R/BaseFunctions.R
In tommasojulitta/FieldSpectroscopyCC: R package for Characterization and Calibration of spectrometers
#####################################################################################################################################################################
StatsOnSpectra<-function(
### Compute statistic across several spectra subset to a specified spectral range
wl ##<< numeric vector: wavelength for each row in spectra
,wlStart=wl[1] ##<< numeric value or vector: first wavelength of the spectral range selection. Default value: first wavelength of wl vector
,wlEnd=wl[length(wl)] ##<< numeric value or vector: last wavelength of the spectral range selection. Default value: last wavelength of wl vector
,spectra=spectra ##<< numeric matrix or data.frame: collection of several spectra acquired by several columns (Digital numbers, Radiance, Reflectance).
,fun='mean' ##<< character or function: function to be applied on each row of the data.frame. Default function: mean
,margin=1 ##<< numeric value: a vector giving the subscripts which the function will be applied over. 1 indicates rows, 2 indicates columns
)
{
if(length(wlStart)>1&length(wlEnd)>1)
{
### In case vector of wlStart and wl end
stat_spectrum<-numeric(length(wlStart))
for( n in 1:length(wlStart)){
spectral_subset<-which(wl>=wlStart[n]&wl<=wlEnd[n])
spectra_subset<-data.frame(spectra[spectral_subset,])
stat<-apply(spectra_subset,margin,fun,na.rm=TRUE)
stat_spectrum<-as.numeric(stat_spectrum)
stat_spectrum[n]<-stat[n]
}
### loop on wl start and end
}else{
### In case wlStart and wl end are single value
spectral_subset<-which(wl>=wlStart&wl<=wlEnd)
spectra_subset<-data.frame(spectra[spectral_subset,])
stat_spectrum<-apply(spectra_subset,margin,fun,na.rm=TRUE)
stat_spectrum<-as.numeric(stat_spectrum)
}
##value<< numeric data.frame containing the computed statistic across the selected spectral range.
return(stat_spectrum)
}
attr(StatsOnSpectra,"ex") <- function(){
data("snr_data")
#perform statistics on spectra
#calculate mean of the signal between 700 and 800 nm
mean<-StatsOnSpectra(wl=snr_data$wl,spectra=snr_data$data_lamp,fun='mean',wlStart=700,wlEnd = 800)
#calculate stadard deviation of the signal between 700 and 800 nm
sd<-StatsOnSpectra(wl=snr_data$wl,spectra=snr_data$data_lamp,fun='sd',wlStart=700,wlEnd = 800)
}
#####################################################################################################################################################################
# checkSpecraFormat <- function(
# data
# ){
# ##TODO check whether wl, data_lamp, and data_dc conform
# return(TRUE)
# }
# attr(StatsOnSpectra,"ex") <- function(){
# # This example appears in the generated help.
# load(file.path(dataDir,"snr_data.Rdata"))
# MeanLampSpectrum<-StatsOnSpectra(wl=snr_data$wl,spectra=snr_data$data_lamp,fun='mean')
# }
#####################################################################################################################################################################
SummarizeSpectra <- function(
### compute common summary statistics on light and dark field spectra
spectra ##<< list with entries wl (wavelength), data_lamp (matrix of several light spectra), and data_dc (matrix of several dark current)
,... ##<< further arguments to \code{\link{StatsOnSpectra}}, such as \code{wlStart} and \code{wlEnd}
){
lamp_mean<-StatsOnSpectra(wl=spectra$wl,spectra=spectra$data_lamp,fun=mean,...)
lamp_sd<-StatsOnSpectra(wl=spectra$wl,spectra=spectra$data_lamp,fun=sd,...)
dc_mean<-StatsOnSpectra(wl=spectra$wl,spectra=spectra$data_dc,fun=mean,...)
dc_sd<-StatsOnSpectra(wl=spectra$wl,spectra=spectra$data_dc,fun=sd,...)
##value<< data.frame with entries
ans <- data.frame(
meanLamp=lamp_mean ##<< mean light spectrum
,sdLamp=lamp_sd ##<< standard deviation across light spectra
,nLamp=ncol(spectra$data_lamp)##<< number of summarized light spectra
,meanDC=dc_mean ##<< mean dark spectrum
,sdDC=dc_sd ##<< standard deviation across dark spectra
,nDar=ncol(spectra$data_dc) ##<< number of summarized dark spectra
)
}
attr(SummarizeSpectra,"ex") <- function(){
data("snr_data")
#perform statistics on spectra
specStats <- SummarizeSpectra(snr_data)
#print header of specStats
head(specStats)
}
#####################################################################################################################################################################
LinearResample<-function(
### Compute linear resample of a spectrum based on specific wavelength of a defined spectrometer
wl ##<< numeric vector: wavelength vector to be resamped
,spectrum ##<< numeric vector: spectrum to be resampled (e.g. radiance, reflectance, digital numbers)
,wlRef ##<< numeric vector: reference wavelength vector to be be used for resampling
){
SpectrumResampled<-approx(wl,spectrum,wlRef);SpectrumResampled<-SpectrumResampled$y
### Compute linear resample
##value<< numeric vector containing the spectrum linearly resample according to the wavelngth vector wlRef.
return(SpectrumResampled)
}
attr(LinearResample,"ex") <- function(){
data("outdoor_rad_cal_data")
data("atmospheric_absorption_regions")
integration_time<-450
#Create matrix for radiometric calibration
DN_mat<-DNSpectralMatrixRadCal(spectra = outdoor_rad_cal_data$DN_matrix,IntegrationTime = integration_time)
#calculate mean of several spectra
radiance_mean<-StatsOnSpectra(wl=outdoor_rad_cal_data$radiance_wl,spectra=outdoor_rad_cal_data$radiance_matrix,fun='mean')
#linear resample at refrence radiance wavelength
radiance_meanRes<-LinearResample(wl = outdoor_rad_cal_data$radiance_wl,spectrum = radiance_mean,wlRef = outdoor_rad_cal_data$DN_wl)
#plot results
x11()
plot(outdoor_rad_cal_data$radiance_wl,radiance_mean,type="l",xlab="WL [nm]",ylab=expression("Radiance [W m"^-2* "sr"^-1* "nm"^-1*"]"),ylim=c(0,5))
lines(outdoor_rad_cal_data$DN_wl,radiance_meanRes,col="blue")
}
#####################################################################################################################################################################
SplineSmoothGapfilling<-function(
### Apply a spline smoothing and gap filling in the region where NA are found
wl ##<< numeric vector: wavelength vector
,spectrum ##<< numeric data.frame: First column wavelength vector, second column spectrum vector
,df = length(wl)/50 ##<< numeric value: the desired equivalent number of degrees of freedom (trace of the smoother matrix)
)
{
data<-data.frame(wl,spectrum)
temp<-na.omit(data)
spline<-smooth.spline(temp$wl,temp$spectrum,df=df)
spline_gapfilling<-predict(spline, data$wl);spline_gapfilling<-spline_gapfilling$y
##value<< numeric vector containing the smoothed spectrum.
return(spline_gapfilling)
}
attr(SplineSmoothGapfilling,"ex") <- function(){
data("outdoor_rad_cal_data")
data("atmospheric_absorption_regions")
integration_time<-450
#Create matrix for radiometric calibration
DN_mat<-DNSpectralMatrixRadCal(spectra = outdoor_rad_cal_data$DN_matrix,IntegrationTime = integration_time)
#calculate mean of several spectra
radiance_mean<-StatsOnSpectra(wl=outdoor_rad_cal_data$radiance_wl,spectra=outdoor_rad_cal_data$radiance_matrix,fun='mean')
#linear resample at refrence radiance wavelength
radiance_mean_res<-LinearResample(wl = outdoor_rad_cal_data$radiance_wl,spectrum = radiance_mean,wlRef = outdoor_rad_cal_data$DN_wl)
wp_coeff_res<-LinearResample(outdoor_rad_cal_data$wp_coef$V1,outdoor_rad_cal_data$wp_coef$V2,outdoor_rad_cal_data$DN_wl)
#calculate mean of several spectra
DN_mean<-StatsOnSpectra(wl=outdoor_rad_cal_data$DN_wl,spectra=outdoor_rad_cal_data$DN_matrix,fun='mean')
#calculate calibration coefficients
rad_cal<-RadiometricCalibration(type=1,wl=outdoor_rad_cal_data$DN_wl,radiance = radiance_mean_res,DN = DN_mean)
#exclude regions of the spectrum affected by atmospheric absorptions and noisy pixels
range_to_exclude<-data.frame(wl_start=c(outdoor_rad_cal_data$DN_wl[1],outdoor_rad_cal_data$DN_wl[length(outdoor_rad_cal_data$DN_wl)-35]),wl_end=c(outdoor_rad_cal_data$DN_wl[30],outdoor_rad_cal_data$DN_wl[length(outdoor_rad_cal_data$DN_wl)]))
atmospheric_absorption_regions<-rbind(atmospheric_absorption_regions,c(range_to_exclude))
exclude_atmospheric_absorption_features<-ExcludeSpectralRegions(wl=outdoor_rad_cal_data$DN_wl, spectrum = rad_cal,SpectralRegion = atmospheric_absorption_regions)
#smooth results
rad_cal_coeff<-SplineSmoothGapfilling(wl=outdoor_rad_cal_data$DN_wl,spectrum = exclude_atmospheric_absorption_features)
x11()
plot(outdoor_rad_cal_data$DN_wl,rad_cal,ylim=c(0,0.000025),xlab="WL [nm]",ylab="Conversion coefficients")
points(outdoor_rad_cal_data$DN_wl,exclude_atmospheric_absorption_features,pch=20,col="red")
lines(outdoor_rad_cal_data$DN_wl,rad_cal_coeff,col="green",lwd=2)
}
#####################################################################################################################################################################
PolSmoothGapfilling<-function(
### Apply a polynomial fitting and gap filling in the region where NA are found
wl ##<< numeric vector: wavelength vector
,spectrum ##<< numeric data.frame: First column wavelength vector, second column spectrum vector
,deg = 4 ##<< numeric value: the degree of the polynomial
)
{
data<-data.frame(wl,spectrum)
temp<-na.omit(data)
y<-temp[,2]
x<-temp[,1]
fit <- lm(y ~ poly(x, deg, raw=TRUE))
predpol<-predict(fit,data.frame(x=wl))
##value<< numeric vector containing the smoothed spectrum.
return(predpol)
}
attr(PolSmoothGapfilling,"ex") <- function(){
data("outdoor_rad_cal_data")
data("atmospheric_absorption_regions")
integration_time<-450
#Create matrix for radiometric calibration
DN_mat<-DNSpectralMatrixRadCal(spectra = outdoor_rad_cal_data$DN_matrix,IntegrationTime = integration_time)
#calculate mean of several spectra
radiance_mean<-StatsOnSpectra(wl=outdoor_rad_cal_data$radiance_wl,spectra=outdoor_rad_cal_data$radiance_matrix,fun='mean')
#linear resample at refrence radiance wavelength
radiance_mean_res<-LinearResample(wl = outdoor_rad_cal_data$radiance_wl,spectrum = radiance_mean,wlRef = outdoor_rad_cal_data$DN_wl)
wp_coeff_res<-LinearResample(outdoor_rad_cal_data$wp_coef$V1,outdoor_rad_cal_data$wp_coef$V2,outdoor_rad_cal_data$DN_wl)
#calculate mean of several spectra
DN_mean<-StatsOnSpectra(wl=outdoor_rad_cal_data$DN_wl,spectra=outdoor_rad_cal_data$DN_matrix,fun='mean')
#calculate calibration coefficients
rad_cal<-RadiometricCalibration(type=1,wl=outdoor_rad_cal_data$DN_wl,radiance = radiance_mean_res,DN = DN_mean)
#exclude regions of the spectrum affected by atmospheric absorptions and noisy pixels
range_to_exclude<-data.frame(wl_start=c(outdoor_rad_cal_data$DN_wl[1],outdoor_rad_cal_data$DN_wl[length(outdoor_rad_cal_data$DN_wl)-35]),wl_end=c(outdoor_rad_cal_data$DN_wl[30],outdoor_rad_cal_data$DN_wl[length(outdoor_rad_cal_data$DN_wl)]))
atmospheric_absorption_regions<-rbind(atmospheric_absorption_regions,c(range_to_exclude))
exclude_atmospheric_absorption_features<-ExcludeSpectralRegions(wl=outdoor_rad_cal_data$DN_wl, spectrum = rad_cal,SpectralRegion = atmospheric_absorption_regions)
#smooth results
rad_cal_coeff<-PolSmoothGapfilling(wl=outdoor_rad_cal_data$DN_wl,spectrum = exclude_atmospheric_absorption_features)
x11()
plot(outdoor_rad_cal_data$DN_wl,rad_cal,ylim=c(0,0.000025),xlab="WL [nm]",ylab="Conversion coefficients")
points(outdoor_rad_cal_data$DN_wl,exclude_atmospheric_absorption_features,pch=20,col="red")
lines(outdoor_rad_cal_data$DN_wl,rad_cal_coeff,col="green",lwd=2)
}
tommasojulitta/FieldSpectroscopyCC documentation built on May 31, 2019, 6:19 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#####################################################################################################################################################################
StatsOnSpectra<-function(
### Compute statistic across several spectra subset to a specified spectral range
wl ##<< numeric vector: wavelength for each row in spectra
,wlStart=wl[1] ##<< numeric value or vector: first wavelength of the spectral range selection. Default value: first wavelength of wl vector
,wlEnd=wl[length(wl)] ##<< numeric value or vector: last wavelength of the spectral range selection. Default value: last wavelength of wl vector
,spectra=spectra ##<< numeric matrix or data.frame: collection of several spectra acquired by several columns (Digital numbers, Radiance, Reflectance).
,fun='mean' ##<< character or function: function to be applied on each row of the data.frame. Default function: mean
,margin=1 ##<< numeric value: a vector giving the subscripts which the function will be applied over. 1 indicates rows, 2 indicates columns
)
{
if(length(wlStart)>1&length(wlEnd)>1)
{
### In case vector of wlStart and wl end
stat_spectrum<-numeric(length(wlStart))
for( n in 1:length(wlStart)){
spectral_subset<-which(wl>=wlStart[n]&wl<=wlEnd[n])
spectra_subset<-data.frame(spectra[spectral_subset,])
stat<-apply(spectra_subset,margin,fun,na.rm=TRUE)
stat_spectrum<-as.numeric(stat_spectrum)
stat_spectrum[n]<-stat[n]
}
### loop on wl start and end
}else{
### In case wlStart and wl end are single value
spectral_subset<-which(wl>=wlStart&wl<=wlEnd)
spectra_subset<-data.frame(spectra[spectral_subset,])
stat_spectrum<-apply(spectra_subset,margin,fun,na.rm=TRUE)
stat_spectrum<-as.numeric(stat_spectrum)
}
##value<< numeric data.frame containing the computed statistic across the selected spectral range.
return(stat_spectrum)
}
attr(StatsOnSpectra,"ex") <- function(){
data("snr_data")
#perform statistics on spectra
#calculate mean of the signal between 700 and 800 nm
mean<-StatsOnSpectra(wl=snr_data$wl,spectra=snr_data$data_lamp,fun='mean',wlStart=700,wlEnd = 800)
#calculate stadard deviation of the signal between 700 and 800 nm
sd<-StatsOnSpectra(wl=snr_data$wl,spectra=snr_data$data_lamp,fun='sd',wlStart=700,wlEnd = 800)
}
#####################################################################################################################################################################
# checkSpecraFormat <- function(
# data
# ){
# ##TODO check whether wl, data_lamp, and data_dc conform
# return(TRUE)
# }
# attr(StatsOnSpectra,"ex") <- function(){
# # This example appears in the generated help.
# load(file.path(dataDir,"snr_data.Rdata"))
# MeanLampSpectrum<-StatsOnSpectra(wl=snr_data$wl,spectra=snr_data$data_lamp,fun='mean')
# }
#####################################################################################################################################################################
SummarizeSpectra <- function(
### compute common summary statistics on light and dark field spectra
spectra ##<< list with entries wl (wavelength), data_lamp (matrix of several light spectra), and data_dc (matrix of several dark current)
,... ##<< further arguments to \code{\link{StatsOnSpectra}}, such as \code{wlStart} and \code{wlEnd}
){
lamp_mean<-StatsOnSpectra(wl=spectra$wl,spectra=spectra$data_lamp,fun=mean,...)
lamp_sd<-StatsOnSpectra(wl=spectra$wl,spectra=spectra$data_lamp,fun=sd,...)
dc_mean<-StatsOnSpectra(wl=spectra$wl,spectra=spectra$data_dc,fun=mean,...)
dc_sd<-StatsOnSpectra(wl=spectra$wl,spectra=spectra$data_dc,fun=sd,...)
##value<< data.frame with entries
ans <- data.frame(
meanLamp=lamp_mean ##<< mean light spectrum
,sdLamp=lamp_sd ##<< standard deviation across light spectra
,nLamp=ncol(spectra$data_lamp)##<< number of summarized light spectra
,meanDC=dc_mean ##<< mean dark spectrum
,sdDC=dc_sd ##<< standard deviation across dark spectra
,nDar=ncol(spectra$data_dc) ##<< number of summarized dark spectra
)
}
attr(SummarizeSpectra,"ex") <- function(){
data("snr_data")
#perform statistics on spectra
specStats <- SummarizeSpectra(snr_data)
#print header of specStats
head(specStats)
}
#####################################################################################################################################################################
LinearResample<-function(
### Compute linear resample of a spectrum based on specific wavelength of a defined spectrometer
wl ##<< numeric vector: wavelength vector to be resamped
,spectrum ##<< numeric vector: spectrum to be resampled (e.g. radiance, reflectance, digital numbers)
,wlRef ##<< numeric vector: reference wavelength vector to be be used for resampling
){
SpectrumResampled<-approx(wl,spectrum,wlRef);SpectrumResampled<-SpectrumResampled$y
### Compute linear resample
##value<< numeric vector containing the spectrum linearly resample according to the wavelngth vector wlRef.
return(SpectrumResampled)
}
attr(LinearResample,"ex") <- function(){
data("outdoor_rad_cal_data")
data("atmospheric_absorption_regions")
integration_time<-450
#Create matrix for radiometric calibration
DN_mat<-DNSpectralMatrixRadCal(spectra = outdoor_rad_cal_data$DN_matrix,IntegrationTime = integration_time)
#calculate mean of several spectra
radiance_mean<-StatsOnSpectra(wl=outdoor_rad_cal_data$radiance_wl,spectra=outdoor_rad_cal_data$radiance_matrix,fun='mean')
#linear resample at refrence radiance wavelength
radiance_meanRes<-LinearResample(wl = outdoor_rad_cal_data$radiance_wl,spectrum = radiance_mean,wlRef = outdoor_rad_cal_data$DN_wl)
#plot results
x11()
plot(outdoor_rad_cal_data$radiance_wl,radiance_mean,type="l",xlab="WL [nm]",ylab=expression("Radiance [W m"^-2* "sr"^-1* "nm"^-1*"]"),ylim=c(0,5))
lines(outdoor_rad_cal_data$DN_wl,radiance_meanRes,col="blue")
}
#####################################################################################################################################################################
SplineSmoothGapfilling<-function(
### Apply a spline smoothing and gap filling in the region where NA are found
wl ##<< numeric vector: wavelength vector
,spectrum ##<< numeric data.frame: First column wavelength vector, second column spectrum vector
,df = length(wl)/50 ##<< numeric value: the desired equivalent number of degrees of freedom (trace of the smoother matrix)
)
{
data<-data.frame(wl,spectrum)
temp<-na.omit(data)
spline<-smooth.spline(temp$wl,temp$spectrum,df=df)
spline_gapfilling<-predict(spline, data$wl);spline_gapfilling<-spline_gapfilling$y
##value<< numeric vector containing the smoothed spectrum.
return(spline_gapfilling)
}
attr(SplineSmoothGapfilling,"ex") <- function(){
data("outdoor_rad_cal_data")
data("atmospheric_absorption_regions")
integration_time<-450
#Create matrix for radiometric calibration
DN_mat<-DNSpectralMatrixRadCal(spectra = outdoor_rad_cal_data$DN_matrix,IntegrationTime = integration_time)
#calculate mean of several spectra
radiance_mean<-StatsOnSpectra(wl=outdoor_rad_cal_data$radiance_wl,spectra=outdoor_rad_cal_data$radiance_matrix,fun='mean')
#linear resample at refrence radiance wavelength
radiance_mean_res<-LinearResample(wl = outdoor_rad_cal_data$radiance_wl,spectrum = radiance_mean,wlRef = outdoor_rad_cal_data$DN_wl)
wp_coeff_res<-LinearResample(outdoor_rad_cal_data$wp_coef$V1,outdoor_rad_cal_data$wp_coef$V2,outdoor_rad_cal_data$DN_wl)
#calculate mean of several spectra
DN_mean<-StatsOnSpectra(wl=outdoor_rad_cal_data$DN_wl,spectra=outdoor_rad_cal_data$DN_matrix,fun='mean')
#calculate calibration coefficients
rad_cal<-RadiometricCalibration(type=1,wl=outdoor_rad_cal_data$DN_wl,radiance = radiance_mean_res,DN = DN_mean)
#exclude regions of the spectrum affected by atmospheric absorptions and noisy pixels
range_to_exclude<-data.frame(wl_start=c(outdoor_rad_cal_data$DN_wl[1],outdoor_rad_cal_data$DN_wl[length(outdoor_rad_cal_data$DN_wl)-35]),wl_end=c(outdoor_rad_cal_data$DN_wl[30],outdoor_rad_cal_data$DN_wl[length(outdoor_rad_cal_data$DN_wl)]))
atmospheric_absorption_regions<-rbind(atmospheric_absorption_regions,c(range_to_exclude))
exclude_atmospheric_absorption_features<-ExcludeSpectralRegions(wl=outdoor_rad_cal_data$DN_wl, spectrum = rad_cal,SpectralRegion = atmospheric_absorption_regions)
#smooth results
rad_cal_coeff<-SplineSmoothGapfilling(wl=outdoor_rad_cal_data$DN_wl,spectrum = exclude_atmospheric_absorption_features)
x11()
plot(outdoor_rad_cal_data$DN_wl,rad_cal,ylim=c(0,0.000025),xlab="WL [nm]",ylab="Conversion coefficients")
points(outdoor_rad_cal_data$DN_wl,exclude_atmospheric_absorption_features,pch=20,col="red")
lines(outdoor_rad_cal_data$DN_wl,rad_cal_coeff,col="green",lwd=2)
}
#####################################################################################################################################################################
PolSmoothGapfilling<-function(
### Apply a polynomial fitting and gap filling in the region where NA are found
wl ##<< numeric vector: wavelength vector
,spectrum ##<< numeric data.frame: First column wavelength vector, second column spectrum vector
,deg = 4 ##<< numeric value: the degree of the polynomial
)
{
data<-data.frame(wl,spectrum)
temp<-na.omit(data)
y<-temp[,2]
x<-temp[,1]
fit <- lm(y ~ poly(x, deg, raw=TRUE))
predpol<-predict(fit,data.frame(x=wl))
##value<< numeric vector containing the smoothed spectrum.
return(predpol)
}
attr(PolSmoothGapfilling,"ex") <- function(){
data("outdoor_rad_cal_data")
data("atmospheric_absorption_regions")
integration_time<-450
#Create matrix for radiometric calibration
DN_mat<-DNSpectralMatrixRadCal(spectra = outdoor_rad_cal_data$DN_matrix,IntegrationTime = integration_time)
#calculate mean of several spectra
radiance_mean<-StatsOnSpectra(wl=outdoor_rad_cal_data$radiance_wl,spectra=outdoor_rad_cal_data$radiance_matrix,fun='mean')
#linear resample at refrence radiance wavelength
radiance_mean_res<-LinearResample(wl = outdoor_rad_cal_data$radiance_wl,spectrum = radiance_mean,wlRef = outdoor_rad_cal_data$DN_wl)
wp_coeff_res<-LinearResample(outdoor_rad_cal_data$wp_coef$V1,outdoor_rad_cal_data$wp_coef$V2,outdoor_rad_cal_data$DN_wl)
#calculate mean of several spectra
DN_mean<-StatsOnSpectra(wl=outdoor_rad_cal_data$DN_wl,spectra=outdoor_rad_cal_data$DN_matrix,fun='mean')
#calculate calibration coefficients
rad_cal<-RadiometricCalibration(type=1,wl=outdoor_rad_cal_data$DN_wl,radiance = radiance_mean_res,DN = DN_mean)
#exclude regions of the spectrum affected by atmospheric absorptions and noisy pixels
range_to_exclude<-data.frame(wl_start=c(outdoor_rad_cal_data$DN_wl[1],outdoor_rad_cal_data$DN_wl[length(outdoor_rad_cal_data$DN_wl)-35]),wl_end=c(outdoor_rad_cal_data$DN_wl[30],outdoor_rad_cal_data$DN_wl[length(outdoor_rad_cal_data$DN_wl)]))
atmospheric_absorption_regions<-rbind(atmospheric_absorption_regions,c(range_to_exclude))
exclude_atmospheric_absorption_features<-ExcludeSpectralRegions(wl=outdoor_rad_cal_data$DN_wl, spectrum = rad_cal,SpectralRegion = atmospheric_absorption_regions)
#smooth results
rad_cal_coeff<-PolSmoothGapfilling(wl=outdoor_rad_cal_data$DN_wl,spectrum = exclude_atmospheric_absorption_features)
x11()
plot(outdoor_rad_cal_data$DN_wl,rad_cal,ylim=c(0,0.000025),xlab="WL [nm]",ylab="Conversion coefficients")
points(outdoor_rad_cal_data$DN_wl,exclude_atmospheric_absorption_features,pch=20,col="red")
lines(outdoor_rad_cal_data$DN_wl,rad_cal_coeff,col="green",lwd=2)
}
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