GEONE is a python package providing a set of tools for geostatistical and multiple-point statistics modeling, comprising:
git clone https://github.com/randlab/geone.git
cd geone
pip install .Note: use pip install . --verbose or pip install . -v for printing (more) messages during the installation.
Remove geone
pip uninstall -y geone
Note: first remove the directory 'geone.egg-info' from the current directory (if present).
Do not launch python from the directory where the installation has been done (with pip), otherwise import geone will fail.
The following python packages are used by GEONE (tested on python 3.11.5):
DEESSE is a parallel software for multiple point statistics (MPS) simulations. MPS allows to generate random fields reproducing the spatial statistics -- within and between variable(s) -- of a training data set (TDS). DEESSE follows an approach based on the direct sampling of the TDS. The simulation grid is sequentially populated by values borrowed from the TDS, selected after a random search for similar patterns. Many features are handled by DEESSE and illustrated in the proposed examples below.
DEESSE can also be used for 3D simulation based on 2D TDS which give the spatial statistics in sections (slices) of the 3D simulation domain. (More generally, 1D or 2D TDS can be used for higher dimensional simulation.) The principle consists in filling the simulation domain by successively simulating sections (according the the given orientation) conditionally to the sections previously simulated. A wrapper for this approach (named DEESSEX referring to crossing-simulation / X-simulation with DEESSE) is proposed in the package GEONE.
Some modules in the package GEONE can be run as a script ('__main__' scope) and provide examples by this way.
Various examples are provided (notebooks in 'examples' directory) to get started with GEONE, as described below.
ex_a_01_image_and_pointset.ipynb: classes for images and point sets, reading from files, writing to files, plottingex_a_02_image2d_rgb.ipynb: reading / writing RGB 2D images, files in png formatex_a_03_image_interpolation.ipynb: some tools to interpolate images (e.g. make them finer or coarser)Multiple-point statistics - simulation using the DEESSE wrapper:
ex_deesse_01_basics.ipynb: basic DEESSE (categorical) simulationsex_deesse_02_additional_outputs_and_simulation_paths.ipynb: retrieving additional output maps and setting the simulation pathex_deesse_03_search_neigbhorhood.ipynb: advanced setting for the search neighborhood ellipsoidex_deesse_04_continous_sim.ipynb: continous simulationsex_deesse_05_geom_transformation.ipynb: simulations with geometrical transformations (rotation / scaling)ex_deesse_06_proba_constraint.ipynb: simulations with probability (proportion) constraintsex_deesse_07_connectivity_data.ipynb: simulations with connectivity dataex_deesse_08_multivariate_sim.ipynb: bivariate simulations - stationary caseex_deesse_09_multivariate_sim2.ipynb: bivariate simulations - setting an auxiliary variable to deal with non-stationarityex_deesse_10_incomplete_image.ipynb: reconstruction of an image using a training data setex_deesse_11_using_mask.ipynb: simulation using a maskex_deesse_12_multiple_TIs.ipynb: simulation using multiple training imagesex_deesse_13_inequality_data.ipynb: simulations with inequality dataex_deesse_14_rotation3D.ipynb: simulations with rotation in 3Dex_deesse_15_block_data.ipynb: simulation with block data, i.e target mean values over block of cellsex_deesse_16_advanced_use_of_pyramids.ipynb: simulation using pyramids (retrieving pyramids, conditioning within pyramids)Multiple-point statistics - X-simulation using the DEESSEX wrapper:
ex_deesseX_01_getting_started.ipynb: getting starting with deesseX, simulation based on XZ and YZ sectionsex_deesseX_02.ipynb: simulation based on XY, XZ and YZ sectionsex_deesseX_03.ipynb: simulation based on XY, XZ and YZ sections and simulation based on XY 2D-section and Z 1D-sectionex_deesseX_04.ipynb: simulation based on XZ and YZ sections, and accounting for non-stationarity (vertical trend)ex_general_multiGaussian.ipynb: functions for multiGaussian estimation and simulation in a grid, and elementary covariance/variogram models (in 1D)ex_grf_1d.ipynb: example for the generation of 1D fieldsex_grf_2d.ipynb: example for the generation of 2D fieldsex_grf_3d.ipynb: example for the generation of 3D fieldsex_geosclassic_1d_1.ipynb:example in 1D for two-point statistics simulation and estimationex_geosclassic_1d_2_non_stat_cov.ipynb:example in 1D with non-stationary covariance modelex_geosclassic_2d_1.ipynb:example in 2D for two-point statistics simulation and estimationex_geosclassic_2d_2_non_stat_cov.ipynb:example in 2D with non-stationary covariance modelex_geosclassic_3d_1.ipynb:example in 3D for two-point statistics simulation and estimationex_geosclassic_3d_2_non_stat_cov.ipynb:example in 3D with non-stationary covariance modelex_geosclassic_indicator_1d.ipynb:example in 1D for two-point statistics simulation and estimation of indicator variablesex_geosclassic_indicator_2d.ipynb:example in 2D for two-point statistics simulation and estimation of indicator variablesex_geosclassic_indicator_3d.ipynb:example in 3D for two-point statistics simulation and estimation of indicator variablesex_geosclassic_image_analysis.ipynb:example for two-point statistics analysis (covariance, variogram, connectivity, ...) of images (maps)ex_vario_analysis_data1D_1.ipynb: example for variogram analysis and ordinary kriging for data in 1Dex_vario_analysis_data1D_2_non_stationary.ipynb: example how dealing with non stationary data set in 1Dex_vario_analysis_data2D_1_omnidirectional.ipynb: example for variogram analysis and ordinary kriging for data in 2D (omni-directional)ex_vario_analysis_data2D_2_general.ipynb: example for variogram analysis and ordinary kriging for data in 2D (general)ex_vario_analysis_data2D_3_non_stationary.ipynb: example how dealing with non stationary data set in 2Dex_vario_analysis_data3D_1_omnidirectional.ipynb: example for variogram analysis and ordinary kriging for data in 3D (omni-directional)ex_vario_analysis_data3D_2_general.ipynb: example for variogram analysis and ordinary kriging for data in 3D (general)ex_vario_analysis_data3D_3_non_stationary.ipynb: example how dealing with non stationary data set in 3Dex_pgs.ipynb: example of pluri-Gaussian simulations in 1D, 2D and 3D (categorical, conditional or not), based on two latent Gaussian fieldsex_acceptRejectSampler.ipynb: example of accept-reject sampler for generating samples according to given density function (uni- or multi-variate)ex_randProcess.ipynb: example of Poisson point process, and Chentsov simulation in 1D, 2D and 3D