Test simulations using Turning Bands method¶

This test is meant to check and demonstrate the simulation using the Turning Bands Method of various covariances. The test is systematically performed on a regular grid.

In [1]:
import gstlearn as gl
import numpy as np           
import gstlearn.plot as gp
In [2]:
nx = [100,100]
dx = [1,1]
grid = gl.DbGrid.create(nx=nx, dx=dx)
varioparam = gl.VarioParam.createMultipleFromGrid(grid, 50)
In [3]:
def represent(type, range=30, param=1):
    model = gl.Model.createFromParam(type=type, range=range, param=param)
    err = gl.simtub(None, grid, model, nbtuba=500)
    vario = gl.Vario.computeFromDb(varioparam, grid)
    
    # Plot
    fig, ax = gp.init(1,2,figsize=(10,5))
    ax[0].raster(grid,name="Simu")
    ax[1].varmod(vario, model)
    fig.decoration(title=type.getDescr())
    
    grid.deleteColumn("Simu")

In the next paragraphs, we perform the simulation of a given model on the grid, and represent the experimental variogram (calculated along the main grid directions) together with the Model.

In [4]:
represent(gl.ECov.SPHERICAL)
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In [5]:
represent(gl.ECov.EXPONENTIAL)
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In [6]:
represent(gl.ECov.GAUSSIAN)
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In [7]:
represent(gl.ECov.CUBIC)
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In [8]:
represent(gl.ECov.LINEAR)
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