R/ptc.read.r
In mlammens/demgsa: Global Sensitivity Analyses Of Demographic Matrix Projection Models
Defines functions
ptc.read
Documented in
ptc.read
#' @export ptc.read
#' @title Read RAMAS GIS Patch (PTC) file
#' @description
#' \code{ptc.read} reads data from a *.ptc file, used in RAMAS GIS -
#' Spatial Data module. This file contains information on the spatial
#' structure of the metapopulation.
#'
#' @param ptcFile The name of the *.ptc file to read
#'
#' @return \code{ptc.read} returns a list object containing all information
#' from the *.ptc file.
ptc.read <- function( ptcFile ) {
########################################################################################
## Read data from a *.ptc file, used in RAMAS GIS -
## Spatial Data module
##
## Args:
## ptcFile: The path and name of a *.ptc file to be read.
##
########################################################################################
# Print the name of the ptc file to the console
print( paste( "Begin ptc.read function with file: ", ptcFile ) )
# Read the *.ptc file in as a long unsorted 'list' structure
Ptc <- readLines( ptcFile )
# Clear first line of "map=\xff". This step is necessary to avoid errors when using
# regex functions in this script
Ptc[1] <- sub("(map=).*","\\1",Ptc[1])
# Set values that will be used through out the function
MigrationLine <- which(Ptc == "Migration")
CorrLine <- which(Ptc == "Correlation")
# Check if Results are present. If yes, set ResLine and LandLine variables
if ( any(grepl( "^Results", Ptc )) ) {
print( paste("ptc.read: Results present in *.ptc file: ", ptcFile) )
ResLine <- grep("Results",Ptc)
LandLine <- grep("Landscape indices",Ptc)
} else {
print( paste("ptc.read: No Results found in *.ptc file: ", ptcFile) )
}
# Create an empty 'list' structure to store ptc file information
Ptc.list <- vector("list",length=0)
## As of RAMAS GIS 5.1, the first 28 lines of the *.ptc file are fixed. The current
## version of this program assumes that within these first 28 lines to location (i.e.
## line number) of some information is fixed.
#
# Landscape input file version: Check that file is a 'Landscape' file and, if yes, get
# first two numbers in string that are separated by a period using regexs
if ( grepl("^Landscape", Ptc[1]) ) {
Ptc.list$version <- sub(".*([0-9]).([0-9]).*","\\1\\2",Ptc[1])
} else {
stop("Error reading *.pct file. Check that this is a correct *.ptc file")
}
# PTC Scenario Title
Ptc.title <- Ptc[2]
# Comment Lines: Four lines of comments in *.ptc file
Ptc.list$comments <- Ptc[3:6]
# Cell length
Ptc.list$cell.length <- as.numeric( Ptc[7] )
# Habitat Suitability Function
Ptc.list$HSI.func <- Ptc[8]
## Habitat Relationships (HSI) section
# Habitat suitability threshold for patches
Ptc.list$HSI.Threshold <- as.numeric( Ptc[10] )
# Neighborhood Distance (cells)
Ptc.list$Neigh.Dist <- as.numeric( Ptc[11] )
# Habitat suitability map color and Input is patch map boolean
color.pmap <- unlist( strsplit( Ptc[12], "," ) )
Ptc.list$HSI.MapColor <- color.pmap[1]
Ptc.list$HSI.IsPatchMap <- as.logical( color.pmap[2] )
# Number of decimals to export in habitat suitability map
Ptc.list$HSI.ExportDecN <- as.numeric( Ptc[13] )
## Link to Metapopulation (MP) section
# Carrying Capacity (K) function
Ptc.list$MP.K.func <- Ptc[14]
# Rmax function
Ptc.list$MP.Rmax.func <- Ptc[15]
# Initial abundance function
Ptc.list$MP.InitN.func <- Ptc[16]
# Relative fecundity function
Ptc.list$MP.RelFec.func <- Ptc[17]
# Relative survival function
Ptc.list$MP.RelSur.func <- Ptc[18]
# *.mp file to get other data from
Ptc.list$MP.DefaultMP <- Ptc[20]
# Catastrophe 1 - Local Prob
Ptc.list$MP.Cat1.LocProb <- Ptc[21]
# Catastrophe 1 - Local Mult
Ptc.list$MP.Cat1.LocMult <- Ptc[22]
# Catastrophe 2 - Local Prob
Ptc.list$MP.Cat2.LocProb <- Ptc[23]
# Catastrophe 2 - Local Mult
Ptc.list$MP.Cat2.LocMult <- Ptc[24]
# Habitat-based distances (from a friction map) (Yes/No)
Ptc.list$MP.HabitatBasedDistances <- Ptc[25]
# Friction map file name
Ptc.list$MP.FrictionMapFilename <- Ptc[26]
# Distance calculation (Edge to Edge, Center to Edge, or Center to Center)
Ptc.list$MP.DistCalc <- Ptc[27]
## Input Maps Information
# Number of input maps
Ptc.list$MapN <- as.numeric(Ptc[28])
## For each map, read in information
# Create an empty AllMapData list
AllMapData <- vector('list',length=0)
if ( Ptc.list$MapN > 0 ){
# Set the number of lines of map information. If the number of maps is non-zero
# then map info will begin at line 29 and continue to 28+(MapN*5)
MapLines <- Ptc[29:(28+(Ptc.list$MapN*5))]
# (Re)Create an empty MapData list
MapData <- vector('list',length=0)
for ( map in 1:Ptc.list$MapN ) {
LineReadOffset <- (Ptc.list$MapN-1)*5
MapData$Name <- MapLines[ 1 + LineReadOffset ]
MapData$FileName <- MapLines[ 2 + LineReadOffset ]
MapData$FileFormat <- MapLines[ 3 + LineReadOffset ]
MapData$Color <- MapLines[ 4 + LineReadOffset ]
MapData$ColN <- as.numeric( MapLines[ 5 + LineReadOffset ] )
# Add new MapData list to AllMapDataList
AllMapData[[ map ]] <- MapData
}
} else {
print( 'No maps included in this *.ptc file.')
}
Ptc.list$MapData <- AllMapData
## Default Population Information
# Note default population information, assuming it is the line immediately preceding
# the MigrationLine.
Ptc.list$DefaultPop <- Ptc[ MigrationLine - 1 ]
## Migration (Dispersal) and Correlation Distance Functions
# UseDispDistFunc: True if dispersal rates are based on dispersal distance function; false if
# they are specified in the dispersal matrix. NOTE: For *.ptc files, this value should
# always be TRUE
Ptc.list$UseDispDistFunc <- as.logical( Ptc[MigrationLine + 1] )
# DispDistFunc: Dispersal-distance function parameters - a, b, c, Dmax - Mij = a exp(-Dij^c/b)
Ptc.list$DispDistFunc <- as.numeric(unlist(strsplit( Ptc[MigrationLine + 2],',' )))
# UseCorrDistFunc: True if correlations between populations is based on correlation distance
# function. NOTE: For *.ptc files, this value should always be TRUE
Ptc.list$UseCorrDistFunc <- as.logical( Ptc[CorrLine +1] )
# CorrDistFunc: Correlation-distance function parameters - a, b, c - Cij = a exp(-Dij^c/b)
Ptc.list$CorrDistFunc <- as.numeric(unlist(strsplit( Ptc[CorrLine + 2],',' )))
##------------------------------------------------------------------------------------##
## Below are data associated with the Results section of a *.ptc file, and while only
## be present in those *.ptc files that have been run. This section includes information
## on the HSI Histogram, which is actually noted prior to the Results line.
if ( any(grepl( "^Results", Ptc )) ) {
## HSI Histogram Information:
# Determine the HSI map line
hsi.map.line <- grep("^HSI map",Ptc)
### FUTURE DEV - Make this information useful in R
Ptc.list$HSI.Histogram <- Ptc[ (hsi.map.line+1):(ResLine-2) ]
Ptc.list$HSI.MapInfo <- Ptc[ (ResLine-1) ]
# Results Date Information
Ptc.list$ResultsDate <- Ptc[ ResLine ]
# Number of populations
PopLine <- unlist( strsplit( Ptc[ ResLine + 1 ]," " ) )
Ptc.list$PopN <- as.numeric(PopLine[1])
## Population Information: This section is similar to the format employed in
## RAMAS Version 5.0.
# Define Population Data Row Names
PopData_df_colnames <- c("name","X_coord","Y_coord","InitAbund","DensDep","MaxR","K","Ksdstr","Allee","KchangeSt","DD_Migr","Cat1.Multiplier","Cat1.Prob","IncludeInSum","StageMatType","RelFec","RelSur","localthr","Cat2.Multiplier","Cat2.Prob","SDMatType","TargetPopK","Cat1.TimeSinceLast","Cat2.TimeSinceLast","RelDisp")
# Read Population Data as data.frame
PopData_df <- read.csv( ptcFile, header=FALSE, skip = (ResLine+1), nrows=Ptc.list$PopN )
PopData_df <- PopData_df[1:length(PopData_df_colnames)]
# Turn NAs into empty strings
PopData_df[is.na(PopData_df)] <- ''
# Assign columns names
names(PopData_df) <- PopData_df_colnames
Ptc.list$PopData_df <- PopData_df
## Population Patch Characteristics
## Total Habitat Suitability (HSI), Patch Area (#Cells), pminX, pminY, pmaxX, pmaxY,
## Population (patch) ID
# PopPatchChar Row Names
PopPatchChar_df_colnames <- c("HSI","Area","pminX","pminY","pmaxX","pmaxY","PopID")
PopPatchChar_df <- read.table( ptcFile, header=FALSE, skip = (ResLine+Ptc.list$PopN+3), nrows=Ptc.list$PopN )
names(PopPatchChar_df) <- PopPatchChar_df_colnames
Ptc.list$PopPatchChar_df <- PopPatchChar_df
## Summary Line
## Not sure what to do with this line yet. It includes the following values:
## Cell length; Max Dispersal Distance; Max Patch Area; Unidentified 1; Unidentified 2
Ptc.list$SummaryLine <- Ptc[ ResLine + (2*Ptc.list$PopN) + 4 ]
## Distance Matrix
## If center-to-center or edge-to-edge distance is selected, then the matrix in the
## *.ptc file is a lower triangular matrix, and reading it in is more complicated
## than simply reading in a square matrix.
# If matrix is square (i.e. "Center to edge")
if ( Ptc.list$MP.DistCalc == "Center to edge" ) {
DistMatr <- read.table( ptcFile, header=FALSE, skip = (ResLine + (2*Ptc.list$PopN) + 4), nrows=Ptc.list$PopN)
DistMatr <- as.matrix(DistMatr)
Ptc.list$DistMatr <- DistMatr
} else if ( any( Ptc.list$MP.DistCalc == c("Edge to edge", "Default: Center to center"))) {
# Use fill function, assuming there are PopN columns
DistMatr <- read.table( ptcFile, header=FALSE, skip = (ResLine + (2*Ptc.list$PopN) + 4), nrows=Ptc.list$PopN, fill=TRUE, col.names = (1:Ptc.list$PopN))
DistMatr <- as.matrix(DistMatr)
DistMatr[is.na(DistMatr)] <- 0
DistMatr <- DistMatr + t(DistMatr)
Ptc.list$DistMatr <- DistMatr
}
## Landscape Indices - Populations
## Five indices for each population (patch):
## Perimeter, Shape Index, Fractal Dim., Core Area, Edge-to-Area Ratio
# PopLandInd Row Names
PopLandInd_df_colnames <- c("perimeter","shp.index","fractal.dim","core.area","edge.area.ratio")
Ptc.list$PopLandInd_df <- read.table( ptcFile, header=FALSE, skip = LandLine, nrows=Ptc.list$PopN, col.names=PopLandInd_df_colnames )
## Landscape Indices - Summary
## Summary values of the Landscape Indices for each population
LandIndSummary_colnames <- c("tot.edge","tot.core","avg.shp","avg.fractal","avg.perimeter")
Ptc.list$LandIndSummary <- read.table( ptcFile, header=FALSE, skip = (LandLine+Ptc.list$PopN), nrows=1, col.names=LandIndSummary_colnames )
## Map averages
## This information is simply being stored as an unsorted list for the time being.
MapAvgLine <- grep("Map averages",Ptc)
Ptc.list$MapAvg <- Ptc[ (MapAvgLine+1):(MapAvgLine+Ptc.list$MapN) ]
}
return(Ptc.list)
} # End ptc.read function
mlammens/demgsa documentation built on Aug. 4, 2020, 1:16 a.m.
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Defines functions ptc.read
Documented in ptc.read
#' @export ptc.read
#' @title Read RAMAS GIS Patch (PTC) file
#' @description
#' \code{ptc.read} reads data from a *.ptc file, used in RAMAS GIS -
#' Spatial Data module. This file contains information on the spatial
#' structure of the metapopulation.
#'
#' @param ptcFile The name of the *.ptc file to read
#'
#' @return \code{ptc.read} returns a list object containing all information
#' from the *.ptc file.
ptc.read <- function( ptcFile ) {
########################################################################################
## Read data from a *.ptc file, used in RAMAS GIS -
## Spatial Data module
##
## Args:
## ptcFile: The path and name of a *.ptc file to be read.
##
########################################################################################
# Print the name of the ptc file to the console
print( paste( "Begin ptc.read function with file: ", ptcFile ) )
# Read the *.ptc file in as a long unsorted 'list' structure
Ptc <- readLines( ptcFile )
# Clear first line of "map=\xff". This step is necessary to avoid errors when using
# regex functions in this script
Ptc[1] <- sub("(map=).*","\\1",Ptc[1])
# Set values that will be used through out the function
MigrationLine <- which(Ptc == "Migration")
CorrLine <- which(Ptc == "Correlation")
# Check if Results are present. If yes, set ResLine and LandLine variables
if ( any(grepl( "^Results", Ptc )) ) {
print( paste("ptc.read: Results present in *.ptc file: ", ptcFile) )
ResLine <- grep("Results",Ptc)
LandLine <- grep("Landscape indices",Ptc)
} else {
print( paste("ptc.read: No Results found in *.ptc file: ", ptcFile) )
}
# Create an empty 'list' structure to store ptc file information
Ptc.list <- vector("list",length=0)
## As of RAMAS GIS 5.1, the first 28 lines of the *.ptc file are fixed. The current
## version of this program assumes that within these first 28 lines to location (i.e.
## line number) of some information is fixed.
#
# Landscape input file version: Check that file is a 'Landscape' file and, if yes, get
# first two numbers in string that are separated by a period using regexs
if ( grepl("^Landscape", Ptc[1]) ) {
Ptc.list$version <- sub(".*([0-9]).([0-9]).*","\\1\\2",Ptc[1])
} else {
stop("Error reading *.pct file. Check that this is a correct *.ptc file")
}
# PTC Scenario Title
Ptc.title <- Ptc[2]
# Comment Lines: Four lines of comments in *.ptc file
Ptc.list$comments <- Ptc[3:6]
# Cell length
Ptc.list$cell.length <- as.numeric( Ptc[7] )
# Habitat Suitability Function
Ptc.list$HSI.func <- Ptc[8]
## Habitat Relationships (HSI) section
# Habitat suitability threshold for patches
Ptc.list$HSI.Threshold <- as.numeric( Ptc[10] )
# Neighborhood Distance (cells)
Ptc.list$Neigh.Dist <- as.numeric( Ptc[11] )
# Habitat suitability map color and Input is patch map boolean
color.pmap <- unlist( strsplit( Ptc[12], "," ) )
Ptc.list$HSI.MapColor <- color.pmap[1]
Ptc.list$HSI.IsPatchMap <- as.logical( color.pmap[2] )
# Number of decimals to export in habitat suitability map
Ptc.list$HSI.ExportDecN <- as.numeric( Ptc[13] )
## Link to Metapopulation (MP) section
# Carrying Capacity (K) function
Ptc.list$MP.K.func <- Ptc[14]
# Rmax function
Ptc.list$MP.Rmax.func <- Ptc[15]
# Initial abundance function
Ptc.list$MP.InitN.func <- Ptc[16]
# Relative fecundity function
Ptc.list$MP.RelFec.func <- Ptc[17]
# Relative survival function
Ptc.list$MP.RelSur.func <- Ptc[18]
# *.mp file to get other data from
Ptc.list$MP.DefaultMP <- Ptc[20]
# Catastrophe 1 - Local Prob
Ptc.list$MP.Cat1.LocProb <- Ptc[21]
# Catastrophe 1 - Local Mult
Ptc.list$MP.Cat1.LocMult <- Ptc[22]
# Catastrophe 2 - Local Prob
Ptc.list$MP.Cat2.LocProb <- Ptc[23]
# Catastrophe 2 - Local Mult
Ptc.list$MP.Cat2.LocMult <- Ptc[24]
# Habitat-based distances (from a friction map) (Yes/No)
Ptc.list$MP.HabitatBasedDistances <- Ptc[25]
# Friction map file name
Ptc.list$MP.FrictionMapFilename <- Ptc[26]
# Distance calculation (Edge to Edge, Center to Edge, or Center to Center)
Ptc.list$MP.DistCalc <- Ptc[27]
## Input Maps Information
# Number of input maps
Ptc.list$MapN <- as.numeric(Ptc[28])
## For each map, read in information
# Create an empty AllMapData list
AllMapData <- vector('list',length=0)
if ( Ptc.list$MapN > 0 ){
# Set the number of lines of map information. If the number of maps is non-zero
# then map info will begin at line 29 and continue to 28+(MapN*5)
MapLines <- Ptc[29:(28+(Ptc.list$MapN*5))]
# (Re)Create an empty MapData list
MapData <- vector('list',length=0)
for ( map in 1:Ptc.list$MapN ) {
LineReadOffset <- (Ptc.list$MapN-1)*5
MapData$Name <- MapLines[ 1 + LineReadOffset ]
MapData$FileName <- MapLines[ 2 + LineReadOffset ]
MapData$FileFormat <- MapLines[ 3 + LineReadOffset ]
MapData$Color <- MapLines[ 4 + LineReadOffset ]
MapData$ColN <- as.numeric( MapLines[ 5 + LineReadOffset ] )
# Add new MapData list to AllMapDataList
AllMapData[[ map ]] <- MapData
}
} else {
print( 'No maps included in this *.ptc file.')
}
Ptc.list$MapData <- AllMapData
## Default Population Information
# Note default population information, assuming it is the line immediately preceding
# the MigrationLine.
Ptc.list$DefaultPop <- Ptc[ MigrationLine - 1 ]
## Migration (Dispersal) and Correlation Distance Functions
# UseDispDistFunc: True if dispersal rates are based on dispersal distance function; false if
# they are specified in the dispersal matrix. NOTE: For *.ptc files, this value should
# always be TRUE
Ptc.list$UseDispDistFunc <- as.logical( Ptc[MigrationLine + 1] )
# DispDistFunc: Dispersal-distance function parameters - a, b, c, Dmax - Mij = a exp(-Dij^c/b)
Ptc.list$DispDistFunc <- as.numeric(unlist(strsplit( Ptc[MigrationLine + 2],',' )))
# UseCorrDistFunc: True if correlations between populations is based on correlation distance
# function. NOTE: For *.ptc files, this value should always be TRUE
Ptc.list$UseCorrDistFunc <- as.logical( Ptc[CorrLine +1] )
# CorrDistFunc: Correlation-distance function parameters - a, b, c - Cij = a exp(-Dij^c/b)
Ptc.list$CorrDistFunc <- as.numeric(unlist(strsplit( Ptc[CorrLine + 2],',' )))
##------------------------------------------------------------------------------------##
## Below are data associated with the Results section of a *.ptc file, and while only
## be present in those *.ptc files that have been run. This section includes information
## on the HSI Histogram, which is actually noted prior to the Results line.
if ( any(grepl( "^Results", Ptc )) ) {
## HSI Histogram Information:
# Determine the HSI map line
hsi.map.line <- grep("^HSI map",Ptc)
### FUTURE DEV - Make this information useful in R
Ptc.list$HSI.Histogram <- Ptc[ (hsi.map.line+1):(ResLine-2) ]
Ptc.list$HSI.MapInfo <- Ptc[ (ResLine-1) ]
# Results Date Information
Ptc.list$ResultsDate <- Ptc[ ResLine ]
# Number of populations
PopLine <- unlist( strsplit( Ptc[ ResLine + 1 ]," " ) )
Ptc.list$PopN <- as.numeric(PopLine[1])
## Population Information: This section is similar to the format employed in
## RAMAS Version 5.0.
# Define Population Data Row Names
PopData_df_colnames <- c("name","X_coord","Y_coord","InitAbund","DensDep","MaxR","K","Ksdstr","Allee","KchangeSt","DD_Migr","Cat1.Multiplier","Cat1.Prob","IncludeInSum","StageMatType","RelFec","RelSur","localthr","Cat2.Multiplier","Cat2.Prob","SDMatType","TargetPopK","Cat1.TimeSinceLast","Cat2.TimeSinceLast","RelDisp")
# Read Population Data as data.frame
PopData_df <- read.csv( ptcFile, header=FALSE, skip = (ResLine+1), nrows=Ptc.list$PopN )
PopData_df <- PopData_df[1:length(PopData_df_colnames)]
# Turn NAs into empty strings
PopData_df[is.na(PopData_df)] <- ''
# Assign columns names
names(PopData_df) <- PopData_df_colnames
Ptc.list$PopData_df <- PopData_df
## Population Patch Characteristics
## Total Habitat Suitability (HSI), Patch Area (#Cells), pminX, pminY, pmaxX, pmaxY,
## Population (patch) ID
# PopPatchChar Row Names
PopPatchChar_df_colnames <- c("HSI","Area","pminX","pminY","pmaxX","pmaxY","PopID")
PopPatchChar_df <- read.table( ptcFile, header=FALSE, skip = (ResLine+Ptc.list$PopN+3), nrows=Ptc.list$PopN )
names(PopPatchChar_df) <- PopPatchChar_df_colnames
Ptc.list$PopPatchChar_df <- PopPatchChar_df
## Summary Line
## Not sure what to do with this line yet. It includes the following values:
## Cell length; Max Dispersal Distance; Max Patch Area; Unidentified 1; Unidentified 2
Ptc.list$SummaryLine <- Ptc[ ResLine + (2*Ptc.list$PopN) + 4 ]
## Distance Matrix
## If center-to-center or edge-to-edge distance is selected, then the matrix in the
## *.ptc file is a lower triangular matrix, and reading it in is more complicated
## than simply reading in a square matrix.
# If matrix is square (i.e. "Center to edge")
if ( Ptc.list$MP.DistCalc == "Center to edge" ) {
DistMatr <- read.table( ptcFile, header=FALSE, skip = (ResLine + (2*Ptc.list$PopN) + 4), nrows=Ptc.list$PopN)
DistMatr <- as.matrix(DistMatr)
Ptc.list$DistMatr <- DistMatr
} else if ( any( Ptc.list$MP.DistCalc == c("Edge to edge", "Default: Center to center"))) {
# Use fill function, assuming there are PopN columns
DistMatr <- read.table( ptcFile, header=FALSE, skip = (ResLine + (2*Ptc.list$PopN) + 4), nrows=Ptc.list$PopN, fill=TRUE, col.names = (1:Ptc.list$PopN))
DistMatr <- as.matrix(DistMatr)
DistMatr[is.na(DistMatr)] <- 0
DistMatr <- DistMatr + t(DistMatr)
Ptc.list$DistMatr <- DistMatr
}
## Landscape Indices - Populations
## Five indices for each population (patch):
## Perimeter, Shape Index, Fractal Dim., Core Area, Edge-to-Area Ratio
# PopLandInd Row Names
PopLandInd_df_colnames <- c("perimeter","shp.index","fractal.dim","core.area","edge.area.ratio")
Ptc.list$PopLandInd_df <- read.table( ptcFile, header=FALSE, skip = LandLine, nrows=Ptc.list$PopN, col.names=PopLandInd_df_colnames )
## Landscape Indices - Summary
## Summary values of the Landscape Indices for each population
LandIndSummary_colnames <- c("tot.edge","tot.core","avg.shp","avg.fractal","avg.perimeter")
Ptc.list$LandIndSummary <- read.table( ptcFile, header=FALSE, skip = (LandLine+Ptc.list$PopN), nrows=1, col.names=LandIndSummary_colnames )
## Map averages
## This information is simply being stored as an unsorted list for the time being.
MapAvgLine <- grep("Map averages",Ptc)
Ptc.list$MapAvg <- Ptc[ (MapAvgLine+1):(MapAvgLine+Ptc.list$MapN) ]
}
return(Ptc.list)
} # End ptc.read function
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