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Note

In this package/documentation, "peak" is often used when referring to the index (i.e. location) of a peak, which is most broadly relevant when speaking of a specific peak

Plateau

A "flat" peak, where the value of the extrema occurs multiple times consecutively, but surrounding elements are less than the extremum. The first occurence of the extrema is considered the peak location. Uncommon for floating-point data.

Example block output
Peak height

Peak height is the value of the signal for a specific peak. Mouse over (or touch) each peak to see its height.

+
Note

In this package/documentation, "peak" is often used when referring to the index (i.e. location) of a peak, which is most broadly relevant when speaking of a specific peak

Plateau

A "flat" peak, where the value of the extrema occurs multiple times consecutively, but surrounding elements are less than the extremum. The first occurence of the extrema is considered the peak location. Uncommon for floating-point data.

Example block output
Peak height

Peak height is the value of the signal for a specific peak. Mouse over (or touch) each peak to see its height.

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Peak promi
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-
Peak width

Peak width is measured as the distance (in units of indices) between the intersection of a horizontal reference line with the signal on either side of a peak, where the height of the reference line is offset from the peak height by a proportion of the peak prominence (keyword argument relheight for the peakwidths functions).

Example block output
"Strict"-ness

The default behavior of peak finding and related functions (e.g. peakproms, etc.) is to only return results that are exactly correct, and to return nothing (i.e. ignore a potential peak), NaN, or missing, as appropriate for a given function. This behavior is controlled by the strict keyword argument (true by default). Setting the strict keyword to false allows these functions to relax some requirements. When strict == false, functions will make optimistic assumptions in an attempt to return useful information (e.g. something not NaN or missing) when data violates default requirements. This can produce results that are not correct according to the strictest definitions of peaks and their characteristics, however, sometimes relaxed behavior is desired/needed.

strict-ness should only affect new peaks/characteristics (i.e. only peaks detected with strict == false). Any observed behavior otherwise (i.e. characteristics of non-strict peaks are altered) is undesired[1] and an issue should be opened.

List of Peaks.jl function behavior/assumptions for `strict == false`
  • maxima/minima finding functions (e.g. findmaxima, etc.) assume that any unobserved (e.g. missing or due to the beginning/end of the array) or NaN data in a window is consistent with a peak. For example:
    • The maximal/minimal value in an incomplete window (e.g. an index i within w elements of the array beginning or end, i-w < firstindex(x) or i+w > lastindex(x)) is assumed to be a peak (i.e. if the array continued, the data would be less/more the current maximal/minimal value). This allows the first or last elements of an array to be considered peaks if the data in the window is ascending or descending.
    • The maximal/minimal value in a window containing missing or NaN elements is assumed to be a peak (i.e. the missing or NaN values would be less/more than the current value if they existed or were real numbers)
  • peakproms uses the larger present (i.e. not NaN or missing) value of the minimum values in each reference range (see prominence definition)
  • peakwidths linearly interpolates across a gap at the width reference level
  • 1We try to ensure that characteristics of non-strict peak will be unaffected during strict == false mode. Any issues with examples of altered characteristics are appreciated, and fixes will be attempted, but we do not guarantee that altered peak characteristics can be prevented.
+
Peak width

Peak width is measured as the distance (in units of indices) between the intersection of a horizontal reference line with the signal on either side of a peak, where the height of the reference line is offset from the peak height by a proportion of the peak prominence (keyword argument relheight for the peakwidths functions).

Example block output
"Strict"-ness

The default behavior of peak finding and related functions (e.g. peakproms, etc.) is to only return results that are exactly correct, and to return nothing (i.e. ignore a potential peak), NaN, or missing, as appropriate for a given function. This behavior is controlled by the strict keyword argument (true by default). Setting the strict keyword to false allows these functions to relax some requirements. When strict == false, functions will make optimistic assumptions in an attempt to return useful information (e.g. something not NaN or missing) when data violates default requirements. This can produce results that are not correct according to the strictest definitions of peaks and their characteristics, however, sometimes relaxed behavior is desired/needed.

strict-ness should only affect new peaks/characteristics (i.e. only peaks detected with strict == false). Any observed behavior otherwise (i.e. characteristics of non-strict peaks are altered) is undesired[1] and an issue should be opened.

List of Peaks.jl function behavior/assumptions for `strict == false`
  • maxima/minima finding functions (e.g. findmaxima, etc.) assume that any unobserved (e.g. missing or due to the beginning/end of the array) or NaN data in a window is consistent with a peak. For example:
    • The maximal/minimal value in an incomplete window (e.g. an index i within w elements of the array beginning or end, i-w < firstindex(x) or i+w > lastindex(x)) is assumed to be a peak (i.e. if the array continued, the data would be less/more the current maximal/minimal value). This allows the first or last elements of an array to be considered peaks if the data in the window is ascending or descending.
    • The maximal/minimal value in a window containing missing or NaN elements is assumed to be a peak (i.e. the missing or NaN values would be less/more than the current value if they existed or were real numbers)
  • peakproms uses the larger present (i.e. not NaN or missing) value of the minimum values in each reference range (see prominence definition)
  • peakwidths linearly interpolates across a gap at the width reference level
diff --git a/dev/how-to/99aefa60.svg b/dev/how-to/9e1100de.svg similarity index 84% rename from dev/how-to/99aefa60.svg rename to dev/how-to/9e1100de.svg index 9b83ec8..9970324 100644 --- a/dev/how-to/99aefa60.svg +++ b/dev/how-to/9e1100de.svg @@ -1,96 +1,96 @@ - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/how-to/2d499983.svg b/dev/how-to/ae32bc02.svg similarity index 86% rename from dev/how-to/2d499983.svg rename to dev/how-to/ae32bc02.svg index 1571012..b43c412 100644 --- a/dev/how-to/2d499983.svg +++ b/dev/how-to/ae32bc02.svg @@ -1,59 +1,59 @@ - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/how-to/26e6ad74.svg b/dev/how-to/ce5cb950.svg similarity index 87% rename from dev/how-to/26e6ad74.svg rename to dev/how-to/ce5cb950.svg index ea4ca4c..3ed1c92 100644 --- a/dev/how-to/26e6ad74.svg +++ b/dev/how-to/ce5cb950.svg @@ -1,54 +1,54 @@ - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/how-to/50ba641c.svg b/dev/how-to/e1c3a1ec.svg similarity index 79% rename from dev/how-to/50ba641c.svg rename to dev/how-to/e1c3a1ec.svg index d7429fe..cf415eb 100644 --- a/dev/how-to/50ba641c.svg +++ b/dev/how-to/e1c3a1ec.svg @@ -1,138 +1,138 @@ - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/how-to/index.html b/dev/how-to/index.html index d2f1d26..39d0a41 100644 --- a/dev/how-to/index.html +++ b/dev/how-to/index.html @@ -7,5 +7,5 @@ y = multisin.(t) .+ 0.1rand(length(t)) pks = findmaxima(y) -plotpeaks(t, y; peaks=pks.indices)Example block output

The simplest way to remove those peaks (assuming the signal is already filtered) is by setting the window w argument in findmaxima and friends:

pks = findmaxima(y, 15)
-f = plotpeaks(t, y; peaks=pks.indices)
Example block output

If only the peaks circled in blue are wanted, then setting the window w too wide won't work, since there are larger peaks that would become dominant.

Example block output

How to filter peaks by peak characteristics

Every peak-characteristic finding function can optionally filter the newly calculated characteristics using the keyword arguments min and max.

Plotting all the peak characteristics and/or looking at the characteristic values can help show which characteristics should be filtered to remove all the unwanted peaks.

plotpeaks!(f, t, y; peaks=pks.indices, prominences=true, widths=true)
Example block output
DataFrame(pks[Not(:data)])
7×5 DataFrame
Rowindicesheightspromswidthsedges
Int64Float64Float64Float64Tuple…
1373.39830.34976819.0733(28.6219, 47.6952)
21025.068835.0532111.484(18.3513, 129.835)
31801.264540.6099224.5815(169.131, 193.712)
42731.32682.542344.1711(251.305, 295.477)
5347-0.5590510.62743623.1592(332.166, 355.326)
6446-2.941210.34075420.0388(436.069, 456.108)
75343.380410.33191616.6846(528.124, 544.809)

Looking at the figure and the characteristic values, we can list the usefulness of each characteristic for filtering:

  • Peak height?
    • There are other peaks around the same height as the peaks we want, so applying a min or max height filter would remove peaks we want, or allow peaks we don't want.
  • Peak prominence?
    • All the peaks we want have similarly small prominences (<1) and the other peaks have much larger prominences (>2). This would be a good filtering option, using peakproms(pks; max=1).
  • Peak width?
    • The peaks we want have fairly similar widths (~15-25 elements wide), and the other peaks have larger widths (>40 elements wide). This would be a good filter, using peakwidths(pks; max=30).

In this case, filtering by peak prominence would be the better choice, because calculating peak widths depends on prominences, so filtering by peak prominence would do the job while avoiding unnecessary work.

In many cases, the desired peaks aren't very different from many other peaks in any one peak characteristic. In these situations, it may be necessary to filter multiple times based on different peak characteristics or different min/max thresholds. There is also a filterpeaks! function which allows you to give a filter predicate and filter by multiple characteristics at once.

+plotpeaks(t, y; peaks=pks.indices)Example block output

The simplest way to remove those peaks (assuming the signal is already filtered) is by setting the window w argument in findmaxima and friends:

pks = findmaxima(y, 15)
+f = plotpeaks(t, y; peaks=pks.indices)
Example block output

If only the peaks circled in blue are wanted, then setting the window w too wide won't work, since there are larger peaks that would become dominant.

Example block output

How to filter peaks by peak characteristics

Every peak-characteristic finding function can optionally filter the newly calculated characteristics using the keyword arguments min and max.

Plotting all the peak characteristics and/or looking at the characteristic values can help show which characteristics should be filtered to remove all the unwanted peaks.

plotpeaks!(f, t, y; peaks=pks.indices, prominences=true, widths=true)
Example block output
DataFrame(pks[Not(:data)])
7×5 DataFrame
Rowindicesheightspromswidthsedges
Int64Float64Float64Float64Tuple…
1373.39830.34976819.0733(28.6219, 47.6952)
21025.068835.0532111.484(18.3513, 129.835)
31801.264540.6099224.5815(169.131, 193.712)
42731.32682.542344.1711(251.305, 295.477)
5347-0.5590510.62743623.1592(332.166, 355.326)
6446-2.941210.34075420.0388(436.069, 456.108)
75343.380410.33191616.6846(528.124, 544.809)

Looking at the figure and the characteristic values, we can list the usefulness of each characteristic for filtering:

  • Peak height?
    • There are other peaks around the same height as the peaks we want, so applying a min or max height filter would remove peaks we want, or allow peaks we don't want.
  • Peak prominence?
    • All the peaks we want have similarly small prominences (<1) and the other peaks have much larger prominences (>2). This would be a good filtering option, using peakproms(pks; max=1).
  • Peak width?
    • The peaks we want have fairly similar widths (~15-25 elements wide), and the other peaks have larger widths (>40 elements wide). This would be a good filter, using peakwidths(pks; max=30).

In this case, filtering by peak prominence would be the better choice, because calculating peak widths depends on prominences, so filtering by peak prominence would do the job while avoiding unnecessary work.

In many cases, the desired peaks aren't very different from many other peaks in any one peak characteristic. In these situations, it may be necessary to filter multiple times based on different peak characteristics or different min/max thresholds. There is also a filterpeaks! function which allows you to give a filter predicate and filter by multiple characteristics at once.

diff --git a/dev/index-1a0a22b6.svg b/dev/index-23c935fc.svg similarity index 84% rename from dev/index-1a0a22b6.svg rename to dev/index-23c935fc.svg index 95b1196..995ee5c 100644 --- a/dev/index-1a0a22b6.svg +++ b/dev/index-23c935fc.svg @@ -1,91 +1,91 @@ - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/index-2f5a4d4b.svg b/dev/index-79cf55f8.svg similarity index 86% rename from dev/index-2f5a4d4b.svg rename to dev/index-79cf55f8.svg index d8c8fb7..6b146ad 100644 --- a/dev/index-2f5a4d4b.svg +++ b/dev/index-79cf55f8.svg @@ -1,54 +1,54 @@ - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/index-836c8f96.svg b/dev/index-f6220e48.svg similarity index 87% rename from dev/index-836c8f96.svg rename to dev/index-f6220e48.svg index b0d3973..cca5ea4 100644 --- a/dev/index-836c8f96.svg +++ b/dev/index-f6220e48.svg @@ -1,46 +1,46 @@ - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/index.html b/dev/index.html index dbda22e..04089ba 100644 --- a/dev/index.html +++ b/dev/index.html @@ -1,7 +1,5 @@ -Home · Peaks
GitHub

Peaks.jl

Peaks.jl is a library for finding peaks (i.e. local maxima and minima) and peak characteristics (e.g. how prominent or wide are peaks, etc) in vector (1D) data signals.

Installation

Peaks.jl can be installed from the Julia REPL by running

] add Peaks
    Updating registry at `~/.julia/registries/General.toml`
-   Resolving package versions...
-   Installed Peaks ─ v0.5.2
+Home · Peaks
GitHub
Peaks.jl
Peaks.jl is a library for finding peaks (i.e. local maxima and minima) and peak characteristics (e.g. how prominent or wide are peaks, etc) in vector (1D) data signals.
Installation
Peaks.jl can be installed from the Julia REPL by running
] add Peaks
   Resolving package versions...
     Updating `~/.julia/environments/v1.10/Project.toml`
   [18e31ff7] + Peaks v0.5.2
     Updating `~/.julia/environments/v1.10/Manifest.toml`
@@ -16,8 +14,5 @@
   [ea8e919c] + SHA v0.7.0
   [fa267f1f] + TOML v1.0.3
   [cf7118a7] + UUIDs
-  [4ec0a83e] + Unicode
-Precompiling project...
-  ✓ Peaks
-  1 dependency successfully precompiled in 1 seconds. 5 already precompiled.
Getting started
Finding peaks
Example block output
To find the peaks in your data you can use the findmaxima function, which returns a NamedTuple with fields for each calculated peak characteristic (indices, heights) and a reference to the data.
julia> indices, heights = findmaxima(y)(indices = [36, 100, 183, 273, 345, 445, 536], heights = [3.300118034348511, 4.982232717207901, 1.197072463435926, 1.2276270625137882, -0.6496226560177698, -3.0322485466891145, 3.300118034348519], data = [0.0, 0.1632851160646655, 0.32610451181030864, 0.4879949200150502, 0.6484979658187734, 0.8071625784012266, 0.9635473614762452, 1.1172229092385426, 1.2677740547092151, 1.4148020378099078  …  3.2394249315275934, 3.2820555214196987, 3.3284358209806952, 3.3784081950151283, 3.4317863042827303, 3.4883561669665792, 3.5478773815030995, 3.6100845038112075, 3.674688571077937, 3.7413787634226736])
When the peaks are plotted over the data, we see that all the local maxima have been identified.
Example block output
Peak characteristics
Peaks have various characteristics, including height, prominence, and width. Peaks.jl exports functions for finding each characteristic: peakheights, peakproms, peakwidths:
julia> indices, proms = peakproms(indices, y)([36, 100, 183, 273, 345, 445, 536], [0.2678694876593948, 4.982232717207901, 0.5474498074181589, 2.4552541250275763, 0.5474498074181591, 0.2678694876593921, 0.26786948765940455])
julia> indices, widths, edges... = peakwidths(indices, y, proms)([36, 100, 183, 273, 345, 445, 536], [17.62631562198532, 111.3881252794683, 24.143545188509734, 44.44393283928167, 23.091125504863612, 18.893643387694567, 17.626315621985214], [28.48833894440662, 18.671388334872134, 169.46890657137365, 251.0, 332.5310934286264, 436.9917020459134, 528.4883389444067], [46.11465456639194, 130.05951361434043, 193.61245175988338, 295.4439328392817, 355.62221893349, 455.885345433608, 546.1146545663919])
Mutating bang ('!') functions are available (i.e. peakproms!, etc.) when allocations are a concern.
Peaks NamedTuple & pipable API
There are Peaks.jl functions that bundle the peaks, peak characteristics, and signal into a convenient NamedTuple:
julia> pks = findmaxima(y);
julia> pks = peakproms(pks);
julia> pks = peakwidths(pks)(indices = [36, 100, 183, 273, 345, 445, 536], heights = [3.300118034348511, 4.982232717207901, 1.197072463435926, 1.2276270625137882, -0.6496226560177698, -3.0322485466891145, 3.300118034348519], data = [0.0, 0.1632851160646655, 0.32610451181030864, 0.4879949200150502, 0.6484979658187734, 0.8071625784012266, 0.9635473614762452, 1.1172229092385426, 1.2677740547092151, 1.4148020378099078  …  3.2394249315275934, 3.2820555214196987, 3.3284358209806952, 3.3784081950151283, 3.4317863042827303, 3.4883561669665792, 3.5478773815030995, 3.6100845038112075, 3.674688571077937, 3.7413787634226736], proms = [0.2678694876593948, 4.982232717207901, 0.5474498074181589, 2.4552541250275763, 0.5474498074181591, 0.2678694876593921, 0.26786948765940455], widths = [17.62631562198532, 111.3881252794683, 24.143545188509734, 44.44393283928167, 23.091125504863612, 18.893643387694567, 17.626315621985214], edges = [(28.48833894440662, 46.11465456639194), (18.671388334872134, 130.05951361434043), (169.46890657137365, 193.61245175988338), (251.0, 295.4439328392817), (332.5310934286264, 355.62221893349), (436.9917020459134, 455.885345433608), (528.4883389444067, 546.1146545663919)])
Mutating functions are also available for the NamedTuple functions; the vectors within the NamedTuple are mutated and re-used in the returned tuple. The NamedTuple functions can also be chained/piped:
julia> pks = findmaxima(y) |> peakproms!(;strict=false) |> peakwidths!(; max=100)(indices = [36, 183, 273, 345, 445, 536], heights = [3.300118034348511, 1.197072463435926, 1.2276270625137882, -0.6496226560177698, -3.0322485466891145, 3.300118034348519], data = [0.0, 0.1632851160646655, 0.32610451181030864, 0.4879949200150502, 0.6484979658187734, 0.8071625784012266, 0.9635473614762452, 1.1172229092385426, 1.2677740547092151, 1.4148020378099078  …  3.2394249315275934, 3.2820555214196987, 3.3284358209806952, 3.3784081950151283, 3.4317863042827303, 3.4883561669665792, 3.5478773815030995, 3.6100845038112075, 3.674688571077937, 3.7413787634226736], proms = [0.2678694876593948, 0.5474498074181589, 2.4552541250275763, 0.5474498074181591, 0.2678694876593921, 0.26786948765940455], widths = [17.62631562198532, 24.143545188509734, 44.44393283928167, 23.091125504863612, 18.893643387694567, 17.626315621985214], edges = [(28.48833894440662, 46.11465456639194), (169.46890657137365, 193.61245175988338), (251.0, 295.4439328392817), (332.5310934286264, 355.62221893349), (436.9917020459134, 455.885345433608), (528.4883389444067, 546.1146545663919)])
Performance tip
Be aware that the NamedTuple functions allocate more memory than the functions with direct/explicit arguments. If maximum performance is needed, mutating functions (e.g. peakproms!, etc) and/or the direct, non-NamedTuple methods are a better choice.
Plotting
The peaks, prominences, and widths can be visualized all together using a Plots.jl recipe plotpeaks:
using Plots
-plotpeaks(t, y; peaks=indices, prominences=true, widths=true)
Example block output

+  [4ec0a83e] + Unicode

Getting started

Finding peaks

Example block output

To find the peaks in your data you can use the findmaxima function, which returns a NamedTuple with fields for each calculated peak characteristic (indices, heights) and a reference to the data.

julia> indices, heights = findmaxima(y)(indices = [36, 100, 183, 273, 345, 445, 536], heights = [3.300118034348511, 4.982232717207901, 1.197072463435926, 1.2276270625137882, -0.6496226560177698, -3.0322485466891145, 3.300118034348519], data = [0.0, 0.1632851160646655, 0.32610451181030864, 0.4879949200150502, 0.6484979658187734, 0.8071625784012266, 0.9635473614762452, 1.1172229092385426, 1.2677740547092151, 1.4148020378099078  …  3.2394249315275934, 3.2820555214196987, 3.3284358209806952, 3.3784081950151283, 3.4317863042827303, 3.4883561669665792, 3.5478773815030995, 3.6100845038112075, 3.674688571077937, 3.7413787634226736])

When the peaks are plotted over the data, we see that all the local maxima have been identified.

Example block output

Peak characteristics

Peaks have various characteristics, including height, prominence, and width. Peaks.jl exports functions for finding each characteristic: peakheights, peakproms, peakwidths:

julia> indices, proms = peakproms(indices, y)([36, 100, 183, 273, 345, 445, 536], [0.2678694876593948, 4.982232717207901, 0.5474498074181589, 2.4552541250275763, 0.5474498074181591, 0.2678694876593921, 0.26786948765940455])
julia> indices, widths, edges... = peakwidths(indices, y, proms)([36, 100, 183, 273, 345, 445, 536], [17.62631562198532, 111.3881252794683, 24.143545188509734, 44.44393283928167, 23.091125504863612, 18.893643387694567, 17.626315621985214], [28.48833894440662, 18.671388334872134, 169.46890657137365, 251.0, 332.5310934286264, 436.9917020459134, 528.4883389444067], [46.11465456639194, 130.05951361434043, 193.61245175988338, 295.4439328392817, 355.62221893349, 455.885345433608, 546.1146545663919])

Mutating bang ('!') functions are available (i.e. peakproms!, etc.) when allocations are a concern.

Peaks NamedTuple & pipable API

There are Peaks.jl functions that bundle the peaks, peak characteristics, and signal into a convenient NamedTuple:

julia> pks = findmaxima(y);
julia> pks = peakproms(pks);
julia> pks = peakwidths(pks)(indices = [36, 100, 183, 273, 345, 445, 536], heights = [3.300118034348511, 4.982232717207901, 1.197072463435926, 1.2276270625137882, -0.6496226560177698, -3.0322485466891145, 3.300118034348519], data = [0.0, 0.1632851160646655, 0.32610451181030864, 0.4879949200150502, 0.6484979658187734, 0.8071625784012266, 0.9635473614762452, 1.1172229092385426, 1.2677740547092151, 1.4148020378099078  …  3.2394249315275934, 3.2820555214196987, 3.3284358209806952, 3.3784081950151283, 3.4317863042827303, 3.4883561669665792, 3.5478773815030995, 3.6100845038112075, 3.674688571077937, 3.7413787634226736], proms = [0.2678694876593948, 4.982232717207901, 0.5474498074181589, 2.4552541250275763, 0.5474498074181591, 0.2678694876593921, 0.26786948765940455], widths = [17.62631562198532, 111.3881252794683, 24.143545188509734, 44.44393283928167, 23.091125504863612, 18.893643387694567, 17.626315621985214], edges = [(28.48833894440662, 46.11465456639194), (18.671388334872134, 130.05951361434043), (169.46890657137365, 193.61245175988338), (251.0, 295.4439328392817), (332.5310934286264, 355.62221893349), (436.9917020459134, 455.885345433608), (528.4883389444067, 546.1146545663919)])

Mutating functions are also available for the NamedTuple functions; the vectors within the NamedTuple are mutated and re-used in the returned tuple. The NamedTuple functions can also be chained/piped:

julia> pks = findmaxima(y) |> peakproms!(;strict=false) |> peakwidths!(; max=100)(indices = [36, 183, 273, 345, 445, 536], heights = [3.300118034348511, 1.197072463435926, 1.2276270625137882, -0.6496226560177698, -3.0322485466891145, 3.300118034348519], data = [0.0, 0.1632851160646655, 0.32610451181030864, 0.4879949200150502, 0.6484979658187734, 0.8071625784012266, 0.9635473614762452, 1.1172229092385426, 1.2677740547092151, 1.4148020378099078  …  3.2394249315275934, 3.2820555214196987, 3.3284358209806952, 3.3784081950151283, 3.4317863042827303, 3.4883561669665792, 3.5478773815030995, 3.6100845038112075, 3.674688571077937, 3.7413787634226736], proms = [0.2678694876593948, 0.5474498074181589, 2.4552541250275763, 0.5474498074181591, 0.2678694876593921, 0.26786948765940455], widths = [17.62631562198532, 24.143545188509734, 44.44393283928167, 23.091125504863612, 18.893643387694567, 17.626315621985214], edges = [(28.48833894440662, 46.11465456639194), (169.46890657137365, 193.61245175988338), (251.0, 295.4439328392817), (332.5310934286264, 355.62221893349), (436.9917020459134, 455.885345433608), (528.4883389444067, 546.1146545663919)])
Performance tip

Be aware that the NamedTuple functions allocate more memory than the functions with direct/explicit arguments. If maximum performance is needed, mutating functions (e.g. peakproms!, etc) and/or the direct, non-NamedTuple methods are a better choice.

Plotting

The peaks, prominences, and widths can be visualized all together using a Plots.jl recipe plotpeaks:

using Plots
+plotpeaks(t, y; peaks=indices, prominences=true, widths=true)
Example block output
diff --git a/dev/reference/index.html b/dev/reference/index.html index 4b071ff..edf2653 100644 --- a/dev/reference/index.html +++ b/dev/reference/index.html @@ -10,7 +10,7 @@ julia> argmaxima([2,0,1,1]; strict=false) 2-element Vector{Int64}: 1 - 3source
Peaks.argminima — Function
argminima(x[, w=1; strict=false]) -> Vector{Int}

Find the indices of local minima in x, where each minima i is either the minimum of x[i-w:i+w] or the first index of a plateau.

A plateau is defined as a minima with consecutive equal (===/egal) minimal values which are bounded by greater values immediately before and after the consecutive minimal values.

When strict == true, no elements in x[i-w:i+w] may be missing or NaN, and the bounds of a plateau must exist. For strict == false, a minima is the minimum of all non-NaN or missing elements in x[i-w:i+w], and plateau bounds are assumed to exist (i.e. missing, NaN, or either end of the array, x[begin-1] or x[end+1], may be treated as the bounds of a plateau).

See also: findminima, findnextminima

Examples

julia> argminima([3,2,3,1,1,3])
+ 3
source
Peaks.argminima — Function
argminima(x[, w=1; strict=false]) -> Vector{Int}

Find the indices of local minima in x, where each minima i is either the minimum of x[i-w:i+w] or the first index of a plateau.

A plateau is defined as a minima with consecutive equal (===/egal) minimal values which are bounded by greater values immediately before and after the consecutive minimal values.

When strict == true, no elements in x[i-w:i+w] may be missing or NaN, and the bounds of a plateau must exist. For strict == false, a minima is the minimum of all non-NaN or missing elements in x[i-w:i+w], and plateau bounds are assumed to exist (i.e. missing, NaN, or either end of the array, x[begin-1] or x[end+1], may be treated as the bounds of a plateau).

See also: findminima, findnextminima

Examples

julia> argminima([3,2,3,1,1,3])
 2-element Vector{Int64}:
  2
  4
@@ -21,29 +21,29 @@
 julia> argminima([2,3,1,1]; strict=false)
 2-element Vector{Int64}:
  1
- 3
source
Peaks.maxima — Function
maxima(x[, w=1; strict=true]) -> Vector{eltype(x)}

Find the values of local maxima in x, where each maxima i is either the maximum of x[i-w:i+w] or the first index of a plateau.

A plateau is defined as a maxima with consecutive equal (===/egal) maximal values which are bounded by lesser values immediately before and after the consecutive maximal values.

See also: argmaxima, findnextmaxima

source
Peaks.minima — Function
minima(x[, w=1; strict=true]) -> Vector{eltype(x)}

Find the values of local minima in x, where each minima i is either the minimum of x[i-w:i+w] or the first index of a plateau.

A plateau is defined as a minima with consecutive equal (===/egal) minimal values which are bounded by greater values immediately before and after the consecutive minimal values.

See also: argminima, findnextminima

source
Peaks.findmaxima — Function
findmaxima(x[, w=1; strict=true]) -> (;indices, heights, data)

Find the indices and values of local maxima in x, where each maxima i is either the maximum of x[i-w:i+w] or the first index of a plateau.

Returns a NamedTuple contains the fields indices, heights, data, which are equivalent to heights = data[indices]. The data field is a reference (not a copy) to the argument x.

A plateau is defined as a maxima with consecutive equal (===/egal) maximal values which are bounded by lesser values immediately before and after the consecutive maximal values.

See also: argmaxima, findnextmaxima

Examples

julia> data = [1, 5, 1, 3, 2];
+ 3
source
Peaks.maxima — Function
maxima(x[, w=1; strict=true]) -> Vector{eltype(x)}

Find the values of local maxima in x, where each maxima i is either the maximum of x[i-w:i+w] or the first index of a plateau.

A plateau is defined as a maxima with consecutive equal (===/egal) maximal values which are bounded by lesser values immediately before and after the consecutive maximal values.

See also: argmaxima, findnextmaxima

source
Peaks.minima — Function
minima(x[, w=1; strict=true]) -> Vector{eltype(x)}

Find the values of local minima in x, where each minima i is either the minimum of x[i-w:i+w] or the first index of a plateau.

A plateau is defined as a minima with consecutive equal (===/egal) minimal values which are bounded by greater values immediately before and after the consecutive minimal values.

See also: argminima, findnextminima

source
Peaks.findmaxima — Function
findmaxima(x[, w=1; strict=true]) -> (;indices, heights, data)

Find the indices and values of local maxima in x, where each maxima i is either the maximum of x[i-w:i+w] or the first index of a plateau.

Returns a NamedTuple contains the fields indices, heights, data, which are equivalent to heights = data[indices]. The data field is a reference (not a copy) to the argument x.

A plateau is defined as a maxima with consecutive equal (===/egal) maximal values which are bounded by lesser values immediately before and after the consecutive maximal values.

See also: argmaxima, findnextmaxima

Examples

julia> data = [1, 5, 1, 3, 2];
 
 julia> pks = findmaxima(data)
-(indices = [2, 4], heights = [5, 3], data = [1, 5, 1, 3, 2])
source
Peaks.findminima — Function
findminima(x[, w=1; strict=true]) -> (;indices, heights, data)

Find the indices and values of local minima in x, where each minima i is either the minimum of x[i-w:i+w] or the first index of a plateau.

Returns a NamedTuple contains the fields indices, heights, data, which are equivalent to heights = data[indices]. The data field is a reference (not a copy) to the argument x.

A plateau is defined as a minima with consecutive equal (===/egal) minimal values which are bounded by greater values immediately before and after the consecutive minimal values.

See also: argminima, findnextminima

Examples

julia> data = [1, 5, 1, 3, 2];
+(indices = [2, 4], heights = [5, 3], data = [1, 5, 1, 3, 2])
source
Peaks.findminima — Function
findminima(x[, w=1; strict=true]) -> (;indices, heights, data)

Find the indices and values of local minima in x, where each minima i is either the minimum of x[i-w:i+w] or the first index of a plateau.

Returns a NamedTuple contains the fields indices, heights, data, which are equivalent to heights = data[indices]. The data field is a reference (not a copy) to the argument x.

A plateau is defined as a minima with consecutive equal (===/egal) minimal values which are bounded by greater values immediately before and after the consecutive minimal values.

See also: argminima, findnextminima

Examples

julia> data = [1, 5, 1, 3, 2];
 
 julia> valleys = findminima(data)
-(indices = [3], heights = [1], data = [1, 5, 1, 3, 2])
source

Peak characteristics & filtering

Peaks.peakproms — Function
peakproms(indices, x; [strict=true, min, max]) -> (indices, proms)
+(indices = [3], heights = [1], data = [1, 5, 1, 3, 2])
source

Peak characteristics & filtering

Peaks.peakproms — Function
peakproms(indices, x; [strict=true, min, max]) -> (indices, proms)
 peakproms(pks::NamedTuple; [strict=true, min, max]) -> NamedTuple

Calculate the prominences of peak indices in x, and remove peaks with prominences less than min and/or greater than max.

Peak prominence is the absolute height (value) difference between the current peak and the larger of the two adjacent smallest magnitude points between the current peak and adjacent larger peaks or signal ends.

If a NamedTuple pks is given, a new NamedTuple is returned with filtered copies of fields from pks. pks must have :indices and :heights fields. If pks has a :proms field, prominences will only be filtered, and not be recalculated. The fields :widths and :edges will also be filtered if present, and any remaining fields will be copied unmodified.

If strict == true, the prominence for a peak with a NaN or missing between the current peak and either adjacent larger peaks will be NaN or missing, otherwise, it will be the larger of the smallest non-NaN or missing values between the current peak and adjacent larger peaks for strict == false.

See also: peakproms!, findmaxima

Examples

julia> pks = findmaxima([0,5,2,3,3,1,4,0]);
 
 julia> pks = peakproms(pks; min=2)
 (indices = [2, 7], heights = [5, 4], data = [0, 5, 2, 3, 3, 1, 4, 0], proms = Union{Missing, Int64}[5, 3])
 
 julia> inds, proms = peakproms(pks.indices, pks.data; max=4)
-([7], Union{Missing, Int64}[3])
source
peakproms(; [strict, min, max]) -> Function

Create a function, f(pks::NamedTuple), that calculates and filters the peak prominences of a copy of its argument, pks, using any given keyword arguments.

Examples

julia> findmaxima([0,5,2,3,3,1,4,0]) |> peakproms(; min=2)
-(indices = [2, 7], heights = [5, 4], data = [0, 5, 2, 3, 3, 1, 4, 0], proms = Union{Missing, Int64}[5, 3])
source
Peaks.peakproms! — Function
peakproms!(indices, x; [strict=true, min, max]) -> (indices, proms)
+([7], Union{Missing, Int64}[3])
source
peakproms(; [strict, min, max]) -> Function

Create a function, f(pks::NamedTuple), that calculates and filters the peak prominences of a copy of its argument, pks, using any given keyword arguments.

Examples

julia> findmaxima([0,5,2,3,3,1,4,0]) |> peakproms(; min=2)
+(indices = [2, 7], heights = [5, 4], data = [0, 5, 2, 3, 3, 1, 4, 0], proms = Union{Missing, Int64}[5, 3])
source
Peaks.peakproms! — Function
peakproms!(indices, x; [strict=true, min, max]) -> (indices, proms)
 peakproms!(pks::NamedTuple; [strict=true, min, max]) -> NamedTuple

Calculate the prominences of peak indices in x, and remove peaks with prominences less than min and/or greater than max.

If a NamedTuple pks is given, a new NamedTuple is returned with the same fields (references) from pks. pks must have :indices and :heights fields. If pks has a :proms field, prominences will only be filtered, and not be recalculated. The fields :widths and :edges will also be filtered (mutated) if present, and any remaining fields will be copied unmodified.

See also: peakproms, findmaxima

Examples

julia> pks = findmaxima([0,5,2,3,3,1,4,0]);
 
 julia> pks = peakproms!(pks; min=2)
 (indices = [2, 7], heights = [5, 4], data = [0, 5, 2, 3, 3, 1, 4, 0], proms = Union{Missing, Int64}[5, 3])
 
 julia> inds, proms = peakproms!(pks.indices, pks.data; max=4)
-([7], Union{Missing, Int64}[3])
source
peakproms!(; [strict, min, max]) -> Function

Create a function, f(pks::NamedTuple), that calculates and filters (mutates) the peak prominences and other fields of its argument, pks, using any given keyword arguments.

Examples

julia> findmaxima([0,5,2,3,3,1,4,0]) |> peakproms!(; min=2)
-(indices = [2, 7], heights = [5, 4], data = [0, 5, 2, 3, 3, 1, 4, 0], proms = Union{Missing, Int64}[5, 3])
source
Peaks.peakwidths — Function
peakwidths(indices, x, proms; [strict=true, relheight=0.5, min, max]) -> (indices, widths, ledge, redge)
+([7], Union{Missing, Int64}[3])
source
peakproms!(; [strict, min, max]) -> Function

Create a function, f(pks::NamedTuple), that calculates and filters (mutates) the peak prominences and other fields of its argument, pks, using any given keyword arguments.

Examples

julia> findmaxima([0,5,2,3,3,1,4,0]) |> peakproms!(; min=2)
+(indices = [2, 7], heights = [5, 4], data = [0, 5, 2, 3, 3, 1, 4, 0], proms = Union{Missing, Int64}[5, 3])
source
Peaks.peakwidths — Function
peakwidths(indices, x, proms; [strict=true, relheight=0.5, min, max]) -> (indices, widths, ledge, redge)
 peakwidths(pks::NamedTuple; [strict=true, relheight=0.5, min, max]) -> NamedTuple

Calculate the widths of peak indices in x at a reference level based on proms and relheight, and removing peaks with widths less than min and/or greater than max. Returns the peaks, widths, and the left and right edges at the reference level.

Peak width is the distance between the signal crossing a reference level before and after the peak. Signal crossings are linearly interpolated between indices. The reference level is the difference between the peak height and relheight times the peak prominence. Width cannot be calculated for a NaN or missing prominence.

If a NamedTuple pks is given, a new NamedTuple is returned with filtered copies of fields from pks. pks must have :indices, :heights, and :proms fields. If pks has :widths and :edges fields, they will not be recalculated, but filtered only. Any remaining fields will be copied unmodified.

If strict == true, the width for a peak with a gap in the signal (e.g. NaN, missing) at the reference level will match the gap (e.g. NaN for NaN, etc.). Otherwise, the signal crossing will be linearly interpolated between the edges of the gap.

See also: peakwidths!, peakproms, findmaxima

Examples

julia> x = Float64[0,5,2,2,3,3,1,4,0];
 
 julia> pks = findmaxima(x) |> peakproms!(;max=2);
@@ -57,8 +57,8 @@
 ([5], [NaN], [NaN], [6.25])
 
 julia> peakwidths(pks.indices, x, pks.proms; strict=false)
-([5], [2.25], [4.0], [6.25])
source
peakwidths(; [strict, relheight, min, max]) -> Function

Create a function, f(pks::NamedTuple), that calculates and filters the peak widths of a copy of its argument, pks, using any given keyword arguments.

Examples

julia> findmaxima([0,5,2,3,3,1,4,0]) |> peakproms() |> peakwidths(; min=1.5)
-(indices = [4], heights = [3], data = [0, 5, 2, 3, 3, 1, 4, 0], proms = Union{Missing, Int64}[1], widths = Union{Missing, Float64}[1.75], edges = Tuple{Union{Missing, Float64}, Union{Missing, Float64}}[(3.5, 5.25)])
source
Peaks.peakwidths! — Function
peakwidths!(indices, x; [strict=true, relheight=0.5, min, max]) -> (indices, widths, ledge, redge)
+([5], [2.25], [4.0], [6.25])
source
peakwidths(; [strict, relheight, min, max]) -> Function

Create a function, f(pks::NamedTuple), that calculates and filters the peak widths of a copy of its argument, pks, using any given keyword arguments.

Examples

julia> findmaxima([0,5,2,3,3,1,4,0]) |> peakproms() |> peakwidths(; min=1.5)
+(indices = [4], heights = [3], data = [0, 5, 2, 3, 3, 1, 4, 0], proms = Union{Missing, Int64}[1], widths = Union{Missing, Float64}[1.75], edges = Tuple{Union{Missing, Float64}, Union{Missing, Float64}}[(3.5, 5.25)])
source
Peaks.peakwidths! — Function
peakwidths!(indices, x; [strict=true, relheight=0.5, min, max]) -> (indices, widths, ledge, redge)
 peakwidths!(pks::NamedTuple; [strict=true, relheight=0.5, min, max]) -> NamedTuple

Calculate the widths of peak indices in x at a reference level based on proms and relheight, removing peaks with widths less than min and/or greater than max. Returns the modified peaks, widths, and the left and right edges at the reference level.

If a NamedTuple pks is given, a new NamedTuple is returned with the same fields (references) from pks. pks must have :indices, :heights, and :proms fields. If pks has :widths and :edges fields, they will not be recalculated, but filtered only. Any remaining fields will be copied unmodified.

See also: peakwidths, peakproms, findmaxima

Examples

julia> x = Float64[0,5,2,2,3,3,1,4,0];
 
 julia> pks = findmaxima(x) |> peakproms!();
@@ -67,8 +67,8 @@
 (indices = [2, 5], heights = [5.0, 3.0], data = [0.0, 5.0, 2.0, 2.0, 3.0, 3.0, 1.0, 4.0, 0.0], proms = [5.0, 1.0], widths = [1.333, 1.75], edges = [(1.5, 2.833), (4.5, 6.25)])
 
 julia> peakwidths!(pks.indices, pks.data, pks.proms; min=1)
-([2, 5], [1.333, 1.75], [1.5, 4.5], [2.833, 6.25])
source
peakwidths!(; [strict, relheight, min, max]) -> Function

Create a function, f(pks::NamedTuple), that calculates and filters (mutates) the peak widths and other fields of its argument, pks, using any given keyword arguments.

Examples

julia> findmaxima([0,5,2,3,3,1,4,0]) |> peakproms!() |> peakwidths!(; min=1.5)
-(indices = [4], heights = [3], data = [0, 5, 2, 3, 3, 1, 4, 0], proms = Union{Missing, Int64}[1], widths = Union{Missing, Float64}[1.75], edges = Tuple{Union{Missing, Float64}, Union{Missing, Float64}}[(3.5, 5.25)])
source
Peaks.peakheights — Function
peakheights(indices, heights; [min, max]) -> (indices, heights)
+([2, 5], [1.333, 1.75], [1.5, 4.5], [2.833, 6.25])
source
peakwidths!(; [strict, relheight, min, max]) -> Function

Create a function, f(pks::NamedTuple), that calculates and filters (mutates) the peak widths and other fields of its argument, pks, using any given keyword arguments.

Examples

julia> findmaxima([0,5,2,3,3,1,4,0]) |> peakproms!() |> peakwidths!(; min=1.5)
+(indices = [4], heights = [3], data = [0, 5, 2, 3, 3, 1, 4, 0], proms = Union{Missing, Int64}[1], widths = Union{Missing, Float64}[1.75], edges = Tuple{Union{Missing, Float64}, Union{Missing, Float64}}[(3.5, 5.25)])
source
Peaks.peakheights — Function
peakheights(indices, heights; [min, max]) -> (indices, heights)
 peakheights(pks::NamedTuple; [min, max]) -> NamedTuple

Return a copy of indices and heights where peaks are removed if their height is less than min and/or greater than max.

If a NamedTuple pks is given, a new NamedTuple is returned with filtered copies of fields from pks. pks must have :indices and :heights fields. The fields :proms, :widths, and :edges will be filtered if present, and any remaining fields will be copied unmodified.

See also: peakproms, peakwidths, findmaxima

Examples

julia> x = [0,5,2,3,3,1,4,0];
 
 julia> pks = findmaxima(x)
@@ -78,8 +78,8 @@
 (indices = [4, 7], heights = [3, 4], data = [0, 5, 2, 3, 3, 1, 4, 0])
 
 julia> inds, heights = peakheights(pks.indices, pks.heights; max=4)
-([4, 7], [3, 4])
source
peakheights(; [min, max]) -> Function

Create a function, f(pks::NamedTuple), that copies and filters the peak heights of its argument, pks, using any given keyword arguments.

Examples

julia> findmaxima([0, 5, 2, 3, 3, 1, 4, 0]) |> peakheights(; max=4)
-(indices = [4, 7], heights = [3, 4], data = [0, 5, 2, 3, 3, 1, 4, 0])
source
Peaks.peakheights! — Function
peakheights!(indices, heights; [min, max]) -> (indices, heights)
+([4, 7], [3, 4])
source
peakheights(; [min, max]) -> Function

Create a function, f(pks::NamedTuple), that copies and filters the peak heights of its argument, pks, using any given keyword arguments.

Examples

julia> findmaxima([0, 5, 2, 3, 3, 1, 4, 0]) |> peakheights(; max=4)
+(indices = [4, 7], heights = [3, 4], data = [0, 5, 2, 3, 3, 1, 4, 0])
source
Peaks.peakheights! — Function
peakheights!(indices, heights; [min, max]) -> (indices, heights)
 peakheights!(pks::NamedTuple; [min, max]) -> NamedTuple

Filter (mutate) and return indices and heights by removing peaks that are less than min and/or greater than max.

If a NamedTuple pks is given, a new NamedTuple is returned with the same fields (references) from pks. pks must have :indices and :heights fields. The fields :proms, :widths, and :edges will be filtered (mutated) if present, and any remaining fields will be referenced unmodified.

See also: peakproms, peakwidths, findmaxima filterpeaks!

Examples

julia> x = [0,5,2,3,3,1,4,0];
 
 julia> pks = findmaxima(x)
@@ -89,8 +89,8 @@
 (indices = [4, 7], heights = [3, 4], data = [0, 5, 2, 3, 3, 1, 4, 0])
 
 julia> inds, heights = peakheights!(pks.indices, pks.heights; min=3.5)
-([7], [4])
source
peakheights!(; [min, max]) -> Function

Create a function, f(pks::NamedTuple), that calculates peak heights and then filters (mutates) the fields of its argument, pks, using any given keyword arguments.

Examples

julia> findmaxima([0, 5, 2, 3, 3, 1, 4, 0]) |> peakheights!(; max=4)
-(indices = [4, 7], heights = [3, 4], data = [0, 5, 2, 3, 3, 1, 4, 0])
source
Peaks.filterpeaks! — Function
filterpeaks!(pks::NT, feature; [min, max]) where {NT<:NamedTuple} -> pks::NT
+([7], [4])
source
peakheights!(; [min, max]) -> Function

Create a function, f(pks::NamedTuple), that calculates peak heights and then filters (mutates) the fields of its argument, pks, using any given keyword arguments.

Examples

julia> findmaxima([0, 5, 2, 3, 3, 1, 4, 0]) |> peakheights!(; max=4)
+(indices = [4, 7], heights = [3, 4], data = [0, 5, 2, 3, 3, 1, 4, 0])
source
Peaks.filterpeaks! — Function
filterpeaks!(pks::NT, feature; [min, max]) where {NT<:NamedTuple} -> pks::NT
 filterpeaks!(pks::NT, mask) -> pks::NT

Filter the standard pks fields where peaks are removed if pks.$feature is less than min and/or greater than max. If a mask is given, a given peak i is filtered (removed) if mask[i] is false.

Standard Peaks.jl fields of pks are :indices, :proms, :heights, :widths, :edges.

Examples

julia> pks = findmaxima([0,5,2,3,3,1,4,0])
 (indices = [2, 4, 7], heights = [5, 3, 4], data = [0, 5, 2, 3, 3, 1, 4, 0])
 
@@ -106,20 +106,20 @@
  1
 
 julia> filterpeaks!(pks, mask)
-(indices = [7], heights = [4], data = [0, 5, 2, 3, 3, 1, 4, 0], proms = Union{Missing, Int64}[3])
source
filterpeaks!(pred, pks) -> NamedTuple

Apply a predicate function pred to NamedTuple slices (the scalar values related to each peak, e.g. (indices=5, heights=3, proms=2)) to and remove a peak if pred returns false.

Examples

julia> pks = findmaxima([0,5,2,3,3,1,4,0])
+(indices = [7], heights = [4], data = [0, 5, 2, 3, 3, 1, 4, 0], proms = Union{Missing, Int64}[3])
source
filterpeaks!(pred, pks) -> NamedTuple

Apply a predicate function pred to NamedTuple slices (the scalar values related to each peak, e.g. (indices=5, heights=3, proms=2)) to and remove a peak if pred returns false.

Examples

julia> pks = findmaxima([0,5,2,3,3,1,4,0])
 (indices = [2, 4, 7], heights = [5, 3, 4], data = [0, 5, 2, 3, 3, 1, 4, 0])
 
 julia> filterpeaks!(pks) do nt
            return nt.heights ≥ 5 || nt.heights ≤ 3
        end
-(indices = [2, 4], heights = [5, 3], data = [0, 5, 2, 3, 3, 1, 4, 0])
source

Convenience functions

Peaks.findnextmaxima — Function
findnextmaxima(x, i[, w=1; strict=true]) -> Int

Find the index of the next maxima in x after or including i, where the maxima i is either the maximum of x[i-w:i+w] or the first index of a plateau. Returns lastindex(x) + 1 if no maxima occur after i.

A plateau is defined as a maxima with consecutive equal (===/egal) maximal values which are bounded by lesser values immediately before and after the consecutive maximal values.

When strict == true, no elements in x[i-w:i+w] may be missing or NaN, and the bounds of a plateau must exist. For strict == false, a maxima is the maximum of all non-NaN or missing elements in x[i-w:i+w], and plateau bounds are assumed to exist (i.e. missing, NaN, or either end of the array, x[begin-1] or x[end+1], may be treated as the bounds of a plateau).

See also: argmaxima

Examples

julia> findnextmaxima([0,2,0,1,1,0], 2)
+(indices = [2, 4], heights = [5, 3], data = [0, 5, 2, 3, 3, 1, 4, 0])
source

Convenience functions

Peaks.findnextmaxima — Function
findnextmaxima(x, i[, w=1; strict=true]) -> Int

Find the index of the next maxima in x after or including i, where the maxima i is either the maximum of x[i-w:i+w] or the first index of a plateau. Returns lastindex(x) + 1 if no maxima occur after i.

A plateau is defined as a maxima with consecutive equal (===/egal) maximal values which are bounded by lesser values immediately before and after the consecutive maximal values.

When strict == true, no elements in x[i-w:i+w] may be missing or NaN, and the bounds of a plateau must exist. For strict == false, a maxima is the maximum of all non-NaN or missing elements in x[i-w:i+w], and plateau bounds are assumed to exist (i.e. missing, NaN, or either end of the array, x[begin-1] or x[end+1], may be treated as the bounds of a plateau).

See also: argmaxima

Examples

julia> findnextmaxima([0,2,0,1,1,0], 2)
 2
 
 julia> findnextmaxima([0,2,0,1,1,0], 3)
 4
-
source
Peaks.findnextminima — Function
findnextminima(x, i[, w=1, strict=true]) -> Int

Find the index of the next minima in x, after or including i, where the minima i is either the minimum of x[i-w:i+w] or the first index of a plateau. Returns lastindex(x) + 1 if no minima occur after i.

A plateau is defined as a minima with consecutive equal (===/egal) minimal values which are bounded by greater values immediately before and after the consecutive minimal values.

When strict == true, no elements in x[i-w:i+w] may be missing or NaN, and the bounds of a plateau must exist. For strict == false, a minima is the minimum of all non-NaN or missing elements in x[i-w:i+w], and plateau bounds are assumed to exist (i.e. missing, NaN, or either end of the array, x[begin-1] or x[end+1], may be treated as the bounds of a plateau).

See also: argminima

Examples

julia> findnextminima([3,2,3,1,1,3], 2)
+
source
Peaks.findnextminima — Function
findnextminima(x, i[, w=1, strict=true]) -> Int

Find the index of the next minima in x, after or including i, where the minima i is either the minimum of x[i-w:i+w] or the first index of a plateau. Returns lastindex(x) + 1 if no minima occur after i.

A plateau is defined as a minima with consecutive equal (===/egal) minimal values which are bounded by greater values immediately before and after the consecutive minimal values.

When strict == true, no elements in x[i-w:i+w] may be missing or NaN, and the bounds of a plateau must exist. For strict == false, a minima is the minimum of all non-NaN or missing elements in x[i-w:i+w], and plateau bounds are assumed to exist (i.e. missing, NaN, or either end of the array, x[begin-1] or x[end+1], may be treated as the bounds of a plateau).

See also: argminima

Examples

julia> findnextminima([3,2,3,1,1,3], 2)
 2
 
 julia> findnextminima([3,2,3,1,1,3], 3)
 4
-
source
Peaks.ismaxima — Function
ismaxima(i, x[, w=1; strict=true]) -> Bool

Test if i is a maxima in x, where the maxima i is either the maximum of x[i-w:i+w] or the first index of a plateau.

A plateau is defined as a maxima with consecutive equal (===/egal) maximal values which are bounded by lesser values immediately before and after the consecutive maximal values.

See also: findnextmaxima

source
Peaks.isminima — Function
isminima(i, x[, w=1; strict=true]) -> Bool

Test if i is a minima in x, where the minima i is either the minimum of x[i-w:i+w] or the first index of a plateau.

A plateau is defined as a minima with consecutive equal (===/egal) minimal values which are bounded by greater values immediately before and after the consecutive minimal values.

See also: findnextminima

source
+source
Peaks.ismaxima — Function
ismaxima(i, x[, w=1; strict=true]) -> Bool

Test if i is a maxima in x, where the maxima i is either the maximum of x[i-w:i+w] or the first index of a plateau.

A plateau is defined as a maxima with consecutive equal (===/egal) maximal values which are bounded by lesser values immediately before and after the consecutive maximal values.

See also: findnextmaxima

source
Peaks.isminima — Function
isminima(i, x[, w=1; strict=true]) -> Bool

Test if i is a minima in x, where the minima i is either the minimum of x[i-w:i+w] or the first index of a plateau.

A plateau is defined as a minima with consecutive equal (===/egal) minimal values which are bounded by greater values immediately before and after the consecutive minimal values.

See also: findnextminima

source