replace_bad_pixs: Replace bad pixels in a spectrum

In photobiology: Photobiological Calculations

Replace bad pixels in a spectrum

Description

This function replaces data for bad pixels by a local estimate, by either simple interpolation or using the algorithm of Whitaker and Hayes (2018).

Usage

replace_bad_pixs(
  x,
  bad.pix.idx = FALSE,
  window.width = min(11, length(x) - 1),
  method = "run.mean",
  na.rm = TRUE
)

Arguments

x	numeric vector containing spectral data.
bad.pix.idx	logical vector or integer. Index into bad pixels in x.
window.width	integer. The full width of the window used for the running mean.
method	character The name of the method: "run.mean" is running mean as described in Whitaker and Hayes (2018); "adj.mean" is mean of adjacent neighbors (isolated bad pixels only).
na.rm	logical Treat NA values as additional bad pixels and replace them.

Value

A logical vector of the same length as x. Values that are TRUE correspond to local spikes in the data.

Replacement values

Simple interpolation enabled by method = "adj.mean" replaces values of isolated bad pixels by the mean of their two closest neighbours. The running mean approach enabled by method = "run.mean" allows the replacement of short runs of bad pixels by the running mean of neighboring pixels within a window of user-specified width. The first approach works well for spectra from array spectrometers to correct for hot and dead pixels in an instrument. The second approach is most suitable for Raman spectra in which spikes triggered by radiation are wider than a single pixel but usually not more than five pixels wide.

Simple interpolation can replace spikes at any position in x, using a single neighbour as replacement at the extremes of x instead of the mean of two neighbours. The running mean approach does not replace those pixels whose distance to the first or last member of x is less than half the window used for the running mean, issuing a warning.

When na.rm = TRUE, NA values are considered "bad pixels" and replaced as such rather than discarded with no replacement. This is the default behaviour.

Note

In the current implementation NA values are not removed, and if they are in the neighborhood of bad pixels, they will result in the generation of additional NAs during their replacement. On the other hand if the NAs locations are listed in bad.pix.idx they will be replaced as any other bad pixel.

References

Whitaker, D. A.; Hayes, K. (2018) A simple algorithm for despiking Raman spectra. Chemometrics and Intelligent Laboratory Systems, 179, 82-84.

See Also

Other peaks and valleys functions: find_peaks(), find_spikes(), get_peaks(), peaks(), spikes(), valleys(), wls_at_target()

Examples

# in a vector
replace_bad_pixs(c(1, 2, NA, 4, 5))

# in a vector
replace_bad_pixs(c(1, 2, 100, 4, 5),
                 method = "adj.mean",
                 bad.pix.idx = c(FALSE, FALSE, TRUE, FALSE, FALSE))

replace_bad_pixs(c(1, 2, 100, 4, 5),
                 method = "adj.mean",
                 bad.pix.idx = 3)

# in a vector
replace_bad_pixs(c(0, 1, 2, 100, 4, 5, 6),
                 method = "run.mean",
                 bad.pix.idx = 4)

# in a vector
replace_bad_pixs(c(1, 1, NA, 1, 1),
                 method = "run.mean",
                 bad.pix.idx = 3)

# in a vector
replace_bad_pixs(c(1, 1, NA, 1, 1),
                 method = "run.mean",
                 bad.pix.idx = 1, na.rm = FALSE)

# In spectrum
# before replacement
white_led.raw_spct$counts_3[120:125]

# replacing bad pixels at index positions 123 and 1994
with(white_led.raw_spct,
     replace_bad_pixs(counts_3, bad.pix.idx = c(123, 1994)))[120:125]

photobiology documentation built on Sept. 15, 2026, 9:06 a.m.