geone


Namegeone JSON
Version 1.2.17 PyPI version JSON
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home_pageNone
SummaryGeostatistics tools and Multiple Point Statistics
upload_time2024-11-28 07:34:28
maintainerNone
docs_urlNone
authorNone
requires_python>=3.7
license LICENSE Copyright (c) 2023 - University of Neuchâtel PREAMBLE GEONE is a python package providing a set of tools for geostatistical and multiple-point statistics modeling. It contains the DEESSE library in the sub-package deesse_core. DEESSE is a commercial product which is not provided as an open source software and belongs to the University of Neuchâtel. GEONE includes a wrapper allowing to launch DEESSE directly in python. GEONE is provided as an open source code with an open access license applicable for all the code except the deesse_core subpackage (see details below). DEESSE and therefore the deesse_core subpackage requires a specific license agreement for commercial use only. The use of DEESSE is free (royalty free) and unlimited for academic research and teaching activities. Its use for industrial and commercial activities is authorized only under a specific license agreement to be concluded with the University of Neuchâtel. Such commercial license must be obtained before any commercial or industrial use to take place and fees will be charged. To obtain such a commercial license from the University of Neuchâtel, the users shall contact directly Prof. Philippe Renard (philippe.renard@unine.ch). LICENSE TERMS FOR THE SOFTWARE GEONE Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files, to deal in the software GEONE (“The Software”) excluding the software DEESSE, without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following condition: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. The permission granted herehover is related to the Software only and does not imply any commercial or industrial license grant concerning DEESSE software also made available by the copyright owners and the University of Neuchâtel on the GitHub platform. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
keywords geostatistics multiple point statistics deesse two point statistics covariance variogram simulation kriging
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bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls. 
            # GEONE

[![Documentation Status](https://readthedocs.org/projects/geone/badge/?version=latest)](https://geone.readthedocs.io/en/latest/?badge=latest)

**Current version : 1.2.17** <!-- Update manually here! see src/geone/_version.py -->

GEONE is a Python3 package providing a set of tools for geostatistical modeling, including:

- multiple-point statistics (MPS) simulation as "DEESSE wrapper"
- geostatistical tools based on two-point statistics, including  "GEOSCLASSIC wrapper"
- miscellaneous algorithms based on random processes

## Documentation, examples and references

The documentation of GEONE is on https://geone.readthedocs.io.

<!-- The notebooks (examples) from the documentation are available in [docs/source/notebooks](./docs/source/notebooks). -->
The notebooks (examples) from the documentation are available in [docs/source/notebooks](https://github.com/randlab/geone/tree/master/docs/source/notebooks).

## Installation

GEONE relies on pre-compiled C libraries (DEESSE and GEOSCLASSIC core)

GEONE is available:

- on [PyPI](https://pypi.org/project/geone) (The Python Package Index), for:
    - linux (x86_64 with GLIBC 2.35 or GLIBC 2.27) and python 3.9 to 3.12
    - mac (x86_64 or arm64) and python 3.9 to 3.12
    - windows and python 3.9 to 3.12
- on the [Github repository](https://github.com/randlab/geone), for:
    - linux (x86_64 with GLIBC 2.35 or GLIBC 2.27) and python 3.7 to 3.12
    - mac (x86_64 or arm64) and python 3.8 to 3.12
    - windows and python 3.7 to 3.12

### Installation from [PyPI](https://pypi.org/project/geone)

In a terminal type 
```
pip install geone
```
Or, equivalently: `python -m pip install geone`.

### Installation from the [Github repository](https://github.com/randlab/geone)

In a terminal, change directory where to download GEONE, and type
```
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.

Alternatively:

- Instead of `git clone ...`, you can download GEONE from the [Github repository](https://github.com/randlab/geone): click on the green button "code" and choose "Download ZIP". 
- Then, unzip the archive on your computer
- Finally, in a terminal, go into the unzipped directory, and type `pip install .`

**Warning - Using GEONE**

If the installation has been done from github, do not launch python from the directory containing the downloaded sources and where the installation has been done (with `pip`), otherwise `import geone` will fail.

### Requirements

The following python packages are used by GEONE (tested on python 3.11.5):

- matplotlib (3.8.1)
- multiprocessing (for parallel processes)
- numpy (tested with version 1.26.0)
- pandas (tested with version 2.1.2)
- pyvista (tested with version 0.42.3)
- scipy (tested with version 1.11.3)

**Warning**

numpy version **less than 2.** is required

### Removing GEONE
In a terminal type 

`pip uninstall -y geone`

*Note: First remove the directory 'geone.egg-info' from the current directory (if present).*

<!--
## References

### Some references about DEESSE
- J. Straubhaar, P. Renard (2021) Conditioning Multiple-Point Statistics Simulation to Inequality Data. Earth and Space Science, [doi:10.1029/2020EA001515](https://dx.doi.org/10.1029/2020EA001515)
- J. Straubhaar, P. Renard, T. Chugunova (2020) Multiple-point statistics using multi-resolution images. Stochastic Environmental Research and Risk Assessment 20, 251-273, [doi:10.1007/s00477-020-01770-8](https://dx.doi.org/10.1007/s00477-020-01770-8)
- J. Straubhaar, P. Renard, G. Mariethoz (2016) Conditioning multiple-point statistics simulations to block data. Spatial Statistics 16, 53-71, [doi:10.1016/j.spasta.2016.02.005](https://dx.doi.org/10.1016/j.spasta.2016.02.005)
- G. Mariethoz, J. Straubhaar, P. Renard, T. Chugunova, P. Biver (2015) Constraining distance-based multipoint simulations to proportions and trends. Environmental Modelling & Software 72, 184-197, [doi:10.1016/j.envsoft.2015.07.007](https://dx.doi.org/10.1016/j.envsoft.2015.07.007)
- G. Mariethoz, P. Renard, J. Straubhaar (2010) The Direct Sampling method to perform multiple-point geostatistical simulation. Water Resources Research 46, W11536, [doi:10.1029/2008WR007621](https://dx.doi.org/10.1029/2008WR007621)

### Reference about DEESSEX
- A. Comunian, P. Renard, J. Straubhaar (2012) 3D multiple-point statistics simulation using 2D training images. Computers & Geosciences 40, 49-65, [doi:10.1016/j.cageo.2011.07.009](https://dx.doi.org/10.1016/j.cageo.2011.07.009)

### Some references about GRF
- J. W. Cooley and J. W. Tukey (1965) An algorithm for machine calculation of complex fourier series. Mathematics of Computation 19(90):297-301, [doi:10.2307/2003354](https://dx.doi.org/10.2307/2003354)
- C. R. Dietrich and G. N. Newsam (1993) A fast and exact method for multidimensional gaussian stochastic simulations. Water Resources Research 29(8):2861-2869, [doi:10.1029/93WR01070](https://dx.doi.org/10.1029/93WR01070)
- A. T. A. Wood and G. Chan (1994) Simulation of stationary gaussian processes in [0,1]^d. Journal of Computational and Graphical Statistics 3(4):409-432, [doi:10.2307/1390903](https://dx.doi.org/10.2307/1390903)

### Other references 
- C. Lantuéjoul (2002) Geostatistical Simulation, Models and Algorithms. Springer Verlag, Berlin, 256 p.
- P. Renard, D. Allard (2013), Connectivity metrics for subsurface flow and transport. Advances in Water Resources 51:168-196, `doi:10.1016/j.advwatres.2011.12.001 <https://doi.org/10.1016/j.advwatres.2011.12.001>`_
- J. Straubhaar, P. Renard (2024), Exploring substitution random functions composed of stationary multi-Gaussian processes. Stochastic Environmental Research and Risk Assessment, `doi:10.1007/s00477-024-02662-x <https://doi.org/10.1007/s00477-024-02662-x>`_
 -->

## License

<!-- See [LICENSE](LICENSE) file. -->
<!-- See [LICENSE](https://geone.readthedocs.io/en/latest/LICENSE.html) file. -->
See [LICENSE](https://github.com/randlab/geone/blob/master/LICENSE) file.

## Authors
GEONE is developed by [Julien Straubhaar](https://www.unine.ch/philippe.renard/home/the-team/julien-straubhaar.html) and [Philippe Renard](https://www.unine.ch/philippe.renard/home/the-team/philippe-renard.html).

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    "description": "# GEONE\n\n[![Documentation Status](https://readthedocs.org/projects/geone/badge/?version=latest)](https://geone.readthedocs.io/en/latest/?badge=latest)\n\n**Current version : 1.2.17** <!-- Update manually here! see src/geone/_version.py -->\n\nGEONE is a Python3 package providing a set of tools for geostatistical modeling, including:\n\n- multiple-point statistics (MPS) simulation as \"DEESSE wrapper\"\n- geostatistical tools based on two-point statistics, including  \"GEOSCLASSIC wrapper\"\n- miscellaneous algorithms based on random processes\n\n## Documentation, examples and references\n\nThe documentation of GEONE is on https://geone.readthedocs.io.\n\n<!-- The notebooks (examples) from the documentation are available in [docs/source/notebooks](./docs/source/notebooks). -->\nThe notebooks (examples) from the documentation are available in [docs/source/notebooks](https://github.com/randlab/geone/tree/master/docs/source/notebooks).\n\n## Installation\n\nGEONE relies on pre-compiled C libraries (DEESSE and GEOSCLASSIC core)\n\nGEONE is available:\n\n- on [PyPI](https://pypi.org/project/geone) (The Python Package Index), for:\n    - linux (x86_64 with GLIBC 2.35 or GLIBC 2.27) and python 3.9 to 3.12\n    - mac (x86_64 or arm64) and python 3.9 to 3.12\n    - windows and python 3.9 to 3.12\n- on the [Github repository](https://github.com/randlab/geone), for:\n    - linux (x86_64 with GLIBC 2.35 or GLIBC 2.27) and python 3.7 to 3.12\n    - mac (x86_64 or arm64) and python 3.8 to 3.12\n    - windows and python 3.7 to 3.12\n\n### Installation from [PyPI](https://pypi.org/project/geone)\n\nIn a terminal type \n```\npip install geone\n```\nOr, equivalently: `python -m pip install geone`.\n\n### Installation from the [Github repository](https://github.com/randlab/geone)\n\nIn a terminal, change directory where to download GEONE, and type\n```\ngit clone https://github.com/randlab/geone.git\ncd geone\npip install .\n```\n\n*Note:* use `pip install . --verbose` or `pip install . -v` for printing (more) messages during the installation.\n\nAlternatively:\n\n- Instead of `git clone ...`, you can download GEONE from the [Github repository](https://github.com/randlab/geone): click on the green button \"code\" and choose \"Download ZIP\". \n- Then, unzip the archive on your computer\n- Finally, in a terminal, go into the unzipped directory, and type `pip install .`\n\n**Warning - Using GEONE**\n\nIf the installation has been done from github, do not launch python from the directory containing the downloaded sources and where the installation has been done (with `pip`), otherwise `import geone` will fail.\n\n### Requirements\n\nThe following python packages are used by GEONE (tested on python 3.11.5):\n\n- matplotlib (3.8.1)\n- multiprocessing (for parallel processes)\n- numpy (tested with version 1.26.0)\n- pandas (tested with version 2.1.2)\n- pyvista (tested with version 0.42.3)\n- scipy (tested with version 1.11.3)\n\n**Warning**\n\nnumpy version **less than 2.** is required\n\n### Removing GEONE\nIn a terminal type \n\n`pip uninstall -y geone`\n\n*Note: First remove the directory 'geone.egg-info' from the current directory (if present).*\n\n<!--\n## References\n\n### Some references about DEESSE\n- J. Straubhaar, P. Renard (2021) Conditioning Multiple-Point Statistics Simulation to Inequality Data. Earth and Space Science, [doi:10.1029/2020EA001515](https://dx.doi.org/10.1029/2020EA001515)\n- J. Straubhaar, P. Renard, T. Chugunova (2020) Multiple-point statistics using multi-resolution images. Stochastic Environmental Research and Risk Assessment 20, 251-273, [doi:10.1007/s00477-020-01770-8](https://dx.doi.org/10.1007/s00477-020-01770-8)\n- J. Straubhaar, P. Renard, G. Mariethoz (2016) Conditioning multiple-point statistics simulations to block data. Spatial Statistics 16, 53-71, [doi:10.1016/j.spasta.2016.02.005](https://dx.doi.org/10.1016/j.spasta.2016.02.005)\n- G. Mariethoz, J. Straubhaar, P. Renard, T. Chugunova, P. Biver (2015) Constraining distance-based multipoint simulations to proportions and trends. Environmental Modelling & Software 72, 184-197, [doi:10.1016/j.envsoft.2015.07.007](https://dx.doi.org/10.1016/j.envsoft.2015.07.007)\n- G. Mariethoz, P. Renard, J. Straubhaar (2010) The Direct Sampling method to perform multiple-point geostatistical simulation. Water Resources Research 46, W11536, [doi:10.1029/2008WR007621](https://dx.doi.org/10.1029/2008WR007621)\n\n### Reference about DEESSEX\n- A. Comunian, P. Renard, J. Straubhaar (2012) 3D multiple-point statistics simulation using 2D training images. Computers & Geosciences 40, 49-65, [doi:10.1016/j.cageo.2011.07.009](https://dx.doi.org/10.1016/j.cageo.2011.07.009)\n\n### Some references about GRF\n- J. W. Cooley and J. W. Tukey (1965) An algorithm for machine calculation of complex fourier series. Mathematics of Computation 19(90):297-301, [doi:10.2307/2003354](https://dx.doi.org/10.2307/2003354)\n- C. R. Dietrich and G. N. Newsam (1993) A fast and exact method for multidimensional gaussian stochastic simulations. Water Resources Research 29(8):2861-2869, [doi:10.1029/93WR01070](https://dx.doi.org/10.1029/93WR01070)\n- A. T. A. Wood and G. 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