MasonProtter

MasonProtter

MasonProtter

MasonProtter

using InteractiveUtils, Pkg

for pk in ["Transducers", "LoopVectorization"]
    if pk ∉ keys(Pkg.project().dependencies)
        Pkg.add(pk)
    end
end

using Transducers, LoopVectorization
function picalc_kernel(r, a, b)

MasonProtter

using BenchmarkTools

function _pi(x::Vector, y::Vector)
    acc = 0
    @inbounds @simd for i ∈ eachindex(x)
        acc += (x[i]*x[i] + y[i]*y[i]) < 1.0
    end
    4acc/length(x)
end

MasonProtter

using BenchmarkTools, StaticArrays, Transducers

function _pi_kernel_static()
    xs = @SVector rand(50)
    ys = @SVector rand(50)
    sum(((x, y),) -> x^2 + y^2 < 1.0, zip(xs, ys))
end
function _pi_kernel(r)
    xs = rand(r)
    ys = rand(r)

MasonProtter

module Syms
export sym, Sym

#--------------------------------------------------------------------------------
# Set up some symbolic types
#--------------------------------------------------------------------------------
abstract type Symbolic{T} end # Symbolic{T} will act like it is <: T

struct Sym{T} <: Symbolic{T}
    name::Symbol

MasonProtter

#+BEGIN_SRC jupyter-julia
using Interpolations
xs = range(-10, 10, length=50) # 50 evenly spaced points from -5 to 5

# model function, some Gaussian
f(x; A=1, σ=2, b=0) = A * exp(-x^2/2σ) + b
ys = f.(xs)

method = BSpline(Cubic(Line(OnGrid()))) #interpolation method
f_itp = scale(interpolate(ys, method), xs) # interpolation function

MasonProtter

#+BEGIN_SRC julia
const ketpat = r"\|.*?\>"
ketrep(str) = "Ket("*(match(r"(?<=\|).*?(?=>)", str).match)*")"

const brapat = r"\<.*?\|"
brarep(str) = "Bra("*(match(r"(?<=<).*?(?=\|)", str).match)*")"

function rep_braket(str)
    replace(replace(str, brapat => brarep), ketpat => ketrep)
end

MasonProtter

#+BEGIN_SRC jupyter-julia
using CUDA
struct StaticString{N} <: AbstractString
    chars::NTuple{N, Char}
end
macro s_str(s)
    chars = tuple(collect(s)...)
    N = length(chars)
    esc(:(StaticString{$N}($chars)))
end