On the online document, there is a simple example of random field generation.
`import gstools as gs
structured field with a size 100x100 and a grid-size of 1x1
x = y = range(100)
model = gs.Gaussian(dim=2, var=1, len_scale=10)
srf = gs.SRF(model)
srf((x, y), mesh_type='structured')
srf.plot()`
The code gives a perfect field like the one below,
However, if I change the model to the Exponential model using
model = gs.Exponential(dim=2, var=1, len_scale=10)
I will get very granular field,
Although, we expect different models will lead to different covariance, I don't think the difference can be so significant.
On the online document, there is a simple example of random field generation. `import gstools as gs
structured field with a size 100x100 and a grid-size of 1x1
x = y = range(100) model = gs.Gaussian(dim=2, var=1, len_scale=10) srf = gs.SRF(model) srf((x, y), mesh_type='structured') srf.plot()`
The code gives a perfect field like the one below,
However, if I change the model to the Exponential model using
model = gs.Exponential(dim=2, var=1, len_scale=10)I will get very granular field,Although, we expect different models will lead to different covariance, I don't think the difference can be so significant.