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View fd_typed.jl
│ ─ %-1 = invoke perform_step!(::OrdinaryDiffEq.ODEIntegrator{Tsit5,true,Array{ForwardDiff.Dual{ForwardDiff.Tag{var"#25#26",Float64},Float64,4},1},Float64,DiffEqBase.NullParameters,Float
View fd_llvm.ll
define void @"julia_perform_step!_19254"(%jl_value_t addrspace(10)* nonnull align 8 dereferenceable(304), %jl_value_t addrspace(10)* nonnull align 8 dereferenceable(544), i8) {
%3 = addrspacecast %jl_value_t addrspace(10)* %0 to %jl_value_t addrspace(11)*
%4 = bitcast %jl_value_t addrspace(11)* %3 to i8 addrspace(11)*
%5 = getelementptr inbounds i8, i8 addrspace(11)* %4, i64 32
%6 = bitcast i8 addrspace(11)* %5 to double addrspace(11)*
%7 = load double, double addrspace(11)* %6, align 8
%8 = getelementptr inbounds i8, i8 addrspace(11)* %4, i64 48
%9 = bitcast i8 addrspace(11)* %8 to %jl_value_t addrspace(10)* addrspace(11)*
%10 = load %jl_value_t addrspace(10)*, %jl_value_t addrspace(10)* addrspace(11)* %9, align 8
YingboMa / specialize_vararg.jl
Created Jan 12, 2020
Got from Mason Protter
View specialize_vararg.jl
# by Mason Protter
using MacroTools: MacroTools, splitdef, combinedef, @capture
macro specialize_vararg(n::Int, fdef)
d = splitdef(fdef)
args = d[:args][end]
@assert d[:args][end] isa Expr && d[:args][end].head == Symbol("...")
args_symbol = d[:args][end].args[]
fdefs = Expr(:block)
for i in 1:n-1
di = deepcopy(d)
View D_sicm.jl
using ModelingToolkit
using ModelingToolkit: Differential, expand_derivatives, Expression, Operation, simplify_constants
function D(f)
(args...) -> begin
syms = map(_->Variable(gensym())(), args)
ex = f(syms...)
ds = map(s->Differential(s), syms)
ops = [expand_derivatives(ds[i](ex)) for i in eachindex(syms)]
rules = Dict( collect(zip(syms, args)) )
View .tmux.conf
set-option -g history-limit 65536
set -g -a terminal-overrides ',*:Ss=\E[%p1%d q:Se=\E[2 q'
set -g mouse on
#bind -n C-k clear-history
# default shell
set-option -g default-shell /usr/bin/fish
# clipboard settings
View heating_equation.jl
using OrdinaryDiffEq, LabelledArrays
N = 40 # Number of heated units
Cu = N == 1 ? [2e7] : (ones(N) .+ range(0,1.348,length=N))*1e7 # "Heat capacity of heated units";
Cd = 2e6*N # "Heat capacity of distribution circuit";
Gh = 200 # "Thermal conductance of heating elements";
Gu = 150 # "Thermal conductance of heated units to the atmosphere";
Qmax = N*3000 # "Maximum power output of heat generation unit";
Teps = 0.5 # "Threshold of heated unit temperature controllers";
View diffeqfastbc.jl
import Base.Broadcast: _broadcast_getindex, preprocess, preprocess_args, Broadcasted, broadcast_unalias, combine_axes, broadcast_shape, check_broadcast_axes, check_broadcast_shape
import Base: copyto!, tail, axes
struct DiffEqBC{T}
@inline axes(b::DiffEqBC) = axes(b.x)
Base.@propagate_inbounds _broadcast_getindex(b::DiffEqBC, i) = _broadcast_getindex(b.x, i)
Base.@propagate_inbounds _broadcast_getindex(b::DiffEqBC{<:AbstractArray{<:Any,0}}, i) = b.x[]
Base.@propagate_inbounds _broadcast_getindex(b::DiffEqBC{<:AbstractVector}, i) = b.x[i[1]]
Base.@propagate_inbounds _broadcast_getindex(b::DiffEqBC{<:AbstractArray}, i) = b.x[i]
View bench_ODEs.jl
using OrdinaryDiffEq, DiffEqDevTools, Plots, ParameterizedFunctions, Sundials, ODEInterfaceDiffEq
using LinearAlgebra
postfix = "PR"
setups = [
YingboMa / lurec.jl
Last active Feb 17, 2019
Sivan Toledo's recursive LU algorithm
View lurec.jl
using LinearAlgebra
lurec(A, blocksize=16) = lurec!(copy(A), Vector{LinearAlgebra.BlasInt}(undef, min(size(A)...)), blocksize)
function lurec!(A::AbstractMatrix{T}, ipiv, blocksize) where T
info = Ref(zero(LinearAlgebra.BlasInt))
m, n = size(A)
mnmin = min(m, n)
reckernel!(A, m, mnmin, ipiv, info, blocksize)
LU{T, typeof(A)}(A, ipiv, info[])
View JacVec_VecJac.jl
using ReverseDiff, ForwardDiff
using Test
vecjac(f, x, v) -> u
function vecjac(f, x, v)
tp = ReverseDiff.InstructionTape()
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