## plotting – Fitting two convoluted peaks

If I have the following data:

https://pastebin.com/ya03z9bP

which plotted as:

``````ListLinePlot(datawithnoliquidline,
PlotStyle -> Directive(Thick, Black),
PlotRange -> {{40, 110}, {-0.02, All}}, Frame -> True,
FrameStyle -> 14, Axes -> False, GridLines -> Automatic,
GridLinesStyle -> Lighter(Gray, .8),
FrameTicks -> {Automatic, Automatic},
FrameLabel -> (Style(#, 20, Bold) & /@ {"T ((Degree)C)",
Row({"!(*SubscriptBox((C), (P)))", " (", " J/gK)"})}),
LabelStyle -> {Black, Bold, 14})
``````

gives two peaks as in the picture:

Questions:
1)How can I correctly fit these two peaks?
2) How can I calculate the areas of the two fitted peaks?

My approach, using gaussian fit (which doesn’t seem correct) is as follows:

``````ff2(x_, areaa1_, areaa3_, siga1_, siga3_, meda1_, meda3_) :=
areaa1 PDF(NormalDistribution(meda1, siga1), x) +
areaa3 PDF(NormalDistribution(meda3, siga3), x) ;

ma1guess = 6;
ma3guess = 1.3;
siga1guess = 4;
siga3guess = 3;
meda1guess = 82;
meda3guess = 97;

averagenematicarea = 1.2;(*Average nematic area from all three DSC
runs*)
STDnematicarea = 0.2; (*Standard deviation of nematic
area*)(*Aceptable shift above and below the nematic area where the
fits will be constraint (e.g if shift is 0.2 and area=1.2, then the
fits would be constraint between 1 J/g and 1.4 J/g)*)

averagesmecticarea = 1;(*Average smectic area from all three DSC runs*)

STDsmecticarea = 0.2; (*Standard deviation of smectic
area*)(*Aceptable shift above and below the nematic area where the
fits will be constraint (e.g if shift is 0.2 and area=1.2, then the
fits would be constraint between 1 J/g and 1.4 J/g)*)

averagenematiconset = 88.2;(*Average nematic onset from all three DSC
runs*)
STDnematiconset = 0.2; (*Standard deviation of nematic
onset*)(*Aceptable shift above and below the nematic onset where the
fits will be constraint (e.g if shift is 0.5 and onset=88, then the
fits would be constraint between 87.5C and 88.5C)*)

nlm3 = NonlinearModelFit(
datawithnoliquidline, {ff2(x, areaa1, areaa3, siga1, siga3, meda1,
meda3), areaa3 >= 0, areaa1 >= 0, 0 <= siga1 <= 20,
0 <= siga3 <= 20, 60 < meda1 < 85,
meda3 - 2*siga3 > 88.6}, {{areaa1, ma1guess}, {areaa3,
ma3guess}, {siga1, siga1guess}, {siga3, siga3guess}, {meda1,
meda1guess}, {meda3, meda3guess}}, x);

fp = nlm3("BestFitParameters");

p1 =(*Original data*)
ListLinePlot(datawithnoliquidline,
PlotStyle -> Directive(Thick, Black),
PlotRange -> {{40, 110}, {-0.02, All}}, Frame -> True,
FrameStyle -> 14, Axes -> False, GridLines -> Automatic,
GridLinesStyle -> Lighter(Gray, .8),
FrameTicks -> {Automatic, Automatic},
FrameLabel -> (Style(#, 20, Bold) & /@ {"T ((Degree)C)",
Row({"!(*SubscriptBox((C), (P)))", " (", " J/gK)"})}),
LabelStyle -> {Black, Bold, 14});
p2 = Plot({nlm3(x),
areaa3 PDF(NormalDistribution(meda3, siga3), x) /.
fp, +areaa1 PDF(NormalDistribution(meda1, siga1), x) /. fp}, {x,
40, 110},
PlotStyle -> {Directive(Red, Dashing({0.02, 0.04}),
AbsoluteThickness(5)), Directive(Green, AbsoluteThickness(2)),
Directive(Orange, AbsoluteThickness(2)),
Directive(Blue, AbsoluteThickness(2)), Directive(Pink, Dashed),
Directive(Cyan, Dashed)}, PlotRange -> All);

Show(p1, p2)
``````

which gives the following:

So, my problem is really fitting correctly the second peak, which I am not sure how to do.

## High volume traffic peaks – WordPress + static pages

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So I need an advice. I’ve got group of pages – two bigger one based on wordpress(around 7gbs in total) and up to 10 static html… | Read the rest of https://www.webhostingtalk.com/showthread.php?t=1819229&goto=newpost

## Network – latency peaks during ping or SSH on Macbook Pro 15 (2017) macOS Catalina 15.4

I have found that when trying to connect to macOS Catalina using ssh over a local wireless network, I am experiencing a serious latency issue that makes it difficult for me to enter anything via ssh. (The latency tested with ping is between 3500 ms and 2 ms.)
I've checked all the forums that mentioned this issue, some of the suggested solutions like disabling DNS or GSAPI, but they don't work for me.
I checked out Wireshark packets and surprisingly found that all ssh packets are still transmitted by IPV6, even if I disabled them in both the router and sshd_config.
I wish there could be some suggestions that can help me solve the problem.

## Divide and Conquer – algorithm to find two peaks

No. Every algorithm needed to locate these two peaks $$Omega (N)$$ assuming that the only operation allowed is to compare two elements in the array, i. H. the comparison mode.

Otherwise, assume an algorithm $$A$$ don't need that much time. To let $$arr (0), arr (1), cdots, arr (N-1)$$ be the given array. Then for some $$N$$ big enough, $$A$$ will have checked at most $$N-3$$ Elements before it ended. Let us assume for all comparisons made by $$A$$will turn out that $$arr (i) Where $$i ,

To let $$arr (a), arr (b), arr (c)$$ be three items that have not been reviewed yet $$a , What ever $$A$$ have done, the two tips could be $${arr (a), arr (N-1) }$$, You can be $${arr (b), arr (N-1) }$$ also. Since $$A$$ can't distinguish between these two cases $$A$$ cannot possibly have determined the two peaks.

Exercise. (less than a minute) Modify the above argument so that it applies in the event that a peak must have both a left and a right neighbor.

## What is the difference between diffraction peaks and lens flare?

When googling for lens reflexes, one often finds images that also contain stars (rays / diffraction peaks) as follows:

What is the difference between diffraction peaks and lens reflections and are the two terms interchangeable?

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## Ignore a peak when fitting a Gaussian with multiple peaks

It would be good if you would add some sample data. Anyway…

The Fit function does not magically find your small points of interest in a long list of data points;) You must first tell them where to look, and then you may need to provide some meaningful starting values ​​for the fit. The fitting algorithm is also applied globally to your entire record, which you do not want, especially not for z. Measurements that may cause significant noise or background data around the peak you want to adjust.

You need to adjust it to the top, which you can do by simply not adding unnecessary data and / or by entering reasonable initial values, such as:

``````truncatedData = data[[500;;1500]];
ff = FindFit[truncatedData,
a*Exp[-(x - b)^2/(2*c^2)] + d,
{{a, max[truncatedData]}, {b, position[abgeschnitteneDatenmax[truncatedDataMax[abgeschnitteneDatenmax[truncatedDataMax[truncatedData]][[1,1]]},
c, d}, x]
``````

Now I've entered ranges 500 through 1500 manually, and they may need to be adjusted. If your data is not always very similar, you should define these areas automatically. If you If you know that a peak is never longer than 300 data points, and the first peak you want to exclude is always within the first 400 data points, you could use the following:

``````peakPos = position[data[[400;;-1]], Max[data[[400;;-1]]]][[1,1]]+399;
lowerLim = peakPos-150;
upperLim = peakPos + 150;
truncatedData = data[[lowerLim;;upperLim]]
``````

However, if these secondary peaks that you want to adjust can be very different, you need to use something smarter to pinpoint the boundaries. You could z. Use the Select function to select all values ​​above your noise baseline, find their position using the Position function, and then use them for the lower and upper bounds.

## Find peaks in the dataset and correlate variables

I have a record that consists of an X variable (column 1) and two Y variables. Y1 shows about 40 peaks while Y2 is a linear function. Can one find the approximate 40 Y2 values ​​for which Y1 is a local peak and create a list?

## suitable – Multi peaks fit (Voigt, Lorentzian or Gaussian)

I recently started Mathematica and have no programming experience.

What I have to do is mount a summit that is probably a combination of two Voigt or Lorentzian. I've tried with existing code in the forum, but without much success. Could you help me with writing? Maybe with comments in the code so I can better understand what we're doing.

My starting point is

``````                data = xrdtable[[1]];
data1 = remainder @ transpose[Rescale /@ (Transpose@data)];
peak function[A_, [Mu]_, [Sigma]_, x_]= A ^ 2 E ^ (- ((x - [Mu]) ^ 2 / (2 [Sigma]^ 2)));

clear[model, modelvalue]
model[data_, n_] : =
module[{dataconfig, modelfunc, objfunc, fitvar, fitres},
dataconfig = {A[#], [Mu][#], [Sigma][#]} & /@ Offer[n];
modefunc = (peakfunc[##, fitvar] & @@@ dataconfig // total);
objfunc =
total[((Sqrt[data[[All, 2]]]) /
dates[[All,
1]]) (Dates[[All, 2]]- (modelfunc /. fitvar -> # &) / @
dates[[All, 1]]) ^ 2];
FindMinimum[objfunc, Join[{}, Flatten@dataconfig]]]model value[data_, n_] /; NumericQ[n] : =
If[n >= 1, model[data, n][[1]]0]fitres = ReleaseHold[
Stop[{Round[{Round[{Runden[{Round[n]model[data1Runde[data1Round[data1Runde[data1Round[n]]}]/.
FindMinimum[ModelValue[data1Runde[Modelvalue[data1Round[Modellwert[data1Runde[modelvalue[data1Round[n]]{n, 5}
Method -> "PrincipalAxis"][[2]]]// Calm

With[{n = 2},
resfunc =
peak function[ON[A[EIN[A[#], [Mu][#], [Sigma][#], x]& /@ Offer[n] /.
model[data1, n][[2]]]Show @ {Plot[Evaluate[resfunc], {x, 0, 1},
PlotStyle -> ({directive[Dashed, Thick,
ColorData["Rainbow"][#]]} & / @
rescale[Range[Length[resfunc]]]), PlotRange -> All,
Frame -> True, Axes -> False],
plot[Evaluate[Total@resfunc], {x, 0, 1},
PlotStyle -> directive[Thick, Red], PlotRange -> All,
Frame -> True, Axes -> False],
graphic[{PointSize[.003], Black, dot @ data1}]}
``````

I'm not quite sure, as you can see that the fit on the right side is not very good (I expect two profiles very close to the yellow and green curves in the second image). I also want to know how to judge if the fit is good enough or not.

Many thanks!

## Filtering – Filter periodic sharp peaks in experimental data

I have experimental data with periodically sharp artifact peaks that mask the real signal, which could be seen as several negative peaks in the first third part of the curve:

Can you somehow filter those spikes? or at least significantly reduce? Here is the record:

``````data = {{5.`, 2.41204`}, {10.`, 2.40477`}, {15.`, 2.40154`}, {20.`,
2.4094`}, {25.`, 2.41592`}, {30.`, 2.40706`}, {35.`,
2.41802`}, {40.`, 2.4171`}, {45.`, 2.40979`}, {50.`
2.4127`}, {55.`, 2.40254`}, {60.`, 2.40753`}, {65.`,
2.41049`}, {70.`, 2.40521`}, {75.`, 2.40446`}, {80.`
2.40692`}, {85.`, 2.40509`}, {90.`, 2.40604`}, {95.`,
2.40464`}, {100.`, 2.40448`}, {105.`, 2.41854`}, {110.`,
2.41627`}, {115.`, 2.40941`}, {120.`, 2.40443`}, {125.`,
2.39909`}, {130.`, 2.41194`}, {135.`, 2.40494`}, {140.`
2.40801`}, {145.`, 2.41532`}, {150.`, 2.41512`}, {155.`,
2.41328`}, {160.`, 2.41383`}, {165.`, 2.40806`}, {170.`
2.3958`}, {175.`, 2.41296`}, {180.`, 2.41556`}, {185.`,
2.40119`}, {190.`, 2.27056`}, {195.`, 2.51902`}, {200.`
2.41532`}, {205.`, 2.36206`}, {210.`, 2.37837`}, {215.`,
2.38771`}, {220.`, 2.39959`}, {225.`, 2.40557`}, {230.`
2.40373`}, {235.`, 2.4045`}, {240.`, 2.39692`}, {245.`
2.38983`}, {250.`, 2.39646`}, {255.`, 2.40411`}, {260.`
2.40749`}, {265.`, 2.41679`}, {270.`, 2.42152`}, {275.`
2.40963`}, {280.`, 2.39891`}, {285.`, 2.39964`}, {290.`
2.39939`}, {295.`, 2.40453`}, {300.`, 2.40663`}, {305.`
2.41569`}, {310.`, 2.40495`}, {315.`, 2.40756`}, {320.`
2.41375`}, {325.`, 2.41212`}, {330.`, 2.41838`}, {335.`
2.41029`}, {340.`, 2.40475`}, {345.`, 2.40698`}, {350.`,
2.41066`}, {355.`, 2.40286`}, {360.`, 2.40045`}, {365.`,
2.38019`}, {370.`, 2.1133`}, {375.`, 2.74495`}, {380.`
2.44314`}, {385.`, 2.23574`}, {390.`, 2.26553`}, {395.`
2.28845`}, {400.`, 2.3039`}, {405.`, 2.32833`}, {410.`,
2.35769`}, {415.`, 2.36831`}, {420.`, 2.38712`}, {425.`
2.39121`}, {430.`, 2.3947`}, {435.`, 2.39861`}, {440.`
2.40759`}, {445.`, 2.41661`}, {450.`, 2.41583`}, {455.`
2.41684`}, {460.`, 2.40925`}, {465.`, 2.40916`}, {470.`
2.41589`}, {475.`, 2.41347`}, {480.`, 2.40624`}, {485.`
2.40825`}, {490.`, 2.41021`}, {495.`, 2.40547`}, {500.`
2.41236`}, {505.`, 2.41382`}, {510.`, 2.40723`}, {515.`
2.40647`}, {520.`, 2.40802`}, {525.`, 2.41601`}, {530.`
2.4071`}, {535.`, 2.4085`}, {540.`, 2.41095`}, {545.`
2.38785`}, {550.`, 2.1242`}, {555.`, 2.74989`}, {560.`
2.47186`}, {565.`, 2.22649`}, {570.`, 2.25904`}, {575.`
2.27855`}, {580.`, 2.30019`}, {585.`, 2.31269`}, {590.`
2.33149`}, {595.`, 2.35533`}, {600.`, 2.37581`}, {605.`
2.38236`}, {610.`, 2.39662`}, {615.`, 2.38875`}, {620.`
2.3945`}, {625.`, 2.40292`}, {630.`, 2.41223`}, {635.`
2.41755`}, {640.`, 2.41581`}, {645.`, 2.42747`}, {650.`
2.42243`}, {655.`, 2.40486`}, {660.`, 2.41097`}, {665.`
2.41289`}, {670.`, 2.4112`}, {675.`, 2.41057`}, {680.`
2.40854`}, {685.`, 2.40255`}, {690.`, 2.40663`}, {695.`,
2,41292`}, {700.`, 2.39958`}, {705.`, 2.40153`}, {710.`,
2.41089`}, {715.`, 2.41834`}, {720.`, 2.41014`}, {725.`
2.38069`}, {730.`, 2.18214`}, {735.`, 2.78697`}, {740.`
2.43799`}, {745.`, 2.2292`}, {750.`, 2.2685`}, {755.`,
2.27852`}, {760.`, 2.29429`}, {765.`, 2.31846`}, {770.`
2.33485`}, {775.`, 2.34926`}, {780.`, 2.36237`}, {785.`
2.37218`}, {790.`, 2.37895`}, {795.`, 2.38696`}, {800.`
2.39067`}, {805.`, 2.40635`}, {810.`, 2.40362`}, {815.`
2.408`}, {820.`, 2.40672`}, {825.`, 2.4003`}, {830.`
2.40356`}, {835.`, 2.40439`}, {840.`, 2.41256`}, {845.`
2.40369`}, {850.`, 2.40362`}, {855.`, 2.40254`}, {860.`
2.40808`}, {865.`, 2.41858`}, {870.`, 2.42083`}, {875.`
2.41221`}, {880.`, 2.41353`}, {885.`, 2.40919`}, {890.`
2.40225`}, {895.`, 2.40183`}, {900.`, 2.41031`}, {905.`
2.36319`}, {910.`, 2.232`}, {915.`, 2.83176`}, {920.`,
2.40338`}, {925.`, 2.24156`}, {930.`, 2.26923`}, {935.`
2.27405`}, {940.`, 2.29832`}, {945.`, 2.32295`}, {950.`,
2.34304`}, {955.`, 2.35416`}, {960.`, 2.36042`}, {965.`,
2.37545`}, {970.`, 2.37944`}, {975.`, 2.38077`}, {980.`
2.38672`}, {985.`, 2.39831`}, {990.`, 2.40744`}, {995.`,
2.41327`}, {1000.`, 2.4076`}, {1005.`, 2.41045`}, {1010.`,
2.41492`}, {1015.`, 2.42123`}, {1020.`, 2.42311`}, {1025.`
2.41626`}, {1030.`, 2.40367`}, {1035.`, 2.4063`}, {1040.`
2.42103`}, {1045.`, 2.41064`}, {1050.`, 2.40684`}, {1055.`
2.40098`}, {1060.`, 2.41097`}, {1065.`, 2.41592`}, {1070.`
2.40849`}, {1075.`, 2.40616`}, {1080.`, 2.41021`}, {1085.`,
2.36027`}, {1090.`, 2.27795`}, {1095.`, 2.84617`}, {1100.`
2.37269`}, {1105.`, 2.25954`}, {1110.`, 2.29005`}, {1115.`
2.29649`}, {1120.`, 2.30906`}, {1125.`, 2.32596`}, {1130.`,
2.33398`}, {1135.`, 2.3494`}, {1140.`, 2.37214`}, {1145.`
2.38268`}, {1150.`, 2.39181`}, {1155.`, 2.39219`}, {1160.`,
2.39735`}, {1165.`, 2.40887`}, {1170.`, 2.40433`}, {1175.`,
2.40305`}, {1180.`, 2.41262`}, {1185.`, 2.40782`}, {1190.`
2.40641`}, {1195.`, 2.40956`}, {1200.`, 2.4138`}, {1205.`
2.42494`}, {1210.`, 2.41362`}, {1215.`, 2.40338`}, {1220.`
2.39176`}, {1225.`, 2.40127`}, {1230.`, 2.41093`}, {1235.`,
2.41167`, {1240.`, 2.41547`}, {1245.`, 2.41724`}, {1250.`
2,41734`}, {1255.`, 2.41807`}, {1260.`, 2.40711`}, {1265.`
2.32865`}, {1270.`, 2.33615`}, {1275.`, 2.83364`}, {1280.`
2.35782`}, {1285.`, 2.2653`}, {1290.`, 2.30087`}, {1295.`
2.30539`}, {1300.`, 2.30546`}, {1305.`, 2.32279`}, {1310.`,
2.34521`}, {1315.`, 2.35093`}, {1320.`, 2.36876`}, {1325.`
2.38492`}, {1330.`, 2.38044`}, {1335.`, 2.38815`}, {1340.`
2.38348`}, {1345.`, 2.40065`}, {1350.`, 2.39982`}, {1355.`
2.39992`}, {1360.`, 2.4054`}, {1365.`, 2.40309`}, {1370.`
2.40594`}, {1375.`, 2.4014`}, {1380.`, 2.40763`}, {1385.`
2.40742`}, {1390.`, 2.40345`}, {1395.`, 2.41129`}, {1400.`
2.41493`}, {1405.`, 2.41004`}, {1410.`, 2.41289`}, {1415.`
2.41294`}, {1420.`, 2.4068`}, {1425.`, 2.4041`}, {1430.`
2.39989`}, {1435.`, 2.41382`}, {1440.`, 2.4151`}, {1445.`
2.28722`}, {1450.`, 2.41435`}, {1455.`, 2.8229`}, {1460.`
2.34602`}, {1465.`, 2.27485`}, {1470.`, 2.28696`}, {1475.`
2.30587`}, {1480.`, 2.32392`}, {1485.`, 2.33769`}, {1490.`,
2.34829`}, {1495.`, 2.35833`}, {1500.`, 2.36606`}, {1505.`
2.38031`}, {1510.`, 2.3882`}, {1515.`, 2.38575`}, {1520.`
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2.40662`}, {1540.`, 2.40589`}, {1545.`, 2.3999`}, {1550.`
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2.41109`}, {1570.`, 2.40651`}, {1575.`, 2.41199`}, {1580.`
2.40756`}, {1585.`, 2.40943`}, {1590.`, 2.40304`}, {1595.`
2.40769`}, {1600.`, 2.40626`}, {1605.`, 2.41822`}, {1610.`
2.41579`}, {1615.`, 2.40967`}, {1620.`, 2.41079`}, {1625.`
2.27913`}, {1630.`, 2.49834`}, {1635.`, 2.79117`}, {1640.`
2.34607`}, {1645.`, 2.30948`}, {1650.`, 2.31606`}, {1655.`
2.31426`}, {1660.`, 2.32389`}, {1665.`, 2.33934`}, {1670.`
2.35389`}, {1675.`, 2.36893`}, {1680.`, 2.37683`}, {1685.`
2.38726`}, {1690.`, 2.37577`}, {1695.`, 2.38969`}, {1700.`
2.39511`}, {1705.`, 2.40145`}, {1710.`, 2.41432`}, {1715.`
2.4107`, {1720.`, 2.40342`}, {1725.`, 2.39728`}, {1730.`,
2.3995`}, {1735.`, 2.41101`}, {1740.`, 2.4087`}, {1745.`
2.40425`}, {1750.`, 2.41393`}, {1755.`, 2.41272`}, {1760.`
2.40541`, {1765.`, 2.40436`}, {1770.`, 2.40444`}, {1775.`,
2.41926`}, {1780.`, 2.41969`}, {1785.`, 2.4193`}, {1790.`
2.4009`}, {1795.`, 2.39214`}, {1800.`, 2.41367`}, {1805.`
2.2484`}, {1810.`, 2.59714`}, {1815.`, 2.76393`}, {1820.`
2.35331`}, {1825.`, 2.33172`}, {1830.`, 2.34277`}, {1835.`,
2.33886`}, {1840.`, 2.34158`}, {1845.`, 2.3528`}, {1850.`,
2.36269`}, {1855.`, 2.36324`}, {1860.`, 2.37877`}, {1865.`
2.38914`}, {1870.`, 2.39899`}, {1875.`, 2.41142`}, {1880.`
2.40601`}, {1885.`, 2.40194`}, {1890.`, 2.39789`}, {1895.`
2.39619`}, {1900.`, 2.40568`}, {1905.`, 2.40624`}, {1910.`,
2.39888}, {1915.`, 2.40384`}, {1920.`, 2.40937`}, {1925.`,
2.40709`}, {1930.`, 2.3954`}, {1935.`, 2.41691`}, {1940.`
2.41117`}, {1945.`, 2.4113`}, {1950.`, 2.41505`}, {1955.`
2.41613`}, {1960.`, 2.39838`}, {1965.`, 2.41402`}, {1970.`
2.42415`}, {1975.`, 2.40659`}, {1980.`, 2.40548`}, {1985.`
2.21571`}, {1990.`, 2.65837`}, {1995.`, 2.73631`}, {2000.`
2.35978`}, {2005.`, 2.34484`}, {2010.`, 2.35112`}, {2015.`,
2.35091`}, {2020.`, 2.35177`}, {2025.`, 2.368`}, {2030.`
2.36698`}, {2035.`, 2.36624`}, {2040.`, 2.37748`}, {2045.`,
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