{"id":56,"date":"2017-12-03T14:46:38","date_gmt":"2017-12-03T13:46:38","guid":{"rendered":"http:\/\/wp.unil.ch\/gaia\/?page_id=56"},"modified":"2021-12-28T08:31:25","modified_gmt":"2021-12-28T07:31:25","slug":"downloads","status":"publish","type":"page","link":"https:\/\/wp.unil.ch\/gaia\/downloads\/","title":{"rendered":"Downloads"},"content":{"rendered":"<p>We try to share our codes on GitHub as much as possible. Check out the GAIA repository here:\u00a0<a href=\"https:\/\/github.com\/GAIA-UNIL\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/github.com\/GAIA-UNIL<\/a>. Below is a description of some elements of this repository. You will also find some other things, such as links to external codes we are typically using or datasets.<\/p>\n<h2><u>Training images<\/u><\/h2>\n<p>A <a href=\"https:\/\/github.com\/GAIA-UNIL\/trainingimages\" target=\"_blank\" rel=\"noopener noreferrer\">set of training images<\/a> is provided on our repository.<\/p>\n<p>If you are wondering where you can find a 3D training image for alluvial environments, check out the aquifers analogs that we published. The data are <a href=\"https:\/\/doi.pangaea.de\/10.1594\/PANGAEA.844167\">here<\/a> and the paper describing it is <a href=\"https:\/\/www.nature.com\/articles\/sdata201533\">here<\/a>.<\/p>\n<h2><u>MPS using QuickSampling<\/u><\/h2>\n<p>The QuickSampling MPS code is <strong>freely<\/strong> available for Linux \/ macOS \/ Windows 10 and is usable form Matlab \/ Python3 and R on the dedicated\u00a0<a href=\"https:\/\/github.com\/GAIA-UNIL\/G2S\" target=\"_blank\" rel=\"noopener noreferrer\">GitHub repository<\/a>. This is our latest MPS implementation, more recent than Direct Sampling, and often faster.<\/p>\n<h2><u>MPS using Direct Sampling<\/u><\/h2>\n<p>The Direct Sampling code comes in many versions. Some are properly compiled codes, other only Matlab scripts:<\/p>\n<h3><strong><b>Compiled libraries:<\/b><\/strong><\/h3>\n<p>Two separate implementations of the Direct Sampling code exist. Both have examples and a proper documentation, but unfortunately cannot be shared on repositories:<\/p>\n<ol>\n<li>The DS implementation of the University of Lausanne can be requested by sending an email to <a href=\"mailto:gregoire.mariethoz@unil.ch\">gregoire.mariethoz@unil.ch<\/a>. In this version, most of the features presented in the literature are present.<\/li>\n<li>The implementation of the University of Neuchatel (called DeeSse) can be obtained by contacting Prof. Philippe Renard (<a href=\"https:\/\/www.unine.ch\/philippe.renard\/home\/the-team\/philippe-renard.html\">https:\/\/www.unine.ch\/philippe.renard\/home\/the-team\/philippe-renard.html<\/a>).<\/li>\n<\/ol>\n<p>Both codes are only available for academic, non-commercial purposes, and to the discretion of the University of Neuchatel who owns the intellectual property of the algorithm.<\/p>\n<p>There is an excellent and freely available MPS library written by Thomas Hansen from the Niels Bohr Institute, available here <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2352711016300164\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2352711016300164<\/a>: .<\/p>\n<h3><strong><b>Matlab<\/b><\/strong><strong><b> versions:<\/b><\/strong><\/h3>\n<p>A <a href=\"https:\/\/wp.unil.ch\/gaia\/mps\/ds-matlab\/\">simple version<\/a> written in Matlab, with a fixed template, is available for demonstration and teaching purposes. <span style=\"color: #ff0000\">IT SHOULD NOT BE USED FOR BENCHMARKING OR FOR COMPARISON WITH OTHER METHODS BECAUSE IT IS EXTREMELY BASIC AND SLOW.<\/span><\/p>\n<p>An <a href=\"https:\/\/github.com\/GAIA-UNIL\/DS_in_Matlab\" target=\"_blank\" rel=\"noopener noreferrer\">advanced version<\/a> of the Matlab Direct Sampling code for is also available. It considers variable lag vectors and flexible data events, and seems to have issues with conditioning (I will fix it when I find the time). <span style=\"color: #ff0000\">THIS ONE SHOULD ALSO NOT BE USED FOR BENCHMARKING OR FOR COMPARISON WITH OTHER METHODS BECAUSE EVEN SLOWER THAN THE SIMPLE VERSION. MOREOVER IT HAS PROBLEMS WITH CONDITIONING.<\/span><\/p>\n<p>Another version of the Direct Sampling is the bunch-pasting strategy of <a href=\"https:\/\/www.minds.ch\/gm\/pdf\/rezaee2013_proof.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">Rezaee et al (2013)<\/a>, for which there is also a <a href=\"https:\/\/wp.unil.ch\/gaia\/files\/2019\/09\/Bunch-DS_Code.zip\">Matlab code<\/a>. <span style=\"color: #ff0000\">AGAIN, IT SHOULD NOT BE USED FOR BENCHMARKING.<\/span><\/p>\n<p>Note that there may be other versions out there that I am not aware of!<\/p>\n<h2><u>MPS using Graph-cuts based simulation<\/u><\/h2>\n<p>We recently developed a graph-cuts based MPS simulation algorithm which is an improvement over Image Quilting. The Matlab code can be found <a href=\"https:\/\/github.com\/GAIA-UNIL\/GraphCuts_MPS\" target=\"_blank\" rel=\"noopener noreferrer\">here<\/a>.<\/p>\n<h2><u>MPS using Image Quilting<\/u><\/h2>\n<p>The Matlab code for MPS simulation by Image Quilting is available <a href=\"https:\/\/github.com\/GAIA-UNIL\/Image-Quilting\" target=\"_blank\" rel=\"noopener noreferrer\">here<\/a>. Note that it uses some of our Matlab\u00a0<a href=\"https:\/\/github.com\/GAIA-UNIL\/MATLAB_Geostat_Utilities\" target=\"_blank\" rel=\"noopener noreferrer\">utilities<\/a>.<\/p>\n<h2><u>General purpose utilities<\/u><\/h2>\n<p><a href=\"https:\/\/github.com\/GAIA-UNIL\/MATLAB_Geostat_Utilities\" target=\"_blank\" rel=\"noopener noreferrer\">Here<\/a> is a set of small Matlab geostatistical tools that you can download and use freely. A basic documentation is also included. Thanks for sending feedback!<\/p>\n<h2><u>MPS-related utilities<\/u><\/h2>\n<p>Our <a href=\"https:\/\/github.com\/GAIA-UNIL\/MPS_patterns_validation\" target=\"_blank\" rel=\"noopener noreferrer\">patterns validation code<\/a> (Windows executable and Fortran code) allows finding out if a training image is compatible with a given data pointset.<\/p>\n<p>Before writing your own code for geostatistical simulation or estimation, check out some nice existing free ones such as <a href=\"https:\/\/sgems.sourceforge.net\/\">SGeMS<\/a> and <a href=\"https:\/\/mgstat.sourceforge.net\/\">mGstat<\/a>.<\/p>\n<h2><u>Inverse modeling<\/u><\/h2>\n<p>If you are working on spatial inverse models with MPS and are looking for a benchmark forward flow and transport model, you can use <a href=\"https:\/\/wp.unil.ch\/gaia\/files\/2019\/09\/ForwardSetup_Laloy_et_al_AWR2016.zip\">one of the case studies<\/a> we developed in our <a href=\"https:\/\/www.minds.ch\/gm\/pdf\/Laloy206_inpress.pdf\">paper on parallel tempering<\/a>. It uses the MAFLOT flow and transport simulation code for the forward problem (<a href=\"https:\/\/www.maflot.com\/Site\/MaFloT.html\">https:\/\/www.maflot.com\/Site\/MaFloT.html<\/a>). The forward problem is very fast, which is needed when investing inverse approaches.<\/p>\n<p>The code for the tempering approach in <a href=\"https:\/\/www.minds.ch\/gm\/pdf\/Laloy206_inpress.pdf\">Laloy et al (2016)<\/a> is available\u00a0<a href=\"https:\/\/wp.unil.ch\/gaia\/files\/2019\/09\/PTSGR_Laloy_et_al_AWR2016.zip\">here<\/a> (read the instructions in the file <span class=\"SpellE\">run_ptsgr.m<\/span>).<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We try to share our codes on GitHub as much as possible. Check out the GAIA repository here:\u00a0https:\/\/github.com\/GAIA-UNIL. Below is a description of some elements of this repository. You will also find some other things, such as links to external codes we are typically using or datasets. Training images A set of training images is &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/wp.unil.ch\/gaia\/downloads\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Downloads&#8221;<\/span><\/a><\/p>\n","protected":false},"author":1001689,"featured_media":0,"parent":0,"menu_order":5,"comment_status":"closed","ping_status":"closed","template":"page-templates\/full-width-page.php","meta":{"_seopress_robots_primary_cat":"","_seopress_titles_title":"","_seopress_titles_desc":"","_seopress_robots_index":"","footnotes":""},"class_list":["post-56","page","type-page","status-publish"],"_links":{"self":[{"href":"https:\/\/wp.unil.ch\/gaia\/wp-json\/wp\/v2\/pages\/56","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp.unil.ch\/gaia\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/wp.unil.ch\/gaia\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/gaia\/wp-json\/wp\/v2\/users\/1001689"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/gaia\/wp-json\/wp\/v2\/comments?post=56"}],"version-history":[{"count":0,"href":"https:\/\/wp.unil.ch\/gaia\/wp-json\/wp\/v2\/pages\/56\/revisions"}],"wp:attachment":[{"href":"https:\/\/wp.unil.ch\/gaia\/wp-json\/wp\/v2\/media?parent=56"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}