Training exposure: Difference between revisions

Revision as of 04:50, 13 May 2012

This page is a knowledge crystal of subtype variable. The page identifier is Op_en5674
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Question

What is the exposure in the Training assessment?

Answer

Training exposure: Difference between revisions(-)
Obs	Year	Unit	Result	Description
1	2012	ug/m3	1 - 2
2	2020	ug/m3	20 - 11

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out <- tidy(op_baseGetData("opasnet_base", "Op_en5674"), direction = "wide")
out <- make.ovariable(out)
cat("Computed training exposures.\n")
print(out)
print(formula.Op_en5674(dependencies.Op_en5674))
cat("Computed training exposures.\n")

Rationale

Formula

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cat("Initiation successful. Now starting the model.\n")

############ tapply of ovariables applies a function to each cell of a ragged array, that is to each (non-empty) group of values given by a unique combination of the levels of certain factors.
### parameters (other parameters are as in generic tapply):
### X an ovariable

setMethod(f = "tapply", 
	signature = signature(X = "ovariable"),
	definition = function(X, INDEX, FUN = NULL, ..., simplify = TRUE) {
		out <- as.data.frame(as.table(tapply(X@sample$Result, INDEX, FUN, ..., simplify = TRUE)))
		X@sample <- out
		return(X)
	}
)

formula.Op_en5674 <- function(x) {
	ueConc <- make.ovariable(x$microenvironment.concentration)
	ueTime <- make.ovariable(x$microenvironment.time)
	out <- ueConc * ueTime
	out <- tapply(out, out@sample[c("Iter", "Unit")], sum)
	return(out)
}

dependencies.Op_en5674 <- list(
	microenvironment.concentration = data.frame(
		Microenvironment = c("Home", "Other"),
		Unit = "ug/m3",
		Result = c(1, 2)),
	microenvironment.time = data.frame(
		Microenvironment = c("Home", "Other"),
		Unit = "ug/m3",
		Result = c(0.7, 0.3))
)

**Urgenche research project 2011 - 2014: city-level climate change mitigation**
Urgenche pages Urgenche main page · Category:Urgenche · Urgenche project page (password-protected) Relevant data Building stock data in Urgenche‎ · Building regulations in Finland · Concentration-response to PM2.5 · Emission factors for burning processes · ERF of indoor dampness on respiratory health effects · ERF of several environmental pollutions · General criteria for land use · Indoor environment quality (IEQ) factors · Intake fractions of PM · Land use in Urgenche · Land use and boundary in Urgenche · Energy use of buildings Relevant methods Building model · Energy balance · Health impact assessment · Opasnet map · Help:Drawing graphs · OpasnetUtils‎ · Recommended R functions‎ · Using summary tables‎ City Kuopio Climate change policies and health in Kuopio (assessment) · Climate change policies in Kuopio (plausible city-level climate policies) · Health impacts of energy consumption in Kuopio · Building stock in Kuopio · Cost curves for energy (prioritization of options) · Energy balance in Kuopio (energy data) · Energy consumption and GHG emissions in Kuopio by sector · Energy consumption classes (categorisation) · Energy consumption of heating of buildings in Kuopio · Energy transformations (energy production and use processes) · Fuels used by Haapaniemi energy plant · Greenhouse gas emissions in Kuopio · Haapaniemi energy plant in Kuopio · Land use in Kuopio · Building data availability in Kuopio · Password-protected pages: File:Heat use in Kuopio.csv · Kuopio housing City Basel Buildings in Basel (password-protected) Energy balances Energy balance in Basel · Energy balance in Kuopio · Energy balance in Stuttgart · Energy balance in Suzhou

Keywords

References

Related files

@@ Line 9: / Line 9: @@
 <t2b index="Year,Observation" locations="Unit,Result,Description" unit="-">
-|ug/m3|10|
+|ug/m3|1 - 2|
-|ug/m3|20|
+|ug/m3|20 - 11|
 </t2b>
@@ Line 20: / Line 20: @@
 		page:Object-oriented_programming_in_Opasnet|name:answer|
 		page:OpasnetBaseUtils|name:generic|
-		page:Cost_curves_for_energy|name:formula
+		page:Training_exposure|name:formula
 	"
 >
-out <- formula.Op_en5674(dependencies.Op_en5674)
+out <- tidy(op_baseGetData("opasnet_base", "Op_en5674"), direction = "wide")
+out <- make.ovariable(out)
 cat("Computed training exposures.\n")
 print(out)
+print(formula.Op_en5674(dependencies.Op_en5674))
+cat("Computed training exposures.\n")
 </rcode>
@@ Line 43: / Line 46: @@
 >
-formula.Op_en5674 <- function(x) {
+cat("Initiation successful. Now starting the model.\n")
-	data <- tidy(op_baseGetData("opasnet_base", x$curves[1]), direction = "wide")
-	curves <- strsplit(as.character(data$Parameters), split = ",")
+############ tapply of ovariables applies a function to each cell of a ragged array, that is to each (non-empty) group of values given by a unique combination of the levels of certain factors.
-	coefficients <- data.frame(array(NA, dim = c(length(curves), 10)))
+### parameters (other parameters are as in generic tapply):
+### X an ovariable
-	for(j in 1:length(curves)) {
+setMethod(f = "tapply",
-		coefficients[j, 1:length(curves[[j]])] <- curves[[j]]
+	signature = signature(X = "ovariable"),
+	definition = function(X, INDEX, FUN = NULL, ..., simplify = TRUE) {
+		out <- as.data.frame(as.table(tapply(X@sample$Result, INDEX, FUN, ..., simplify = TRUE)))
+		X@sample <- out
+		return(X)
 	}
-	coefficients[nrow(coefficients) + 1, 1] <- x$deviation
+)
-	for(k in 1:ncol(coefficients)) {
-		coefficients[, k] <- ifelse(is.na(coefficients[, k]), 0, as.numeric(coefficients[, k]))
+formula.Op_en5674 <- function(x) {
-	}
+	ueConc <- make.ovariable(x$microenvironment.concentration)
-	x.new <- polyroot(as.vector(colSums(coefficients)))
+	ueTime <- make.ovariable(x$microenvironment.time)
-	x.new <- ifelse(abs(Im(x.new)) < 1e-6, Re(x.new), 1e+30) # Take only the real roots.
+	out <- ueConc * ueTime
-	x.new <- x.new[abs(x.new) == min(abs(x.new))] # Pick the root that is closest to zero (=BAU).
+	out <- tapply(out, out@sample[c("Iter", "Unit")], sum)
-	y.new <- 0
+	return(out)
-	for(k in 1:ncol(coefficients)) {
-		y.new <- y.new + coefficients[, k] * x.new^(k-1)
-	}
-	temp <- data[1, ]
-	temp[1, ] <- "Deviation"
-	data <- rbind(data, temp)
-	data <- cbind(data, data.frame(Result = y.new))
-	return(data)
 }
 dependencies.Op_en5674 <- list(
-	curves = c("Op_en5478", "Cost curves for energy"),
+	microenvironment.concentration = data.frame(
-	bau = c("Op_en5469", "Energy balance in Kuopio"),
+		Microenvironment = c("Home", "Other"),
-	deviation = -5
+		Unit = "ug/m3",
+		Result = c(1, 2)),
+	microenvironment.time = data.frame(
+		Microenvironment = c("Home", "Other"),
+		Unit = "ug/m3",
+		Result = c(0.7, 0.3))
 )

Training exposure: Difference between revisions

Revision as of 04:50, 13 May 2012

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