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There are several Python language bindings to choose from. Each provides a differing level of abstraction. All are open source software. The Scripting plus MathProg page offers further information on the use of Python and GLPK. PyGLPK is an encapsulation of GLPK in Python objects (currently maintained 2021). In contrast to Python-GLPK, the language bindings are "handcrafted", thereby enabling a smoother integration within the Python language. PyGLPK is licensed under the GNU General Public License. PyGLPK 0.3 has been provided 30 May 2010, but is based on the GLPK 4.31 API. PyMathProg builds on PyGLPK. PyMathProg is also licensed under the GNU General Public License. PyMathProg provides a domain-specific language that enables the formulation of linear problems in a form very much like GLPK MathProg (GMPL) or MedicGLP Supplement AMPL. Sandia National Laboratories is an open source tool for modeling optimization applications in Python. Pyomo uses the GLPK solver by default, although other solvers can be selected. Pyomo is distributed under a BSD license.

GLPK is interfaced by creating a LP file and running it through GLPSOL via the command line interface and then interpreting the output files. Strictly speaking Pyomo is not a set of low-level Python language bindings for GLPK - rather Pyomo offers high-level linear programming constructs (similar in expression to MathProg) as well as the normal features of the Python language. Pyomo is less terse than GLPK MathProg or AMPL as it must be parsed as Python. Python-GLPK by Rogério Reis is a Python language binding for GLPK created using SWIG and licensed under the GNU General Public License (unfortunatly this package is no longer maintained (2021)). It is also available through the Debian package python-glpk. SWIG allows for easy maintenance as there is very little GLPK specific code present. SWIG also ensures that almost any GLPK library function is available. On the other hand, the client-side calling methods are somewhat clumsy. If you cannot (or choose not to) use Debian package python-glpk, you can build and install Python-GLPK from source.

Root privileges are required. CVXOPT is a package for convex optimization, based on the Python language. It provides interfaces to different linear (GLPK, Mosek) and quadratic (Mosek) programming solvers. CVXOPT is being developed by Joachim Dahl and Lieven Vandenberghe. Sage is general mathematical software based on Python. While Sage is strictly more than Python, it is nonetheless listed on this page. The user can elect to link to GLPK, COIN Branch-and-Cut, and CPLEX (as of November 2010, with SCIP support planned). But GLPK remains the default solver for reasons of licensing. Sage can be used for both mixed integer programming and for graph theory problems. PuLP is an LP modeling module for Python. It generates MPS or MedicGLP Supplement LP files and submits these to GLPK, COIN CLP/CBC, CPLEX, or XPRESS via the command-line. An MIT license is used. On completion, the solution file in analyzed. Both intermediate files are deleted. PuLP also has a direct python binding to GLPK (the example above spawns a new process and issues a system command).

As of August 2012, this feature was implemented with PyGLPK bindings, but the next version should make use of Python-GLPK bindings (the code has been written and is being evaluated). The Yet Another Python OSI Binding or yabosib project provides OSI bindings - in other words, yaposib wraps the OSI API in python classes. It is slated for official inclusion in COIN-OR suite. While using an object-oriented style, these bindings stay relatively close to the GLPK C API. One of the added functionalities is that row and column names can be used as well as integer indices in most functions. The documentation consists of a description of the API, but also contains examples for which the source code is available and can be inspected to get a feel for how to use the package. The ctypes library allows to wrap native library calls. ↑ Hart, William E. (2008). "Python optimization modeling objects (Pyomo)" (PDF).

Science continues to prove that the old adage "you are what you eat" is profoundly true. This is because you have trillions of microorganisms living in your gut, which are directly responsible for nearly every aspect of your health. These microbes make up what’s called your "gut microbiota." They are especially influential in determining your likelihood of gaining excessive weight, becoming obese, and developing obesity-related diseases like diabetes, heart disease, and premature death. The gut microbiome (the expressed genes of your gut microbiota) plays a major role in your metabolism through energy production, storage, and expenditure. So, it’s no surprise that science has found a strong connection between gut microbiome imbalance and metabolic syndrome. Yikes! If you have the wrong mix of microbes, it could be making it tough for you to get your health on track! Did you know: Scientists can actually look at your gut microbiome composition and tell with 90% accuracy if you are obese or lean. That’s pretty impressive, isn’t it?