W_tmp[:,j]) for j in 1:k] chg = maximum(s) end function main(m::Int, k::Int) W = Array{Float64}(m, k) Wp = Array{Float64}(m, k) fill!(W, 3) fill!(Wp, 5) chg = find_chg(k,m,W,Wp) println("chg = ", chg) end main(100, 100)
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using Iconv using Test @test iconv("笨熊", "UTF-8", "GBK") == togbk("笨熊") == g"笨熊" @test iconv(g"笨熊", "GBK", "UTF-8") == toutf8(g"笨熊") == b"笨熊"
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module GHWT_tf_1d include("utils.jl") using ..GraphSignal, ..GraphPartition, ..BasisSpecification, LinearAlgebra include("common.jl") export ghwt_tf_bestbasis, eghwt_bestbasis """ coeffdict = tf_init(dmatrix::Matrix{Float64},GP::GraphPart) Store the expanding coeffcients from matrix into a list of dictionary (inbuilt hashmap in Julia) ### Input Arguments * `dmatrix`: The expanding GHWT coefficients of all levels corresponding to input GP * `GP::GraphPart`: an input GraphPart object ### Output Arguments * `coeffdict`: The expanding GHWT coeffcients stored in a list of "dictionary" (inbuilt hashmap in Julia), * `coeffdict`: The entry `coeffdict[j][(k,l)]` corresponds to the coefficient of basis-vector on level j with region k and tag l. Copyright 2018 The Regents of the University of California Implemented by Yiqun Shao (Adviser: Dr. Naoki Saito) """ function tf_init(dmatrix::Matrix{Float64},GP::GraphPart) # Obtain the tag information tag = convert(Array{Int,2},GP.tag) # Obtain the region information tag_r = convert(Array{Int,2},rs_to_region(GP.rs, GP.tag)) (m,n) = size(dmatrix) # Initialize coeffdict coeffdict = Array{Dict{Tuple{Int,Int},Float64}}(undef, n) # Fill in the values with the rule that `coeffdict[j][(k,l)]` represents # the coefficient of basis-vector on level j with region k and tag l. for i = 1:n coeffdict[i]= Dict{Tuple{Int,Int},Float64}((tag_r[j,i],tag[j,i]) => dmatrix[j,i] for j=1:m) end return coeffdict end """ coeffdict_new,tag_tf = tf_core_new(coeffdict::Array{Dict{Tuple{Int,Int},Float64},1}) One forward iteration of time-frequency adapted GHWT method. For each entry in `coeffdict_new`, we compare two (or one) entries in 'coeffdict' on time-direction and two (or one) entries in 'coeffdict' on frequency-direction. Those two groups reprensent the same subspace. We compare the cost-functional value of them and choose the smaller one as a new entry in 'coeffdict_new'. ### Input Arguments * `coeffdict`: The entries of which reprensents the cost functional value of some basis-vectors' coefficients. ### Output Arguments * `coeffdict_new`: The entries of which represents the cost functional value of some basis-vectors' coefficients * `tag_tf`: Indicating whether the time-direction (0) or frequency direction (1) was chosen for each entry in coeffdict_new. Copyright 2018 The Regents of the University of California Implemented by Yiqun Shao (Adviser: Dr. Naoki Saito) """ function tf_core_new(coeffdict::Array{Dict{Tuple{Int,Int},Float64},1}) # We choose '1-norm' as the optional cost functional costfun = cost_functional(1) jmax = length(coeffdict) # Initialization coeffdict_new = Array{Dict{Tuple{Int,Int},Float64}}(undef, jmax-1) tag_tf = Array{Dict{Tuple{Int,Int},Bool}}(undef, jmax-1) # Iterate through levels for j = 1:(jmax-1) # the temporary dictionary, which will be the j-th level of `coeffdict_new` temp_coeff = Dict{Tuple{Int,Int},Float64}() # the temporary dictionary, which will be the j-th level of 'tag_tf' temp_tf = Dict{Tuple{Int,Int},Bool}() # iterate through the entries in coeffdict on level j for key in keys(coeffdict[j]) # coeffdict[j][k,l] represent the entry on level j with region k and tag l k = key[1] # region index l = key[2] # tag index # only look at the entry with even tag l to avoid duplication if l%2 == 0 # search for the (j,k,l+1) entry. # the (j,k,l) and (j,k,l+1) span the same subspace as (j+1,2*k,l/2) and (j+1,2*k+1,l/2) # (j,k,l) and (j,k,l+1) are `frequency-direction` # (j,k,l) and (j,k,l+1) are `time-direction` if haskey(coeffdict[j],(k,l+1)) # check for degenerate case ((j,k,l+1) doesn't exist) freqcos = costfun([coeffdict[j][(k,l)],coeffdict[j][(k,l+1)]]) else freqcos = costfun([coeffdict[j][(k,l)]]) end if ~haskey(coeffdict[j+1],(2*k,l/2)) # check for degenerate case ((j+1,2*k,l/2) or (j+1,2*k+1,l/2) doesn't exist) timecos = costfun([coeffdict[j+1][(2*k+1,l/2)]]) elseif ~haskey(coeffdict[j+1],(2*k+1,l/2)) timecos = costfun([coeffdict[j+1][(2*k,l/2)]]) else timecos = costfun([coeffdict[j+1][(2*k+1,l/2)],coeffdict[j+1][(2*k,l/2)]]) end # compare the cost-functional value and record into 'tag_tf' if timecos <= freqcos temp_coeff[(k,l/2)] = timecos temp_tf[(k,l/2)] = false else temp_coeff[(k,l/2)] = freqcos temp_tf[(k,l/2)] = true end end end coeffdict_new[j] = temp_coeff tag_tf[j] = temp_tf end return coeffdict_new,tag_tf end """ tag_tf_b_new = tf_basisrecover_new(tag_tf_b::Array{Dict{Tuple{Int,Int},Bool}},tag_tf_f::Array{Dict{Tuple{Int,Int},Bool}}) One backward iteration of time-frequency adapted GHWT method to recover the best-basis from the `tag_tf`s recorded. ### Input Arguments * `tag_tf_b`: The `dictionary` recording the time-or-frequency information on some iteration 'i' in the main algorithm * `tag_tf_f`: The `dictionary` recording the time-or-frequency information on some iteration 'i+1' in the main algorithm ### Output Arguments * `tag_tf_b_new`: The updated 'tag_tf_b'. Eventually the 'tag_tf' on iteration 1 will represent the selected best-basis Copyright 2018 The Regents of the University of California Implemented by Yiqun Shao (Adviser: Dr. Naoki Saito) """ function tf_basisrecover_new(tag_tf_b::Array{Dict{Tuple{Int,Int},Bool}},tag_tf_f::Array{Dict{Tuple{Int,Int},Bool}}) # Initialization jmax = length(tag_tf_b) #tag_tf_b_new = Array{Dict{Tuple{Int,Int},Bool}}(jmax) tag_tf_b_new = Array{Dict{Tuple{Int,Int},Bool}}(undef, jmax) for j = 1:jmax tag_tf_b_new[j] = Dict{Tuple{Int,Int},Bool}() end # Iterate on the levels for j = 1:(jmax-1) for key in keys(tag_tf_f[j]) k = key[1] l = key[2] # The entries on frequency-direction are selected if tag_tf_f[j][(k,l)] == true if ~haskey(tag_tf_b[j],(k,2*l)) tag_tf_b_new[j][(k,2*l+1)] = tag_tf_b[j][(k,2*l+1)] elseif ~haskey(tag_tf_b[j],(k,2*l+1)) tag_tf_b_new[j][(k,2*l)] = tag_tf_b[j][(k,2*l)] else tag_tf_b_new[j][(k,2*l)] = tag_tf_b[j][(k,2*l)] tag_tf_b_new[j][(k,2*l+1)] = tag_tf_b[j][(k,2*l+1)] end else # The entries on time-direction are selected if ~haskey(tag_tf_b[j+1],(2*k,l)) tag_tf_b_new[j+1][(2*k+1,l)] = tag_tf_b[j+1][(2*k+1,l)] elseif ~haskey(tag_tf_b[j+1],(2*k+1,l)) tag_tf_b_new[j+1][(2*k,l)] = tag_tf_b[j+1][(2*k,l)] else tag_tf_b_new[j+1][(2*k+1,l)] = tag_tf_b[j+1][(2*k+1,l)] tag_tf_b_new[j+1][(2*k,l)] = tag_tf_b[j+1][(2*k,l)] end end end end return tag_tf_b_new end """ (dvec, BS) = ghwt_tf_bestbasis(dmatrix::Array{Float64,3}, GP::GraphPart; cfspec::Float64 = 1.0, flatten::Any = 1.0) Implementation of time-frequency adapted GHWT method = eGHWT. Modified from the algorithm in the paper: "A Fast Algorithm for Adapted Time Frequency Tilings" by Christoph M. Thiele and Lars F. Villemoes. ### Input Arguments * `dmatrix::Array{Float64,3}`: the matrix of expansion coefficients * `GP::GraphPart`: an input GraphPart object * `cfspec::Any`: the specification of cost functional to be used (default: 1.0, i.e., 1-norm) * `flatten::Any`: the method for flattening vector-valued data to scalar-valued data (default: 1.0, i.e., 1-norm) ### Output Arguments * `dvec::Matrix{Float64}`: the vector of expansion coefficients corresponding to the eGHWT best basis * `BS::BasisSpec`: a BasisSpec object which specifies the eGHWT best basis Copyright 2018 The Regents of the University of California Implemented by Yiqun Shao (Adviser: Dr. Naoki Saito) """ function ghwt_tf_bestbasis(dmatrix::Array{Float64,3}, GP::GraphPart; cfspec::Float64 = 1.0, flatten::Any = 1.0) # determine the cost functional to be used costfun = cost_functional(cfspec) # constants and dmatrix cleanup fcols = Base.size(dmatrix,3) dmatrix[ abs.(dmatrix) .< 10^2 * eps() ] .= 0 dmatrix0 = deepcopy(dmatrix) # keep the original dmatrix as dmatrix0 # "flatten" dmatrix if fcols > 1 if !isnothing(flatten) dmatrix = dmatrix_flatten(dmatrix, flatten)[:,:,1] end end dmatrix = dmatrix.^cfspec # Initialization. Store the expanding coeffcients from matrix into a list of dictionary (inbuilt hashmap in Julia) # The entry `coeffdict[j][(k,l)]` corresponds to the coefficient of basis-vector on level j with region k and tag l. tag = convert(Array{Int,2},GP.tag) tag_r = rs_to_region(GP.rs, GP.tag) (m,n) = size(dmatrix) coeffdict = Array{Dict{Tuple{Int,Int},Float64}}(undef, n) for i = 1:n coeffdict[i]= Dict{Tuple{Int,Int},Float64}((tag_r[j,i],tag[j,i]) => dmatrix[j,i] for j=1:m) end # TAG_tf stores the time-or-frequency information on every iteration # COEFFDICT stores the corresponding cost-functional values. COEFFDICT = Vector(undef, n) TAG_tf = Vector(undef, n) # Initialization of the first iteration COEFFDICT[1] = coeffdict TAG_tf_init = Array{Dict{Tuple{Int,Int},Bool}}(undef, n) for j = 1:n TAG_tf_init[j] = Dict(key => true for key in keys(coeffdict[j])) end TAG_tf[1] = TAG_tf_init # Iterate forward. For each entry in `COEFFDICT[i+1]`, we compare two (or one) entries in 'COEFFDICT[i]' on time-direction and two (or one) on frequency-direction. # Those two groups reprensent the same subspace. We compare the cost-functional value of them and choose the smaller one as a new entry in 'COEFFDICT[i+1]' # 'TAG_tf[i+1]' records if frequency-direction (1) or time-direction (0) was chosen. for i = 1:(n-1) COEFFDICT[i+1], TAG_tf[i+1] = tf_core_new(COEFFDICT[i]) end # Iterate backward with the existing tag_tf information to recover the best-basis. bestbasis_tag = TAG_tf[n] for i = (n-1):-1:1 bestbasis_tag = tf_basisrecover_new(TAG_tf[i],bestbasis_tag) end # Change the data structure from dictionary to matrix #bestbasis = zeros(m,n) #bestbasis_tag_matrix = zeros(Int,m,n) levlist = Vector{Tuple{Int,Int}}(undef, 0) dvec = Vector{Float64}(undef, 0) for j = 1:n for i = 1:m k = tag_r[i,j] l = tag[i,j] if haskey(bestbasis_tag[j],(k,l)) #bestbasis_tag_matrix[i,j] = 1 push!(levlist, (i,j)) #bestbasis[i,j] = dmatrix[i,j] push!(dvec, dmatrix[i,j]) end end end BS = BasisSpec(levlist, c2f = true, description = "eGHWT Best Basis") dvec = dmatrix2dvec(dmatrix0, GP, BS) return dvec, BS end # of function ghwt_tf_bestbasis const eghwt_bestbasis = ghwt_tf_bestbasis # This should be a useful alias! end # of module GHWT_tf_1d
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using Test using StaticArrays using IntervalArithmetic # using Revise using HDGElasticity function allapprox(v1,v2) return all(v1 .≈ v2) end @test HDGElasticity.linear_map_slope(1.,2.,[1.,2.],[3.,4.]) ≈ [2.,2.] map = HDGElasticity.LineMap(0.,1.,[0.,0.],[1.,1.]) @test map.xiL ≈ 0.0 @test map.xiR ≈ 1.0 @test allapprox(map.xL,[0.,0.]) @test allapprox(map.xR,[1.,1.]) @test allapprox(map.slope,[1.,1.]) @test HDGElasticity.dimension(map) == 2 @test allapprox(map(0.),[0.,0.]) @test allapprox(map(0.5),[0.5,0.5]) map = HDGElasticity.LineMap([0.5,0.5],[1.,0.5]) @test map.xiL ≈ -1.0 @test map.xiR ≈ 1.0 @test allapprox(map.xL,[0.5,0.5]) @test allapprox(map.xR,[1.0,0.5]) @test allapprox(map.slope,[0.5,0.0]/2) map = HDGElasticity.LineMap([0.,0.],[1.,1.]*sqrt(2)) @test allapprox(map(-1),[0.,0.]) @test allapprox(map(1),[1,1]*sqrt(2)) @test allapprox(HDGElasticity.jacobian(map),[1,1]/sqrt(2)) @test allapprox(HDGElasticity.determinant_jacobian(map),1.) @test allapprox(HDGElasticity.inverse_jacobian(map),[1,1]*sqrt(2)) xiL = [0.,0.] xiR = [1.,1.] xL = [0.,0.] xR = [2.,1.] map = HDGElasticity.CellMap(xiL,xiR,xL,xR) @test allapprox(map.xiL,xiL) @test allapprox(map.xiR,xiR) @test allapprox(map.xL,xL) @test allapprox(map.xR,xR) @test HDGElasticity.dimension(map) == 2 @test allapprox(map([0.5,0.5]),[1.,0.5]) @test allapprox(map([0.25,0.75]),[0.5,0.75]) @test allapprox(HDGElasticity.jacobian(map),[2.,1.]) @test allapprox(HDGElasticity.inverse_jacobian(map),[0.5,1.]) @test HDGElasticity.determinant_jacobian(map) ≈ 2.0 @test allapprox(HDGElasticity.face_determinant_jacobian(map),[2.,1.,2.,1.]) HDGElasticity.update_range!(map,[1.,1.],[2.,2.]) @test allapprox(map([0.5,0.5]),[1.5,1.5]) @test allapprox(map([0.75,0.25]),[1.75,1.25]) @test allapprox(HDGElasticity.jacobian(map),[1.,1.]) @test HDGElasticity.determinant_jacobian(map) ≈ 1.0 map = HDGElasticity.CellMap(2) @test allapprox(map.xiL,[-1.,-1.]) @test allapprox(map.xiR,[+1.,+1.]) @test allapprox(map.xL,[-1.,-1.]) @test allapprox(map.xR,[+1.,+1.]) @test allapprox(map.slope,[1.,1.]) HDGElasticity.update_range!(map,[0.,0.],[1.,1.]) @test allapprox(map.xL,[0.,0.]) @test allapprox(map.xR,[1.,1.]) @test allapprox(map.slope,[0.5,0.5]) box = IntervalBox(1..2,3..5) HDGElasticity.update_range!(map,box) @test allapprox(map.xL,[1.,3.]) @test allapprox(map.xR,[2.,5.]) @test allapprox(map.slope,[0.5,1.]) map = HDGElasticity.CellMap([1.,2.],[2.,4.]) @test allapprox(map.xiL,[-1.,-1.]) @test allapprox(map.xiR,[1.,1.]) @test allapprox(map.xL,[1.,2.]) @test allapprox(map.xR,[2.,4.]) @test allapprox(HDGElasticity.jacobian(map),[0.5,1.]) @test HDGElasticity.determinant_jacobian(map) ≈ 0.5 xi = IntervalBox(-1..1,2) x = IntervalBox(0..1,2) map = HDGElasticity.CellMap(xi,x) @test allapprox(map.xiL,[-1.,-1.]) @test allapprox(map.xiR,[1.,1.]) @test allapprox(map.xL,[0.,0.]) @test allapprox(map.xR,[1.,1.]) map = HDGElasticity.CellMap(IntervalBox(0..2,1..2)) @test allapprox(map.xiL,[-1.,-1.]) @test allapprox(map.xiR,[1.,1.]) @test allapprox(map.xL,[0.,1.]) @test allapprox(map.xR,[2.,2.]) fmaps = HDGElasticity.reference_cell_facemaps(2) @test allapprox(fmaps[1](-1.0),[-1.,-1.]) @test allapprox(fmaps[1](+1.0),[+1.,-1.]) @test allapprox(fmaps[2](-1.0),[+1.,-1.]) @test allapprox(fmaps[2](+1.0),[+1.,+1.]) @test allapprox(fmaps[3](-1.0),[-1.,+1.]) @test allapprox(fmaps[3](+1.0),[+1.,+1.]) @test allapprox(fmaps[4](-1.0),[-1.,-1.]) @test allapprox(fmaps[4](+1.0),[-1.,+1.])
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struct BlockIndices i1::UnitRange{Int} i2::UnitRange{Int} isdiag::Bool end # Remove isdiag field from comparison for dict Base.isequal(b1::BlockIndices, b2::BlockIndices) = b1.i1 == b2.i1 && b1.i2 == b2.i2 Base.hash(b::BlockIndices, h::UInt) = hash((b.i1, b.i2), h) """ SparseBlocks A type for speeding up expressions like the following: A[rows, cols] .= data A[rows, cols] .+= data where `A` is a `SparseMatrixCSC` and `rows` and `cols` are `UnitRange`s. The `data` is usually a `Matrix` but will work with any `AbstractArray` or collection that supports broadcasting. The key assumption is that the sparsity in `A` is known and fixed, specified by a list of "blocks," or a chunk of consecutive rows and columns, addressable by two `UnitRange` objects. Usage consists of two phases: 1) initialization, where the sparsity structure is specified, and 2) usage, where the precomputed data in this struct is used to speed up indexing operations. ## Initialization To specify the sparsity structure, use [`addblock!`](@ref). For example, we could do the following: sb = SparseBlocks(10,10) # blocks for a 10x10 sparse matrix addblock!(sb, 1:2, 1:2) # nonzeros block in top left corner addblock!(sb, 1:3, 8:10) # nonzeros block in top right corner After adding all of our blocks, we initialize the sparsity structure via [`initialize!`](@ref): A = spzeros(10,10) initialize!(sb, A) After calling `initialize!`, the matrix `A` will have the correct number of nonzero entries. The speed of the indexing operations is given by using cached conversions from the entries of each block to the storage location in the nonzeros vector of `A`, so it's critical that the sparsity structure of `A` does not change! ## Usage Once initialized, we can use the cached information in this object to speed up writing to our sparse matrix `A`. Basic usage is as simple as wrapping our indices with an indexing call to our `SparseBlocks` object: A[sb[1:2, 1:2]] .= randn(2,2) Note that these indices must exactly match one of the argument pairs passed to [`addblock`](@ref). For best performance, you can cache the [`SparseBlockIndex`](@ref) returned from this object: inds = sb[1:2, 1:2] # cache this somewhere for future indexing calls A[inds] .= randn(2,2) All of these methods avoid dynamic memory allocations and are about 5-20x faster indexing directly into the sparse matrix. """ struct SparseBlocks n::Int m::Int inds::Dict{BlockIndices,Matrix{Int}} SparseBlocks(n,m) = new(n,m,Dict{BlockIndices,Matrix{Int}}()) end """ addblock!(blocks, rows, cols) Add a nonzero block for the sparse matrix, spanning the block defined by consecutive `rows` and `cols` ranges. All calls to `addblock!` should be made before calling [`initialize!`](@ref). """ function addblock!(blocks::SparseBlocks, i1::UnitRange, i2::UnitRange, isdiag::Bool=false) if i1.start < 1 || i1.stop > blocks.n || i2.start < 1 || i2.stop > blocks.m throw(BoundsError(spzeros(blocks.n, blocks.m), (i1, i2))) end block = BlockIndices(i1, i2, isdiag) if isdiag blocks.inds[block] = zeros(Int, min(length(i1), length(i2)), 1) else blocks.inds[block] = zeros(Int, length(i1), length(i2)) end blocks end """ initialize!(blocks::SparseBlocks, A::SparseMatrixCSC) Initialize the sparsity structure specified by `blocks` (via previous calls to [`addblock!`](@ref)). Writes epsilon values to the nonzero blocks in `A` to initialize the data storage for the sparse matrix. The indices into this storage is computed and stored in `blocks`. """ function initialize!(blocks::SparseBlocks, A::SparseMatrixCSC{T}) where T n,m = blocks.n, blocks.m if size(A) != (n,m) throw(DimensionMismatch("Dimension of sparse matrix doesn't match expected size of ($n,$m).")) end @assert size(A) == (blocks.n, blocks.m) for block in keys(blocks.inds) if block.isdiag for (i,j) in zip(block.i1, block.i2) A[i,j] = eps(T) end else A[block.i1, block.i2] .= eps(T) end end inds = blocks.inds for (block,inds) in pairs(blocks.inds) rows = block.i1 cols = block.i2 n = length(rows) if block.isdiag for (j,(r,c)) in enumerate(zip(rows, cols)) nzind = getnzind(A, r, c) if !isempty(nzind) inds[j] = nzind[1] end end else for (j,col) in enumerate(cols) nzind = getnzind(A, rows[1], col) if !isempty(nzind) inds[:,j] .= nzind[1] - 1 .+ (1:n) end end end end blocks end # Get the index into the nonzero vector for the given row and column function getnzind(A,row,col) rows = view(A.rowval, nzrange(A,col)) istart = searchsorted(rows, row) .+ A.colptr[col] .- 1 return istart end """ SparseBlockIndex A custom index for sparse arrays with a fixed sparsity structure, especially for those whose nonzeros appear as dense blocks. This is created by indexing into a [`SparseBlocks`] object, using indices to one of the cached blocks. This object can then be used as a index for those same indices, creating a [`SparseBlockView`](@ref) object that efficiently maps cartesian indices into the nonzeros vector of the sparse matrix. """ struct SparseBlockIndex block::BlockIndices nzinds::Matrix{Int} end function Base.getindex(blocks::SparseBlocks, i1::UnitRange, i2::UnitRange) block0 = BlockIndices(i1, i2, false) block = getkey(blocks.inds, block0, nothing) if isnothing(block) throw(KeyError((i1, i2))) end SparseBlockIndex(block, blocks.inds[block]) end """ SparseBlockView A custom view into a sparse matrix with known sparsity, and whose nonzero entries appears as dense blocks. Can be used a normal array, where operations on the elements write and read directly from the nonzeros vector of the sparse matrix. """ struct SparseBlockView{Tv,Ti} <: AbstractMatrix{Tv} data::SparseMatrixCSC{Tv,Ti} block::SparseBlockIndex end # Needed to support expressions like A[B] .= data Broadcast.dotview(A::AbstractSparseMatrix, block::SparseBlockIndex) = SparseBlockView(A, block) # Define this type when indexing into a SparseMatrixCSC with a SparseBlockIndex Base.getindex(A::AbstractSparseMatrix, block::SparseBlockIndex) = SparseBlockView(A, block) # Array interface Base.size(B::SparseBlockView) = size(B.block.nzinds) Base.getindex(B::SparseBlockView, index) = getindex(B.data.nzval, B.block.nzinds[index]) Base.getindex(B::SparseBlockView, I::Vararg{Int,2}) = getindex(B.data.nzval, B.block.nzinds[I...]) Base.setindex!(B::SparseBlockView, v, index) = setindex!(B.data.nzval, v, B.block.nzinds[index]) Base.setindex!(B::SparseBlockView, v, I::Vararg{Int,2}) = setindex!(B.data.nzval, v, B.block.nzinds[I...]) Base.IndexStyle(::SparseBlockView) = IndexLinear() # Broadcasting interface struct SparseViewStyle <: Broadcast.BroadcastStyle end Base.BroadcastStyle(::Type{<:SparseBlockView}) = SparseViewStyle() # Always use the new broadcasting style over others Base.BroadcastStyle(::SparseViewStyle, ::Broadcast.BroadcastStyle) = SparseViewStyle() # Handle generic expressions like B .= f.(arg) function Broadcast.materialize!(B::SparseBlockView, bc::Broadcast.Broadcasted{Broadcast.DefaultMatrixStyle}) for i in eachindex(bc) B[i] = bc[i] end B end # Handle generic expressions like B .= f.(B, g.(args...)) function Broadcast.materialize!(B::SparseBlockView, bc::Broadcast.Broadcasted{SparseViewStyle}) for i in eachindex(bc) B[i] = bc.f(B[i], bc[i]) end B end # Specialization of B .= data::AbstractMatrix function Broadcast.materialize!(B::SparseBlockView, bc::Broadcast.Broadcasted{Broadcast.DefaultMatrixStyle,Nothing,typeof(identity),<:Tuple{<:AbstractMatrix}}) data = bc.args[1] for i in eachindex(B) B[i] = data[i] end B end # Specialization of B .= f.(B, data::AbstractMatrix) # which includes B .+= data::AbstractMatrix and variants function Broadcast.materialize!(B::SparseBlockView, bc::Broadcast.Broadcasted{SparseViewStyle,Nothing,<:Any,<:Tuple{<:SparseBlockView,<:AbstractArray}}) data = bc.args[2] for i in eachindex(B) B[i] = bc.f(B[i], data[i]) end B end # Specialization of B .= D::Diagonal function Broadcast.materialize!(B::SparseBlockView, bc::Broadcast.Broadcasted{<:LinearAlgebra.StructuredMatrixStyle{<:Diagonal}, Nothing, typeof(identity), <:Tuple{<:Diagonal}}) D = bc.args[1] if B.block.block.isdiag for i in eachindex(B) B[i] = D.diag[i] end else for i = 1:minimum(size(B)) B[i,i] = D.diag[i] end end end function Broadcast.materialize!(B::SparseBlockView, bc::Broadcast.Broadcasted{SparseViewStyle,Nothing,<:Any,<:Tuple{<:SparseBlockView,<:Diagonal}}) D = bc.args[2] f = bc.f if B.block.block.isdiag for i in eachindex(B) B[i] = f(B[i], D.diag[i]) end else for i = 1:minimum(size(B)) B[i,i] = f(B[i], D.diag[i]) end end end # Specialize B' .= bc function Broadcast.materialize!(B::Adjoint{T, <:SparseBlockView{T}}, bc::Broadcast.Broadcasted{Broadcast.DefaultMatrixStyle,Nothing,typeof(identity),<:Tuple{<:AbstractMatrix}}) where T data = bc.args[1] for i in eachindex(bc) it = CartesianIndex(i[2], i[1]) B.parent[it] = data[i] end B end # Specialize B = UpperTriangular(data) function Broadcast.materialize!(B::SparseBlockView, bc::Broadcast.Broadcasted{<:LinearAlgebra.StructuredMatrixStyle{<:UpperTriangular}}) for r = 1:size(B,1) for c = r:size(B,2) i = CartesianIndex(r,c) B[r,c] = bc[i] end end end # Specialize B = LowerTriangular(data) function Broadcast.materialize!(B::SparseBlockView, bc::Broadcast.Broadcasted{<:LinearAlgebra.StructuredMatrixStyle{<:LowerTriangular}}) for c = 1:size(B,2) for r = c:size(B,1) i = CartesianIndex(r,c) B[r,c] = bc[i] end end end
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2.481339
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using OffsetArrays using StaticArrays using DataStructures export pgbf, cgbf, contract, addbf!, PGBF, CGBF, build_basis,eri_fetcher, Shell, Basis export m2ao, shell_indices,nao """ PGBF(expn,xyz,I,J,K,norm) Create a primitive Gaussian basis function g(x,y,z) = norm * (x-x0)^I (y-y0)^J (z-z0)^K exp(-expn*r^2) The function parameters `xyz` correspond to [x0,y0,z0]. """ mutable struct PGBF expn::Float64 xyz::SVector{3,Float64} I::Int64 J::Int64 K::Int64 norm::Float64 end """ pgbf(expn,x=0,y=0,z=0,I=0,J=0,K=0,norm=1) Helper function to create a normalized PGBF with some optional defaults set. """ function pgbf(expn,x=0.0,y=0.0,z=0.0,I=0,J=0,K=0,norm=1) p = PGBF(expn,SA[x,y,z],I,J,K,norm) normalize!(p) return p end function amplitude(bf::PGBF,x,y,z) dx,dy,dz = dxyz = bf.xyz-SA[x,y,z] r2 = dist2(dxyz) return bf.norm*(dx^bf.I)*(dy^bf.J)*(dz^bf.K)*exp(-bf.expn*r2) end (bf::PGBF)(x,y,z) = amplitude(bf::PGBF,x,y,z) function normalize!(pbf::PGBF) pbf.norm /= sqrt(overlap(pbf,pbf)) end """ CGBF(xyz,I,J,K,norm,[pbgfs],[coefs]) Create a contracted Gaussian basis function made of the functions in [pgbfs] with coefficients [coefs]. Also track the origin `xyz` and powers `I,J,K`. """ mutable struct CGBF xyz::SVector{3,Float64} I::Int64 J::Int64 K::Int64 norm::Float64 pgbfs::Vector{PGBF} coefs::Vector{Float64} end """ cgbf(expn,x=0,y=0,z=0,I=0,J=0,K=0,norm=1) Helper function to create a CGBF with optional defaults. """ cgbf(x=0.0,y=0.0,z=0.0,I=0,J=0,K=0) = CGBF(SA[x,y,z],I,J,K,1.0,[],[]) amplitude(bf::CGBF,x,y,z) = bf.norm*sum(c*amplitude(pbf,x,y,z) for (c,pbf) in primitives(bf)) (bf::CGBF)(x,y,z) = amplitude(bf::CGBF,x,y,z) function normalize!(bf::CGBF) bf.norm /= sqrt(overlap(bf,bf)) end primitives(a::CGBF) = zip(a.coefs,a.pgbfs) function addbf!(cbf::CGBF,expn,coef) Base.push!(cbf.pgbfs,pgbf(expn,cbf.xyz...,cbf.I,cbf.J,cbf.K)) Base.push!(cbf.coefs,coef) normalize!(cbf) end contract(f,a::CGBF,b::CGBF) = a.norm*b.norm*sum(ca*cb*f(abf,bbf) for (ca,abf) in primitives(a) for (cb,bbf) in primitives(b)) function contract(f,a::CGBF,b::CGBF,c::CGBF,d::CGBF) s = 0 for (ca,abf) in primitives(a) for (cb,bbf) in primitives(b) for (cc,cbf) in primitives(c) for (cd,dbf) in primitives(d) s += ca*cb*cc*cd*f(abf,bbf,cbf,dbf) end end end end return a.norm*b.norm*c.norm*d.norm*s end function shells(atoms::Vector{Atom},name="sto3g") data = basis_data[name] shs = Shell[] for atom in atoms for (sym,primlist) in data[atom.atno] L = lvalue[sym] expns = [expn for (expn,coef) in primlist] coefs = [coef for (expn,coef) in primlist] bfs = CGBF[] for (I,J,K) in shell_indices[L] cbf = cgbf(atom.xyz...,I,J,K) push!(bfs,cbf) for (expn,coef) in zip(expns,coefs) addbf!(cbf,expn,coef) end end push!(shs,Shell(atom.xyz,lvalue[sym],expns,coefs,bfs)) end end return shs end function build_basis(atoms::Vector{Atom},name="sto3g") shs = shells(atoms,name) bfs = CGBF[] ishs = Int[] mshs = Int[] for (ish,sh) in enumerate(shs) for (msh,(I,J,K)) in enumerate(shell_indices[sh.L]) cbf = cgbf(sh.xyz...,I,J,K) push!(bfs,cbf) push!(ishs,ish) push!(mshs,msh) for (expn,coef) in zip(sh.expns,sh.coefs) addbf!(cbf,expn,coef) end end end return Basis(bfs,shs,ishs,mshs) end """ Shell(xyz,L,expns,coeffs) Structure for a basis function shell, containing multiple CGBFs of different angular momenta. """ mutable struct Shell xyz::SVector{3,Float64} L::Int expns::Vector{Float64} coefs::Vector{Float64} cgbfs::Vector{CGBF} end nbf(sh::Shell) = length(shell_indices(sh.L)) function pgbfs(sh::Shell) x,y,z = sh.xyz pgbfs = PGBF[] for (I,J,K) in shell_indices[sh.L] for (expn,coef) in zip(sh.expns,sh.coefs) push!(pgbfs,pgbf(expn,x,y,z,I,J,K)) end end return pgbfs end """ Basis(cgbfs,shells,ishell,mshell) Structure to hold a basis set, with info about shells and other data """ mutable struct Basis # subset of AbstractVector{CGBF}? cgbfs::Vector{CGBF} shells::Vector{Shell} ishell::Vector{Int64} # Which shell corresponds to bf i mshell::Vector{Int64} # Which m-value (px,dxy, etc.) corresponds to bf i end Base.size(b::Basis) = (length(b.cgbfs),) Base.length(b::Basis) = length(b.cgbfs) Base.iterate(b::Basis,i::Int) = iterate(b.cgbfs,i) Base.iterate(b::Basis) = iterate(b.cgbfs) Base.getindex(b::Basis, i::Int) = b.cgbfs[i] nbf(b::Basis) = length(b.cgbfs) nshells(b::Basis) = length(b.shells) """ eri_fetcher Compute all of the required ijkl terms that go into an ERI record, and compute the required calls to hrr, and how to unpack the results into the record. eri_fetcher returns a dictionary such that the integral structure may be formed via: ``` fetcher[ishell,jshell,kshell,lshell] = (ijkl,hi,hj,hk,hl) hrrs = hrr(ishell,jshell,kshell,lshell) ints[ijkl] = hrrs[hi,hj,hk,hl] ``` """ function eri_fetcher(bfs::Basis) fetcher = DefaultOrderedDict{NTuple{4, Int}, Vector{NTuple{5, Int}}}(Vector{NTuple{5, Int}}[]) for (index,ijkl) in enumerate(iiterator(length(bfs))) i,j,k,l = ijkl li,mi = bfs.ishell[i],bfs.mshell[i] lj,mj = bfs.ishell[j],bfs.mshell[j] lk,mk = bfs.ishell[k],bfs.mshell[k] ll,ml = bfs.ishell[l],bfs.mshell[l] push!(fetcher[li,lj,lk,ll],(index,mi,mj,mk,ml)) end return fetcher end function eri_fetcher(shs::Vector{Shell}) fetcher = DefaultOrderedDict{NTuple{4, Int}, Vector{NTuple{5, Int}}}(Vector{NTuple{5, Int}}[]) nsh = length(shs) ijk2lm = make_ijk2lm() i = 0 index = 0 for (ish,shi) in enumerate(shs) shi = shs[ish] for ibf in shi.cgbfs i += 1 mi = ijk2lm[ibf.I,ibf.J,ibf.K][2] j = 0 for (jsh,shj) in enumerate(take(shs,ish)) for jbf in shj.cgbfs j += 1 if j > i break end mj = ijk2lm[jbf.I,jbf.J,jbf.K][2] k = 0 for (ksh,shk) in enumerate(shs) for kbf in shk.cgbfs k += 1 mk = ijk2lm[kbf.I,kbf.J,kbf.K][2] l = 0 for (lsh,shl) in enumerate(take(shs,ksh)) @inbounds for lbf in shl.cgbfs l += 1 if l > k break end if k*l > i*j break end ml = ijk2lm[lbf.I,lbf.J,lbf.K][2] index += 1 push!(fetcher[ish,jsh,ksh,lsh],(index,mi,mj,mk,ml)) end # lbf end # lsh end # kbf end # ksh end # jbf end # jsh end # ibf end # ish return fetcher end # shell_indices map from a shell l-value to the Cartesian version of m-values that are the # powers of the Cartesian Gaussian basis functions. # # If desired, we can also invert shell_indices to map IJK triplets to l,m pairs: # IJK2lm = Dict(IJK =>(l,m) for l in 0:4 for (m,IJK) in enumerate(shell_indices[l])) const shell_indices = Dict( 0 => [MVector(0,0,0)], # 1 1 => [MVector(1,0,0),MVector(0,1,0),MVector(0,0,1)], # 3 2 => [MVector(2,0,0),MVector(1,1,0),MVector(1,0,1),MVector(0,2,0),MVector(0,1,1),MVector(0,0,2)], 3 => [MVector(3,0,0),MVector(2,1,0),MVector(2,0,1), MVector(1,2,0),MVector(1,0,2),MVector(1,1,1), MVector(0,3,0),MVector(0,2,1),MVector(0,1,2),MVector(0,0,3)], # 10 4 => [MVector(4,0,0),MVector(3,1,0),MVector(3,0,1),MVector(2,2,0),MVector(2,1,1),MVector(2,0,2), MVector(1,3,0),MVector(1,2,1),MVector(1,1,2),MVector(1,0,3), MVector(0,4,0),MVector(0,3,1),MVector(0,2,2),MVector(0,1,3),MVector(0,0,4)] # 15 ) llabel = Dict(0=>"s",1=>"p",2=>"d",3=>"f",4=>"g",5=>"h") lvalue = merge(Dict((v,k) for (k,v) in llabel),Dict((uppercase(v),k) for (k,v) in llabel)) bflabel(bf) = llabel[bf.I+bf.J+bf.K]*bfpow("x",bf.I)*bfpow("y",bf.J)*bfpow("z",bf.K) function bfpow(s,j) if j == 0 return "" elseif j == 1 return s end return "$s$j" end "make_m2ao - Make map between a sequential list of (mx,my,mz) values and ao indices" function make_m2ao(lmax=4) m2ao = Dict{Vector{Int64}, Int64}() iao = 0 for l in 0:lmax for ms in shell_indices[l] iao += 1 m2ao[ms] = iao end end return m2ao end function make_ao2m(lmax=4) ao2m = MVector{3, Int64}[] for l in 0:lmax for ms in shell_indices[l] push!(ao2m,ms) end end return ao2m end const m2ao = make_m2ao() const ao2m = make_ao2m() "make_nao - Number of AOs for system with l shells" make_nao(l) = sum(length(shell_indices[i]) for i in 0:l) const nao = OffsetArray([make_nao(l) for l in 0:4],0:4) # Data functions and tables over basis functions # todo: keep a constant of maxao or something # and use it to construct tables function make_shift_index() n = length(ao2m) shift_index = zeros(Int,n,3) for a in 1:n m = ao2m[a] for i in 1:3 if m[i] == 0 shift_index[a,i] = 0 else mm = copy(m) mm[i] -= 1 am = m2ao[mm] shift_index[a,i] = am end end end return shift_index end const shift_index = make_shift_index() function make_shift_direction() n = length(ao2m) shift_direction = zeros(Int,n) for a in 1:n shift_direction[a] = argmax(ao2m[a]) end return shift_direction end const shift_direction = make_shift_direction() function make_shell_number() n = length(ao2m) shell_number = zeros(Int,n) for a in 1:n shell_number[a] = sum(ao2m[a]) end return shell_number end const shell_number = make_shell_number() function make_shift_index_plus() n = length(ao2m) maxsh = shell_number[n] shift_index = zeros(Int,n,3) for a in 1:n m = ao2m[a] for i in 1:3 if sum(m) == maxsh shift_index[a,i] = 0 else mm = copy(m) mm[i] += 1 am = m2ao[mm] shift_index[a,i] = am end end end return shift_index end const shift_index_plus = make_shift_index_plus() function make_ijk2lm(maxl=4) ijk2lm = Dict{NTuple{3,Int},NTuple{2,Int}}() for l in 0:maxl for (m,(i,j,k)) in enumerate(shell_indices[l]) ijk2lm[i,j,k] = (l,m) end end return ijk2lm end
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1.779658
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export Domain, SegmentDomain, tocanonical, fromcanonical, fromcanonicalD, ∂ export chebyshevpoints, fourierpoints, isambiguous, arclength export components, component, ncomponents # add indexing for all spaces, not just DirectSumSpace # mimicking scalar vs vector # prectype gives the precision, including for Vec prectype(::Type{D}) where {D<:Domain} = float(eltype(eltype(D))) prectype(d::Domain) = prectype(typeof(d)) #TODO: bivariate AnyDomain struct AnyDomain <: Domain{UnsetNumber} end struct EmptyDomain <: Domain{Nothing} end isambiguous(::AnyDomain) = true dimension(::AnyDomain) = 1 complexlength(::AnyDomain) = NaN arclength(::AnyDomain) = NaN arclength(::EmptyDomain) = false arclength(::DomainSets.EmptySpace) = false isempty(::AnyDomain) = false reverseorientation(a::Union{AnyDomain,EmptyDomain}) = a canonicaldomain(a::Union{AnyDomain,EmptyDomain}) = a indomain(x::Domain,::EmptyDomain) = false convert(::Type{Domain{T}}, ::AnyDomain) where T = Domain(T) union(::AnyDomain, d::Domain) = d union(d::Domain, ::AnyDomain) = d union(::EmptyDomain, ::EmptyDomain) = EmptyDomain() union(::EmptyDomain, a::Domain) = a union(a::Domain, ::EmptyDomain) = a ##General routines isempty(::EmptyDomain) = true ## Interval DomainSets abstract type SegmentDomain{T} <: Domain{T} end abstract type AbstractSegment{T} <: SegmentDomain{T} end const IntervalOrSegment{T} = Union{AbstractInterval{T}, AbstractSegment{T}} const IntervalOrSegmentDomain{T} = Union{AbstractInterval{T}, SegmentDomain{T}} canonicaldomain(d::IntervalOrSegmentDomain) = ChebyshevInterval{real(prectype(d))}() domainscompatible(a,b) = domainscompatible(domain(a),domain(b)) domainscompatible(a::Domain,b::Domain) = isambiguous(a) || isambiguous(b) || isapprox(a,b) ##TODO: Should fromcanonical be fromcanonical!? #TODO consider moving these leftendpoint(d::IntervalOrSegmentDomain{T}) where {T} = fromcanonical(d,-one(eltype(T))) rightendpoint(d::IntervalOrSegmentDomain{T}) where {T} = fromcanonical(d,one(eltype(T))) indomain(x,::AnyDomain) = true function indomain(x,d::SegmentDomain) T=float(real(prectype(d))) y=tocanonical(d,x) ry=real(y) iy=imag(y) sc=norm(fromcanonicalD(d,ry<-1 ? -one(ry) : (ry>1 ? one(ry) : ry))) # scale based on stretch of map on projection to interal dy=fromcanonical(d,y) # TODO: use isapprox once keywords are fast ((isinf(norm(dy)) && isinf(norm(x))) || norm(dy-x) ≤ 1000eps(T)*max(norm(x),1)) && -one(T)-100eps(T)/sc ≤ ry ≤ one(T)+100eps(T)/sc && -100eps(T)/sc ≤ iy ≤ 100eps(T)/sc end ncomponents(s::Domain) = 1 components(s::Domain) = [s] function components(s::Domain,k) k ≠ 1 && throw(BoundsError()) s end issubcomponent(a::Domain,b::Domain) = a in components(b) ##### canoncial """ canonicaldomain(d) returns a domain which we map to for operations. For example, the canonical domain for an interval [a,b] is [-1,1] """ function canonicaldomain end """ tocanonical(d, x) maps the point `x` in `d` to a point in `canonical(d,x)` """ function tocanonical end issubset(a::Domain,b::Domain) = a==b ## conveninece routines ones(d::Domain) = ones(prectype(d),Space(d)) zeros(d::Domain) = zeros(prectype(d),Space(d)) function commondomain(P::AbstractVector) ret = AnyDomain() for op in P d = domain(op) @assert ret == AnyDomain() || d == AnyDomain() || ret == d if d != AnyDomain() ret = d end end ret end commondomain(P::AbstractVector,g::AbstractArray{T}) where {T<:Number} = commondomain(P) commondomain(P::AbstractVector,g) = commondomain([P;g]) domain(::Number) = AnyDomain() ## rand rand(d::IntervalOrSegmentDomain,k...) = fromcanonical.(Ref(d),2rand(k...)-1) checkpoints(d::IntervalOrSegmentDomain) = fromcanonical.(Ref(d),[-0.823972,0.01,0.3273484]) ## boundary boundary(d::SegmentDomain) = [leftendpoint(d),rightendpoint(d)] #TODO: Points domain ## map domains # we auto vectorize arguments tocanonical(d::Domain,x,y,z...) = tocanonical(d,Vec(x,y,z...)) fromcanonical(d::Domain,x,y,z...) = fromcanonical(d,Vec(x,y,z...)) mappoint(d1::Domain,d2::Domain,x...) = fromcanonical(d2,tocanonical(d1,x...)) invfromcanonicalD(d::Domain,x...) = 1/fromcanonicalD(d,x...) ## domains in higher dimensions ## sorting # we sort spaces lexigraphically by default for OP in (:<,:(<=),:>,:(>=),:(isless)) @eval $OP(a::Domain,b::Domain)=$OP(string(a),string(b)) end ## Other special domains struct PositiveIntegers <: Domain{Int} end struct Integers <: Domain{Int} end const ℕ = PositiveIntegers() const ℤ = Integers()
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2.579621
1,796
cluster = [1 1 1 1; 2 2 3 3; 4 4 5 5] X = Array(reshape(1:24, 2, 3, 4)) @testset "pool" begin @testset "GlobalPool" begin glb_cltr = [1 1 1 1; 1 1 1 1; 1 1 1 1] p = GlobalPool(:add, 3, 4) @test p(X) == sumpool(glb_cltr, X) end @testset "LocalPool" begin p = LocalPool(:add, cluster) @test p(X) == sumpool(cluster, X) end @testset "TopKPool" begin N = 10 k, in_channel = 4, 7 X = rand(in_channel, N) for T = [Bool, Float64] adj = rand(T, N, N) p = TopKPool(adj, k, in_channel) @test eltype(p.p) === Float32 @test size(p.p) == (in_channel,) @test eltype(p.Ã) === T @test size(p.Ã) == (k, k) y = p(X) @test size(y) == (in_channel, k) end end end
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1.737705
488
blockvalid(x, v) = count(isequal(v), x) ≤ 1 function backtrack!(x) pos = findfirst(isequal(0), x) isa(pos, Nothing) && return true iloc = 3div(pos[1]-1, 3) .+ (1:3) jloc = 3div(pos[2]-1, 3) .+ (1:3) for k in 1:9 x[pos] = k blockvalid(view(x, pos[1], :), k) || continue blockvalid(view(x, :, pos[2]), k) || continue blockvalid(view(x, iloc, jloc), k) || continue backtrack!(x) && return true end x[pos] = 0 return false end function ssolve(lines, i) t = [lines[10i-j][k] - '0' for j in 8:-1:0, k in 1:9] backtrack!(t) sum([100, 10, 1] .* t[1, 1:3]) end lines = readlines("p096_sudoku.txt") @time sum(ssolve(lines, i) for i in 1:50) @time sum(ssolve(lines, i) for i in 1:50)
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1.989529
382
abstract type RegressionType end struct OLS robust::Bool function OLS(robust=true) new(robust) end end
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2.309091
55
geneCols = Dict{String,Symbol}( "aliases" => :GeneSymbolAlias, "symbol" => :OfficialGeneSymbol, "region" => :GeneRegion, "regionStart" => :GeneStart, "regionEnd" => :GeneEnd, "id" => :EnsemblGeneId, "version" => :EnsemblGeneVersion, "biotype" => :EnsemblGeneBiotype, "name" => :GeneName ); transcriptCols = Dict{String,Symbol}( "region" => :TranscriptRegion, "regionStart" => :TranscriptStart, "regionEnd" => :TranscriptEnd, "id" => :EnsemblTranscriptId, "version" => :EnsemblTranscriptVersion, "biotype" => :EnsemblTranscriptBiotype ); proteinCols = Dict{String,Symbol}( "id" => :EnsemblProteinId, "version" => :EnsemblProteinVersion ); elTypeDict = Dict{String, Union}( # "aliases" => Vector{String}, "aliases" => Union{String, Missing}, "symbol" => Union{String, Missing}, "region" => Union{String, Missing}, "regionStart" => Union{Int, Missing}, "regionEnd" => Union{Int, Missing}, "id" => Union{String, Missing}, "version" => Union{Int, Missing}, "biotype" => Union{String, Missing}, "name" => Union{String, Missing} ); function parseGngJson(json::Array{Any,1}) ######################### # Count number or rows and create columns # In the case of the columns only look at the first entry # First four columns are inputDB, inputID, targetID and targetDB nCols = 4; colNames = Symbol["InputId", "InputSourceDb", "TargetId", "TargetDb"]; colNamesUnstack = Symbol["InputId", "InputSourceDb"]; colElTypes = Union[Union{String, Missing}, Union{String, Missing}, Union{String, Missing}, Union{String, Missing}]; # Gene column names and types map(i->haskey(geneCols,i) && (push!(colNames, geneCols[i]), push!(colNamesUnstack, geneCols[i]), push!(colElTypes, elTypeDict[i])), keys(json[1]["row"][1]["gene"])) # Transcript: # Dict{String,Any} with 4 entries: # "peptide" => Dict{String,Any}(Pair{String,Any}("id", "ENSP00000381185"),Pair{String,Any}("targetIds", Any[Dict{String,Any}(Pair{String,Any}("dbName", "UniProtKB/TrEMBL"),Pair{String,Any}("id", "A0A024R2B3"))]),Pair{String,A… # "id" => "ENST00000398117" # "targetIds" => Any[] # "region" => "18" map(i->haskey(transcriptCols,i) && (push!(colNames, transcriptCols[i]), push!(colNamesUnstack, transcriptCols[i]), push!(colElTypes, elTypeDict[i])), keys(json[1]["row"][1]["gene"]["transcript"][1])) # number of cols minus 1 because of targetIds, e.g.: # Dict{String,Any} with 3 entries: # "id" => "ENSP00000381185" # "targetIds" => Any[Dict{String,Any}(Pair{String,Any}("dbName", "UniProtKB/TrEMBL"),Pair{String,Any}("id", "A0A024R2B3"))] # "version" => 1 map(i->haskey(proteinCols,i) && (push!(colNames, proteinCols[i]), push!(colNamesUnstack, proteinCols[i]), push!(colElTypes, elTypeDict[i])), keys(json[1]["row"][1]["gene"]["transcript"][1]["peptide"])) ################## # Rows nRows = 0; for entry in json # For each gene entry # nRows += 1; # Number of external gene identifiers gene = entry["row"][1]["gene"]; nRows += (length(gene["targetIds"]) * length(gene["transcript"])); for transcript in gene["transcript"] # Transcript: # Dict{String,Any} with 4 entries: # "peptide" => Dict{String,Any}(Pair{String,Any}("id", "ENSP00000381185"),Pair{String,Any}("targetIds", Any[Dict{String,Any}(Pair{String,Any}("dbName", "UniProtKB/TrEMBL"),Pair{String,Any}("id", "A0A024R2B3"))]),Pair{String,A… # "id" => "ENST00000398117" # "targetIds" => Any[] # "region" => "18" nRows += length(transcript["targetIds"]); # Peptide: # Dict{String,Any} with 3 entries: # "id" => "ENSP00000381185" # "targetIds" => Any[Dict{String,Any}(Pair{String,Any}("dbName", "UniProtKB/TrEMBL"),Pair{String,Any}("id", "A0A024R2B3"))] # "version" => 1 nRows += length(transcript["peptide"]["targetIds"]); end # println(entry["row"][1]) end nRows x = DataFrames.DataFrame( colElTypes, colNames, nRows # 100 ); lowG = 1; highG = 1; lowT = 1; highT = 1; for entry in json # For each gene entry lowT = lowG; highT = lowT; highG = lowG; # Number of external gene identifiers gene = entry["row"][1]["gene"]; externalIds = String[]; externalDbs = String[]; # In the case of external IDs add entries to arrays !isempty(gene["targetIds"]) && map(i->(push!(externalIds, i["id"]), push!(externalDbs, i["dbName"])), gene["targetIds"]); externalGeneIds = copy(externalIds); externalGeneDbs = copy(externalDbs); # nRows += length(gene["targetIds"]) for transcript in gene["transcript"] # In the case of external IDs add entries to arrays !isempty(transcript["targetIds"]) && map(i->(push!(externalIds, i["id"]), push!(externalDbs, i["dbName"])), transcript["targetIds"]); peptide = transcript["peptide"]; !isempty(peptide["targetIds"]) && map(i->(push!(externalIds, i["id"]), push!(externalDbs, i["dbName"])), peptide["targetIds"]); highT += (length(externalIds) - 1); # for # map(i->haskey(geneCols,i) && (push!(colNames, geneCols[i]), push!(colElTypes, elTypeDict[i])), keys(json[1]["row"][1]["gene"])) # for epEntry in transcript["peptide"]["targetIds"] map(key->haskey(transcriptCols,key) && (x[transcriptCols[key]][lowT:highT] = transcript[key]), keys(transcript)) map(key->haskey(proteinCols,key) && (x[proteinCols[key]][lowT:highT] = peptide[key]), keys(peptide)) # map(key->haskey(geneCols,key) && (println("col: $(geneCols[key]) = $(gene[key])")), keys(gene)) # map(key->haskey(geneCols,key) && (println("$(collect(lowT:highT)) -> $(fill(gene[key], high))")), keys(gene)) x[:TargetId][lowT:highT] = externalIds; x[:TargetDb][lowT:highT] = externalDbs; externalIds = copy(externalGeneIds); externalDbs = copy(externalGeneDbs); # x[low,:EnsemblGeneId] = gene["id"]; # x[low,:EnsemblTranscriptId] = transcript["id"]; # x[low,:EnsemblProteinId] = peptide["id"]; # println("[$i] : $externalId, $externalDb") # println.("enspId: $(transcript["peptide"]["id"]) -> $(transcript["peptide"]["targetIds"])") lowT = highT + 1; highT = lowT; end highG = highT-1; # Fill in gene information for (key, value) in gene if haskey(geneCols, key) && !isempty(value) val = value; if key == "aliases" val = join(value, ","); end x[geneCols[key]][lowG:highG] = val; end end # map(key->haskey(geneCols,key) && (x[geneCols[key]][lowG:highG] = fill(join(gene[key], ","), (highG - lowG + 1))), keys(gene)); x[:InputId][lowG:highG] = entry["row"][1]["inputId"]; x[:InputSourceDb][lowG:highG] = entry["row"][1]["inputDb"]; lowG = highG + 1; end x return x; end function unstackDf(x::D, keyCols::S = Vector{Symbol}(), keyColsElType::T = Vector{DataType}()) where { D<:DataFrames.DataFrame, S<:Vector{Symbol}, T<:Vector{DataType}} if isempty(keyCols) keyCols = names(x)[map(i->!in(i,["TargetId", "TargetDb"]), collect(String,names(x)))]; keyColsElType = DataFrames.eltypes(x)[map(i->!in(i,["TargetId", "TargetDb"]), collect(String,names(x)))]; elseif length(keyCols != length(keyColsElType)) error("keyCols and keyColsElType have to be of same length.!"); end colNames = copy(keyCols); colElTypes = copy(keyColsElType); append!(colNames, DataFrames.identifier.(collect(unique(x[:TargetDb])))); append!(colElTypes, fill(Union{String, Missing}, (length(colNames) - length(keyCols)))); nRows = DataFrames.nrow(unique(x[keyCols])); xDf = DataFrames.DataFrame( colElTypes, colNames, nRows ); sortByCols = copy(keyCols); push!(sortByCols, :TargetDb); sort!(x, cols = sortByCols); idx = 0; key = ""; for row in DataFrames.eachrow(x) keyCurr = join(map(i->i[2], row[keyCols])); if key != keyCurr idx += 1; key = keyCurr; map(i->xDf[idx,i[1]] = i[2], row[keyCols]); end colId = DataFrames.identifier(row[:TargetDb]) xDf[idx, colId] = DataFrames.ismissing(xDf[idx, colId]) ? row[:TargetId] : xDf[idx, colId] * "," * row[:TargetId]; end return xDf; end
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2.120162
4,186
using Documenter using MERAKit makedocs(sitename = "MERAKit.jl", modules = [MERAKit]) deploydocs(repo = "github.com/mhauru/MERAKit.jl.git")
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2.389831
59
__precompile__() module CoordinateTransformations using StaticArrays using Rotations export RotMatrix, Quat, SpQuat, AngleAxis, RodriguesVec, RotX, RotY, RotZ, RotXY, RotYX, RotZX, RotXZ, RotYZ, RotZY, RotXYX, RotYXY, RotZXZ, RotXZX, RotYZY, RotZYZ, RotXYZ, RotYXZ, RotZXY, RotXZY, RotYZX, RotZYX # Core methods export compose, ∘, transform_deriv, transform_deriv_params export Transformation, IdentityTransformation # 2D coordinate systems and their transformations export Polar export PolarFromCartesian, CartesianFromPolar # 3D coordinate systems and their transformations export Spherical, Cylindrical export SphericalFromCartesian, CartesianFromSpherical, CylindricalFromCartesian, CartesianFromCylindrical, CylindricalFromSpherical, SphericalFromCylindrical # Common transformations export AbstractAffineMap export AffineMap, LinearMap, Translation include("core.jl") include("coordinatesystems.jl") include("affine.jl") # Deprecations export transform Base.@deprecate_binding AbstractTransformation Transformation Base.@deprecate transform(transformation::Transformation, x) transformation(x) end # module
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3.025974
385
using Manifolds, Manopt, Test, ManifoldsBase using Random Random.seed!(42) # Test the additional manifold functions # @testset "Additional Manifold functions" begin @testset "mid point & reflect" begin M = Sphere(2) p = [1.0, 0.0, 0.0] q = [0.0, 1.0, 0.0] r = mid_point(M,p,q) r2 = similar(r) mid_point!(M,r2,p,q) r3 = shortest_geodesic(M,p,q,0.5) r4 = mid_point(M,p,q,q) @test isapprox(M,r,r2) @test isapprox(M,r2,r3) @test isapprox(M,r3,r4) r5 = similar(r4) mid_point!(M,r5,p,q,q) @test isapprox(M,r4,r5) r4 = mid_point(M,p,-p,q) r5 = similar(r4) mid_point!(M,r5,p,-p,q) @test isapprox(M,r4,q) @test isapprox(M,r4,r5) @test isapprox(M,reflect(M,p,q),-q) f = x->x @test reflect(M,f,q) == q M2 = Euclidean(2) p2 = [1.0, 0.0] q2 = [0.0, 1.0] s = mid_point(M2,p2,q2) s2 = similar(s) mid_point!(M2,s2,p2,q2) @test s==s2 @test s==(p2+q2)/2 s = mid_point(M2,p2,q2,s) @test s==s2 s2 = similar(s) mid_point!(M2,s2,p2,q2,s) @test s==s2 end @testset "random" begin Mc = Circle() pc = random_point(Mc) @test is_manifold_point(Mc, pc, true) Xc = random_tangent(Mc,pc) @test is_tangent_vector(Mc, pc, Xc, true) Me = Euclidean(3) pe = random_point(Me) @test is_manifold_point(Me, pe, true) Xe = random_tangent(Me,pe) @test is_tangent_vector(Me, pe, Xe, true) Mg = Grassmann(3,2) pg = random_point(Mg) @test is_manifold_point(Mg, pg, true) Xg = random_tangent(Mg,pg) @test is_tangent_vector(Mg, pg, Xg, true; atol=10^(-14)) Mp = ProductManifold(Mg,Me) pp = random_point(Mp) @test is_manifold_point(Mp, pp, true) Xp = random_tangent(Mp,pp) @test is_tangent_vector(Mp, pp, Xp,true; atol=10^(-15)) Mp2 = PowerManifold(Me, NestedPowerRepresentation(),4) pp2 = random_point(Mp2) @test is_manifold_point(Mp2,pp2) Xp2 = random_tangent(Mp2,pp2) @test is_tangent_vector(Mp2,pp2,Xp2) Mp3 = PowerManifold(Me, ArrayPowerRepresentation(), 4) pp3 = random_point(Mp3) @test is_manifold_point(Mp3,pp3) Xp3 = random_tangent(Mp3,pp3) @test is_tangent_vector(Mp3,pp3,Xp3) Mr = Rotations(3) pr = random_point(Mr) @test is_manifold_point(Mr,pr) Xr = random_tangent(Mr,pr) @test is_tangent_vector(Mr,pr,Xr) Mspd = SymmetricPositiveDefinite(3) pspd = random_point(Mspd) @test is_manifold_point(Mspd,pspd;atol=10^(-14)) Xspd = random_tangent(Mspd,pspd) @test is_tangent_vector(Mspd,pspd,Xspd, true; atol=10^(-15)) Xspd2 = random_tangent(Mspd,pspd, Val(:Rician)) @test is_tangent_vector(Mspd,pspd,Xspd2,true;atol=10^(-15)) Mst = Stiefel(3,2) pst = random_point(Mst) @test is_manifold_point(Mst, pst, true) Msp = Sphere(2) psp = random_point(Msp) @test is_manifold_point(Msp, psp, true) Xsp = random_tangent(Msp,psp) @test is_tangent_vector(Msp,psp,Xsp,true;atol=10^(-15)) Mh = Hyperbolic(2) ph = [0.0,0.0,1.0] Xh = random_tangent(Mh, ph) @test is_tangent_vector(Mh, ph, Xh, true) end end
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1.69927
2,055
using Documenter push!(LOAD_PATH,"../src/") using BasesAndSamples makedocs( sitename = "BasesAndSamples.jl Documentation", format = Documenter.HTML(), modules = [BasesAndSamples] ) # Documenter can also automatically deploy documentation to gh-pages. # See "Hosting Documentation" and deploydocs() in the Documenter manual # for more information. deploydocs( repo = "github.com/nanleij/BasesAndSamples.jl.git", devbranch="main" )
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2.954248
153
import GreyDecision.GreyNumbers: GreyNumber import GreyDecision.Utility: makeminmax import LinearAlgebra: det @testset "Transpose" begin x = [ GreyNumber(5.0) GreyNumber(3.0) GreyNumber(-2.0); GreyNumber(-8.0) GreyNumber(6.0) GreyNumber(1.0); GreyNumber(10.0) GreyNumber(7.0) GreyNumber(13.0) ] xt = transpose(x) @test size(xt) == size(x) @test xt[1, 2] == x[2, 1] @test xt[1, 3] == x[3, 1] @test xt[1, 1] == x[1, 1] @test xt[2, 2] == x[2, 2] @test xt[3, 3] == x[3, 3] end @testset "det(x)" begin x = [ GreyNumber(5.0) GreyNumber(3.0) GreyNumber(-2.0); GreyNumber(-8.0) GreyNumber(6.0) GreyNumber(1.0); GreyNumber(10.0) GreyNumber(7.0) GreyNumber(13.0) ] y = [ 5.0 3.0 -2.0; -8.0 6.0 1.0; 10.0 7.0 13.0 ] xdet = det(transpose(x) * x) ydet = det(transpose(y) * y) @test ydet == xdet.a @test ydet == xdet.b end @testset "inv(x)" begin atol = 0.001 x = [ GreyNumber(5.0) GreyNumber(3.0) GreyNumber(-2.0); GreyNumber(-8.0) GreyNumber(6.0) GreyNumber(1.0); GreyNumber(10.0) GreyNumber(7.0) GreyNumber(13.0) ] xtx = transpose(x) * x invxyx = inv(xtx) result = invxyx * xtx @test isapprox(result[1,1].a, 1.0, atol = atol) @test isapprox(result[2,2].a, 1.0, atol = atol) @test isapprox(result[3,3].a, 1.0, atol = atol) @test isapprox(result[1,2].a, 0.0, atol = atol) @test isapprox(result[1,3].a, 0.0, atol = atol) @test isapprox(result[2,1].a, 0.0, atol = atol) @test isapprox(result[2,3].a, 0.0, atol = atol) @test isapprox(result[3,1].a, 0.0, atol = atol) @test isapprox(result[3,2].a, 0.0, atol = atol) end
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1.861702
940
include("../src/experiments/0_setup.jl") import DataFrames.DataFrame using DataFrames, CSV using FastGroupBy using Base.Threads K = 100 tries = vcat([Int(2^k-1) for k = 7:31], 3_000_000_000) for N in tries println(N) if N < 2_000_000 by = nothing; val = nothing; gc() srand(1) by = rand(Int64(1):Int64(round(N/K)), N); val = rand(Int32(1):Int32(5), N); sp = @elapsed sumby_sortperm(by, val) CSV.write(string("benchmark/out/64/sp$N $(replace(string(now()),":","")).csv"),DataFrame(sp = sp)) end by = nothing; val = nothing; gc() srand(1) by = rand(Int64(1):Int64(round(N/K)), N); val = rand(Int32(1):Int32(5), N); srs = @elapsed sumby_multi_rs(by, val) CSV.write(string("benchmark/out/64/mrs$N $(replace(string(now()),":","")).csv"),DataFrame(srs = srs)) if N < 2_000_000_000 by = nothing; val = nothing; gc() srand(1) by = rand(Int64(1):Int64(round(N/K)), N); val = rand(Int32(1):Int32(5), N); srg = @elapsed sumby_radixgroup(by, val) CSV.write(string("benchmark/out/64/srg$N $(replace(string(now()),":","")).csv"),DataFrame(srg = srg)) end by = nothing; val = nothing; gc() srand(1) by = rand(Int64(1):Int64(round(N/K)), N); val = rand(Int32(1):Int32(5), N); srs = @elapsed sumby_radixsort(by, val) CSV.write(string("benchmark/out/64/srs$N $(replace(string(now()),":","")).csv"),DataFrame(srs = srs)) end tries = vcat([Int(2^k-1) for k = 7:31], 3_000_000_000) for N in tries println(N) if N < 400_000 by = nothing; val = nothing; gc() srand(1) by = rand(Int32(1):Int32(round(N/K)), N); val = rand(Int32(1):Int32(5), N); sp = @elapsed sumby_sortperm(by, val) CSV.write(string("benchmark/out/sp$N $(replace(string(now()),":","")).csv"),DataFrame(sp = sp)) end by = nothing; val = nothing; gc() srand(1) by = rand(Int32(1):Int32(round(N/K)), N); val = rand(Int32(1):Int32(5), N); srs = @elapsed sumby_multi_rs(by, val) CSV.write(string("benchmark/out/mrs$N $(replace(string(now()),":","")).csv"),DataFrame(srs = srs)) if N < 2_000_000_000 by = nothing; val = nothing; gc() srand(1) by = rand(Int32(1):Int32(round(N/K)), N); val = rand(Int32(1):Int32(5), N); srg = @elapsed sumby_radixgroup(by, val) CSV.write(string("benchmark/out/srg$N $(replace(string(now()),":","")).csv"),DataFrame(srg = srg)) end by = nothing; val = nothing; gc() srand(1) by = rand(Int32(1):Int32(round(N/K)), N); val = rand(Int32(1):Int32(5), N); srs = @elapsed sumby_radixsort(by, val) CSV.write(string("benchmark/out/srs$N $(replace(string(now()),":","")).csv"),DataFrame(srs = srs)) end
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2.077094
1,349
using FedDCD include("./PrimalMethods.jl") include("./DualMethods.jl") # Softmax for mnist fileTrain = "data/mnist.scale" fileTest = "data/mnist.scale.t" λ = 1e-2 participationRate = 0.3 localLr = 1e-2 numRounds = 100 # - FedAvg μ = 0.0 RunFedAvgAndProx( fileTrain, fileTest, λ, μ, participationRate, localLr, numRounds, "results/exp1/FedAvg_logReg_mnist_lambda1e-2.txt" ) # - FedProx μ = 1e-4 RunFedAvgAndProx( fileTrain, fileTest, λ, μ, participationRate, localLr, numRounds, "results/exp1/FedProx_logReg_mnist_lambda1e-2.txt" ) # - Scaffold RunScaffold( fileTrain, fileTest, λ, participationRate, localLr, numRounds, "results/exp1/Scaffold_logReg_mnist_lambda1e-2.txt" ) # Softmax for rcv1 fileTrain = "data/rcv1_train.binary" fileTest = "data/rcv1_train.binary" λ = 1e-3 participationRate = 0.3 localLr = 1e-1 numRounds = 100 # - FedAvg μ = 0.0 RunFedAvgAndProx( fileTrain, fileTest, λ, μ, participationRate, localLr, numRounds, "results/exp1/FedAvg_logReg_rcv1_lambda1e-2.txt" ) # - FedProx μ = 1e-4 RunFedAvgAndProx( fileTrain, fileTest, λ, μ, participationRate, localLr, numRounds, "results/exp1/FedProx_logReg_rcv1_lambda1e-2.txt" ) # - Scaffold RunScaffold( fileTrain, fileTest, λ, participationRate, localLr, numRounds, "results/exp1/Scaffold_logReg_rcv1_lambda1e-2.txt" ) # Softmax + MLP for mnist fileTrain = "data/mnist.scale" fileTest = "data/mnist.scale.t" λ = 1e-2 participationRate = 0.3 localLr = 1e-2 numRounds = 100 # - FedAvg μ = 0.0 # - FedAvg RunFedAvgAndProxNN( fileTrain, fileTest, λ, μ, participationRate, localLr, numRounds, "results/exp1/FedAvg_MLP_mnist_lambda1e-2.txt" ) # - FedProx μ = 1e-4 RunFedAvgAndProxNN( fileTrain, fileTest, λ, μ, participationRate, localLr, numRounds, "results/exp1/FedProx_MLP_mnist_lambda1e-2.txt" ) # - Scaffold RunScaffoldNN( fileTrain, fileTest, λ, participationRate, localLr, numRounds, "results/exp1/Scaffold_MLP_mnist_lambda1e-2.txt" ) # Softmax for mnist fileTrain = "data/mnist.scale" fileTest = "data/mnist.scale.t" λ = 1e-2 participationRate = 0.1 localLr = 1e-3 numRounds = 500 # - FedAvg μ = 0.0 RunFedAvgAndProx( fileTrain, fileTest, λ, μ, participationRate, localLr, numRounds, "results/exp2/FedAvg_logReg_mnist_lambda1e-2_tau1e-1.txt" ) # - FedProx μ = 1e-4 RunFedAvgAndProx( fileTrain, fileTest, λ, μ, participationRate, localLr, numRounds, "results/exp2/FedProx_logReg_mnist_lambda1e-2_tau1e-1.txt" ) # - Scaffold RunScaffold( fileTrain, fileTest, λ, participationRate, localLr, numRounds, "results/exp2/Scaffold_logReg_mnist_lambda1e-2_tau1e-1.txt" ) # Softmax for rcv1 fileTrain = "data/rcv1_train.binary" fileTest = "data/rcv1_train.binary" λ = 1e-3 participationRate = 0.1 localLr = 1e-1 numRounds = 500 # - FedAvg μ = 0.0 RunFedAvgAndProx( fileTrain, fileTest, λ, μ, participationRate, localLr, numRounds, "results/exp2/FedAvg_logReg_rcv1_lambda1e-2_tau1e-1.txt" ) # - FedProx μ = 1e-4 RunFedAvgAndProx( fileTrain, fileTest, λ, μ, participationRate, localLr, numRounds, "results/exp2/FedProx_logReg_rcv1_lambda1e-2_tau1e-1.txt" ) # - Scaffold RunScaffold( fileTrain, fileTest, λ, participationRate, localLr, numRounds, "results/exp2/Scaffold_logReg_rcv1_lambda1e-2_tau1e-1.txt" ) # Softmax + MLP for mnist fileTrain = "data/mnist.scale" fileTest = "data/mnist.scale.t" λ = 1e-2 participationRate = 0.1 localLr = 1e-2 numRounds = 500 # - FedAvg μ = 0.0 # - FedAvg RunFedAvgAndProxNN( fileTrain, fileTest, λ, μ, participationRate, localLr, numRounds, "results/exp2/FedAvg_MLP_mnist_lambda1e-2_tau1e-1.txt" ) # - FedProx μ = 1e-4 RunFedAvgAndProxNN( fileTrain, fileTest, λ, μ, participationRate, localLr, numRounds, "results/exp2/FedProx_MLP_mnist_lambda1e-2_tau1e-1.txt" ) # - Scaffold RunScaffoldNN( fileTrain, fileTest, λ, participationRate, localLr, numRounds, "results/exp2/Scaffold_MLP_mnist_lambda1e-2_tau1e-1.txt" ) ####################################################################################################### ################################## # Participation rate = 0.05 ################################## # Softmax for mnist fileTrain = "data/mnist.scale" fileTest = "data/mnist.scale.t" λ = 1e-2 participationRate = 0.05 localLr = 1e-3 numRounds = 1000 # - FedAvg μ = 0.0 RunFedAvgAndProx( fileTrain, fileTest, λ, μ, participationRate, localLr, numRounds, "results/exp2/FedAvg_logReg_mnist_lambda1e-2_tau5e-2.txt" ) # - FedProx μ = 1e-4 RunFedAvgAndProx( fileTrain, fileTest, λ, μ, participationRate, localLr, numRounds, "results/exp2/FedProx_logReg_mnist_lambda1e-2_tau5e-2.txt" ) # - Scaffold RunScaffold( fileTrain, fileTest, λ, participationRate, localLr, numRounds, "results/exp2/Scaffold_logReg_mnist_lambda1e-2_tau5e-2.txt" ) # Softmax for rcv1 fileTrain = "data/rcv1_train.binary" fileTest = "data/rcv1_train.binary" λ = 1e-3 participationRate = 0.05 localLr = 1e-1 numRounds = 1000 # - FedAvg μ = 0.0 RunFedAvgAndProx( fileTrain, fileTest, λ, μ, participationRate, localLr, numRounds, "results/exp2/FedAvg_logReg_rcv1_lambda1e-2_tau5e-2.txt" ) # - FedProx μ = 1e-4 RunFedAvgAndProx( fileTrain, fileTest, λ, μ, participationRate, localLr, numRounds, "results/exp2/FedProx_logReg_rcv1_lambda1e-2_tau5e-2.txt" ) # - Scaffold RunScaffold( fileTrain, fileTest, λ, participationRate, localLr, numRounds, "results/exp2/Scaffold_logReg_rcv1_lambda1e-2_tau5e-2.txt" ) # Softmax + MLP for mnist fileTrain = "data/mnist.scale" fileTest = "data/mnist.scale.t" λ = 1e-2 participationRate = 0.05 localLr = 1e-2 numRounds = 1000 # - FedAvg μ = 0.0 # - FedAvg RunFedAvgAndProxNN( fileTrain, fileTest, λ, μ, participationRate, localLr, numRounds, "results/exp2/FedAvg_MLP_mnist_lambda1e-2_tau5e-2.txt" ) # - FedProx μ = 1e-4 RunFedAvgAndProxNN( fileTrain, fileTest, λ, μ, participationRate, localLr, numRounds, "results/exp2/FedProx_MLP_mnist_lambda1e-2_tau5e-2.txt" ) # - Scaffold RunScaffoldNN( fileTrain, fileTest, λ, participationRate, localLr, numRounds, "results/exp2/Scaffold_MLP_mnist_lambda1e-2_tau5e-2.txt" ) # Softmax for mnist fileTrain = "data/mnist.scale" fileTest = "data/mnist.scale.t" λ = 1e-2 participationRate = 0.3 localLr = 1e-2 numRounds = 100 # Experiment 2 # rcv1 # τ = 0.3 RunFedDCD( "data/rcv1_train.binary", "data/rcv1_train.binary", 1e-3, 0.3, 0.1, 100, "results/exp2/FedDCD_rcv1_lambda1e-3_tau3e-1.txt" ) RunAccFedDCD( "data/rcv1_train.binary", "data/rcv1_train.binary", 1e-3, 0.3, 0.1, 100, "results/exp2/AccFedDCD_rcv1_lambda1e-3_tau3e-1.txt" ) # τ = 0.1 RunFedDCD( "data/rcv1_train.binary", "data/rcv1_train.binary", 1e-3, 0.1, 0.1, 500, "results/exp2/FedDCD_rcv1_lambda1e-3_tau1e-1.txt" ) RunAccFedDCD( "data/rcv1_train.binary", "data/rcv1_train.binary", 1e-3, 0.1, 0.1, 500, "results/exp2/AccFedDCD_rcv1_lambda1e-3_tau1e-1.txt" ) # τ = 0.05 RunFedDCD( "data/rcv1_train.binary", "data/rcv1_train.binary", 1e-3, 0.05, 0.1, 1000, "results/exp2/FedDCD_rcv1_lambda1e-3_tau5e-2.txt" ) RunAccFedDCD( "data/rcv1_train.binary", "data/rcv1_train.binary", 1e-3, 0.05, 0.1, 1000, "results/exp2/AccFedDCD_rcv1_lambda1e-3_tau5e-2.txt" ) # mnist # τ = 0.3 RunFedDCDNN( "data/mnist.scale", "data/mnist.scale.t", 1e-2, 0.3, 0.1, 100, "results/exp2/FedDCD_mnist_lambda1e-2_tau3e-1.txt" ) RunAccFedDCDNN( "data/mnist.scale", "data/mnist.scale.t", 1e-2, 0.3, 0.1, 100, "results/exp2/AccFedDCD_mnist_lambda1e-2_tau3e-1.txt" ) # τ = 0.1 RunFedDCDNN( "data/mnist.scale", "data/mnist.scale.t", 1e-2, 0.1, 0.1, 500, "results/exp2/FedDCD_mnist_lambda1e-2_tau1e-1.txt" ) RunAccFedDCDNN( "data/mnist.scale", "data/mnist.scale.t", 1e-2, 0.1, 0.1, 500, "results/exp2/AccFedDCD_mnist_lambda1e-2_tau1e-1.txt" ) # τ = 0.05 RunFedDCDNN( "data/mnist.scale", "data/mnist.scale.t", 1e-2, 0.05, 0.1, 1000, "results/exp2/FedDCD_mnist_lambda1e-2_tau5e-2.txt" ) RunAccFedDCDNN( "data/mnist.scale", "data/mnist.scale.t", 1e-2, 0.05, 0.1, 1000, "results/exp2/AccFedDCD_mnist_lambda1e-2_tau5e-2.txt" )
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# # Recursive Fourier Propagation for BoostFractor # # V: 2019-08-06 # # Stefan Knirck # export dancer, dance_intro, cheerleader """ Propagates the fields through the system * `amin`: Mimum (local) amplitude of a field, in order to be propagated * `nmax`: Maximum number of beam iteration steps, directly equivalent to how many boundaries a beam 'sees' at most * `bdry`: SetupBoundaries-Objekt, containing all relevant geometrical information (disk positions, epsilon, etc) * `f`: Frequency * `prop`: Propagator Function to use. Standard is propagator() * `reflect`: If nothing (standar value), the axion-induced signal is computed. If set, this field defines a beam, for which the reflected beam will be calculated * `Xset`, `Yset`: Explicitly set the coordinate system for the fields * `returnsum`: If false, the out-propagating contributions after each iteration will be returned, without summing. * `immediatesum`: If false, the out-propagating contributions will be saved and summed up at the end. """ function dancer(amin, nmax, sbdry::SetupBoundaries, coords::CoordinateSystem; f=10.0e9, prop=propagator, emit=nothing, reflect=nothing, diskR=0.1, returnsum=true, immediatesum=true) # Make dancer swallow the same SetupBoundaries object as cheerleader and transformer bdry = deepcopy(sbdry) bdry.eps = bdry.eps[2:end] bdry.distance = bdry.distance[2:end] bdry.relative_tilt_x = bdry.relative_tilt_x[2:end] bdry.relative_tilt_y = bdry.relative_tilt_y[2:end] bdry.relative_surfaces = bdry.relative_surfaces[2:end,:,:] append!(bdry.r, 0.0) rightmoving = 1 leftmoving = 2 fields = Array{Complex{Float64}}(zeros(length(bdry.distance), 2, length(coords.X), length(coords.Y))) #fields = SharedArray(Complex{Float64}, length(bdry.distance), 2, length(coords.X), length(coords.Y)) # number of regions -----^ ^ ^ # number of propagation directions -------^ ^ # dimensions of the fields at each position -------------^ # Pre-allocate memory if immediatesum Eout = Array{Complex{Float64}}(zeros(length(coords.X), length(coords.Y),1)) else Eout = Array{Complex{Float64}}(zeros(length(coords.X), length(coords.Y),nmax+1)) end # TODO: propagation through and emission from last bdry to the right if reflect === nothing if emit === nothing fields = dance_intro(bdry,coords;diskR=diskR) else fields = emit end # Eout = else fields[length(bdry.distance), leftmoving, :, :] = reflect # Eout = end lambda = wavelength(f) n = 0 # Iteration Count a = 1.0 # Maximum Field Amplitude in the iteration # The measure of music (Takt) .... da capo al fine while n <= nmax && a >= amin # The "legs" of the dancer dancing in parallel # @sync is important to make sure that the outer loop waits for the inner one to finish... #@sync @parallel for i in 1:1:length(bdry.r) # The beats # Move left leg (rightmoving) if i > 1 # In place propagate left leg: fields[i-1,rightmoving,:,:] = prop(fields[i-1,rightmoving,:,:], bdry.distance[i-1], diskR, bdry.eps[i-1], bdry.relative_tilt_x[i-1], bdry.relative_tilt_y[i-1], bdry.relative_surfaces[i-1,:,:], lambda, coords) end # Move right leg (leftmoving) if i < length(bdry.r) fields[i,leftmoving,:,:] = prop(fields[i,leftmoving,:,:], bdry.distance[i], diskR, bdry.eps[i], bdry.relative_tilt_x[i], bdry.relative_tilt_y[i], bdry.relative_surfaces[i,:,:], lambda, coords) end # Reflection & Transmission if i == 1 # Leftmost case fields[i, leftmoving,:,:] .*= -bdry.r[i] # The right-moving does not exist and about the transmitted one we dont care (yet) elseif i == length(bdry.r) # Rightmost case # The rightmoving is transmitted to Eout (yeah! that'll be our result!) # old version without pre-allocation #Eout = cat(3,Eout, (1+bdry.r[i])*fields[i-1, rightmoving,:,:]) # with pre-allocated array: if immediatesum Eout[:,:,1] .+= (1+bdry.r[i]).*fields[i-1, rightmoving,:,:] else Eout[:,:,n+1] = (1+bdry.r[i]).*fields[i-1, rightmoving,:,:] end # And reflected as well. fields[i-1, rightmoving,:,:] .*= bdry.r[i] else # Standard Case # field(i-1) = transmit(field(i)) + reflect(field(i-1)) # field(i) = transmit(field(i-1)) + reflect(field(i)) # Basically the above, but as much in-place as possible # Always the field on the left gets a copy FieldArrivedLeft = copy(fields[i-1, rightmoving,:,:]) # Then we in-place update it fields[i-1, rightmoving,:,:] .*= bdry.r[i] fields[i-1, rightmoving,:,:] .+= (1-bdry.r[i]).*fields[i, leftmoving,:,:] # Then we in-place update the other one with the previously made backup fields[i, leftmoving,:,:] .*= -bdry.r[i] fields[i, leftmoving,:,:] .+= (1+bdry.r[i]).*FieldArrivedLeft end end # Now the directions have changed lmv = copy(leftmoving) leftmoving = rightmoving rightmoving = lmv # a is the maximum field that still occurs in the system if amin != 0 a = maximum(abs.(fields)) end # n is the iteration count n += 1 end # Dance Outro # The summation at the end is to give the opportunity to intelligently sum # since different rays might have very different orders of magnitude # The other reason is, that we are able to return the different components # such that we can get immediate access to less accurate results # TODO: See if Julia is really using the most intelligent summing strategy if returnsum return sum(reverse(Eout, dims = 3), dims = 3)[:,:,1] else return Eout end end """ dance_intro(bdry::SetupBoundaries, X, Y; bfield=nothing, velocity_x=0, f=10e9,diskR=0.1) Initialize EM fields. Can include velocity effects. """ function dance_intro(bdry::SetupBoundaries, coords::CoordinateSystem; bfield=nothing, velocity_x=0, f=10e9,diskR=0.1) # Initialize the variable we want to return fields_initial = Array{Complex{Float64}}(zeros(length(bdry.distance), 2, length(coords.X), length(coords.Y))) # Inaccuracies of the emitted fields, BField and Velocity Effects ################### if bfield === nothing # Make sure that there is only emission on the disk surfaces bfield = [x^2 + y^2 > diskR^2 ? 0.0 : 1.0 for z in ones(length(bdry.distance)+1), x in coords.X, y in coords.Y] end if velocity_x != 0 lambda = wavelength(f) Ma_PerMeter = 2pi/lambda # k = 2pi/lambda (c/f = lambda) bfield .*= [exp(-1im*Ma_PerMeter*(-velocity_x)*x) for z in ones(length(bdry.distance)+1), x in coords.X, y in coords.Y] end #################################################################################### # Iterate over the gaps and initialize the emissions from them ##################### # This implements Theoretical Foundations (arxiv: 1612.07057) (3.3) for n in 1:length(bdry.distance) ax_rightmoving = 0 if n == 1 if bdry.r[1] == 1 ax_rightmoving = -1.0 else #TODO ax_rightmoving = 0 end else # Right-Moving in that gap eps_i = bdry.eps[n-1] eps_m = bdry.eps[n] denominator = eps_i * sqrt(eps_m) + eps_m * sqrt(eps_i) #ax_i = sqrt(eps_i) * (1 - eps_m/eps_i) / denominator + 0im ax_rightmoving = sqrt(eps_m) * (1 - eps_i/eps_m) / denominator +0.0im end # Left-Moving in that gap eps_i = bdry.eps[n] eps_m = (n == length(bdry.distance)) ? 1 : bdry.eps[n+1] # Rightmost epsilon is 1. denominator = eps_i * sqrt(eps_m) + eps_m * sqrt(eps_i) ax_leftmoving = sqrt(eps_i) * (1 - eps_m/eps_i) / denominator +0.0im #ax_m = sqrt(eps_m) * (1 - eps_i/eps_m) / denominator +0.0im # Fill the initial array fields_initial[n,1,:,:] = [1.0*ax_rightmoving + 0.0im for x in coords.X, y in coords.Y] .* bfield[n,:,:] fields_initial[n,2,:,:] = [1.0*ax_leftmoving + 0.0im for x in coords.X, y in coords.Y] .* bfield[n+1,:,:] end return fields_initial end """ cheerleader(amin, nmax, bdry::SetupBoundaries; f=10.0e9, prop=propagator, emit=nothing, reflect=nothing, Xset=X, Yset=Y, diskR=0.1, returnboth=false) New Recursive Fourier Propagation implementation. # Arguments: - `amin`: Mimum (local) amplitude of a field, in order to be propagated - `nmax`: Maximum number of beam iteration steps, directly equivalent to how many boundaries a beam 'sees' at most - `bdry::SetupBoundaries`: Properties of dielectric boundaries - `f::Float64` ```> 0```: Frequency of EM radiation - `prop`: Propagator Function to use. Standard is propagator(). - `emit`: Explicitly set the axion-induced fields emitted from each boundary (to the left and to the right). If ``nothing`` fields are initialized according to uniform, homogeneous external B-field with zero axion velocity. - `reflect`: If `nothing` (standar value), the axion-induced signal is computed. If set, this field defines a beam, for which the reflected beam will be calculated - `Xset` and `Yset`: Explicitly set the coordinate system for the fields - `diskR`: Radius of dielectric disk - `returnboth::Bool`: If `true` cheerleader returns fields leaving on left and right. If `false` only returns fields leaving on right. See [`dancer`](@ref) for old version. """ function cheerleader(amin, nmax, bdry::SetupBoundaries, coords::CoordinateSystem; f=10.0e9, prop=propagator, emit=nothing, reflect=nothing, diskR=0.1, returnboth=false) # Before speed of light was 3e8 here, but this means an error at the permil level, i.e. order ~20MHz at 20GHz, # if fixing lambda to 1.5 cm, one gets a shift of roughly 10MHz lambda = wavelength(f) # Pre-allocate memory # Note that fields[0,:,:] contains the fields leaving the system on the left # and fields[length(bdry.distance)+1,:,:] the fields leaving on the right #fields = OffsetArray(::Array{Complex{Float64}}}, 0:length(bdry.distance)+1, 1:length(X), 1:length(Y)) fields = Array{Complex{Float64}}(zeros( length(bdry.distance)+2, length(coords.X), length(coords.Y))) # number of regions + 2 outporpagating --^ ^ # dimensions of the fields at each position ------------------------------^ # In a next step this could/should be generalized in the SetupBoundaries structure.. reflectivities_leftmoving = -bdry.r reflectivities_rightmoving = bdry.r transmissivities_leftmoving = 1 .- bdry.r transmissivities_rightmoving = 1 .+ bdry.r ### Indexing of arrays, note the specific indexing of the regions, different from dancer()!. #Boundaries: [ 1, 2, 3, 4 ] # ####### | | | | ####### # # <-- # | | | | # --> # # ####### | | | | ####### #Regions: [ 1 , 2 , 3 , 4 , 5 ] # emit in this function expects for each region left- and right-propagating fields. # Note that this is different from dancer() since here we do not take the mirror # explicit, such that also a transmissivity can be calculated # TODO: propagation through and emission from last bdry to the right if reflect === nothing if emit === nothing emit = Array{Complex{Float64}}(zeros(length(bdry.distance), 2, length(coords.X), length(coords.Y))) # we may reuse the dance_intro-function in the standard case bdrycpy = deepcopy(bdry) bdrycpy.distance = bdrycpy.distance[2:end] bdrycpy.eps = bdrycpy.eps[2:end] emit[2:end,:,:,:] = dance_intro(bdrycpy,coords,diskR=diskR) end else emit = Array{Complex{Float64}}(zeros(length(bdry.distance), 2, length(coords.X), length(coords.Y))) # This should be right like this, it is explicitly taken care of at the bottom... emit[length(bdry.distance), 2, :, :] = reflect end # Initialize fields ........................................ # Add everything which is emitted to the right fields = copy(emit[:,1,:,:]) # Push forward the fields rightmoving... for i in 2:1:length(bdry.distance)-1 # Propagate to the right fields[i,:,:] = prop(fields[i,:,:], bdry.distance[i], diskR, bdry.eps[i], bdry.relative_tilt_x[i], bdry.relative_tilt_y[i], bdry.relative_surfaces[i,:,:], lambda, coords) # Reflect and Transmit fields[i+1,:,:] .+= transmissivities_rightmoving[i].*fields[i,:,:] fields[i,:,:] .*= reflectivities_rightmoving[i] end # Add everything which is emitted to the left (except last gap) fields[1:length(bdry.distance)-1,:,:] .+= emit[1:length(bdry.distance)-1,2,:,:] #fields .+= emit[:,2,:,:] # The last gap is not supposed to contain left-propagating stuff, # so we have to treat it explicitly (not elegant, but ok) let i = length(bdry.distance)-1 # Transmit fields[i,:,:] .+= transmissivities_leftmoving[i].*emit[i+1,2,:,:] # Reflect fields[i+1,:,:] .+= reflectivities_leftmoving[i].*emit[i+1,2,:,:] end # Main iterations ............................................... n = 0 # Iteration Count a = 1.0 # Maximum Field Amplitude in the iteration while n <= nmax && a >= amin # Iterate over regions # The first region explicitly let i = 1 # Do NOT Propagate to the fields in the current gap to the right # Region 1 always contains left-moving fields. # Propagate to the fields in the next gap to the left fields[i+1,:,:] = prop(fields[i+1,:,:], bdry.distance[i+1], diskR, bdry.eps[i+1], bdry.relative_tilt_x[i+1], bdry.relative_tilt_y[i+1], bdry.relative_surfaces[i+1,:,:], lambda, coords) # Reflect and Transmit # No backup copy needed since nothing is coming from the i = 1 region # Transmit fields[i,:,:] .+= transmissivities_leftmoving[i].*fields[i+1,:,:] # Reflect fields[i+1,:,:] .*= reflectivities_leftmoving[i] end # Push forward... for i in 2:1:length(bdry.distance)-2 # Propagate to the fields in the current gap to the right fields[i,:,:] = prop(fields[i,:,:], bdry.distance[i], diskR, bdry.eps[i], bdry.relative_tilt_x[i], bdry.relative_tilt_y[i], bdry.relative_surfaces[i,:,:], lambda, coords) # Propagate to the fields in the next gap to the left fields[i+1,:,:] = prop(fields[i+1,:,:], bdry.distance[i+1], diskR, bdry.eps[i+1], bdry.relative_tilt_x[i+1], bdry.relative_tilt_y[i+1], bdry.relative_surfaces[i+1,:,:], lambda, coords) # Reflect and Transmit fields_next_gap_copy = copy(fields[i+1,:,:]) fields[i+1,:,:] .*= reflectivities_leftmoving[i] fields[i+1,:,:] .+= transmissivities_rightmoving[i].*fields[i,:,:] fields[i,:,:] .*= reflectivities_rightmoving[i] fields[i,:,:] .+= transmissivities_leftmoving[i].*fields_next_gap_copy end let i = length(bdry.distance)-1 # Propagate to the fields in the current gap to the right fields[i,:,:] = prop(fields[i,:,:], bdry.distance[i], diskR, bdry.eps[i], bdry.relative_tilt_x[i], bdry.relative_tilt_y[i], bdry.relative_surfaces[i,:,:], lambda, coords) # DO NOT Propagate to the fields in the next gap to the left # Since it only contains fields propagating to the right # Reflect and Transmit fields[i+1,:,:] .+= transmissivities_rightmoving[i].*fields[i,:,:] fields[i,:,:] .*= reflectivities_rightmoving[i] end # The last region always contains right-moving fields and is automatically added up correctly # Check convergence if amin != 0 a = maximum(abs.(fields)) end # n is the iteration count n += 1 end if returnboth return fields[1,:,:], fields[length(bdry.distance),:,:] else return fields[length(bdry.distance),:,:] end end
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"""A scirntific module (calculate exp(x))""" module SCI abstract type AbstractProblem end struct Problem{T} <: AbstractProblem x::T end abstract type AbstractAlgorithm end struct Builtin <: AbstractAlgorithm end Base.@kwdef struct Taylor <: AbstractAlgorithm n::Int = 10 end default_algorithm(prob::Problem) = Builtin() struct Solution{R, P<:AbstractProblem, A<:AbstractAlgorithm} result::R; prob::P; alg::A end solve(prob::AbstractProblem) = solve(prob, default_algorithm(prob)) solve(prob::AbstractProblem, alg::Builtin) = Solution(exp(prob.x), prob, alg) solve(prob::AbstractProblem, alg::Taylor) = Solution(sum(prob.x^k/factorial(k) for k in 0:alg.n), prob, alg) using COM COM.prettify(sol::Solution{R, P, A}) where {R, P<:AbstractProblem, A<:Builtin} = """ Problem: x = $(sol.prob.x) Algorithm: builtin exp(x) Result: $(sol.result) """ COM.prettify(sol::Solution{R, P, A}) where {R, P<:AbstractProblem, A<:Taylor} = """ Problem: x = $(sol.prob.x) Algorithm: Taylor series of exp(x) upto degree $(sol.alg.n) Result: $(sol.result) """ end
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395
mutable struct Attribute name::Symbol value::String Attribute(name::Symbol) = new(name, "") end const NULL_ATTRIBUTE = Attribute(:NULL) mutable struct ChartType concreteName::String attributes::Vector{Attribute} ancestors::Vector{ChartType} ChartType(attributes::Vector{A}, ancestors::Vector{ChartType}=ChartType[]) where {A<:Attribute} = new("NONE", convert(Vector{Attribute}, attributes), ancestors) ChartType(concreteName::String, attributes::Vector{A}, ancestors::Vector{ChartType}=ChartType[]) where {A<:Attribute} = new(concreteName, convert(Vector{Attribute}, attributes), ancestors) end function Base.deepcopy(chart_type::ChartType) name = chart_type.concreteName attributes = Vector{Attribute}(undef, length(chart_type.attributes)) for (i,a) in enumerate(chart_type.attributes) attributes[i] = deepcopy(a) end ancestors = Vector{ChartType}(undef, length(chart_type.ancestors)) for (i,ancestor) in enumerate(chart_type.ancestors) ancestors[i] = deepcopy(ancestor) end ChartType(name, attributes, ancestors) end function get_all_attributes(chart_type::ChartType) retval = Attribute[] for ancestor in chart_type.ancestors append!(retval, get_all_attributes(ancestor)) end append!(retval, chart_type.attributes) retval end function Base.getindex(chart_type::ChartType, s::Symbol) for a in chart_type.attributes if a.name == s return a end end for ancestor in chart_type.ancestors a = ancestor[s] if a.name != :NULL return a end end NULL_ATTRIBUTE end function Base.setindex!(chart_type::ChartType, v::String, s::Symbol) for a in chart_type.attributes if a.name == s a.value = v return end end for ancestor in chart_type.ancestors a = ancestor[s] if a.name != :NULL a.value = v return end end end const BaseChart = ChartType([Attribute(:width), Attribute(:height)]) const ColorChart = ChartType([Attribute(:colors), Attribute(:colorAccessor), Attribute(:colorDomain)]) const CoordinateGridChart = ChartType([Attribute(:zoomScale), Attribute(:zoomOutRestrict), Attribute(:mouseZoomable), Attribute(:x), Attribute(:xUnits), Attribute(:xAxis), Attribute(:elasticX), Attribute(:xAxisPadding), Attribute(:y), Attribute(:yAxis), Attribute(:elasticY), Attribute(:renderHorizontalGridLines), Attribute(:renderVerticalGridLines), Attribute(:yAxisPadding), Attribute(:round), Attribute(:keyAccessor), Attribute(:valueAccessor), Attribute(:xAxisLabel), Attribute(:yAxisLabel)], [BaseChart, ColorChart]) const AbstractBubbleChart = ChartType([Attribute(:r), Attribute(:radiusValueAccessor), Attribute(:minRadiusWithLabel), Attribute(:maxBubbleRelativeSize)], [ColorChart]) const WidgetChart = ChartType([Attribute(:dimension), Attribute(:group)]) # StackableChart = ChartType(false, [Attribute{}]) const PieChart = ChartType("pieChart", [Attribute(:slicesCap), Attribute(:innerRadius), Attribute(:radius), Attribute(:cx), Attribute(:cy), Attribute(:minAngleForLabel)], [ColorChart, BaseChart]) const BarChart = ChartType("barChart", [Attribute(:centerBar), Attribute(:gap)], [CoordinateGridChart]) # StackableChart const LineChart = ChartType("lineChart", [Attribute(:renderArea), Attribute(:dotRadius)], [CoordinateGridChart]) # StackableChart const RowChart = ChartType("rowChart", [Attribute(:gap), Attribute(:elasticX), Attribute(:labelOffsetX), Attribute(:labelOffsetY)], [ColorChart, BaseChart]) const BubbleChart = ChartType("bubbleChart", [Attribute(:elasticRadius)], [AbstractBubbleChart, CoordinateGridChart]) const DataCountWidget = ChartType("dataCount", Attribute[], [WidgetChart]) const DataTableWidget = ChartType("dataTable", [Attribute(:size), Attribute(:columns), Attribute(:sortBy), Attribute(:order)], [WidgetChart]) # DC Chart mutable struct DCChart group::Group typ::ChartType title::String parent::String function DCChart( typ::ChartType, group::Group; title::String = "Chart for " * string(group.dim.name), parent::String = @sprintf("chart_%06d", rand(0:999999)), ) new(group, typ, title, parent) end end function randomize_parent(chart::DCChart) chart.parent = @sprintf("chart_%06d", rand(0:999999)) Union{} end function Base.write(io::IO, chart::DCChart, indent::Int) tabbing = " "^indent println(io, tabbing, "var ", chart.parent, " = dc.", chart.typ.concreteName, "(\"#", chart.parent, "\")") # TODO: add chart grouping println(io, tabbing, " .dimension(", chart.group.dim.name, ")") print(io, tabbing, " .group(", chart.group.name, ")") attributes = get_all_attributes(chart.typ) for (i,a) in enumerate(attributes) if !isempty(a.value) print(io, "\n", tabbing, " .", string(a.name), "(", a.value, ")") end end println(io, ";") end # DC Widget mutable struct DCWidget typ::ChartType parent::String html::String columns::Vector{Symbol} group_results::Bool function DCWidget( typ::ChartType, columns::Vector{Symbol} = Symbol[], group_results::Bool = false, html::String = "", parent::String = @sprintf("chart_%06d", rand(0:999999)), ) new(typ, parent, html, columns, group_results) end end # Inelegant way to handle the two types of widgets. Change if time permits. function randomize_parent(widget::DCWidget) widget.parent = @sprintf("chart_%06d", rand(0:999999)) if widget.typ.concreteName == "dataCount" widget.html = string("<div id=\"", widget.parent, """\"><span class="filter-count"></span> selected out of <span class="total-count"></span> records</div>""") elseif widget.typ.concreteName == "dataTable" html_str = IOBuffer() if !widget.group_results write(html_str, " <style> #", widget.parent, " .dc-table-group{display:none} </style>") end write(html_str, " <table class=\"table table-hover\" id=\"", widget.parent, "\"> <thead> <tr>") for key in widget.columns write(html_str, "<th>", key, "</th>") end write(html_str, "</tr> </thead> </table>") widget.html = String(take!(html_str)) end Union{} end function Base.write(io::IO, chart::DCWidget, indent::Int) tabbing = " "^indent print(io, tabbing, "var ", chart.parent, " = dc.", chart.typ.concreteName, "(\"#", chart.parent, "\")") # TODO: add chart grouping attributes = get_all_attributes(chart.typ) for (i,a) in enumerate(attributes) if !isempty(a.value) print(io, "\n", tabbing, " .", string(a.name), "(", a.value, ")") end end println(io, ";") end """ can_infer_chart(arr::AbstractVector) Whether chart inference is supported for the given array type. """ isna(x) = isa(x, Missing) can_infer_chart(arr::AbstractVector) = !any(isna, arr) can_infer_chart(arr::AbstractVector{I}) where {I<:Integer} = !any(isna, arr) can_infer_chart(arr::AbstractVector{F}) where {F<:AbstractFloat} = !any(isna, arr) && !any(isinf, arr) && !any(isnan, arr) can_infer_chart(arr::AbstractVector{S}) where {S<:AbstractString} = !any(isna, arr) """ infer_chart(arr::AbstractVector, group::Group) Constructs a Chart suitable for the type in arr. """ infer_chart(arr::AbstractVector{R}, group::Group) where {R<:Real} = barchart(arr, group) infer_chart(arr::AbstractVector{S}, group::Group) where {S<:AbstractString} = piechart(arr, group) function scale_default(arr::AbstractVector{R}) where R<:Real @sprintf("d3.scale.linear().domain([%d,%d])", floor(Int, minimum(arr)), ceil(Int, maximum(arr))) end function size_default!(chart::ChartType) chart[:width] = "300.0" chart[:height] = "225.0" end """ barchart Infer construction of a DC barchart based on the given group. """ function barchart(arr::AbstractVector{I}, group::Group) where I<:Integer chart = deepcopy(BarChart) size_default!(chart) chart[:x] = scale_default(arr) chart[:xUnits] = "dc.units.fp.precision(.0)" DCChart(chart, group) end function barchart(arr::AbstractVector{F}, group::Group) where F<:AbstractFloat chart = deepcopy(BarChart) size_default!(chart) chart[:centerBar] = "true" chart[:x] = scale_default(arr) chart[:xUnits] = "dc.units.fp.precision($(group.dim.bin_width))" DCChart(chart, group) end """ piechart Infer construction of a DC piechart based on the given group. """ function piechart(arr::AbstractVector{S}, group::Group) where S<:AbstractString chart = deepcopy(PieChart) size_default!(chart) chart[:radius] = string(parse(Float64, chart[:height].value)*0.4) chart[:slicesCap] = "10" DCChart(chart, group) end function piechart(arr::AbstractVector{I}, group::Group) where I<:Integer chart = deepcopy(PieChart) size_default!(chart) DCChart(chart, group) end """ linechart Infer construction of a DC linechart based on the given group. """ function linechart(arr::AbstractVector{I}, group::Group) where I<:Integer chart = deepcopy(LineChart) size_default!(chart) chart[:x] = scale_default(arr) chart[:xUnits] = "dc.units.fp.precision(.0)" DCChart(chart, group) end function linechart(arr::AbstractVector{F}, group::Group) where F<:AbstractFloat chart = deepcopy(LineChart) size_default!(chart) chart[:x] = scale_default(arr) chart[:xUnits] = "dc.units.fp.precision($(group.dim.bin_width))" DCChart(chart, group) end """ bubblechart Construct an empty custom DC bubblechart. """ function bubblechart(group::Group) chart = deepcopy(BubbleChart) size_default!(chart) DCChart(chart, group) end function _generate_accessor(col::Symbol) if col == :DCCount return "function (d) { return d.value.DCCount;}" else return string("function (d) { return d.value.", col, "_sum;}") end end """ bubbleChart Construct a bubblechart using the given master grouping and sums given by `x_col`, `y_col`, and `r_col` for x position, y position, and radius. """ function bubblechart(group::Group, x_col::Symbol, y_col::Symbol, r_col::Symbol) chart = deepcopy(BubbleChart) size_default!(chart) chart[:width] = string(parse(Float64, chart[:width].value) * 1) chart[:x] = "d3.scale.linear().domain([0,150])" # Will get overriden by elasticX chart[:elasticX] = "true" chart[:elasticY] = "true" chart[:elasticRadius] = "true" chart[:xAxisPadding] = "50" chart[:yAxisPadding] = "50" chart[:keyAccessor] = _generate_accessor(x_col) chart[:valueAccessor] = _generate_accessor(y_col) chart[:radiusValueAccessor] = _generate_accessor(r_col) chart[:maxBubbleRelativeSize] = "0.2" DCChart(chart, group) end """ rowchart Infer construction of a DC rowchart based on the given group. """ function rowchart(arr::AbstractVector{S}, group::Group) where S<:AbstractString chart = deepcopy(RowChart) size_default!(chart) DCChart(chart, group) end function rowchart(arr::AbstractVector{I}, group::Group) where I<:Integer chart = deepcopy(RowChart) size_default!(chart) DCChart(chart, group) end """ datacountwidget Construct a DC DataCountWidget. """ function datacountwidget() chart = deepcopy(DataCountWidget) chart[:dimension] = "cf" chart[:group] = "all" dcwidget = DCWidget(chart) randomize_parent(dcwidget) dcwidget end """ datatablewidget Construct a DC DataTableWidget. """ function datatablewidget(col::Symbol, columns::Vector{Symbol}, group_results::Bool=false) chart = deepcopy(DataTableWidget) chart[:dimension] = string(col) if group_results chart[:group] = string("function(d) {return d.", col, ";}") else chart[:group] = """function(d) {return "Showing All Results";}""" end col_str = IOBuffer() print(col_str, "[\n") for key in columns print(col_str, "function(d) {return d.", key, ";},\n") end print(col_str, "]") chart[:columns] = String(take!(col_str)) chart[:size] = "15" dcwidget = DCWidget(chart, columns, group_results) randomize_parent(dcwidget) dcwidget end function datatablewidget(columns::Vector{Symbol}) datatablewidget(columns[1], columns, false) end #= # Chart types DataCountWidget <: BaseChart DataTableWidget <: BaseChart BubbleChart <: AbstractBubbleChart, CoordinateGridChart CompositeChart <: CoordinateGridChart GeoCloroplethChart <: ColorChart, BaseChart BubbleOverlayChart <: AbstractBubbleChart, BaseChart RowChart <: ColorChart, BaseChart Legend NumberDisplay <: BaseChart =#
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function _make_spectogram_gui() popupmenu_spect = Menu() popupmenu_spect_freq = MenuItem("Frequency Range") popupmenu_spect_win = MenuItem("Window Size") popupmenu_spect_overlap = MenuItem("Window Overlap") push!(popupmenu_spect,popupmenu_spect_freq) push!(popupmenu_spect,popupmenu_spect_win) push!(popupmenu_spect,popupmenu_spect_overlap) popupmenu_spect_freq_select=Menu(popupmenu_spect_freq) if VERSION > v"0.7-" spect_f_handles=Array{MenuItemLeaf}(undef,0) else spect_f_handles=Array{MenuItemLeaf}(0) end spect_f_options=[300; 1000; 3000; 7500; 15000] push!(spect_f_handles,MenuItem(string(spect_f_options[1]))) push!(popupmenu_spect_freq_select,spect_f_handles[1]) for i=2:5 push!(spect_f_handles,MenuItem(spect_f_handles[i-1],string(spect_f_options[i]))) push!(popupmenu_spect_freq_select,spect_f_handles[i]) end popupmenu_spect_win_select=Menu(popupmenu_spect_win) if VERSION > v"0.7-" spect_w_handles=Array{MenuItemLeaf}(undef,0) else spect_w_handles=Array{MenuItemLeaf}(0) end spect_w_options=[10; 50; 100] push!(spect_w_handles,MenuItem(string(spect_w_options[1]))) push!(popupmenu_spect_win_select,spect_w_handles[1]) for i=2:3 push!(spect_w_handles,MenuItem(spect_w_handles[i-1],string(spect_w_options[i]))) push!(popupmenu_spect_win_select,spect_w_handles[i]) end Gtk.showall(popupmenu_spect) (spect_w_handles,spect_f_handles, popupmenu_spect) end function add_spect_callbacks(spect_f_handles,spect_w_handles,handles) for i=1:5 signal_connect(spect_popup_freq_cb,spect_f_handles[i],"activate",Void,(),false,(handles,i-1)) end for i=1:3 signal_connect(spect_popup_win_cb,spect_w_handles[i],"activate",Void,(),false,(handles,i-1)) end nothing end function prepare_spectrogram(ctx,han) myheight=height(ctx) mywidth=width(ctx) set_source_rgb(ctx,1.0,1.0,1.0) move_to(ctx,50.0,myheight-35) show_text(ctx,"-1") move_to(ctx,50.0,myheight-35) show_text(ctx,"-1.0") move_to(ctx,(mywidth-50.0)/2+50,myheight-35) show_text(ctx,"-0.5") move_to(ctx,mywidth-20.0,myheight-35) show_text(ctx,"0.0") move_to(ctx,mywidth/2,myheight-20) show_text(ctx,"Time (s)") move_to(ctx,10.0,myheight-140) rotate(ctx,-pi/2) show_text(ctx,"Frequency") identity_matrix(ctx) move_to(ctx,35.0,myheight-50.0) rotate(ctx,-pi/2) show_text(ctx,"0.0") identity_matrix(ctx) move_to(ctx,35.0,myheight-50-125) rotate(ctx,-pi/2) show_text(ctx,string(round(Int64,han.spect.f_max*han.spect.f_div/2))) identity_matrix(ctx) nothing end function draw_spectrogram(rhd::RHD2000,han::Gui_Handles) ctx=Gtk.getgc(han.c) x=Gtk.cairo_surface(han.c) data = ccall((:cairo_image_surface_get_data,Cairo._jl_libcairo),Ptr{UInt32},(Ptr{Void},),x.ptr) c_h=round(Int64,height(ctx)) c_w=round(Int64,width(ctx))-50 for i=(size(rhd.v,1)+1):rhd.sr han.v_s[i-SAMPLES_PER_DATA_BLOCK] = han.v_s[i] end count=1 if han.band_widgets.lfp_en[han.sc.spike] for i=(rhd.sr-SAMPLES_PER_DATA_BLOCK+1):rhd.sr han.v_s[i] = rhd.lfps[count,han.sc.spike] count+=1 end else for i=(rhd.sr-SAMPLES_PER_DATA_BLOCK+1):rhd.sr han.v_s[i] = rhd.v[count,han.sc.spike] count+=1 end end plot_spectrogram(han.v_s,rhd.sr,han.spect) in_w = han.spect.t_max in_h = han.spect.f_max scale_w = c_w / in_w scale_h = 250 / in_h mymin=minimum(han.spect.out) mymax=maximum(han.spect.out) myrange=mymax-mymin rgb_mat = zeros(UInt32,in_h,in_w) for h=1:in_h for w=1:in_w startround = (han.spect.out[h,w]-mymin)/myrange*255 myinput::UInt8 = (startround >= 255) ? 255 : floor(UInt8,startround)+1 myblue=jet_b[myinput] mygreen=jet_g[myinput] myred=jet_r[myinput] rgb_mat[h,w] = 0 rgb_mat[h,w] |= myblue # blue rgb_mat[h,w] |= (mygreen << 8) #green rgb_mat[h,w] |= (myred << 16) #red end end for h=1:250 for w=1:c_w val = rgb_mat[ceil(Int,h/scale_h),ceil(Int,w/scale_w)] unsafe_store!(data,val,(c_h-h-50)*(c_w+50)+w+50) end end nothing end function plot_spectrogram(s,fs,spect) S = spectrogram(s,spect.win_width_s,spect.win_overlap_s; fs=fs, window=hanning) temp = power(S) for i=1:spect.f_max for j=1:spect.t_max spect.out[i,j]=log10(temp[i,j]) end end nothing end function spect_popup_freq_cb(widget::Ptr,user_data::Tuple{Gui_Handles,Int64}) han, event_id = user_data if event_id == 0 f_max = 300 elseif event_id == 1 f_max = 1000 elseif event_id == 2 f_max = 3000 elseif event_id == 3 f_max = 7500 else f_max = 15000 end han.spect = Spectrogram(han.spect.fs; win_width_t = han.spect.win_width_t, win_overlap_t = han.spect.win_overlap_t, f_max = f_max) nothing end function spect_popup_win_cb(widget::Ptr,user_data::Tuple{Gui_Handles,Int64}) han, event_id = user_data if event_id == 0 mywin = .01 elseif event_id == 1 mywin = .05 else mywin = .1 end han.spect = Spectrogram(han.spect.fs; win_width_t = mywin, win_overlap_t = han.spect.win_overlap_t, f_max = han.spect.f_max*han.spect.f_div) nothing end
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module Analysis using Oceananigans using JLD2 using Plots using Oceananigans.Grids using Oceananigans.Fields: offset_data import Oceananigans.Fields: Field Field(loc::Tuple, grid::AbstractGrid, raw_data::Array) = Field(loc, CPU(), grid, nothing, offset_data(raw_data, grid, loc)) field_from_file(file, loc, name, iter) = Field(loc, file["serialized/grid"], file["timeseries/$name/$iter"]) CellFileField(file, name, iter) = Field((Cell, Cell, Cell), file["serialized/grid"], file["timeseries/$name/$iter"]) XFaceFileField(file, name, iter) = Field((Face, Cell, Cell), file["serialized/grid"], file["timeseries/$name/$iter"]) YFaceFileField(file, name, iter) = Field((Cell, Face, Cell), file["serialized/grid"], file["timeseries/$name/$iter"]) max_abs_tracer(file, iter) = maximum(abs, interior(CellFileField(file, :c, iter))) tracer_variance(file, iter) = 1/2 * sum(interior(CellFileField(file, :c, iter)).^2) kinetic_energy(file, iter) = 1/2 * (sum(interior(XFaceFileField(file, :u, iter)).^2) + sum(interior(YFaceFileField(file, :v, iter)).^2)) function oceananigans_statistics(filename) file = jldopen(filename) iterations = parse.(Int, keys(file["timeseries/t"])) t = [ file["timeseries/t/$iter"] for iter in iterations] e = [ kinetic_energy(file, iter) for iter in iterations] χ = [ tracer_variance(file, iter) for iter in iterations] c★ = [ max_abs_tracer(file, iter) for iter in iterations] close(file) Δχ = χ ./ χ[1] Δe = e ./ e[1] return t, Δe, Δχ, c★ end function plot_resolutions(t, data, resolutions; kwargs...) plt = plot(t[1], data[1]; label = "N² = $(resolutions[1])²", kwargs...) for i = 2:length(data) plot!(plt, t[i], data[i]; label = "N² = $(resolutions[i])²", kwargs...) end return plt end end # module
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using BenchmarkTools BenchmarkTools.DEFAULT_PARAMETERS.samples = 100 function compute(file::String)::Int chars = Dict(Char(Int('A') + i) => i + 1 for i ∈ 0:25) return sum(i * chars[c] for (i, name) ∈ enumerate(sort(split(replace(file, "\"" => ""), ","))) for c ∈ name) end file = read("problems/0022/p022_names.txt", String) compute(file) @benchmark compute(file)
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""" data_harvest(all=true, paper=false) Makes all the figures from the paper for the notebook. This function calls PlotData to make the figures one at a time. """ function data_harvest(all=true, paper=false) cvals = [.5, .99, 1] itvals = [10, 10, 30] level=5 for ic=1:3 figure(ic) PlotData(cvals[ic]; ptitle=~paper) end if all for ic=1:3 figure(ic+3) PlotData(cvals[ic]; half=true, ptitle=~paper) end end end """ PlotData(c; half=false, level=5, ptitle=true) Generates a figure from the paper using the precomputed data from data_populate. """ function PlotData(c; half=false, level=5, ptitle=true) if half nfig=2 T=Float16 TI=[Float16] leadtitle="Half Precision: c = " else nfig=4 T=Float64 TI=[Float64, Float32] leadtitle="Single and Double Precision: c = " end ftitle=string(leadtitle,string(c)) c==1.0 ? (nits=31) : (nits=11) maxit=nits-1 #aymin=1.e-15 aymin=ymin(c,half) Datain=zeros(nits,level,nfig) fname=Fname(c,T) readmpdata(fname, Datain) for ic=1:nfig for iz = 1:level Datain[:,iz,ic] .= Datain[:,iz,ic]./Datain[1,iz,ic] end end for T in TI PlotHist(Datain, level, maxit, aymin, T) end if ptitle PyPlot.suptitle(ftitle) end end function ymin(c, half) ymin = 1.e-15 if half if c == 1.0 ymin = 1.e-6 else ymin = 1.e-10 end end return ymin end function PlotHist(DataC::Array{Float64,3}, pmax, maxit, aymin, T) fmtplot = ("k-", "k--", "k-.", "k:", "k>:") gxlabel = "Nonlinear Iterations" gylabel = L"$|| F ||/||F_0||$" if T==Float16 b=1 pstart=1 else b=2 pstart = (T==Float32)*3 + (T==Float64)*1 end subplot(b,2,pstart) for ip=1:pmax semilogy(0:maxit,DataC[:,ip,pstart],fmtplot[ip]); axis([0.0, maxit, aymin, 1.0]) if pstart == 1 legend(["1024", "2048", "4096", "8192", "16384"]) end title(string(prestring(T),",", "analytic Jacobian")) xlabel(gxlabel) ylabel(gylabel) end subplot(b,2,pstart+1) for ip=1:pmax semilogy(0:maxit,DataC[:,ip,pstart+1],fmtplot[ip]) title(string(prestring(T),",", "finite difference Jacobian")) xlabel(gxlabel) if T != Float16 ylabel(gylabel) end axis([0.0, maxit, aymin, 1.0]) end end function prestring(T) if T==Float64 pstr="Double precision" elseif T==Float32 pstr="Single precision" else pstr="Half precision" end return pstr end
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352, 13, 15, 12962, 198, 437, 198, 437, 198, 198, 8818, 662, 8841, 7, 51, 8, 198, 361, 309, 855, 43879, 2414, 198, 220, 220, 279, 2536, 2625, 25628, 15440, 1, 198, 17772, 361, 309, 855, 43879, 2624, 198, 220, 220, 279, 2536, 2625, 28008, 15440, 1, 198, 17772, 198, 220, 220, 279, 2536, 2625, 31305, 15440, 1, 198, 437, 198, 7783, 279, 2536, 198, 437, 198 ]
2.188984
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export ft_getstatus function ft_getstatus(ft_handle::Culong) amountinrxqueue = Ref{Cuint}() amountintxqueue = Ref{Cuint}() eventstatus = Ref{Cuint}() ft_status = ccall((:FT_GetStatus, d2xx), Cuint, (Culong,Ref{Cuint},Ref{Cuint},Ref{Cuint}), ft_handle, amountinrxqueue, amountintxqueue, eventstatus) checkstatus(ft_status) return (convert(Int32,amountinrxqueue[]), convert(Int32,amountintxqueue[]),convert(Int32,eventstatus[])) end
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""" localizationsplot(localizations::Vector{Localizations}, color::Symbol) localizationsplot(localizations::Vector{Vector{Localization}}, colors::Vector{Symbol}) Plot localizations by spatial coordinates. Assumes that the y-coordinate is inverted. Defaults to 0-to-40960 on each axis, but option may be specified using keyword arguments xlims=(x1, x2) and ylims=(y1, y2). Colors are specified in the documentation for Plots.jl. Can tune opacity with opacity= keword argument. factor= can be tuned for display in the pane or for saving and printing (recommended 4, default 1). """ plotlocalizations(locs::Vector{Localization}, color::Symbol; kargs...) = plotlocalizations([locs], [color]; kargs...) function plotlocalizations(localizations::Vector{Vector{Localization}}, colors::Vector{Symbol}; xlims=(0, 40960), ylims=(0,40960), opacity=0.75, factor=1) channelcount = length(localizations) plot(framestyle=:none, size=((1024,1024) .* factor)) for i ∈ 1:channelcount coords = localizations[i] |> extractcoordinates scatter!(coords[1,:], coords[2,:], marker=(1 * factor, stroke(0), opacity, colors[i])) end plot!(aspect_ratio=:equal, xlims=xlims, yaxis=(ylims, :flip), legend=:none, grid=:hide, ticks=(0)) end
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2.978774
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include("prewrap/bitmasks.jl") include("prewrap/types.jl") include("prewrap/handles.jl") include("prewrap/pointers.jl") include("prewrap/conversions.jl") include("prewrap/errors.jl") include("prewrap/spirv.jl")
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2.573171
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struct StreamerInfo streamer dependencies end struct Streamers tkey::TKey refs::Dict{Int32, Any} elements::Vector{StreamerInfo} end Base.length(s::Streamers) = length(s.elements) function Base.show(io::IO, s::Streamers) for streamer_info in s.elements println(io, "$(streamer_info.streamer.fName)") # streamer = streamer_info.streamer # print(io, "$(streamer.fName): fType = $(streamer.fType), ") # print(io, "fTypeName: $(streamer.fTypeName)") end end # Structures required to read streamers struct TStreamerInfo fName fTitle fCheckSum fClassVersion fElements end function unpack(io, tkey::TKey, refs::Dict{Int32, Any}, T::Type{TStreamerInfo}) preamble = Preamble(io, T) fName, fTitle = nametitle(io) fCheckSum = readtype(io, UInt32) fClassVersion = readtype(io, Int32) fElements = readobjany!(io, tkey, refs) endcheck(io, preamble) T(fName, fTitle, fCheckSum, fClassVersion, fElements) end safename(s::AbstractString) = replace(s, "::" => "_3a3a_") function initialise_streamer(s::StreamerInfo) # FIXME Abstract is not needed when switched to autogenerated streamers base = Symbol(safename(s.streamer.fName)) supername = Symbol(:Abstract, base) if !isdefined(@__MODULE__, supername) @debug "Defining abstract type $supername" @eval abstract type $(supername) <: ROOTStreamedObject end end name = Symbol(base, Symbol("_$(s.streamer.fClassVersion)")) if !isdefined(@__MODULE__, name) @debug " creating versioned struct '$name <: $supername'" @eval struct $(name) <: $(supername) end # FIXME create the stream!() functions somewhere here... # println(name) # @eval struct $name <: ROOTStreamedObject # data::Dict{Symbol, Any} # end else @debug "Not defining $name since it already has a bootstrapped version" end end """ function Streamers(io) Reads all the streamers from the ROOT source. """ function Streamers(io) refs = Dict{Int32, Any}() start = position(io) tkey = unpack(io, TKey) if iscompressed(tkey) @debug "Compressed stream at $(start)" seekstart(io, tkey) compression_header = unpack(io, CompressionHeader) #FIXME for some reason we need to re-pack such that it ends at exact bytes. skipped = position(io) - start # notice our `TKey` size is not the same as official TKey, can't use sizeof() io_buf = IOBuffer(read(io, tkey.fNbytes - skipped)) if String(compression_header.algo) == "ZL" stream = IOBuffer(read(ZlibDecompressorStream(io_buf), tkey.fObjlen)) elseif String(compression_header.algo) == "XZ" stream = IOBuffer(read(XzDecompressorStream(io_buf), tkey.fObjlen)) elseif String(compression_header.algo) == "L4" skip(io_buf, 8) #skip checksum stream = IOBuffer(lz4_decompress(read(io_buf), tkey.fObjlen)) else error("Unsupported compression type '$(String(compression_header.algo))'") end else @debug "Unompressed stream at $(start)" stream = io end preamble = Preamble(stream, Streamers) skiptobj(stream) name = readtype(stream, String) size = readtype(stream, Int32) streamer_infos = Vector{StreamerInfo}() @debug "Found $size streamers, continue with parsing." for i ∈ 1:size obj = readobjany!(stream, tkey, refs) if typeof(obj) == TStreamerInfo @debug " processing streamer info for '$(obj.fName)' (v$(obj.fClassVersion))" @debug " number of dependencies: $(length(obj.fElements.elements))" dependencies = Set() for element in obj.fElements.elements if typeof(element) == TStreamerBase @debug " + adding dependency '$(element.fName)'" push!(dependencies, element.fName) else @debug " - skipping dependency '$(element.fName)' with type '$(typeof(element))'" end end @debug " => finishing dependency readout for: $(obj.fName)" push!(streamer_infos, StreamerInfo(obj, dependencies)) else @debug " not a TStreamerInfo but '$(typeof(obj))', skipping." end # FIXME why not just skip a byte? skip(stream, readtype(stream, UInt8)) end endcheck(stream, preamble) streamer_infos = topological_sort(streamer_infos) for streamer_info in streamer_infos initialise_streamer(streamer_info) end Streamers(tkey, refs, streamer_infos) end """ function topological_sort(streamer_infos) Sort the streamers with respect to their dependencies and keep only those which are not defined already. The implementation is based on https://stackoverflow.com/a/11564769/1623645 """ function topological_sort(streamer_infos) @debug "Starting topological sort of streamers" provided = Set{String}() sorted_streamer_infos = [] while length(streamer_infos) > 0 remaining_items = [] emitted = false @debug " number of remaining streamers to sort: $(length(streamer_infos))" for streamer_info in streamer_infos # if all(d -> isdefined(@__MODULE__, Symbol(d)) || d ∈ provided, streamer_info.dependencies) # if !isdefined(@__MODULE__, Symbol(streamer_info.streamer.fName)) && aliasfor(streamer_info.streamer.fName) === nothing @debug " processing '$(streamer_info.streamer.fName)' with $(length(streamer_info.dependencies))' dependencies" if length(streamer_infos) == 1 || all(d -> d ∈ provided, streamer_info.dependencies) if aliasfor(streamer_info.streamer.fName) === nothing push!(sorted_streamer_infos, streamer_info) end push!(provided, streamer_info.streamer.fName) emitted = true else push!(remaining_items, streamer_info) end end if !emitted for streamer_info in streamer_infos filter!(isequal(streamer_info), remaining_items) end end streamer_infos = remaining_items end @debug "Finished the topological sort of streamers" sorted_streamer_infos end """ function readobjany!(io, tkey::TKey, refs) The main entrypoint where streamers are parsed and cached for later use. The `refs` dictionary holds the streamers or parsed data which are reused when already available. """ function readobjany!(io, tkey::TKey, refs) beg = position(io) - origin(tkey) bcnt = readtype(io, UInt32) if Int64(bcnt) & Const.kByteCountMask == 0 || Int64(bcnt) == Const.kNewClassTag # New class or 0 bytes version = 0 start = 0 tag = bcnt bcnt = 0 else version = 1 start = position(io) - origin(tkey) tag = readtype(io, UInt32) end if Int64(tag) & Const.kClassMask == 0 # reference object if tag == 0 return missing elseif tag == 1 error("Returning parent is not implemented yet") elseif !haskey(refs, tag) # skipping seek(io, origin(tkey) + beg + bcnt + 4) return missing else return refs[tag] end elseif tag == Const.kNewClassTag cname = readtype(io, CString) streamer = getfield(@__MODULE__, Symbol(cname)) if version > 0 refs[start + Const.kMapOffset] = streamer else refs[length(refs) + 1] = streamer end obj = unpack(io, tkey, refs, streamer) if version > 0 refs[beg + Const.kMapOffset] = obj else refs[length(refs) + 1] = obj end return obj else # reference class, new object ref = Int64(tag) & ~Const.kClassMask haskey(refs, ref) || error("Invalid class reference.") streamer = refs[ref] obj = unpack(io, tkey, refs, streamer) if version > 0 refs[beg + Const.kMapOffset] = obj else refs[length(refs) + 1] = obj end return obj end end struct TList preamble name size objects end Base.length(l::TList) = length(l.objects) function unpack(io, tkey::TKey, refs::Dict{Int32, Any}, T::Type{TList}) preamble = Preamble(io, T) skiptobj(io) name = readtype(io, String) size = readtype(io, Int32) objects = [] for i ∈ 1:size push!(objects, readobjany!(io, tkey, refs)) skip(io, readtype(io, UInt8)) end endcheck(io, preamble) TList(preamble, name, size, objects) end struct TObjArray name low elements end function unpack(io, tkey::TKey, refs::Dict{Int32, Any}, T::Type{TObjArray}) preamble = Preamble(io, T) skiptobj(io) name = readtype(io, String) size = readtype(io, Int32) low = readtype(io, Int32) elements = Vector{Any}(undef, size) for i in 1:size ele = readobjany!(io, tkey, refs) # !ismissing(ele) && @show ele.fName elements[i] = ele end endcheck(io, preamble) return TObjArray(name, low, elements) end abstract type AbstractTStreamerElement end @premix @with_kw mutable struct TStreamerElementTemplate version fOffset fName fTitle fType fSize fArrayLength fArrayDim fMaxIndex fTypeName fXmin fXmax fFactor end @TStreamerElementTemplate mutable struct TStreamerElement end @pour initparse begin fields = Dict{Symbol, Any}() end function parsefields!(io, fields, T::Type{TStreamerElement}) preamble = Preamble(io, T) fields[:version] = preamble.version fields[:fOffset] = 0 fields[:fName], fields[:fTitle] = nametitle(io) fields[:fType] = readtype(io, Int32) fields[:fSize] = readtype(io, Int32) fields[:fArrayLength] = readtype(io, Int32) fields[:fArrayDim] = readtype(io, Int32) n = preamble.version == 1 ? readtype(io, Int32) : 5 fields[:fMaxIndex] = [readtype(io, Int32) for _ in 1:n] fields[:fTypeName] = readtype(io, String) if fields[:fType] == 11 && (fields[:fTypeName] == "Bool_t" || fields[:fTypeName] == "bool") fields[:fType] = 18 end fields[:fXmin] = 0.0 fields[:fXmax] = 0.0 fields[:fFactor] = 0.0 if preamble.version == 3 fields[:fXmin] = readtype(io, Float64) fields[:fXmax] = readtype(io, Float64) fields[:fFactor] = readtype(io, Float64) end endcheck(io, preamble) end function unpack(io, tkey::TKey, refs::Dict{Int32, Any}, T::Type{TStreamerElement}) @initparse parsefields!(io, fields, T) T(;fields...) end @TStreamerElementTemplate mutable struct TStreamerBase fBaseVersion end function parsefields!(io, fields, T::Type{TStreamerBase}) preamble = Preamble(io, T) parsefields!(io, fields, TStreamerElement) fields[:fBaseVersion] = fields[:version] >= 2 ? readtype(io, Int32) : 0 endcheck(io, preamble) end function unpack(io, tkey::TKey, refs::Dict{Int32, Any}, T::Type{TStreamerBase}) @initparse parsefields!(io, fields, T) T(;fields...) end @TStreamerElementTemplate mutable struct TStreamerBasicType end function parsefields!(io, fields, ::Type{TStreamerBasicType}) parsefields!(io, fields, TStreamerElement) if Const.kOffsetL < fields[:fType] < Const.kOffsetP fields[:fType] -= Const.kOffsetP end basic = true if fields[:fType] ∈ (Const.kBool, Const.kUChar, Const.kChar) fields[:fSize] = 1 elseif fields[:fType] in (Const.kUShort, Const.kShort) fields[:fSize] = 2 elseif fields[:fType] in (Const.kBits, Const.kUInt, Const.kInt, Const.kCounter) fields[:fSize] = 4 elseif fields[:fType] in (Const.kULong, Const.kULong64, Const.kLong, Const.kLong64) fields[:fSize] = 8 elseif fields[:fType] in (Const.kFloat, Const.kFloat16) fields[:fSize] = 4 elseif fields[:fType] in (Const.kDouble, Const.kDouble32) fields[:fSize] = 8 elseif fields[:fType] == Const.kCharStar fields[:fSize] = sizeof(Int) else basic = false end if basic && fields[:fArrayLength] > 0 fields[:fSize] *= fields[:fArrayLength] end end function unpack(io, tkey::TKey, refs::Dict{Int32, Any}, T::Type{TStreamerBasicType}) @initparse preamble = Preamble(io, T) parsefields!(io, fields, T) endcheck(io, preamble) T(;fields...) end @TStreamerElementTemplate mutable struct TStreamerBasicPointer fCountVersion fCountName fCountClass end function unpack(io, tkey::TKey, refs::Dict{Int32, Any}, T::Type{TStreamerBasicPointer}) @initparse preamble = Preamble(io, T) parsefields!(io, fields, TStreamerElement) fields[:fCountVersion] = readtype(io, Int32) fields[:fCountName] = readtype(io, String) fields[:fCountClass] = readtype(io, String) endcheck(io, preamble) T(;fields...) end @TStreamerElementTemplate mutable struct TStreamerLoop fCountVersion fCountName fCountClass end components(::Type{TStreamerLoop}) = [TStreamerElement] function parsefields!(io, fields, ::Type{TStreamerLoop}) fields[:fCountVersion] = readtype(io, Int32) fields[:fCountName] = readtype(io, String) fields[:fCountClass] = readtype(io, String) end function unpack(io, tkey::TKey, refs::Dict{Int32, Any}, T::Type{TStreamerLoop}) @initparse preamble = Preamble(io, T) for component in components(T) parsefields!(io, fields, component) end parsefields!(io, fields, T) endcheck(io, preamble) T(;fields...) end abstract type AbstractTStreamSTL end @TStreamerElementTemplate mutable struct TStreamerSTL <: AbstractTStreamSTL fSTLtype fCtype end @TStreamerElementTemplate mutable struct TStreamerSTLstring <: AbstractTStreamSTL fSTLtype fCtype end function unpack(io, tkey::TKey, refs::Dict{Int32, Any}, ::Type{T}) where T <: AbstractTStreamSTL @initparse if T == TStreamerSTLstring wrapper_preamble = Preamble(io, T) end preamble = Preamble(io, T) parsefields!(io, fields, TStreamerElement) fields[:fSTLtype] = readtype(io, Int32) fields[:fCtype] = readtype(io, Int32) if fields[:fSTLtype] == Const.kSTLmultimap || fields[:fSTLtype] == Const.kSTLset if startswith(fields[:fTypeName], "std::set") || startswith(fields[:fTypeName], "set") fields[:fSTLtype] = Const.kSTLset elseif startswith(fields[:fTypeName], "std::multimap") || startswith(fields[:fTypeName], "multimap") fields[:fSTLtype] = Const.kSTLmultimap end end endcheck(io, preamble) if T == TStreamerSTLstring endcheck(io, wrapper_preamble) end T(;fields...) end const TObjString = String function unpack(io, tkey::TKey, refs::Dict{Int32, Any}, T::Type{TObjString}) preamble = Preamble(io, T) skiptobj(io) value = readtype(io, String) endcheck(io, preamble) T(value) end abstract type AbstractTStreamerObject end function unpack(io, tkey::TKey, refs::Dict{Int32, Any}, ::Type{T}) where T<:AbstractTStreamerObject @initparse preamble = Preamble(io, T) parsefields!(io, fields, TStreamerElement) endcheck(io, preamble) T(;fields...) end @TStreamerElementTemplate mutable struct TStreamerObject <: AbstractTStreamerObject end @TStreamerElementTemplate mutable struct TStreamerObjectAny <: AbstractTStreamerObject end @TStreamerElementTemplate mutable struct TStreamerObjectAnyPointer <: AbstractTStreamerObject end @TStreamerElementTemplate mutable struct TStreamerObjectPointer <: AbstractTStreamerObject end @TStreamerElementTemplate mutable struct TStreamerString <: AbstractTStreamerObject end abstract type ROOTStreamedObject end # function stream(io, ::Type{T}) where {T<:ROOTStreamedObject} # fields = Dict{Symbol, Any}() # preamble = Preamble(io, T) # stream!(io, fields, T{preamble.version}) # endcheck(io, preamble) # T(fields) # end function stream!(io, fields, ::Type{T}) where {T<:ROOTStreamedObject} preamble = Preamble(io, T) streamer_name = Symbol(T, "_$(preamble.version)") # @show streamer_name mod, typename = split(String(streamer_name), ".") # @show mod typename streamer = getfield(@__MODULE__, Symbol(typename)) # @show streamer readfields!(io, fields, streamer) endcheck(io, preamble) end function unpack(io, tkey::TKey, refs::Dict{Int32, Any}, ::Type{T}) where {T<:ROOTStreamedObject} cursor = Cursor(position(io), io, tkey, refs) @initparse preamble = Preamble(io, T) streamer_name = Symbol(T, "_$(preamble.version)") mod, typename = split(String(streamer_name), ".") streamer = getfield(@__MODULE__, Symbol(typename)) readfields!(cursor, fields, streamer) streamer(;cursor=cursor, fields...) end # function stream!(io, fields, ::Type{T{V}}) where {V, T<:ROOTStreamedObject} # println("Don't know how to stream $T") # end struct TObject <: ROOTStreamedObject end parsefields!(io, fields, ::Type{TObject}) = skiptobj(io) struct TString <: ROOTStreamedObject end unpack(io, tkey::TKey, refs::Dict{Int32, Any}, ::Type{TString}) = readtype(io, String) struct Undefined <: ROOTStreamedObject skipped_bytes end Base.show(io::IO, u::Undefined) = print(io, "$(typeof(u)) ($(u.skipped_bytes) bytes)") function unpack(io, tkey::TKey, refs::Dict{Int32, Any}, T::Type{Undefined}) preamble = Preamble(io, T) bytes_to_skip = preamble.cnt - 6 skip(io, bytes_to_skip) endcheck(io, preamble) Undefined(bytes_to_skip) end const TArrayD = Vector{Float64} const TArrayI = Vector{Int32} function readtype(io, T::Type{Vector{U}}) where U <: Union{Integer, AbstractFloat} size = readtype(io, eltype(T)) [readtype(io, eltype(T)) for _ in 1:size] end
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2.331013
7,758
using comorbid using Test @testset "comorbid.jl" begin @test isapprox(exact_burden_term([.1], [.8], [true]), [.08]) end
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2.272727
55
module mcrosssection1 using FinEtools using FinEtoolsFlexBeams.CrossSectionModule using Test function test() cs = CrossSectionModule.CrossSectionCircle(s -> 5.9910, s -> [0.0, 0.0, 1.0]) par = cs.parameters(0.0) for (c, r) in zip((par.A, par.J, par.I1, par.I2, par.I3), (112.75829799164978, 2023.5649824692157, 2023.5649824692157, 1011.7824912346078, 1011.7824912346078)) @test c ≈ r end true end end using .mcrosssection1 mcrosssection1.test() module mcrosssection2 using FinEtools using FinEtoolsFlexBeams.CrossSectionModule using Test function test() cs = CrossSectionModule.CrossSectionRectangle(s -> 42.0, s -> 4.2, s -> [0.0, 0.0, 1.0]) par = cs.parameters(0.0) for (c, r) in zip((par.A, par.J, par.I1, par.I2, par.I3), (176.4, 970.849152, 26190.108000000004, 259.30800000000005, 25930.800000000003)) @test c ≈ r end true end end using .mcrosssection2 mcrosssection2.test() module mcrosssection3 using FinEtools using FinEtoolsFlexBeams.CrossSectionModule using Test function test() cs = CrossSectionModule.CrossSectionRectangle(s -> 1.3 * 4.2, s -> 4.2, s -> [0.0, 0.0, 1.0]) par = cs.parameters(0.0) for (c, r) in zip((par.A, par.J, par.I1, par.I2, par.I3), (22.932000000000006, 71.66370760423138, 90.68000760000004, 33.71004000000001, 56.969967600000025)) @test c ≈ r end true end end using .mcrosssection3 mcrosssection3.test() module mcrosssection4 using FinEtools using FinEtoolsFlexBeams.CrossSectionModule using Test function test() R = 0.5 cs = CrossSectionModule.CrossSectionCircle(s -> (1/2+2*s)*R, s -> [0.0, 0.0, 1.0]) par = cs.parameters(0.0) for (c, r) in zip((par.A, par.J, par.I1, par.I2, par.I3), (0.19634954084936207, 0.006135923151542565, 0.006135923151542565, 0.0030679615757712823, 0.0030679615757712823)) @test c ≈ r end true end end using .mcrosssection4 mcrosssection4.test()
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2.238095
861
# ------------------------------------- # # Helper methods used in generated code # # ------------------------------------- # # inlined function to extract a single variable. If `x` is a vector then # extract a single element. If `x` is a Matrix then extract one column of the # matrix @inline _unpack_var(x::AbstractVector, i::Integer) = x[i] @inline _unpack_var(x::AbstractMatrix, i::Integer, ::Type{COrder}) = view(x, :, i) @inline _unpack_var(x::AbstractMatrix, i::Integer, ::Type{FOrder}) = view(x, i, :) @inline _unpack_var(x::AbstractMatrix, i::Integer) = view(x, :, i) # inlined function to extract a single observations of a vector fo variables. @inline _unpack_obs(x::AbstractMatrix, i::Integer, ::Type{COrder}) = view(x, i, :) @inline _unpack_obs(x::AbstractMatrix, i::Integer, ::Type{FOrder}) = view(x, :, i) @inline _unpack_obs(x::AbstractMatrix, i::Integer) = view(x, i, :) @inline _unpack_obs(x::AbstractVector, i::Integer) = x # similar to _unpack_var, but instead assigns one element of a vector or one # column of a matrix @inline _assign_var(lhs::AbstractVector, rhs::Number, i) = setindex!(lhs, rhs, i) @inline _assign_var(lhs::AbstractMatrix, rhs::AbstractVector, i) = setindex!(lhs, rhs, :, i) @inline _assign_var(lhs::AbstractMatrix, rhs::AbstractVector, i, ::Type{COrder}) = setindex!(lhs, rhs, :, i) @inline _assign_var(lhs::AbstractMatrix, rhs::AbstractVector, i, ::Type{FOrder}) = setindex!(lhs, rhs, i, :) # determine the size of the output variable, given the number of expressions # in the equation and all the input arguments _output_size(n_expr::Int, args::AbstractVector...) = (n_expr,) _output_size(n_expr::Int, arg::AbstractMatrix) = (size(arg, 1), n_expr) function _output_size(n_expr::Int, args...) n_row = 0 # get maximum number of rows among matrix arguments for a in args if isa(a, AbstractMatrix) nr = size(a, 1) if n_row > 0 # we already found another matrix argument, now we can enforce # that all matrix arguments have conformable shapes if nr != n_row msg = string("Unconformable argument sizes. For vectorized", " Core.evaluation all matrix arguments must have ", "the same number of rows.") throw(DimensionMismatch(msg)) end else # we need to update n_row n_row = nr end end end (n_row, n_expr) end _output_size(n_expr::Int, ::Type{COrder}, args...) = _output_size(n_expr::Int, args...) function _output_size(n_expr::Int, order::Type{FOrder}, args...) s = _output_size(n_expr::Int, order::Type{FOrder}, args...) (s[2],s[1]) end # Allocate an array of eltype `T` for `n_expr` variables that corresponds # to input arguments `args...` _allocate_out(T::Type, n_expr::Int, args::AbstractVector...) = Array{T}(undef, n_expr) _allocate_out(T::Type, order::Union{Type{COrder},Type{FOrder}}, n_expr::Int, arg::AbstractMatrix) = Array{T}(undef, _output_size(n_expr, order, arg)) _allocate_out(T::Type, n_expr::Int, arg::AbstractMatrix) = Array{T}(undef, _output_size(n_expr, arg)) function _allocate_out(T::Type, n_expr::Int, args...) Array{T}(undef, _output_size(n_expr, args...)) end function _allocate_out(T::Type, order::Union{Type{COrder},Type{FOrder}}, n_expr::Int, args...) Array{T}(undef, _output_size(n_expr, order, args...)) end ## Triangular solver _expr_or_number(x::Union{AbstractString,Symbol,Expr}) = _to_expr(x) _expr_or_number(x::Number) = x inf_to_Inf(x::Number) = x inf_to_Inf(x::Symbol) = x in (:inf, :Inf) ? Inf : x inf_to_Inf(ex::Expr) = Expr(ex.head, map(inf_to_Inf, ex.args)...) _to_Float64(x::Real) = convert(Float64, x) _to_Float64(x::AbstractArray) = map(Float64, x) function solution_order(d::OrderedDict, it::IncidenceTable, pre_solved::Vector{Symbol}=Symbol[]) # unpack some data vars = collect(keys(d)) n = length(d) # allocate out = zeros(Int, length(d)) # Start with indices for equations that are purely numerical front = setdiff(1:n, keys(it.by_eq)) out[front] = 1:length(front) solved = vcat(pre_solved, vars[front]) to_solve = deepcopy(it.by_eq) # now start stepping through equations ix = length(front) for _junk in 2:n+1 for (eq, eq_vars) in to_solve can_solve = true for (var, dates) in eq_vars if !in(var, solved) can_solve = false break end end if can_solve out[eq] = ix+=1 push!(solved, vars[eq]) pop!(to_solve, eq) end end end !isempty(to_solve) && error("Not triangular system") return sortperm(out) end function solution_order(_d::AbstractDict, pre_solved::Vector{Symbol}=Symbol[]) d = OrderedDict(_d) it = Dolang.IncidenceTable(collect(values(d))) solution_order(d, it, pre_solved) end solve_triangular_system(d::AbstractDict) = solve_triangular_system(OrderedDict(d)) function solve_triangular_system(d::OrderedDict) sol_order = solution_order(d) # extract expressions and variable names in proper order nms = collect(keys(d))[sol_order] exprs = collect(values(d))[sol_order] # build expression to Core.evaluate system in correct order to_eval = Expr(:block) to_eval.args = [:($(i[1])=$(i[2])) for i in zip(nms, exprs)] # add one line to return a tuple of all data ret = Expr(:tuple); ret.args = nms # now Core.evaluate and get data data = eval(:( let $to_eval $ret end )) OrderedDict{Symbol,Real}(zip(nms, data)) end mutable struct TriangularSystemException <: Exception missing end """ Solves triangular system specified by incidence dictionary. ``` system = Dict(0=>[1,2], 1=>[], 2=>[1] ) solve_dependency(system) ``` or ``` system = Dict(:x=>[:y,:z], :y=>[], :z=>[:y] ) solve_dependency(system) ``` Optionally, one can add specify which subset of variables to solve so that unrequired variables will be ommited in the solution. In ``` system = Dict(:x=>[:y,:z], :y=>[], :z=>[:y], :p=>[:x,y,z] ) solve_dependency(system, [:x,:y,:z]) ``` the answer is the same as before since `:p` is not needed to define the values of `[:x,:y,:z]`. """ function solve_dependencies(deps::AbstractDict{T,Set{T}}, unknowns=nothing) where T solution = [] if unknowns == nothing needed = Set(keys(deps)) else needed = Set(unknowns) end while length(needed)>0 tt0 = (length(needed), length(solution)) if length(needed)==0 return solution else for k in needed # check whether k is solved if k in solution pop!(needed, k) elseif issubset(deps[k], solution) push!(solution, k) else needed = union(deps[k], needed) end end tt = (length(needed), length(solution)) if tt == tt0 mis = join([string(e) for e in needed], ", ") exc = TriangularSystemException(mis) throw(exc) end end end return solution end function get_dependencies(defs::AbstractDict{T,U}; target=:all) where T where U deps = OrderedDict{Any,Set{Any}}() def_keys = keys(defs) for (k, v) in (defs) # get list of all symbols in this equation # _syms = collect(values(list_variables(v))) # allsyms = length(_syms) > 0 ? Set(union(_syms...)) : Set() if target==:variables allsyms = list_variables(v) elseif target==:parameters allsyms = list_parameters(v) else ll = list_symbols(v) allsyms = cat(ll.parameters, ll.variables; dims=1) end # intersect that with the keys in our definitions ii = intersect(allsyms, def_keys) ij = Set(ii) deps[k] = ij end deps end function reorder_triangular_block(defs::AbstractDict{T,U}) where T where U deps = get_dependencies(defs) sol = Dolang.solve_dependencies(deps) return OrderedDict((k,defs[k]) for k in sol) end """Solves definitions blocks Keys are timed variables in canonical form (e.g. `(:v,0)`) at date t=0. Values are expressions, possibly referencing key variables at different dates. The system is recursively solved for the unknowns, by default the keys. """ function solve_definitions(defs::AbstractDict{Tuple{Symbol, Int}, <:SymExpr}, unknowns=keys(defs)) # defs should map timed-vars to expressions. _defs = deepcopy(defs) for (v,t) in collect(keys(_defs)) # t should always be 0 @assert t==0 for shift in (-1,1) _defs[(v,shift)] = Dolang.time_shift(_defs[(v,t)], shift) end end deps = Dolang.get_dependencies(_defs) solution = Dolang.solve_dependencies(deps, unknowns) reordered = OrderedDict() for k in solution reordered[k] = _defs[k] end return reordered end
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#= # 5. Viscous flow about a moving body In this notebook we will demonstrate the simulation of a moving body. It is straightforward to set up a moving body. The main caveat is that the simulation is slower, because the integrator must update the operators continuously throughout the simulation. We will demonstrate this on an oscillating flat plate. =# using ViscousFlow #- using Plots #= ### Problem specification and discretization For simplicity, we will not create a free stream in this problem. Everything here is the usual. =# my_params = Dict() my_params["Re"] = 200 #- xlim = (-1.0,1.0) ylim = (-1.0,1.0) my_params["grid Re"] = 4.0 g = setup_grid(xlim,ylim,my_params) Δs = surface_point_spacing(g,my_params) #= ### Set up body Set up the plate and place it at the origin. (We don't actually have to move it, since it defaults to the origin, but it's helpful to put this here in case we wish to initialize it differently.) =# body = Plate(1.0,Δs) T = RigidTransform((0,0),0) T(body) #= ### Set the body motion Now we specify the body motion. We will use oscillatory pitch-heave kinematics for this: =# a = 0.25 # location of pitch axis, a = 0.5 is leading edge ϕp = -π/2 # phase lag of pitch ϕh = 0.0 # phase lag of heave A = 0.25 # amplitude/chord fstar = 1/π # fc/U α₀ = 0 # mean angle of attack Δα = 10π/180 # amplitude of pitching U₀ = 0.0 # translational motion (set to zero in place of free stream) K = π*fstar # reduced frequency, K = πfc/U oscil1 = RigidBodyTools.PitchHeave(U₀,a,K,ϕp,α₀,Δα,A,ϕh) motion = RigidBodyMotion(oscil1) # We can inspect the kinematics in this `motion` by plotting them: plot(motion) #= ### Define the boundary condition functions Instead of using the default boundary condition functions, we define special ones here that provide the instantaneous surface velocity (i.e. the velocity of every surface point) from the prescribed motion. Every surface has an "exterior" and "interior" side. For a flat plate, these two sides are the upper and lower sides, and both sides are next to the fluid, so both of them are assigned the prescribed velocity of the plate. (For closed bodies, we would assign this velocity to only one of the sides, and zero to the other side. We will see an example of this in a later case.) We pack these into a special dictionary and pass these to the system construction. =# function my_vsplus(t,base_cache,phys_params,motions) vsplus = zeros_surface(base_cache) surface_velocity!(vsplus,base_cache,motions,t) return vsplus end function my_vsminus(t,base_cache,phys_params,motions) vsminus = zeros_surface(base_cache) surface_velocity!(vsminus,base_cache,motions,t) return vsminus end bcdict = Dict("exterior" => my_vsplus, "interior" => my_vsminus) #= ### Construct the system structure Here, we supply the motion and boundary condition functions as additional arguments. =# sys = viscousflow_system(g,body,phys_params=my_params,motions=motion,bc=bcdict); #= Before we solve the problem, it is useful to note that the Reynolds number we specified earlier may not be the most physically-meaningful Reynolds number. More relevant in this problem is the Reynolds number based on the maximum body speed. =# Umax, imax, tmax, bmax = maxlistvelocity(sys) Re_eff = my_params["Re"]*Umax #- u0 = init_sol(sys) tspan = (0.0,10.0) integrator = init(u0,tspan,sys) #= ### Solve This takes longer per time step than it does for stationary bodies. Here, we only run it for 1.5 time units just to demonstrate it. =# @time step!(integrator,1.5) #= ### Examine the solution Let's look at a few snapshots of the vorticity field. Note that the plotting here requires us to explicitly call the [`surfaces`](@ref) function to generate the instantaneous configuration of the plate. =# sol = integrator.sol plt = plot(layout = (1,3), size = (800, 300), legend=:false) tsnap = 0.5:0.5:1.5 for (i, t) in enumerate(tsnap) plot!(plt[i],vorticity(sol,sys,t),sys,layers=false,title="t = $(round(t,digits=2))",clim=(-5,5),levels=range(-5,5,length=30),color = :RdBu) plot!(plt[i],surfaces(sol,sys,t)) end plt # and the forces sol = integrator.sol fx, fy = force(sol,sys,1); #- plot( plot(sol.t,2*fx,xlim=(0,Inf),ylim=(-3,3),xlabel="Convective time",ylabel="\$C_D\$",legend=:false), plot(sol.t,2*fy,xlim=(0,Inf),ylim=(-6,6),xlabel="Convective time",ylabel="\$C_L\$",legend=:false), size=(800,350) )
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module TreeTensors importall Base using Tensors include("Trees.jl") include("ModeTrees.jl") # Tree tensor networks export TreeTensor typealias TensorDict{T} Dict{Tree,Tensor{T}} immutable TreeTensor{T} mtree::AbstractModeTree tensors::TensorDict{T} end (::Type{TreeTensor{T}}){T}(mtree) = TreeTensor(mtree, TensorDict{T}()) # Basic functions export modetree, tree Tensors.scalartype{T}(::Type{TreeTensor{T}}) = T modetree(x::TreeTensor) = x.mtree tree(x::TreeTensor) = tree(modetree(x)) copy(x::TreeTensor) = TreeTensor(x.mtree, TensorDict{scalartype(x)}(x.tensors)) convert{T}(::Type{TreeTensor{T}}, x::TreeTensor) = TreeTensor(x.mtree, convert(TensorDict{T},x.tensors)) copyconvert{T}(::Type{TreeTensor{T}}, x::TreeTensor{T}) = copy(x) copyconvert{T}(::Type{TreeTensor{T}}, x::TreeTensor) = convert(TreeTensor{T}, x) # Indexing and iteration for f in ( :getindex, :setindex!, :start, :next, :done, :eltype, :length, :keys, :values, :keytype, :valtype ) @eval $f(x::TreeTensor, args...) = $f(x.tensors, args...) end function index(x::TreeTensor, s::Symbol) if s == :root return tree(x) else throw(KeyError(s)) end end getindex(x::TreeTensor, s::Symbol) = x[index(x,s)] setindex!(x::TreeTensor, xv, s::Symbol) = x[index(x,s)] = xv # Initialization ones{T}(::Type{T}, mtree::AbstractModeTree) = TreeTensor(mtree, Dict{Tree,Tensor{T}}( v => ones(T, [[Mode(e,1) for e in neighbor_edges(v)]; mtree[v]]) for v in vertices(mtree, root_to_leaves) )) eye{T}(::Type{T}, mtree::AbstractModeTree) = TreeTensor(square(mtree), Dict{Tree,Tensor{T}}( v => begin t = eye(T, mtree[v]); t.modes = [t.modes; [Mode(e,1) for e in neighbor_edges(v)]]; t end for v in vertices(mtree, root_to_leaves) )) function rand{T}(::Type{T}, mtree::AbstractModeTree, r) evaluate(r::Int,e) = r evaluate(r::Dict,e) = r[e] return TreeTensor(mtree, Dict{Tree,Tensor{T}}( v => rand(T, [[Mode(e,evaluate(r,e)) for e in neighbor_edges(v)]; mtree[v]]) for v in vertices(mtree, root_to_leaves) )) end for f in (:ones, :rand, :eye) @eval $f(mtree::AbstractModeTree, args...) = $f(Float64, mtree, args...) end for f in (:ones, :rand, :eye) @eval $f(x::TreeTensor, args...) = $f(scalartype(x), modetree(x), args...) end # Conversion to and from full export decompose function decompose(x::Tensor, mtree, rank) y = TreeTensor{scalartype(x)}(mtree) for (v,p) in edges(y, leaves_to_root) b,s,y[v] = svd(x, [child_edges(v,p);mlabel(mtree[v])], PairSet(v,p), rank) x = scale!(b,s) end y[:root] = x return y end export contract!, contract function contract!(x::TreeTensor) for (v,p) in edges(x, leaves_to_root) x[p] *= x[v] end return x[:root] end contract(x::TreeTensor) = contract!(copy(x)) # Arithmetic function +(x::TreeTensor, y::TreeTensor) @assert modetree(x) == modetree(y) "x and y must have the same mode tree" mtree = modetree(x) return TreeTensor( mtree, Dict{Tree,Tensor{promote_type(scalartype(x), scalartype(y))}}( v => padcat(x[v],y[v], neighbor_edges(v)) for v in vertices(mtree, root_to_leaves) ) ) end function *(x::TreeTensor, y::TreeTensor) @assert unsquare(modetree(x)) == unsquare(modetree(y)) "x and y must have the same mode tree" mtree = modetree(x) return TreeTensor( mtree, Dict{Tree,Tensor{promote_type(scalartype(x),scalartype(y))}}( v => mergem!(tag(x[v], 1,neighbor_edges(v))*tag(y[v], 2,neighbor_edges(v)), Dict([tag(1,e),tag(2,e)] => e for e in neighbor_edges(v))) for v in vertices(mtree, root_to_leaves) ) ) end *(a::Number, x::TreeTensor) = (y = copyconvert(TreeTensor{promote_type(typeof(a),scalartype(x))}, x); y[:root] *= a; return y) *(x::TreeTensor, a::Number) = a*x scale!(a::Number, x::TreeTensor) = (x[:root] *= a; return x) scale!(x::TreeTensor, a::Number) = scale!(a,x) -(x::TreeTensor, y::TreeTensor) = x + (-1)*y # Transposition and conjugation for f in (:conj,:transpose,:ctranspose) f! = Symbol(string(f)*"!") @eval begin function Base.$f(t::TreeTensor) return TreeTensor( modetree(t), Dict{Tree,Tensor{scalartype(t)}}( v => $f(tv) for (v,tv) in t ) ) end function Base.$f!(t::TreeTensor) for v in keys(t) t[v] = $f!(t[v]) end return t end end end # Orthogonalisation and truncation export orthogonalize!, orthogonalize function orthogonalize!(x::TreeTensor) for (v,p) in edges(x, leaves_to_root) x[v],c = qr(x[v], PairSet(v,p)) x[p] = c*x[p] end return x end orthogonalize(x::TreeTensor) = orthogonalize!(copy(x)) export truncate!, truncate function truncate!(x::TreeTensor, rank) orthogonalize!(x) s = Dict{PairSet{Tree}, Tensor{real(scalartype(x))}}() for (v,p) in edges_with_root(x, root_to_leaves) for u in children(v,p) e = PairSet(u,v) b,s[e],d = svd(x[v], e, maxrank()) x[u] = scale!(s[e],d)*x[u] x[v] = scale!(b,s[e]) end x[v] = resize(x[v], Dict(e => rank(s[e].data) for e in neighbor_edges(v))) scale!(x[v], [resize(1./s[e], Dict(e => msize(x[v],e))) for e in child_edges(v,p)]...) end return x end truncate(x::TreeTensor, rank) = truncate!(copy(x), rank) # Contracted subtrees function contracted_subtree(v::Tree,p::Tree, c::TreeTensor, args::TreeTensor...) cv = retag!(tag!(conj(args[1][v]), 1, neighbor_edges(v)), :C => :_, :R => :C) for u in children(v,p) cv *= c[u] end for i = 2:length(args)-1 cv *= tag(args[i][v], i, neighbor_edges(v)) end cv *= retag!(tag(args[end][v], length(args), neighbor_edges(v)), :C => :_) return cv end function contracted_subtrees(args::TreeTensor...) c = TreeTensor( modetree(args[1]), TensorDict{scalartype(args[1])}() ) for (v,p) in edges(c, leaves_to_root) c[v] = contracted_subtree(v,p,c,args...) end return c end # Norm and dot export norm! norm!(x::TreeTensor) = norm(orthogonalize!(x)[:root]) norm( x::TreeTensor) = norm(orthogonalize( x)[:root]) function dot(x::TreeTensor, y::TreeTensor) return scalar( contracted_subtree( tree(x),tree(x), contracted_subtrees(x,y), x,y ) ) end # Local solvers function localmatrix(A, v, xAx) lA = tag(A[v], 2, neighbor_edges(v)) for u in neighbors(v) lA *= xAx[u] end retag!(lA, 1 => :R, 3 => :C) return lA end function localrhs(b, v, xb) lb = tag(b[v], 2, neighbor_edges(v)) for u in neighbors(v) lb *= xb[u] end untag!(lb, 1) return lb end function localmatvecfunc(A, v, xAx) Av = tag(A[v],2,neighbor_edges(v)) c = [retag(xAx[u], 3 => :C, 1 => :R) for u in neighbors(v)] if length(c) == 3 return xv -> begin (c[3]*((c[1]*Av)*(c[2]*xv))) end elseif length(c) == 2 return xv -> begin (c[2]*(Av*(c[1]*xv))) end elseif length(c) == 1 return xv -> begin (c[1]*Av*xv) end else error("Local matvec structure not implemented!") end end function localproblem(x,A,b, v, xAx,xb) modes = x[v].modes matvec = localmatvecfunc(A, v, xAx) return x[v][mlabel(modes)], xv -> (xv = Tensor(modes,xv); matvec(xv)[mlabel(modes)]), localrhs(b,v,xb)[mlabel(modes)] end # Local solvers using IterativeSolvers abstract LocalSolver export DenseSolver immutable DenseSolver <: LocalSolver end function localsolve!(x,A,b, solver::DenseSolver, v, xAx, xb) x[v] = localmatrix(A,v,xAx)\localrhs(b,v,xb) end export GMRES immutable GMRES <: LocalSolver tol::Float64 maxiter::Int restart::Int warn::Bool end GMRES(; tol = sqrt(eps()), maxiter = 1, restart = 20, warn = false) = GMRES(tol, maxiter, restart, warn) function localsolve!(x,A,b, solver::GMRES, v, xAx,xb) _,hist = gmres!( localproblem(x,A,b, v, xAx,xb)...; tol = solver.tol, maxiter = solver.maxiter, restart = solver.restart ) if !hist.isconverged && solver.warn warn( "Local GMRES iteration did not convergence.\n"* " LSE size: "*string(length(x[v]))*"\n"* " Threshold: "*string(hist.threshold)*"\n"* " Residuals: "*string(hist.residuals) ) end end export CG immutable CG <: LocalSolver tol::Float64 maxiter::Int warn::Bool end CG(; tol = sqrt(eps()), maxiter = 20, warn = false) = CG(tol, maxiter, warn) function localsolve!(x,A,b, solver::CG, v, xAx,xb) _,hist = cg!( localmatvec(x,A,b, v, xAx,xb)...; tol = solver.tol, maxiter = solver.maxiter ) if !hist.isconverged && solver.warn warn( "Local CG iteration did not convergence.\n"* " LSE size: "*string(length(x[v]))*"\n"* " Threshold: "*string(hist.threshold)*"\n"* " Residuals: "*string(hist.residuals) ) end end # ALS linear solver export als_solve! function als_solve!( x,A,b, solver = GMRES(); maxiter::Int = 20, tol = sqrt(eps(real(scalartype(x)))) ) updatenorms = zeros(real(scalartype(x)), maxiter) orthogonalize!(x) xAx = contracted_subtrees(x,A,x) xb = contracted_subtrees(x,b) for i = 1:maxiter for (u,v) in edges(x, both_ways) # Solve xu = copy(x[u]) localsolve!(x,A,b, solver, u, xAx,xb) updatenorms[i] = max(updatenorms[i], norm(xu - x[u])) # Orthogonalize x[u],r = qr(x[u],PairSet(u,v)) x[v] = r*x[v] # Compute contracted subtrees xAx[u] = contracted_subtree(u,v, xAx, x,A,x) xb[u] = contracted_subtree(u,v, xb, x,b) end # Convergence check if updatenorms[i] < tol*norm(x[:root]) return x, ConvergenceHistory(true, tol*norm(x[:root]), i, updatenorms[1:i]) end end return x, ConvergenceHistory(false, tol*norm(x[:root]), maxiter, updatenorms) end # ALS sum export als_sum! function als_sum!{T}( y,x::AbstractVector{TreeTensor{T}}; maxiter::Int = 20, tol = sqrt(eps(real(T))) ) updatenorms = zeros(real(T), maxiter) orthogonalize!(y) yx = [contracted_subtrees(y,x) for x in x] for i = 1:maxiter for (u,v) in edges(y, both_ways) # Project yunew = sum([localrhs(x, u, yx) for (x,yx) in zip(x,yx)]) updatenorms[i] = max(updatenorms[i], norm(yunew - y[u])) y[u] = yunew # Orthogonalize y[u],r = qr(y[u],PairSet(u,v)) y[v] = r*y[v] # Compute contracted subtrees for (x,yx) in zip(x,yx) local x,yx yx[u] = contracted_subtree(u,v, yx, y,x) end end # Convergence check if updatenorms[i] < tol*norm(y[:root]) return y, ConvergenceHistory(true, tol*norm(y[:root]), i, updatenorms[1:i]) end end return y, ConvergenceHistory(false, tol*norm(y[:root]), maxiter, updatenorms) end # ALS AXPY export als_axpy! function als_axpy!( z,A,x,y; maxiter::Int = 20, tol = sqrt(eps(real(scalartype(x)))) ) updatenorms = zeros(real(scalartype(x)), maxiter) orthogonalize!(z) zAx = contracted_subtrees(z,A,x) zy = contracted_subtrees(z,y) for i = 1:maxiter for (u,v) in edges(z, both_ways) # Project zunew = localmatvecfunc(A, u, zAx)(x[u]) + localrhs(y, u, zy) updatenorms[i] = max(updatenorms[i], norm(zunew - z[u])) z[u] = zunew # Orthogonalize z[u],r = qr(z[u],PairSet(u,v)) z[v] = r*z[v] # Compute contracted subtrees zAx[u] = contracted_subtree(u,v, zAx, z,A,x) zy[u] = contracted_subtree(u,v, zy, z,y) end # Convergence check if updatenorms[i] < tol*norm(z[:root]) return z, ConvergenceHistory(true, tol*norm(z[:root]), i, updatenorms[1:i]) end end return z, ConvergenceHistory(false, tol*norm(z[:root]), maxiter, updatenorms) end ## ALSSD linear solver # #function alssd_solve!( # x,A,b, solver = GMRES(); # residualrank = 4, maxiter::Int = 20, tol = sqrt(eps(real(scalartype(x)))) #) # residualnorms = zeros(real(scalartype(x)), maxiter) # tol *= norm(b) # # z = rand(x, residualrank) # for i = 1:maxiter # als_axpy!(z, -A,x,b; maxiter = 1) # # residualnorms[i] = norm!(z) # if residualnorms[i] < tol # return x,ConvergenceHistory(true, tol, i, residualnorms[1:i]) # end # # x += z # als_solve!(x,A,b, solver; maxiter = 1) # truncate!(x, adaptive(tol)) # end # return x #end ## ALS operator inversion # #function als_inv!( # X,A, solver = GMRES(); # maxiter::Int = 20, tol = sqrt(eps(real(scalartype(X)))) #) # updatenorms = zeros(real(scalartype(X)), maxiter) # # orthogonalize!(X) # XAX = contracted_subtrees(X,A,X) # XAXt = contracted_subtrees(X,A,X) # xb = contracted_subtrees(x,b) # for i = 1:maxiter # for (u,v) in edges(x, both_ways) # # Solve # xu = copy(x[u]) # localsolve!(x,A,b, solver, u, xAx,xb) # updatenorms[i] = max(updatenorms[i], norm(xu - x[u])) # # # Orthogonalize # x[u],r = qr(x[u],PairSet(u,v)) # x[v] = r*x[v] # # # Compute contracted subtrees # xAx[u] = contracted_subtree(u,v, xAx, x,A,x) # xb[u] = contracted_subtree(u,v, xb, x,b) # end # # # Convergence check # if updatenorms[i] < tol*norm(x[:root]) # return x, ConvergenceHistory(true, tol*norm(x[:root]), i, updatenorms[1:i]) # end # end # return x, ConvergenceHistory(false, tol*norm(x[:root]), maxiter, updatenorms) #end end # module
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400, 10126, 27493, 1, 59, 77, 1, 9, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 366, 220, 1874, 312, 723, 82, 25, 366, 9, 8841, 7, 10034, 13, 411, 312, 723, 82, 8, 198, 220, 220, 220, 220, 220, 220, 220, 1267, 198, 220, 220, 220, 886, 198, 437, 628, 198, 2, 27249, 14174, 1540, 332, 198, 198, 39344, 435, 82, 62, 82, 6442, 0, 198, 8818, 435, 82, 62, 82, 6442, 0, 7, 198, 220, 220, 220, 2124, 11, 32, 11, 65, 11, 1540, 332, 796, 6951, 19535, 9783, 220, 198, 220, 220, 220, 3509, 2676, 3712, 5317, 796, 1160, 11, 284, 75, 796, 19862, 17034, 7, 25386, 7, 5305, 7, 1416, 282, 433, 2981, 7, 87, 35514, 198, 8, 198, 220, 220, 220, 2325, 36686, 579, 82, 796, 1976, 27498, 7, 5305, 7, 1416, 282, 433, 2981, 7, 87, 36911, 3509, 2676, 8, 628, 220, 220, 220, 29617, 519, 20996, 1096, 0, 7, 87, 8, 198, 220, 220, 220, 2124, 31554, 796, 23407, 62, 7266, 83, 6037, 7, 87, 11, 32, 11, 87, 8, 198, 220, 220, 220, 2124, 65, 796, 23407, 62, 7266, 83, 6037, 7, 87, 11, 65, 8, 198, 220, 220, 220, 329, 1312, 796, 352, 25, 9806, 2676, 198, 220, 220, 220, 220, 220, 220, 220, 329, 357, 84, 11, 85, 8, 287, 13015, 7, 87, 11, 1111, 62, 1322, 8, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1303, 4294, 303, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2124, 84, 796, 4866, 7, 87, 58, 84, 12962, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 17205, 6442, 0, 7, 87, 11, 32, 11, 65, 11, 1540, 332, 11, 334, 11, 2124, 31554, 11, 30894, 8, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2325, 36686, 579, 82, 58, 72, 60, 796, 3509, 7, 929, 19608, 268, 579, 82, 58, 72, 4357, 2593, 7, 87, 84, 532, 2124, 58, 84, 60, 4008, 628, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1303, 47664, 519, 20996, 1096, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2124, 58, 84, 4357, 81, 796, 10662, 81, 7, 87, 58, 84, 4357, 47, 958, 7248, 7, 84, 11, 85, 4008, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2124, 58, 85, 60, 796, 374, 9, 87, 58, 85, 60, 628, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1303, 3082, 1133, 23407, 13284, 6037, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2124, 31554, 58, 84, 60, 796, 23407, 62, 7266, 21048, 7, 84, 11, 85, 11, 2124, 31554, 11, 2124, 11, 32, 11, 87, 8, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2124, 65, 58, 84, 60, 796, 23407, 62, 7266, 21048, 7, 84, 11, 85, 11, 2124, 65, 11, 2124, 11, 65, 8, 198, 220, 220, 220, 220, 220, 220, 220, 886, 628, 220, 220, 220, 220, 220, 220, 220, 1303, 35602, 12745, 2198, 198, 220, 220, 220, 220, 220, 220, 220, 611, 2325, 36686, 579, 82, 58, 72, 60, 1279, 284, 75, 9, 27237, 7, 87, 58, 25, 15763, 12962, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1441, 2124, 11, 35602, 12745, 18122, 7, 7942, 11, 284, 75, 9, 27237, 7, 87, 58, 25, 15763, 46570, 1312, 11, 2325, 36686, 579, 82, 58, 16, 25, 72, 12962, 198, 220, 220, 220, 220, 220, 220, 220, 886, 198, 220, 220, 220, 886, 198, 220, 220, 220, 1441, 2124, 11, 35602, 12745, 18122, 7, 9562, 11, 284, 75, 9, 27237, 7, 87, 58, 25, 15763, 46570, 3509, 2676, 11, 2325, 36686, 579, 82, 8, 198, 437, 628, 198, 2, 27249, 2160, 198, 198, 39344, 435, 82, 62, 16345, 0, 198, 8818, 435, 82, 62, 16345, 0, 90, 51, 92, 7, 198, 220, 220, 220, 331, 11, 87, 3712, 23839, 38469, 90, 27660, 51, 22854, 90, 51, 11709, 26, 198, 220, 220, 220, 3509, 2676, 3712, 5317, 796, 1160, 11, 284, 75, 796, 19862, 17034, 7, 25386, 7, 5305, 7, 51, 22305, 198, 8, 198, 220, 220, 220, 2325, 36686, 579, 82, 796, 1976, 27498, 7, 5305, 7, 51, 828, 3509, 2676, 8, 628, 220, 220, 220, 29617, 519, 20996, 1096, 0, 7, 88, 8, 198, 220, 220, 220, 331, 87, 796, 685, 3642, 20216, 62, 7266, 83, 6037, 7, 88, 11, 87, 8, 329, 2124, 287, 2124, 60, 198, 220, 220, 220, 329, 1312, 796, 352, 25, 9806, 2676, 198, 220, 220, 220, 220, 220, 220, 220, 329, 357, 84, 11, 85, 8, 287, 13015, 7, 88, 11, 1111, 62, 1322, 8, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1303, 4935, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 331, 403, 413, 796, 2160, 26933, 12001, 81, 11994, 7, 87, 11, 334, 11, 331, 87, 8, 329, 357, 87, 11, 28391, 8, 287, 19974, 7, 87, 11, 28391, 8, 12962, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2325, 36686, 579, 82, 58, 72, 60, 796, 3509, 7, 929, 19608, 268, 579, 82, 58, 72, 4357, 2593, 7, 88, 403, 413, 532, 331, 58, 84, 60, 4008, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 331, 58, 84, 60, 796, 331, 403, 413, 628, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1303, 47664, 519, 20996, 1096, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 331, 58, 84, 4357, 81, 796, 10662, 81, 7, 88, 58, 84, 4357, 47, 958, 7248, 7, 84, 11, 85, 4008, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 331, 58, 85, 60, 796, 374, 9, 88, 58, 85, 60, 628, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1303, 3082, 1133, 23407, 13284, 6037, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 329, 357, 87, 11, 28391, 8, 287, 19974, 7, 87, 11, 28391, 8, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1957, 2124, 11, 28391, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 331, 87, 58, 84, 60, 796, 23407, 62, 7266, 21048, 7, 84, 11, 85, 11, 331, 87, 11, 331, 11, 87, 8, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 886, 198, 220, 220, 220, 220, 220, 220, 220, 886, 628, 220, 220, 220, 220, 220, 220, 220, 1303, 35602, 12745, 2198, 198, 220, 220, 220, 220, 220, 220, 220, 611, 2325, 36686, 579, 82, 58, 72, 60, 1279, 284, 75, 9, 27237, 7, 88, 58, 25, 15763, 12962, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1441, 331, 11, 35602, 12745, 18122, 7, 7942, 11, 284, 75, 9, 27237, 7, 88, 58, 25, 15763, 46570, 1312, 11, 2325, 36686, 579, 82, 58, 16, 25, 72, 12962, 198, 220, 220, 220, 220, 220, 220, 220, 886, 198, 220, 220, 220, 886, 198, 220, 220, 220, 1441, 331, 11, 35602, 12745, 18122, 7, 9562, 11, 284, 75, 9, 27237, 7, 88, 58, 25, 15763, 46570, 3509, 2676, 11, 2325, 36686, 579, 82, 8, 198, 437, 628, 198, 2, 27249, 317, 27481, 56, 198, 198, 39344, 435, 82, 62, 897, 9078, 0, 198, 8818, 435, 82, 62, 897, 9078, 0, 7, 198, 220, 220, 220, 1976, 11, 32, 11, 87, 11, 88, 26, 198, 220, 220, 220, 3509, 2676, 3712, 5317, 796, 1160, 11, 284, 75, 796, 19862, 17034, 7, 25386, 7, 5305, 7, 1416, 282, 433, 2981, 7, 87, 35514, 198, 8, 198, 220, 220, 220, 2325, 36686, 579, 82, 796, 1976, 27498, 7, 5305, 7, 1416, 282, 433, 2981, 7, 87, 36911, 3509, 2676, 8, 628, 220, 220, 220, 29617, 519, 20996, 1096, 0, 7, 89, 8, 198, 220, 220, 220, 1976, 31554, 796, 23407, 62, 7266, 83, 6037, 7, 89, 11, 32, 11, 87, 8, 198, 220, 220, 220, 1976, 88, 796, 23407, 62, 7266, 83, 6037, 7, 89, 11, 88, 8, 198, 220, 220, 220, 329, 1312, 796, 352, 25, 9806, 2676, 198, 220, 220, 220, 220, 220, 220, 220, 329, 357, 84, 11, 85, 8, 287, 13015, 7, 89, 11, 1111, 62, 1322, 8, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1303, 4935, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1976, 403, 413, 796, 1957, 6759, 35138, 20786, 7, 32, 11, 334, 11, 1976, 31554, 5769, 87, 58, 84, 12962, 1343, 1957, 81, 11994, 7, 88, 11, 334, 11, 1976, 88, 8, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2325, 36686, 579, 82, 58, 72, 60, 796, 3509, 7, 929, 19608, 268, 579, 82, 58, 72, 4357, 2593, 7, 89, 403, 413, 532, 1976, 58, 84, 60, 4008, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1976, 58, 84, 60, 796, 1976, 403, 413, 628, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1303, 47664, 519, 20996, 1096, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1976, 58, 84, 4357, 81, 796, 10662, 81, 7, 89, 58, 84, 4357, 47, 958, 7248, 7, 84, 11, 85, 4008, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1976, 58, 85, 60, 796, 374, 9, 89, 58, 85, 60, 628, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1303, 3082, 1133, 23407, 13284, 6037, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1976, 31554, 58, 84, 60, 796, 23407, 62, 7266, 21048, 7, 84, 11, 85, 11, 1976, 31554, 11, 1976, 11, 32, 11, 87, 8, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1976, 88, 58, 84, 60, 796, 23407, 62, 7266, 21048, 7, 84, 11, 85, 11, 1976, 88, 11, 1976, 11, 88, 8, 198, 220, 220, 220, 220, 220, 220, 220, 886, 628, 220, 220, 220, 220, 220, 220, 220, 1303, 35602, 12745, 2198, 198, 220, 220, 220, 220, 220, 220, 220, 611, 2325, 36686, 579, 82, 58, 72, 60, 1279, 284, 75, 9, 27237, 7, 89, 58, 25, 15763, 12962, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1441, 1976, 11, 35602, 12745, 18122, 7, 7942, 11, 284, 75, 9, 27237, 7, 89, 58, 25, 15763, 46570, 1312, 11, 2325, 36686, 579, 82, 58, 16, 25, 72, 12962, 198, 220, 220, 220, 220, 220, 220, 220, 886, 198, 220, 220, 220, 886, 198, 220, 220, 220, 1441, 1976, 11, 35602, 12745, 18122, 7, 9562, 11, 284, 75, 9, 27237, 7, 89, 58, 25, 15763, 46570, 3509, 2676, 11, 2325, 36686, 579, 82, 8, 198, 437, 628, 198, 2235, 8355, 5432, 35, 14174, 1540, 332, 198, 2, 198, 2, 8818, 435, 824, 67, 62, 82, 6442, 0, 7, 198, 2, 220, 220, 220, 2124, 11, 32, 11, 65, 11, 1540, 332, 796, 6951, 19535, 9783, 220, 198, 2, 220, 220, 220, 29598, 43027, 796, 604, 11, 3509, 2676, 3712, 5317, 796, 1160, 11, 284, 75, 796, 19862, 17034, 7, 25386, 7, 5305, 7, 1416, 282, 433, 2981, 7, 87, 35514, 198, 2, 8, 198, 2, 220, 220, 220, 29598, 27237, 82, 796, 1976, 27498, 7, 5305, 7, 1416, 282, 433, 2981, 7, 87, 36911, 3509, 2676, 8, 198, 2, 220, 220, 220, 284, 75, 1635, 28, 2593, 7, 65, 8, 198, 2, 198, 2, 220, 220, 220, 1976, 796, 43720, 7, 87, 11, 29598, 43027, 8, 198, 2, 220, 220, 220, 329, 1312, 796, 352, 25, 9806, 2676, 198, 2, 220, 220, 220, 220, 220, 220, 220, 435, 82, 62, 897, 9078, 0, 7, 89, 11, 532, 32, 11, 87, 11, 65, 26, 3509, 2676, 796, 352, 8, 198, 2, 198, 2, 220, 220, 220, 220, 220, 220, 220, 29598, 27237, 82, 58, 72, 60, 796, 2593, 0, 7, 89, 8, 198, 2, 220, 220, 220, 220, 220, 220, 220, 611, 29598, 27237, 82, 58, 72, 60, 1279, 284, 75, 198, 2, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1441, 2124, 11, 3103, 332, 12745, 18122, 7, 7942, 11, 284, 75, 11, 1312, 11, 29598, 27237, 82, 58, 16, 25, 72, 12962, 198, 2, 220, 220, 220, 220, 220, 220, 220, 886, 198, 2, 198, 2, 220, 220, 220, 220, 220, 220, 220, 2124, 15853, 1976, 198, 2, 220, 220, 220, 220, 220, 220, 220, 435, 82, 62, 82, 6442, 0, 7, 87, 11, 32, 11, 65, 11, 1540, 332, 26, 3509, 2676, 796, 352, 8, 198, 2, 220, 220, 220, 220, 220, 220, 220, 40122, 378, 0, 7, 87, 11, 29605, 7, 83, 349, 4008, 198, 2, 220, 220, 220, 886, 198, 2, 220, 220, 220, 1441, 2124, 198, 2, 437, 628, 198, 2235, 27249, 10088, 287, 9641, 198, 2, 198, 2, 8818, 435, 82, 62, 16340, 0, 7, 198, 2, 220, 220, 220, 1395, 11, 32, 11, 1540, 332, 796, 6951, 19535, 9783, 220, 198, 2, 220, 220, 220, 3509, 2676, 3712, 5317, 796, 1160, 11, 284, 75, 796, 19862, 17034, 7, 25386, 7, 5305, 7, 1416, 282, 433, 2981, 7, 55, 35514, 198, 2, 8, 198, 2, 220, 220, 220, 2325, 36686, 579, 82, 796, 1976, 27498, 7, 5305, 7, 1416, 282, 433, 2981, 7, 55, 36911, 3509, 2676, 8, 198, 2, 198, 2, 220, 220, 220, 29617, 519, 20996, 1096, 0, 7, 55, 8, 198, 2, 220, 220, 220, 1395, 25922, 796, 23407, 62, 7266, 83, 6037, 7, 55, 11, 32, 11, 55, 8, 198, 2, 220, 220, 220, 1395, 25922, 83, 796, 23407, 62, 7266, 83, 6037, 7, 55, 11, 32, 11, 55, 8, 198, 2, 220, 220, 220, 2124, 65, 796, 23407, 62, 7266, 83, 6037, 7, 87, 11, 65, 8, 198, 2, 220, 220, 220, 329, 1312, 796, 352, 25, 9806, 2676, 198, 2, 220, 220, 220, 220, 220, 220, 220, 329, 357, 84, 11, 85, 8, 287, 13015, 7, 87, 11, 1111, 62, 1322, 8, 198, 2, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1303, 4294, 303, 198, 2, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2124, 84, 796, 4866, 7, 87, 58, 84, 12962, 198, 2, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 17205, 6442, 0, 7, 87, 11, 32, 11, 65, 11, 1540, 332, 11, 334, 11, 2124, 31554, 11, 30894, 8, 198, 2, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2325, 36686, 579, 82, 58, 72, 60, 796, 3509, 7, 929, 19608, 268, 579, 82, 58, 72, 4357, 2593, 7, 87, 84, 532, 2124, 58, 84, 60, 4008, 198, 2, 198, 2, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1303, 47664, 519, 20996, 1096, 198, 2, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2124, 58, 84, 4357, 81, 796, 10662, 81, 7, 87, 58, 84, 4357, 47, 958, 7248, 7, 84, 11, 85, 4008, 198, 2, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2124, 58, 85, 60, 796, 374, 9, 87, 58, 85, 60, 198, 2, 198, 2, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1303, 3082, 1133, 23407, 13284, 6037, 198, 2, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2124, 31554, 58, 84, 60, 796, 23407, 62, 7266, 21048, 7, 84, 11, 85, 11, 2124, 31554, 11, 2124, 11, 32, 11, 87, 8, 198, 2, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2124, 65, 58, 84, 60, 796, 23407, 62, 7266, 21048, 7, 84, 11, 85, 11, 2124, 65, 11, 2124, 11, 65, 8, 198, 2, 220, 220, 220, 220, 220, 220, 220, 886, 198, 2, 198, 2, 220, 220, 220, 220, 220, 220, 220, 1303, 35602, 12745, 2198, 198, 2, 220, 220, 220, 220, 220, 220, 220, 611, 2325, 36686, 579, 82, 58, 72, 60, 1279, 284, 75, 9, 27237, 7, 87, 58, 25, 15763, 12962, 198, 2, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1441, 2124, 11, 35602, 12745, 18122, 7, 7942, 11, 284, 75, 9, 27237, 7, 87, 58, 25, 15763, 46570, 1312, 11, 2325, 36686, 579, 82, 58, 16, 25, 72, 12962, 198, 2, 220, 220, 220, 220, 220, 220, 220, 886, 198, 2, 220, 220, 220, 886, 198, 2, 220, 220, 220, 1441, 2124, 11, 35602, 12745, 18122, 7, 9562, 11, 284, 75, 9, 27237, 7, 87, 58, 25, 15763, 46570, 3509, 2676, 11, 2325, 36686, 579, 82, 8, 198, 2, 437, 198, 198, 437, 1303, 8265, 198 ]
1.924533
7,394
function simple_lp(bridged) MOI.empty!(bridged) @test MOI.is_empty(bridged) # add 10 variables - only diagonal is relevant X = MOI.add_variables(bridged, 2) # add sdp constraints - only ensuring positivenesse of the diagonal vov = MOI.VectorOfVariables(X) c1 = MOI.add_constraint(bridged, MOI.ScalarAffineFunction([ MOI.ScalarAffineTerm(2.0, X[1]), MOI.ScalarAffineTerm(1.0, X[2]) ], 0.0), MOI.EqualTo(4.0)) c2 = MOI.add_constraint(bridged, MOI.ScalarAffineFunction([ MOI.ScalarAffineTerm(1.0, X[1]), MOI.ScalarAffineTerm(2.0, X[2]) ], 0.0), MOI.EqualTo(4.0)) b1 = MOI.add_constraint(bridged, MOI.ScalarAffineFunction([ MOI.ScalarAffineTerm(1.0, X[1]) ], 0.0), MOI.GreaterThan(0.0)) b2 = MOI.add_constraint(bridged, MOI.ScalarAffineFunction([ MOI.ScalarAffineTerm(1.0, X[2]) ], 0.0), MOI.GreaterThan(0.0)) MOI.set(bridged, MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}(), MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.([-4.0, -3.0], [X[1], X[2]]), 0.0) ) MOI.set(bridged, MOI.ObjectiveSense(), MOI.MIN_SENSE) MOI.optimize!(bridged) obj = MOI.get(bridged, MOI.ObjectiveValue()) @test obj ≈ -9.33333 atol = 1e-2 Xr = MOI.get(bridged, MOI.VariablePrimal(), X) @test Xr ≈ [1.3333, 1.3333] atol = 1e-2 end function simple_lp_2_1d_sdp(bridged) MOI.empty!(bridged) @test MOI.is_empty(bridged) # add 10 variables - only diagonal is relevant X = MOI.add_variables(bridged, 2) # add sdp constraints - only ensuring positivenesse of the diagonal vov = MOI.VectorOfVariables(X) c1 = MOI.add_constraint(bridged, MOI.ScalarAffineFunction([ MOI.ScalarAffineTerm(2.0, X[1]), MOI.ScalarAffineTerm(1.0, X[2]) ], 0.0), MOI.EqualTo(4.0)) c2 = MOI.add_constraint(bridged, MOI.ScalarAffineFunction([ MOI.ScalarAffineTerm(1.0, X[1]), MOI.ScalarAffineTerm(2.0, X[2]) ], 0.0), MOI.EqualTo(4.0)) b1 = MOI.add_constraint(bridged, MOI.VectorOfVariables([X[1]]), MOI.PositiveSemidefiniteConeTriangle(1)) b2 = MOI.add_constraint(bridged, MOI.VectorOfVariables([X[2]]), MOI.PositiveSemidefiniteConeTriangle(1)) MOI.set(bridged, MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}(), MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.([-4.0, -3.0], [X[1], X[2]]), 0.0) ) MOI.set(bridged, MOI.ObjectiveSense(), MOI.MIN_SENSE) MOI.optimize!(bridged) obj = MOI.get(bridged, MOI.ObjectiveValue()) @test obj ≈ -9.33333 atol = 1e-2 Xr = MOI.get(bridged, MOI.VariablePrimal(), X) @test Xr ≈ [1.3333, 1.3333] atol = 1e-2 end function lp_in_SDP_equality_form(bridged) MOI.empty!(bridged) @test MOI.is_empty(bridged) # add 10 variables - only diagonal is relevant X = MOI.add_variables(bridged, 10) # add sdp constraints - only ensuring positivenesse of the diagonal vov = MOI.VectorOfVariables(X) cX = MOI.add_constraint(bridged, vov, MOI.PositiveSemidefiniteConeTriangle(4)) c1 = MOI.add_constraint(bridged, MOI.ScalarAffineFunction([ MOI.ScalarAffineTerm(2.0, X[1]), MOI.ScalarAffineTerm(1.0, X[3]), MOI.ScalarAffineTerm(1.0, X[6]) ], 0.0), MOI.EqualTo(4.0)) c2 = MOI.add_constraint(bridged, MOI.ScalarAffineFunction([ MOI.ScalarAffineTerm(1.0, X[1]), MOI.ScalarAffineTerm(2.0, X[3]), MOI.ScalarAffineTerm(1.0, X[10]) ], 0.0), MOI.EqualTo(4.0)) MOI.set(bridged, MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}(), MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.([-4.0, -3.0], [X[1], X[3]]), 0.0) ) MOI.set(bridged, MOI.ObjectiveSense(), MOI.MIN_SENSE) MOI.optimize!(bridged) obj = MOI.get(bridged, MOI.ObjectiveValue()) @test obj ≈ -9.33333 atol = 1e-2 Xr = MOI.get(bridged, MOI.VariablePrimal(), X) @test Xr ≈ [1.3333, .0, 1.3333, .0, .0, .0, .0, .0, .0, .0] atol = 1e-2 end function lp_in_SDP_inequality_form(optimizer) MOI.empty!(optimizer) @test MOI.is_empty(optimizer) # add 10 variables - only diagonal is relevant X = MOI.add_variables(optimizer, 3) # add sdp constraints - only ensuring positivenesse of the diagonal vov = MOI.VectorOfVariables(X) cX = MOI.add_constraint(optimizer, vov, MOI.PositiveSemidefiniteConeTriangle(2)) c1 = MOI.add_constraint(optimizer, MOI.VectorAffineFunction(MOI.VectorAffineTerm.([1,1],[ MOI.ScalarAffineTerm(2.0, X[1]), MOI.ScalarAffineTerm(1.0, X[3]), ]), [-4.0]), MOI.Nonpositives(1)) c2 = MOI.add_constraint(optimizer, MOI.VectorAffineFunction(MOI.VectorAffineTerm.([1,1],[ MOI.ScalarAffineTerm(1.0, X[1]), MOI.ScalarAffineTerm(2.0, X[3]), ]), [-4.0]), MOI.Nonpositives(1)) MOI.set(optimizer, MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}(), MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.([4.0, 3.0], [X[1], X[3]]), 0.0) ) MOI.set(optimizer, MOI.ObjectiveSense(), MOI.MAX_SENSE) MOI.optimize!(optimizer) obj = MOI.get(optimizer, MOI.ObjectiveValue()) @test obj ≈ 9.33333 atol = 1e-2 Xr = MOI.get(optimizer, MOI.VariablePrimal(), X) @test Xr ≈ [1.3333, .0, 1.3333] atol = 1e-2 c1_d = MOI.get(optimizer, MOI.ConstraintDual(), c1) c2_d = MOI.get(optimizer, MOI.ConstraintDual(), c2) end function sdp_from_moi(optimizer) # min X[1,1] + X[2,2] max y # X[2,1] = 1 [0 y/2 [ 1 0 # y/2 0 <= 0 1] # X >= 0 y free # Optimal solution: # # ⎛ 1 1 ⎞ # X = ⎜ ⎟ y = 2 # ⎝ 1 1 ⎠ MOI.empty!(optimizer) @test MOI.is_empty(optimizer) X = MOI.add_variables(optimizer, 3) vov = MOI.VectorOfVariables(X) cX = MOI.add_constraint(optimizer, vov, MOI.PositiveSemidefiniteConeTriangle(2)) c = MOI.add_constraint(optimizer, MOI.VectorAffineFunction([MOI.VectorAffineTerm(1,MOI.ScalarAffineTerm(1.0, X[2]))], [-1.0]), MOI.Zeros(1)) MOI.set(optimizer, MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}(), MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(1.0, [X[1], X[3]]), 0.0)) MOI.set(optimizer, MOI.ObjectiveSense(), MOI.MIN_SENSE) MOI.optimize!(optimizer) @test MOI.get(optimizer, MOI.TerminationStatus()) == MOI.OPTIMAL @test MOI.get(optimizer, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT @test MOI.get(optimizer, MOI.DualStatus()) == MOI.FEASIBLE_POINT @test MOI.get(optimizer, MOI.ObjectiveValue()) ≈ 2 atol=1e-2 Xv = ones(3) @test MOI.get(optimizer, MOI.VariablePrimal(), X) ≈ Xv atol=1e-2 # @test MOI.get(optimizer, MOI.ConstraintPrimal(), cX) ≈ Xv atol=1e-2 # @test MOI.get(optimizer, MOI.ConstraintDual(), c) ≈ 2 atol=1e-2 # @show MOI.get(optimizer, MOI.ConstraintDual(), c) end function double_sdp_from_moi(optimizer) # solve simultaneously two of these: # min X[1,1] + X[2,2] max y # X[2,1] = 1 [0 y/2 [ 1 0 # y/2 0 <= 0 1] # X >= 0 y free # Optimal solution: # # ⎛ 1 1 ⎞ # X = ⎜ ⎟ y = 2 # ⎝ 1 1 ⎠ MOI.empty!(optimizer) @test MOI.is_empty(optimizer) X = MOI.add_variables(optimizer, 3) Y = MOI.add_variables(optimizer, 3) vov = MOI.VectorOfVariables(X) vov2 = MOI.VectorOfVariables(Y) cX = MOI.add_constraint(optimizer, vov, MOI.PositiveSemidefiniteConeTriangle(2)) cY = MOI.add_constraint(optimizer, vov2, MOI.PositiveSemidefiniteConeTriangle(2)) c = MOI.add_constraint(optimizer, MOI.VectorAffineFunction([MOI.VectorAffineTerm(1,MOI.ScalarAffineTerm(1.0, X[2]))], [-1.0]), MOI.Zeros(1)) c2 = MOI.add_constraint(optimizer, MOI.VectorAffineFunction([MOI.VectorAffineTerm(1,MOI.ScalarAffineTerm(1.0, Y[2]))], [-1.0]), MOI.Zeros(1)) MOI.set(optimizer, MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}(), MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(1.0, [X[1], X[end], Y[1], Y[end]]), 0.0)) MOI.set(optimizer, MOI.ObjectiveSense(), MOI.MIN_SENSE) MOI.optimize!(optimizer) @test MOI.get(optimizer, MOI.TerminationStatus()) == MOI.OPTIMAL @test MOI.get(optimizer, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT @test MOI.get(optimizer, MOI.DualStatus()) == MOI.FEASIBLE_POINT @test MOI.get(optimizer, MOI.ObjectiveValue()) ≈ 2*2 atol=1e-2 Xv = ones(3) @test MOI.get(optimizer, MOI.VariablePrimal(), X) ≈ Xv atol=1e-2 Yv = ones(3) @test MOI.get(optimizer, MOI.VariablePrimal(), Y) ≈ Yv atol=1e-2 # @test MOI.get(optimizer, MOI.ConstraintPrimal(), cX) ≈ Xv atol=1e-2 # @test MOI.get(optimizer, MOI.ConstraintDual(), c) ≈ 2 atol=1e-2 # @show MOI.get(optimizer, MOI.ConstraintDual(), c) end function double_sdp_with_duplicates(optimizer) MOI.empty!(optimizer) x = MOI.add_variable(optimizer) X = [x, x, x] vov = MOI.VectorOfVariables(X) cX = MOI.add_constraint(optimizer, vov, MOI.PositiveSemidefiniteConeTriangle(2)) c = MOI.add_constraint(optimizer, MOI.VectorAffineFunction([MOI.VectorAffineTerm(1,MOI.ScalarAffineTerm(1.0, X[2]))], [-1.0]), MOI.Zeros(1)) MOI.set(optimizer, MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}(), MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(1.0, [X[1], X[3]]), 0.0)) MOI.set(optimizer, MOI.ObjectiveSense(), MOI.MIN_SENSE) MOI.optimize!(optimizer) @test MOI.get(optimizer, MOI.TerminationStatus()) == MOI.OPTIMAL @test MOI.get(optimizer, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT @test MOI.get(optimizer, MOI.DualStatus()) == MOI.FEASIBLE_POINT @test MOI.get(optimizer, MOI.ObjectiveValue()) ≈ 2 atol=1e-2 Xv = ones(3) @test MOI.get(optimizer, MOI.VariablePrimal(), X) ≈ Xv atol=1e-2 end function sdp_wiki(optimizer) # https://en.wikipedia.org/wiki/Semidefinite_programming MOI.empty!(optimizer) @test MOI.is_empty(optimizer) X = MOI.add_variables(optimizer, 6) vov = MOI.VectorOfVariables(X) cX = MOI.add_constraint(optimizer, vov, MOI.PositiveSemidefiniteConeTriangle(3)) cd1 = MOI.add_constraint(optimizer, MOI.VectorAffineFunction([MOI.VectorAffineTerm(1,MOI.ScalarAffineTerm(1.0, X[1]))], [-1.0]), MOI.Zeros(1)) cd1 = MOI.add_constraint(optimizer, MOI.VectorAffineFunction([MOI.VectorAffineTerm(1,MOI.ScalarAffineTerm(1.0, X[3]))], [-1.0]), MOI.Zeros(1)) cd1 = MOI.add_constraint(optimizer, MOI.VectorAffineFunction([MOI.VectorAffineTerm(1,MOI.ScalarAffineTerm(1.0, X[6]))], [-1.0]), MOI.Zeros(1)) c12_ub = MOI.add_constraint(optimizer, MOI.VectorAffineFunction([MOI.VectorAffineTerm(1,MOI.ScalarAffineTerm(1.0, X[2]))], [0.1]), MOI.Nonpositives(1)) # x <= -0.1 -> x + 0.1 <= 0 c12_lb = MOI.add_constraint(optimizer, MOI.VectorAffineFunction([MOI.VectorAffineTerm(1,MOI.ScalarAffineTerm(-1.0, X[2]))], [-0.2]), MOI.Nonpositives(1)) # x >= -0.2 -> -x + -0.2 <= 0 c23_ub = MOI.add_constraint(optimizer, MOI.VectorAffineFunction([MOI.VectorAffineTerm(1,MOI.ScalarAffineTerm(1.0, X[5]))], [-0.5]), MOI.Nonpositives(1)) # x <= 0.5 -> x - 0.5 <= 0 c23_lb = MOI.add_constraint(optimizer, MOI.VectorAffineFunction([MOI.VectorAffineTerm(1,MOI.ScalarAffineTerm(-1.0, X[5]))], [0.4]), MOI.Nonpositives(1)) # x >= 0.4 -> -x + 0.4 <= 0 MOI.set(optimizer, MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}(), MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(1.0, [X[4]]), 0.0)) MOI.set(optimizer, MOI.ObjectiveSense(), MOI.MIN_SENSE) MOI.optimize!(optimizer) obj = MOI.get(optimizer, MOI.ObjectiveValue()) @test obj ≈ -0.978 atol=1e-2 MOI.set(optimizer, MOI.ObjectiveSense(), MOI.MAX_SENSE) MOI.optimize!(optimizer) obj = MOI.get(optimizer, MOI.ObjectiveValue()) @test obj ≈ 0.872 atol=1e-2 end simple_lp(optimizer_bridged) simple_lp_2_1d_sdp(optimizer_bridged) lp_in_SDP_equality_form(optimizer_bridged) lp_in_SDP_inequality_form(optimizer_bridged) sdp_from_moi(optimizer_bridged) double_sdp_from_moi(optimizer_bridged) double_sdp_with_duplicates(optimizer_bridged) sdp_wiki(optimizer_bridged) # print test const optimizer_print = MOI.instantiate( ()->ProxSDP.Optimizer( log_freq = 10, log_verbose = true, timer_verbose = true, extended_log = true, extended_log2 = true, tol_gap = 1e-4, tol_feasibility = 1e-4), with_bridge_type = Float64) sdp_wiki(optimizer_print) # eig solvers default_solver = MOI.get(optimizer_bridged, MOI.RawOptimizerAttribute("eigsolver")) min_size_krylov_eigs = MOI.get(optimizer_bridged, MOI.RawOptimizerAttribute("min_size_krylov_eigs")) MOI.set(optimizer_bridged, MOI.RawOptimizerAttribute("eigsolver"), 1) MOI.set(optimizer_bridged, MOI.RawOptimizerAttribute("min_size_krylov_eigs"), 1) sdp_wiki(optimizer_bridged) MOI.set(optimizer_bridged, MOI.RawOptimizerAttribute("eigsolver"), 2) sdp_wiki(optimizer_bridged) MOI.set(optimizer_bridged, MOI.RawOptimizerAttribute("min_size_krylov_eigs"), min_size_krylov_eigs) MOI.set(optimizer_bridged, MOI.RawOptimizerAttribute("eigsolver"), default_solver) MOI.set(optimizer_bridged, MOI.RawOptimizerAttribute("full_eig_decomp"), true) sdp_wiki(optimizer_bridged) MOI.set(optimizer_bridged, MOI.RawOptimizerAttribute("full_eig_decomp"), false)
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2.027293
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#= # MNIST We begin by importing the required packages. We load MNIST via the MLDatasets.jl package. =# import Makie import CairoMakie import MLDatasets import Flux import RestrictedBoltzmannMachines as RBMs import ConvolutionalRBMs as ConvRBMs using Statistics: mean, var, std using ValueHistories: MVHistory using Random: bitrand using RestrictedBoltzmannMachines: visible, hidden, weights, log_pseudolikelihood, transfer_sample nothing #hide #= Useful function to plot MNIST digits. =# """ imggrid(A) Given a four dimensional tensor `A` of size `(width, height, ncols, nrows)` containing `width x height` images in a grid of `nrows x ncols`, this returns a matrix of size `(width * ncols, height * nrows)`, that can be plotted in a heatmap to display all images. """ function imggrid(A::AbstractArray{<:Any,4}) (width, height, ncols, nrows) = size(A) return reshape(permutedims(A, (1,3,2,4)), width * ncols, height * nrows) end imggrid_border(A::AbstractArray{<:Any,4}, borderval = 1) = imggrid(bordered(A, borderval)) function bordered(A::AbstractArray{<:Any,4}, borderval = 1) return mapslices(A; dims=(1,2)) do img::AbstractMatrix [ fill(borderval, 1, size(img, 2) + 2); fill(borderval, size(img, 1)) img fill(borderval, size(img, 1)); fill(borderval, 1, size(img, 2) + 2) ] end end #= Load MNIST dataset. =# Float = Float32 train_x, train_y = MLDatasets.MNIST.traindata() digit = 2 train_x = Array{Float}(train_x[:, :, train_y .== digit] .≥ 0.5) train_y = train_y[train_y .== digit] println(length(train_y), " training images (for digit = $digit)") nothing #hide # Reshape for convolutional input train_x = reshape(train_x, 1, 28, 28, :) # channel dims, input dims, batch dims nothing #hide # Initialize the convolutional RBM. rbm = ConvRBMs.BinaryConvRBM(Float, 1, 16, (15,15); pad=:same, pool=true) RBMs.initialize!(rbm, train_x) nothing #hide # Pseudolikelihood before training idx = rand(1:size(train_x)[end], 256) mean(@time log_pseudolikelihood(rbm, train_x[:,:,:,idx])) # Initialize training batchsize = 256 optim = Flux.ADAM() vm = transfer_sample(visible(rbm), falses(1, 28, 28, batchsize)) # fantasy chains history = MVHistory() nothing #hide # Train! @time for iter in 1:20 ConvRBMs.pcd!(rbm, train_x; vm, history, batchsize, optim, epochs=5) lpl = log_pseudolikelihood(rbm, train_x[:, :, :, rand(1:size(train_x)[end], 1024)]) push!(history, :lpl_ave, mean(lpl)) push!(history, :lpl_std, std(lpl)) end nothing #hide # Plot of log-pseudolikelihood of trian data during learning. fig = Makie.Figure(resolution=(600,300)) ax = Makie.Axis(fig[1,1], xlabel = "train time", ylabel="pseudolikelihood") Makie.band!(ax, get(history, :lpl_ave)[1], get(history, :lpl_ave)[2] - get(history, :lpl_std)[2]/2, get(history, :lpl_ave)[2] + get(history, :lpl_std)[2]/2, color=:lightblue ) Makie.lines!(ax, get(history, :lpl_ave)..., color=:blue) fig #= Now let's generate some random RBM samples. =# nrows, ncols = 10, 15 nsteps = 1000 fantasy_F = zeros(nrows*ncols, nsteps) fantasy_x = bitrand(1,28,28,nrows*ncols) fantasy_F[:,1] .= RBMs.free_energy(rbm, fantasy_x) @time for t in 2:nsteps fantasy_x .= RBMs.sample_v_from_v(rbm, fantasy_x) fantasy_F[:,t] .= RBMs.free_energy(rbm, fantasy_x) end nothing #hide # Check equilibration of sampling fig = Makie.Figure(resolution=(400,300)) ax = Makie.Axis(fig[1,1], xlabel="sampling time", ylabel="free energy") fantasy_F_μ = vec(mean(fantasy_F; dims=1)) fantasy_F_σ = vec(std(fantasy_F; dims=1)) Makie.band!(ax, 1:nsteps, fantasy_F_μ - fantasy_F_σ/2, fantasy_F_μ + fantasy_F_σ/2) Makie.lines!(ax, 1:nsteps, fantasy_F_μ) fig # Plot the resulting samples. fig = Makie.Figure(resolution=(40ncols, 40nrows)) ax = Makie.Axis(fig[1,1], yreversed=true) Makie.image!(ax, imggrid(reshape(fantasy_x, 28, 28, ncols, nrows)), colorrange=(1,0)) Makie.hidedecorations!(ax) Makie.hidespines!(ax) fig # Plot the filters learned wncols = 4; wnrows = 4 fig = Makie.Figure(resolution=(80wncols, 80wnrows)) ax = Makie.Axis(fig[1,1], yreversed=true) Makie.image!(ax, imggrid_border(reshape(rbm.w ./ maximum(abs, rbm.w; dims=(2,3)), 15, 15, wncols, wnrows))) Makie.hidedecorations!(ax) Makie.hidespines!(ax) fig
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""" Return the gradient of the learnable parameters w.r.t. some objective """ function grad end function grad! end """ Proximal operator of a function (https://en.wikipedia.org/wiki/Proximal_operator) """ function prox end function prox! end """ Anything that takes an input and performs some kind of function to produce an output. For example a linear prediction function. """ abstract type Transformation end abstract type StochasticTransformation <: Transformation end abstract type Learnable <: Transformation end """ Do a forward pass, and return the output """ function transform end function transform! end """ Baseclass for any prediction model that can be minimized. This means that an object of a subclass contains all the information needed to compute its own current loss. """ abstract type Minimizable <: Learnable end function update end function update! end function learn end function learn! end # -------------------------------------------------------------------- import Base: AbstractSet "A continuous range (inclusive) between a lo and a hi" struct IntervalSet{T} <: AbstractSet{T} lo::T hi::T end function IntervalSet(lo::A, hi::B) where {A,B} T = promote_type(A,B) IntervalSet{T}(convert(T,lo), convert(T,hi)) end # numeric interval randtype(s::IntervalSet{T}) where T <: Number = Float64 Base.rand(s::IntervalSet{T}, dims::Integer...) where T <: Number = rand(dims...) .* (s.hi - s.lo) .+ s.lo Base.in(x::Number, s::IntervalSet{T}) where T <: Number = s.lo <= x <= s.hi Base.length(s::IntervalSet{T}) where T <: Number = 1 Base.:(==)(s1::IntervalSet{T}, s2::IntervalSet{T}) where T = s1.lo == s2.lo && s1.hi == s2.hi # vector of intervals randtype(s::IntervalSet{T}) where T <: AbstractVector = Vector{Float64} Base.rand(s::IntervalSet{T}) where T <: AbstractVector = Float64[rand() * (s.hi[i] - s.lo[i]) + s.lo[i] for i=1:length(s)] Base.in(x::AbstractVector, s::IntervalSet{T}) where T <: AbstractVector = all(i -> s.lo[i] <= x[i] <= s.hi[i], 1:length(s)) Base.length(s::IntervalSet{T}) where T <: AbstractVector = length(s.lo) "Set of discrete items" struct DiscreteSet{T<:AbstractArray} <: AbstractSet{T} items::T end randtype(s::DiscreteSet) = eltype(s.items) Base.rand(s::DiscreteSet, dims::Integer...) = rand(s.items, dims...) Base.in(x, s::DiscreteSet) = x in s.items Base.length(s::DiscreteSet) = length(s.items) Base.getindex(s::DiscreteSet, i::Int) = s.items[i] Base.:(==)(s1::DiscreteSet, s2::DiscreteSet) = s1.items == s2.items # operations on arrays of sets randtype(sets::AbstractArray{S,N}) where {S <: AbstractSet, N} = Array{promote_type(map(randtype, sets)...), N} Base.rand(sets::AbstractArray{S}) where S <: AbstractSet = eltype(randtype(sets))[rand(s) for s in sets] function Base.rand(sets::AbstractArray{S}, dim1::Integer, dims::Integer...) where S <: AbstractSet A = Array{randtype(sets)}(undef, dim1, dims...) for i in eachindex(A) A[i] = rand(sets) end A end function Base.in(xs::AbstractArray, sets::AbstractArray{S}) where S <: AbstractSet size(xs) == size(sets) && all(map(in, xs, sets)) end "Groups several heterogenous sets. Used mainly for proper dispatch." struct TupleSet{T<:Tuple} <: AbstractSet{T} sets::T end TupleSet(sets::AbstractSet...) = TupleSet(sets) # rand can return arrays or tuples, but defaults to arrays randtype(sets::TupleSet, ::Type{Vector}) = Vector{promote_type(map(randtype, sets.sets)...)} Base.rand(sets::TupleSet, ::Type{Vector}) = eltype(randtype(sets, Vector))[rand(s) for s in sets.sets] randtype(sets::TupleSet, ::Type{Tuple}) = Tuple{map(randtype, sets.sets)...} Base.rand(sets::TupleSet, ::Type{Tuple}) = map(rand, sets.sets) function Base.rand(sets::TupleSet, ::Type{OT}, dim1::Integer, dims::Integer...) where OT A = Array{randtype(sets, OT)}(undef, dim1, dims...) for i in eachindex(A) A[i] = rand(sets, OT) end A end Base.length(sets::TupleSet) = sum(length(s) for s in sets.sets) Base.iterate(sets::TupleSet) = iterate(sets.sets) Base.iterate(sets::TupleSet, i) = iterate(sets.sets, i) randtype(sets::TupleSet) = randtype(sets, Vector) Base.rand(sets::TupleSet, dims::Integer...) = rand(sets, Vector, dims...) Base.in(x, sets::TupleSet) = all(map(in, x, sets.sets)) "Returns an AbstractSet representing valid input values" function inputdomain end "Returns an AbstractSet representing valid output/target values" function targetdomain end
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# should be your environment and have for nothing to do whith the speed_test cd("/Users/frank/.julia/dev/TypeDBClient_Speed") using Pkg Pkg.activate(".") using TypeDBClient using TypeDBClient: CoreSession, CoreClient using UUIDs g = TypeDBClient client = g.CoreClient("127.0.0.1",1729) Optional{T} = Union{Nothing,T} # @info "deleting all databases" # dbs = g.get_all_databases(client) # for item in dbs # g.delete_database(client, item.name) # end @info "create database typedb" if !g.contains_database(client, "typedb") g.create_database(client, "typedb") end sess = CoreSession(client, "typedb" , g.Proto.Session_Type.DATA, request_timout=Inf) function trans_func(task) return task end function coreTransaction(session::g.CoreSession, sessionId::g.Bytes, type::g.EnumType, options::g.TypeDBOptions, grpc_controller; request_timout::Real=session.request_timeout) type = type options = options input_channel = Channel{g.Proto.Transaction_Client}(10) proto_options = g.copy_to_proto(options, g.Proto.Options) res_imm = g.Proto.transaction(session.client.core_stub.asyncStub , grpc_controller, input_channel, trans_func) req_result, status = fetch(res_imm) output_channel = g.grpc_result_or_error(req_result, status, result->result) println(status) # open_req = g.TransactionRequestBuilder.open_req(session.sessionID, type, proto_options,session.networkLatencyMillis) # bidirectionalStream = g.BidirectionalStream(input_channel, output_channel, status) # trans_id = uuid4() # result = g.CoreTransaction(type, options, bidirectionalStream, trans_id, sessionId, request_timout, session) # # The following is only for warming up Transaction. If we didn't do this # # it could happen that a transaction reach a timeout. # req_result = g.execute(result, open_req, false) # kind_of_result = g.Proto.which_oneof(req_result, :res) # getproperty(req_result, kind_of_result) return input_channel end @info "beginning Threads" client_in = CoreClient("127.0.0.1", 1729) sess_in = CoreSession(client_in, "typedb", g.Proto.Session_Type.DATA, request_timout=Inf) grpc_controller = g.gRPCController(request_timeout=sess_in.request_timeout) Threads.@threads for i in 1:2 @info "here I am" res = coreTransaction(sess_in, sess_in.sessionID, g.Proto.Transaction_Type.WRITE, g.TypeDBOptions(), grpc_controller, request_timout = sess_in.request_timeout) @info "I'm close to end" end # close(sess)
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abstract type AbstractLearningUpdate end function update!(model, lu::T, opt, s_t::Array{Array{AF, 1}, 1}, a_t::Array{<:Integer, 1}, s_tp1::Array{Array{AF, 1}, 1}, r::Array{AF, 1}, terminal, args...) where {AF<:AbstractFloat, T<:AbstractLearningUpdate} update!(model, lu, opt, hcat(s_t...), a_t, hcat(s_tp1...), r, terminal, args...) end # function update!(model, # lu::T, # opt, # s_t::Array{Array{AF, 1}, 1}, # a_t::Array{<:Integer, 1}, # s_tp1::Array{Array{AF, 1}, 1}, # r::Array{AF, 1}, # terminal) where {AF<:AbstractFloat, T<:AbstractLearningUpdate} # update!(model, lu, opt, hcat(s_t...), a_t, hcat(s_tp1...), r, terminal) # end # function update!(model::GVFNetwork, # lu, opt, s_t, a_t, s_tp1, r, terminal) # update!(model.gvf_model, lu[1], opt, model.horde, s_t, a_t, s_tp1, r, terminal) # update!(model, lu[2], opt, s_t, a_t, s_tp1, r, terminal) # end abstract type AbstractQLearning <: AbstractLearningUpdate end struct QLearning <: AbstractQLearning γ::Float32 end function loss(lu::QLearning, model, s_t, a_t, s_tp1, r, terminal, target_model) γ = lu.γ.*(1 .- terminal) action_idx = [CartesianIndex(a_t[i], i) for i in 1:length(terminal)] q_tp1 = maximum(target_model(s_tp1); dims=1)[1,:] target = (r .+ γ.*q_tp1) q_t = model(s_t)[action_idx] return Flux.mse(target, q_t) end function loss(lu::QLearning, model, s_t, a_t, s_tp1, r, terminal, target_model::Nothing) γ = lu.γ.*(1 .- terminal) action_idx = [CartesianIndex(a_t[i], i) for i in 1:length(terminal)] q_tp1 = Flux.Tracker.data(maximum(model(s_tp1); dims=1)[1,:]) target = (r .+ γ.*q_tp1) q_t = model(s_t)[action_idx] return Flux.mse(target, q_t) end struct DoubleQLearning <: AbstractQLearning γ::Float32 end function loss(lu::DoubleQLearning, model, s_t, a_t, s_tp1, r, terminal, target_model) γ = lu.γ.*(1 .- terminal) action_idx = [CartesianIndex(a_t[i], i) for i in 1:length(terminal)] q̃_tp1 = Flux.data(model(s_tp1)) q̃_tp1_argmax = findmax(q̃_tp1; dims=1) action_tp1 = [q̃_tp1_argmax[2][i] for i in 1:length(terminal)] q_tp1 = target_model(s_tp1)[action_tp1] target = (r .+ γ.*q_tp1) q_t = model(s_t)[action_idx] return Flux.mse(target, q_t) end function loss(lu::DoubleQLearning, model, s_t, a_t, s_tp1, r, terminal, target_model::Nothing) γ = lu.γ.*(1 .- terminal) action_idx = [CartesianIndex(a_t[i], i) for i in 1:length(terminal)] q̃_tp1 = Flux.data(model(s_tp1)) q̃_tp1_argmax = findmax(q̃_tp1; dims=1) action_tp1 = [q̃_tp1_argmax[2][i] for i in 1:length(terminal)] q_tp1 = Flux.data(model(s_tp1)[action_tp1]) target = (r .+ γ.*q_tp1) q_t = model(s_t)[action_idx] return Flux.mse(target, q_t) end function update!(model, lu::LU, opt, s_t::Array{<:AbstractFloat, 2}, a_t::Array{<:Integer, 1}, s_tp1::Array{<:AbstractFloat, 2}, r::Array{<:AbstractFloat, 1}, terminal, target_model) where {LU<:AbstractQLearning} ps = params(model) gs = Flux.gradient(ps) do loss(lu, model, s_t, a_t, s_tp1, r, terminal, target_model) end Flux.Optimise.update!(opt, ps, gs) end struct TDLearning end function loss(lu::TDLearning, model, s_t, a_t, s_tp1, r, terminal, target_model, horde::H) where {H<:Horde} # get GVF horde parameters p = [RLCore.get(horde, s_t[:,i], a_t, s_tp1[:,i]) for i in 1:length(terminal)] c = hcat(getindex.(p, 1)...) γ = hcat(getindex.(p, 2)...) π = hcat(getindex.(p, 3)...) v_t = model(s_t) v_tp1 = target_model(s_tp1) target = c .+ γ.*v_tp1 # -> Matrix (preds × batch_size) return sum((target .- v_t).^2) * (1 // length(terminal)) end loss(lu::TDLearning, model, s_t, a_t, s_tp1, r, terminal, target_model::Nothing, horde::H) where {H<:Horde} = loss(lu, model, s_t, a_t, s_tp1, r, terminal, (x)->Flux.data(model(x)), horde) struct AuxQLearning{T<:AbstractQLearning} <: AbstractLearningUpdate β::Float32 q_learning::T td_learning::TDLearning end function update!( model, lu::AQL, opt, s_t::Array{<:AbstractFloat, 2}, a_t::Array{<:Integer, 1}, s_tp1::Array{<:AbstractFloat, 2}, r::Array{<:AbstractFloat, 1}, terminal, target_model, horde::H) where {AQL<:AuxQLearning, H<:RLCore.Horde} num_gvfs = length(horde) ps = params(model) gs = Flux.gradient(ps) do ℒ_q = loss(lu.q_learning, (x)->model(x)[1:(end-num_gvfs), :], s_t, a_t, s_tp1, r, terminal, (x)->target_model(x)[1:(end-num_gvfs), :]) # @show ℒ_q ℒ_td = loss(lu.td_learning, (x)->model(x)[(end-num_gvfs+1):end, :], s_t, a_t, s_tp1, r, terminal, (x)->target_model(x)[(end-num_gvfs+1):end, :], horde) # @show ℒ_td return ℒ_q + lu.β*ℒ_td end Flux.Optimise.update!(opt, ps, gs) end function update!( model, lu::AuxQLearning, opt, s_t::Array{<:AbstractFloat, 2}, a_t::Array{<:Integer, 1}, s_tp1::Array{<:AbstractFloat, 2}, r::Array{<:AbstractFloat, 1}, terminal, target_model, horde::Nothing) update!(model, lu.q_learning, opt, s_t, a_t, s_tp1, r, terminal, target_model) end
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1.932168
2,860
function asymmetry(df, id, x, y) result = DataFrame(TrackID = Int[], len = Int[], asymmetry = Float64[]) @inbounds for n = minimum(df[!, id]):maximum(df[!, id]) m = extract(df, n, id, [x, y]) T = Matrix{Float64}(undef, 2, 2) mean_x = @views mean(m[:, 1]) mean_y = @views mean(m[:, 2]) t1 = 0.0 t2 = 0.0 t3 = 0.0 N = size(m, 1) for t = 1:N _x = m[t, 1] - mean_x _y = m[t, 2] - mean_y t1 += abs2(_x) t2 += _x * _y t3 += abs2(_y) end t1 /= N t2 /= N t3 /= N T[1, 1] = t1 T[1, 2] = t2 T[2, 1] = t2 T[2, 2] = t3 λ1, λ2 = eigvals(T) A = -log(1 - abs2(λ1 - λ2)/(2*abs2(λ1 + λ2))) push!(result, [Int(n), Int(N), A]) end result end function non_gaussian_parameter(df, x, frame=:FRAME2) Δ = diff(df[!, x]) prepend!(Δ, NaN) Δ[findall(x -> x == 1, df[!, frame])] .= NaN filter!(!isnan, Δ) a = mean(Δ.^4) b = mean(abs2.(Δ))^2 a/3b - 1 end function efficiency(df, id, x, y) result = DataFrame(TrackID = Int[], value = Float64[]) @inbounds for n in 1:maximum(df[!, id]) m = extract(df, n, id, [x, y]) c = 0.0 T = size(m, 1) for t in 1:T-1 c += squared_displacement(m, t, 1) end c /= (T-1) c₀ = squared_displacement(m, 1, T-1) push!(result, [n, c₀/c]) end result end function _largest_distance(m) T = size(m, 1) d = displacement(m, 1) for δ = 1:T-1 for t = 1:T-δ dₜ = displacement(m, t, δ) if dₜ > d d = dₜ end end end d end function _total_length(m) T = size(m, 1) c = 0.0 for t = 1:T-1 c += displacement(m, t, 1) end c end function fractal_dimension(df, id, x, y) result = DataFrame(TrackID = Int[], value = Float64[]) @inbounds @simd for n in minimum(df[!, id]):maximum(df[!, id]) m = extract(df, n, id, [x, y]) N = size(m, 1)-1 d = _largest_distance(m) L = _total_length(m) push!(result, [n, log(N)/log(N*d/L)]) end result end function gaussianity( df::DataFrame, id::Symbol = :TrackID, x::Symbol = :POSITION_X, y::Symbol = :POSITION_Y, ) gaussianity = DataFrame(TrackID = Int64[], gaussianity = Float64[], delta_t = Int64[], n = Int64[]) @inbounds for n in sort(collect(Set(df[!, id]))) m = extract(df, Int(n), id, [x, y]) T = size(m, 1) for δ = 1:T-1 r⁴ = 0.0 r² = 0.0 @simd for t = 1:(T-δ) r⁴ += abs2(squared_displacement(m, t, δ)) r² += squared_displacement(m, t, δ) end r⁴ /= (T - δ) r² /= (T - δ) push!(gaussianity, [n, r⁴/(2*abs2(r²)) - 1, δ, T - δ]) end end gaussianity end
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""" generate_array( array_1::Array{Int64}, array_2::Array{Int64}, array_merged::Array{Int64}, i::Int64, j::Int64, k::Int64, l::Int64, size::Int64, flag::Bool, result::Array{Any,1} ) `generate_array` is a modified [Breadth-First-Search]https://en.wikipedia.org/wiki/Breadth-first_search] for generating the result-array with the possible combinations of two merged arrays with ascending ordering. # Arguments - `array_1::Array{Int64}`: First sorted array - `array_2::Array{Int64}`: Second sorted array - `array_merged::Array{Int64}`: Merged sorted array - `i::Int64`: Outer lower increment of first array - `j::Int64`: Outer higher increment of first array - `k::Int64`: Outer lower increment of second array - `l::Int64`: Outer higher increment of second array - `size::Int64`: Current size - `flag::Bool`: Switching between first and second array - `result::Array{Any,1}`: Result list of the merged arrays """ function generate_array( array_1::Array{Int64}, array_2::Array{Int64}, array_merged::Array{Int64}, i::Int64, j::Int64, k::Int64, l::Int64, size::Int64, flag::Bool, result::Array{Any,1}, ) if flag if size > 1 push!(result, array_merged[1:size]) end for m in i:k # Define the first element if size < 2 array_merged[size] = array_1[m] generate_array(array_1, array_2, array_merged, m + 1, j, k, l, size, !flag, result) else # Going to the third and higher element of array_1 if array_1[m] > array_merged[size] array_merged[size + 1] = array_1[m] generate_array( array_1, array_2, array_merged, m + 1, j, k, l, size + 1, !flag,result ) end end end else # Going to the second of array_2 for n in j:l if array_2[n] > array_merged[size] array_merged[size + 1] = array_2[n] generate_array( array_1, array_2, array_merged, i, n + 1, k, l, size + 1, !flag,result ) end end end return result end """ combinations_of_2arrays(array_1::Array{Int64,1}, array_2::Array{Int64,1})) Provides all combinations of sorted arrays with an increasing number of elements. Original idea # Arguments - `array_1::Array{Int64,1}`: First sorted array - `array_2::Array{Int64,1}`: Second sorted array # Examples ```julia-repl julia> import ClassicAlgorithmsCollections julia> arr_1 = [10, 15, 25] julia> arr_2 = [5, 20, 30] julia> ClassicAlgorithmsCollections.combinations_of_2arrays(arr_1, arr_2) [10, 20] [10, 20, 25, 30] [10, 30] [15, 20] [15, 20, 25, 30] [15, 30] [25, 30] ``` """ function combinations_of_2arrays(array_1::Array{Int64,1}, array_2::Array{Int64,1}) length_array_1, length_array_2 = length(array_1), length(array_2) init_array = zeros(Int64, (length_array_1 + length_array_2)) # Create array-list for the searching solution result = Array{Any,1}() return generate_array( array_1, array_2, init_array, 1, 1, length_array_1, length_array_2, 1, true, result ) end
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1.88247
2,008
include("eggify.jl") using Metatheory.Library using Metatheory.EGraphs.Schedulers mult_t = commutative_monoid(:(*), 1) plus_t = commutative_monoid(:(+), 0) minus_t = @theory begin a - a => 0 a + (-b) => a - b end mulplus_t = @theory begin 0 * a => 0 a * 0 => 0 a * (b + c) == ((a * b) + (a * c)) a + (b * a) => ((b + 1) * a) end pow_t = @theory begin (y^n) * y => y^(n + 1) x^n * x^m == x^(n + m) (x * y)^z == x^z * y^z (x^p)^q == x^(p * q) x^0 => 1 0^x => 0 1^x => 1 x^1 => x inv(x) == x^(-1) end function customlt(x, y) if typeof(x) == Expr && Expr == typeof(y) false elseif typeof(x) == typeof(y) isless(x, y) elseif x isa Symbol && y isa Number false else true end end canonical_t = @theory begin # restore n-arity (x + (+)(ys...)) => +(x, ys...) ((+)(xs...) + y) => +(xs..., y) (x * (*)(ys...)) => *(x, ys...) ((*)(xs...) * y) => *(xs..., y) (*)(xs...) |> Expr(:call, :*, sort!(xs; lt = customlt)...) (+)(xs...) |> Expr(:call, :+, sort!(xs; lt = customlt)...) end cas = mult_t ∪ plus_t ∪ minus_t ∪ mulplus_t ∪ pow_t theory = cas query = cleanast(:(a + b + (0 * c) + d)) function simplify(ex) g = EGraph(ex) params = SaturationParams( scheduler = BackoffScheduler, timeout = 20, schedulerparams = (1000, 5), # fuel and bantime ) report = saturate!(g, cas, params) println(report) res = extract!(g, astsize) res = rewrite(res, canonical_t; clean = false, m = @__MODULE__) # this just orders symbols and restores n-ary plus and mult res end ########################################### params = SaturationParams(timeout = 20, schedulerparams = (1000, 5)) for i in 1:2 ex = simplify(:(a + b + (0 * c) + d)) println("Best found: $ex") end open("src/main.rs", "w") do f write(f, rust_code(theory, query)) end
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2.235653
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using Documenter, StingerGraphs makedocs() deploydocs( julia = "nightly", repo = "github.com/stingergraph/StingerGraphs.jl.git" )
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using RCall function load_R_library(libname) reval("library($libname)") end
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struct DBFGS{T1,T2,T3} <: QuasiNewton{T1} approx::T1 theta::T2 P::T3 end DBFGS(approx) = DBFGS(approx, 0.2, nothing) DBFGS(; inverse = true, theta = 0.2) = DBFGS(inverse ? Inverse() : Direct(), theta, nothing) hasprecon(::DBFGS{<:Any,<:Any,<:Nothing}) = NoPrecon() hasprecon(::DBFGS{<:Any,<:Any,<:Any}) = HasPrecon() summary(dbfgs::DBFGS{Inverse}) = "Inverse Damped BFGS" summary(dbfgs::DBFGS{Direct}) = "Direct Damped BFGS" function update!(scheme::DBFGS{<:Direct,<:Any,<:Any}, B, s, y) # We could write this as # B .+= (y*y')/dot(s, y) - (B*s)*(s'*B)/(s'*B*s) # B .+= (y*y')/dot(s, y) - b*b'/dot(s, b) # where b = B*s # But instead, we split up the calculations. First calculate the denominator # in the first term σ = dot(s, y) ρ = inv(σ) # scalar # Then calculate the vector b b = B * s # vector temporary sb = dot(s, b) if σ ≥ scheme.theta * sb θ = 1.0 # Calculate one vector divided by dot(s, b) ρbb = inv(sb) * b # And calculate B .+= (inv(σ) * y) * y' .- ρbb * b' else θ = 0.8 * sb / (sb - σ) r = y * θ + (1 - θ) * b # Calculate one vector divided by dot(s, b) ρbb = inv(dot(s, b)) * b # And calculate B .+= (inv(dot(s, r)) * r) * r' .- ρbb * b' end end function update(scheme::DBFGS{<:Direct,<:Any}, B, s, y) # As above, but out of place σ = dot(s, y) b = B * s sb = dot(s, b) if σ ≥ scheme.theta * sb θ = 1.0 # Calculate one vector divided by dot(s, b) ρbb = inv(sb) * b # And calculate return B = B .+ (inv(σ) * y) * y' .- ρbb * b' else θ = 0.8 * sb / (sb - σ) r = y * θ + (1 - θ) * b # Calculate one vector divided by dot(s, b) ρbb = inv(dot(s, b)) * b # And calculate return B = B .+ (inv(dot(s, r)) * r) * r' .- ρbb * b' end end function update(scheme::DBFGS{<:Inverse,<:Any}, H, s, y) σ = dot(s, y) ρ = inv(σ) # if isfinite(ρ) C = (I - ρ * s * y') H = C * H * C' + ρ * s * s' # end H end function update!(scheme::DBFGS{<:Inverse,<:Any}, H, s, y) σ = dot(s, y) ρ = inv(σ) if isfinite(ρ) Hy = H * y H .= H .+ ((σ + y' * Hy) .* ρ^2) * (s * s') Hys = Hy * s' Hys .= Hys .+ Hys' H .= H .- Hys .* ρ end H end function update!(scheme::DBFGS{<:Inverse,<:Any}, A::UniformScaling, s, y) update(scheme, A, s, y) end function update!(scheme::DBFGS{<:Direct,<:Any}, A::UniformScaling, s, y) update(scheme, A, s, y) end
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