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### A Pluto.jl notebook ###
# v0.19.32

using Markdown
using InteractiveUtils

# ╔═╡ a353c1f3-0921-4c9b-ab1d-a34cb262b3d9
# following docs at https://docs.julialang.org/en/v1/manual/methods/#Function-like-objects-1
# lets make a functor

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# written in Julia just because its easier and more compact to annotate the code with types (mainly vector vs scalar)
# and group the data into structs.... we will write everything in python
# the goal is just to get a sense of what functions we need from the abaco hardware side (abaco_*)
# and what functions we want to expose from some analysis package, and what data all of those will want

const Vec = Vector{Float64}
struct S21
    frequency::Vec
    transmission::Vec
    phase::Vec  # future

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What tasks should mass_infinity accomplish?

• Take raw data records of fixed length and noise. Return basis for analyzing that data, and future data.
  • Requires cuts to select down to clean pulses from which to make the basis. This suggests a sort of self consistent cut procedure. Luckily Alpert et all describe such a procedure https://link.springer.com/article/10.1007/s10909-015-1402-y
• Take raw data records. Return subspace representation + residual std deviation.
• Take subspace representation and spectral information. Return rough calibration. J?
• Take J and ??. Return various corrected Js.
  • Baseline-J Correlation Correction
  • Subsample arrival time Correction
• Time Drift Correction

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const NEIG_py = [[1, 4, 5], [0, 2, 4, 5, 6], [1, 3, 5, 6, 7], [2, 6, 7], [0, 1, 5, 8, 9], [0, 1, 2, 4, 6, 8, 9, 10], [1, 2, 3, 5, 7, 9, 10, 11], [2, 3, 6, 10, 11], [4, 5, 9, 12, 13], [4, 5, 6, 8, 10, 12, 13, 14], [5, 6, 7, 9, 11, 13, 14, 15], [6, 7, 10, 14, 15], [8, 9, 13], [8, 9, 10, 12, 14], [9, 10, 11, 13, 15], [10, 11, 14]];
const NEIG = [n.+1 for n in NEIG_py]
function enlarge(path::Vector{Int})
    (push!(copy(path),loc) for loc in NEIG[path[end]] if !(loc in path))
end
collect(enlarge([1]))
function enlargepaths(paths)
    Iterators.Flatten(enlarge(path) for path in paths)
end
collect(enlargepaths([[1],[2]]))

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using BenchmarkTools

dirname = "anotherdir"
isdir(dirname) || mkdir(dirname)
fname = joinpath(dirname,"stackexchange46809845_f.jl")
funname = "myfun"
funstr = """$funname() = rand(100)"""
open(fname,"w") do f
    write(f,funstr)
end

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using HDF5

"HDF5 appears to be inefficent for small writes, so this a simple buffer that
allows me to write to HDF5 only once per unit time (typically one second) to
limit the number of small writes."
mutable struct BufferedHDF5Dataset{T}
  ds::HDF5Dataset
  v::Vector{T}
  lasti::Int64 # last index in hdf5 dataset
  timeout_s::Float64 # interval in seconds at which to transfer data from v to ds

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using MacroTools, Base.Test

"@parsetype T ex
replace all numeric literals that are supertypes of `T` with type `T`"
macro parsetype(Tsym, ex)
    T=getfield(Main, Tsym)
    ST=parsesuper(T)
    parsetype(T, ST, ex)
end

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package main

import "time"
import "fmt"
func counter(c chan int, N int) {
	for j := 1; j <= N; j++ {
		c <- j
	}
}

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function searchsortedfirst2vec(a,b)
	[searchsortedfirst(a,bb, 1, length(a), Base.Sort.Forward) for bb in b]
end
c=searchsorted2(a,b)

# returns an array of indicies into a such that each points is the first value in a[i] greater than b[i]
# assumes both a and b are sorted
function searchsortedfirst2a(a,b,o::Base.Sort.Ordering=Base.Sort.Forward)
	alow = 1

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