Archiv des Autors: Martin Stoppacher

# -------------

# region

# World
# Euro area
# European Union
# Europe & Central Asia
# Arab World
# North America
# Latin America & Caribbean
# Middle East & North Africa
# Sub-Saharan Africa 
# East Asia & Pacific
# South Asia

target.area <- which(renewable.electricity.output$country=="World")
renewable.electricity.output[target.area,]
plot(renewable.electricity.output[target.area,4:3],type="l",
     main=titel,sub=subtitel,ylab="%",xlab="",xlim=c(1980,2020),ylim=c(1,90),
     axes=TRUE)
text(1985, 20, "World",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Euro area")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="red")
text(1985, 12, "Euro area", col="red",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Arab World")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="green")
text(1985, 5, "Arab World", col="green",cex=0.7)

target.area <- which(renewable.electricity.output$country=="North America")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="blue",lty=2)
text(1985, 17, "North America", col="blue",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Latin America & Caribbean")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="yellow")
text(1985, 65, "Latin America & Caribbean", col="yellow",cex=0.7)

# target.area <- which(renewable.electricity.output$country=="Middle East & North Africa")
# renewable.electricity.output[target.area,]
# lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="orange")
# text(1985, 5, "Middle East & North Africa", col="orange",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Sub-Saharan Africa")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="cyan",lty=3)
text(1985, 23, "Sub-Saharan Africa", col="cyan",cex=0.7)

target.area <- which(renewable.electricity.output$country=="East Asia & Pacific")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="magenta",lty=4)
text(1985, 14.5, "East Asia & Pacific", col="magenta",cex=0.7)

target.area <- which(renewable.electricity.output$country=="South Asia")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="pink",lty=5)
text(1985, 28, "South Asia", col="pink",cex=0.7)

# -------------

# region

# World

# Euro area

# European Union

# Europe & Central Asia

# Arab World

# North America

# Latin America & Caribbean

# Middle East & North Africa

# Sub-Saharan Africa

# East Asia & Pacific

# South Asia

target.area <- which(renewable.electricity.output$country=="World")

renewable.electricity.output[target.area,]

plot(renewable.electricity.output[target.area,4:3],type="l",

main=titel,sub=subtitel,ylab="%",xlab="",xlim=c(1980,2020),ylim=c(1,90),

axes=TRUE)

text(1985, 20, "World",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Euro area")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="red")

text(1985, 12, "Euro area", col="red",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Arab World")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="green")

text(1985, 5, "Arab World", col="green",cex=0.7)

target.area <- which(renewable.electricity.output$country=="North America")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="blue",lty=2)

text(1985, 17, "North America", col="blue",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Latin America & Caribbean")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="yellow")

text(1985, 65, "Latin America & Caribbean", col="yellow",cex=0.7)

# target.area <- which(renewable.electricity.output$country=="Middle East & North Africa")

# renewable.electricity.output[target.area,]

# lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="orange")

# text(1985, 5, "Middle East & North Africa", col="orange",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Sub-Saharan Africa")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="cyan",lty=3)

text(1985, 23, "Sub-Saharan Africa", col="cyan",cex=0.7)

target.area <- which(renewable.electricity.output$country=="East Asia & Pacific")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="magenta",lty=4)

text(1985, 14.5, "East Asia & Pacific", col="magenta",cex=0.7)

target.area <- which(renewable.electricity.output$country=="South Asia")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="pink",lty=5)

text(1985, 28, "South Asia", col="pink",cex=0.7)

Renewable electricity output – World Map

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http://host6.ssl-net.net/martinstoppacher_com/blog/?p=1194

# ----------------

library(rworldmap)

options("scipen"=100, "digits"=4)

year <- 2015
target.year <- which(renewable.electricity.output$year==year)
head(renewable.electricity.output[target.year,])

renewable.electricity.output.year <- na.omit(renewable.electricity.output[target.year,])
renewable.electricity.output.year <- renewable.electricity.output.year[which(renewable.electricity.output.year$EG.ELC.RNEW.ZS>0),]

mapped_data <- joinCountryData2Map(renewable.electricity.output.year, joinCode = "ISO2", nameJoinColumn = "iso2c")
par(mai=c(0,0,0.2,0),xaxs="i",yaxs="i")
mapCountryData(mapped_data, nameColumnToPlot = "EG.ELC.RNEW.ZS",mapTitle=paste(titel,subtitel))

mapCountryData(mapped_data, nameColumnToPlot = "EG.ELC.RNEW.ZS",mapRegion="world",mapTitle=paste(titel,subtitel),
               numCats=2,catMethod=c(0:100),addLegend=FALSE,colourPalette=c("red","green"))

# ----------------

library(rworldmap)

options("scipen"=100, "digits"=4)

year <- 2015

target.year <- which(renewable.electricity.output$year==year)

head(renewable.electricity.output[target.year,])

renewable.electricity.output.year <- na.omit(renewable.electricity.output[target.year,])

renewable.electricity.output.year <- renewable.electricity.output.year[which(renewable.electricity.output.year$EG.ELC.RNEW.ZS>0),]

mapped_data <- joinCountryData2Map(renewable.electricity.output.year, joinCode = "ISO2", nameJoinColumn = "iso2c")

par(mai=c(0,0,0.2,0),xaxs="i",yaxs="i")

mapCountryData(mapped_data, nameColumnToPlot = "EG.ELC.RNEW.ZS",mapTitle=paste(titel,subtitel))

mapCountryData(mapped_data, nameColumnToPlot = "EG.ELC.RNEW.ZS",mapRegion="world",mapTitle=paste(titel,subtitel),

numCats=2,catMethod=c(0:100),addLegend=FALSE,colourPalette=c("red","green"))

mapCountryData(mapped_data, nameColumnToPlot = "EG.ELC.RNEW.ZS",mapRegion="world",mapTitle=paste(titel,subtitel),
               numCats=3,catMethod=c(0:100),addLegend=FALSE,colourPalette=c("red","yellow","green"))
legend(-179, -10, legend=c("low", "medium","high"), col=c("red", "yellow","green"), lty=1:3, cex=0.6)

mapCountryData(mapped_data, nameColumnToPlot = "EG.ELC.RNEW.ZS",mapRegion="world",mapTitle=paste(titel,subtitel),

numCats=3,catMethod=c(0:100),addLegend=FALSE,colourPalette=c("red","yellow","green"))

legend(-179, -10, legend=c("low", "medium","high"), col=c("red", "yellow","green"), lty=1:3, cex=0.6)

Renewable electricity output – Statistics

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# Renewable electricity output (% of total electricity output) - EG.ELC.RNEW.ZS

1	# Renewable electricity output (% of total electricity output) - EG.ELC.RNEW.ZS

library("XML")
library("WDI")
library("gridExtra")
library("scatterplot3d")

options(scipen=100) # turning of scientific notation

titel<-"Renewable electricity output"
subtitel<-"(% of total electricity output)"

renewable.electricity.output <- WDI(country="all", indicator="EG.ELC.RNEW.ZS", start=1900, end=2019)
str(renewable.electricity.output)

# 'data.frame':	15840 obs. of  4 variables:
#   $ iso2c         : chr  "1A" "1A" "1A" "1A" ...
# $ country       : chr  "Arab World" "Arab World" "Arab World" "Arab World" ...
# $ EG.ELC.RNEW.ZS: num  NA NA NA NA 2.92 ...
# $ year          : num  2019 2018 2017 2016 2015 ...

head(renewable.electricity.output)

# iso2c    country EG.ELC.RNEW.ZS year
# 1    1A Arab World             NA 2019
# 2    1A Arab World             NA 2018
# 3    1A Arab World             NA 2017
# 4    1A Arab World             NA 2016
# 5    1A Arab World       2.920702 2015
# 6    1A Arab World       3.278073 2014

library("XML")

library("WDI")

library("gridExtra")

library("scatterplot3d")

options(scipen=100) # turning of scientific notation

titel<-"Renewable electricity output"

subtitel<-"(% of total electricity output)"

renewable.electricity.output <- WDI(country="all", indicator="EG.ELC.RNEW.ZS", start=1900, end=2019)

str(renewable.electricity.output)

# 'data.frame': 15840 obs. of 4 variables:

# $ iso2c : chr "1A" "1A" "1A" "1A" ...

# $ country : chr "Arab World" "Arab World" "Arab World" "Arab World" ...

# $ EG.ELC.RNEW.ZS: num NA NA NA NA 2.92 ...

# $ year : num 2019 2018 2017 2016 2015 ...

head(renewable.electricity.output)

# iso2c country EG.ELC.RNEW.ZS year

# 1 1A Arab World NA 2019

# 2 1A Arab World NA 2018

# 3 1A Arab World NA 2017

# 4 1A Arab World NA 2016

# 5 1A Arab World 2.920702 2015

# 6 1A Arab World 3.278073 2014

# selection of the year 2015

year <- 2015
target.year <- which(renewable.electricity.output$year==year)
head(renewable.electricity.output[target.year,])

# iso2c                                     country EG.ELC.RNEW.ZS year
# 5      1A                                  Arab World       2.920702 2015
# 65     S3                      Caribbean small states       8.914780 2015
# 125    B8              Central Europe and the Baltics      20.252851 2015
# 185    V2                  Early-demographic dividend      18.104862 2015
# 245    Z4                         East Asia & Pacific      20.412466 2015
# 305    4E East Asia & Pacific (excluding high income)      23.394930 2015

# selection of the year 2015

year <- 2015

target.year <- which(renewable.electricity.output$year==year)

head(renewable.electricity.output[target.year,])

# iso2c country EG.ELC.RNEW.ZS year

# 5 1A Arab World 2.920702 2015

# 65 S3 Caribbean small states 8.914780 2015

# 125 B8 Central Europe and the Baltics 20.252851 2015

# 185 V2 Early-demographic dividend 18.104862 2015

# 245 Z4 East Asia & Pacific 20.412466 2015

# 305 4E East Asia & Pacific (excluding high income) 23.394930 2015

# income categories

– Low income
– Lower middle income
– Upper middle income
– High income

target.income <- which(renewable.electricity.output$country=="Low income")
renewable.electricity.output[target.income,]
plot(renewable.electricity.output[target.income,4:3],type="l",
     main=titel,sub=subtitel,ylab="%",xlab="",xlim=c(1980,2020),ylim=c(10,90),
     axes=TRUE)
text(1985, 65, "Low income",cex=0.7)

target.income <- which(renewable.electricity.output$country=="Low income")

renewable.electricity.output[target.income,]

plot(renewable.electricity.output[target.income,4:3],type="l",

main=titel,sub=subtitel,ylab="%",xlab="",xlim=c(1980,2020),ylim=c(10,90),

axes=TRUE)

text(1985, 65, "Low income",cex=0.7)

target.income <- which(renewable.electricity.output$country=="Lower middle income")
renewable.electricity.output[target.income,]
lines(renewable.electricity.output[target.income,4:3],type="l",main=titel,ylab="%",xlab="",col="red")
text(1985, 20, "Lower middle income", col="red",cex=0.7)

target.income <- which(renewable.electricity.output$country=="Upper middle income")
renewable.electricity.output[target.income,]
lines(renewable.electricity.output[target.income,4:3],type="l",main=titel,ylab="%",xlab="",col="green")
text(1985, 25, "Upper middle income", col="green",cex=0.7)

target.income <- which(renewable.electricity.output$country=="High income")
renewable.electricity.output[target.income,]
lines(renewable.electricity.output[target.income,4:3],type="l",main=titel,ylab="%",xlab="",col="blue")
text(1985, 15, "High income", col="blue",cex=0.7)

target.income <- which(renewable.electricity.output$country=="Lower middle income")

renewable.electricity.output[target.income,]

lines(renewable.electricity.output[target.income,4:3],type="l",main=titel,ylab="%",xlab="",col="red")

text(1985, 20, "Lower middle income", col="red",cex=0.7)

target.income <- which(renewable.electricity.output$country=="Upper middle income")

renewable.electricity.output[target.income,]

lines(renewable.electricity.output[target.income,4:3],type="l",main=titel,ylab="%",xlab="",col="green")

text(1985, 25, "Upper middle income", col="green",cex=0.7)

target.income <- which(renewable.electricity.output$country=="High income")

renewable.electricity.output[target.income,]

lines(renewable.electricity.output[target.income,4:3],type="l",main=titel,ylab="%",xlab="",col="blue")

text(1985, 15, "High income", col="blue",cex=0.7)

# -------------

# region

# World
# Euro area
# European Union
# Europe & Central Asia
# Arab World
# North America
# Latin America & Caribbean
# Middle East & North Africa
# Sub-Saharan Africa 
# East Asia & Pacific
# South Asia

target.area <- which(renewable.electricity.output$country=="World")
renewable.electricity.output[target.area,]
plot(renewable.electricity.output[target.area,4:3],type="l",
     main=titel,sub=subtitel,ylab="%",xlab="",xlim=c(1980,2020),ylim=c(1,90),
     axes=TRUE)
text(1985, 20, "World",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Euro area")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="red")
text(1985, 12, "Euro area", col="red",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Arab World")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="green")
text(1985, 5, "Arab World", col="green",cex=0.7)

target.area <- which(renewable.electricity.output$country=="North America")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="blue",lty=2)
text(1985, 17, "North America", col="blue",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Latin America & Caribbean")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="yellow")
text(1985, 65, "Latin America & Caribbean", col="yellow",cex=0.7)

# target.area <- which(renewable.electricity.output$country=="Middle East & North Africa")
# renewable.electricity.output[target.area,]
# lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="orange")
# text(1985, 5, "Middle East & North Africa", col="orange",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Sub-Saharan Africa")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="cyan",lty=3)
text(1985, 23, "Sub-Saharan Africa", col="cyan",cex=0.7)

target.area <- which(renewable.electricity.output$country=="East Asia & Pacific")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="magenta",lty=4)
text(1985, 14.5, "East Asia & Pacific", col="magenta",cex=0.7)

target.area <- which(renewable.electricity.output$country=="South Asia")
renewable.electricity.output[target.area,]
lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="pink",lty=5)
text(1985, 28, "South Asia", col="pink",cex=0.7)

# -------------

# region

# World

# Euro area

# European Union

# Europe & Central Asia

# Arab World

# North America

# Latin America & Caribbean

# Middle East & North Africa

# Sub-Saharan Africa

# East Asia & Pacific

# South Asia

target.area <- which(renewable.electricity.output$country=="World")

renewable.electricity.output[target.area,]

plot(renewable.electricity.output[target.area,4:3],type="l",

main=titel,sub=subtitel,ylab="%",xlab="",xlim=c(1980,2020),ylim=c(1,90),

axes=TRUE)

text(1985, 20, "World",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Euro area")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="red")

text(1985, 12, "Euro area", col="red",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Arab World")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="green")

text(1985, 5, "Arab World", col="green",cex=0.7)

target.area <- which(renewable.electricity.output$country=="North America")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="blue",lty=2)

text(1985, 17, "North America", col="blue",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Latin America & Caribbean")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="yellow")

text(1985, 65, "Latin America & Caribbean", col="yellow",cex=0.7)

# target.area <- which(renewable.electricity.output$country=="Middle East & North Africa")

# renewable.electricity.output[target.area,]

# lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="orange")

# text(1985, 5, "Middle East & North Africa", col="orange",cex=0.7)

target.area <- which(renewable.electricity.output$country=="Sub-Saharan Africa")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="cyan",lty=3)

text(1985, 23, "Sub-Saharan Africa", col="cyan",cex=0.7)

target.area <- which(renewable.electricity.output$country=="East Asia & Pacific")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="magenta",lty=4)

text(1985, 14.5, "East Asia & Pacific", col="magenta",cex=0.7)

target.area <- which(renewable.electricity.output$country=="South Asia")

renewable.electricity.output[target.area,]

lines(renewable.electricity.output[target.area,4:3],type="l",main=titel,ylab="%",xlab="",col="pink",lty=5)

text(1985, 28, "South Asia", col="pink",cex=0.7)

Using trigonometric functions in R

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R uses radiant as input for trigonometric functions.

# calculating rad
pi/2
90*pi/180 # transforming degree into radiant
degr<-c(0:360)
degr
rad<-grad*(pi/180)
rad

# calculating rad

pi/2

90*pi/180 # transforming degree into radiant

degr<-c(0:360)

degr

rad<-grad*(pi/180)

rad

Now we can plot the function.

sin(rad)
plot(sin(rad))

1 2	sin(rad) plot(sin(rad))

And by playing with the functions we get a funny graphic output.

plot(asin(sin(rad)))
plot(asin(sin(rad)),type="l")
points(sin(rad))
points(sin(rad)*-1,col="green")
lines(asin(sin(rad))*-1)
lines(1/sin(rad),col="red")
lines(1/sin(rad)*-1,col="blue")
points(x=91,y=0)
points(x=271,y=0)

plot(asin(sin(rad)))

plot(asin(sin(rad)),type="l")

points(sin(rad))

points(sin(rad)*-1,col="green")

lines(asin(sin(rad))*-1)

lines(1/sin(rad),col="red")

lines(1/sin(rad)*-1,col="blue")

points(x=91,y=0)

points(x=271,y=0)

And if we include the tangent, the graphic looks like this:

tan(rad)
tangens <- tan(rad)
tangens[91]<-0
tangens[271]<-0
plot(tangens,type="l")
lines(sin(rad),type="l",col="red")
lines(cos(rad),type="l",col="green")

tan(rad)

tangens <- tan(rad)

tangens[91]<-0

tangens[271]<-0

plot(tangens,type="l")

lines(sin(rad),type="l",col="red")

lines(cos(rad),type="l",col="green")

lines(tangens,type="l",col="red")
lines(1/tangens,type="l",col="green")
# dividing by 10 for visual reasons
lines(tangens/10,type="l",col="red")
lines(1/tangens/10,type="l",col="green")

lines(tangens,type="l",col="red")

lines(1/tangens,type="l",col="green")

# dividing by 10 for visual reasons

lines(tangens/10,type="l",col="red")

lines(1/tangens/10,type="l",col="green")

plot(tangens,type="l")
lines(1/tangens,type="l")
plot(tangens,type="l")
lines(1/tangens,type="l",col="green")

plot(tangens,type="l")

lines(1/tangens,type="l")

plot(tangens,type="l")

lines(1/tangens,type="l",col="green")

Audio file conversion with afconvert (mac)

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I was looking for a simple and elegant way to convert a high amount of audio files from one format (.caf) to another (.aif). The solution i found is a very elegant one and also comes included with your operating system – if using a MAC.

afconvert -f AIFF -d BEI24@48000 "Wow Bass 03.caf" "Wow Bass 03.aif"

1	afconvert -f AIFF -d BEI24@48000 "Wow Bass 03.caf" "Wow Bass 03.aif"

And now here is the most amazing part. It is super easy to execute the conversion of multiple files by just one command line.

for i in *.caf; do afconvert -f AIFF -d BEI24@48000 "$i" "${i%.caf}.aif"; done

1	for i in *.caf; do afconvert -f AIFF -d BEI24@48000 "$i" "${i%.caf}.aif"; done

or to run through subdirectories:

for d in */ ; do cd $d; pwd; cd ..; done
for d in */ ; do cd $d; for i in *.caf; do echo *caf; pwd; done; cd ..; done;
for d in */ ; do cd $d; for i in *.caf; do afconvert -f AIFF -d BEI24@48000 "$i" "${i%.caf}.aif"; done; cd ..; done

for d in */ ; do cd $d; pwd; cd ..; done

for d in */ ; do cd $d; for i in *.caf; do echo *caf; pwd; done; cd ..; done;

for d in */ ; do cd $d; for i in *.caf; do afconvert -f AIFF -d BEI24@48000 "$i" "${i%.caf}.aif"; done; cd ..; done

or with recursion by using find:

find -name "*.caf"
find . -type f -iname '*.caf' -print | while read -r name; do cp "$name" "/.../..."; done
find . -type f -iname '*.caf' -print | while read -r name; do afconvert -f AIFF -d BEI24@48000 "$name" "/Volumes/Daten/Logic/test/${name%.*}.aif"; done

find -name "*.caf"

find . -type f -iname '*.caf' -print | while read -r name; do cp "$name" "/.../..."; done

find . -type f -iname '*.caf' -print | while read -r name; do afconvert -f AIFF -d BEI24@48000 "$name" "/Volumes/Daten/Logic/test/${name%.*}.aif"; done

Key	linear PCM format
LE	Little Endian
BE	Big Endian
F	Floating point
I	Integer
UI	Unsigned integer
8/16/24/32/64	Number of bits

Number of bits	Information Size
8	256
16	65536
24	16777216
32	4294967296
64	18446744073709551616

afconvert -hf

1	afconvert -hf

Audio file and data formats:	data_formats:
‚3gpp‘ = 3GP Audio (.3gp)	‚Qclp‘ ‚aac ‚ ‚aace‘ ‚aach‘ ‚aacl‘ ‚aacp‘ ’samr‘
‚3gp2‘ = 3GPP-2 Audio (.3g2)	Qclp‘ ‚aac ‚ ‚aace‘ ‚aach‘ ‚aacl‘ ‚aacp‘ ’samr‘
‚adts‘ = AAC ADTS (.aac, .adts)	‚aac ‚ ‚aach‘ ‚aacp‘
‚ac-3‘ = AC3 (.ac3)	‚ac-3‘
‚AIFC‘ = AIFC (.aifc, .aiff, .aif)	I8 BEI16 BEI24 BEI32 BEF32 BEF64 UI8 ‚ulaw‘ ‚alaw‘ ‚MAC3‘ ‚MAC6‘ ‚ima4‘ ‚QDMC‘ ‚QDM2‘ ‚Qclp‘ ‚agsm‘
‚AIFF‘ = AIFF (.aiff, .aif)	I8 BEI16 BEI24 BEI32
‚amrf‘ = AMR (.amr)	’samr‘
‚m4af‘ = Apple MPEG-4 Audio (.m4a, .m4r)	‚aac ‚ ‚aace‘ ‚aach‘ ‚aacl‘ ‚aacp‘ ‚alac‘
‚caff‘ = CAF (.caf)	‚.mp1‘ ‚.mp2‘ ‚.mp3‘ ‚QDM2‘ ‚QDMC‘ ‚Qclp‘ ‚Qclq‘ ‚aac ‚ ‚aace‘ ‚aach‘ ‚aacl‘ ‚aacp‘ ‚alac‘ ‚alaw‘ ‚dvi8‘ ‚ilbc‘ ‚ima4‘ I8 BEI16 BEI24 BEI32 BEF32 BEF64 LEI16 LEI24 LEI32 LEF32 LEF64 ‚ms\x00\x02‘ ‚ms\x00\x11‘ ‚ms\x001‘ ‚paac‘ ’samr‘ ‚ulaw‘
‚MPG1‘ = MPEG Layer 1 (.mp1, .mpeg, .mpa)	‚.mp1‘
‚MPG2‘ = MPEG Layer 2 (.mp2, .mpeg, .mpa)	‚.mp2‘
‚MPG3‘ = MPEG Layer 3 (.mp3, .mpeg, .mpa)	‚.mp3‘
‚mp4f‘ = MPEG-4 Audio (.mp4)	data_formats: ‚aac ‚ ‚aace‘ ‚aach‘ ‚aacl‘ ‚aacp‘
‚NeXT‘ = NeXT/Sun (.snd, .au)	I8 BEI16 BEI24 BEI32 BEF32 BEF64 ‚ulaw‘
‚Sd2f‘ = Sound Designer II (.sd2)	I8 BEI16 BEI24 BEI32
‚WAVE‘ = WAVE (.wav)	UI8 LEI16 LEI24 LEI32 LEF32 LEF64 ‚ulaw‘ ‚alaw‘

Supported Audio File and Data Formats in OS X

My Sounds (Freesound.org)

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This is a collection of my sounds at freesound.org. I try to keep the list up to date but sometimes there are more sounds available on freesound therefore please check out my freesound account and freesound in general. „Freesound is a collaborative database of Creative Commons Licensed sounds. Browse, download and share sounds.“ – freesound.org

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Creating a beat frequency interference with R

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A beat frequency is a mix of two frequencies which are very close to each other but not similar. The trick is that they are to close to each other to be separated by the human ear as two distinct frequencies, thus generating a single tone with fluctuating amplitude behavior – a periodic change in volume. In Fact this effect just appears within the human brain, therefore the two tones can be measured physically by using the appropriate instruments. Further more the effect also works in a binaural situation where one ear can only hear one frequency respectively.

The following graphic shows two almost similar sinus waves, one at 440 Hz and one slightly below, at 435 Hz. The sound data is produced for exactly 2 seconds of time at a 44100 Hz sample rate, giving us 88200 sample points for 2 seconds. The first three demonstrations of the graph show only the beginning of the wave whereas the last one presents the combination of both signals for the complete 2 seconds.

435Hz-440Hz-beatsfrequency-4plots

Basically a combination of two sinus waves can be mathematically represented by:

And if we assume that both amplitudes are the same we get the reduced form by:

It is interesting to understand that the resulting frequency of the beat, i.e. the recognized periodic fluctuation of volume, is given by:

440Hz – Sinus – 2 seconds

435Hz – Sinus – 2 seconds

435 & 440Hz – Sinus – resulting beat frequency – 2 seconds

The oscillations in this post are simple created in R by using standard mathematical functions in combination with the time series package in R. In addition the seewave package is used to store the sinus waves as a .wav file to the system.

install.packages("seewave")
require("seewave")

1 2	install.packages("seewave") require("seewave")

The time series package handles data as equispaced points in time. This is in accordance with the sampling of continuous sound signals as the become digitized. A common used sampling frequency for CD quality is 44.1 kHz which results in 88.2k sample points for a length of 2 seconds.

s1<-sin(2*pi*440*seq(0,2,length.out=88200))
s1<-ts(data=s1, start=0, frequency=44100)

jpeg(filename = "440Hz Sinus.jpg", width=880,height=880,res=100)
plot(head(s1,401),type="l",ylab="")
dev.off()

s2<-sin(2*pi*435*seq(0,2,length.out=88200))
s2<-ts(data=s2, start=0, frequency=44100)

jpeg(filename = "435Hz Sinus.jpg", width=880,height=880,res=100)
plot(head(s2,401),type="l",ylab="")
dev.off()

s3<-(s1+s2)/2

s1<-sin(2*pi*440*seq(0,2,length.out=88200))

s1<-ts(data=s1, start=0, frequency=44100)

jpeg(filename = "440Hz Sinus.jpg", width=880,height=880,res=100)

plot(head(s1,401),type="l",ylab="")

dev.off()

s2<-sin(2*pi*435*seq(0,2,length.out=88200))

s2<-ts(data=s2, start=0, frequency=44100)

jpeg(filename = "435Hz Sinus.jpg", width=880,height=880,res=100)

plot(head(s2,401),type="l",ylab="")

dev.off()

s3<-(s1+s2)/2

For ease of use the summation of the amplitude 2a becomes reduced to a by division.

f<-44100
savewav(s1,f=f ,filename = "s1.wav")
savewav(s2,f=f ,filename = "s2.wav")
savewav(s3,f=f ,filename = "s3.wav")

f<-44100

savewav(s1,f=f ,filename = "s1.wav")

savewav(s2,f=f ,filename = "s2.wav")

savewav(s3,f=f ,filename = "s3.wav")

The graphical representation of the sound can easily be saved as a .jpg file to the system.

jpeg(filename = "435Hz-440Hz-beatsfrequency-4plots.jpg", width=880,height=880,res=100)
par(mfrow=c(2,2))
plot(head(s1,401),type="l",ylab="",main="Sinus 440Hz")
plot(head(s2,401),type="l",ylab="",main="Sinus 435Hz")
plot(head(s3,17640),type="l",ylab="",main="440Hz-335Hz Beat")
plot(s3,type="l",ylab="",main="440Hz-335Hz Beat")
dev.off()

jpeg(filename = "435Hz-440Hz-beatsfrequency-4plots.jpg", width=880,height=880,res=100)

par(mfrow=c(2,2))

plot(head(s1,401),type="l",ylab="",main="Sinus 440Hz")

plot(head(s2,401),type="l",ylab="",main="Sinus 435Hz")

plot(head(s3,17640),type="l",ylab="",main="440Hz-335Hz Beat")

plot(s3,type="l",ylab="",main="440Hz-335Hz Beat")

dev.off()

In addition to the sample above we can also see and hear what it is like when the beat effect fades out and the brain starts to recognize two different tones. Therefore the next few examples present the resulting wave after summing two different frequencies, where one is always 440 Hz.

beatsfrequency-9examples

435 & 440Hz – Sinus – resulting beat frequency – 2 seconds

425 & 440Hz – Sinus – resulting (beat) frequency – 2 seconds

415 & 440Hz – Sinus – resulting (beat) frequency – 2 seconds

405 & 440Hz – Sinus – resulting (beat) frequency – 2 seconds

395 & 440Hz – Sinus – resulting (beat) frequency – 2 seconds

485 & 440Hz – Sinus – resulting (beat) frequency – 2 seconds

jpeg(filename = "beatsfrequency-9examples.jpg", width=880,height=880,res=100)
par(mfrow=c(3,3))
for(i in 1:9){
e<-445-i*10
s1<-sin(2*pi*440*seq(0,2,length.out=88200))
s1<-ts(data=s1, start=0, frequency=44100)
s2<-sin(2*pi*e*seq(0,2,length.out=88200))
s2<-ts(data=s2, start=0, frequency=44100)
s3<-s1+s2
#jpeg(filename = "beatsfrequency.jpg", width=880,height=880,res=100)
plot(head(s3/2,17640),type="l",ylab="",xlab="",main=paste(e,"Hz"))
#plot(s3,type="l")
#dev.off()
f<-44100
savewav((s3/2),f=f ,filename = paste(e,"Hz - 440Hz-beat.wav"))
}
dev.off()

jpeg(filename = "beatsfrequency-9examples.jpg", width=880,height=880,res=100)

par(mfrow=c(3,3))

for(i in 1:9){

e<-445-i*10

s1<-sin(2*pi*440*seq(0,2,length.out=88200))

s1<-ts(data=s1, start=0, frequency=44100)

s2<-sin(2*pi*e*seq(0,2,length.out=88200))

s2<-ts(data=s2, start=0, frequency=44100)

s3<-s1+s2

#jpeg(filename = "beatsfrequency.jpg", width=880,height=880,res=100)

plot(head(s3/2,17640),type="l",ylab="",xlab="",main=paste(e,"Hz"))

#plot(s3,type="l")

#dev.off()

f<-44100

savewav((s3/2),f=f ,filename = paste(e,"Hz - 440Hz-beat.wav"))

}

dev.off()

http://cran.r-project.org/web/views/TimeSeries.html
http://cran.r-project.org/web/packages/seewave/index.html

Latex code for the Formulas above:

sin(2 \pi f_1t)+sin(2 \pi f_2t)=2sin(2 \pi \frac{f_1+f_2}{2} t)sin(2 \pi \frac{f_1-f_2}{2} t)

x=(a_2-a_1)cos \omega_2t+2a_1cos\frac{\omega_1+\omega_2}{2}tcos\frac{\omega_2-\omega_1}{2}t

x=2a_1cos\frac{\omega_1+\omega_2}{2}tcos\frac{\omega_2-\omega_1}{2}t

\omega_a=\frac{\omega_1+\omega_2}{2}

\omega_b=\frac{\omega_2-\omega_1}{2}

\omega_s=\omega_2-\omega_1

f_s=f_2-f_1

sin(2 \pi f_1t)+sin(2 \pi f_2t)=2sin(2 \pi \frac{f_1+f_2}{2} t)sin(2 \pi \frac{f_1-f_2}{2} t)

x=(a_2-a_1)cos \omega_2t+2a_1cos\frac{\omega_1+\omega_2}{2}tcos\frac{\omega_2-\omega_1}{2}t

x=2a_1cos\frac{\omega_1+\omega_2}{2}tcos\frac{\omega_2-\omega_1}{2}t

\omega_a=\frac{\omega_1+\omega_2}{2}

\omega_b=\frac{\omega_2-\omega_1}{2}

\omega_s=\omega_2-\omega_1

f_s=f_2-f_1

S&P 500 growth 1975 – 2014

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Just a few graphs on the development of the S&P500 Index from 1975 – 2014, using data from yahoo finance.

The first graph shows the price movement of the index for the time period from 1975 to 2014 (available data at yahoo finance), with slower but rather constant growth in the first half of the time period and higher growth in the second. And of course the two major set backs from 2000-2003 and 2007-2009 are clearly visible.

SP500.jpg1975-2015

The next graph presents the same time series separated into decades, merged together into one graph. It is easy to see that there is a constant positive development within the different decades (with respect to the choice of truncation). The question arising now is, in which period did the most change in index value occur. The answer to this question kind of turns the plot on its head. It can be found in the next graph.

SP500.jpg 1975-2005 4decades-new

Regarding the question from above the following plot shows the same development in percent values, for the individual decades, included into one graph. Now the separation between the periods becomes more clear, with the highest movement between 1985-1995 and the lowest between 2005 and 2014.

SP500.jpg 1975-2005 4decades percent-new

An other interesting effect appears when we modify the truncation of the single decades. E.g. if we observe the time frames from 1980-1990, 1990-2000, 2000-2010, an so on. 3 decades of the index development are strongly positive whereas 1 decade shows a negative development (2000-2010), indicating the possible importance of timing models.

SP500.jpg 1975-2005 4otherdecades percent2-new

http://finance.yahoo.com/q?s=%5EGSPC

Syndication feed reader using the Project Rome API – (1_FeedReader.java)

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/* ***************************************************************************** */
/* Example of a syndication feed reader using the Project Rome API               */
/* current version of Rome: rome1.0.jar (2009)      https://rome.dev.java.net/   */
/* You need to implement this API plus the JDOM API                              */
/* jdom.jar  ,   you can find this at               https://jdom.org/            */
/* Parts of this example are taken from the PRome Web Page tutorials             */
/* https://rome.dev.java.net/  author: Alejandro Abdelnur                        */
/*                                                                               */
/* This class retrieves a syndfeed from the web and prints its                   */
/* pure content to the system                                                    */
/* created by Martin Stoppacher       date:  26.12.2009                          */
/* ***************************************************************************** */

import java.net.URL;
   /* Class URL represents a Uniform Resource Locator, a pointer to a "resource" */
   /* on the World Wide Web                                                      */
import java.io.InputStreamReader;
   /* An InputStreamReader is a bridge from byte streams to character streams    */
import com.sun.syndication.feed.synd.SyndFeed;
   /* This is the Bean interface for all types of feeds.                         */
import com.sun.syndication.io.SyndFeedInput;
   /* Parses an XML document (File, InputStream, Reader, W3C SAX InputSource, W3C*/
   /* DOM Document or JDom DOcument) into an WireFeed (RSS/Atom).                */
import com.sun.syndication.io.XmlReader;
   /* Character stream that handles (or at least attemtps to) all the necessary  */
   /* Voodo to figure out the charset encoding of the XML document within        */
   /* the stream.                                                                */

public class 1_FeedReader {

    public static void main(String[] args) {
        boolean ok = false;
        if (args.length==1) {
            try {
                URL feedUrl = new URL(args[0]);   /*creates a string with the URL*/

                SyndFeedInput input = new SyndFeedInput();    /* new input object*/
                
                SyndFeed feed = input.build(new XmlReader(feedUrl));
                                     /* reads feed from URL and puts it into feed*/

                System.out.println(feed);              /* outputs file to system */

                ok = true;
            }
            catch (Exception ex) {
                ex.printStackTrace();
                System.out.println("ERROR: "+ex.getMessage());
            }
        }

      if (!ok) {
      System.out.println();
      System.out.println("FeedReader reads and prints any RSS/Atom feed type.");
      System.out.println("The first parameter must be the" 
                                                   +"URL of the feed to read.");
      
      System.out.println();
      }
    }

}

/* ***************************************************************************** */

/* Example of a syndication feed reader using the Project Rome API */

/* current version of Rome: rome1.0.jar (2009) https://rome.dev.java.net/ */

/* You need to implement this API plus the JDOM API */

/* jdom.jar , you can find this at https://jdom.org/ */

/* Parts of this example are taken from the PRome Web Page tutorials */

/* https://rome.dev.java.net/ author: Alejandro Abdelnur */

/* */

/* This class retrieves a syndfeed from the web and prints its */

/* pure content to the system */

/* created by Martin Stoppacher date: 26.12.2009 */

/* ***************************************************************************** */

import java.net.URL;

/* Class URL represents a Uniform Resource Locator, a pointer to a "resource" */

/* on the World Wide Web */

import java.io.InputStreamReader;

/* An InputStreamReader is a bridge from byte streams to character streams */

import com.sun.syndication.feed.synd.SyndFeed;

/* This is the Bean interface for all types of feeds. */

import com.sun.syndication.io.SyndFeedInput;

/* Parses an XML document (File, InputStream, Reader, W3C SAX InputSource, W3C*/

/* DOM Document or JDom DOcument) into an WireFeed (RSS/Atom). */

import com.sun.syndication.io.XmlReader;

/* Character stream that handles (or at least attemtps to) all the necessary */

/* Voodo to figure out the charset encoding of the XML document within */

/* the stream. */

public class 1_FeedReader {

public static void main(String[] args) {

boolean ok = false;

if (args.length==1) {

try {

URL feedUrl = new URL(args[0]); /*creates a string with the URL*/

SyndFeedInput input = new SyndFeedInput(); /* new input object*/

SyndFeed feed = input.build(new XmlReader(feedUrl));

/* reads feed from URL and puts it into feed*/

System.out.println(feed); /* outputs file to system */

ok = true;

}

catch (Exception ex) {

ex.printStackTrace();

System.out.println("ERROR: "+ex.getMessage());

}

if (!ok) {

System.out.println();

System.out.println("FeedReader reads and prints any RSS/Atom feed type.");

System.out.println("The first parameter must be the"

+"URL of the feed to read.");

System.out.println();

}

Implementing a MySQL DB connection via ooRexx by using BSF4ooRexx – (3_MySql_connector.rxj)

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/* ***************************************************************************** */
/* This is a short example of implementing a MySQL DB connection via ooRexx      */ 
/* by using BSF4ooRexx. Using the Java - mysql connector via bean scripting      */
/* in ooRexx                                                                     */
/* created by Martin Stoppacher - 26.12.2009                                     */
/* ***************************************************************************** */

say hello access to a my sql database via java using bsf4rexx
uid = "root";
url = "jdbc:mysql://localhost/test";
pw = "password";

mydrive=.bsf~new('com.mysql.jdbc.Driver')        /* establishe the db connection */
man=bsf.loadClass("java.sql.DriverManager")
man~registerDriver(mydrive)

conn=man~getConnection(url, uid, pw)                  /* connecting the database */

stmt=conn~createStatement                                       /* fetching data */
rset=stmt~executeQuery("select max(Nr)+1 as maximum from mytest")

rset~next; i = rset~getString("maximum"); say i;
stmt~executeUpdate("insert into mytest values(" || i ||",'BSF4ooRexx')") 

rset=stmt~executeQuery("select NR, NAME from mytest")           /* fetching data */

say "NR______NAME"                                                 /* print data */
do while rset~next
say rset~getString("nr")  || ______ || rset~getString("NAME")
end

say end

::requires BSF.CLS                                       /* get the Java support */

/* ***************************************************************************** */

/* This is a short example of implementing a MySQL DB connection via ooRexx */

/* by using BSF4ooRexx. Using the Java - mysql connector via bean scripting */

/* in ooRexx */

/* created by Martin Stoppacher - 26.12.2009 */

/* ***************************************************************************** */

say hello access to a my sql database via java using bsf4rexx

uid = "root";

url = "jdbc:mysql://localhost/test";

pw = "password";

mydrive=.bsf~new('com.mysql.jdbc.Driver') /* establishe the db connection */

man=bsf.loadClass("java.sql.DriverManager")

man~registerDriver(mydrive)

conn=man~getConnection(url, uid, pw) /* connecting the database */

stmt=conn~createStatement /* fetching data */

rset=stmt~executeQuery("select max(Nr)+1 as maximum from mytest")

rset~next; i = rset~getString("maximum"); say i;

stmt~executeUpdate("insert into mytest values(" || i ||",'BSF4ooRexx')")

rset=stmt~executeQuery("select NR, NAME from mytest") /* fetching data */

say "NR______NAME" /* print data */

do while rset~next

say rset~getString("nr") || ______ || rset~getString("NAME")

end

say end

::requires BSF.CLS /* get the Java support */

MST Blog

Econometrics, Programming, Engineering, …

Archiv des Autors: Martin Stoppacher

Renewable electricity output – Regions

Renewable electricity output – World Map

Renewable electricity output – Statistics

Using trigonometric functions in R

Audio file conversion with afconvert (mac)

My Sounds (Freesound.org)

Creating a beat frequency interference with R

S&P 500 growth 1975 – 2014

Syndication feed reader using the Project Rome API – (1_FeedReader.java)

Implementing a MySQL DB connection via ooRexx by using BSF4ooRexx – (3_MySql_connector.rxj)

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