(1) a command line version (peakfit.m) for Matlab or Octave,The difference between them is that peakfit.m is completely controlled by command-line input arguments and returns its information via command-line output arguments; ipf.m allows interactive control via keypress commands. For automating the fitting of large numbers of signals, peakfit.m is better; ipf.m is best for exploring signals to determine the optimum fitting range, peak shapes, number of peaks, baseline correction mode, etc. Otherwise they have the same curve-fitting capabilities. The peak shape models available are illustrated in this graphic. See Notes and Hints for more information and useful suggestions.
(2) a keypress operated interactive version (ipf.m) for Matlab only.
Peakfit.m is a user-defined command window peak fitting function for Matlab or Octave, usable from a remote terminal. It is written as a self-contained function in a single m-file. (To view of download, click Peakfit.m). It takes data in the form of a 2 x n matrix that has the independent variables (X-values) in row 1 and the dependent variables (Y-values) in row 2, or as a single dependent variable vector. The syntax is [FitResults, GOF, baseline, coeff, residuals, xi, yi, BootResults]=peakfit(signal, center, window, NumPeaks, peakshape, extra, NumTrials, start, AUTOZERO, fixedwidth, plots, bipolar, minwidth, DELTA, clipheight)). Only the first argument, the data matrix, is required. The other input and output arguments are explained below.
The screen display is shown on the right; the upper panel shows the data as blue dots, the combined model as a red line (ideally overlapping the blue dots), and the model components as green lines. The dotted magenta lines are the first-guess peak positions for the last fit. The lower panel shows the residuals (difference between the data and the model).
You can download a ZIP file
containing peakfit.m, DemoPeakFit.m,
ipf.m, Demoipf.m, some sample data for testing, and
a test script (testpeakfit.m) that runs
all the examples sequentially to test for proper operation.
For a discussion of the accuracy and precision of peak
parameter measurement using peakfit.m, click here.
Version 7 and later support unconstrained variable
shapes that have three iterated variables: 30=variable-alpha
Voigt; 31=variable time constant ExpGaussian; 32=variable
shape Pearson; 33=variable Gaussian/ Lorentzian blend. These
determine the positions, width, and shape factor by
iteration. Version
history.
Peakfit.m can be called with several optional additional arguments. All input arguments (except the signal itself) can be replaced by zeros to use their default values.
peakfit(signal);
Performs an iterative least-squares fit of a single
unconstrained Gaussian peak to the entire data matrix "signal",
which has x values in row 1 and Y values in row 2 (e.g. [x y]) or
which may be a single signal vector (in which case the data points
are plotted against their index numbers on the x axis).
peakfit(signal,center,window);
Fits a single unconstrained Gaussian peak to a portion of
the matrix "signal". The portion is centered on the x-value
"center" and has width "window" (in x units).
In this and in all following
examples, set "center" and "window" both to 0 to fit the entire
signal.
peakfit(signal,center,window,NumPeaks);
"NumPeaks" = number of peaks in the model (default is 1 if
not specified).
peakfit(signal,center,window,NumPeaks,peakshape);
Specifies the peak shape of the model: "peakshape" =
1-23. (1=unconstrained Gaussian, 2=unconstrained Lorentzian,
3=logistic distribution, 4=Pearson, 5=exponentially
broadened Gaussian; 6=equal-width Gaussians, 7=equal-width
Lorentzians, 8=exponentially broadened equal-width
Gaussians, 9=exponential pulse, 10= up-sigmoid (logistic function), 11=fixed-width
Gaussians, 12=fixed-width
Lorentzians, 13=Gaussian/Lorentzian
blend; 14=bifurcated Gaussian, 15=Breit-Wigner-Fano
resonance; 16=Fixed-position
Gaussians; 17=Fixed-position Lorentzians; 18=exponentially
broadened Lorentzian; 19=alpha function; 20=Voigt profile;
21=triangular; 22=multiple shapes; 23=down-sigmoid;
25=lognormal distribution; 26=slope (see
Example 28); 28=polynomial (Example 30); 29=articulated linear
segmented (see Example 29); 30=independently-variable alpha
Voigt; 31=independently-variable time constant ExpGaussian;
32=independently-variable Pearson; 33=independently-variable
Gaussian/Lorentzian blend; 34=fixed-height Gaussian. The peakshape
can be a vector of different shapes for each peak, e.g. [1
2 1] for three peaks in a Gaussian, Lorentzian, Gaussian sequence.
(The function ShapeDemo demonstrates
most of the basic peak shapes graphically,
showing the variable-shape peaks as multiple lines).
Note: "unconstrained"
simply means that the position, height, and width of each
peak in
the model can vary independently of the other peaks, as
opposed to the equal-width, fixed-width, or fixed position
variants. Shapes 4, 5, 13, 14, 15, 18, and 20 are
constrained to the same shape constant; shapes
30-33
are completely unconstrained in position, width and
shape; their shape variables are determined
iteratively.
peakfit(signal,center,window,NumPeaks,peakshape,extra)
Specifies the value of 'extra', used in the Pearson,
exponentially-broadened Gaussian, Gaussian/Lorentzian
blend, bifurcated Gaussian, and Breit-Wigner-Fano
shapes to fine-tune the peak shape. In version 5,
'extra' can be a vector of different extra values for each peak).
peakfit(signal,center,window,NumPeaks,peakshape,
extra, NumTrials);
Restarts the fitting process "NumTrials" times and selects
the best one (with lowest fitting error). NumTrials can be any
positive integer (default is 1). In may cases, NumTrials=1 will be
sufficient, but if that does not give consistent results, increase
NumTrials until the result are stable.
peakfit(signal,center,window,NumPeaks,peakshape,extra,
NumTrials, start)
Specifies the first guesses vector "firstguess" for the
peak positions and widths, e.g. start=[position1 width1 position2
width2 ...]
peakfit(signal,center,window,NumPeaks,peakshape,extra,
NumTrials, start, autozero)
As above, but "autozero" sets the baseline correction mode in
the last argument: autozero=0
(default) does not subtract
baseline from data segment;. autozero=1 interpolates a linear
baseline from the edges of the data segment and subtracts it
from the signal (assumes that the peak
returns to the baseline at the edges of the signal);
autozero=2, like mode 1 except that it
computes a quadratic curved baseline; autozero=3
compensates for a flat baseline without reference to
the signal itself (does not require that the signal return to
the baseline at the edges of the signal, as does modes 1 and 2).
Coefficients of the polynomial baselines are returned in the
third output argument "baseline".
peakfit(signal,0,0,0,0,0,0,0,2)
Use zeros as placeholders to use the default values of
input arguments. In this case, autozero is set to 2, but all
others are the default values.
Example 8. As
above,
returns the vector xi containing 200 interpolated x-values for
the model peaks and the matrix y1 containing the y values of
each model peak at each xi. Type plot(xi,yi(1,:)) to plot peak 1 or plot(xi,yi) to plot all peaks.
>
[FitResults,LowestError,residuals,xi,yi]=
peakfit(smatrix,.4,.7,2,1,0,10)
Example 9. Fitting a single
unconstrained Gaussian on a linear background, using the linear
autozero mode
(9th input argument = 1)
>>x=[0:.1:10]';y=10-x+exp(-(x-5).^2);peakfit([x
y],5,8,0,0,0,0,0,1)
Example 10. Fits a group of three peaks near x=2400 in DataMatrix3 with three equal-width exponentially-broadened Gaussians.
>> load DataMatrix3
>>
[FitResults,FitError]= peakfit(DataMatrix3,2400,440,3,8,31,1)
Peak number Position
Height
Width Peak area
1
2300.5
0.82546
60.535
53.188
2
2400.4
0.48312
60.535
31.131
3
2500.6
0.84799
60.535
54.635
FitError = 0.19975
Example
11. Example
of an unstable fit to a signal consisting of two unconstrained
Gaussian peaks of equal height (1.0). The peaks are too highly
overlapped for a stable fit, even though the fit error is
small and the residuals are unstructured. Each time you
re-generate this signal, it gives a different fit, with the
peaks heights varying about 15% from signal to signal.
>> x=[0:.1:10]';
y=exp(-(x-5.5).^2)+exp(-(x-4.5).^2)+.01*randn(size(x));
[FitResults,FitError]=peakfit([x y],5,19,2,1)
Peak number
Position
Height
Width Peak area
1
4.4059
0.80119
1.6347
1.3941
2
5.3931
1.1606
1.7697
2.1864
FitError = 0.598
Much more stable results can be
obtained using the equal-width Gaussian model (peakfit([x y],5,19,2,6)),
but that is justified only if the experiment is
legitimately expected to yield peaks of equal width. See http://terpconnect.umd.edu/~toh/spectrum/CurveFittingC.html#Peak_width_constraints.
Example 12. Demonstration of the four autozero modes, for a single Gaussian on large baseline, with position=10, height=1, and width=1.66. The autozero mode is specified by the 9th input argument (0,1,2, or 3).
Autozero=0 means to ignore the baseline (default mode if not specified). In this case, this leads to large errors.
>> x=8:.05:12;y=1+exp(-(x-10).^2);
>> [FitResults,FitError,baseline]=peakfit([x;y],0,0,1,1,0,1,0,0)
FitResults =
1 10 1.8561 3.612 5.7641
FitError =5.387
baseline = 0Autozero=1 subtracts linear baseline from edge to edge. Does not work well in this case because the signal does not return completely to the baseline at the edges.
Autozero=3 subtracts a flat baseline automatically, without requiring that the signal returns to baseline at the edges. This mode works best for this signal.
>> [FitResults,FitError,baseline]=peakfit([x;y],0,0,1,1,0,1,0,1)
FitResults =
1 9.9984 0.96161 1.5586 1.5914
FitError = 1.9801
baseline = 0.0012608 1.0376
Autozero=2 subtracts quadratic baseline from edge to edge. Does not work well in this case because the signal does not return completely to the baseline at the edges.
>> [FitResults,FitError,baseline]=peakfit([x;y],0,0,1,1,0,1,0,2)
FitResults =
1 9.9996 0.81762 1.4379 1.2501
FitError = 1.8205
baseline = -0.046619 0.9327 -3.469
>> [FitResults,FitError,baseline]=peakfit([x;y],0,0,1,1,0,1,0,3)
FitResults =
1 10 1.0001 1.6653 1.7645
FitError = 0.0037056
baseline = 0.99985
In the following example, the baseline is strongly sloped, but straight. In that case the most accurate result is obtained by using a 2-peak fit, fitting the baseline with a variable-slope straight line (shape 26, peakfit version 6 only).
>> x=8:.05:12;y=x+exp(-(x-10).^2);
>> [FitResults,FitError]=peakfit([x;y],0,0,2,[1 26],[1 1],1,0)
FitResults =
1 10 1 1.6651 1.7642
2 4.485 0.22297 0.05 40.045
FitError =0.093
In this example, the baseline is curved, so the best choice is autozero=2:
>> x=[0:.1:10]';y=1./(1+x.^2)+exp(-(x-5).^2);
>> [FitResults,FitError,baseline]=peakfit([x y],5,5.5,0,0,0,0,0,2)
FitResults =
1 5.0091 0.97108 1.603 1.6569
FitError = 0.97661
baseline = 0.0014928 -0.038196 0.22735
Example 13. Same
as example 4, but with fixed-width Gaussian (shape
11), width=1.666.
>>
x=[0:.1:10];y=exp(-(x-5).^2)+.5*exp(-(x-3).^2)+.1*randn(size(x));
>> [FitResults,FitError]=peakfit([x' y'],0,0,2,11,0,0,0,0,1.666)
Peak number Position
Height
Width Peak area
1
3.9943
0.49537
1.666
0.87849
2
5.9924
0.98612
1.666
1.7488
Example 14. Peak
area measurements. Same as the example in Figure 15 on Integration and Peak Area
Measurement. All four peaks have the same
theoretical peak area (1.772). The four peaks can be fit
together in one fitting operation using a 4-peak Gaussian
model, with only rough estimates of the first-guess positions
and widths. The peak areas thus measured are much more
accurate than the perpendicular drop method:
>>
x=[0:.01:18];
>>
y=exp(-(x-4).^2)+exp(-(x-9).^2)+exp(-(x-12).^2)+exp(-(x-13.7).^2);
>> peakfit([x;y],0,0,4,1,0,1,[4 2 9 2 12 2 14
2],0,0)
Peak number Position
Height
Width Peak area
1
4
1
1.6651
1.7725
2
9
1
1.6651
1.7725
3
12
1
1.6651
1.7724
4
13.7
1
1.6651
1.7725
This
works well even in the presence of substantial amounts of random
noise:
>> x=[0:.01:18];
y=exp(-(x-4).^2)+exp(-(x-9).^2)+exp(-(x-12).^2)+exp(-(x-13.7).^2)+.1.*randn(size(x));
>> peakfit([x;y],0,0,4,1,0,1,[4 2 9 2 12 2 14 2],0,0)
Peak number Position
Height
Width Peak area
1
4.0086
0.98555
1.6693
1.7513
2
9.0223
1.0007
1.669
1.7779
3
11.997
1.0035
1.6556
1.7685
4
13.701
1.0002
1.6505
1.7573
Sometimes experimental
peaks are effected by exponential broadening, which does not by
itself change the true peak areas, but does shift peak positions
and increases peak width, overlap, and asymmetry, as shown when
you try to fit the peaks with Gaussians. Using the same
noise signal from above:
>> y1=ExpBroaden(y',-50);
>> peakfit([x;y1'],0,0,4,1,50,1,0,0,0)
ans =
1
4.4538
0.83851
1.9744 1.7623
2
9.4291
0.8511
1.9084 1.7289
3
12.089
0.59632
1.542 0.97883
4
13.787
1.0181
2.4016 2.6026
Peakfit.m
(and ipf.m) have an exponentially-broadened Gaussian peak shape
(shape #5) that works better in those cases:, recovering the original peak positions,
heights, widths, and areas. (Adding a first-guess vector as the
8th argument may improve the reliability of the fit in some
cases).
>> y1=ExpBroaden(y',-50);
>>
peakfit([x;y1'],0,0,4,5,50,1,[4 2 9 2 12 2 14
2],0,0)
ans=
Peak#
Position
Height
Width Area
1
4
1
1.6651
1.7725
2
9
1
1.6651
1.7725
3
12
1
1.6651
1.7725
4
13.7
0.99999
1.6651
1.7716
Example 15. Displays a table of
parameter error estimates. See DemoPeakfitBootstrap for a
self-contained demo of this function.
>> x=0:.05:9;
y=exp(-(x-5).^2)+.5*exp(-(x-3).^2)+.01*randn(1,length(x));
>> [FitResults,LowestError,baseline,residuals,xi,yi,BootstrapErrors]=
peakfit([x;y],0,0,2,6,0,1,0,0,0);
Peak
#1
Position
Height
Width Area
Bootstrap Mean: 2.9987
0.49717
1.6657 0.88151
Bootstrap STD: 0.0039508 0.0018756
0.0026267 0.0032657
Bootstrap IQR: 0.0054789 0.0027461
0.0032485 0.0044656
Percent RSD: 0.13175
0.37726 0.15769
0.37047
Percent IQR: 0.18271
0.55234 0.19502
0.50658
Peak #2
Position
Height
Width Area
Bootstrap Mean: 4.9997
0.99466
1.6657 1.7636
Bootstrap STD: 0.001561 0.0014858
0.00262 0.0025372
Bootstrap IQR: 0.002143 0.0023511
0.00324 0.0035296
Percent RSD:
0.031241 0.14938
0.15769 0.14387
Percent IQR:
0.042875 0.23637
0.19502 0.20014
Example 21. “Humps” function fit
with two Voigt profiles (shape 20), alpha=1.7, flat baseline
mode (3).
[FitResults,FitError]=peakfit(humps(0:.01:2),
71, 140, 2, 20, 1.7, 1, ...[31 4.7 90 8.8],
3)
FitResults = 1 31.047 96.762 4.6785 2550.1 2 90.09 22.935 8.8253 1089.5 FitError =0.80501
Example 22. peakfitdemob.m. Illustrated on the right. Measurement of three weak Gaussian peaks at x=100, 250, 400, superimposed in a very strong Gaussian baseline plus noise. The peakfit function fits four peaks, treating the baseline as a 4th peak whose peak position is negative. This requires specifying a "start" vector, like Example 7. You can test the reliability of this method by changing the peak parameters in lines 11, 12, and 13 and see if the peakfit function will successfully track the changes and give accurate results for the three peaks without having to change the start vector. See Example 9 on iSignal.html for other ways to handle this signal. (The true peaks heights are 1, 2, and 3, respectively).
Example 33: Version 7.4.
Fixed-height Gaussians (heights specified in 10th
input argument).
>>
x=[0:.1:10];y=exp(-(x-5).^2)+.5*exp(-(x-3).^2)+.1*randn(size(x));peakfit([x'
y'],0,0,2,34,0,0,0,0,[.5 1])
ans =
1
3.0437
0.5
1.8591 0.98943
2
5.0797
1
1.5663 1.6672
Note:
to display the FitResults table with column labels, call
peakfit.m with output arguments [FitResults...] and
type :
disp(' Peak number
Position
Height
Width Peak
area');disp(FitResults)
If you have no idea where to start, you can use the Interactive Peak Fitter
(ipf.m) to quickly try different fitting regions, peak
shapes, numbers of peaks, baseline correction modes, etc. When you
get a good fit, you can press the "W" key to print out the
command line statement for peakfit.m that will perform that fit in
a single line of code, with or without graphics.
DemoPeakFit.m is a demonstration script for peakfit.m. It generates an overlapping Gaussian peak signal, adds normally-distributed noise, fits it with the peakfit.m function (in line 78), repeats this many times ("NumRepeats" in line 20), then compares the peak parameters (position, height, width, and area) of the measurements to their actual values and computes accuracy (percent error) and precision (percent relative standard deviation). You can change any of the initial values in lines 13-30. Here is a typical result for a two-peak signal with Gaussian peaks:
Percent errors of measured parameters: Position
Height
Width Area
0.048404
0.07906
0.12684 0.19799
0.023986 0.38235
-0.36158 -0.067655
Average Percent Parameter Error for all peaks:
0.036195
0.2307
0.24421 0.13282
In these results, you can see that the accuracy and precision (%RSD) of the peak position measurements are always the best, followed by peak height, and then the peak width and peak area, which are usually the worst.
DemoPeakFitTime.m is a simple script that demonstrates how to apply multiple curve fits to a signal that is changing with time. The signal contains two noisy Gaussian peaks (similar to the illustration at the right) in which the peak position of the second peak increases with time and the other parameters remain constant (except for the noise). The script creates a set of 100 noisy signals (on line 5) containing two Gaussian peaks where the position of the second peak changes with time (from x=6 to 8) and the first peak remains the same. Then it fits a 2-Gaussian model to each of those signals (on line 8), stores the FitResults in a 100 × 2 × 5 matrix, displays the signals and the fits graphically with time (click to play animation), then plots the measured peak position of the two peaks vs time on line 12.
Which to use: iPeak, iSignal, or Peakfit? Read this comparison of all three. Or download these Matlab demos that compare iPeak.m with Peakfit.m for signals with a few peaks and signals with many peaks and that shows how to adjust iPeak to detect broad or narrow peaks. DemoPeakfitBootstrap demonstrates the ability of peakfit to compute estimates of the errors in the measured peak parameters. These are self-contained demos that include all required Matlab functions. Just place them in your path and click Run or type their name at the command prompt. Or you can download all these demos together in idemos.zip. peakfitVSfindpeaks.m performs a direct comparison of the peak parameter accuracy of findpeaks vs peakfit.
2. Automatically
finding and Fitting Peaks.
findpeaksfit.m
is essentially a combination of findpeaks.m
and peakfit.m. It
uses the number of peaks found and the peak positions and widths
determined by findpeaks as input for the peakfit.m function, which
then fits the entire signal with the specified peak model.
This combination function is more convenient that using findpeaks
and peakfit separately. It yields better values that findpeaks
alone, because peakfit fits the entire peak, not just the top
part, and because it deals with non-Gaussian and overlapped peaks.
However, it fits only those peaks that are found by findpeaks, so
you will have to make sure that every peak that contributes to
your signal is located by findpeaks. The syntax is
function [P,FitResults,LowestError,residuals,xi,yi] =
findpeaksfit(x, y, SlopeThreshold, AmpThreshold,
smoothwidth, peakgroup, smoothtype, peakshape, extra,
NumTrials, autozero, fixedparameters, plots)
The first seven input arguments are exactly the same as for the findpeaks.m
function; if you have been using findpeaks or iPeak to find and
measure peaks in your signals, you can use those same input
argument values for findpeaksfit.m. The remaining six input
arguments of findpeaksfit.m are for the peakfit function; if you
have been using peakfit.m or ipf.m
to fit peaks in your signals, you can use those same input
argument values for findpeaksfit.m. Type "help findpeaksfit" for
more information. This function is included in the ipf11.zip distribution.
Like iPeak and iSignal, ipf starts out by showing the entire signal in the lower panel and the selected region in the upper panel (adjusted by the same cursor controls keys as iPeak and iSignal). After performing a fit (figure on the right), the upper panel shows the data as blue dots, the total model as a red line (ideally overlapping the blue dots), and the model components as green lines. The dotted magenta lines are the first-guess peak positions for the last fit. The lower panel shows the residuals (difference between the data and the model). Important note: Make sure you don't click on the “Show Plot Tools” button in the toolbar above the figure; that will disable normal program functioning. If you do; close the Figure window and start again.
(The function ShapeDemo demonstrates the basic peak shapes graphically, showing the variable-shape peaks as multiple lines)
Practical examples with
experimental data:
1. In this example, a sample of room air is analyzed by gas chromatography (data source). The resulting chromatogram (right) shows two slightly overlapping peaks, the first for oxygen and the second for nitrogen. The area under each peak is presumed to be proportional to the gas composition. The peaks are visibly asymmetric, but an exponentially-broadened Gaussian (a commonly-encountered peak shape in chromatography) gives a good fit to the data, using ipf.m and adjusting the exponential term with the A and Z keys to get the best fit. The results, shown in the ipf.m screen on the right and in the Q-key report below, show that the peak areas are in a ratio of 23% and 77%. This is fairly close to the actual 21% and 78% composition, and the results would have been even more accurate if argon were included or if the areas were calibrated for the different detector responses of nitrogen and oxygen.
Percent Fitting
Error = 2.9318% Elapsed time = 11.5251 sec.
Peak# Position Height
Width Area
1 4.8385
17762 0.081094 1533.2
2 5.1439
47142 0.10205
5119.2
Peak Shape = Gaussian
Number of peaks = 3
Fitted range = 5 - 6.64
Percent Error = 7.4514 Elapsed time = 0.19741
Sec.
Peak# Position
Height
Width Area
1
5.33329
14.8274
0.262253 4.13361
2
5.80253
26.825
0.326065 9.31117
3
6.27707
22.1461
0.249248 5.87425
Autozero OFF |
Linear autozero |
Quadratic autozero |
Observe that the RSD of the peak position is best (lowest), followed by height and width and area. This is a typical pattern, seen before. Also, be aware that the reliability of the computed variability depends on the assumption that the noise in the signal is representative of the average noise in repeated measurements. If the number of data points in the signal is small, these estimates can be very approximate.
A likely pitfall with the bootstrap method when applied to iterative fits is the possibility that one (or more) of the bootstrap fits will go astray, that is, will result in peak parameters that are wildly different from the norm, causing the estimated variability of the parameters to be too high. For that reason, in ipf 8.12, the interquartile range (IQR) as well as the standard deviation (STD) is reported. The IQR is more robust to outliers. For a normal distribution, the interquartile range is equal to 1.34896 times the standard deviation. But if one or more of the bootstrap sample fits fails, resulting in a distribution of peak parameters with large outliers, the STD will be much greater that the IQR. In that case, a more realistic estimate of variability is IRQ/1.34896. It's best to try to increase the fit stability by choosing a better model (e.g. using an equal-width or fixed-width model, or a fixed-position shape, if appropriate), adjusting the fitted range (pan and zoom keys), the background subtraction (T or B keys), or the start positions (C key), and/or selecting a higher number of fit trials per bootstrap (which will increase the computation time). As a quick preliminary test of bootstrap fit stability, pressing the N key will perform a single fit to a single random bootstrap sample and plot the result; do that several times to see whether the bootstrap fits are stable enough to be worth computing a 100-sample bootstrap. Note: it's normal for the stability of the bootstrap sample fits (N key) to be poorer than the full-sample fits (F key), because the latter includes only the variability caused by changing the starting positions for one set of data and noise, whereas the N and V keys aim to include the variability caused by the random noise in the sample by fitting bootstrap sub-samples. Moreover, the best estimates of the measured peak parameters are those obtained by the normal fits of the full signal (F and X keys), not the means reported for the bootstrap samples (V and N keys), because there are more independent data points in the full fits and because the bootstrap means are influenced by the outliers that occur more commonly in the bootstrap fits. The bootstrap results are useful only for estimating the variability of the peak parameters, not for estimating their mean values. The N and V keys are also very useful way to determine if you are using too many peaks in your model; superfluous peaks will be very unstable when N is press repeatedly and will have much higher standard deviation of its peak height when the V key is used.
19. (version 11 and above) Shift-o fits a simple polynomial (linear, quadratic, cubic, etc) to the segment of the signal displayed in the upper panel and displays the polynomial coefficients (in descending powers) and the R^{2}.
20. (version 11.1 and above) If some peaks are saturated (clipped at a maximum height), you can make the program ignore the saturated points by pressing Shift-M and entering the maximum Y values to keep. Y values above this limit will simply be ignored; peaks below this limit will be fit as usual.
21.(version 11.1 and above) To constrain the model to peaks above a certain width, press Shift-W and enter the minimum peak with allowed.
a) The execution time can vary over a factor of 4 or 5 or more between different computers, (e.g. a small laptop with 1.6 GHz, dual core Athlon CPU with 4 Gbytes RAM, vs a desktop with a 3.4 GHz i7 CPU with 16 Gbytes RAM). Run the Matlab "bench.m" benchmark test to see how your computer stacks up.Other factors that are less important are the number of data points in the fitted region (but only if the number of data points is very large; see PeakfitTimeTest3.m) and the starting values (good starting values can reduce execution time slightly; PeakfitTimeTest2.m and PeakfitTimeTest2a.m have examples). Some of these TimeTest scripts need DataMatrix2 and DataMatrix3.
b) The execution time increases with the square of the number of peaks in the model. (See PeakfitTimeTest.m).
c) The execution time varies greatly (sometimes by a factor of 100 or more) with the peak shape, with the exponentially-broadened Gaussian being the slowest and the fixed-width Gaussian being the fastest. See PeakfitTimeTest2.m and PeakfitTimeTest2a.m The equal-width and fixed-width shape variations are always faster.
d) The execution time increases directly with NumTrials in peakfit.m. The "Best of 10 trials" function (X key in ipf.m) takes about 10 times longer than a single fit.
That's it! Your new shape will now be shape 21 (or whatever was the shape number of the old shape you sacrificed) and, in ipf.m, will be activated by the same keystroke used by the old shape (e.g. Shift-T for the triangular, key number 84).
- Open peakfit.m or ipf.m in the Matlab editor and re-write the old shape function (such as "triangle.m", located near the end of the code - there is one of those functions for each shape) by changing name of the function and the mathematics of the assignment statement (e.g. g = 1-(1./wid).*abs(x-pos);). You can use the same variables ('x' for the independent variable, 'pos' for peak position, 'wid' for peak width, etc). Scale your function to have a peak height of 1.0 (e.g. after computing y as a function of x, divide by max(y)).
- Use the search function in Matlab to find all instances of the name of the old function and replace it with the new name, checking "Wrap around" but leaving "Match case" and "Whole word" unchecked in the Search box. If you do it right, for example, all instances of "triangular" and all instances of "fittriangular' will be modified with your new name replacing "triangular". Save the result (or Save as... with a modified file name).
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