Thin the density of wavelength values
Increase the wavelength step in stored spectral data in featureless regions to save storage space.
thin_wl(x, ...)
## Default S3 method:
thin_wl(x, ...)
## S3 method for class 'generic_spct'
thin_wl(x, max.wl.step = 10, max.slope.delta = 0.001, col.names, ...)
## S3 method for class 'source_spct'
thin_wl(
x,
max.wl.step = 10,
max.slope.delta = 0.001,
unit.out = getOption("photobiology.radiation.unit", default = "energy"),
...
)
## S3 method for class 'response_spct'
thin_wl(
x,
max.wl.step = 10,
max.slope.delta = 0.001,
unit.out = getOption("photobiology.radiation.unit", default = "energy"),
...
)
## S3 method for class 'filter_spct'
thin_wl(
x,
max.wl.step = 10,
max.slope.delta = 0.001,
qty.out = getOption("photobiology.filter.qty", default = "transmittance"),
...
)
## S3 method for class 'reflector_spct'
thin_wl(x, max.wl.step = 10, max.slope.delta = 0.001, ...)
## S3 method for class 'raw_spct'
thin_wl(x, max.wl.step = 10, max.slope.delta = 0.001, col.names, ...)
## S3 method for class 'cps_spct'
thin_wl(x, max.wl.step = 10, max.slope.delta = 0.001, col.names, ...)
## S3 method for class 'object_spct'
thin_wl(x, max.wl.step = 10, max.slope.delta = 0.001, col.names, ...)
## S3 method for class 'chroma_spct'
thin_wl(x, ...)
## S3 method for class 'calibration_spct'
thin_wl(x, ...)
## S3 method for class 'generic_mspct'
thin_wl(x, max.wl.step = 10, max.slope.delta = 0.001, ...)
## S3 method for class 'chroma_mspct'
thin_wl(x, ...)
## S3 method for class 'calibration_mspct'
thin_wl(x, ...)x |
An R object |
... |
additional named arguments passed down to |
max.wl.step |
numeric. Largest allowed wavelength difference between adjacent spectral values in nanometres (nm). |
max.slope.delta |
numeric in 0 to 1. Largest allowed change in relative slope of the spectral quantity per nm betweem adjacent pairs of values. |
col.names |
character. Name of the column of |
unit.out |
character Allowed values "energy", and "photon", or its alias "quantum". |
qty.out |
character Allowed values "transmittance", and "absorbance". |
The algorithm used for spectra is "naive" in an effort to keep it efficient. It works by iteratively attempting to delete every other observation along wavelengths, based on the criteria for maximum wavelength step and maximum relative step in the spectral variable between adjacent data values.
An object of the same class as x but with a reduced density of
wavelength values in those regions were slope is shallow and featureless.
default: Default for generic function
generic_spct:
source_spct:
response_spct:
filter_spct:
reflector_spct:
raw_spct:
cps_spct:
object_spct:
chroma_spct:
calibration_spct:
generic_mspct:
chroma_mspct:
calibration_mspct:
The value of max.slope.delta is expressed as relative change in
the slope of spectral variable per nanometre. This means that values
between 0.0005 and 0.005 tend to work reasonably well. The best value
will depend on the wavelength step of the input and noise in data. A
moderate smoothing before thinning can sometimes help in the case of
noisy data.
The amount of thinning is almost always less than the value of criteria
passed as argument as it is based on existing wavelength values. For
example if we start with a spectrum with a uniform wavelength step of 1 nm,
possible steps in the thinned spectrum are 2, 4, 8, 16, 32, etc. nm. The
algorithm, does work with any step sizes, regular or variable in the input.
Thinning is most effective for spectra with large "featureless" regions as
the algorithm attempts not to discard information, contrary to smoothing or
interpolation.
Other experimental utility functions:
collect2mspct(),
drop_user_cols(),
uncollect2spct()
nrow(yellow_gel.spct) wl_stepsize(yellow_gel.spct) thinned.spct <- thin_wl(yellow_gel.spct) nrow(thinned.spct) wl_stepsize(thinned.spct)
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