Testing the Rule¶
import pandas as pd
import gstlearn as gl
import gstlearn.plot as gp
import gstlearn.document as gdoc
import random as rnd
gdoc.setNoScroll()
ndim = 2
gl.defineDefaultSpace(gl.ESpaceType.RN, ndim)
gl.set_test_discrete(False)
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 = gl.Rule.createFromNames(["S", "T", "F1", "F2", "F3"])
rule.setFaciesName(1, "MyFacies")
rule.setFaciesValue(2, 6)
We can check the additional characteristics attached to the Rule.
rule.display()
Lithotype Rule ============== - Number of nodes = 5 - Number of facies = 3 - Number of thresholds along G1 = 1 - Number of thresholds along G2 = 1 Node Characteristics -------------------- Node F1 - Facies 1 Node F2 - Facies 2 Node F3 - Facies 3 Facies Characteristics ---------------------- Facies 1 - Name = F1 - Color = #1f77b4ff - Assigned Value = 1 Facies 2 - Name = MyFacies - Color = #ff7f0eff - Assigned Value = 2 Facies 3 - Name = F3 - Color = #2ca02cff - Assigned Value = 6
These characteristics are used for the representation of the Rule itself.
res = gp.plot(rule)
We perform a non-conditional simulation using PGS method.
grid = gl.DbGrid.create(nx=[110, 110])
model1 = gl.Model.createFromParam(gl.ECov.CUBIC, ranges=[20, 30])
model2 = gl.Model.createFromParam(gl.ECov.EXPONENTIAL, 30, 1.0, 1.0)
nbsimu = 1
ruleprop = gl.RuleProp.createFromRule(rule)
err = gl.simpgs(dbin=None, dbout=grid, ruleprop=ruleprop, model1=model1, model2=model2)
The following statistics demonstrate that the "Facies" variable (produced by PGS) has values coming from the assigned values per facies, as defined in Rule.
grid.getStatsAsTable()
Number Minimum Maximum Mean St. Dev. Variance
rank 12100.000 1.000 12100.000 6050.500 3492.969 12200833.250
x1 12100.000 0.000 109.000 54.500 31.753 1008.250
x2 12100.000 0.000 109.000 54.500 31.753 1008.250
Facies 12100.000 1.000 6.000 2.954 2.055 4.222
Representing the grid of facies with the colors as defined in the Rule. The Legend is adapted.
ax = gp.plot(grid, rule=rule, flagLegend=True)
We sample the previous simulated grid in order to generate a Data base constituted of isolated points (named "data")
ndat = 20
randomlist = rnd.sample(range(0, grid.getNSample()), ndat)
data = gl.Db.createReduce(grid, ranks=randomlist)
The graphic representation of symbol (using the "nameColor" argument) is adapted to the representation of categorial information.
ax = gp.plot(data, nameColor="Facies", rule=rule, s=50, flagLegendColor=True)
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).
fig, ax = gp.init()
ax = gp.plot(grid, rule=rule, flagLegend=True)
ax = gp.plot(
data, nameColor="Facies", rule=rule, s=50, flagLegendColor=True, edgecolors="white"
)
gp.close()
