Add another example, including field transf.

examples/00_misc/06_fourier.py

@@ -1,6 +1,6 @@
 """
-Generating a Periodic Random Field
-----------------------------------
+Generating a Simple Periodic Random Field
+-----------------------------------------
 
 In this simple example we are going to learn how to generate periodic spatial
 random fields. The Fourier method comes naturally with the property of

examples/00_misc/07_fourier_trans.py

@@ -0,0 +1,41 @@
+"""
+Generating a Transformed Periodic Random Field
+----------------------------------------------
+
+Building on the precious example, we are now going to generate periodic
+spatial random fields with a transformation applied, resulting in a level set.
+"""
+
+import numpy as np
+import gstools as gs
+
+# We start off by defining the spatial grid.
+x = np.linspace(0, 500, 300)
+y = np.linspace(0, 500, 200)
+
+# Instead of using a Gaussian covariance model, we will use the much rougher
+# exponential model and we will introduce an anisotropy by using two different
+# length scales in the x- and y-axes
+model = gs.Exponential(dim=2, var=2, len_scale=[30, 20])
+
+# Very similar as before, setting up the spatial random field
+srf = gs.SRF(
+    model,
+    generator="Fourier",
+    modes_no=[30, 20],
+    modes_truncation=[30, 20],
+    seed=1681903,
+)
+# and computing it
+srf((x, y), mesh_type='structured')
+
+# With the field generated, we can now apply transformations
+# starting with a discretization of the field into 4 different values
+thresholds = np.linspace(np.min(srf.field), np.max(srf.field), 4)
+srf.transform("discrete", store="transform_discrete", values=thresholds)
+srf.plot("transform_discrete")
+
+# This is already a nice result, but we want to pronounce the peaks of the
+# field. We can do this by applying a log-normal transformation on top
+srf.transform("lognormal", field="transform_discrete", store="transform_lognormal")
+srf.plot("transform_lognormal")