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If you specify csv as the data type for a parameter, you can enter several values in succession, as a series of numbers, in an input field.

Number series are usually imported from measuring instruments, but can also be entered manually (when entering manually, the individual values are entered separately with the Enter key).

In the figure above, the numerical series burning rate was calculated from the numerical series burning distance and burning time.
Burning rate = (Burning distance / Burning time)*60

This was done by dividing the 1st value of the burning distance by the 1st value of the burning time and multiplying by 60 (254 / 202)*60 = 75.45.
Divide the 2nd value of the burning distance by the 2nd value of the burning time and multiply by 60 (123 / 202)*60 = 36.53.
etc.

The formula is as follows:

csvmultiply(csvdivide(m['BV']['1'], m['BV']['2']), 60)

Functions for calculating with number sequences

Function	Designation	Example
average	average(Number series) forms the average value of the number series	mittelwert(m['BV']['1']) = average(254; 123; 100; 254; 254) = 197
median	median(Number series) forms the median of the number series. If the number series consists of an even number of numbers, median calculates the average of the two middle numbers.	median(m['BV']['1']) = 254
stabw or stabws	stabw(Number series) forms the standard deviation of a sample:	stabw(m['BV']['1']) = 78,47
stabwn	stabwn(Number series) is the standard deviation of a population:	stabwn(m['BV']['1']) = 70,19
summe	summe(Number series) forms the sum of a number series	summe(m['BV']['1']) = 985
produkt	produkt(Number series) forms the product of the Number series	produkt(m['BV']['1']) = 201560887200
min	min(Number series) returns the smallest number of the number series	min(m['BV']['1']) = 100
max	max(Number series) gives the largest number of the Number series	max(m['BV']['1']) = 254
first	first(Number series) returns the first value of a number series	first(m['BV']['1']) = 254
last	last(Number series) returns the last value of a number series	last(m['BV']['1']) = 254
csventry	csventry(Position, values) returns the value at a certain position of a number series. The position starts counting at 0.	z.B.: csventry(this(1), this(0)) returns the following result:
csvadd	csvadd(Number series A, Number series B) adds the 1st value of Number series A to the 1st value of Number series B, etc. and returns a Number series again.	csvadd(m['BV']['1'], m['BV']['2']) = 456; 325; 150; 456; 456
csvminus	csvminus(Number series A, Number series B) subtracts the 1st value of Number series A from the 1st value of Number series B, etc. and returns a Number series again.	csvminus(m['BV']['1'], m['BV']['2']) = 52; -79; 50; 52; 52
csvmultiply	csvmultiply(Number series A, Number series B) multiplies the 1st value of Number series A by the 1st value of Number series B, etc. and returns a Number series again.	csvmultiply(m['BV']['1'], m['BV']['2']) = 51308; 24846; 5000; 51308; 51308
csvdivide	csvdivide(Number series A, Number series B) divides the 1st value of Number series A with the 1st value of Number series B, etc. and returns a Number series again.	csvdivide(m['BV']['1'], m['BV']['2']) = 1,26; 0,61; 2,00; 1,26; 1,26
csvpow	csvpow(Number series A, Number series B)Takes the 1st value of number series A as the base and the 1st value of number series B as the power, etc. and returns a number series again.	csvpow(10, 3) = 1000
csvanzahl	csvanzahl(Number series) returns as a result the number of numbers in the Number series	csvanzahl(1,26; 0,61; 2,00; 1,26; 1,26) = 5
csvverketten	csvverketten(Number series, Number series) allows the concatenation of two number seriesn	csvverketten(15; 16, 'mg/l') = 15mg/l; 16mg/l
csvmittelwert	csvmittelwert(Data series A, Data series B,...) allows calculation of the mean for each row of multiple data series.	csvmittelwert(5;10,15;20) = 10;15
csvmedian	csvmedian(Data series A, Data series B,...) allows determination of the median for each row of multiple data series. In case of an even number of data series, the mean of the middle values is calculated.	csvmedian(5;10,15;20,20;25) = 15;20
csvstabw	csvstabw(Data series A, Data series B,...) Determines the standard deviation for each row of multiple data series.	csvstabw(1;1,1.5;2,2;3) = 0.5;1
csvstabwn	csvstabwn(Data series A, Data series B,...) Determines the population standard deviation for each row of multiple data series.	csvstabwn(1;1,1.5;2,2;3) = 0.41;0.82
csvstabws	csvstabws(Data series A, Data series B,...) Determines the sample standard deviation for each row of multiple data series.	csvstabws(1;1,1.5;2,2;3) = 0.5;1
csvround	csvround(Data series, Number of decimal places) Rounds a floating-point value for each row of a data series.	csvround(5.42;4.37,1) = 5.4;4.4
csvlog	csvlog(Data series) natural logarithm for each row of a data series.	csvlog(10;15) = 2.3026;2.7081
csvlog10	csvlog10(Data series) Logarithm base 10 for each row of a data series.	csvlog10(10;15) = 2.3026;2.7081
csvexponentialformat	csvexponentialformat(Data series) The numbers are displayed in exponential notation (scientific notation) with one decimal place for each row of a data series.	csvexponentialformat(1005;3400) = 1.0 x 10<sup>3</sup>;3.4 x 10<sup>3</sup>
csvlinks	csvlinks(String, Number of characters) Cuts characters from the left for each row of a data series.	csvlinks(Hello;World,3) = Hel;Wor
csvrechts	csvright(String, Number of characters) Cuts characters from the right for each row of a data series.	csvright(Hello;World,3) = llo;rld

By combining the above functions, the above calculation of the firing rate can be realised as follows:

Burning rate = csvmultiply(csvdivide(m['BV']['1'], m['BV']['2']), 60) = 75,45; 36,53; 120,00; 75,45; 75,45

Logical operations with Number seriesn

Example of logical operations with number series

csvwenn and csvvergleich are mostly used in the following combination:

csvwenn(csvvergleich(a, Vergleichsoperator, b), a, b)

Whereby a and b is a number series and (>, <, <=, >=, !=, ==) are used as comparison operators.

The following is an example of how we can use the number series a and b and then return the larger value line by line:

a = {1, 3, 4}
b = {7, 1, -1}

csvwenn(csvvergleich(a, '>', b), a, b) = {7, 3, 4}

the result comes about because the result of the csv comparison looks like this
csvvergleich({1, 3, 4}, '>', {7, 1, -1}) = {0, 1, 1}

Application example csvund
→ csvund requires that the two csvvergleicher apply
csvwenn( csvund( csvvergleich(a, ’>’ , 0), csvvergleich(b, ‘>’, 0) ), ‘<0’, csvadd(a,b) ) = {8, 4, ‘<0’}