Tutorials (Python)

Case studies

Oise

Non-stationary Kriging along Oise river. 1) Creating the necessary files (time consuming) 2) Kriging

Standard 2-D case

Standard workflow on a 2-D data set. Performs variography and modeling, estimation and simulation, in a monovariate and bivariate cases.
Basic Objects

Point Process

Several mechanisms for generating Point Process: Poisson, Poisson with regionalized intensity, Repulsion

Anamorphosis

An example for Gaussian Anamorphosis

Faults

Faults: how to define them; their impact on variography and estimation

Neighborhood

Definition of the Neighborhood concept. This tutorial gives hints on the parameters of the Moving Neighborhood

Polygons

Tutorial to demonstrate the use of a Polygon: established as the Convex Hull of data; after dilation

Selectivity curves

Calculation and visualization of experimental selectivity curves

Non Stationary Covariances

Handle non stationary covariances for kriging, covariance evaluation, and SPDE approach
Meshing Technique

Meshing

Description of various meshings. Focus on the Turbo Meshing (based on an internal grid, possibly rotated)

Meshing on a Manifold

Generalization of the Meshing expanded to the case of any manifold
Methodology

Discrete Disjunctive Kriging

An example of Discrete Disjunctive Kriging (DDK)

PluriGaussian Simulations

Pluri-Gaussian simulations performed in 2D

Point & Block Kriging

Implementation of Kriging for various targets: for Points, for regular blocks, for irregular blocks

Interpolations

Various interpolations from a set of Points to the nodes of a regular Grid.

Mathematical Morphology

Definition of several tools from the Mathematical Morphology applied to regular grids

Potential Model

Demonstration of the estimation based on the Potential Model. Examples in 1-D, 2-D and 3-D cases

Variograms in 3-D

Example of definition, calculation and visualization of variogram calculate in 3-D

Cross-validation

Example of Cross-validation calculation with various output formats

Super Kriging

Estimation in the framework of Super Kriging

Simulations in 3-D

Simulations on a 3-D grid. Visualization in Fence Diagram

Spectral Simulation on Sphere

Simulations performed on a Sphere of the Spectral Model

Spatial Sampling Density Variance



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Boolean Model

Perform conditional and non-conditional simulation with a variety of different object families

Projection Pursuit Multivariate Transform

Projection Pursuit Multivariate Transform: its calculation and application to simulated multivariate data set

Scatter and H-scatter plots

Calculating and representing scatter plots or h-scatter plots

CoKriging vs Kriging

CoKriging in Heterotopic case compared to Kriging

Log-Likelihood

Calculation of the Log-Likelihood applied to the definition of the pattern for the linkage of the drift functions in the multivariable framework

Turning Band Simulations

Simulation using the Turning Bands Method of various covariances
Tools for Python environment

Python class overload (in Python)

Demonstration of the overload of a class of gstlearn in Python

Db to Panda Frame

Hints for converting a Data Base from gstlearn library into a Panda Data Frame

Graphic 3-D

Presentation of the graphic facility to produce information in 3-D, either on a Sphere or in R3 Euclidean space

Overload the Drift

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Spatio-Temporal

Spatio-Temporal Simulation

Simulations of a Spatio-Temporal phenomenon

Diffusion Advection

Simulation of a Diffusion-Advection Model
Data Base Management

Data Base management (Db)

Management of the Data Base internal to gstlearn Library (Db)

Simulation post-processing

The Simulation post-processing is illustrated through an Upscaling capability

Grid to Grid manipulation

Grid to Grid transformations

Grid Refinement

Grid Refinement techniques and performances

Migration Facility

Migration of one or several variables from one data organization to another. Particular use of the Ball Tree sorting.

Statistics on Db

Statistics performed on Point and Grid data bases

Spill Point

Calculate the Spill Point on a Surface
SPDE

SPDE

Estimation and Simulations performed in the framework of SPDE

SPDE with variable anisotropy

Estimation and Simulations with non-stationary anisotropy of the Covariance Model, treated in the SPDE formalism

SPDE for Markovian Model

Simulations with a Markovian Model, treated in the SPDE formalism

SPDE simulation on a sphere

Simulations performed on a Sphere, treated in the SPDE formalism

SPDE for Spiral Anisotropy

Estimation and Simulations performed with a non-stationary anisotropy of the covariance (spiral form), treated in the SPDE formalism