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

using Markdown
using InteractiveUtils

# ╔═╡ 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
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

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

# ╔═╡ 35436842-9dc0-4541-8d45-abf3d174ccd1
md" ### Filtering signals
- Waveform, inlcuding oscillatory noise is contained within +- $(nsigma)
- Filter potential signals signal > $(nsigma)
- Filter persistent bkgnd signal < -$(nsigma) 
"

# ╔═╡ 657c7a1a-431d-47d4-ab64-8740e2d29505
md"### Finding signal
- Selecting data above $nsigma shows peak candidates that can be single electrons
"

# ╔═╡ f5815f51-5ce3-43a4-a262-3cf94529022f
md"### Peak structure
- The structure of the peaks shows potential signals, together with some background. We can use a peak finding algorithm. 
"

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

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

# ╔═╡ 0c3983ed-e586-44b1-a82a-94539c355139
md"- The algorithm finds a large number of maxima. This is not surprising, given the oscillatory noise.
"

# ╔═╡ 7ebf77fd-cd26-421a-82d2-7cbb74e3c4e7
md"### Find peak prominence"

# ╔═╡ 5c37131b-6ef1-410b-9548-41abba13b2d6
md"- Most maxima have small values of prominence, tipically below $nsigma sigma
- We can select suitable peak candidates filtering on prominence > $nsigma sigma"

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

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

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

# ╔═╡ 8634524d-0f37-4e65-9cc6-f7abb5626294
md"### Peak finding in negative (background) signals
- To understand the noise, we can now repeat the same analysis over the negative pulses, which are purely due to noise.
- We can observe that the prom cut is effective to eliminate most of the peak (only one survives the cut), while in this case, the width cut does not add extra suppression. 
"

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

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

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

# ╔═╡ 188f9e91-2111-4731-9582-cdb8b1d54013
md"#### Selection result
- We can see that the two prominent peaks are found by the algo.
- The width of the peaks is underestimated in this case due to the noise that introduces a positive swing. This is why it is convenient to reduce the width cut. 
"

# ╔═╡ 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
"

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

# ╔═╡ dab28bf1-3c97-4562-be02-17e2486d9ccc
md"## Separation between positive and negative signals 
- Prominence does not separate from positive and negative signals (but is already biased because we are selecting 3 sigma signals anyway)
- Width is a clear indicator, we can see two peaks, probably due to the noise that distorts the wide peak
- This analysis suggests that cut on prominence at 3 sigma and width at 2.5 wil effectively select the signal
"

# ╔═╡ 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"

# ╔═╡ 45ca1374-d892-4076-b03f-355cd85068e1
md"## BOLD-062 A1"

# ╔═╡ d20c6608-85f0-4f73-8f99-a0594768e7d3
md"### 650 nm"

# ╔═╡ 8602010c-bf34-479d-b05b-3d093cb7ff42
png("waveform.png")

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

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

# ╔═╡ b20d8cbc-e4c8-4e51-bb5c-f6315df1ccea
first(wvfm1,3)

# ╔═╡ 42dc83fb-5da1-401c-8303-5e233577a4e6
last(wvfm1,3)

# ╔═╡ 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))
"

# ╔═╡ 01becbfa-d619-41c7-87ed-0ca447625c90
promsel = meanAmp + nsigma * stdAmp

# ╔═╡ 6a0f7c24-cc8f-4a14-8fdd-70ab6d9a9d06
wvfmFlt = filter(row -> row[:Ampl] > meanAmp + nsigma * stdAmp, wvfm1);

# ╔═╡ f7bc85d0-c386-4c70-af24-9b2dd44544fb
pks, vals = findmaxima(wvfmFlt.Ampl);

# ╔═╡ 17900908-bffe-4c05-a846-930b6c781522
begin
	scatter(pks, vals, markersize=3, label=false)
	hline!([meanAmp + nsigma * stdAmp], label=false)
	hline!([meanAmp + nsigma2 * stdAmp], label=false)
	xlabel!("peak index")
	ylabel!("I (mV)")
	
end

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

# ╔═╡ f1e45ed4-223f-4b13-9e46-11713c094e94
begin
	scatter(pks, proms, markersize=3, label=false)
	hline!([meanAmp + nsigma * stdAmp], label=false)
	hline!([meanAmp + nsigma2 * stdAmp], label=false)
	xlabel!("peak index")
	ylabel!("Prominence of peak (mV)")
	
end

# ╔═╡ 95d5e897-162c-4d63-a0c6-d7916af1448b
peaks2, proms2 = peakproms(pks, wvfmFlt.Ampl; minprom=promsel)

# ╔═╡ a4802f08-e703-45f9-8f7b-e3cd5c991ab6
_, widths, leftedge, rightedge = peakwidths(peaks2, wvfmFlt.Ampl, proms2);

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

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

# ╔═╡ 60bfadce-187b-4a7d-a849-f37fb7f068b1
npeaks = length(peaks3)

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

# ╔═╡ be4b932d-98f8-495b-ae2a-8a293c54738c
pheigth = [wvfmFlt.Ampl[i] for i in peaks3]

# ╔═╡ c5d56314-1690-4f04-92b0-00a8984b1e5c
ptime = [wvfmFlt.Time[i] for i in peaks3]

# ╔═╡ 13792c29-3966-49d2-a452-6fc8e9ef1f48
begin
	plot(wvfmFlt.Ampl, lw=2, label=false)
	scatter!(peaks3, pheigth, label="peak")
	scatter!(leftedge2, xbase, label="leftedge")
	scatter!(rightedge2, xbase, label="rightedge")
	hline!([promsel], label=false)
	hline!([meanAmp + nsigma2 * stdAmp], label=false)
	xlabel!("t (μs)")
	ylabel!("peak index")
	
end

# ╔═╡ 97f6b530-c549-4ade-b6b0-1c80e0beb0d5
wvfmFlt2 = filter(row -> row[:Ampl] < meanAmp - nsigma * stdAmp, wvfm1);

# ╔═╡ a4228bd2-6d87-4efe-b35c-89dcd8854586
begin
	scatter(wvfmFlt2.Time, wvfmFlt2.Ampl, markersize=3, label=false)
	hline!([meanAmp - nsigma * stdAmp], label=false)
	hline!([meanAmp - nsigma2 * stdAmp], label=false)
	xlabel!("t (μs)")
	ylabel!("I (mV)")
	
end

# ╔═╡ ff68768d-629c-46e0-8b87-889fa5568340
begin
	plot(wvfmFlt2.Ampl, lw=2, label=false)
	hline!([meanAmp - nsigma2 * stdAmp], label=false)
	hline!([meanAmp - nsigma * stdAmp], label=false)
	xlabel!("t (μs)")
	ylabel!("peak index")
	
end

# ╔═╡ 081a1ea1-e64d-4caa-9a6c-66ffad77ca6b
bpks, bvals = findminima(wvfmFlt2.Ampl)

# ╔═╡ 66301099-cd5d-4b2d-baf9-26b3bf74f1c3
bpeaks, bproms2 = peakproms(bpks, wvfmFlt.Ampl; minprom=promsel)

# ╔═╡ e362b8dd-b17e-44b2-8cb0-e153db79db13
_, bwidths2, bleftedge, brightedge = peakwidths(bpeaks, wvfmFlt.Ampl, bproms2)

# ╔═╡ cdb514fa-1d30-47fc-b592-3e6eed88f681
bpmx = [wvfmFlt2.Ampl[i] for i in bpeaks]

# ╔═╡ 5a63ab7e-e29e-42e8-9108-abf23f50c57c
begin
	plot(wvfmFlt2.Ampl, lw=2, label=false)
	scatter!(bpeaks, bpmx, label="peak")
	hline!([meanAmp - nsigma2 * stdAmp], label=false)
	hline!([meanAmp - nsigma * stdAmp], label=false)
	xlabel!("t (μs)")
	ylabel!("I (mV)")
	
end

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

# ╔═╡ fff73c5f-0fef-48b5-825e-1a677d52d0ca
wvfm3 = read_waveform(csvf,200);

# ╔═╡ 1928ef8d-d7df-4b2b-9b9a-f958cacecaa3
md"### plot_waveform
	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)

# ╔═╡ e6a30344-8bda-4988-8ccb-5b7b323e46a9
plot_waveform(wvfmFlt, meanAmp, stdAmp, nsigma, nsigma2, sct=true)

# ╔═╡ 4e3574c4-1857-4101-9e69-f7dbba19ba48
plot_waveform(wvfmFlt, meanAmp, stdAmp, nsigma, nsigma2, trace=true)

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

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

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

# ╔═╡ 66cb6d51-f8c0-4d6e-8fca-2efeff9ec36c
plot_waveform(wvfm2, meanAmp2, stdAmp2, nsigma, nsigma2)

# ╔═╡ 2d9f1953-fd36-44d9-9376-f6fd4a7756e4
meanAmp3, stdAmp3 = mean_and_std(wvfm3)

# ╔═╡ dd0e4114-fd5f-4685-81d6-7900ffe44ad9

plot_waveform(wvfm3, meanAmp3, stdAmp3, nsigma, nsigma2)

# ╔═╡ b6a05ec9-0341-432e-87a7-d3bff76fd0a6
function display_waveform(csvf, i0, nsigma, nsigma2)
	wvfm = read_waveform(csvf,i0)
	meanAmp, stdAmp = mean_and_std(wvfm)
	plot_waveform(wvfm, meanAmp, stdAmp, nsigma, nsigma2)
end

# ╔═╡ 1e9d4e87-aa89-4b3e-885f-0f553c0724ec
md"### select_peaks
	select_peaks(wvfm, nsigma; wsel=2.5, signal=true)
"

# ╔═╡ 8458b6e4-4c73-4b8a-b891-c4ca38d63988
function select_peaks(wvfm, nsigma; wsel=2.5, signal=true)
	meanAmp = mean(wvfm.Ampl)
	stdAmp = std(wvfm.Ampl)
	if signal == true
		promsel = meanAmp + nsigma * stdAmp
		wvfmFlt = filter(row -> row[:Ampl] > promsel, wvfm)
		pks, vals = findmaxima(wvfmFlt.Ampl)
	else
		promsel = meanAmp -nsigma * stdAmp
		wvfmFlt = filter(row -> row[:Ampl] < promsel, wvfm)
		pks, vals = findminima(wvfmFlt.Ampl)
	end
	peaks2, proms = peakproms(pks, wvfmFlt.Ampl; minprom=promsel)
	if length(peaks2) == 0
		return Nothing 
	end
	peaks2, widths, leftedge, rightedge = peakwidths(peaks2, wvfmFlt.Ampl, proms; minwidth=wsel)

	ys = [wvfmFlt.Ampl[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
		

# ╔═╡ 78b4fe63-d016-4f52-8086-9e6520cafcd5
function select_peaks(csvf, i0, il; nsigma=3.0, wmax=2.5)
	SPK = []
	
	for i in i0:il
		wvfm = read_waveform(csvf,i)
		
		result = select_peaks(wvfm, nsigma; wsel=wmax)
		if result != Nothing
			_, speaks = result
			push!(SPK,speaks)
		end
	end
	SPK
end

# ╔═╡ 853787cc-229c-4b0b-afe6-4bc36666dec6
result = select_peaks(wvfm2, nsigma; wsel=3.0);

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

# ╔═╡ c317ba94-ed7b-4585-a41f-fce3c97f816e
if result != Nothing
	plot(wvflt.Ampl, lw=2, label=false)
	scatter!(speaks.peaks, speaks.ys, label="peak")
	scatter!(speaks.leftedge, speaks.xbase, label="leftedge")
	scatter!(speaks.rightedge, speaks.xbase, label="rightedge")
	hline!([speaks.promsel], label=false)
	xlabel!("index")
	ylabel!("I (mV)")
	
end 

# ╔═╡ 8f5253c3-ee2e-4617-af81-9b53782cb08b
result2 = select_peaks(wvfm3, nsigma; wsel=3.0)

# ╔═╡ 2535c853-5d38-4f3c-acab-5fe321243023
wvflt2, speaks2 = result2;

# ╔═╡ 5881d208-bca1-4a71-9d94-72abcd263252
speaks2

# ╔═╡ 52c020fd-6994-4530-8017-bc33722aab16
begin
	if result2 != Nothing
		#wvflt2, speaks2 = result2
		plot(wvflt2.Ampl, lw=2, label=false)
		scatter!(speaks2.peaks, speaks2.ys, label="peak")
		scatter!(speaks2.leftedge, speaks2.xbase, label="leftedge")
		scatter!(speaks2.rightedge, speaks2.xbase, label="rightedge")
		hline!([speaks2.promsel], label=false)
		xlabel!("index")
		ylabel!("I (mV)")
	end
end

# ╔═╡ 231125a5-f2c8-4ff2-bb36-88c2bbaef8cc
bresult2 = select_peaks(wvfm3, nsigma; wsel=0.0, signal=false);

# ╔═╡ 153c324a-9861-4ad6-b5fe-0438a54d0275
bwvflt2, bspeaks2 = bresult2;

# ╔═╡ 56ff95ea-a7b7-4cc2-b202-4408711cd188
begin
	if bresult2 != Nothing
		#wvflt2, speaks2 = result2
		plot(bwvflt2.Ampl, lw=2, label=false)
		scatter!(bspeaks2.peaks, bspeaks2.ys, label="peak")
		scatter!(bspeaks2.leftedge, bspeaks2.xbase, label="leftedge")
		scatter!(bspeaks2.rightedge, bspeaks2.xbase, label="rightedge")
		hline!([bspeaks2.promsel], label=false)
		xlabel!("index")
		ylabel!("I (mV)")
	end
end

# ╔═╡ af7add3b-c1ad-4fbb-bac9-41ba3cafa716
spks = select_peaks(csvf, 9, 999; nsigma=3.0, wmax=2.5)

# ╔═╡ e5e4df13-c2d0-4376-9ecc-36332dfdc61d
times = reduce(vcat,[spks[i].xs for i in 1:length(spks)])

# ╔═╡ 0bcb141b-b4db-4c31-9919-745a8bb3d91d
hpkp = lfi.LaserLab.hist1d(times, 20, 0.0, 2.0)

# ╔═╡ f3084ec2-e1eb-4b7b-8e42-d2931d284493
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

# ╔═╡ f24f35a5-9075-430c-adcb-62946b2c189e
md"### prominence
	function prominence(csvf, i0, il, nsigma=3.0)
"

# ╔═╡ 71b9f8dd-688a-4a33-8af9-ac4b6e21e052
function prominence(csvf, i0, il, nsigma=3.0)
	PROM = Float64[]
	WP   = Float64[]
	BPROM = Float64[]
	WN   = Float64[]
	for i in i0:il
		wvfm = read_waveform(csvf,i)
		
		result = select_peaks(wvfm, 0; wsel=0.0)
		if result != Nothing
			_, speaks = result
			append!(PROM,speaks.proms)
		end

		bresult = select_peaks(wvfm, 0; wsel=0.0, signal=false)
		if bresult != Nothing
			_, speaks = bresult
			append!(BPROM,speaks.proms)
		end

		result = select_peaks(wvfm, nsigma; wsel=0.0)
		if result != Nothing
			_, speaks = result
			append!(WP,speaks.widths)
		end

		bresult = select_peaks(wvfm, nsigma; wsel=0.0, signal=false)
		if bresult != Nothing
			_, speaks = bresult
			append!(WN,speaks.widths)
		end
	end
	return PROM, WP, BPROM, WN
end

# ╔═╡ afe971a3-8f38-4341-868c-846089ea70f8
PP, WP, PN, WN = prominence(csvf, 9, 999, 3)

# ╔═╡ 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 (+)")

# ╔═╡ 1a9b1e50-8f69-4e92-8815-e73cb0b871e2
hN = lfi.LaserLab.hist1d(PN, 20, 0.0, 10.0)

# ╔═╡ 6b29e574-7c04-4247-8c1a-6e77de81c266
lfi.LaserLab.hist1d(hN, "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 (+)")

# ╔═╡ 3fb3a578-7702-48c9-9711-121e3c7d0165
hWN = lfi.LaserLab.hist1d(WN, 20, 0.0, 10.0)

# ╔═╡ 941be823-7792-490c-b69c-b8493569cc81
lfi.LaserLab.hist1d(hWN, "Width (-)")

# ╔═╡ 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
fls = glob_files("../data/scope/signal_650nm/")

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

# ╔═╡ 3ddc6f6e-216b-4f56-95a2-dd9616c1ce51
spk650 = select_peaks(fls, 1, 995; nsigma=3.0, wmax=2.5)

# ╔═╡ 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

# ╔═╡ c7c7c532-aa8f-484e-b118-8f8656dd53e1
fa1650 = glob_files2("../data/scope/A1/"; wvl="732nm") 

# ╔═╡ 23b8db87-3d65-4335-b7e9-c5f5910de531
nfa1650 = length(fa1650)

# ╔═╡ 94a5b96b-14d5-47f6-9fd5-b024b56557ef
p1 = display_waveform(fa1650, 50, 3.0, 5.0)

# ╔═╡ 5975d71a-a9dd-4576-b388-e11c2ba4e52d
a1PP, a1WP, a1PN, a1WN = prominence(fa1650, 1, nfa1650, 3)

# ╔═╡ f3aa07d7-7d4b-4aea-9fce-a000c2e7d2da
begin
	a1hP = lfi.LaserLab.hist1d(a1PP, 20, 0.0, 10.0)
	lfi.LaserLab.hist1d(a1hP, "Prominence (+)")
end


# ╔═╡ ed195b52-9174-4d0f-9c5a-dc7ee972d75d
begin
	a1hN = lfi.LaserLab.hist1d(a1PN, 20, 0.0, 10.0)
	lfi.LaserLab.hist1d(a1hN, "Prominence (-)")
end

# ╔═╡ 6c060e3e-e993-46b0-917d-df9451b93fe2
begin
	a1hW = lfi.LaserLab.hist1d(a1WP, 20, 0.0, 10.0)
	lfi.LaserLab.hist1d(a1hW, "Width (+)")
end

# ╔═╡ 1e5649f2-2c8b-4533-a1f1-b8f279952ed8
begin
	a1hWN = lfi.LaserLab.hist1d(a1WN, 20, 0.0, 10.0)
	lfi.LaserLab.hist1d(a1hWN, "Width (-)")
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)

# ╔═╡ 8183fd70-9e17-44dc-882e-a8b9f6d5ee42
begin
	spk650nm2 = select_peaks(fls, 1000, 1999; nsigma=3.0, wmax=2.5)
	t650nm2 = reduce(vcat,[spk650nm2[i].xs for i in 1:length(spk650nm2)])
	ht650nm2 = lfi.LaserLab.hist1d(t650nm2, 20, 0.0, 2.0)
	coef650nm2, stderr650nm2, tft650nm2 = fit_peaks(ht650nm2; pa0=[100.0, 0.5], i0=2)
	plot_fit(ht650nm2, coef650nm2, tft650nm2)
end


# ╔═╡ baa9f00a-a008-4cfd-9181-1457f128042e
begin
	spk650nm3 = select_peaks(fls, 2000, 2999; nsigma=3.0, wmax=2.5)
	t650nm3 = reduce(vcat,[spk650nm3[i].xs for i in 1:length(spk650nm3)])
	ht650nm3 = lfi.LaserLab.hist1d(t650nm3, 20, 0.0, 2.0)
	coef650nm3, stderr650nm3, tft650nm3 = fit_peaks(ht650nm3; pa0=[100.0, 0.5], i0=2)
	plot_fit(ht650nm3, coef650nm3, tft650nm3)
end

# ╔═╡ 17b2db84-e0a6-4092-9795-db5877c30cbd
begin
	spk650nm4 = select_peaks(fls, 3000, 3994; nsigma=3.0, wmax=2.5)
	t650nm4 = reduce(vcat,[spk650nm4[i].xs for i in 1:length(spk650nm4)])
	ht650nm4 = lfi.LaserLab.hist1d(t650nm4, 20, 0.0, 2.0)
	coef650nm4, stderr650nm4, tft650nm4 = fit_peaks(ht650nm4; pa0=[100.0, 0.5], i0=2)
	plot_fit(ht650nm4, coef650nm4, tft650nm4)
end

# ╔═╡ d68fa4d5-6f6c-459c-9cfd-024a68f5e837
begin
	spk700nm = select_peaks(fls700, 1, 2997; nsigma=3.0, wmax=2.5)
	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, 1998; nsigma=3.0, wmax=2.5)
	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

# ╔═╡ 2b43e522-6654-4196-8e14-2525d542e5ef
begin
	spka1650nm = select_peaks(fa1650, 1, nfa1650; nsigma=2.0, wmax=4.0)
	ta1650nm = reduce(vcat,[spka1650nm[i].xs for i in 1:length(spka1650nm)])
	hta1650nm = lfi.LaserLab.hist1d(ta1650nm, 20, 0.0, 2.0)
	coefa1650nm, stderra1650nm, tfta1650nm = fit_peaks(hta1650nm; pa0=[100.0, 0.5])
	plot_fit(hta1650nm, coefa1650nm, tfta1650nm)
end

# ╔═╡ f315cad7-100b-407c-8e0d-2ce8b3669126
length(ta1650nm)

# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
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"
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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"
DataFrames = "~1.3.2"
Distributions = "~0.25.49"
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"
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version = "1.1.0"

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git-tree-sha1 = "62e51b39331de8911e4a7ff6f5aaf38a5f4cc0ae"
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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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