R/mp.read.r
In mlammens/demgsa: Global Sensitivity Analyses Of Demographic Matrix Projection Models
Defines functions
mp.read
Documented in
mp.read
#' @export mp.read
#' @title Read RAMAS (c) *.MP file
#' @description
#' \code{mp.read} reads in the *.MP file used in the RAMAS Metapop software.
#' The *.MP file is a simple text file containing all of the values necessary
#' to configure a metapopulation simulation.
#'
#' @param mpFile The name of the *.MP file to be read.
#' @param verbose Print function progress and checkpoints.
#' @param results Read results saved in the *.MP file.
#'
#' @return \code{mp.read} returns a nested list object. The first level includes
#' two elements, the version of the *.MP file and list names "mp.file". The
#' "mp.file" list is composed of 52 elements that provide all of the information
#' found in the *.MP file.
#'
#' @details
#' mp.file 'list' structure elements. Most element names from RAMAS Metapop source code
#' The assignment of each element of the mp.file list is based on the Metapopulation input file
#' type for version 5.0.
#' \enumerate{
#' \item MaxRep: maximum number of replications (integer; 1-10000)
#' \item MaxDur: Duration of simulation (integer; 1-500)
#' \item Demog_Stoch: Use demographic stochasticity? (boolean)
#' \item Stages: Number of stages (integer; 1-50)
#' \item Stages.IgnoreConstraints: Ingore constraints? (boolean - TRUE/FALSE)
#' \item Cat1: Information associated with catastrophe 1
#' This information is subdivided in RAMAS Input, but will not be subdivided
#' in the first version of the sensitivity analysis package
#' \item Cat2: Information associated with catastrophe 2
#' See note associated with Cat1
#' \item DDActing: Information regarding density dependence
#' \item Distrib: Distribution (Normal or Lognormal) to use for Environmental
#' Stochasticity.
#' \item AdvancedStochSetting: Advanced Environmental Stoch. Settings: (0 or PoolVar or
#' NegSurvCorr)
#' \item dispCV: Coefficient of variation for dispersal
#' \item WhenBelow: When below local threshold (count in total or don't count or assume dead)
#' \item corrwith: Within population correlation -
#' 1 - (F, S, K Correlated)
#' 2 - (F and S correlated, K independent)
#' 3 - (F, S, K independent
#' \item DispersalDependsOnTargetPopK: Just like the variable name implie; boolean
#' \item DDBasis: Density basis type (char)
#' \item PopSpecificDD: Density dependence type is population specific (boolean - Yes/No)
#' \item DDforAllPop: Density dependence type for all populations (char)
#' \item UserDllFileName: Filename for user-defined density dependence function
#' \item TimeStepSize: Time step size
#' \item TimeStepUnits: Time step units
#' \item SexStructure: Description of Sex Structure (char)
#' \item FemaleStages: Number of female stages
#' \item MatingSystem: Description of Mating system selected (char)
#' \item FemalesPerMale: Number of females per male as given by user
#' \item MalesPerFemale: Number of males per female as given by user
#' \item CVerror: sampling error for N
#' \item FirstTforRisk: Initial number of time steps to exclude from risk calculations
#' Population Parameters
#' \item PopList: Individual population data - saved as a 'nested list' structure, one 'list' for
#' each population.
#' Dispersal (Migration) Parameters
#' \item UseDispDistFunc: True if dispersal rates are based on dispersal distance function; false if
#' they are specified in the dispersal matrix
#' \item DispDistFunc: Dispersal-distance function parameters - a, b, c, Dmax - Mij = a exp(-Dij^c/b)
#' \item DispMatr: Dispersal matrix, as defined or calculated in 'fill.matrix.df.r'
#' Correlation Parameters
#' \item UseCorrDistFunc: True if correlations between populations is based on correlation distance
#' function; False if they are specified in the correlation matrix
#' \item CorrDistFunc: Correlation-distance function parameters - a, b, c - Cij = a exp(-Dij^c/b)
#' \item CorrMatr: User specified correlation matrix, as defined or calculated in 'fill.matrix.df.r'
#' Stage Matrices Information
#' \item StMatrNumber: number of stage matrices defined by user in *.mp file
#' \item StMatr: a 'list' object containing information about each stage matrix. For each stage
#' matrix the following information is stored:
#' StMatrName: Name of the stage matrix
#' StMatrSurvMult: Survival multiplier for stage matrix (for generating new matrices; not used in simulation)
#' StMatrFecMult: Fecundity multiplier for stage matrix (for generating new matrices; not used in simulation)
#' Matr: a matrix object with numeric values
#' Standard Deviation Matrices Information
#' \item SDMatrNumber: number of st. dev. matrices defined by user in *.mp file
#' \item SDMatr: a 'list' object containing information for each st.dev. matrix. For each st.dev.
#' matrix the following information is stored:
#' SDMatrName: Name of the st.dev. matrix
#' Matr: a matrix object of numeric values
#' \item ConstraintsMatr: Constraints Matrix
#' \item StMig: Stage Relative Migration (Dispersal) Rates
#' \item Cat1EffMat: Catastrophe 1 effects on vital rates; one row per stage, seperated by spaces
#' \item Cat1EffNst: Catastrophe 1 effects on abundances
#' \item Cat2EffMat: Catastrophe 2 effects on vital rates; one row per stage, seperated by spaces
#' \item Cat2EffNst: Catastrophe 2 effects on abundances
#' \item StInit: Initial Abundance for each stage for each population
#' Stage Properties
#' \item StProp: a 'list' object containing the following information for each stage
#' StName: Name of stage
#' StWeight: Relative weight of stage
#' StExclude: Exclude from total (boolean: TRUE or FALSE)
#' StBasisForDD: Basis for density-dependence (boolean: TRUE or FALSE)
#' StBreeding: Proportion of stage physically capable of breeding
#' Population Management Actions
#' \item NPopManage: Number of population managment actions
#' \item PopManageProp: All of the lines associated with population management, unpartitioned
#' \item ExtinctThr: Extinction threshold
#' \item ExplodeThr: Explosion threshold
#' \item stepsize: step size
#' \item PopData_df: Population level information in data.frame format
#'}
mp.read <- function(mpFile, verbose = FALSE, results = FALSE) {
# Inform the user that the mp.read() function has been called.
if(verbose){
print( paste( "Begin mp.read function with file: ", mpFile ) )
}
# Save the file path to mpFile
mpFilePath <- mpFile
# Read *.mp files into a long unsorted 'list' structure, 1 element per line in the file.
mpFile <- readLines(mpFile)
# Clear first line of "map=\xff". This step is needed because the "\x" leads to regex problems :(
mpFile[1] <- sub('map.*','',mpFile[1])
# Set values that will be used through out the function
FirstPopLine <- 45 # Line 45 correlates with first population data line
MigrationLine <- which(mpFile == "Migration")
CorrLine <- which(mpFile == "Correlation")
ConstraintsLine <- which(mpFile == "Constraints Matrix")
# Create mp.file list and initiate with length 0
mp.file <- vector("list",length = 0)
# Get Metapop Version information using metapopversion() function
mp.file$RAMAS.Version <- metapopversion(mpFile)
# MaxRep: Number of replications (integer; 1-100000)
mp.file$MaxRep <- as.numeric(mpFile[7])
# MaxDur: Duration of simulation (integer; 1-500)
mp.file$MaxDur <- as.numeric(mpFile[8])
# Demog_Stoch: Use demographic stochasticity? (boolean)
mp.file$Demog_Stoch <- as.logical(mpFile[9])
# Stages: Number of stages (integer; 1-50)
# Stages.IgnoreConstraints: Ingore constraints? (boolean - TRUE/FALSE)
stageLine <- unlist(strsplit(mpFile[10],' '))
mp.file$Stages <- as.numeric(stageLine[1])
mp.file$Stages.IgnoreConstraints <- as.logical(stageLine[2])
# Cat1: Information associated with catastrophe 1
mp.file$Cat1$Name <- mpFile[11]
# Check if Cat1 probability is a single number or a PCH file
if(grepl(pattern = "PCH", ignore.case = TRUE, x = mpFile[12])){
mp.file$Cat1$Probability <- mpFile[12]
} else {
mp.file$Cat1$Probability <- as.numeric(mpFile[12])
# If this value was blank, this might be NA, in which case, convert to 0
mp.file$Cat1$Probability <-
ifelse(is.na(mp.file$Cat1$Probability), yes = 0, no = mp.file$Cat1$Probability)
}
mp.file$Cat1$Extent <- mpFile[13]
mp.file$Cat1$Effect <- mpFile[14]
mp.file$Cat1$Spread <- mpFile[15]
mp.file$Cat1$Infect.Prob <- as.numeric(mpFile[16])
mp.file$Cat1$Spread.Dist.Params <- as.numeric(unlist(strsplit( mpFile[17],',' )))
# Cat2: Information associated with catastrophe 2
mp.file$Cat2$Name <- mpFile[18]
# Check if Cat2 probability is a single number or a PCH file
if(grepl(pattern = "PCH", ignore.case = TRUE, x = mpFile[19])){
mp.file$Cat2$Probability <- mpFile[19]
} else {
mp.file$Cat2$Probability <- as.numeric(mpFile[19])
# If this value was blank, this might be NA, in which case, convert to 0
mp.file$Cat2$Probability <-
ifelse(is.na(mp.file$Cat2$Probability), yes = 0, no = mp.file$Cat2$Probability)
}
mp.file$Cat2$Extent <- mpFile[20]
mp.file$Cat2$Effect <- mpFile[21]
mp.file$Cat2$Spread <- mpFile[22]
mp.file$Cat2$Infect.Prob <- as.numeric(mpFile[23])
mp.file$Cat2$Spread.Dist.Params <- as.numeric(unlist(strsplit( mpFile[24],',' )))
# Catastrophe 2 Reset Vital Rate Trend After Catastrophe (True or False),
# comma (,), Correlation between the two catastrophes (zero or ...)
mp.file$Cat2$Reset.and.Correlation <- mpFile[25]
# DDActing: Information regarding density dependence
mp.file$DDActing <- mpFile[26]
# Distrib: Distribution (Normal or Lognormal) to use for Environmental
# Stochasticity.
# AdvancedStochSetting: Advanced Environmental Stoch. Settings: (0 or PoolVar or
# NegSurvCorr)
stochLine <- unlist(strsplit(mpFile[27],','))
mp.file$Distrib <- stochLine[1]
mp.file$AdvancedStochSetting <- stochLine[2]
# dispCV: Coefficient of variation for dispersal
mp.file$dispCV <- as.numeric(mpFile[28])
# WhenBelow: When below local threshold (count in total or don't count or assume dead)
mp.file$WhenBelow <- mpFile[29]
# corrwith: Within population correlation -
# 1 - (F, S, K Correlated)
# 2 - (F and S correlated, K independent)
# 3 - (F, S, K independent
mp.file$corrwith <- mpFile[30]
# DispersalDependsOnTargetPopK: Just like the variable name implie; boolean
mp.file$DispersalDependsOnTargetPopK <- mpFile[31]
# DDBasis: Density basis type (char). Possiblities are 'AllStages', 'SelectedStages', 'FecundityWeighted'
mp.file$DDBasis <- mpFile[32]
# PopSpecificDD: Density dependence type is population specific (boolean - Yes/No)
mp.file$PopSpecificDD <- mpFile[33]
# DDforAllPop: Density dependence type for all populations (char)
mp.file$DDforAllPop <- mpFile[34]
# UserDllFileName: Filename for user-defined density dependence function
mp.file$UserDllFileName <- mpFile[35]
# TimeStepSize: Time step size (integer)
mp.file$TimeStepSize <- mpFile[36]
# TimeStepUnits: Time step units (char)
mp.file$TimeStepUnits <- mpFile[37]
# SexStructure: Description of Sex Structure (char)
mp.file$SexStructure <- mpFile[38]
# FemaleStages: Number of female stages (integer)
mp.file$FemaleStages <- mpFile[39]
# MatingSystem: Description of Mating system selected (char)
mp.file$MatingSystem <- mpFile[40]
# FemalesPerMale: Number of females per male as given by user (number)
mp.file$FemalesPerMale <- as.numeric(mpFile[41])
# MalesPerFemale: Number of males per female as given by user
mp.file$MalesPerFemale <- as.numeric(mpFile[42])
# CVerror: sampling error for N
mp.file$CVerror <- as.numeric(mpFile[43])
# FirstTforRisk: Initial number of time steps to exclude from risk calculations
mp.file$FirstTforRisk <- as.numeric(mpFile[44])
# ----------------------------------------------------------------------------------------------- #
# PopList: Population level information
if(verbose){
print( "mp.read: Reading population information")
}
# First determine the number of populations (popNumber). In version 5.0, population data begins at line 45
# and the 'Migration' section begins immediately after the last population's data
# The population level information is stored in two different structures - 1) a List format that
# corresponds with how information is stored in RAMAS and 2) a data.frame that is convenient
# for functions.
##### TO DO - Below is setup for if there is no user defined variables
PopNumber <- MigrationLine - FirstPopLine
if (PopNumber < 1) {
stop("Error: mp.read: Insufficient number of populations. Check 'first pop. line' value in mp.read.r")
}
PopRawData <- mpFile[FirstPopLine:(FirstPopLine + PopNumber - 1)]
# Create population data list object
PopData <- vector('list',length=0) # Initiate a PopData list
AllPopData <- vector('list',length=0) # Initiate a AllPopData list
PopData_df_rownames <- vector() # Initiate a row names vector
if ( mp.file$RAMAS.Version == 50 ) {
PopData_df_rownames <- c("name","X_coord","Y_coord","InitAbund","DensDep","MaxR","K","Ksdstr","Allee","KchangeSt","DD_Migr","Cat1.Multiplier","Cat1.Prob","IncludeInSum","StageMatType","RelFec","RelSur","localthr","Cat2.Multiplier","Cat2.Prob","SDMatType","TargetPopK","Cat1.TimeSinceLast","Cat2.TimeSinceLast","RelDisp")
for ( pop in 1:PopNumber ) {
popLine <- unlist(strsplit( PopRawData[ pop ], ',' ))
PopData$name <- popLine[1]
PopData$X_coord <- as.numeric(popLine[2])
PopData$Y_coord <- as.numeric(popLine[3])
PopData$InitAbund <- popLine[4]
PopData$DensDep <- popLine[5]
PopData$MaxR <- popLine[6]
PopData$K <- popLine[7]
PopData$Ksdstr <- popLine[8]
PopData$Allee <- popLine[9]
PopData$KchangeSt <- popLine[10]
PopData$DD_Migr <- popLine[11]
PopData$Cat1.Multiplier <- popLine[12]
PopData$Cat1.Prob <- popLine[13]
PopData$IncludeInSum <- popLine[14]
PopData$StageMatType <- popLine[15]
PopData$RelFec <- popLine[16]
PopData$RelSur <- popLine[17]
PopData$localthr <- popLine[18]
PopData$Cat2.Multiplier <- popLine[19]
PopData$Cat2.Prob <- popLine[20]
PopData$SDMatType <- popLine[21]
PopData$TargetPopK <- popLine[22]
PopData$Cat1.TimeSinceLast <- popLine[23]
PopData$Cat2.TimeSinceLast <- popLine[24]
PopData$RelDisp <- popLine[25]
AllPopData[[pop]] <- PopData # Add PopData to full list of population data
}
} else if ( mp.file$RAMAS.Version >= 51 ) {
PopData_df_rownames <- c("name","X_coord","Y_coord","InitAbund","DensDep","MaxR","K","Ksdstr","Allee","KchangeSt","DD_Migr","Cat1.Multiplier","Cat1.Prob","IncludeInSum","StageMatType","RelFec","RelSur","localthr","Cat2.Multiplier","Cat2.Prob","SDMatType","TargetPopK","Cat1.TimeSinceLast","Cat2.TimeSinceLast","RelDisp","RelVarFec","RelVarSurv")
for ( pop in 1:PopNumber ) {
popLine <- unlist(strsplit( PopRawData[ pop ], ',' ))
PopData$name <- popLine[1]
PopData$X_coord <- as.numeric(popLine[2])
PopData$Y_coord <- as.numeric(popLine[3])
PopData$InitAbund <- as.numeric(popLine[4])
PopData$DensDep <- popLine[5]
PopData$MaxR <- as.numeric(popLine[6])
PopData$K <- as.numeric(popLine[7])
PopData$Ksdstr <- as.numeric(popLine[8])
PopData$Allee <- as.numeric(popLine[9])
PopData$KchangeSt <- popLine[10]
PopData$DD_Migr <- popLine[11]
PopData$Cat1.Multiplier <- popLine[12]
PopData$Cat1.Prob <- popLine[13]
PopData$IncludeInSum <- popLine[14]
PopData$StageMatType <- popLine[15]
PopData$RelFec <- popLine[16]
PopData$RelSur <- popLine[17]
PopData$localthr <- popLine[18]
PopData$Cat2.Multiplier <- popLine[19]
PopData$Cat2.Prob <- popLine[20]
PopData$SDMatType <- popLine[21]
PopData$TargetPopK <- popLine[22]
PopData$Cat1.TimeSinceLast <- popLine[23]
PopData$Cat2.TimeSinceLast <- popLine[24]
PopData$RelDisp <- popLine[25]
PopData$RelVarFec <- popLine[26]
PopData$RelVarSurv <- popLine[27]
# ## Look for user defined density dependence parameters
# if (length(popLine)>27){
# for (udd_par in 1:(length(popLine)-27)){
# PopData$UserDef
# }
# }
AllPopData[[pop]] <- PopData # Add PopData to full list of population data
}
}
mp.file$PopList <- AllPopData
# Create population data data.frame
PopData_df <- read.csv( mpFilePath, header=FALSE, skip=44, nrows=PopNumber )
###browser()
### Only use as many columns as there are elements in the PopData_df_rownames. The rest of the columns
### are associated with user defined density dependence parameters, not used at this time.
### PopData_df <- PopData_df[1:length(PopData_df_rownames)]
# Turn NAs into empty strings
PopData_df[is.na(PopData_df)] <- ''
# Check if PopData_df includes user defined d-d values
if (ncol(PopData_df)>27){
# Get the number of userd defined d-d pars
Num_udd_pars <- ncol(PopData_df)-27
udd_names <- paste('udd_',1:Num_udd_pars,sep='')
PopData_df_rownames <- c(PopData_df_rownames,udd_names)
}
# Assign columns names
names(PopData_df) <- PopData_df_rownames
###browser()
# ----------------------------------------------------------------------------------------------- #
# ----------------------------------------------------------------------------------------------- #
# Dispersal (Migration) Data
if(verbose){
print( "mp.read: Reading dispersal (migration) information" )
}
# UseDispDistFunc: True if dispersal rates are based on dispersal distance function; false if
# they are specified in the dispersal matrix
mp.file$UseDispDistFunc <- as.logical( mpFile[MigrationLine + 1] )
# DispDistFunc: Dispersal-distance function parameters - a, b, c, Dmax - Mij = a exp(-Dij^c/b)
mp.file$DispDistFunc <- as.numeric(unlist(strsplit( mpFile[MigrationLine + 2],',' )))
# DispMatr: User specified dispersal matrix. If the user selected dispersal based on
# disp-dist function then there are no rows for dispersal matrix. The definition of
# DispMatr has non-intuitive indexing to account for the fact that this first two lines
# after "Migration" are not part of the matrix, they are UseDispDistFunc and
# DispDistFunc, respectively.
if ( mp.file$UseDispDistFunc ) {
# migLines is a variable used to identify the number of lines used to define the
# disp-dist func parameters and the disp matrix, if one is defined
migLines <- 1 #Only one line necessary for migration parameters
mp.file$DispMatr <- fill.matrix.df( PopData_df, mp.file$DispDistFunc, 'disp' )
} else {
migLines <- (1 + PopNumber)
DispMatr <- mpFile[ (MigrationLine + 3):(MigrationLine + 1 + migLines) ]
mp.file$DispMatr <- matrix(as.numeric(unlist(strsplit( DispMatr,',' ))), nrow = PopNumber, byrow = TRUE)
}
# ----------------------------------------------------------------------------------------------- #
# ----------------------------------------------------------------------------------------------- #
# Correlation Data
if(verbose){
print( "mp.read: Reading correlation information")
}
# UseCorrDistFunc: True if correlations between populations is based on correlation distance
# function; False if they are specified in the correlation matrix
mp.file$UseCorrDistFunc <- as.logical( mpFile[CorrLine +1] )
# CorrDistFunc: Correlation-distance function parameters - a, b, c - Cij = a exp(-Dij^c/b)
mp.file$CorrDistFunc <- as.numeric(unlist(strsplit( mpFile[CorrLine + 2],',' )))
# CorrMatr: User specified correlation matrix. Similar to dispersl matrix (See notes for DispMatr
# above), however the matrix is a lower triangular matrix, thus it is not saved as a matrix object
if ( mp.file$UseCorrDistFunc ) {
corrLines <- 1
# Create a Correlation Matrix from the Correlation Distance function. If there is only one
# population, then the number 1 is returned
mp.file$CorrMatr <- fill.matrix.df( PopData_df, mp.file$CorrDistFunc, 'corr' )
} else {
if(verbose){
print("Using Correlation matrix") ### WARNING LINE
}
# Define a new function used to read correlation distance matrices
# addZeroes: used to read a correlation distance matrix. This function adds zeroes to each line
# of the lower triangular corr-dist matrix stored in the *.mp file
addZeroes <- function( vect ) {
zeroes <- PopNumber - length(vect)
new_vect <- c( vect, rep(0, zeroes) )
return(new_vect)
}
corrLines <- (1 + PopNumber)
# If user defined, the corr-matr is a lower-triangular matrix. To make comparisons easy, the
# matrix is transformed into a 'matrix' object
#
# Read corrMatr as list from readLines input
corrMatr <- mpFile[ (CorrLine+3):(CorrLine+2+PopNumber) ]
# Split each list element by comma
corrMatr <- strsplit( corrMatr, ',' )
# Make each list element a vector of numeric values
corrMatr <- lapply( corrMatr, as.numeric)
# Apply the addZeroes function
corrMatr <- lapply( corrMatr, addZeroes )
# Make the 'list' into a 'matrix'
corrMatr <- do.call( rbind, corrMatr )
# Set CorrMatr in mp.file 'list'
mp.file$CorrMatr <- corrMatr
}
# ----------------------------------------------------------------------------------------------- #
# ----------------------------------------------------------------------------------------------- #
# Stage Matrices Information
# Determine the number of stage matrix types (i.e., how many stage matrices are defined)
# The number of stage matices (stage matrix types) is the first number of the line that
# contains the phrase "stage matrix" in it.
stgMatrLine <- grep('stage matrix', mpFile, ignore.case=TRUE)
StMatrNumber <- unlist(strsplit( mpFile[stgMatrLine],' ' ))
StMatrNumber <- as.numeric( StMatrNumber[1] )
# StMatrNumber: number of stage matrices defined by user in *.mp file
mp.file$StMatrNumber <- StMatrNumber
#
# StMatr: a list object containing information about each stage matrix.
StMatr <- vector('list',length=0) # Create an empty stage matrix
oneMatr <- 4 + mp.file$Stages # The number of lines of information for one matrix
allMatr <- StMatrNumber * oneMatr # The number of lines of information for all matrices
# Extract from mpFile all of the lines of information for all of the matrices
AllStMatrLines <- mpFile[ (stgMatrLine + 1):(stgMatrLine + 1 + allMatr) ]
#
AllStMatr <- vector('list',length=0) # Create an empty list of stage matrices
for ( matr in 1:mp.file$StMatrNumber ) {
LineAdd <- matr - 1
# StMatrName: Name of the stage matrix
StMatr$StMatrName <- AllStMatrLines[ 1 + (LineAdd*oneMatr) ]
# StMatrSurvMult: Survival multiplier for stage matrix (for generating new matrices; not used in simulation)
StMatr$StMatrSurvMult <- AllStMatrLines[ 2 + (LineAdd*oneMatr) ]
# StMatrFecMult: Fecundity multiplier for stage matrix (for generating new matrices; not used in simulation)
StMatr$StMatrFecMult <- AllStMatrLines[ 3 + (LineAdd*oneMatr) ]
# Matr: a matrix object with numeric values
dumbMatr <- AllStMatrLines[ (5 + (LineAdd*oneMatr)):( (4 + mp.file$Stages) + (LineAdd*oneMatr) ) ]
StMatr$Matr <- matrix(as.numeric(unlist(strsplit( dumbMatr,' ' ))), nrow = mp.file$Stages, byrow = TRUE)
AllStMatr[[matr]] <- StMatr
}
mp.file$StMatr <- AllStMatr
# ----------------------------------------------------------------------------------------------- #
# ----------------------------------------------------------------------------------------------- #
# Standard Deviation Matrices Information
# Determine the number of std. dev. matrix types.
# The number of standard deviation matrices (st dev matrix types) is the first number of the line that
# contains the phrase "st.dev. matrix" in it.
sdMatrLine <- grep('st.dev. matrix', mpFile, ignore.case=TRUE)
sdMatrNumber <- unlist(strsplit( mpFile[sdMatrLine],' ' ))
sdMatrNumber <- as.numeric( sdMatrNumber[1] )
# SDMatrNumber: number of st. dev. matrices defined by user in *.mp file
mp.file$SDMatrNumber <- sdMatrNumber
#
# SDMatr: a list object containing information for each st.dev. matrix.
SDMatr <- vector('list',length=0) # Create an empty st.dev. matrix list object
oneSDMatr <- 1 + mp.file$Stages # The number of lines of information for one st.dev. matrix
allSDMatr <- sdMatrNumber * oneSDMatr # The number of lines of information for all st.dev. matrices
# Extract from mpFile all of the lines of information for all of the matrices
AllSDMatrLines <- mpFile[ (sdMatrLine + 1):(sdMatrLine + 1 + allSDMatr) ]
#
AllSDMatr <- vector('list',length=0) # Create an empty list of std deviation matrices
for ( matr in 1:mp.file$SDMatrNumber ) {
LineAdd <- matr - 1 # Addition factor to skip to the lines associated with the matrix of interest
# SDMatrName: Name of the st.dev. matrix
SDMatr$SDMatrName <- AllSDMatrLines[ 1 + (LineAdd*oneSDMatr) ]
# Matr: a matrix object of numeric values
dumbMatr <- AllSDMatrLines[ (2 + (LineAdd*oneSDMatr)):( (1 + mp.file$Stages) + (LineAdd*oneSDMatr) ) ]
SDMatr$Matr <- matrix(as.numeric(unlist(strsplit( dumbMatr,' ' ))), nrow = mp.file$Stages, byrow = TRUE)
AllSDMatr[[matr]] <- SDMatr
}
mp.file$SDMatr <- AllSDMatr
# ----------------------------------------------------------------------------------------------- #
# ConstraintsMatr: Constraints Matrix
dumbMatr <- mpFile[ (ConstraintsLine + 1):(ConstraintsLine + mp.file$Stages) ]
mp.file$ConstraintsMatr <- matrix(as.numeric(unlist(strsplit( dumbMatr,' '))),nrow = mp.file$Stages, byrow = TRUE)
# StMig: Stage Relative Migration (Dispersal) Rates
mp.file$StMig <- as.numeric(unlist(strsplit( mpFile[ ConstraintsLine + mp.file$Stages + 1 ],' ' )))
# Cat1EffMat: Catastrophe 1 effects on vital rates; one row per stage, seperated by spaces
dumbMatr <- mpFile[ (ConstraintsLine + mp.file$Stages + 2):(ConstraintsLine + 2*mp.file$Stages + 1) ]
mp.file$Cat1EffMat <- matrix(as.numeric(unlist(strsplit( dumbMatr,' ' ))), nrow = mp.file$Stages, byrow = TRUE)
# Cat1EffNst: Catastrophe 1 effects on abundances
mp.file$Cat1EffNst <- as.numeric(unlist(strsplit( mpFile[ ConstraintsLine + 2*mp.file$Stages + 2 ],' ' )))
# Cat2EffMat: Catastrophe 2 effects on vital rates; one row per stage, seperated by spaces
dumbMatr <- mpFile[ (ConstraintsLine + 2*mp.file$Stages + 3):(ConstraintsLine + 3*mp.file$Stages + 2) ]
mp.file$Cat2EffMat <- matrix(as.numeric(unlist(strsplit( dumbMatr,' ' ))), nrow = mp.file$Stages, byrow = TRUE)
# Cat2EffNst: Catastrophe 2 effects on abundances
mp.file$Cat2EffNst <- as.numeric(unlist(strsplit( mpFile[ ConstraintsLine + 3*mp.file$Stages + 3 ],' ' )))
# StInit: Initial Abundance for each stage for each population
InitAbLine <- ConstraintsLine + 3*mp.file$Stages + 4 # First line
InitAb <- mpFile[ InitAbLine:(InitAbLine + PopNumber - 1) ]
mp.file$StInit <- matrix(as.numeric(unlist(strsplit( InitAb,' ' ))), nrow = PopNumber, byrow = TRUE)
# ----------------------------------------------------------------------------------------------- #
# Stage Properties
InitStPropLine <- InitAbLine + PopNumber # First line of stage properties in the *.mp file
# StProp: a list object containing information for each stage
StProp <- vector('list',length=0) # Create an empty stage property list object
# For each stage there are five properties, thus five lines of information
allStProp <- 5 * mp.file$Stages
AllStPropLines <- mpFile[ InitStPropLine:(InitStPropLine + allStProp - 1) ]
#
AllStProp <- vector('list',length=0) # Create an empty list of St.Prop. lists
for ( stg in 1:mp.file$Stages ) {
firstinfo <- (stg - 1)*5 + 1 # First line of info for stage 'stg'
# StName: Name of stage
StProp$StName <- AllStPropLines[ firstinfo ]
# StWeight: Relative weight of stage
StProp$StWeight <- AllStPropLines[ firstinfo + 1 ]
#StExclude: Exclude from total (boolean: TRUE or FALSE)
StProp$StExclude <- AllStPropLines[ firstinfo + 2 ]
#StBasisForDD: Basis for density-dependence (boolean: TRUE or FALSE)
StProp$StBasisForDD <- AllStPropLines[ firstinfo + 3 ]
#StBreeding: ###### WHAT IS THIS????
StProp$StBreeding <- AllStPropLines[ firstinfo + 4 ]
AllStProp[[ stg ]] <- StProp
}
mp.file$StProp <- AllStProp
# ----------------------------------------------------------------------------------------------- #
# ----------------------------------------------------------------------------------------------- #
# Population Management Actions
# NPopManage: The number of population managment actions
mgmntLine <- grep('pop mgmnt',mpFile,ignore.case=TRUE)
NPopManage <- unlist(strsplit(mpFile[mgmntLine],' '))
NPopManage <- as.numeric(NPopManage[1])
mp.file$NPopManage <- NPopManage
if ( NPopManage > 0 ) {
# PopManageProp: All of the lines associated with population management, unpartitioned
####mp.file$PopManageProp <- mpFile[ (mgmntLine + 1):(mgmntLine + NPopManage) ]
PopManageProp.colNames <- c('active', 'mng.type', 'from.pop', 'to.pop',
'begin.time','end.time','period','when','num.or.prop',
'number','proportion',
'from.stage','to.stage','cond.type','cond.abund.low',
'cond.abund.high','from.all.stgs','cond.quant.2','cond.func.N1',
'cond.func.N2','abund.each.stg_div_all.stg',
'abund.each.pop_div_all.pop')
mp.file$PopManageProp <- read.table( mpFilePath , skip=mgmntLine, nrows=NPopManage,
col.names=PopManageProp.colNames)
} else {
mp.file$PopManageProp <- "NA" # Fill with 'NA' value
}
# ----------------------------------------------------------------------------------------------- #
# The next three elements of the list are defined based on the position of the population
# management line. This may change in the future and have to be adjusted
# ExtinctThr: Extinction Threshold
mp.file$ExtinctThr <- as.numeric( mpFile[ mgmntLine + NPopManage + 1 ] )
# ExplodeThr: Explosion Threshold
mp.file$ExplodeThr <- as.numeric( mpFile[ mgmntLine + NPopManage + 2 ] )
# stepsize: Stepsize
mp.file$stepsize <- as.numeric( mpFile[ mgmntLine + NPopManage + 3 ] )
# ----------------------------------------------------------------------------------------------- #
# The last element of the list is the population data data.frame
mp.file$PopData_df <- PopData_df
## ******************************************************************** ##
## BEGIN MP RESULTS READ SECTION
## ******************************************************************** ##
if(results){
# Create results list and initiate with length 0
results <- vector("list",length = 0)
# Find begining of simulation results
res.start <- grep('Simulation results', mpFile, ignore.case=TRUE)
# Check to see that there are results in this *.mp file
if( !length(res.start) ) {
stop( paste('No Simulation results found in *.mp file:', mpFilePath) )
}
if(verbose){
print('mp.read.results: Reading simulation results')
}
# Get number of replications in simulation
SimRepLine <- unlist( strsplit ( mpFile[ (res.start + 1) ], ' ' ) )
results$SimRep <- as.numeric( SimRepLine[1] )
# Read in Pop. ALL Results
pop.all.line <- grep( 'Pop. ALL', mpFile )
results$PopAll <- read.table( mpFilePath, skip=pop.all.line, nrows=mp.file$MaxDur )
names( results$PopAll ) <- c('Mean', 'StDev', 'Min', 'Max')
# Read in individual population Results
# PopInd variable is a 3-dim Array of size 'Duration of Simulation' x 4 x 'Number of Populations'
# The second dimension (length=4) corresponds to the Mean, StDev, Min, and Max population size
###browser()
PopInd <- vector()
# Calculate start of individual population information
pop.ind.start <- pop.all.line + mp.file$MaxDur + 1
# Calculate end of individual population information
pop.ind.stop <- pop.all.line + mp.file$MaxDur + (mp.file$MaxDur+1)*PopNumber
# Identify where the population ID lines are (i.e., the lines that say Pop. #)
pop.ind.ID.lines <-
seq(from=(pop.all.line+mp.file$MaxDur+1),
to=(pop.all.line+mp.file$MaxDur+((mp.file$MaxDur+1)*PopNumber)),
by=(mp.file$MaxDur+1))
# Make a vector of all lines
pop.ind.lines <- pop.ind.start:pop.ind.stop
# Remove ID lines
pop.ind.lines <- setdiff(pop.ind.lines,pop.ind.ID.lines)
# Get these values
pvals <- mpFile[pop.ind.lines]
# Covert to numeric
pvals.num <- as.numeric(unlist(strsplit(pvals, split=" ")))
# Convert to matrix. There are allways four columns in these matrices.
pop.vals <- matrix(pvals.num,ncol=4,byrow=TRUE)
# Make a lits of PopNumber matrices
pop.vals.list <- lapply(split(pop.vals,0:(nrow(pop.vals)-1)%/%mp.file$MaxDur),matrix,nrow=mp.file$MaxDur)
# Convert the list to an array
pop.vals.array <- array(unlist(pop.vals.list),c(mp.file$MaxDur,4,PopNumber))
results$PopInd <- pop.vals.array
#
# for ( pop in 1:PopNumber ){
# # Number of lines past Pop. ALL to skip to start 'pop' values. Last '+1' for Pop. # Label
# start.pop <- pop.all.line + pop*(mp.file$MaxDur +1) + 1
# # Number of lines past Pop. ALL to skip to stop 'pop' values.
# stop.pop <- (start.pop-1) + mp.file$MaxDur
# # Get pop values from mpFile. Initially is read as characters
# pvals <- mpFile[start.pop:stop.pop]
# # Covert to numeric
# pvals.num <- as.numeric(unlist(strsplit(pvals, split=" ")))
# # Convert to matrix. There are allways four columns in these matrices.
# pop.vals <- matrix(pvals.num,ncol=4,byrow=TRUE)
# # Combine new matrix with PopInd matrix
# PopInd <- c(PopInd,pop.vals)
# }
# results$PopInd <- array( PopInd, dim=c(mp.file$MaxDur,4,PopNumber) )
# Read in Occupancy Results - a summary stat. of number of patches occupied at each time step during a simulation
occ.line <- grep( '^Occupancy', mpFile ) # Note carrot used to capture line that begins with 'Occupancy'
results$Occupancy <- read.table( mpFilePath, skip=occ.line, nrows=mp.file$MaxDur )
names( results$Occupancy ) <- c('Mean', 'StDev', 'Min', 'Max')
# Read in Local Occupancy Results - a summary stat. for occupancy rate (prop. of time patches remained occupied)
occ.loc.line <- grep( 'Local Occupancy', mpFile )
results$LocOccupancy <- read.table( mpFilePath, skip=occ.loc.line, nrows=PopNumber )
names( results$LocOccupancy ) <- c('Mean', 'StDev', 'Min', 'Max')
# Read Min., Max., and Ter. - the min, max, and final population abundance values for each
# replication of the mp model. each column is ordered seperately
rep.line <- grep( 'Min. Max. Ter.', mpFile )
results$Replications <- read.table( mpFilePath, skip=rep.line, nrows=results$SimRep )
names( results$Replications ) <- c('Min', 'Max', 'Ter')
# Read Time to cross - used to determine quasi-extinction/ -explosion risk. The number of
# rows in the mp file depends on the stepsize. Each row is a time-step and the first col
# is the number of times the pop. abund. crossed the min threshold for the first time in that
# time step and the second is associated with crossing the max threshold
t.cross.line <- grep( 'Time to cross', mpFile )
t.cross.rows <- (mp.file$MaxDur %/% mp.file$stepsize) + (mp.file$MaxDur %% mp.file$stepsize)
results$TimeCross <- read.table( mpFilePath, skip=t.cross.line, nrows=t.cross.rows )
names( results$TimeCross ) <- c('QuasiExtinct','QuasiExpl')
# Read Final stage abundances results
# results$FinalStAb variable is a 3-dim Array of size Numer of Stages (rows) x 4 (col) x Number of Populations (slices)
# to call the results of one populaiton (e.g., Pop. 1) use third index (e.g., results$FinalStAb[,,1] )
# Columns of the matrix are Mean, StDev, Min, Max
fin.stg.ab.line <- grep( 'Final stage abundances', mpFile )
fin.stg.ab.rows <- PopNumber * mp.file$Stages
FinalStAb <- as.matrix( read.table( mpFilePath, skip=fin.stg.ab.line, nrows=fin.stg.ab.rows ) )
# Seperate out FinalStAb into the different populations
fsa.first <- 1 # Initial first line for partitioning Final Stage Abundance matrix
fsa.list <- lapply(split(FinalStAb,0:(nrow(FinalStAb)-1)%/%mp.file$Stages),matrix,nrow=mp.file$Stages)
fsa.array <- array(unlist(fsa.list),c(mp.file$Stages,4,PopNumber))
results$FinalStAb <- fsa.array
#
# FinalStAb.vect <- vector()
# for ( pop in 1:PopNumber ){
# fsa.last <- pop*mp.file$Stages
# FinalStAb.vect <- c( FinalStAb.vect, FinalStAb[ fsa.first:fsa.last, ] )
# fsa.first <- fsa.last + 1
# }
# results$FinalStAb <- array( FinalStAb.vect, dim=c(mp.file$Stages, 4, PopNumber) )
# Read LocExtDur results
loc.ext.dur.line <- grep( 'LocExtDur', mpFile )
results$LocExtDur <- read.table( mpFilePath, skip=loc.ext.dur.line, nrow=PopNumber )
names( results$LocExtDur ) <- c('Mean','StDev','Max','Min')
# Read Harvest results
# First line is the total harvest results, the second line is the number of lines dedicated
# to individual time units for harvest
harvest.line <- grep( '^Harvest', mpFile )
results$HarvestTot <- read.table( mpFilePath, skip=harvest.line, nrow=1 )
names( results$HarvestTot ) <- c('Mean','StDev','Min','Max')
# Determine number of time steps with harvest data
harvest.steps <- mpFile[ harvest.line + 2 ]
harvest.steps <- unlist( strsplit( harvest.steps, ' ' ) )
harvest.steps <- as.numeric( harvest.steps[1] )
if ( harvest.steps > 0 ) {
results$HarvestSteps <- read.table( mpFilePath, skip=(harvest.line + 2), nrow=harvest.steps )
names( results$HarvestSteps ) <- c('Time', 'Mean', 'StDev', 'Min', 'Max')
}
# Read RiskOfLowHarvest results
risk.harvest.line <- grep( 'RiskOfLowHarvest', mpFile )
results$RiskLowHarvest <- read.table( mpFilePath, skip=risk.harvest.line, nrow=results$SimRep )
# Read Average stage abundances results
# First line after 'avg.st.ab.line' is the number of populations and number of time
# steps recorded (dependent on maxdur and stepsize)
# After this, there are popnumber * time steps lines by number of stages columns
# The values are the stage abundance values for each stage in each population
avg.st.ab.line <- grep( 'Average stage abundances', mpFile )
ab.steps <- mpFile[ avg.st.ab.line + 1 ]
ab.steps <- unlist( strsplit( ab.steps, ' ' ) )
ab.pops <- as.numeric( ab.steps[1] )
ab.steps <- as.numeric( ab.steps[2] )
avg.st.ab.rows <- ab.pops * ab.steps
AvgStAb <- as.matrix( read.table( mpFilePath, skip=(avg.st.ab.line + 1), nrow=avg.st.ab.rows ) )
# Seperate out AvgStAb into different populations
asb.first <- 1
AvgStAb.vect <- vector()
for ( pop in 1:ab.pops ){
asb.last <- pop*ab.steps
AvgStAb.vect <- c( AvgStAb.vect, AvgStAb[ asb.first:asb.last, ] )
asb.first <- asb.last + 1
}
results$AvgStAb <- array( AvgStAb.vect, dim=c(ab.steps, mp.file$Stages, ab.pops) )
mp.file$results <- results
}
return(mp.file)
} # End mp.read function
mlammens/demgsa documentation built on Aug. 4, 2020, 1:16 a.m.
R Package Documentation
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Defines functions mp.read
Documented in mp.read
#' @export mp.read
#' @title Read RAMAS (c) *.MP file
#' @description
#' \code{mp.read} reads in the *.MP file used in the RAMAS Metapop software.
#' The *.MP file is a simple text file containing all of the values necessary
#' to configure a metapopulation simulation.
#'
#' @param mpFile The name of the *.MP file to be read.
#' @param verbose Print function progress and checkpoints.
#' @param results Read results saved in the *.MP file.
#'
#' @return \code{mp.read} returns a nested list object. The first level includes
#' two elements, the version of the *.MP file and list names "mp.file". The
#' "mp.file" list is composed of 52 elements that provide all of the information
#' found in the *.MP file.
#'
#' @details
#' mp.file 'list' structure elements. Most element names from RAMAS Metapop source code
#' The assignment of each element of the mp.file list is based on the Metapopulation input file
#' type for version 5.0.
#' \enumerate{
#' \item MaxRep: maximum number of replications (integer; 1-10000)
#' \item MaxDur: Duration of simulation (integer; 1-500)
#' \item Demog_Stoch: Use demographic stochasticity? (boolean)
#' \item Stages: Number of stages (integer; 1-50)
#' \item Stages.IgnoreConstraints: Ingore constraints? (boolean - TRUE/FALSE)
#' \item Cat1: Information associated with catastrophe 1
#' This information is subdivided in RAMAS Input, but will not be subdivided
#' in the first version of the sensitivity analysis package
#' \item Cat2: Information associated with catastrophe 2
#' See note associated with Cat1
#' \item DDActing: Information regarding density dependence
#' \item Distrib: Distribution (Normal or Lognormal) to use for Environmental
#' Stochasticity.
#' \item AdvancedStochSetting: Advanced Environmental Stoch. Settings: (0 or PoolVar or
#' NegSurvCorr)
#' \item dispCV: Coefficient of variation for dispersal
#' \item WhenBelow: When below local threshold (count in total or don't count or assume dead)
#' \item corrwith: Within population correlation -
#' 1 - (F, S, K Correlated)
#' 2 - (F and S correlated, K independent)
#' 3 - (F, S, K independent
#' \item DispersalDependsOnTargetPopK: Just like the variable name implie; boolean
#' \item DDBasis: Density basis type (char)
#' \item PopSpecificDD: Density dependence type is population specific (boolean - Yes/No)
#' \item DDforAllPop: Density dependence type for all populations (char)
#' \item UserDllFileName: Filename for user-defined density dependence function
#' \item TimeStepSize: Time step size
#' \item TimeStepUnits: Time step units
#' \item SexStructure: Description of Sex Structure (char)
#' \item FemaleStages: Number of female stages
#' \item MatingSystem: Description of Mating system selected (char)
#' \item FemalesPerMale: Number of females per male as given by user
#' \item MalesPerFemale: Number of males per female as given by user
#' \item CVerror: sampling error for N
#' \item FirstTforRisk: Initial number of time steps to exclude from risk calculations
#' Population Parameters
#' \item PopList: Individual population data - saved as a 'nested list' structure, one 'list' for
#' each population.
#' Dispersal (Migration) Parameters
#' \item UseDispDistFunc: True if dispersal rates are based on dispersal distance function; false if
#' they are specified in the dispersal matrix
#' \item DispDistFunc: Dispersal-distance function parameters - a, b, c, Dmax - Mij = a exp(-Dij^c/b)
#' \item DispMatr: Dispersal matrix, as defined or calculated in 'fill.matrix.df.r'
#' Correlation Parameters
#' \item UseCorrDistFunc: True if correlations between populations is based on correlation distance
#' function; False if they are specified in the correlation matrix
#' \item CorrDistFunc: Correlation-distance function parameters - a, b, c - Cij = a exp(-Dij^c/b)
#' \item CorrMatr: User specified correlation matrix, as defined or calculated in 'fill.matrix.df.r'
#' Stage Matrices Information
#' \item StMatrNumber: number of stage matrices defined by user in *.mp file
#' \item StMatr: a 'list' object containing information about each stage matrix. For each stage
#' matrix the following information is stored:
#' StMatrName: Name of the stage matrix
#' StMatrSurvMult: Survival multiplier for stage matrix (for generating new matrices; not used in simulation)
#' StMatrFecMult: Fecundity multiplier for stage matrix (for generating new matrices; not used in simulation)
#' Matr: a matrix object with numeric values
#' Standard Deviation Matrices Information
#' \item SDMatrNumber: number of st. dev. matrices defined by user in *.mp file
#' \item SDMatr: a 'list' object containing information for each st.dev. matrix. For each st.dev.
#' matrix the following information is stored:
#' SDMatrName: Name of the st.dev. matrix
#' Matr: a matrix object of numeric values
#' \item ConstraintsMatr: Constraints Matrix
#' \item StMig: Stage Relative Migration (Dispersal) Rates
#' \item Cat1EffMat: Catastrophe 1 effects on vital rates; one row per stage, seperated by spaces
#' \item Cat1EffNst: Catastrophe 1 effects on abundances
#' \item Cat2EffMat: Catastrophe 2 effects on vital rates; one row per stage, seperated by spaces
#' \item Cat2EffNst: Catastrophe 2 effects on abundances
#' \item StInit: Initial Abundance for each stage for each population
#' Stage Properties
#' \item StProp: a 'list' object containing the following information for each stage
#' StName: Name of stage
#' StWeight: Relative weight of stage
#' StExclude: Exclude from total (boolean: TRUE or FALSE)
#' StBasisForDD: Basis for density-dependence (boolean: TRUE or FALSE)
#' StBreeding: Proportion of stage physically capable of breeding
#' Population Management Actions
#' \item NPopManage: Number of population managment actions
#' \item PopManageProp: All of the lines associated with population management, unpartitioned
#' \item ExtinctThr: Extinction threshold
#' \item ExplodeThr: Explosion threshold
#' \item stepsize: step size
#' \item PopData_df: Population level information in data.frame format
#'}
mp.read <- function(mpFile, verbose = FALSE, results = FALSE) {
# Inform the user that the mp.read() function has been called.
if(verbose){
print( paste( "Begin mp.read function with file: ", mpFile ) )
}
# Save the file path to mpFile
mpFilePath <- mpFile
# Read *.mp files into a long unsorted 'list' structure, 1 element per line in the file.
mpFile <- readLines(mpFile)
# Clear first line of "map=\xff". This step is needed because the "\x" leads to regex problems :(
mpFile[1] <- sub('map.*','',mpFile[1])
# Set values that will be used through out the function
FirstPopLine <- 45 # Line 45 correlates with first population data line
MigrationLine <- which(mpFile == "Migration")
CorrLine <- which(mpFile == "Correlation")
ConstraintsLine <- which(mpFile == "Constraints Matrix")
# Create mp.file list and initiate with length 0
mp.file <- vector("list",length = 0)
# Get Metapop Version information using metapopversion() function
mp.file$RAMAS.Version <- metapopversion(mpFile)
# MaxRep: Number of replications (integer; 1-100000)
mp.file$MaxRep <- as.numeric(mpFile[7])
# MaxDur: Duration of simulation (integer; 1-500)
mp.file$MaxDur <- as.numeric(mpFile[8])
# Demog_Stoch: Use demographic stochasticity? (boolean)
mp.file$Demog_Stoch <- as.logical(mpFile[9])
# Stages: Number of stages (integer; 1-50)
# Stages.IgnoreConstraints: Ingore constraints? (boolean - TRUE/FALSE)
stageLine <- unlist(strsplit(mpFile[10],' '))
mp.file$Stages <- as.numeric(stageLine[1])
mp.file$Stages.IgnoreConstraints <- as.logical(stageLine[2])
# Cat1: Information associated with catastrophe 1
mp.file$Cat1$Name <- mpFile[11]
# Check if Cat1 probability is a single number or a PCH file
if(grepl(pattern = "PCH", ignore.case = TRUE, x = mpFile[12])){
mp.file$Cat1$Probability <- mpFile[12]
} else {
mp.file$Cat1$Probability <- as.numeric(mpFile[12])
# If this value was blank, this might be NA, in which case, convert to 0
mp.file$Cat1$Probability <-
ifelse(is.na(mp.file$Cat1$Probability), yes = 0, no = mp.file$Cat1$Probability)
}
mp.file$Cat1$Extent <- mpFile[13]
mp.file$Cat1$Effect <- mpFile[14]
mp.file$Cat1$Spread <- mpFile[15]
mp.file$Cat1$Infect.Prob <- as.numeric(mpFile[16])
mp.file$Cat1$Spread.Dist.Params <- as.numeric(unlist(strsplit( mpFile[17],',' )))
# Cat2: Information associated with catastrophe 2
mp.file$Cat2$Name <- mpFile[18]
# Check if Cat2 probability is a single number or a PCH file
if(grepl(pattern = "PCH", ignore.case = TRUE, x = mpFile[19])){
mp.file$Cat2$Probability <- mpFile[19]
} else {
mp.file$Cat2$Probability <- as.numeric(mpFile[19])
# If this value was blank, this might be NA, in which case, convert to 0
mp.file$Cat2$Probability <-
ifelse(is.na(mp.file$Cat2$Probability), yes = 0, no = mp.file$Cat2$Probability)
}
mp.file$Cat2$Extent <- mpFile[20]
mp.file$Cat2$Effect <- mpFile[21]
mp.file$Cat2$Spread <- mpFile[22]
mp.file$Cat2$Infect.Prob <- as.numeric(mpFile[23])
mp.file$Cat2$Spread.Dist.Params <- as.numeric(unlist(strsplit( mpFile[24],',' )))
# Catastrophe 2 Reset Vital Rate Trend After Catastrophe (True or False),
# comma (,), Correlation between the two catastrophes (zero or ...)
mp.file$Cat2$Reset.and.Correlation <- mpFile[25]
# DDActing: Information regarding density dependence
mp.file$DDActing <- mpFile[26]
# Distrib: Distribution (Normal or Lognormal) to use for Environmental
# Stochasticity.
# AdvancedStochSetting: Advanced Environmental Stoch. Settings: (0 or PoolVar or
# NegSurvCorr)
stochLine <- unlist(strsplit(mpFile[27],','))
mp.file$Distrib <- stochLine[1]
mp.file$AdvancedStochSetting <- stochLine[2]
# dispCV: Coefficient of variation for dispersal
mp.file$dispCV <- as.numeric(mpFile[28])
# WhenBelow: When below local threshold (count in total or don't count or assume dead)
mp.file$WhenBelow <- mpFile[29]
# corrwith: Within population correlation -
# 1 - (F, S, K Correlated)
# 2 - (F and S correlated, K independent)
# 3 - (F, S, K independent
mp.file$corrwith <- mpFile[30]
# DispersalDependsOnTargetPopK: Just like the variable name implie; boolean
mp.file$DispersalDependsOnTargetPopK <- mpFile[31]
# DDBasis: Density basis type (char). Possiblities are 'AllStages', 'SelectedStages', 'FecundityWeighted'
mp.file$DDBasis <- mpFile[32]
# PopSpecificDD: Density dependence type is population specific (boolean - Yes/No)
mp.file$PopSpecificDD <- mpFile[33]
# DDforAllPop: Density dependence type for all populations (char)
mp.file$DDforAllPop <- mpFile[34]
# UserDllFileName: Filename for user-defined density dependence function
mp.file$UserDllFileName <- mpFile[35]
# TimeStepSize: Time step size (integer)
mp.file$TimeStepSize <- mpFile[36]
# TimeStepUnits: Time step units (char)
mp.file$TimeStepUnits <- mpFile[37]
# SexStructure: Description of Sex Structure (char)
mp.file$SexStructure <- mpFile[38]
# FemaleStages: Number of female stages (integer)
mp.file$FemaleStages <- mpFile[39]
# MatingSystem: Description of Mating system selected (char)
mp.file$MatingSystem <- mpFile[40]
# FemalesPerMale: Number of females per male as given by user (number)
mp.file$FemalesPerMale <- as.numeric(mpFile[41])
# MalesPerFemale: Number of males per female as given by user
mp.file$MalesPerFemale <- as.numeric(mpFile[42])
# CVerror: sampling error for N
mp.file$CVerror <- as.numeric(mpFile[43])
# FirstTforRisk: Initial number of time steps to exclude from risk calculations
mp.file$FirstTforRisk <- as.numeric(mpFile[44])
# ----------------------------------------------------------------------------------------------- #
# PopList: Population level information
if(verbose){
print( "mp.read: Reading population information")
}
# First determine the number of populations (popNumber). In version 5.0, population data begins at line 45
# and the 'Migration' section begins immediately after the last population's data
# The population level information is stored in two different structures - 1) a List format that
# corresponds with how information is stored in RAMAS and 2) a data.frame that is convenient
# for functions.
##### TO DO - Below is setup for if there is no user defined variables
PopNumber <- MigrationLine - FirstPopLine
if (PopNumber < 1) {
stop("Error: mp.read: Insufficient number of populations. Check 'first pop. line' value in mp.read.r")
}
PopRawData <- mpFile[FirstPopLine:(FirstPopLine + PopNumber - 1)]
# Create population data list object
PopData <- vector('list',length=0) # Initiate a PopData list
AllPopData <- vector('list',length=0) # Initiate a AllPopData list
PopData_df_rownames <- vector() # Initiate a row names vector
if ( mp.file$RAMAS.Version == 50 ) {
PopData_df_rownames <- 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")
for ( pop in 1:PopNumber ) {
popLine <- unlist(strsplit( PopRawData[ pop ], ',' ))
PopData$name <- popLine[1]
PopData$X_coord <- as.numeric(popLine[2])
PopData$Y_coord <- as.numeric(popLine[3])
PopData$InitAbund <- popLine[4]
PopData$DensDep <- popLine[5]
PopData$MaxR <- popLine[6]
PopData$K <- popLine[7]
PopData$Ksdstr <- popLine[8]
PopData$Allee <- popLine[9]
PopData$KchangeSt <- popLine[10]
PopData$DD_Migr <- popLine[11]
PopData$Cat1.Multiplier <- popLine[12]
PopData$Cat1.Prob <- popLine[13]
PopData$IncludeInSum <- popLine[14]
PopData$StageMatType <- popLine[15]
PopData$RelFec <- popLine[16]
PopData$RelSur <- popLine[17]
PopData$localthr <- popLine[18]
PopData$Cat2.Multiplier <- popLine[19]
PopData$Cat2.Prob <- popLine[20]
PopData$SDMatType <- popLine[21]
PopData$TargetPopK <- popLine[22]
PopData$Cat1.TimeSinceLast <- popLine[23]
PopData$Cat2.TimeSinceLast <- popLine[24]
PopData$RelDisp <- popLine[25]
AllPopData[[pop]] <- PopData # Add PopData to full list of population data
}
} else if ( mp.file$RAMAS.Version >= 51 ) {
PopData_df_rownames <- 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","RelVarFec","RelVarSurv")
for ( pop in 1:PopNumber ) {
popLine <- unlist(strsplit( PopRawData[ pop ], ',' ))
PopData$name <- popLine[1]
PopData$X_coord <- as.numeric(popLine[2])
PopData$Y_coord <- as.numeric(popLine[3])
PopData$InitAbund <- as.numeric(popLine[4])
PopData$DensDep <- popLine[5]
PopData$MaxR <- as.numeric(popLine[6])
PopData$K <- as.numeric(popLine[7])
PopData$Ksdstr <- as.numeric(popLine[8])
PopData$Allee <- as.numeric(popLine[9])
PopData$KchangeSt <- popLine[10]
PopData$DD_Migr <- popLine[11]
PopData$Cat1.Multiplier <- popLine[12]
PopData$Cat1.Prob <- popLine[13]
PopData$IncludeInSum <- popLine[14]
PopData$StageMatType <- popLine[15]
PopData$RelFec <- popLine[16]
PopData$RelSur <- popLine[17]
PopData$localthr <- popLine[18]
PopData$Cat2.Multiplier <- popLine[19]
PopData$Cat2.Prob <- popLine[20]
PopData$SDMatType <- popLine[21]
PopData$TargetPopK <- popLine[22]
PopData$Cat1.TimeSinceLast <- popLine[23]
PopData$Cat2.TimeSinceLast <- popLine[24]
PopData$RelDisp <- popLine[25]
PopData$RelVarFec <- popLine[26]
PopData$RelVarSurv <- popLine[27]
# ## Look for user defined density dependence parameters
# if (length(popLine)>27){
# for (udd_par in 1:(length(popLine)-27)){
# PopData$UserDef
# }
# }
AllPopData[[pop]] <- PopData # Add PopData to full list of population data
}
}
mp.file$PopList <- AllPopData
# Create population data data.frame
PopData_df <- read.csv( mpFilePath, header=FALSE, skip=44, nrows=PopNumber )
###browser()
### Only use as many columns as there are elements in the PopData_df_rownames. The rest of the columns
### are associated with user defined density dependence parameters, not used at this time.
### PopData_df <- PopData_df[1:length(PopData_df_rownames)]
# Turn NAs into empty strings
PopData_df[is.na(PopData_df)] <- ''
# Check if PopData_df includes user defined d-d values
if (ncol(PopData_df)>27){
# Get the number of userd defined d-d pars
Num_udd_pars <- ncol(PopData_df)-27
udd_names <- paste('udd_',1:Num_udd_pars,sep='')
PopData_df_rownames <- c(PopData_df_rownames,udd_names)
}
# Assign columns names
names(PopData_df) <- PopData_df_rownames
###browser()
# ----------------------------------------------------------------------------------------------- #
# ----------------------------------------------------------------------------------------------- #
# Dispersal (Migration) Data
if(verbose){
print( "mp.read: Reading dispersal (migration) information" )
}
# UseDispDistFunc: True if dispersal rates are based on dispersal distance function; false if
# they are specified in the dispersal matrix
mp.file$UseDispDistFunc <- as.logical( mpFile[MigrationLine + 1] )
# DispDistFunc: Dispersal-distance function parameters - a, b, c, Dmax - Mij = a exp(-Dij^c/b)
mp.file$DispDistFunc <- as.numeric(unlist(strsplit( mpFile[MigrationLine + 2],',' )))
# DispMatr: User specified dispersal matrix. If the user selected dispersal based on
# disp-dist function then there are no rows for dispersal matrix. The definition of
# DispMatr has non-intuitive indexing to account for the fact that this first two lines
# after "Migration" are not part of the matrix, they are UseDispDistFunc and
# DispDistFunc, respectively.
if ( mp.file$UseDispDistFunc ) {
# migLines is a variable used to identify the number of lines used to define the
# disp-dist func parameters and the disp matrix, if one is defined
migLines <- 1 #Only one line necessary for migration parameters
mp.file$DispMatr <- fill.matrix.df( PopData_df, mp.file$DispDistFunc, 'disp' )
} else {
migLines <- (1 + PopNumber)
DispMatr <- mpFile[ (MigrationLine + 3):(MigrationLine + 1 + migLines) ]
mp.file$DispMatr <- matrix(as.numeric(unlist(strsplit( DispMatr,',' ))), nrow = PopNumber, byrow = TRUE)
}
# ----------------------------------------------------------------------------------------------- #
# ----------------------------------------------------------------------------------------------- #
# Correlation Data
if(verbose){
print( "mp.read: Reading correlation information")
}
# UseCorrDistFunc: True if correlations between populations is based on correlation distance
# function; False if they are specified in the correlation matrix
mp.file$UseCorrDistFunc <- as.logical( mpFile[CorrLine +1] )
# CorrDistFunc: Correlation-distance function parameters - a, b, c - Cij = a exp(-Dij^c/b)
mp.file$CorrDistFunc <- as.numeric(unlist(strsplit( mpFile[CorrLine + 2],',' )))
# CorrMatr: User specified correlation matrix. Similar to dispersl matrix (See notes for DispMatr
# above), however the matrix is a lower triangular matrix, thus it is not saved as a matrix object
if ( mp.file$UseCorrDistFunc ) {
corrLines <- 1
# Create a Correlation Matrix from the Correlation Distance function. If there is only one
# population, then the number 1 is returned
mp.file$CorrMatr <- fill.matrix.df( PopData_df, mp.file$CorrDistFunc, 'corr' )
} else {
if(verbose){
print("Using Correlation matrix") ### WARNING LINE
}
# Define a new function used to read correlation distance matrices
# addZeroes: used to read a correlation distance matrix. This function adds zeroes to each line
# of the lower triangular corr-dist matrix stored in the *.mp file
addZeroes <- function( vect ) {
zeroes <- PopNumber - length(vect)
new_vect <- c( vect, rep(0, zeroes) )
return(new_vect)
}
corrLines <- (1 + PopNumber)
# If user defined, the corr-matr is a lower-triangular matrix. To make comparisons easy, the
# matrix is transformed into a 'matrix' object
#
# Read corrMatr as list from readLines input
corrMatr <- mpFile[ (CorrLine+3):(CorrLine+2+PopNumber) ]
# Split each list element by comma
corrMatr <- strsplit( corrMatr, ',' )
# Make each list element a vector of numeric values
corrMatr <- lapply( corrMatr, as.numeric)
# Apply the addZeroes function
corrMatr <- lapply( corrMatr, addZeroes )
# Make the 'list' into a 'matrix'
corrMatr <- do.call( rbind, corrMatr )
# Set CorrMatr in mp.file 'list'
mp.file$CorrMatr <- corrMatr
}
# ----------------------------------------------------------------------------------------------- #
# ----------------------------------------------------------------------------------------------- #
# Stage Matrices Information
# Determine the number of stage matrix types (i.e., how many stage matrices are defined)
# The number of stage matices (stage matrix types) is the first number of the line that
# contains the phrase "stage matrix" in it.
stgMatrLine <- grep('stage matrix', mpFile, ignore.case=TRUE)
StMatrNumber <- unlist(strsplit( mpFile[stgMatrLine],' ' ))
StMatrNumber <- as.numeric( StMatrNumber[1] )
# StMatrNumber: number of stage matrices defined by user in *.mp file
mp.file$StMatrNumber <- StMatrNumber
#
# StMatr: a list object containing information about each stage matrix.
StMatr <- vector('list',length=0) # Create an empty stage matrix
oneMatr <- 4 + mp.file$Stages # The number of lines of information for one matrix
allMatr <- StMatrNumber * oneMatr # The number of lines of information for all matrices
# Extract from mpFile all of the lines of information for all of the matrices
AllStMatrLines <- mpFile[ (stgMatrLine + 1):(stgMatrLine + 1 + allMatr) ]
#
AllStMatr <- vector('list',length=0) # Create an empty list of stage matrices
for ( matr in 1:mp.file$StMatrNumber ) {
LineAdd <- matr - 1
# StMatrName: Name of the stage matrix
StMatr$StMatrName <- AllStMatrLines[ 1 + (LineAdd*oneMatr) ]
# StMatrSurvMult: Survival multiplier for stage matrix (for generating new matrices; not used in simulation)
StMatr$StMatrSurvMult <- AllStMatrLines[ 2 + (LineAdd*oneMatr) ]
# StMatrFecMult: Fecundity multiplier for stage matrix (for generating new matrices; not used in simulation)
StMatr$StMatrFecMult <- AllStMatrLines[ 3 + (LineAdd*oneMatr) ]
# Matr: a matrix object with numeric values
dumbMatr <- AllStMatrLines[ (5 + (LineAdd*oneMatr)):( (4 + mp.file$Stages) + (LineAdd*oneMatr) ) ]
StMatr$Matr <- matrix(as.numeric(unlist(strsplit( dumbMatr,' ' ))), nrow = mp.file$Stages, byrow = TRUE)
AllStMatr[[matr]] <- StMatr
}
mp.file$StMatr <- AllStMatr
# ----------------------------------------------------------------------------------------------- #
# ----------------------------------------------------------------------------------------------- #
# Standard Deviation Matrices Information
# Determine the number of std. dev. matrix types.
# The number of standard deviation matrices (st dev matrix types) is the first number of the line that
# contains the phrase "st.dev. matrix" in it.
sdMatrLine <- grep('st.dev. matrix', mpFile, ignore.case=TRUE)
sdMatrNumber <- unlist(strsplit( mpFile[sdMatrLine],' ' ))
sdMatrNumber <- as.numeric( sdMatrNumber[1] )
# SDMatrNumber: number of st. dev. matrices defined by user in *.mp file
mp.file$SDMatrNumber <- sdMatrNumber
#
# SDMatr: a list object containing information for each st.dev. matrix.
SDMatr <- vector('list',length=0) # Create an empty st.dev. matrix list object
oneSDMatr <- 1 + mp.file$Stages # The number of lines of information for one st.dev. matrix
allSDMatr <- sdMatrNumber * oneSDMatr # The number of lines of information for all st.dev. matrices
# Extract from mpFile all of the lines of information for all of the matrices
AllSDMatrLines <- mpFile[ (sdMatrLine + 1):(sdMatrLine + 1 + allSDMatr) ]
#
AllSDMatr <- vector('list',length=0) # Create an empty list of std deviation matrices
for ( matr in 1:mp.file$SDMatrNumber ) {
LineAdd <- matr - 1 # Addition factor to skip to the lines associated with the matrix of interest
# SDMatrName: Name of the st.dev. matrix
SDMatr$SDMatrName <- AllSDMatrLines[ 1 + (LineAdd*oneSDMatr) ]
# Matr: a matrix object of numeric values
dumbMatr <- AllSDMatrLines[ (2 + (LineAdd*oneSDMatr)):( (1 + mp.file$Stages) + (LineAdd*oneSDMatr) ) ]
SDMatr$Matr <- matrix(as.numeric(unlist(strsplit( dumbMatr,' ' ))), nrow = mp.file$Stages, byrow = TRUE)
AllSDMatr[[matr]] <- SDMatr
}
mp.file$SDMatr <- AllSDMatr
# ----------------------------------------------------------------------------------------------- #
# ConstraintsMatr: Constraints Matrix
dumbMatr <- mpFile[ (ConstraintsLine + 1):(ConstraintsLine + mp.file$Stages) ]
mp.file$ConstraintsMatr <- matrix(as.numeric(unlist(strsplit( dumbMatr,' '))),nrow = mp.file$Stages, byrow = TRUE)
# StMig: Stage Relative Migration (Dispersal) Rates
mp.file$StMig <- as.numeric(unlist(strsplit( mpFile[ ConstraintsLine + mp.file$Stages + 1 ],' ' )))
# Cat1EffMat: Catastrophe 1 effects on vital rates; one row per stage, seperated by spaces
dumbMatr <- mpFile[ (ConstraintsLine + mp.file$Stages + 2):(ConstraintsLine + 2*mp.file$Stages + 1) ]
mp.file$Cat1EffMat <- matrix(as.numeric(unlist(strsplit( dumbMatr,' ' ))), nrow = mp.file$Stages, byrow = TRUE)
# Cat1EffNst: Catastrophe 1 effects on abundances
mp.file$Cat1EffNst <- as.numeric(unlist(strsplit( mpFile[ ConstraintsLine + 2*mp.file$Stages + 2 ],' ' )))
# Cat2EffMat: Catastrophe 2 effects on vital rates; one row per stage, seperated by spaces
dumbMatr <- mpFile[ (ConstraintsLine + 2*mp.file$Stages + 3):(ConstraintsLine + 3*mp.file$Stages + 2) ]
mp.file$Cat2EffMat <- matrix(as.numeric(unlist(strsplit( dumbMatr,' ' ))), nrow = mp.file$Stages, byrow = TRUE)
# Cat2EffNst: Catastrophe 2 effects on abundances
mp.file$Cat2EffNst <- as.numeric(unlist(strsplit( mpFile[ ConstraintsLine + 3*mp.file$Stages + 3 ],' ' )))
# StInit: Initial Abundance for each stage for each population
InitAbLine <- ConstraintsLine + 3*mp.file$Stages + 4 # First line
InitAb <- mpFile[ InitAbLine:(InitAbLine + PopNumber - 1) ]
mp.file$StInit <- matrix(as.numeric(unlist(strsplit( InitAb,' ' ))), nrow = PopNumber, byrow = TRUE)
# ----------------------------------------------------------------------------------------------- #
# Stage Properties
InitStPropLine <- InitAbLine + PopNumber # First line of stage properties in the *.mp file
# StProp: a list object containing information for each stage
StProp <- vector('list',length=0) # Create an empty stage property list object
# For each stage there are five properties, thus five lines of information
allStProp <- 5 * mp.file$Stages
AllStPropLines <- mpFile[ InitStPropLine:(InitStPropLine + allStProp - 1) ]
#
AllStProp <- vector('list',length=0) # Create an empty list of St.Prop. lists
for ( stg in 1:mp.file$Stages ) {
firstinfo <- (stg - 1)*5 + 1 # First line of info for stage 'stg'
# StName: Name of stage
StProp$StName <- AllStPropLines[ firstinfo ]
# StWeight: Relative weight of stage
StProp$StWeight <- AllStPropLines[ firstinfo + 1 ]
#StExclude: Exclude from total (boolean: TRUE or FALSE)
StProp$StExclude <- AllStPropLines[ firstinfo + 2 ]
#StBasisForDD: Basis for density-dependence (boolean: TRUE or FALSE)
StProp$StBasisForDD <- AllStPropLines[ firstinfo + 3 ]
#StBreeding: ###### WHAT IS THIS????
StProp$StBreeding <- AllStPropLines[ firstinfo + 4 ]
AllStProp[[ stg ]] <- StProp
}
mp.file$StProp <- AllStProp
# ----------------------------------------------------------------------------------------------- #
# ----------------------------------------------------------------------------------------------- #
# Population Management Actions
# NPopManage: The number of population managment actions
mgmntLine <- grep('pop mgmnt',mpFile,ignore.case=TRUE)
NPopManage <- unlist(strsplit(mpFile[mgmntLine],' '))
NPopManage <- as.numeric(NPopManage[1])
mp.file$NPopManage <- NPopManage
if ( NPopManage > 0 ) {
# PopManageProp: All of the lines associated with population management, unpartitioned
####mp.file$PopManageProp <- mpFile[ (mgmntLine + 1):(mgmntLine + NPopManage) ]
PopManageProp.colNames <- c('active', 'mng.type', 'from.pop', 'to.pop',
'begin.time','end.time','period','when','num.or.prop',
'number','proportion',
'from.stage','to.stage','cond.type','cond.abund.low',
'cond.abund.high','from.all.stgs','cond.quant.2','cond.func.N1',
'cond.func.N2','abund.each.stg_div_all.stg',
'abund.each.pop_div_all.pop')
mp.file$PopManageProp <- read.table( mpFilePath , skip=mgmntLine, nrows=NPopManage,
col.names=PopManageProp.colNames)
} else {
mp.file$PopManageProp <- "NA" # Fill with 'NA' value
}
# ----------------------------------------------------------------------------------------------- #
# The next three elements of the list are defined based on the position of the population
# management line. This may change in the future and have to be adjusted
# ExtinctThr: Extinction Threshold
mp.file$ExtinctThr <- as.numeric( mpFile[ mgmntLine + NPopManage + 1 ] )
# ExplodeThr: Explosion Threshold
mp.file$ExplodeThr <- as.numeric( mpFile[ mgmntLine + NPopManage + 2 ] )
# stepsize: Stepsize
mp.file$stepsize <- as.numeric( mpFile[ mgmntLine + NPopManage + 3 ] )
# ----------------------------------------------------------------------------------------------- #
# The last element of the list is the population data data.frame
mp.file$PopData_df <- PopData_df
## ******************************************************************** ##
## BEGIN MP RESULTS READ SECTION
## ******************************************************************** ##
if(results){
# Create results list and initiate with length 0
results <- vector("list",length = 0)
# Find begining of simulation results
res.start <- grep('Simulation results', mpFile, ignore.case=TRUE)
# Check to see that there are results in this *.mp file
if( !length(res.start) ) {
stop( paste('No Simulation results found in *.mp file:', mpFilePath) )
}
if(verbose){
print('mp.read.results: Reading simulation results')
}
# Get number of replications in simulation
SimRepLine <- unlist( strsplit ( mpFile[ (res.start + 1) ], ' ' ) )
results$SimRep <- as.numeric( SimRepLine[1] )
# Read in Pop. ALL Results
pop.all.line <- grep( 'Pop. ALL', mpFile )
results$PopAll <- read.table( mpFilePath, skip=pop.all.line, nrows=mp.file$MaxDur )
names( results$PopAll ) <- c('Mean', 'StDev', 'Min', 'Max')
# Read in individual population Results
# PopInd variable is a 3-dim Array of size 'Duration of Simulation' x 4 x 'Number of Populations'
# The second dimension (length=4) corresponds to the Mean, StDev, Min, and Max population size
###browser()
PopInd <- vector()
# Calculate start of individual population information
pop.ind.start <- pop.all.line + mp.file$MaxDur + 1
# Calculate end of individual population information
pop.ind.stop <- pop.all.line + mp.file$MaxDur + (mp.file$MaxDur+1)*PopNumber
# Identify where the population ID lines are (i.e., the lines that say Pop. #)
pop.ind.ID.lines <-
seq(from=(pop.all.line+mp.file$MaxDur+1),
to=(pop.all.line+mp.file$MaxDur+((mp.file$MaxDur+1)*PopNumber)),
by=(mp.file$MaxDur+1))
# Make a vector of all lines
pop.ind.lines <- pop.ind.start:pop.ind.stop
# Remove ID lines
pop.ind.lines <- setdiff(pop.ind.lines,pop.ind.ID.lines)
# Get these values
pvals <- mpFile[pop.ind.lines]
# Covert to numeric
pvals.num <- as.numeric(unlist(strsplit(pvals, split=" ")))
# Convert to matrix. There are allways four columns in these matrices.
pop.vals <- matrix(pvals.num,ncol=4,byrow=TRUE)
# Make a lits of PopNumber matrices
pop.vals.list <- lapply(split(pop.vals,0:(nrow(pop.vals)-1)%/%mp.file$MaxDur),matrix,nrow=mp.file$MaxDur)
# Convert the list to an array
pop.vals.array <- array(unlist(pop.vals.list),c(mp.file$MaxDur,4,PopNumber))
results$PopInd <- pop.vals.array
#
# for ( pop in 1:PopNumber ){
# # Number of lines past Pop. ALL to skip to start 'pop' values. Last '+1' for Pop. # Label
# start.pop <- pop.all.line + pop*(mp.file$MaxDur +1) + 1
# # Number of lines past Pop. ALL to skip to stop 'pop' values.
# stop.pop <- (start.pop-1) + mp.file$MaxDur
# # Get pop values from mpFile. Initially is read as characters
# pvals <- mpFile[start.pop:stop.pop]
# # Covert to numeric
# pvals.num <- as.numeric(unlist(strsplit(pvals, split=" ")))
# # Convert to matrix. There are allways four columns in these matrices.
# pop.vals <- matrix(pvals.num,ncol=4,byrow=TRUE)
# # Combine new matrix with PopInd matrix
# PopInd <- c(PopInd,pop.vals)
# }
# results$PopInd <- array( PopInd, dim=c(mp.file$MaxDur,4,PopNumber) )
# Read in Occupancy Results - a summary stat. of number of patches occupied at each time step during a simulation
occ.line <- grep( '^Occupancy', mpFile ) # Note carrot used to capture line that begins with 'Occupancy'
results$Occupancy <- read.table( mpFilePath, skip=occ.line, nrows=mp.file$MaxDur )
names( results$Occupancy ) <- c('Mean', 'StDev', 'Min', 'Max')
# Read in Local Occupancy Results - a summary stat. for occupancy rate (prop. of time patches remained occupied)
occ.loc.line <- grep( 'Local Occupancy', mpFile )
results$LocOccupancy <- read.table( mpFilePath, skip=occ.loc.line, nrows=PopNumber )
names( results$LocOccupancy ) <- c('Mean', 'StDev', 'Min', 'Max')
# Read Min., Max., and Ter. - the min, max, and final population abundance values for each
# replication of the mp model. each column is ordered seperately
rep.line <- grep( 'Min. Max. Ter.', mpFile )
results$Replications <- read.table( mpFilePath, skip=rep.line, nrows=results$SimRep )
names( results$Replications ) <- c('Min', 'Max', 'Ter')
# Read Time to cross - used to determine quasi-extinction/ -explosion risk. The number of
# rows in the mp file depends on the stepsize. Each row is a time-step and the first col
# is the number of times the pop. abund. crossed the min threshold for the first time in that
# time step and the second is associated with crossing the max threshold
t.cross.line <- grep( 'Time to cross', mpFile )
t.cross.rows <- (mp.file$MaxDur %/% mp.file$stepsize) + (mp.file$MaxDur %% mp.file$stepsize)
results$TimeCross <- read.table( mpFilePath, skip=t.cross.line, nrows=t.cross.rows )
names( results$TimeCross ) <- c('QuasiExtinct','QuasiExpl')
# Read Final stage abundances results
# results$FinalStAb variable is a 3-dim Array of size Numer of Stages (rows) x 4 (col) x Number of Populations (slices)
# to call the results of one populaiton (e.g., Pop. 1) use third index (e.g., results$FinalStAb[,,1] )
# Columns of the matrix are Mean, StDev, Min, Max
fin.stg.ab.line <- grep( 'Final stage abundances', mpFile )
fin.stg.ab.rows <- PopNumber * mp.file$Stages
FinalStAb <- as.matrix( read.table( mpFilePath, skip=fin.stg.ab.line, nrows=fin.stg.ab.rows ) )
# Seperate out FinalStAb into the different populations
fsa.first <- 1 # Initial first line for partitioning Final Stage Abundance matrix
fsa.list <- lapply(split(FinalStAb,0:(nrow(FinalStAb)-1)%/%mp.file$Stages),matrix,nrow=mp.file$Stages)
fsa.array <- array(unlist(fsa.list),c(mp.file$Stages,4,PopNumber))
results$FinalStAb <- fsa.array
#
# FinalStAb.vect <- vector()
# for ( pop in 1:PopNumber ){
# fsa.last <- pop*mp.file$Stages
# FinalStAb.vect <- c( FinalStAb.vect, FinalStAb[ fsa.first:fsa.last, ] )
# fsa.first <- fsa.last + 1
# }
# results$FinalStAb <- array( FinalStAb.vect, dim=c(mp.file$Stages, 4, PopNumber) )
# Read LocExtDur results
loc.ext.dur.line <- grep( 'LocExtDur', mpFile )
results$LocExtDur <- read.table( mpFilePath, skip=loc.ext.dur.line, nrow=PopNumber )
names( results$LocExtDur ) <- c('Mean','StDev','Max','Min')
# Read Harvest results
# First line is the total harvest results, the second line is the number of lines dedicated
# to individual time units for harvest
harvest.line <- grep( '^Harvest', mpFile )
results$HarvestTot <- read.table( mpFilePath, skip=harvest.line, nrow=1 )
names( results$HarvestTot ) <- c('Mean','StDev','Min','Max')
# Determine number of time steps with harvest data
harvest.steps <- mpFile[ harvest.line + 2 ]
harvest.steps <- unlist( strsplit( harvest.steps, ' ' ) )
harvest.steps <- as.numeric( harvest.steps[1] )
if ( harvest.steps > 0 ) {
results$HarvestSteps <- read.table( mpFilePath, skip=(harvest.line + 2), nrow=harvest.steps )
names( results$HarvestSteps ) <- c('Time', 'Mean', 'StDev', 'Min', 'Max')
}
# Read RiskOfLowHarvest results
risk.harvest.line <- grep( 'RiskOfLowHarvest', mpFile )
results$RiskLowHarvest <- read.table( mpFilePath, skip=risk.harvest.line, nrow=results$SimRep )
# Read Average stage abundances results
# First line after 'avg.st.ab.line' is the number of populations and number of time
# steps recorded (dependent on maxdur and stepsize)
# After this, there are popnumber * time steps lines by number of stages columns
# The values are the stage abundance values for each stage in each population
avg.st.ab.line <- grep( 'Average stage abundances', mpFile )
ab.steps <- mpFile[ avg.st.ab.line + 1 ]
ab.steps <- unlist( strsplit( ab.steps, ' ' ) )
ab.pops <- as.numeric( ab.steps[1] )
ab.steps <- as.numeric( ab.steps[2] )
avg.st.ab.rows <- ab.pops * ab.steps
AvgStAb <- as.matrix( read.table( mpFilePath, skip=(avg.st.ab.line + 1), nrow=avg.st.ab.rows ) )
# Seperate out AvgStAb into different populations
asb.first <- 1
AvgStAb.vect <- vector()
for ( pop in 1:ab.pops ){
asb.last <- pop*ab.steps
AvgStAb.vect <- c( AvgStAb.vect, AvgStAb[ asb.first:asb.last, ] )
asb.first <- asb.last + 1
}
results$AvgStAb <- array( AvgStAb.vect, dim=c(ab.steps, mp.file$Stages, ab.pops) )
mp.file$results <- results
}
return(mp.file)
} # End mp.read function
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