text:
### A Pluto.jl notebook ###
# v0.18.0

using Markdown
using InteractiveUtils

# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
    quote
        local iv = try Base.loaded_modules[Base.PkgId(Base.UUID("6e696c72-6542-2067-7265-42206c756150"), "AbstractPlutoDingetjes")].Bonds.initial_value catch; b -> missing; end
        local el = $(esc(element))
        global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)
        el
    end
end

# ╔═╡ 52d4f953-98d1-4016-b2b3-7c234b012189
begin
	using Plots
	using Printf
	using Unitful
	using DataFrames
	using CSV
	using Images
	using Interpolations
	using QuadGK
	using UnitfulEquivalences
	using Markdown
	using InteractiveUtils
	using PlutoUI
	using LsqFit
	using Distributions
	using Statistics
	using StatsBase
end

# ╔═╡ 91726783-795a-4da6-9ca7-2d176ba328ca
begin
	using Peaks
	using Glob
	using FFTW
	using DSP
end

# ╔═╡ b4f98dfc-9e44-11ec-3621-4526f6f187bd
md"# RuSL Lifetime analysis"

# ╔═╡ 1e429641-5615-4c2f-b466-a5e1d5cbc297
import Unitful:
    nm, μm, mm, cm, m, km,
    mg, g, kg,
    ps, ns, μs, ms, s, minute, hr, d, yr, Hz, kHz, MHz, GHz,
    eV,
    μJ, mJ, J,
	μW, mW, W,
    A, N, mol, mmol, V, L, M

# ╔═╡ 860a66e0-9fdb-4537-865e-bf70e7fa289a
import PhysicalConstants.CODATA2018: N_A

# ╔═╡ 1665009d-f4a8-4d3e-bc9a-3cca8c26c213
function ingredients(path::String)
	# this is from the Julia source code (evalfile in base/loading.jl)
	# but with the modification that it returns the module instead of the last object
	name = Symbol(basename(path))
	m = Module(name)
	Core.eval(m,
        Expr(:toplevel,
             :(eval(x) = $(Expr(:core, :eval))($name, x)),
             :(include(x) = $(Expr(:top, :include))($name, x)),
             :(include(mapexpr::Function, x) = $(Expr(:top, :include))(mapexpr, $name, x)),
             :(include($path))))
	m
end

# ╔═╡ c9e85963-fe90-48c4-8e20-fcc92c31ce99
lfi = ingredients("../src/LaserLab.jl")

# ╔═╡ f3fea484-8430-47ab-986b-7b170bef4ba6
PlutoUI.TableOfContents(title="Table of Contents", indent=true)

# ╔═╡ 9e58009d-0244-49db-801e-362bcb895eb0
md" ## Introduction
Data was taken with the TOPATU setup, with the following elements:

1. EPL480 nm laser operating at 250 kHz (5 μs pulses).
2. A PMT H13543 from Hamamatsu, with 20 % QE at 500 nm, 10.6 % QE at 700 nm and 8 % QE at 800 nm.
3. Data was taken with three different band filters, centered at 660, 700 and 730 nm.
4. Coincidence were triggered in a scope, at 10 MHz rate, the trigger signal was given by the laser.

   **Features of the data**

- The PMT data is somewhat noisy. Thus, it is necessary to characterise single electron pulses by a peak search analysis.
"

# ╔═╡ aedcf3c2-7d51-466c-a88e-2c681679b8b0
load("../notebooks/img/setupPMTScope.png")

# ╔═╡ a6cd504a-1afc-4be0-81d2-0de827650526
begin
	spG = lfi.LaserLab.CsvG(';',',')  # spanish: decimals represented with ',' delimited with ';'
	enG = lfi.LaserLab.CsvG(',','.')  # english
	enG2 = lfi.LaserLab.CsvG('\t','.')  # english
	println("")
end

# ╔═╡ 01355b47-de67-4104-a6bc-3d773b156bae
md"## Data files
- The data files are stored in directories, which in turn contain csv files, one file per trace (waveform)
- We can store all the files using glob
"

# ╔═╡ 4db00abd-5074-41fc-bbf5-18f5f2fa08d0
csvf = glob("../data/scope/signal_650nm/rep00/C1*.csv")

# ╔═╡ 010c2f50-ef21-44df-a1d4-cebfe4e94783
md"## Signal features
- To ilustrate the signal's features, first we read a waveform. The data comes in seconds and volts, signals are negative. As a first setp we read the data, invert the signals, & transform amplitude to mV and times to μs
- We use Julia's multiple dispatch feature to define two versions of *read_waveform*, one that takes the explicit adress of the directory and file, and one that takes a vector of strings produced by glob and the file number"

# ╔═╡ 2a9e671f-33c4-42be-99c4-e02f74b01cab
md"### Reading waveforms
- Function read_waveform allows reading waveforms one by one
"

# ╔═╡ 727c419a-7e04-4c24-9870-549bcc5fed59
begin
	nsigma = 3.0
	nsigma2 = 5.0
end

# ╔═╡ 6fa25494-93a2-48ad-892e-5081d0914400
md"## Characterising signals"

# ╔═╡ f88b142f-c42b-46d7-90e1-a657946df80e
md"### Plotting waveform"

# ╔═╡ 43f951f5-3c82-4c9a-8e22-249310dfa588
md"### Plotting waveform FFT"

# ╔═╡ 7f638d01-4f8e-46ce-bc98-6eb73763e213
md"- Notice that noise frequency appear above 200 MHz, thus a low-pass filter can be used to filter them away"

# ╔═╡ ef104366-3c34-4658-9317-25e8c438cdcc
md"## Filtering the signal"

# ╔═╡ 4e35809a-0a39-432e-8175-bb06fb571194
md"### Butterworth Filter"

# ╔═╡ 4ba8b710-3760-4647-8294-5c5f0be7d97b
md"### Chebyshev filter"

# ╔═╡ 84dba0b8-fe30-42ee-8542-233160cb4bf6
md"### Same features of the filters"

# ╔═╡ 35436842-9dc0-4541-8d45-abf3d174ccd1
md" ## Filtering signals using a Chebyshev filter
"

# ╔═╡ c669b848-0d0d-4a82-bfcc-f13319f5bc4d
md"## Peak finding algorithm step-by-step
"

# ╔═╡ 5a03e28b-921a-4f33-a8f2-2e642b2f8f01
md"### Subtract the base line"

# ╔═╡ 3e54ffba-6521-4236-bafd-0caaa207a518
md"### Find the maxima in the landscape"

# ╔═╡ f8c63ce1-9365-4de0-aa3b-dbc04d82aeb1
promsel=0.5

# ╔═╡ 7ebf77fd-cd26-421a-82d2-7cbb74e3c4e7
md"### Find peak prominence
- minimum peak prominence to retain the peak = $promsel
"

# ╔═╡ fc15250c-faa0-4feb-9034-2c978a6f5e40
md"### Find peak width"

# ╔═╡ 52379219-9f4e-442b-ab8d-8965955e27b5
wsel = 4.0

# ╔═╡ 1b420392-e906-4fb8-a5ea-04d07c51efa5
#peaks3, widths2, leftedge2, rightedge2 = peakwidths(peaks2, wvfmFlt.Ampl, proms2; minwidth=wsel)

# ╔═╡ 27491d67-b26d-4ec3-8799-86c7cff3c90d
md"### Plot the peaks"

# ╔═╡ 9f5f8052-6ee6-402a-ac0a-cfac3ab782bd
md"## Formalizing analysis
- Let now formalise the analysis above
"

# ╔═╡ ed36670d-74fa-4ca7-b815-63022739ec48
md"### read waveform, compute stats
- We can also read waveforms specifying a file number in csvg vector (obtained by glob)
"

# ╔═╡ 0b4ce181-8a10-47ad-bf70-43adc6645fd1
#meanAmp2, stdAmp2 = mean_and_std(wvfm2)

# ╔═╡ b35c0b28-6fd2-4490-9c22-8692a798575a
md"### Filter signal
"

# ╔═╡ a1a50633-bd5d-4148-8825-c0b0855087a9
md"### Subtract baseline
"

# ╔═╡ 2673dfd5-b088-4f35-8669-29e8f9806fe4
md"### Select peaks
"

# ╔═╡ bf46ed11-6f1c-4f1d-b010-82f6e9988f7c
@bind sprom NumberField(0.0:10.0:0.1; default=0.5)

# ╔═╡ d9c433bf-4d96-4ee9-a98c-056454db37ea
@bind sw NumberField(0.0:10.0; default=0.0)

# ╔═╡ ecd4080b-38f0-4191-8cd0-d64c6d3674be
md"- Adjust sliders to select on:
- prominence => : $sprom
- Peak width => : $sw

- Set for example the prominence cut at 0.1. One observes that the reflection peaks are not eliminated. This is easily done setting prominence at 0.5
"

# ╔═╡ 3c7a48b4-c8a8-4891-84ed-0ba146eee549
md"## A few more examples
- Simply change the file number below to examine examples.
- Try for example 50. With a cut on width of 3 no peaks are selected. with a cut on 2.5 one peak is selected. The width measured by the algorithm is 2.8 and is again distorted by noise.
- Event 60 shows four peaks with a cut at 2.5 and 3 peaks with a cut on 3. It is hard to decide if the peak eliminated by the cut at 3 is signal or noise
- Event 100 shows very clear two peaks with a cut at 3 on width
- Event 200 shows four peaks, all rather convincing with a cut at 3
"

# ╔═╡ 0bf5a3d6-a66e-458a-9852-93bb4f00d54c
@bind fileNumber NumberField(1:900; default=1)

# ╔═╡ fa13e453-124b-43c1-84c0-4d0e7ce1ea94
md"## Statistics
- The next step is to study the cuts on a statistical basis.
"

# ╔═╡ b0a7208d-26c1-4ea5-9072-f43a70d9e1ca
md"- Cut on prominence ~ 0.5"

# ╔═╡ bc3ddf9f-76ca-4c78-b140-ed29747aaed2
md"- Cut on with ~ 6"

# ╔═╡ ab03fda5-fbf7-4ae2-a743-b57e95211dad
md"## Selection"

# ╔═╡ b66b58c1-99f7-40c9-824e-74fdf3da4220
md"## Fits"

# ╔═╡ f6530e2b-ba30-47be-918d-8ac316053dc9
md"# Analysis"

# ╔═╡ b3a05a67-3844-4ef8-9bdf-e631477e2b15
md"## Test sample"

# ╔═╡ d5679626-ae35-4532-9949-87a3991f10e4
md"### Peaks for 650 nm"

# ╔═╡ 4a6b9446-8724-4d03-a2d0-48c3d26057b4
md"### Peaks for 700 nm"

# ╔═╡ a9f1938c-93c8-4e6f-91ad-8b38d0b2f6ff
md"### Peaks for 732 nm"

# ╔═╡ fde49db9-165b-4b55-928b-b0d3f2610a31
md"## Functions and data structures"

# ╔═╡ 500c0b3b-fc21-45a7-b03d-07c413a947c9
md"
	struct SPeaks
"

# ╔═╡ 79c95b20-7b71-414f-b5d6-41e4cfe373ca
struct SPeaks
	peaks::Vector{Int64}
	proms::Vector{Float64}
	widths::Vector{Float64}
	leftedge::Vector{Float64}
	rightedge::Vector{Float64}
	xs::Vector{Float64}
	ys::Vector{Float64}
	xbase::Vector{Float64}
	promsel::Float64
end

# ╔═╡ fe91ac2c-b95a-4d5e-a2fe-f09f43297d4d
md"
	read_waveform(wdir::String, wname::String)
"

# ╔═╡ e673cdda-7032-4600-bbd0-01a291aa3eb5
begin
function read_waveform(wdir::String, wname::String)
	wvfm = lfi.LaserLab.load_df_from_csv(wdir, wname, enG,5)
	wvfm[!, :Ampl] = -1000.0 * wvfm[!, :Ampl]
	wvfm[!, :Time] = 1e+6  .* wvfm[!, :Time]
	wvfm
end
function read_waveform(csvf::Vector{String}, fnumber::Integer)
	wvfm2 = DataFrame(CSV.File(csvf[fnumber], header=5, delim=enG.delim, decimal=enG.decimal))
	wvfm2[!, :Ampl] = -1000.0 * wvfm2[!, :Ampl]
	wvfm2[!, :Time] = 1e+6  .* wvfm2[!, :Time]
	wvfm2
end
end

# ╔═╡ f7643ff9-a02d-473b-8492-3dc380f38383
wvfm1 = read_waveform("/Users/jj/JuliaProjects/LaserLab/data/scope/signal_650nm/rep00", "C1--Trace--00005.csv");

# ╔═╡ 61993e02-4a99-457d-b677-605cf7534e87
size(wvfm1)

# ╔═╡ 726c769b-763b-43da-b1d3-bd012120602e
meanAmp = mean(wvfm1.Ampl)

# ╔═╡ 11563c92-e839-4bfb-98f3-ca2598b95696
stdAmp = std(wvfm1.Ampl)

# ╔═╡ 40523ce0-4cdd-43d4-8c37-3147f3d37a27
md" ### Computing mean and std of waveform
- mean amplitude = $(round(meanAmp, sigdigits=2))
- std of mean = $(round(stdAmp, sigdigits=2))
"

# ╔═╡ 3c0c557e-bbe3-49b7-b422-1217fb2f7fda
first(wvfm1)

# ╔═╡ 8fcae1eb-ffd9-4353-953d-7245559bdd19
wvfm2 = read_waveform(csvf,30);

# ╔═╡ fff73c5f-0fef-48b5-825e-1a677d52d0ca
wvfm3 = read_waveform(csvf,fileNumber);

# ╔═╡ 96dad766-398d-414c-ac66-dfaa76947ffc
md"
		sampling_rate(wvfm1, ws=5.0e-6)
  Given waveform **wvfm** with length **ws** (in seconds), returns the sampling rate
"

# ╔═╡ b43916ba-0499-4d82-b81c-49aba21ee145
sampling_rate(wvfm, ws=5.0e-6) = uconvert(GHz, (length(wvfm.Time) / ws) * Hz)

# ╔═╡ 8e99255c-0357-425f-978b-4bf3eb9428ce
begin
	fs = uconvert(Hz, sampling_rate(wvfm1))/Hz
	fl = 150.0MHz # filter high frequency noise
	flh = uconvert(Hz, fl)/Hz
end

# ╔═╡ 5dfbb58a-1411-4264-9540-8cc26263910f
md"
		sampling_period(wvfm, ws=5.0e-6)
  Given waveform **wvfm** with length **ws** (in seconds), returns the sampling period
"

# ╔═╡ ff872f5f-b10b-4065-8850-d81f9d6aece3
sampling_period(wvfm, ws=5.0e-6) = uconvert(ns, 1.0 / sampling_rate(wvfm, ws))

# ╔═╡ f25d1e02-4933-4864-9d34-bd061ced9eb8
md"- number of samples in the waveform (5 μs long) = $(length(wvfm1.Time))
- Sampling rate = $(round(sampling_rate(wvfm1)/GHz, sigdigits=2)) GHz
- sampling period = $(round(sampling_period(wvfm1)/ns, sigdigits=2)) ns"

# ╔═╡ cfaf2b26-e39d-47b5-8e99-8d35dbd7b3ac
md"
		fourier_transform(wvfm1)

   Given waveform **wvfm1** returns the sampling period, fourier transform amplitude and frequency
"

# ╔═╡ 13e729c1-15ff-4fcf-b3df-9247fda54109
function fourier_transform(wvfm1)
	ts = sampling_period(wvfm1)
	tss = uconvert(s, ts)/s           # tss is the sampling period in seconds
	F = fft(wvfm1.Ampl) |> fftshift   # fourier transform
	freqs = fftfreq(length(wvfm1.Time), 1.0/tss) |> fftshift  # frequencies in Hz
	ts, F, freqs
end

# ╔═╡ 604081ba-c975-414a-a8b6-68a6c401172f
function plot_fft(wvfm1)
	ts, F, freqs = fourier_transform(wvfm1)
	xl = Integer(length(freqs))
	xi = Integer(2 + xl/2)
	fmhz = freqs[xi:xl] * 1e-6      # frequencies in MHz
	ampl = abs.(F)[xi:xl]
	plot(fmhz, ampl)
	xlabel!("frequency (MHz)")
	ylabel!("Amplitude")
end

# ╔═╡ 10504712-31e8-4d00-8ebf-59d4feb0ff5f
plot_fft(wvfm1)

# ╔═╡ 1928ef8d-d7df-4b2b-9b9a-f958cacecaa3
md"
	plot_waveform(wvfm, mean, std, nsigma, nsigma2; sctter=false)
"

# ╔═╡ 17fffef0-699e-4aa2-8931-41afa2ef4da1
function plot_waveform(wvfm, mean, std, nsigma, nsigma2; sct=false, trace=false)
	if sct
		p1 = scatter(wvfm.Time, wvfm.Ampl,  markersize=2,
				  color = :black,
	    		  label=false,
				  fmt = :png)
	elseif trace
		p1 = plot(wvfm.Ampl, lw=2, label=false, fmt = :png)
	else
		p1 = plot(wvfm.Time, wvfm.Ampl, lw=2, label=false, fmt = :png)
	end
	hline!([mean - nsigma * std], label=false, fmt = :png)
	hline!([mean + nsigma * std], label=false, fmt = :png)
	hline!([mean - nsigma2 * std], label=false, fmt = :png)
	hline!([mean + nsigma2 * std], label=false, fmt = :png)
	xlabel!("t (μs)")
	ylabel!("I (mV)")
	p1
end

# ╔═╡ c688b96b-002f-4550-844d-505416bce77e
plot_waveform(wvfm1, meanAmp, stdAmp, nsigma, nsigma2)

# ╔═╡ 0bb31d5b-6d5c-49cc-9ddc-1675dfe4c98f
md"
	function mean_and_std(wvfm)
"

# ╔═╡ 8fe40af8-5374-48ed-831e-69b52a9e3ffc
function mean_and_std(wvfm)
	mean(wvfm.Ampl), std(wvfm.Ampl)
end

# ╔═╡ f3b5c2b0-1039-4edf-9efe-6da3665e5b39
md"
	filter_signal(wvfm, flh, fs, filtertype, n=4, ϵ=1.0)

 Given waveform **wvfm**, with sampling rate **fs** (in seconds) and cutoff frequency **flh**, the function returns a filter (Butterworth, or Chebyshev), frequency response and filtered signal
"

# ╔═╡ 84c75a93-92ce-4df4-be33-9fd71bffb2ca
function filter_signal(wvfm, flh, fs, filtertype, n=4, ϵ=1.0)

	responsetype = Lowpass(flh; fs=fs)

	if filtertype == "Butterworth"
		designmethod = Butterworth(n)
	elseif filtertype == "Chebyshev1"
		ripple = 10.0*log10(ϵ^2 + 1.0)
		designmethod = Chebyshev1(n, ripple)
	end
	filter = digitalfilter(responsetype, designmethod)
	H, w = freqresp(filter)
	H, filter, filt(filter, wvfm.Ampl)
end

# ╔═╡ 00c92e6b-749e-4858-96ee-be4a0a056fbb
hbw, fltbw, fsgnbw = filter_signal(wvfm1, flh, fs, "Butterworth",4, 1.0);

# ╔═╡ 27749777-9a67-42dc-91d4-e17ed99f8bb3
begin
	plot(wvfm1.Time, wvfm1.Ampl, lw=2, label="unfiltered")
	plot!(wvfm1.Time, fsgnbw, w=2, label="Butter filter")
	xlabel!("Time (μs)")
	ylabel!("Amplitude")
end

# ╔═╡ 78d6a1e6-f820-41ba-8050-ecaed514fd96
hcb, fltcb, fsgncb = filter_signal(wvfm1, flh, fs, "Chebyshev1",4, 2.0);

# ╔═╡ 88e18ad4-f478-4e5f-a239-91f18b781937
begin
	plot(wvfm1.Time, wvfm1.Ampl, lw=2, label="unfiltered")
	plot!(wvfm1.Time, fsgncb, lw=2, label="Chebyshev filter")
	xlabel!("Time (μs)")
	ylabel!("Amplitude")
end

# ╔═╡ 68df6f2c-f614-4f66-be31-180823f51edb
begin
	plot(abs.(hbw), lw=2, label="Butter")
	plot!(abs.(hcb), lw=2, label="Cheby")
	xlabel!("Samples")
	ylabel!("Frequency")
end

# ╔═╡ 76eed385-e4c0-4c25-9e8e-6d558b683f13
begin
	plot(amp2db.(abs.(hbw)), lw=2, label="Butter")
	plot!(amp2db.(abs.(hcb)), lw=2, label="Cheby")
	xlabel!("Samples")
	ylabel!("Amplitude")
end

# ╔═╡ 35f0fda8-7e9a-4f5c-bbd3-979e8d04ebfa
function filter_signal_cb(wvfm1; fl=200.0MHz, n=4, ϵ=2)
	fs = uconvert(Hz, sampling_rate(wvfm1))/Hz
	flh = uconvert(Hz, fl)/Hz
	hcb, fltcb, fsgncb = filter_signal(wvfm1, flh, fs, "Chebyshev1",n, ϵ)
	wvfm1[!, "fAmpl"] = fsgncb
	wvfm1
end

# ╔═╡ 842d5c9d-598c-4565-8526-09b6df3a1d2f
fwvfm1 = filter_signal_cb(wvfm1; fl=200.0MHz);

# ╔═╡ 6da51a94-3d8b-447a-80fa-81eba8ced45c
first(fwvfm1,5)

# ╔═╡ d29de4fc-c875-4567-b9b8-4932a53c9d4e
fwvfm2 = filter_signal_cb(wvfm2; fl=200.0MHz);

# ╔═╡ b0e0c6da-7e0f-4923-a215-0559d0d072c6
fwvfm3 = filter_signal_cb(wvfm3; fl=200.0MHz);

# ╔═╡ f20b33b0-c0ec-4ee7-8273-6b738eb44dd7
function thrx(fwvfm1; nsigma=1, waveform="filtered")
	if waveform=="filtered"
		ampl = "fAmpl"
	else
		ampl = "Ampl"
	end

	meanfs = mean(fwvfm1[!, ampl])
	stdfs = std(fwvfm1[!, ampl])
	thrp = meanfs + stdfs * nsigma
	thrn = meanfs - stdfs * nsigma
	meanfs, stdfs, thrp, thrn
end

# ╔═╡ a461a88c-534b-465c-8dab-9d1e2dc49287
meanfs, stdfs, thrp, thrn = thrx(fwvfm1; nsigma=2.0)

# ╔═╡ 50304255-941d-4870-856d-c4a2de6f36a7
md" - After filtering:
- mean = $(round(meanfs, sigdigits=2))
- std = $(round(stdfs, sigdigits=2))
- thrp = $(round(thrp, sigdigits=2))
- thrn = $(round(thrn, sigdigits=2))
"

# ╔═╡ 6a0f7c24-cc8f-4a14-8fdd-70ab6d9a9d06
wvfmFlt = filter(row -> row[:fAmpl] > thrp, fwvfm1);

# ╔═╡ f7bc85d0-c386-4c70-af24-9b2dd44544fb
upks, uvals = findmaxima(wvfmFlt.fAmpl)

# ╔═╡ 359ea578-cdcc-4a96-86e0-d334ad220702
pks, proms = peakproms(upks, wvfmFlt.fAmpl; minprom=promsel)

# ╔═╡ c09b06c6-f35b-4350-8012-5f907c53f8f3
_, uproms = peakproms(pks, wvfmFlt.fAmpl)

# ╔═╡ 60bfadce-187b-4a7d-a849-f37fb7f068b1
npeaks = length(pks)

# ╔═╡ 1520fcbe-030f-4a38-b5bf-dcd5b05b8a62
xbase = ones(npeaks,1)*[promsel]

# ╔═╡ 1d36a8cc-b262-4b28-8ff6-47606f8b276d
_, widths, leftedge, rightedge = peakwidths(pks, wvfmFlt.fAmpl, proms)

# ╔═╡ 297d7b98-5a48-484d-81b1-f8d0e121ab83
wr = [round(widths[i], sigdigits=2) for i in 1:length(widths)]

# ╔═╡ be4b932d-98f8-495b-ae2a-8a293c54738c
pheigth = [wvfmFlt.fAmpl[i] for i in pks]

# ╔═╡ c5d56314-1690-4f04-92b0-00a8984b1e5c
ptime = [wvfmFlt.Time[i] for i in pks]

# ╔═╡ 13792c29-3966-49d2-a452-6fc8e9ef1f48
begin
	plot(wvfmFlt.fAmpl, lw=2, label=false)
	scatter!(pks, pheigth, label="peak")
	scatter!(leftedge, xbase, label="leftedge")
	scatter!(rightedge, xbase, label="rightedge")
	hline!([promsel], label=false)
	xlabel!("peak index")
	ylabel!("Amplitude")

end

# ╔═╡ 9fa21941-3586-43bc-adeb-92cc1ae805a7
meanAmp2, stdAmp2, thrp3s, thrn3s = thrx(wvfm2; nsigma=3.0, waveform="raw")

# ╔═╡ c31442a6-7480-422f-926d-f97c44996e22
fmeanAmp2, fstdAmp2, thrp2, thrn2 = thrx(wvfm2; nsigma=2.0, waveform="filtered")

# ╔═╡ 3979e3b9-dddc-433f-ab69-6fc1265bdf4f
wvfmFlt2 = filter(row -> row[:fAmpl] > thrp2, fwvfm2);

# ╔═╡ c0b2f470-5a64-41a6-a65b-e6c7c45bdc86
meanAmp3, stdAmp3, thr3p3s, thr3n3s = thrx(wvfm3; nsigma=3.0, waveform="raw")

# ╔═╡ 771ad8be-26d7-400f-b91b-850b4f5416be
fmeanAmp3, fstdAmp3, thrp3, thrn3 = thrx(wvfm3; nsigma=2.0, waveform="filtered")

# ╔═╡ d859f2d1-27bd-42c5-89a5-5ebffb9afc8b
wvfmFlt3 = filter(row -> row[:fAmpl] > thrp3, wvfm2);

# ╔═╡ 843d888f-1e8d-4875-b175-1bc0a98f3320
function plot_filtered_waveform(wvfm; window=false, waveform="filtered", wstart=-1, wend=-1, thrp=0, thrn=0)

	if waveform=="filtered"
		ampl = "fAmpl"
	else
		ampl = "Ampl"
	end

	if window
		plot(wvfm.Time[wstart:wend], wvfm[wstart:wend, ampl], lw=2, label=waveform, fmt = :png)
		scatter!(wvfm.Time[wstart:wend], wvfm[wstart:wend, ampl],  markersize=2,
					  color = :black, label=false, fmt = :png)
	else
		plot(wvfm.Time, wvfm[!, ampl], lw=2, label=waveform, fmt = :png)
		scatter!(wvfm.Time, wvfm[!, ampl], markersize=2,
					  color = :black, label=false, fmt = :png)
	end

	hline!([thrp], label=false, fmt = :png)
	hline!([thrn], label=false, fmt = :png)
	xlabel!("Time (μs)")
	ylabel!("Amplitude")
end

# ╔═╡ 8e09dc6f-755e-49bb-8fde-ad29b9eb4bd2
plot_filtered_waveform(fwvfm1; window=false, wstart=701, wend=900, thrp=thrp, thrn=thrn)

# ╔═╡ 583a4fe4-07f7-4c1a-9420-bd2bcd0dcd93
plot_filtered_waveform(fwvfm1; window=true, wstart=501, wend=1500, thrp=thrp, thrn=thrn)

# ╔═╡ 53d6b897-7e60-4b24-ab6e-1e07be1ad43f
plot_filtered_waveform(fwvfm1; window=true, wstart=1200, wend=3500, thrp=thrp, thrn=thrn)

# ╔═╡ eac497f9-8c5a-42a9-af97-aaeddb3c0823
plot_filtered_waveform(wvfmFlt; window=false, thrp=thrp, thrn=thrn)

# ╔═╡ b3ddc6fe-6752-4f53-8eee-da30555fd95f
plot_filtered_waveform(wvfm2; window=false, waveform="raw", thrp=thrp3s, thrn=thrn3s)

# ╔═╡ 218b7cbc-641d-4b55-bcb3-f5572fffaa32
plot_filtered_waveform(fwvfm2; window=false, thrp=thrp2, thrn=thrn2)

# ╔═╡ a2f24a67-c112-4b10-94e8-10d078c29181
plot_filtered_waveform(wvfmFlt2; window=false, thrp=thrp2, thrn=thrn2)

# ╔═╡ 1cc2b8a5-87cd-4d84-a26d-905d5cec362e
plot_filtered_waveform(wvfm3; window=false, waveform="raw", thrp=thr3p3s, thrn=thr3n3s)

# ╔═╡ a09a6487-3bb6-4ece-ba5e-7b298a507666
plot_filtered_waveform(wvfm3; window=false, thrp=thrp3, thrn=thrn3)

# ╔═╡ 7b31594c-849e-4456-87fb-8224773f7a77
plot_filtered_waveform(wvfmFlt3; window=false, thrp=thrp3, thrn=thrn3)

# ╔═╡ 58d706c0-44f5-4514-b87b-eaf1f1940448
function select_filtered_peaks(wvfm, thrp; promsel = 0.1, wsel=2.5)

	wvfmFlt = filter(row -> row[:fAmpl] > thrp, wvfm)
	pks, vals = findmaxima(wvfmFlt.fAmpl)
	peaks2, proms = peakproms(pks, wvfmFlt.fAmpl; minprom=promsel)

	if length(peaks2) == 0
		return Nothing
	end

	peaks2, widths, leftedge, rightedge = peakwidths(peaks2, wvfmFlt.fAmpl, proms; minwidth=wsel)

	ys = [wvfmFlt.fAmpl[i] for i in peaks2]
	xs = [wvfmFlt.Time[i] for i in peaks2]
	npeaks = length(peaks2)
	xbase = ones(npeaks,1)*[promsel]

	return wvfmFlt, SPeaks(peaks2, proms, widths, leftedge, rightedge, xs,ys, xbase, promsel)
end

# ╔═╡ 853787cc-229c-4b0b-afe6-4bc36666dec6
result = select_filtered_peaks(fwvfm2, thrp2; promsel = sprom, wsel=sw);

# ╔═╡ 9428c207-98e0-4fcd-8477-d2d92a15d3c9
if result != Nothing
	wvflt, speaks = result
	println("")
end

# ╔═╡ 83a0e714-a16f-4d73-8f86-64bb0bc8c54c
result3 = select_filtered_peaks(wvfm3, thrp3; promsel = sprom, wsel=sw);

# ╔═╡ 02203388-bfcc-4f9e-ad23-695155034be1
if result3 != Nothing
	wvflt3, speaks3 = result3
	println("")
end

# ╔═╡ c1894849-5d29-4857-a715-9b91ffe8163d
function plot_filtered_peaks(wvfmFlt, spks)
	plot(wvfmFlt.fAmpl, lw=2, label=false)
	scatter!(spks.peaks, spks.ys, label="peak")
	scatter!(spks.leftedge, spks.xbase, label="leftedge")
	scatter!(spks.rightedge, spks.xbase, label="rightedge")
	hline!([spks.promsel], label=false)
	xlabel!("peak index")
	ylabel!("peak amplitude")
end

# ╔═╡ b49aaeaf-63ea-4bf1-a61b-beca60c8df98
plot_filtered_peaks(wvflt, speaks)

# ╔═╡ bcb5df7a-66be-40a2-a17b-f1dd2ef70c64
plot_filtered_peaks(wvflt3, speaks3)

# ╔═╡ 2089aeed-5c35-4df0-bda6-f2162f3d3407
function prom_and_width(csvf, i0, il; fl=200.0MHz, nsigma=2.0, promsel=0.0, wsel=0.0)

	PROM = Float64[]
	WP   = Float64[]

	for i in i0:il
		wvfm = read_waveform(csvf,i)
		fwvfm = filter_signal_cb(wvfm; fl=fl)
		fmeanAmp, fstdAmp, thrp, thrn = thrx(wvfm; nsigma=nsigma, waveform="filtered")
		result = select_filtered_peaks(wvfm, thrp; promsel = promsel, wsel=wsel)

		if result != Nothing
			_, speaks = result
			append!(PROM,speaks.proms)
			append!(WP,speaks.widths)
		end
	end
	return PROM, WP
end

# ╔═╡ afe971a3-8f38-4341-868c-846089ea70f8
PP, WP = prom_and_width(csvf, 1, 990)

# ╔═╡ 67cd1b0d-89cc-46ca-ae4b-3900d072ea68
hP = lfi.LaserLab.hist1d(PP, 20, 0.0, 10.0)

# ╔═╡ 321b373a-5fda-4c9c-b1ad-34d1f82d086b
lfi.LaserLab.hist1d(hP, "Prominence (+)")

# ╔═╡ aad39fc9-433c-4155-92a7-6462971dbf22
hW = lfi.LaserLab.hist1d(WP, 20, 0.0, 10.0)

# ╔═╡ 015712c4-b2b9-48d4-a076-427a8d0a0baa
lfi.LaserLab.hist1d(hW, "Width (+)")

# ╔═╡ 03492420-09ff-48fd-82d7-daadf2b41ec3
PP2, WP2 = prom_and_width(csvf, 1, 990; promsel=0.5)

# ╔═╡ 07c0a448-8acd-4387-a755-4bdfa06254df
hP2 = lfi.LaserLab.hist1d(PP2, 20, 0.0, 10.0)

# ╔═╡ f93d9148-1fca-4161-8d27-03d6635da764
lfi.LaserLab.hist1d(hP2, "Prominence (+)")

# ╔═╡ e9144680-0127-4cbd-8408-6fec1f412fc8
hW2 = lfi.LaserLab.hist1d(WP2, 20, 0.0, 10.0)

# ╔═╡ d87eaa68-8c56-48c5-b19f-2f95c70f0e6b
lfi.LaserLab.hist1d(hW2, "Width (+)")

# ╔═╡ 24cf8503-fdde-4202-8f45-4b0a6b57108a
function select_peaks(csvf, i0, il; fl=200.0MHz, nsigma=2.0, promsel=0.5, wsel=6.0)
	SPK = []
	for i in i0:il
		wvfm = read_waveform(csvf,i)
		fwvfm = filter_signal_cb(wvfm; fl=fl)
		fmeanAmp, fstdAmp, thrp, thrn = thrx(wvfm; nsigma=nsigma)
		result = select_filtered_peaks(wvfm, thrp; promsel = promsel, wsel=wsel)
		if result != Nothing
			_, speaks = result
			push!(SPK,speaks)
		end
	end
	SPK
end

# ╔═╡ 966845c7-03ed-4bed-bc9c-6c79af302b43
spks = select_peaks(csvf, 1, 990; fl=200.0MHz, nsigma=2.0, promsel=0.5, wsel=6.0)

# ╔═╡ ab7c483e-f8db-4556-9d78-8a87a7c2cf77
times = reduce(vcat,[spks[i].xs for i in 1:length(spks)])

# ╔═╡ fee7bf69-da15-484b-82ce-de0246a49b75
hpkp = lfi.LaserLab.hist1d(times, 20, 0.0, 2.0)

# ╔═╡ 6d5bc9d2-3c7a-497b-9372-e4d43eabf3c4
lfi.LaserLab.hist1d(hpkp, "Peaks (+)")

# ╔═╡ 8065bf42-8d71-4c99-85c6-a2b6fd2e2087
begin
	expo(t, N, λ) = N*exp(-t/λ)
	mexp(t, p) = p[1] * exp.(-t/p[2])
	pa0 = [100.0, 0.5]
	tdata = lfi.LaserLab.centers(hpkp)
	vdata = hpkp.weights
	fit = curve_fit(mexp, tdata, vdata, pa0)
	cofe = coef(fit)
	stder = stderror(fit)
	tft = expo.(tdata, coef(fit)...)
	println("")
end

# ╔═╡ 73878b74-389a-4306-834b-9d9a126ba95d
cofe

# ╔═╡ d73cae53-b429-4c06-a63b-9bc876387256
stder

# ╔═╡ baba5028-d85c-415f-95f6-0393ce686249
begin
ps1 = scatter(tdata, vdata, yerr=sqrt.(vdata), markersize=2,
		color = :black,
	    label="data",
		fmt = :png)
pp = plot(ps1, tdata, tft, lw=2, label="μ = $(round(cofe[2]*1000, sigdigits=2)) μs", fmt = :png)
xlabel!("t (μs)")
ylabel!("frequency")
end

# ╔═╡ c056af02-693b-4837-ab7b-1c4c7b7a9ca5
md"### glob_files
	function glob_files(path)
"

# ╔═╡ 8d02c42c-452e-4ad3-9b27-95ac7ea5e210
function glob_files(path)
	sdr = string(path, "/rep*")
	drs = glob(sdr)
	FLS = String[]
	for dr in drs
		fls   = string(dr, "/C1*.csv")
		csf = glob(fls)
		append!(FLS, csf)
	end
	FLS
end

# ╔═╡ 3fff2102-a8b4-44ad-870f-7837ae874c2e
fls650 = glob_files("../data/scope/signal_650nm/")

# ╔═╡ 938ff2ea-13af-4bce-98d9-8ef5349a7f3d
nfls650 = length(fls650)

# ╔═╡ 03b1941f-9dc7-4c5d-9188-ebd68e3d6cd7
spk650 = select_peaks(fls650, 1, nfls650; fl=200.0MHz, nsigma=2.0, promsel=0.5, wsel=6.0)

# ╔═╡ 5e93697b-1924-4ece-bab5-7795e2a5f015
begin
	t650nm = reduce(vcat,[spk650[i].xs for i in 1:length(spk650)])
	ht650nm = lfi.LaserLab.hist1d(t650nm, 20, 0.0, 2.0)
end

# ╔═╡ 2a21f18e-20e6-41ab-88d1-391a1f71839a
fls700 = glob_files("../data/scope/signal_700nm/")

# ╔═╡ 6ef46751-1053-4fea-9f89-96e5355d0177
nfls700 = length(fls700)

# ╔═╡ 07f496b8-6147-43ad-ad64-07938321626d
fls732 = glob_files("../data/scope/signal_732nm/")

# ╔═╡ 9bff1c70-4d62-4571-b03f-329f6bffb390
nfls732 = length(fls732)

# ╔═╡ 380a7a82-0924-42b6-8698-7573b389e9ad
function glob_files2(path; wvl="650nm")
	sdr = string(path, "/", wvl,"*")
	drs = glob(sdr)
	FLS = String[]
	for dr in drs
		fls   = string(dr, "/C1*.csv")
		csf = glob(fls)
		append!(FLS, csf)
	end
	FLS
end

# ╔═╡ f62e8d24-10c6-42e9-a68e-f2d68f950ed1
md"### fit_peaks
	fit_peaks(htime; pa0=[100.0, 0.5], i0=1)
"

# ╔═╡ e45bd546-c5fc-4914-b237-3df5f473363d
function fit_peaks(htime; pa0=[100.0, 0.5], i0=1)
	expo(t, N, λ) = N*exp(-t/λ)
	mexp(t, p) = p[1] * exp.(-t/p[2])
	tdata = lfi.LaserLab.centers(htime)
	vdata = htime.weights
	il = length(tdata)
	fit = curve_fit(mexp, tdata[i0:il], vdata[i0:il], pa0)
	coef(fit), stderror(fit), expo.(tdata, coef(fit)...)
end

# ╔═╡ b75f91ae-7c35-4e80-bac9-fe7dfa1bcd3d
coef650nm, stderr650nm, tft650nm = fit_peaks(ht650nm; pa0=[100.0, 0.5], i0=1)

# ╔═╡ 5e6ea853-c7a2-44ce-acb5-628ed36f8180
md"### plot_fit
	plot_fit(htime, coeff, tft)
"

# ╔═╡ 8953d6fb-f9ad-4f95-b022-5ac95058dbf7
function plot_fit(htime, coeff, tft)
	tdata =lfi.LaserLab.centers(htime)
	vdata =htime.weights
	ps1 = scatter(tdata, vdata, yerr=sqrt.(vdata), markersize=2,
				  color = :black,
	    		  label="data",
				  fmt = :png)
	pp = plot(ps1, tdata, tft, lw=2, label="μ = $(round(coeff[2]*1000, sigdigits=2)) ns", fmt = :png)
	xlabel!("t (μs)")
	ylabel!("frequency")
end

# ╔═╡ a49b2124-a1cc-44a8-9993-a6e35fe34cbd
plot_fit(ht650nm, coef650nm, tft650nm)

# ╔═╡ d68fa4d5-6f6c-459c-9cfd-024a68f5e837
begin
	spk700nm = select_peaks(fls700, 1, nfls700; fl=200.0MHz, nsigma=2.0, promsel=0.5, wsel=6.0)
	t700nm = reduce(vcat,[spk700nm[i].xs for i in 1:length(spk700nm)])
	ht700nm = lfi.LaserLab.hist1d(t700nm, 20, 0.0, 2.0)
	coef700nm, stderr700nm, tft700nm = fit_peaks(ht700nm; pa0=[100.0, 0.5])
	plot_fit(ht700nm, coef700nm, tft700nm)
end

# ╔═╡ dd70e052-8dbd-4364-acec-9dfb3f907d6c
begin
	spk732nm = select_peaks(fls732, 1, nfls732; fl=200.0MHz, nsigma=2.0, promsel=0.5, wsel=6.0)
	t732nm = reduce(vcat,[spk732nm[i].xs for i in 1:length(spk732nm)])
	ht732nm = lfi.LaserLab.hist1d(t732nm, 20, 0.0, 2.0)
	coef732nm, stderr732nm, tft732nm = fit_peaks(ht732nm; pa0=[100.0, 0.5])
	plot_fit(ht732nm, coef732nm, tft732nm)
end

# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
DSP = "717857b8-e6f2-59f4-9121-6e50c889abd2"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
FFTW = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341"
Glob = "c27321d9-0574-5035-807b-f59d2c89b15c"
Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"
LsqFit = "2fda8390-95c7-5789-9bda-21331edee243"
Markdown = "d6f4376e-aef5-505a-96c1-9c027394607a"
Peaks = "18e31ff7-3703-566c-8e60-38913d67486b"
PhysicalConstants = "5ad8b20f-a522-5ce9-bfc9-ddf1d5bda6ab"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
QuadGK = "1fd47b50-473d-5c70-9696-f719f8f3bcdc"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
UnitfulEquivalences = "da9c4bc3-91c8-4f02-8a40-6b990d2a7e0c"

[compat]
CSV = "~0.10.2"
DSP = "~0.7.5"
DataFrames = "~1.3.2"
Distributions = "~0.25.49"
FFTW = "~1.4.6"
Glob = "~1.3.0"
Images = "~0.25.1"
Interpolations = "~0.13.5"
LsqFit = "~0.12.1"
Peaks = "~0.4.0"
PhysicalConstants = "~0.2.1"
Plots = "~1.26.0"
PlutoUI = "~0.7.35"
QuadGK = "~2.4.2"
StatsBase = "~0.33.16"
Unitful = "~1.11.0"
UnitfulEquivalences = "~0.2.0"
"""

# ╔═╡ 00000000-0000-0000-0000-000000000002
PLUTO_MANIFEST_TOML_CONTENTS = """
# This file is machine-generated - editing it directly is not advised

julia_version = "1.7.2"
manifest_format = "2.0"

[[deps.AbstractFFTs]]
deps = ["ChainRulesCore", "LinearAlgebra"]
git-tree-sha1 = "6f1d9bc1c08f9f4a8fa92e3ea3cb50153a1b40d4"
uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
version = "1.1.0"

[[deps.AbstractPlutoDingetjes]]
deps = ["Pkg"]
git-tree-sha1 = "8eaf9f1b4921132a4cff3f36a1d9ba923b14a481"
uuid = "6e696c72-6542-2067-7265-42206c756150"
version = "1.1.4"

[[deps.Adapt]]
deps = ["LinearAlgebra"]
git-tree-sha1 = "af92965fb30777147966f58acb05da51c5616b5f"
uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
version = "3.3.3"

[[deps.ArgTools]]
uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f"

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[[deps.SuiteSparse]]
deps = ["Libdl", "LinearAlgebra", "Serialization", "SparseArrays"]
uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9"

[[deps.TOML]]
deps = ["Dates"]
uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76"

[[deps.TableTraits]]
deps = ["IteratorInterfaceExtensions"]
git-tree-sha1 = "c06b2f539df1c6efa794486abfb6ed2022561a39"
uuid = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c"
version = "1.0.1"

[[deps.Tables]]
deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "TableTraits", "Test"]
git-tree-sha1 = "bb1064c9a84c52e277f1096cf41434b675cd368b"
uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
version = "1.6.1"

[[deps.Tar]]
deps = ["ArgTools", "SHA"]
uuid = "a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"

[[deps.TensorCore]]
deps = ["LinearAlgebra"]
git-tree-sha1 = "1feb45f88d133a655e001435632f019a9a1bcdb6"
uuid = "62fd8b95-f654-4bbd-a8a5-9c27f68ccd50"
version = "0.1.1"

[[deps.Test]]
deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

[[deps.TiffImages]]
deps = ["ColorTypes", "DataStructures", "DocStringExtensions", "FileIO", "FixedPointNumbers", "IndirectArrays", "Inflate", "OffsetArrays", "PkgVersion", "ProgressMeter", "UUIDs"]
git-tree-sha1 = "991d34bbff0d9125d93ba15887d6594e8e84b305"
uuid = "731e570b-9d59-4bfa-96dc-6df516fadf69"
version = "0.5.3"

[[deps.TiledIteration]]
deps = ["OffsetArrays"]
git-tree-sha1 = "5683455224ba92ef59db72d10690690f4a8dc297"
uuid = "06e1c1a7-607b-532d-9fad-de7d9aa2abac"
version = "0.3.1"

[[deps.TranscodingStreams]]
deps = ["Random", "Test"]
git-tree-sha1 = "216b95ea110b5972db65aa90f88d8d89dcb8851c"
uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
version = "0.9.6"

[[deps.URIs]]
git-tree-sha1 = "97bbe755a53fe859669cd907f2d96aee8d2c1355"
uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
version = "1.3.0"

[[deps.UUIDs]]
deps = ["Random", "SHA"]
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"

[[deps.UnPack]]
git-tree-sha1 = "387c1f73762231e86e0c9c5443ce3b4a0a9a0c2b"
uuid = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
version = "1.0.2"

[[deps.Unicode]]
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"

[[deps.UnicodeFun]]
deps = ["REPL"]
git-tree-sha1 = "53915e50200959667e78a92a418594b428dffddf"
uuid = "1cfade01-22cf-5700-b092-accc4b62d6e1"
version = "0.4.1"

[[deps.Unitful]]
deps = ["ConstructionBase", "Dates", "LinearAlgebra", "Random"]
git-tree-sha1 = "b649200e887a487468b71821e2644382699f1b0f"
uuid = "1986cc42-f94f-5a68-af5c-568840ba703d"
version = "1.11.0"

[[deps.UnitfulEquivalences]]
deps = ["Unitful"]
git-tree-sha1 = "76fc2f7fdc87531a1018eb7d647df7c29daf36b7"
uuid = "da9c4bc3-91c8-4f02-8a40-6b990d2a7e0c"
version = "0.2.0"

[[deps.Unzip]]
git-tree-sha1 = "34db80951901073501137bdbc3d5a8e7bbd06670"
uuid = "41fe7b60-77ed-43a1-b4f0-825fd5a5650d"
version = "0.1.2"

[[deps.Wayland_jll]]
deps = ["Artifacts", "Expat_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Pkg", "XML2_jll"]
git-tree-sha1 = "3e61f0b86f90dacb0bc0e73a0c5a83f6a8636e23"
uuid = "a2964d1f-97da-50d4-b82a-358c7fce9d89"
version = "1.19.0+0"

[[deps.Wayland_protocols_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4528479aa01ee1b3b4cd0e6faef0e04cf16466da"
uuid = "2381bf8a-dfd0-557d-9999-79630e7b1b91"
version = "1.25.0+0"

[[deps.WeakRefStrings]]
deps = ["DataAPI", "InlineStrings", "Parsers"]
git-tree-sha1 = "c69f9da3ff2f4f02e811c3323c22e5dfcb584cfa"
uuid = "ea10d353-3f73-51f8-a26c-33c1cb351aa5"
version = "1.4.1"

[[deps.WoodburyMatrices]]
deps = ["LinearAlgebra", "SparseArrays"]
git-tree-sha1 = "de67fa59e33ad156a590055375a30b23c40299d3"
uuid = "efce3f68-66dc-5838-9240-27a6d6f5f9b6"
version = "0.5.5"

[[deps.XML2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "Zlib_jll"]
git-tree-sha1 = "1acf5bdf07aa0907e0a37d3718bb88d4b687b74a"
uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a"
version = "2.9.12+0"

[[deps.XSLT_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Libgpg_error_jll", "Libiconv_jll", "Pkg", "XML2_jll", "Zlib_jll"]
git-tree-sha1 = "91844873c4085240b95e795f692c4cec4d805f8a"
uuid = "aed1982a-8fda-507f-9586-7b0439959a61"
version = "1.1.34+0"

[[deps.Xorg_libX11_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll", "Xorg_xtrans_jll"]
git-tree-sha1 = "5be649d550f3f4b95308bf0183b82e2582876527"
uuid = "4f6342f7-b3d2-589e-9d20-edeb45f2b2bc"
version = "1.6.9+4"

[[deps.Xorg_libXau_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4e490d5c960c314f33885790ed410ff3a94ce67e"
uuid = "0c0b7dd1-d40b-584c-a123-a41640f87eec"
version = "1.0.9+4"

[[deps.Xorg_libXcursor_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXfixes_jll", "Xorg_libXrender_jll"]
git-tree-sha1 = "12e0eb3bc634fa2080c1c37fccf56f7c22989afd"
uuid = "935fb764-8cf2-53bf-bb30-45bb1f8bf724"
version = "1.2.0+4"

[[deps.Xorg_libXdmcp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fe47bd2247248125c428978740e18a681372dd4"
uuid = "a3789734-cfe1-5b06-b2d0-1dd0d9d62d05"
version = "1.1.3+4"

[[deps.Xorg_libXext_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "b7c0aa8c376b31e4852b360222848637f481f8c3"
uuid = "1082639a-0dae-5f34-9b06-72781eeb8cb3"
version = "1.3.4+4"

[[deps.Xorg_libXfixes_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "0e0dc7431e7a0587559f9294aeec269471c991a4"
uuid = "d091e8ba-531a-589c-9de9-94069b037ed8"
version = "5.0.3+4"

[[deps.Xorg_libXi_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXfixes_jll"]
git-tree-sha1 = "89b52bc2160aadc84d707093930ef0bffa641246"
uuid = "a51aa0fd-4e3c-5386-b890-e753decda492"
version = "1.7.10+4"

[[deps.Xorg_libXinerama_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll"]
git-tree-sha1 = "26be8b1c342929259317d8b9f7b53bf2bb73b123"
uuid = "d1454406-59df-5ea1-beac-c340f2130bc3"
version = "1.1.4+4"

[[deps.Xorg_libXrandr_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll"]
git-tree-sha1 = "34cea83cb726fb58f325887bf0612c6b3fb17631"
uuid = "ec84b674-ba8e-5d96-8ba1-2a689ba10484"
version = "1.5.2+4"

[[deps.Xorg_libXrender_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "19560f30fd49f4d4efbe7002a1037f8c43d43b96"
uuid = "ea2f1a96-1ddc-540d-b46f-429655e07cfa"
version = "0.9.10+4"

[[deps.Xorg_libpthread_stubs_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "6783737e45d3c59a4a4c4091f5f88cdcf0908cbb"
uuid = "14d82f49-176c-5ed1-bb49-ad3f5cbd8c74"
version = "0.1.0+3"

[[deps.Xorg_libxcb_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"]
git-tree-sha1 = "daf17f441228e7a3833846cd048892861cff16d6"
uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b"
version = "1.13.0+3"

[[deps.Xorg_libxkbfile_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "926af861744212db0eb001d9e40b5d16292080b2"
uuid = "cc61e674-0454-545c-8b26-ed2c68acab7a"
version = "1.1.0+4"

[[deps.Xorg_xcb_util_image_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "0fab0a40349ba1cba2c1da699243396ff8e94b97"
uuid = "12413925-8142-5f55-bb0e-6d7ca50bb09b"
version = "0.4.0+1"

[[deps.Xorg_xcb_util_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll"]
git-tree-sha1 = "e7fd7b2881fa2eaa72717420894d3938177862d1"
uuid = "2def613f-5ad1-5310-b15b-b15d46f528f5"
version = "0.4.0+1"

[[deps.Xorg_xcb_util_keysyms_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "d1151e2c45a544f32441a567d1690e701ec89b00"
uuid = "975044d2-76e6-5fbe-bf08-97ce7c6574c7"
version = "0.4.0+1"

[[deps.Xorg_xcb_util_renderutil_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "dfd7a8f38d4613b6a575253b3174dd991ca6183e"
uuid = "0d47668e-0667-5a69-a72c-f761630bfb7e"
version = "0.3.9+1"

[[deps.Xorg_xcb_util_wm_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "e78d10aab01a4a154142c5006ed44fd9e8e31b67"
uuid = "c22f9ab0-d5fe-5066-847c-f4bb1cd4e361"
version = "0.4.1+1"

[[deps.Xorg_xkbcomp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxkbfile_jll"]
git-tree-sha1 = "4bcbf660f6c2e714f87e960a171b119d06ee163b"
uuid = "35661453-b289-5fab-8a00-3d9160c6a3a4"
version = "1.4.2+4"

[[deps.Xorg_xkeyboard_config_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xkbcomp_jll"]
git-tree-sha1 = "5c8424f8a67c3f2209646d4425f3d415fee5931d"
uuid = "33bec58e-1273-512f-9401-5d533626f822"
version = "2.27.0+4"

[[deps.Xorg_xtrans_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845"
uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10"
version = "1.4.0+3"

[[deps.Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"

[[deps.Zstd_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "e45044cd873ded54b6a5bac0eb5c971392cf1927"
uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
version = "1.5.2+0"

[[deps.libass_jll]]
deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "5982a94fcba20f02f42ace44b9894ee2b140fe47"
uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0"
version = "0.15.1+0"

[[deps.libblastrampoline_jll]]
deps = ["Artifacts", "Libdl", "OpenBLAS_jll"]
uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"

[[deps.libfdk_aac_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55"
uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280"
version = "2.0.2+0"

[[deps.libpng_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c"
uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
version = "1.6.38+0"

[[deps.libsixel_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "78736dab31ae7a53540a6b752efc61f77b304c5b"
uuid = "075b6546-f08a-558a-be8f-8157d0f608a5"
version = "1.8.6+1"

[[deps.libvorbis_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
git-tree-sha1 = "b910cb81ef3fe6e78bf6acee440bda86fd6ae00c"
uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
version = "1.3.7+1"

[[deps.nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"

[[deps.p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"

[[deps.x264_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2"
uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a"
version = "2021.5.5+0"

[[deps.x265_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9"
uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76"
version = "3.5.0+0"

[[deps.xkbcommon_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"]
git-tree-sha1 = "ece2350174195bb31de1a63bea3a41ae1aa593b6"
uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd"
version = "0.9.1+5"
"""

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max_stars_repo_name: proscrite/LaserLab

max_issues_repo_name: proscrite/LaserLab

max_forks_repo_name: proscrite/LaserLab


text:
require("lru")

const TestLRU = UnboundedLRU{ASCIIString, ASCIIString}()
const TestBLRUs = BoundedLRU{ASCIIString, ASCIIString}(100)
const TestBLRUm = BoundedLRU{ASCIIString, ASCIIString}(1000)
const TestBLRUl = BoundedLRU{ASCIIString, ASCIIString}(10000)
const TestBLRUxl = BoundedLRU{ASCIIString, ASCIIString}(100000)

get_str(i) = ascii(vcat(map(x->[x>>4; x&0x0F], reinterpret(Uint8, [int32(i)]))...))

isbounded{L<:LRU}(::Type{L}) = any(map(n->n==:maxsize, L.names))
isbounded{L<:LRU}(l::L) = isbounded(L)

nmax = int(logspace(2, 5, 4))

println("LRU consistency tests")
for lru in (
            TestLRU,
            TestBLRUs,
            TestBLRUm,
            TestBLRUl,
            TestBLRUxl,
            )
    for n in nmax
        empty!(lru)
        @printf("  %s, %d items\n", lru, n)
        print("    Simple eviction: ")
        for i in 1:n
            str = get_str(i)
            lru[str] = str
            @assert lru.q[1].v == str
            if isbounded(lru) && length(lru) >= lru.maxsize
                tailstr = get_str(i-lru.maxsize+1)
                @assert lru.q[end].v == tailstr
            end
        end
        println("pass")

        print("    Lookup, random access: ")
        for i in 1:n
            str = get_str(rand(1:n))
            if haskey(lru, str) # the bounded LRUs can have cache misses
                blah = lru[str]
                @assert lru.q[1].v == blah
            end
        end
        println("pass")
    end
    empty!(lru)
end

ext: jl

max_stars_repo_name: jakebolewski/julia

max_issues_repo_name: jakebolewski/julia

max_forks_repo_name: jakebolewski/julia


text:
type SDF <: SDFDynamicSystem
    omg::Float64
    zta::Float64
    u0::Float64
    v0::Float64

    SDF(omg::Float64, zta::Float64) = new(omg, zta, 0.0, 0.0)
end

function SDF(m::Float64, c::Float64, k::Float64)
    omg = sqrt(k/m)
    zta = c/(2.0*m*omg)
    SDF(omg, zta)
end

function systemEquation(s::SDF, p::Function)
    function dydt(y::AbstractArray{Float64,1}, t::Float64)
        [y[2]; p(t)-2.0*s.zta*s.omg*y[2]-s.omg*s.omg*y[1]]
    end
    dydt
end

function response(s::SDF, p::Function, t::AbstractArray{Float64,1})
    y0 = [s.u0; s.v0]

    f = systemEquation(s, p)

    y = ode4(f, y0, t)

    u = y[:,1]
    v = y[:,2]
    fdz = 2.0*s.zta*s.omg*v
    a = map(p,t)-fdz-s.omg*s.omg*u

    [u v a fdz zeros(length(t))]
end

max_stars_repo_name: Panchatantra/DSR

max_issues_repo_name: Panchatantra/DSR

max_forks_repo_name: Panchatantra/DSR