GeoStatTools provides geostatistical tools for random field generation and variogram estimation based on many readily provided and even user-defined covariance models.
GSTools can be installed via conda on Linux, Mac, and Windows. Install the package by typing the following command in a command terminal:
conda install gstools
In case conda forge is not set up for your system yet, see the easy to follow instructions on conda forge. Using conda, the parallelized version of GSTools should be installed.
GSTools can be installed via pip on Linux, Mac, and Windows. On Windows you can install WinPython to get Python and pip running. Install the package by typing the following into command in a command terminal:
pip install gstools
To get the latest development version you can install it directly from GitHub:
pip install git+git://github.com/GeoStat-Framework/GSTools.git@develop
If something went wrong during installation, try the
-I flag from pip.
To enable the OpenMP support, you have to provide a C compiler, Cython and OpenMP. To get all other dependencies, it is recommended to first install gstools once in the standard way just decribed. Simply use the following commands:
pip install gstools pip install -I --no-deps --global-option="--openmp" gstools
Or for the development version:
pip install git+git://github.com/GeoStat-Framework/GSTools.git@develop pip install -I --no-deps --global-option="--openmp" git+git://github.com/GeoStat-Framework/GSTools.git@develop
-I --no-deps force pip to reinstall gstools but not the dependencies.
At the moment you can cite the Zenodo code publication of GSTools:
If you want to cite a specific version, have a look at the Zenodo site.
A publication for the GeoStat-Framework is in preperation.
Tutorials and Examples¶
The documentation also includes some tutorials, showing the most important use cases of GSTools, which are
- Random Field Generation
- The Covariance Model
- Variogram Estimation
- Random Vector Field Generation
- Conditioned random field generation
- Field transformations
- Miscellaneous examples
Some more examples are provided in the examples folder.
Spatial Random Field Generation¶
The core of this library is the generation of spatial random fields. These fields are generated using the randomisation method, described by Heße et al. 2014.
Gaussian Covariance Model¶
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()
import gstools as gs # structured field with a size 100x100x100 and a grid-size of 1x1x1 x = y = z = range(100) model = gs.Gaussian(dim=3, var=0.6, len_scale=20) srf = gs.SRF(model) srf((x, y, z), mesh_type='structured') srf.vtk_export('3d_field') # Save to a VTK file for ParaView mesh = srf.to_pyvista() # Create a PyVista mesh for plotting in Python mesh.threshold_percent(0.5).plot()
Estimating and fitting variograms¶
The spatial structure of a field can be analyzed with the variogram, which contains the same information as the covariance function.
All covariance models can be used to fit given variogram data by a simple interface.
This is an example of how to estimate the variogram of a 2 dimensional unstructured field and estimate the parameters of the covariance model again.
import numpy as np import gstools as gs # generate a synthetic field with an exponential model x = np.random.RandomState(19970221).rand(1000) * 100. y = np.random.RandomState(20011012).rand(1000) * 100. model = gs.Exponential(dim=2, var=2, len_scale=8) srf = gs.SRF(model, mean=0, seed=19970221) field = srf((x, y)) # estimate the variogram of the field with 40 bins bins = np.arange(40) bin_center, gamma = gs.vario_estimate_unstructured((x, y), field, bins) # fit the variogram with a stable model. (no nugget fitted) fit_model = gs.Stable(dim=2) fit_model.fit_variogram(bin_center, gamma, nugget=False) # output ax = fit_model.plot(x_max=40) ax.plot(bin_center, gamma) print(fit_model)
Stable(dim=2, var=1.92, len_scale=8.15, nugget=0.0, anis=[1.], angles=[0.], alpha=1.05)
Kriging and Conditioned Random Fields¶
An important part of geostatistics is Kriging and conditioning spatial random fields to measurements. With conditioned random fields, an ensemble of field realizations with their variability depending on the proximity of the measurements can be generated.
For better visualization, we will condition a 1d field to a few “measurements”, generate 100 realizations and plot them:
import numpy as np import matplotlib.pyplot as plt import gstools as gs # conditions cond_pos = [0.3, 1.9, 1.1, 3.3, 4.7] cond_val = [0.47, 0.56, 0.74, 1.47, 1.74] gridx = np.linspace(0.0, 15.0, 151) # spatial random field class model = gs.Gaussian(dim=1, var=0.5, len_scale=2) srf = gs.SRF(model) srf.set_condition(cond_pos, cond_val, "ordinary") # generate the ensemble of field realizations fields =  for i in range(100): fields.append(srf(gridx, seed=i)) plt.plot(gridx, fields[i], color="k", alpha=0.1) plt.scatter(cond_pos, cond_val, color="k") plt.show()
User defined covariance models¶
One of the core-features of GSTools is the powerful
class, which allows to easy define covariance models by the user.
Here we re-implement the Gaussian covariance model by defining just the
which takes a non-dimensional distance
h = r/l
import numpy as np import gstools as gs # use CovModel as the base-class class Gau(gs.CovModel): def cor(self, h): return np.exp(-h**2)
And that’s it! With
Gau you now have a fully working covariance model,
which you could use for field generation or variogram fitting as shown above.
Incompressible Vector Field Generation¶
Using the original Kraichnan method, incompressible random spatial vector fields can be generated.
import numpy as np import gstools as gs x = np.arange(100) y = np.arange(100) model = gs.Gaussian(dim=2, var=1, len_scale=10) srf = gs.SRF(model, generator='VectorField') srf((x, y), mesh_type='structured', seed=19841203) srf.plot()
After you have created a field, you may want to save it to file, so we provide
a handy VTK export routine using the
vtk_export() or you could
create a VTK/PyVista dataset for use in Python with to
import gstools as gs 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.vtk_export("field") # Saves to a VTK file mesh = srf.to_pyvista() # Create a VTK/PyVista dataset in memory mesh.plot()
Which gives a RectilinearGrid VTK file
field.vtr or creates a PyVista mesh
in memory for immediate 3D plotting in Python.