Thread: [python-control-announce] python-control 0.7.0 now available

Version 0.7.0 of the python-control toolbox is now available for download on SourceForge, as well as via pip and conda.  This version was completed by in April 2015 (sorry for the delays in posting!).  A short list of changes is appended to the bottom of this message.

Mac users can now install slycot (and python-control) without needing a FORTRAN compiler, simply using

  pip install slycot
  pip install control

This also works on linux, but it builds from source => you still need a FORTRAN compiler. To install in a user directory, use 'pip install —user …’.

Both slycot and python-control can also be installed with conda, again without needing a fortran compiler:

  conda config --add channels http://conda.binstar.org/cwrowley
  conda install slycot
  conda install control

The conda install works on both Mac and Linux.

Documentation is now available at

  http://python-control.readthedocs.org/

Thanks to Clancy Rowley, Rene van Paassen, James Goppert, Scott Livingston and others who contributed to this release.

-richard

Changes since previous release (0.6d):

* Fixes to docstrings and examples (multiple)
* Bug fix to gain margin calculation for phase crossing through zero
* c2d functionality added for MIMO state-space systems
* Faster versions of freqresp for transfer functions and state space models
* Fixed bug in dare so that it returns stabilizing solution
* Faster version of force_response (lsim)
* Installation via pip and conda
* Gain vector now optional in root_locus

A full list of changes is available on https://github.com/python-control/python-control/commits/master.

Version 0.8.0 of the python-control toolbox is now available for download on GitHub, as well as via pip and conda.  A short list of changes is appended to the bottom of this message.

Installation instructions:

 pip install slycot	# optional
 pip install control

You should get slycot v0.3.0 and python-control v0.8.0.  In order to install slycot via pip you will need to have a Fortran compiler on your system.  Note that python-control depends on numpy, scipy, and matplotlib.

Binary installations are available for Linux and Mac platforms using conda and conda-forge:

  conda install -c conda-forge control

Slycot should be installed automatically, without the need for a Fortran compiler.  (Unfortunately, conda-forge does not yet support Fortran for Windows builds => you have to install from source.)

Documentation is available at

   http://python-control.readthedocs.org/

Thanks to Jason Moore, Rene van Paassen, Rory Yorke, Yuichi Nagayama, Mitchell Clement, Kurt Sansom, Jed Frey, gonmolina, Alessio Russo, Joseph Slater, Lorenz Halt, Tyler Veness and others who contributed to this release.

-richard

Changes since previous release (0.7.0):

* Updated and corrected docstrings 
* Better support for MIMO systems in `pole()` and `zero()` functions 
* Support for SciPy-1.0.0 updates to the `scipy.signal` module that is used for some python-control functions and support for `numpy` data types (int32, int64, etc) in all python-control functions 
* Support for static gains (0D state space systems and constant transfer functions)
* New functions `obsv` (observable canonical form), `augw` and `mixsyn` (mixed sensitivity synthesis)

* Full list of merged pull requests(https://github.com/python-control/python-control/pulls?q=is%3Amerged+milestone%3A0.8.0+is%3Apr

* Full list of commits(https://github.com/python-control/python-control/compare/v0.7.0...0.8.0

* GitHub release page: https://github.com/python-control/python-control/releases/tag/0.8.0

Version 0.8.1 of the python-control toolbox is now available for download on GitHub, as well as via pip and conda.  A short list of changes is appended to the bottom of this message.

Installation instructions:

 pip install slycot # optional
 pip install control

You should get slycot v0.3.0 and python-control v0.8.1.  In order to install slycot via pip you will need to have a Fortran compiler on your system.  Note that python-control depends on numpy, scipy, and matplotlib.

Binary installations are available for Linux and Mac platforms using conda and conda-forge:

 conda install -c conda-forge control

Slycot should be installed automatically, without the need for a Fortran compiler.  (Unfortunately, conda-forge does not yet support Fortran for Windows builds => you have to install from source.)

Documentation is available at

  http://python-control.readthedocs.org/

Thanks to

-richard

Changes since previous release (0.8.0):

This release fixes a number of small issues and adds a few new features:

* A new canonical function modal_form can be used to convert a state space system to modal form (ipa-lth)
* Improved pole placement algorithm place_varga (rabraker)
* Bug fix in computing poles of MIMO transfer functions (murrayrm)
* Indexing of MIMO transfer functions (hungpham2511)
* Additional small bug fixes (adm78, stertingen)
* Documentation updates and fixes (joaoantoniocardoso, murrayrm)

* List of all changes in this release: https://github.com/python-control/python-control/compare/0.8.0...0.8.1

Version 0.8.2 of the python-control toolbox is now available for download on GitHub, as well as via pip and conda.  This is mainly a maintenance release with lots of small bug fixes and performance improvements, plus a few new features (dynamic sisotool, latex output for state space systems in Jupyter notebooks, step_info, etc).  A short list of changes is appended to the bottom of this message.  

Installation instructions:

pip install slycot	# optional
pip install control

You should get slycot v0.3.3 and python-control v0.8.2.  In order to install slycot via pip you will need to have a Fortran compiler on your system.  Note that python-control depends on numpy, scipy, and matplotlib.

Binary installations are available using conda and conda-forge:

conda install -c conda-forge control

Slycot should be installed automatically, without the need for a Fortran compiler (support for Windows builds of slycot are coming soon; stay tuned…).

Documentation is available at

 http://python-control.readthedocs.org/

NOTE FOR MacOS USERS: There are some issues with the way that the matplotlib backend works and some people (including me -:) have noticed crashes with the default settings using conda downloads.  This appears to be a matplotlib/MacOS issue, which you can fix it by setting the MPLBACKEND environment variable to TkAgg

 export MPLBACKEND="TkAgg” 

More info here: https://matplotlib.org/faq/osx_framework.html

-richard

* Fixed a number of small bugs , including discrete time simulations (bnavigator), integer values in state space matrics (don4get), missing zeros on pole/zero plots (Sup3rGeo)

* First cut at a new step_info() function, similar to MATLAB (joaoantoniocardoso)

* Added a new function for dynamically setting gain in sisotool (icam0)

* Anti-stabilizing solutions for Riccati equations are now available (dbacc)

* Printout out state space systems in Jupyter notebooks now formats using LaTeX (alchemyst)

* Additional performance and visualiation improvements: _remove_useless_states (adm78), margins on Bode plots ( icam0), improved damping lines in discrete pzmap (Sup3rGeo)

* List of all changes in this release: https://github.com/python-control/python-control/compare/0.8.1...0.8.2

* Special thanks to Jason Moore (moorepants) for help with debugging the conda-forge recipe.

Version 0.8.2 of the python-control toolbox is now available for download on GitHub, as well as via pip and conda.  This is mainly a maintenance release with lots of small bug fixes and performance improvements, plus a few new features (dynamic sisotool, latex output for state space systems in Jupyter notebooks, step_info, etc).  A short list of changes is appended to the bottom of this message.  

Installation instructions:

pip install slycot	# optional
pip install control

You should get slycot v0.3.3 and python-control v0.8.2.  In order to install slycot via pip you will need to have a Fortran compiler on your system.  Note that python-control depends on numpy, scipy, and matplotlib.

Binary installations are available using conda and conda-forge:

conda install -c conda-forge control

Slycot should be installed automatically, without the need for a Fortran compiler (support for Windows builds of slycot are coming soon; stay tuned…).

Documentation is available at

 http://python-control.readthedocs.org/

NOTE FOR MacOS USERS: There are some issues with the way that the matplotlib backend works and some people (including me -:) have noticed crashes with the default settings using conda downloads.  This appears to be a matplotlib/MacOS issue, which you can fix it by setting the MPLBACKEND environment variable to TkAgg

 export MPLBACKEND="TkAgg” 

More info here: https://matplotlib.org/faq/osx_framework.html

-richard

* Fixed a number of small bugs , including discrete time simulations (bnavigator), integer values in state space matrics (don4get), missing zeros on pole/zero plots (Sup3rGeo)

* First cut at a new step_info() function, similar to MATLAB (joaoantoniocardoso)

* Added a new function for dynamically setting gain in sisotool (icam0)

* Anti-stabilizing solutions for Riccati equations are now available (dbacc)

* Printout out state space systems in Jupyter notebooks now formats using LaTeX (alchemyst)

* Additional performance and visualiation improvements: _remove_useless_states (adm78), margins on Bode plots ( icam0), improved damping lines in discrete pzmap (Sup3rGeo)

* List of all changes in this release: https://github.com/python-control/python-control/compare/0.8.1...0.8.2

* Special thanks to Jason Moore (moorepants) for help with debugging the conda-forge recipe.

This is excellent.  Keep up the good work.

Will the conda install work in windows?  If not, what would it take to make
it work?  This would be great for my students.


--
Ryan Krauss, Ph.D.
Associate Professor
Mechanical Engineering
Southern Illinois University Edwardsville

On Sun, Oct 18, 2015 at 12:07 PM, Richard Murray <mu...@cd...>
wrote:

> Version 0.7.0 of the python-control toolbox is now available for download
> on SourceForge, as well as via pip and conda.  This version was completed
> by in April 2015 (sorry for the delays in posting!).  A short list of
> changes is appended to the bottom of this message.
>
> Mac users can now install slycot (and python-control) without needing a
> FORTRAN compiler, simply using
>
>   pip install slycot
>   pip install control
>
> This also works on linux, but it builds from source => you still need a
> FORTRAN compiler. To install in a user directory, use 'pip install —user …’.
>
> Both slycot and python-control can also be installed with conda, again
> without needing a fortran compiler:
>
>   conda config --add channels http://conda.binstar.org/cwrowley
>   conda install slycot
>   conda install control
>
> The conda install works on both Mac and Linux.
>
> Documentation is now available at
>
>   http://python-control.readthedocs.org/
>
> Thanks to Clancy Rowley, Rene van Paassen, James Goppert, Scott Livingston
> and others who contributed to this release.
>
> -richard
>
> Changes since previous release (0.6d):
>
> * Fixes to docstrings and examples (multiple)
> * Bug fix to gain margin calculation for phase crossing through zero
> * c2d functionality added for MIMO state-space systems
> * Faster versions of freqresp for transfer functions and state space models
> * Fixed bug in dare so that it returns stabilizing solution
> * Faster version of force_response (lsim)
> * Installation via pip and conda
> * Gain vector now optional in root_locus
>
> A full list of changes is available on
> https://github.com/python-control/python-control/commits/master.
>
>
>
>
> ------------------------------------------------------------------------------
> _______________________________________________
> python-control-announce mailing list
> pyt...@li...
> https://lists.sourceforge.net/lists/listinfo/python-control-announce
>