Popular compilers such as GCC, Clang, and Visual Studio support C++20 modules along with CMake.
We provide a CMake project of recent C++ language standad examples, including C++20 modules.
The C++ standard compiler support table is not always up to date. That’s why we make CMake projects to test compiler support of newer language standard features.
In Python Matplotlib or Matlab, making a plot with log-scaled (instead of default linear-scaled) axes can use the functions like “loglog()”, “semilogx()”, or “semilogy()”. Here we show the more general object-oriented syntax for each of a 2-D line “plot()” and 2-D pseudocolor “pcolor()” and then set the axes scale properties.
We arbitrarily use log abscissa scale and linear ordinate scale, but the same syntax applies to other combinations.
2-D line plot:
import numpy as np
from matplotlib.pyplot import figure, show
x = np.logspace(0, 10, 100)
y = np.log(x)**2
fg = figure()
ax = fg.gca()
ax.plot(x, y)
ax.set_xscale('log')
ax.set_xlabel('x')
ax.set_ylabel('y')
show()pseudocolor pcolormesh(): observe the stretching of the ticks along the y-axis. In some cases it’s helpful to set the axis limits manually to avoid whitespace past the last data point.
import numpy as np
from matplotlib.pyplot import figure, show
d = np.random.rand(10, 10)
x = np.linspace(1, 10, d.shape[0])
y = np.logspace(0, 1, d.shape[1])
fg = figure()
ax = fg.gca()
ax.pcolormesh(x, y, d)
ax.set_yscale('log')
ax.set_ylim(y[0], y[-1])
ax.set_xlabel('x')
ax.set_ylabel('y')
show()2-D line plot:
x = logspace(0, 10, 100);
y = log(x).^2;
fg = figure();
ax = axes(fg);
plot(ax, x, y);
ax.XScale = 'log';
xlabel(ax, 'x');
ylabel(ax, 'y');pseudocolor pcolor(): observe the stretching of the ticks along the y-axis. In some cases it’s helpful to set the axis limits manually to avoid whitespace past the last data point.
d = rand(10, 10);
x = linspace(1, 10, size(d, 1));
y = logspace(0, 1, size(d, 2));
fg = figure();
ax = axes(fg);
pcolor(ax, x, y, d);
ax.YScale = 'log';
xlabel(ax, 'x');
ylabel(ax, 'y');If one suspects a website has been compromised, don’t use a standard web browser to access the site as there could be zero-day malware on the site. Consider Terminal programs that don’t have JavaScript enabled like curl or lynx if necessary to browse the site, preferably from a VM or other isolated computing resource. These programs are also not immune from security vulnerabilities.
DNSViz helps visualize the DNS chain. Keep in mind DNS and nameserver updates can take minutes to hours to propagate.
macOS:
dscacheutil -q host -a name host.invalidLinux / macOS / WSL:
dig +trace host.invalidIf the DNS entries seem valid, consider that the web hosting server (that sends the HTML files to browser) may be compromised.
Matlab Compiler compiles existing .m Matlab script to run as an executable on another PC without Matlab. Matlab Compiler Runtime MCR is used on computers that don’t have Matlab to run the compiled Matlab code.
Caveats:
Compiling computer: ensure Matlab Compiler is installed:
assert(license('test', 'compiler') == 1)Example program “mymcc.m”:
function Y = mymcc()
X = 0:0.01:2*3.14;
Y = sin(X);
plot(X,Y)
title('Test of MCR')
xlabel('x')
ylabel('y')
disp('I ran an MCR program!')
endCompile “.m” file in Matlab:
mcc -m -v mymcc.mRun compiled Matlab program:
./run_mymcc.sh mymccI ran an MCR program!
and show a Matlab plot window showing a sine wave. Close the plot window to end the execution of your program.
Notes:
GNU Octave does not currently have the ability to compile “.m” files. Octave mkoctfile is to distribute C / C++ code that calls Octave functions–and ABI-compatible Octave must be installed on the user computers
There can be substantial speed boosts from Intel compilers with Intel CPUs. Intel oneAPI gives advanced debuggers and performance measurements. Intel oneMKL can give a significant speed boost to ABI-compatible compilers for certain math operations.
For Windows, use the oneAPI Command Prompt.
Otherwise, specify environment variables CC, CXX, FC to indicate desired compilers
via script:
Build with CMake:
cmake -B build
cmake --build buildTo see the compiler commands CMake is issuing, use
cmake --build build -vRefer to Intel Link Advisor for supported compiler / operating system / MKL combinations.
Get runtime confirmation that MKL is being used via
MKL_VERBOSE.
Linux:
MKL_VERBOSE=1 ./mytestWindows
set MKL_VERBOSE=1
mytest.exeThat gives verbose text output upon use of MKL functions. That runtime option does slow down MKL performance, so normally we don’t use it.
Native virtualization has a “guest” OS with the same CPU architecture as the “host” physical CPU. Non-native emulation generally runs slower than native virtualization. Non-native virtualization means a host computer (such as Apple Silicon) can emulate any supported CPU architecture. Apple Silicon is ARM64, but with virtualization such as UTM / QEMU the Apple Silicon CPU can emulate ARM32, x86_64, MIPS, RISC-V, PowerPC, and more within the container.
QEMU emulator is available on Homebrew for Apple Silicon and can emulate a different CPU architecture or run native architecture at nearly full performance. UTM is a containerized emulation based off of QEMU for iOS and macOS–like QEMU, the same CPU architecture is virtualized at near full performance, while non-native virtualization is emulated with slower performance. When creating a new virtual machine in UTM, the first questions include whether the VM will be virtualized (native) or emulated (non-native) and the CPU architecture. UTM works with native virtualized Windows 11 for ARM, Linux, and emulates many architectures, even old PowerPC macOS guest images.
VirtualBox is an open-source native virtualization application that generally targets x86_64 CPUs. VirtualBox “Developer Preview” for Apple Silicon is available from the Nightly Builds as “macOS/ARM64 BETA”. The Oracle developer notes that the VirtualBox Apple Silicon beta is not yet ready for production use.
Commercial paid Apple Silicon virtualization: these native virtualization applications are not open-source. They run native virtual machines on Apple Silicon including Windows 11 ARM.
HDF5 is a popular data container format, a filesystem within a file. Many programs supporting HDF5 like Matlab can read and plot data. It is useful to have a standalone simple data browser like HDFview.
HDFview from the HDF Group can read HDF5, NetCDF4, and FITS. HDFview enables editing (writing) as well as reading HDF5. One can simply download the HDFview binaries, or use package managers:
apt install hdfviewbrew install hdfviewViTables is a Python-based HDF5 GUI.
The Java-based PanoplyJ is available for macOS, Linux and Windows.
To ensure the user set compile flags take precedence by appearing later in the command line vs. CMake’s automatic flags, use add_compile_options to set language-specific or project-wide compiler options. target_compile_options is similar, but applies to only one target.
The
generator expression
$<$<COMPILE_LANGUAGE:C>:...> is used to only add the flag for the C language.
This is important as many compiler options are language-specific, particularly for C / C++ and Fortran projects.
If one truly needs to wipe out all CMake automatic flags, try settingCMAKE_
Matlab
function arguments validation
syntax is generally recommended over legacy validateattributes() and inputParser().
Function arguments validation specification coerces the input and/or output variables to the class declaration given if possible, and errors otherwise.
datetime.empty() that allows initializing an empty array.Matlab argument validation syntax coerces class at runtime.
Git uses the EDITOR environment variable to determine which text editor to use for commit messages. Since the commit message editing is typically small and simple, it may be desired to set a distinct text editor just for Git. This is done via Git global config:
git config --global core.editor "nano"