Scientific Computing

Fortran 2018 added syntax useful for polymorphism including select rank, which allows rank polymorphism and select type, which allows class polymorphism. For reference, GCC ≥ 7 has select type and GCC ≥ 10 has select rank. Intel oneAPI supports both.

To be compatible with older compilers as well as handle cases where it’s simply more concise to use Fortran 2003 generic procedure interfaces, each procedure must be unambiguously distinguishable. Several criteria make a procedure distinguishable. Here we focus on having at least one non-optional argument that is TKR-distinct (Type, Kind, Rank).

Notice that this example has the first variable non-optional to make the procedures TKR-distinct.

module mod1

implicit none (type, external)

interface manyranks
  procedure m0,m1
end interface manyranks

private
public :: manyranks

contains

subroutine m0(val1, val2)
real, intent(in) :: val1
real, intent(in), optional :: val2
!! omitted code
end subroutine m0

subroutine m1(val1, val2)
real, intent(in) :: val1(:)
real, intent(in), optional :: val2(:)
!! omitted code
end subroutine m1

end module mod1

The code will compile as above. If you add optional to both “val1”, the compilation will fail like:

• GCC: Ambiguous interfaces in generic interface 'manyranks' for 'm0' at (1) and 'm1' at (2)
• Intel: #5286: Ambiguous generic interface MANYRANKS: previously declared specific procedure M0 is not distinguishable from this declaration. [M1]

Although this example used rank polymorphism, the same issue arises when using any of type, kind or rank (TKR) generic procedures–there must be an unambiguous resolution with at least one non-optional argument. This polymorphism is implemented at runtime, and so there is no guarantee of non-ambiguity when all arguments are optional.

CMake add_test() can be dynamically set by arbitrarily complex foreach(), if(), etc. logic. A subset of tests can be configured dynamically after the tests are declared. A list of tests enabled is retrieved by the TESTS directory property. The variable “test_names” contains all the test names previously added in the CMakeLists.txt via “add_test()” in a list in the DIRECTORY scope.

get_property(test_names DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR} PROPERTY TESTS)

set_property(TEST ${test_names} PROPERTY TIMEOUT 60)

get_property(test_names GLOBAL PROPERTY TESTS) will return an empty list–DIRECTORY scope must be used.

CMake has a corresponding meta package system CPack, which generates configuration files for numerous packaging systems. Distributing code and binary executablees and libraries to users without requiring them to compile a project is done via these packages. CPack creates these binary packages like Windows .msi, Linux .deb/.rpm, macOS .dmg, etc. CPack also creates traditional source archives as are also generated by GitHub Releases, but with fine-grained control of the contents.

Assuming the PROJECT_BINARY_DIR is “build”, CPack generates build/CPackConfig.cmake for binary packages and build/CPackSourceConfig.cmake for source packages. CPackConfig.cmake is generated according to install() commands in the CMakeLists.txt files of the project.

Note that in general “install()” DESTINATION should always use relative paths. CPack ignores install() items with absolute DESTINATION.

CPackSourceConfig.cmake works the opposite way–it includes everything not excluded by CPACK_SOURCE_IGNORE_FILES, so we make a file cmake/.cpack_ignore with regex excluding non-source files. As a last step at the end of the main CMakeLists.txt after all install(), we include cmake/cpack.cmake:

As usual:

cmake -B build
cmake --build build

The distribution packaged .zip / .tar.gz files under build/package are generated by:

cpack --config build/CPackSourceConfig.cmake

cpack --config build/CPackConfig.cmake

These can be built by the CI system and uploaded for distribution on GitHub Releases, etc. by configuring the .github/workflows/ci.yml accordingly.

In general, programs don’t usually print to console the integer return code from the main procedure. The program may well print some message indicating success or failure, but maybe not. When calling executables from a compiled or scripted language such as Fortran, C, Python or Matlab, it’s often vital to know the value of the integer return code as a signal that the program thought it was successful or not. Further, some program crashes do not emit any console text, and could make the user think the program was successful.

To help eliminate doubt, issue a command to print the last error code to console when working with command line programs. The method to print this integer code depends on the shell. From Terminal / Command Prompt, the return code from the last command is printed by:

• Unix-like shell: echo $?
• Windows command prompt: echo %errorlevel%
• PowerShell: echo $lastexitcode

The HDF5 format can be used for almost anything, including image stacks. To signal graphical interactive HDF5 viewers that a dataset is an image stack, add HDF5 image metadata, as in these Python and Matlab examples. HDFview will show an optional video player for this dataset due to the image metadata added.

Most CI systems will not build if a git commit -m message includes a string like [skip ci]. For example:

git commit -m "update install docs [skip ci]"

Some CI systems have additional custom commit keywords that allow skipping only that CI:

• GitHub Actions: [skip actions]
• Azure Pipelines: [skip azp]
• Travis-CI: [skip travis]
• AppVeyor: [skip appveyor]
• CircleCI: [skip circle]
• GitLab: git push -o ci.skip git push option
• Bitbucket [skip ci]

Google Shared Drive is for groups of people with Gmail addresses (corporate or personal). Public folder or limited access link-sharing of folders and files in Shared Drives are possible as with personal Google Drive. Moving files is done in bundles of files, not by folder. Google Drive for desktop works for Shared Drives like individual Google Drive for read/write access. Google Shared Drives and personal Google Drive can be synced with rclone, which is especially handy for HPC and cloud computing. If using Windows Subsystem for Linux, keep the files on the native Windows filesystem.

Two of the most important aspects of a build system are generation speed and build/rebuild speed. For non-trivial projects in any compiled language, build speed can be a significant pain point. Build speed is the time it takes to first build project targets. Rebuild speed is the time taken on subsequent builds of a target, where most build artifacts can be reused, but the dependencies and sources must be scanned for changes since last build.

Even for medium projects, the seconds lost rebuilding add up lost productivity for developers. CMake and Meson are among the fastest generators, while SCons and Autotools are among the slowest. CMake and Meson have full support for Ninja. Ninja is generally significantly faster than GNU Make, particularly for the case where only a few out of very many files need to be rebuilt.

Meson is a Python program without external dependencies. It’s easy to run the latest development snapshot or track Git changes as a result.

pip install meson ninja

Since Meson generates Ninja build files, we installed Ninja as well. With Meson, the code build process goes like:

meson setup build

meson compile -C build

CMake has existed since the early 2000’s, and has broad usage. Build a CMake project like:

cmake -B build

cmake --build build

HTML plotting is important for communicating key data to colleagues, the general public and policymakers. HTML plots allow easy sharing of interactive data plots to any web browser. There are numerous HTML plotting methods available from Python. These methods are completely open source and work in any web browser. The HTML file can be shared by several means:

• email attachment
• embedded in a webpage as an HTML5 iframe
• Ipython notebook
• hosted plotting service (Plotly, figshare, et al)

Matplotlib HTMLWriter can plot animated sequences. More powerful HTML plotting capability requires mpld3. Virtually any Matplotlib plot can be converted to HTML for display in any web browser. mpld3 uses HTML and D3.js to animate the plots.

from matplotlib.pyplot import figure
import mpld3

fig = figure()
ax = fig.gca()
ax.plot([1,2,3,4])

mpld3.show(fig)

mpld3.show() converts any Matplotlib plot to HTML and opens the figure in the web browser. mpld3.save_html(fig,'myfig.html') method saves figures to HTML files for archiving or uploading as an iframe to a website, etc. D3.js enabled interactive elements are available from the mpld3 API. Note the template_type='simple' keyword for .save_html(), which can increase robustness across web browsers.

The open source Plotly library can plot completely offline, without Plotly servers. Plotly can make offline Python plots that work in any web browser. Plotly offline plots don’t need IPython/Jupyter, although you can certainly use them as well.

Offline plotting is important as it doesn’t rely on external proprietary services that could prevent future users from making plots. The Plotly API is available for Python, R, Matlab, JavaScript, Scala and a growing number of other programming languages. Plotly examples show the simple syntax. When using plain Python, be sure to use plotly.offline.plot() as plotly.offline.iplot (iplot) will silently fail to do anything!

The Trendnet TEG-S80g version 3.0 - 4.1 lack LED speed indicators, with only active (plugin detection) indicators for each port.

The TRENDnet TEG-S80g model has been reliable for me across a decade of use in small instrument networks with challenging physical environments.