ParaMonte Python release notes

This project follows Semantic Versioning. To access the latest release of the package, visit the ParaMonte GitHub repository release page or the ParaMonte page on the Python Package Index.

Version 2.x.x

Version 2.5.2 – January 8, 2021

  • Minor enhancements to the checkForUpdate() function of paramonte module. This function now relies on a more robust method of latest-version-checking.

Version 2.5.1 – January 3, 2021

  • Minor enhancements and bug fixes.

Version 2.5.0 – January 1, 2021

Major enhancements

  • This release is a a major step toward further portability of the kernel routines of the ParaMonte::Python library. The kernel library dependencies are now properly handled and recognized at runtime without such aggressive actions as permanently redefining the environmental path variables, most importantly, PATH and LD_LIBRARY_PATH on Linux/macOS.

  • The ParaMonte::Python library is now capable of recognizing the existing MPI libraries such as MPICH and OpenMPI on user’s system and avoid further installation of a new MPI library if it is deemed unnecessary.

  • The ParaMonte kernel routines are now capable of handling user-input file paths that contain white-space (blank) or other exotic characters.

  • Enhancements to the build() function of the paramonte module.

Minor enhancements

  • Typo-fixes in the documentation of the library.

  • The ParaMonte::Python library packages for different Operating systems and processor architecture are now separate from each other. This change was made to lower the overall size of ParaMonte::Python by only keeping the relevant files in each packaging of the library. The current release contains three separate packages for ParaMonte::Python,

    • libparamonte_python_windows_x64,
    • libparamonte_python_darwin_x64,
    • libparamonte_python_linux_x64.

Version 2.4.0 – December 17, 2020

Major enhancements

  • This update presents several major performance, accuracy, and verification enhancements to the ParaMonte kernel routines, in particular, to the ParaDRAM sampler.

  • An extensive set of over 866 tests have been added that test all aspects of the ParaMonte kernel library.

  • The issue of Windows file locking, that led to the occasional crashes of the ParaDRAM and ParaDISE simulations in multiChain parallelism mode, is now resolved.

  • All overflow / underflow exceptions are now properly handled.

  • The ParaDRAM class in paramonte is now also available as Paradram and paradram, although the original label will remain the default preferred method of ParaDRAM object instantiation.

Minor enhancements

  • The interactive mode for displaying plots is now automatically on. The plots are not automatically displayed in ipython sessions.

Version 2.3.1 – November 15, 2020

Minor enhancements

  • Minor enhancements to the Kernel library build scripts and dependencies management.

Version 2.3.0 – October 29, 2020

Enhancements

  • The IO debugging info of all ParaMonte samplers have been enhanced. In cases of wrong syntax or syntax-breaking input values in the simulation output files, the error messages are now more informative and point directly to the exact location of of error in the input file.

  • The Integrated Autocorrelation (IAC) for sample refinement in ParaDRAM sampler of ParaMonte is now set to the average of all variables’ IAC values instead of the maximum IAC value. This will lead to less aggressive decorrelation of the final sample, which means significantly larger final sample sizes, without compromising the i.i.d. property of the final refined sample. This behavior can be reversed back to the original by specifying “max” or “maximum” along with the requested refinement method, SampleRefinementMethod = "batchmeans-max" or SampleRefinementMethod = "BatchMeans-maximum" (case-insensitive).

Version 2.2.5 – October 20, 2020

Minor enhancements

  • Enhancements to the messages for incompatible architecture.

Version 2.2.4 – October 18, 2020

Minor enhancements

  • The bug preventing the setting of SpecDRAM specifications is now fixed.

Version 2.2.3 – October 15, 2020

Minor enhancements

  • Further enhancements and corrections to the checkForUpdate() function of paramonte module.

Version 2.2.2 – October 14, 2020

Minor enhancements

  • The checkForUpdate() function of paramonte module now works fine when a newer version of the software is available.

Version 2.2.1 – October 11, 2020

Minor enhancements

  • Documentation enhancements and typo fixes.

Version 2.2.0 – October 11, 2020

Major enhancements

  • The ParaMonte::Python output file parsers method are now capable of parsing simulation output contents directly from the web. All that is needed, is to provide the link to the web file as the input file name to the simulation output file parser methods (e.g., readSample(), readChain(), readReport(), readProgress(), readRestart(), …).

Minor enhancements

  • A bug in the y-axis variable names in the heatmap plot is now fixed.

Version 2.1.1 – October 9, 2020

Minor enhancements

  • A Linux bug in the installation of the MPI library is now fixed.

Version 2.1.0 – October 3, 2020

Minor enhancements

  • A new simulation specification overwriteRequested has been added to all ParaMonte samplers. If True and the ParaMonte sampler detects an existing set of old simulation output files in the output path of the current simulation with the same names as the output file names of the current simulation, then, the ParaMonte sampler will overwrite the existing simulation files.

Version 2.0.9 – October 2, 2020

Minor enhancements

  • Minor correction to the value of __version__, now representing solely the version number.

  • A simple example-usage Python script is now added to the README.md file of the package.

Version 2.0.8 – September 29, 2020

Minor enhancements

  • Enhanced error messages for situations when the MPI library cannot be found on the system.

Version 2.0.7 – September 26, 2020

Minor enhancements

  • The guidelines for the installation of the MPI library on macOS and Linux have been improved.

Version 2.0.6 – September 25, 2020

Minor enhancements

  • The explicit dependencies on scipy, matplotlib, and seaborn are now removed from the PyPI setup file of the ParaMonte library as these are only required for the post-processing and visualizations of the simulation results. From now on, only numpy and pandas are the minimally-required Python modules, and practically, only numpy.

  • Two new functions verifyDependencyVersion() and getDependencyVersion() are now added to the library that can check for the existence of the ParaMonte library’s visualization dependencies and their required minimum versions.

  • The seaborn Python library has now decided to deprecate the distplot() function. The corresponding visualization method in the ParaMonte library has been now updated to a more appropriate name and underlying function.

Version 2.0.4 – September 22, 2020

Minor enhancements

  • The output of the plotting functions is now stored as a list in the currentFig temporary component of the visualization objects. This way, access to multiple individual objects on the active plot is maintained instead of only the last object. Overall, this is a minor change that will not cause any noticeable change in the behavior of the library in almost in all use cases.

  • A minor bug regarding the input value for the outputDelimiter attribute of the spec component of the ParaMonteSampler() class,
    used in the readTabular() internal method, is now fixed.

Version 2.0.3 – September 11, 2020

Minor enhancements

  • Minor enhancement to checkForUpdate() method of the paramonte module.

Version 2.0.2 – September 11, 2020

Minor enhancements

  • Minor enhancement to checkForUpdate() method of the paramonte module.

Version 2.0.1 – September 10, 2020

Minor enhancements

  • LGPL3 LICENSE is now switched to MIT LICENSE.md file.

  • A fix to the brew software installation now avoids the seemingly-unavoidable crash.

Version 2.0.0 – September 6, 2020

Major enhancements to the ParaMonte / ParaDRAM sampler interfaces

  • The entire ParaMonte Python interface library has been revamped. The new naming conventions, visualization, and computing tools are significantly nicer to deal with and in some cases, orders of magnitude faster than the previous major release.

  • The kernel density estimates and visualization tools are now on average 100 times or more faster than the previous release of the library.

  • Several new post-processing functionalities have now been added, such as the ability to seamlessly parse the contents of the output *_report.txt, *_restart.txt, and *_progress.txt simulation files, in addition to the other output files (*_sample.txt and *_chain.txt) that could be parsed in the previous versions.

  • The new major release also includes 3D visualization tools, such as 3D line, scatter, or line+scatter plots as well as fast 2D and 3D kernel density estimate contour plotting tools.

Minor enhancements

  • The simulation output files reading is now completely overhauled. In particular, the output file reader methods are now capable of handling input file paths that point to a directory. In such cases, it will search the input directory for files matching the requested file name pattern. If no input file is provided to the file reader methods, the current working directory will be search for the the potential simulation files that match the requested pattern.

  • The error-signaling behavior of the library now is very much controlled, that is, upon code failure, it does not automatically shutdown the Python kernel in Jupyter Notebooks. The library now simply throws an error message upon failing instead of restarting the environment.

  • The single value assignment to spec.targetAcceptanceRate component of a ParaDRAM object is now properly handled. For example, the following code is valid as expected, 
    import paramonte as pm
pmpd = pm.ParaDRAM()
pmpd.spec.targetAcceptanceRate = 0.23 # this is now valid
pmpd.spec.targetAcceptanceRate = [0.2, 0.3] # this is also valid, which limits the acceptance rate to the specified range
  • The minimum required dependency versions are now raised to the following, 
    python_requires = ">=3.5"
install_requires = [ "numpy>=1.18.0"
                   , "scipy>=1.4.0"
                   , "pandas>=1.0.0"
                   , "seaborn>=0.10.0"
                   , "matplotlib>=3.2.0"
                   ]

Version 1.x.x

Version 1.1.1 – June 7, 2020

Minor enhancements

  • The _ScatterLinePlot dangling class is removed from the package.

Version 1.1.0 – June 1, 2020

  • Major enhancements to the ParaMonte kernel library.
  • Major bug fixes in the ParaMonte Python library.
  • The ParaMonte kernel and Python interface versions are now reposted separately as components of the paramonte module.

Version 1.0.12 – April 6, 2020

  • Minor enhancements and bug fixes to the kernel routines.

Version 1.0.11 – April 4, 2020

  • Minor enhancements and bug fixes to the GridPlot.

Version 1.0.10 – March 28, 2020

  • Minor bug fix.

Version 1.0.9 – March 27, 2020

  • Minor enhancements.

Version 1.0.8 – March 27, 2020

  • Minor enhancements.

Version 1.0.7 – March 26, 2020

  • Minor corrections.

Version 1.0.6 – March 22, 2020

  • Minor bug fixes.

Version 1.0.5 – March 21, 2020

  • Minor bug fix.

Version 1.0.4 – March 20, 2020

  • support for macOS (Darwin) added.

Version 1.0.3 – February 13, 2020

  • Minor bug fixes.

Version 1.0.2 – February 13, 2020

  • Minor bug fixes to the parallel routines.

Version 1.0.1 – February 13, 2020

  • Minor bug fixes.

Version 1.0.0 – January 1, 2020 – Initial release

  • This is the first public release of the ParaMonte library.

New features

  • ParaDRAM sampler: Parallel Delayed-Rejection Adaptive Metropolis-Hastings Markov Chain Monte Carlo Sampler.
  • ParaMonte Interface to the Python Programming languages.
  • ParaMonte simulation-output visualization via the ParaMonte Python interface.


If you have any questions about the topics discussed on this page, feel free to ask in the comment section below, or raise an issue on the GitHub page of the library, or reach out to the ParaMonte library authors.