| Name | transportmaps JSON |
| Version |
3.0.11
JSON |
| download |
| home_page | |
| Summary | Tools for the construction of transport maps |
| upload_time | 2024-01-11 23:54:23 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.8 |
| license | LGPL |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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==============
Transport Maps
==============
This package provides basic functionalities for the construction of monotonic transport maps.
* `Binary (PyPi) <https://pypi.python.org/pypi/TransportMaps>`_
* `Source (Bitbucket) <https://bitbucket.org/dabi86/transportmaps>`_
* `Homepage <http://transportmaps.mit.edu/docs/>`_
* `Q&A & Bug-tracking <https://bitbucket.org/dabi86/transportmaps/issues?status=new&status=open>`_
Supported systems
-----------------
* \*nix like OS (Linux, Unix, ...)
* Mac OS
Other operating systems have not been tested and they likely require a more complex procedure for the installation (this includes the Microsoft Windows family..).
We reccommend to work in a virtual environment using `virtualenv <https://virtualenv.readthedocs.io/en/latest/>`_ or `Anaconda <https://www.continuum.io/why-anaconda>`_.
Installation requirements
-------------------------
* `gcc <https://gcc.gnu.org/>`_ (or an alternative C/C++ compiler)
* `gfortran <https://gcc.gnu.org/fortran/>`_ (or an alternative Fortran compiler)
Automatic installation
----------------------
First of all make sure to have the latest version of `pip <https://pypi.python.org/pypi/pip>`_ installed
$ pip install --upgrade pip
The package and its python dependencies can be installed running the command:
$ pip install --upgrade TransportMaps
If one whish to enable some of the optional dependencies:
$ pip install --upgrade TransportMaps[SUITESPARSE]
These options will install the following modules:
* DOLFIN -- dolfin package for Partial Differential Equations
* SUITESPARSE -- scikit-sparse
* This requires `scitik-sparse <https://github.com/scikit-sparse/scikit-sparse>`_ and the package `libsuitesparse-dev <https://packages.ubuntu.com/focal/libsuitesparse-dev>`_
* HMC -- Hamiltonian Monte Carlo `pyhmc <https://pythonhosted.org/pyhmc/>`_ (it requires having numpy and cython already installed)
Running the Unit Tests
----------------------
Unit tests are available and can be run through the commands:
>>> import TransportMaps as TM
>>> TM.tests.run_tests()
Or directly using the bash command:
$ tmap-run-tests
The Git repository also contains a docker-compose configuration file to test the whole suite on several
versions of python.
There are >2000 unit tests, and it will take some time to run all of them.
Credits
-------
This sofware has been developed and is being maintained by the `Uncertainty Quantification Group <http//uqgroup.mit.edu>`_ at MIT, under the guidance of Prof. Youssef Marzouk.
**Developing team**
| Daniele Bigoni – [`www <http://limitcycle.it/dabi/>`_]
| Alessio Spantini
| Rebecca Morrison
| Ricardo M. Baptista
|
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"description": "==============\nTransport Maps\n==============\n\nThis package provides basic functionalities for the construction of monotonic transport maps.\n\n* `Binary (PyPi) <https://pypi.python.org/pypi/TransportMaps>`_\n* `Source (Bitbucket) <https://bitbucket.org/dabi86/transportmaps>`_\n* `Homepage <http://transportmaps.mit.edu/docs/>`_\n* `Q&A & Bug-tracking <https://bitbucket.org/dabi86/transportmaps/issues?status=new&status=open>`_\n\nSupported systems\n-----------------\n\n* \\*nix like OS (Linux, Unix, ...)\n* Mac OS\n\nOther operating systems have not been tested and they likely require a more complex procedure for the installation (this includes the Microsoft Windows family..).\n\nWe reccommend to work in a virtual environment using `virtualenv <https://virtualenv.readthedocs.io/en/latest/>`_ or `Anaconda <https://www.continuum.io/why-anaconda>`_.\n\nInstallation requirements\n-------------------------\n\n* `gcc <https://gcc.gnu.org/>`_ (or an alternative C/C++ compiler)\n* `gfortran <https://gcc.gnu.org/fortran/>`_ (or an alternative Fortran compiler)\n\nAutomatic installation\n----------------------\n\nFirst of all make sure to have the latest version of `pip <https://pypi.python.org/pypi/pip>`_ installed\n\n $ pip install --upgrade pip\n\nThe package and its python dependencies can be installed running the command:\n\n $ pip install --upgrade TransportMaps\n\nIf one whish to enable some of the optional dependencies:\n\n $ pip install --upgrade TransportMaps[SUITESPARSE]\n\nThese options will install the following modules:\n\n* DOLFIN -- dolfin package for Partial Differential Equations\n\n* SUITESPARSE -- scikit-sparse\n\n * This requires `scitik-sparse <https://github.com/scikit-sparse/scikit-sparse>`_ and the package `libsuitesparse-dev <https://packages.ubuntu.com/focal/libsuitesparse-dev>`_\n\n* HMC -- Hamiltonian Monte Carlo `pyhmc <https://pythonhosted.org/pyhmc/>`_ (it requires having numpy and cython already installed)\n\nRunning the Unit Tests\n----------------------\n\nUnit tests are available and can be run through the commands:\n\n >>> import TransportMaps as TM\n >>> TM.tests.run_tests()\n\nOr directly using the bash command:\n\n $ tmap-run-tests\n\nThe Git repository also contains a docker-compose configuration file to test the whole suite on several\nversions of python.\n\nThere are >2000 unit tests, and it will take some time to run all of them.\n\nCredits\n-------\n\nThis sofware has been developed and is being maintained by the `Uncertainty Quantification Group <http//uqgroup.mit.edu>`_ at MIT, under the guidance of Prof. Youssef Marzouk.\n\n**Developing team**\n\n| Daniele Bigoni \u2013 [`www <http://limitcycle.it/dabi/>`_]\n| Alessio Spantini\n| Rebecca Morrison\n| Ricardo M. Baptista\n|\n",
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