Global variables

ndim = 2
err = defineDefaultSpace(ESpaceType_RN(), ndim)

Defining the Rule

Creating the Rule and defining specifically the name of the second facies (its rank is entered as 1 as the argument is 0-based) and the assigned value for the third facies. The other values are kept unchanged, as set by default.

rule = Rule_createFromNames(c("S", "T", "F1", "F2", "F3"))
err = rule$setFaciesName(1, "MyFacies")
err = rule$setFaciesValue(2, 6)

We can check the additional characteristics attached to the Rule.

err = rule$display()
## 
## Lithotype Rule
## ==============
## - Number of nodes               = 5
## - Number of facies              = 3
## - Number of thresholds along G1 = 1
## - Number of thresholds along G2 = 1
## - Description: S T F1 F2 F3 
## 
## 
## Facies Characteristics
## ----------------------
## Facies 1 - Name = F1 - Color = Blue - Assigned Value = 1
## Facies 2 - Name = MyFacies - Color = Orange - Assigned Value = 2
## Facies 3 - Name = F3 - Color = Green - Assigned Value = 6

These characteristics are used for the representation of the Rule itself.

plot.init() +
  plot.rule(rule, flagLegend=TRUE) +
  plot.decoration(title="Rule Definition")

We perform a non-conditional simulation using PGS method.

grid = DbGrid_create(nx=c(110,110))
model1 = Model_createFromParam(ECov_CUBIC(), ranges=c(20,30))
model2 = Model_createFromParam(ECov_EXPONENTIAL(), 30, 1., 1.)

nbsimu = 1
ruleprop = RuleProp_createFromRule(rule)
err = simpgs(dbin=NULL, dbout=grid, ruleprop=ruleprop,
             model1=model1, model2=model2)

Representing the grid of facies with the colors as defined in the Rule. The Legend is adapted.

p = plot.init()
p = p + plot(grid, name="Facies", rule=rule, flagLegend=TRUE,
             legendName="Facies")
p = p + plot.decoration(title = "Facies")
plot.end(p)

We sample the previous simulated grid in order to generate a Data base constituted of isolated points (named “data”)

ndat = 20
randomlist = sample(0:grid$getNSample()-1, size = ndat, replace = TRUE)
data = Db_createReduce(grid, ranks=randomlist)

The graphic representation of symbol (using the “nameColor” argument) is adapted to the representation of categorial information.

p = plot.init()
  p = p + plot.symbol(db = data,
             nameColor = "Facies", rule=rule,
             size = 3, flagLegend=TRUE)
  p = p + plot.decoration(title = "Sampling locations")
  plot.end(p)

The following plot demonstrates the possibility of overlaying the two previous graphic representations. Note that the symbols are represented with an additional colored edge (white).

p = plot.init()
  p = p + plot(grid, name="Facies", rule=rule, flagLegend=TRUE,
             legendName="Facies")
  p = p + plot.symbol(db = data,
             nameColor = "Facies", rule=rule, borderColor="white",
             size = 3, flagLegend=TRUE)
  p = p + plot.decoration(title = "Facies and Sampling")
  plot.end(p)