Demonstration of gstlearn for a 2-D case study

This file demonstrates the use of Selectivity curves

%%javascript
IPython.OutputArea.prototype._should_scroll = function(lines) {
    return false;
}

Import packages

import numpy as np
import pandas as pd
import sys
import os
import matplotlib.pyplot as plt
import gstlearn as gl
import gstlearn.plot as gp

Reading the Grid file

!wget -q https://soft.minesparis.psl.eu/gstlearn/data/Selectivity/Grid_100.ascii

filename = "Grid_100.ascii"
db100 = gl.DbGrid.createFromNF(filename)
db100.display()

Data Base Grid Characteristics
==============================

Data Base Summary
-----------------
File is organized as a regular grid
Space dimension              = 2
Number of Columns            = 4
Maximum Number of UIDs       = 4
Total number of samples      = 100

Grid characteristics:
---------------------
Origin :      0.000     0.000
Mesh   :      1.000     1.000
Number :         10        10

Variables
---------
Column = 0 - Name = rank - Locator = NA
Column = 1 - Name = x1 - Locator = x1
Column = 2 - Name = x2 - Locator = x2
Column = 3 - Name = z1 - Locator = z1
 

Plotting the grid of samples

gp.setDefaultGeographic(xlim=[-1,10],ylim=[-1,10])
gp.printDefault()

Non geographical defaults:
- Figure dimensions = [5, 5]
- Limits along X (not defined)
- Limits along Y (not defined)
- Aspect = auto
Geographical defaults:
- Figure dimensions = [8, 8]
- Limits along X = [-1, 10]
- Limits along Y = [-1, 10]
- Aspect = 1

fig, ax = gp.initGeographic()
ax.raster(db100, name="z1")
ax.decoration(title="Data")
plt.show()

fig, ax = gp.initGeographic()
ax.literal(db100, name="z1")
ax.decoration(title="Data")
plt.show()

fig, ax = gp.init()
ax.histogram(db100, name="z1", bins=20)
ax.decoration(title="Data")
plt.show()

dum = db100.statistics(["z1"],[gl.EStatOption.MEAN, gl.EStatOption.VAR])

     1.531     1.615
 

Creating the grid of blocks by averaging samples 2 by 2

db25 = gl.DbGrid.create(nx=[5,5], dx=[2,2], x0=[0.5,0.5])
dum = gl.dbStatisticsOnGrid(db100, db25, gl.EStatOption.MEAN, namconv = gl.NamingConvention(""))

fig, ax = gp.initGeographic()
ax.raster(db25, name="z1")
ax.decoration(title="Blocks")
plt.show()

fig, ax = gp.initGeographic()
ax.literal(db25,name="z1")
ax.decoration(title="Blocks")
plt.show()

fig, ax = gp.init()
ax.histogram(db25, name="z1", bins=10)
ax.decoration(title="Blocks")
plt.show()

dum = db25.statistics(["z1"],[gl.EStatOption.MEAN, gl.EStatOption.VAR])

     1.531     0.974
 

Creating a samping grid keeping only the upper right corner sample for each block

db25s = gl.DbGrid.create(nx=[5,5],dx=[2,2],x0=[0.5,0.5])
dum = gl.migrate(db100,db25s,name="z1",namconv=gl.NamingConvention("",False))

fig, ax = gp.initGeographic()
ax.raster(db25s, name="z1")
ax.decoration(title="Sampling")
plt.show()

fig, ax = gp.initGeographic()
ax.literal(db25s,name="z1")
ax.decoration(title="Sampling")
plt.show()

dum = db25s.statistics(["z1"],[gl.EStatOption.MEAN, gl.EStatOption.VAR])

     1.364     1.165
 

Using the Selectivity Curves

We compare the selectivity curves between Data and Blocks:

selectivity = gl.Selectivity(100)
table100 = selectivity.eval(db100, True)
table25  = selectivity.eval(db25,  True)
table25s = selectivity.eval(db25s, True)

table100.getColumnNames()

('Z-Cut', 'T-estim', 'Q-estim', 'B-estim', 'M-estim', 'T-stdev', 'Q-stdev')

• Ore tonnage as a function of the cutoff

fig, ax = gp.init()
ax.table(table100,[1,0],color='blue')
ax.table(table25,[1,0],color='red')
ax.decoration(title="T(z)")
plt.show()

• Metal as a function of the cutoff

fig, ax = gp.init()
ax.table(table100,[2,0],color='blue')
ax.table(table25,[2,0],color='red')
ax.decoration(title="Q(z)")
plt.show()

• Recovered grade as a function of the cutoff

fig, ax = gp.init()
ax.table(table100,[4,0],color='blue')
ax.table(table25,[4,0],color='red')
ax.decoration(title="M(z)")
plt.show()

• Conventional Benefit as a function of the cutoff

fig, ax = gp.init()
ax.table(table100,[3,0],color='blue')
ax.table(table25,[3,0],color='red')
ax.decoration(title="B(z)")
plt.show()

• Metal as a function of Ore Tonnage

fig, ax = gp.init()
ax.table(table100,[2,1],color='blue')
ax.table(table25,[2,1],color='red')
ax.plot([0.,1.], [0.,db100.getMean("z1")], linestyle='dashed')
ax.decoration(title="Q(T)")
plt.show()

Regressions

Display regressions

fig, ax = gp.init()
ax.correlation(db25,namex="z1",namey="z1",db2=db25s, asPoint=True, diagLine=True, regrLine=True)
ax.decoration(ylabel="Blocks",xlabel="Samples",title="Block vs. Sample")
plt.show()

fig, ax = gp.init()
ax.correlation(db25s,namex="z1",namey="z1",db2=db25, asPoint=True, diagLine=True, regrLine=True)
ax.decoration(xlabel="Blocks",ylabel="Samples",title="Sample vs. Block")
plt.show()

Comparing selectivity curves

fig, ax = gp.init()
ax.table(table100,[2,1],color='blue')
ax.table(table25,[2,1],color='red')
ax.table(table25s,[2,1],color='green')
ax.plot([0.,1.], [0.,db100.getMean("z1")], linestyle='dashed')
ax.plot([0.,1.], [0.,db25s.getMean("z1")], linestyle='dashed')
ax.decoration(title="Q(T)")
plt.show()