Namespace corrections for v0.11.0-9002

NAMESPACE

@@ -24,6 +24,12 @@ S3method(UWEDGE,default)
 S3method(UWEDGE,rcomp)
 S3method(accuracy,DataFrameStack)
 S3method(accuracy,data.frame)
+S3method(as.AnisotropyRangeMatrix,AnisotropyRangeMatrix)
+S3method(as.AnisotropyRangeMatrix,AnisotropyScaling)
+S3method(as.AnisotropyRangeMatrix,default)
+S3method(as.AnisotropyScaling,AnisotropyRangeMatrix)
+S3method(as.AnisotropyScaling,AnisotropyScaling)
+S3method(as.AnisotropyScaling,numeric)
 S3method(as.CompLinModCoReg,CompLinModCoReg)
 S3method(as.CompLinModCoReg,LMCAnisCompo)
 S3method(as.DataFrameStack,array)

R/gmAnisotropy.R

@@ -3,6 +3,30 @@
 #### Anisotropy ------------------
 
 
+#' Convert to anisotropy scaling matrix
+#' 
+#' Convert an anisotropy specification to a scaling matrix
+#'
+#' @param x an matrix to be tagged as anisotropy scaling matrix
+#'
+#' @return An anisotropy scaling matrix \eqn{A} is such that for any 
+#' lag vector \eqn{h}, the variogram model turns isotropic in terms 
+#' of \eqn{u'=h'\cdot A}. This function does not check any special
+#' property for this matrix! You should probably be using `anis_GSLIBpar2A()` 
+#' isntead, and leave `AnisotropyScaling()` for internal uses.
+#'
+#'    
+#' @export
+#' @family anisotropy
+#' @examples
+#' ( l = anis_GSLIBpar2A(angles=30, ratios=0.5)) 
+#' ( ll = unclass(l) )
+#' AnisotropyScaling(l)
+AnisotropyScaling = function(x){
+  class(x) = "AnisotropyScaling"
+  return(x)
+}
+
 #' Convert to anisotropy scaling matrix
 #' 
 #' Convert an anisotropy specification to a scaling matrix
@@ -21,31 +45,27 @@
 #' are given in degrees, all ratios must be smaller or equal to 1. This follows gstat (and hence
 #' GSlib) conventions; see gstat::vgm() for details.
 #'    
+#' @family anisotropy
 #' @export
+#' @aliases as.AnisotropyScaling.numeric
 #' @examples
 #' ( l = anis_GSLIBpar2A(angles=30, ratios=0.5)) 
 #' ( ll = unclass(l) )
-#' AnisotropyScaling(l)
-AnisotropyScaling = function(x){
-  class(x) = "AnisotropyScaling"
-  return(x)
-}
-
-#' @describeIn AnisotropyScaling coerce function to anisotropy scaling matrix
+#' as.AnisotropyScaling(ll)
 #' @export
 as.AnisotropyScaling <- function(x){ UseMethod("as.AnisotropyScaling", x) }
 
-# @describeIn AnisotropyScaling identity
-# @method as.AnisotropyScaling AnisotropyScaling
-# @export
+#' @describeIn as.AnisotropyScaling identity method
+#' @method as.AnisotropyScaling AnisotropyScaling
+#' @export
 as.AnisotropyScaling.AnisotropyScaling = function(x){
   x
 } 
 
 
-# @describeIn AnisotropyScaling  from a vector of numbers
-# @method as.AnisotropyScaling numeric
-# @export
+#' @describeIn as.AnisotropyScaling  from a vector of numbers
+#' @method as.AnisotropyScaling numeric
+#' @export
 as.AnisotropyScaling.double <-as.AnisotropyScaling.numeric <- function(x){
   if(length(dim(x))==0){
     if(length(x)==2) return(anis2D_par2A(ratio=x[2], angle=x[1], inv=TRUE))
@@ -61,9 +81,9 @@ as.AnisotropyScaling.double <-as.AnisotropyScaling.numeric <- function(x){
   }
 }
 
-# @describeIn AnisotropyScaling  from an AnisotropicRangeMatrix
-# @method as.AnisotropyScaling AnisotropyRangeMatrix
-# @export
+#' @describeIn as.AnisotropyScaling from an AnisotropicRangeMatrix
+#' @method as.AnisotropyScaling AnisotropyRangeMatrix
+#' @export
 as.AnisotropyScaling.AnisotropyRangeMatrix = function(x){
   AnisotropyScaling(solve(chol(x)))
 }
@@ -81,8 +101,8 @@ as.AnisotropyScaling.AnisotropyRangeMatrix = function(x){
 #' @param checkValidity boolean, should validity be checked?
 #'
 #' @return the same matrix with a class attribute
+#' @family anisotropy
 #' @export
-#'
 AnisotropyRangeMatrix = function(x, checkValidity=TRUE){
   if(checkValidity){
     if(length(dim(x))==3){
@@ -99,24 +119,35 @@ AnisotropyRangeMatrix = function(x, checkValidity=TRUE){
   return(x)
 }
 
-#' @describeIn AnisotropyRangeMatrix  Convert to anisotropy range matrix
+#' Force a matrix to be anisotropy range matrix,
+#' 
+#' Force a matrix M to be considered an anisotropy range matrix, i.e 
+#' with ranges and orientations, 
+#' such that \eqn{u = sqrt(h' * M^{-1} * h)} allows to use an isotropic
+#' variogram.
+#'
+#' @param x  matrix simmetric positive definite (i.e. M above)
+#'
+#' @return the same anisotropy, specified as `M`
+#' @family anisotropy
 #' @export
 as.AnisotropyRangeMatrix <- function(x){ UseMethod("as.AnisotropyRangeMatrix", x) }
 
-# @describeIn AnisotropyRangeMatrix  Convert to anisotropy range matrix
-# @method as.AnisotropyRangeMatrix default
-# @export
+
+#' @describeIn as.AnisotropyRangeMatrix  Default conversion to anisotropy range matrix
+#' @method as.AnisotropyRangeMatrix default
+#' @export
 as.AnisotropyRangeMatrix.default <- function(x) as.AnisotropyRangeMatrix(as.AnisotropyScaling(x))
 
 
-# @describeIn AnisotropyRangeMatrix  Convert to anisotropy range matrix
-# @method as.AnisotropyRangeMatrix AnisotropyRangeMatrix
-# @export
+#' @describeIn as.AnisotropyRangeMatrix  identity conversion
+#' @method as.AnisotropyRangeMatrix AnisotropyRangeMatrix
+#' @export
 as.AnisotropyRangeMatrix.AnisotropyRangeMatrix <- function(x) x
 
-# @describeIn AnisotropyRangeMatrix  Convert to anisotropy range matrix
-# @method as.AnisotropyRangeMatrix AnisotropyScaling
-# @export
+#' @describeIn AnisotropyRangeMatrix  Convert from AnisotropyScaling
+#' @method as.AnisotropyRangeMatrix AnisotropyScaling
+#' @export
 as.AnisotropyRangeMatrix.AnisotropyScaling <- function(x) AnisotropyRangeMatrix(solve(tcrossprod(x)), checkValidity=FALSE)
 
 
@@ -145,8 +176,9 @@ as.AnisotropyRangeMatrix.AnisotropyScaling <- function(x) AnisotropyRangeMatrix(
 #' 
 #' Other values are meaningless.
 #'
+#' @family anisotropy
 #' @export
-anis_GSLIBpar2A = function(ratios, angles, inv=FALSE){
+  anis_GSLIBpar2A = function(ratios, angles, inv=FALSE){
   if( (length(ratios)==1) & length(angles)==1){
     anis2D_par2A(ratio=ratios, angle=angles, inv=inv)
   }else if( (length(ratios)==1) & length(angles)==1){
@@ -196,7 +228,7 @@ anis2D_par2A = function(ratio, angle, inv=FALSE){
   # rotation to new coordinates and scaling:
   A = R %*% S # t(Rinv) %*% S # because t(Rinv) = R in orthogonal matrices
   A = rbind(cbind(A,0),c(0,0,1))
-  class(A) = c("AnisotropyScaling","Anisotropy")
+  class(A) = "AnisotropyScaling"
   return(A)
 }
 
@@ -240,6 +272,6 @@ anis3D_par2A = function(ratios, angles, inv=FALSE){
   S = diag(c(1, ratios^((1-2*inv)/2) ))
   # rotation to new coordinates and scaling:
   A = R %*% S # t(Rinv) %*% S # because t(Rinv) = R in orthogonal matrices
-  class(A) = c("AnisotropyScaling","Anisotropy")
+  class(A) = "AnisotropyScaling"
   return(A)
 }

R/variograms.R

@@ -512,13 +512,14 @@ is.isotropic.LMCAnisCompo = function(x, tol=1e-10, ...){
 #'
 #' @return a logical, TRUE if the object is an anisotropy specification; FALSE otherwise 
 #' @export
+#' @family anisotropy
 #'
 #' @examples
 #' a =  anis2D_par2A(0.5, 30)
 #' a
 #' is.anisotropySpecification(a)
 is.anisotropySpecification = function(x){
-  "Anisotropy" %in% class(x)  |  inherits(x, "Anisotropy")
+  length(grep("Anisotropy", class(x))) |  inherits(x, "Anisotropy")
 } 
 
 

vignettes/SimulatingMicrostructures.Rmd

@@ -91,18 +91,18 @@ E.g. `angle = 45` with `ratio = 0.5` would result into NE-SW elongated crystals.
 
 For Quartz, let's assume we have mostly roundish/quadratic grains and only a minor preferred orientation of the crystals, so that they are slightly oriented along NE-SW:
 ```{r p-qu}
-p1_qu = anis2D_par2A(ratio = .9, angle = 45)
+p1_qu = anis_GSLIBpar2A(ratios = .9, angles = 45)
 ```
 
 The Feldspars have a more pronounced elongated shape, also with preferred orientation along the same direction as the Quartz grains:
 ```{r p-fsp}
-p2_fsp = anis2D_par2A(ratio = .6, angle = 45)
+p2_fsp = anis_GSLIBpar2A(ratios = .6, angles = 45)
 ```
 
-Function `anis2D_par2A` returns the Cholesky decomposition of the anisotropy matrix. So, if the anistropy of the mineral phases had been generated by this function, then we need to calculate the cross product of the matrices to obtain the anistropy matrices `A`:
+Function `anis_GSLIBpar2A` returns the inverse Cholesky decomposition of the anisotropy matrix. So, if the anistropy of the mineral phases had been generated by this function, then we need to re-calculate the  anistropy range matrices `A`:
 ```{r}
-A1_qu = tcrossprod(p1_qu)   
-A2_fsp = tcrossprod(p2_fsp)
+A1_qu = as.AnisotropyRangeMatrix(p1_qu)   
+A2_fsp = as.AnisotropyRangeMatrix(p2_fsp)
 ```
 
 This results into
@@ -121,20 +121,26 @@ There is also the option to directly generate the anisotropy matrix.
 In this case, the matrix can be written manually:
 The Biotite occurs as thin plates and has a slightly different preferred orientation then the two main minerals:
 ```{r}
-A3_bi = matrix(data = c(1, .6, 0, .6, 1, 0, 0, 0, 1), ncol = 3)
+A3_bi = AnisotropyRangeMatrix(
+    matrix(data = c(1, .6, 0, .6, 1, 0, 0, 0, 1), ncol = 3)
+  )
 A3_bi
 ```
 
 For the fracture and joint systems the anisotropy is very pronounced, because they are very thin but passing through longer distances through the rock.
 Accordingly, the anisotropy matrix should be chosen similar to this:
 ```{r}
-A4_frac = matrix(data = c(1, -.999, 0, -.999, 1, 0, 0, 0, 1), ncol = 3)
+A4_frac = AnisotropyRangeMatrix(
+  matrix(data = c(1, -.999, 0, -.999, 1, 0, 0, 0, 1), ncol = 3)
+    )
 A4_frac
 ```
 
 And let's assume, that the joint system is orthogonal to the fracture system:
 ```{r}
-A5_joints = matrix(data = c(1, .999, 0, .999, 1, 0, 0, 0, 1), ncol = 3)
+A5_joints = AnisotropyRangeMatrix(
+    matrix(data = c(1, .999, 0, .999, 1, 0, 0, 0, 1), ncol = 3)
+  )
 A5_joints
 ```
 
@@ -287,7 +293,7 @@ For example, in the simulation above, the geologist would expect, that the fract
 
 Changing the orientation of the fractures:
 ```{r}
-p4_frac = anis2D_par2A(ratio = .01, angle = 95)
+p4_frac = anis_GSLIBpar2A(ratio = .01, angle = 95)
 A4_frac = tcrossprod(p4_frac)
 A4_frac
 ```