gives a binary list in which 1s correspond to peak positions in data.


detects peaks that survive Gaussian blurring up to scale σ.


detects peaks with minimum sharpness s.


detects only peaks with values greater than t.


uses different scales for thresholding sharpness and value.

Details and Options

  • PeakDetect finds local maxima using the given constraints.
  • PeakDetect[data] automatically chooses constraints to return a set of peaks.
  • Input data can be of one of the following forms:
  • {y1,y2,}a list of values
    TimeSeries[]regularly sampled time series object
    EventSeries[]regularly sampled event series object
  • PeakDetect[data,0,0,-] returns all the peaks.
  • PeakDetect[data,σ,s,t] is equivalent to PeakDetect[data,σ,{s,σ},{t,0}].
  • PeakDetect takes a Padding option. By default, Padding->"Reversed" is used.


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Basic Examples  (1)

Find dominant peaks in a list:

Visualize detected peaks:

Scope  (12)

Data  (4)

Peaks of a 1D list:

Peaks of a TimeSeries object:

Peaks of an EventSeries object:

Find peaks:

Peaks of a list of Quantity objects:

Threshold for values greater than 30 meters:

Parameters  (8)

By default, an automatic scale is used:

Find all peaks at scale :

Compute peaks at different scales:

When finding peaks at scale , only peaks that sustain a blur up to scale are returned:

Signal and its blurred version at scale :

By default, peaks are not filtered based on their sharpness, equivalent to :

Specify minimum sharpness value :

Sharpness, defined by the negative second derivative, should be greater than the specified :

Specify a minimum height value :

Apply the value threshold after smoothing the data using a scale :

Options  (1)

Padding  (1)

By default, Padding->"Reversed" is used:

Specify the padding:

Applications  (3)

Find peaks of the recent sunspot activity:

Peaks in a discrete-time Fourier transform:

Mean daily temperatures for Chicago during a period of two months:

Find peaks in the temperature:

Create a new time series with peaks stored using MetaInformation:

Plot the mean temperatures with warmer days marked:

Possible Issues  (1)

Symmetric twin peaks that merge under Gaussian blurring may survive as peaks:

Wolfram Research (2014), PeakDetect, Wolfram Language function,


Wolfram Research (2014), PeakDetect, Wolfram Language function,


@misc{reference.wolfram_2020_peakdetect, author="Wolfram Research", title="{PeakDetect}", year="2014", howpublished="\url{}", note=[Accessed: 16-April-2021 ]}


@online{reference.wolfram_2020_peakdetect, organization={Wolfram Research}, title={PeakDetect}, year={2014}, url={}, note=[Accessed: 16-April-2021 ]}


Wolfram Language. 2014. "PeakDetect." Wolfram Language & System Documentation Center. Wolfram Research.


Wolfram Language. (2014). PeakDetect. Wolfram Language & System Documentation Center. Retrieved from