This function replaces data for bad pixels by a local estimate, by either simple interpolation or using the algorithm of Whitaker and Hayes (2018).
replace_bad_pixs(
x,
bad.pix.idx = FALSE,
window.width = 11,
method = "run.mean",
na.rm = TRUE
)
A logical vector of the same length as x
. Values that are TRUE
correspond to local spikes in the data.
numeric vector containing spectral data.
logical vector or integer. Index into bad pixels in
x
.
integer. The full width of the window used for the running mean.
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).
logical Treat NA
values as additional bad pixels and
replace them.
Simple interpolation replaces values of isolated bad pixels by the mean of their two closest neighbors. The running mean approach 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.
Whitaker, D. A.; Hayes, K. (2018) A simple algorithm for despiking Raman spectra. Chemometrics and Intelligent Laboratory Systems, 179, 82-84.
Other peaks and valleys functions:
find_peaks()
,
find_spikes()
,
get_peaks()
,
peaks()
,
spikes()
,
valleys()
,
wls_at_target()
# in a vector
replace_bad_pixs(c(1, 1, 45, 1, 1), bad.pix.idx = 3)
# 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]
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