nullptr is a better defined replacement for NULL in the
C++11 standard
in header <cstddef> and in the
C23 standard
in header <stddef.h>.
Include these system headers in source code to be platform independent. Modern version of compilers such as GCC have been refining internal includes to have narrower include scope.
The basic structure for a rapidly updating animated plot with Matplotlib, without using
matplotlib.animation
is described below for imshow() and pcolormesh().
NOTE: matplotlib.pyplot.draw()
only takes effect on the CURRENT (most recently called) axes.
If updating multiple axes, call draw() after each set of axes is modified.
Both examples assume:
from matplotlib.pyplot import figure, draw, pause
# imgs is a N x X x Y image stack
fg = figure()
ax = fg.gca()If the pause() statement time is too small, Matplotlib may not update the plot at all.
Consider your display may not be able to update faster than about pause(0.01).
More examples with speed comparison showing that imshow is faster than pcolormesh generally.
imshow:
h = ax.imshow(imgs[0]) # set initial display dimensions
for img in imgs:
h.set_data(img)
draw(), pause(1e-3)pcolormesh:
h = ax.pcolormesh(imgs[0]) # set initial display dimensions
for img in imgs:
h.set_array(img.ravel())
draw(), pause(1e-3)Intel oneAPI 2023 provides GNU-like and MSVC-like compiler drivers for Windows. This is a good thing, but CMake had assumptions that only C++ compiler driver icpx OR icx would be present. CMake 3.25.2 fixed oneAPI 2023 compiler detection on Windows. Long term, GNU-like support for Windows may also come. Until you update CMake, you can workaround this issue of C++ oneAPI compiler detection on Windows by configuring the CMake project like:
cmake -Bbuild -DCMAKE_CXX_COMPILER=icxNumerous programs can write data in VTK file formats accessible from VTK-compatible programs such as ParaView. One of these format is “.vtu”. Adaptive Mesh Refinement (AMR) library ForestClaw has its own routines to write VTK data.
The base 2D VTK writing is done in “forestclaw/src/patches/clawpatch/fclaw2d_clawpatch_output_vtk.c”. The function “fclaw2d_vtk_write_header()” writes the XML header for UnstructuredGrid.
Visual Studio executables built in Debug mode by default pop up modal debug windows if an unhandled exception occurs. This can be annoying to developers particularly when unit testing a project. On remote systems, modal windows can become a real issue if the modal window is accidentally off-screen. In such cases it is sometimes hard to get the modal window back to the main desktop to be closed.
Adding a few lines of code to the C++ program works around this issue by redirecting the error text to stderr console and not popping up the modal window. _CrtSetReportMode keeps the model window from appearing. _CrtSetReportFile redirects the message text to stderr so that the message can be diagnosed.
This is also relevant to continuous integration systems such as GitHub Actions, which may hang with an unrealized modal dialog otherwise.
#ifdef _MSC_VER
#include <crtdbg.h>
#endif
int main(){
#ifdef _MSC_VER
_CrtSetReportMode(_CRT_ASSERT, _CRTDBG_MODE_FILE);
_CrtSetReportFile(_CRT_ASSERT, _CRTDBG_FILE_STDERR);
_CrtSetReportMode(_CRT_WARN, _CRTDBG_MODE_FILE);
_CrtSetReportFile(_CRT_WARN, _CRTDBG_FILE_STDERR);
_CrtSetReportMode(_CRT_ERROR, _CRTDBG_MODE_FILE);
_CrtSetReportFile(_CRT_ERROR, _CRTDBG_FILE_STDERR);
#endif
// rest of program
}Visual Studio can detect memory leaks in programs with _CrtDumpMemoryLeaks. This minimal example doesn’t do the printout. Using these checks requires the project is build in Debug mode.
More complete working minimal example that prints the memory diagnostics with Visual Studio. On Linux, Valgrind can be used to detect memory leaks. Numerous other free memory checkers are available and work with CMake CTest frontend.
#ifdef _MSC_VER
#include <crtdbg.h>
#endif
int main(void){
char* c
c = malloc( 100 );
// unfreed memory, a deliberate leak
// near the end of the function to be checked
#ifdef _MSC_VER
_CrtDumpMemoryLeaks();
#endif
return 0;
}Matplotlib on any platform can use FFmpeg, Avconv or Mencoder to directly write lossy or lossless compressed movies created from sequences of plots.
Instead of creating hundreds of PNGs, or skipping plots and missing details, Matplotlib movies of a large sequence of plots is highly effective for many processes that evolve across time and/or space.
Alternatively, convert a stack of PNGs to AVI. It’s simpler and often faster and more robust to use Matplotlib.animation.
Quality: the default auto bitrate makes excessively compressed, blocky movies.
Override the default auto-bitrate with the following snippet:
import matplotlib.animation as anim
Writer = anim.writers['ffmpeg']
writer = Writer(fps=15, codec='mpeg4', bitrate=1e6)
#
with writer.saving(fg, fn,100):
# code to plot/update figure
writer.grab_frame(facecolor='k')In matplotlib_writeavi.py,
just four added lines of code do the AVI writing.
First line tells Matplotlib to use
FFmpeg.
Second line tells Matplotlib to make a lossless FFV1 video at 15 frames/sec.
One can optionally use codec='mpeg4', but lossy encoding can wash out details of plots.
Third line says to use 100 DPI (smaller DPI–smaller file and movie size).
import matplotlib.animation as anim
#...
Writer = anim.writers['ffmpeg']
writer = Writer(fps=15, codec='ffv1')
# ...
with writer.saving(fg, fn, 100):
# ...
writer.grab_frame(facecolor='k')For problems playing back the .avi file, try omitting the codec='ffv1' parameter.
Minimum AVI frame rate: less than 3 fps can invoke bugs on VLC. VLC has trouble with slow frame rate video from any source.
The macOS terminal defaults to UTF8. When SSHing into a macOS computer from a non-macOS computer, or any computer with a different locale, there may be problems running programs on the remote where locale is important. For example, a Linux system with “C” locale may cause .zip archive extraction on remote macOS to fail like:
Pathname cannot be converted from UTF-8 to current locale.
Locally on the macOS computer (or using Remote Desktop over SSH), check locale with:
% locale
LANG="en_US.UTF-8"
LC_COLLATE="en_US.UTF-8"
LC_CTYPE="en_US.UTF-8"
LC_MESSAGES="en_US.UTF-8"
LC_MONETARY="en_US.UTF-8"
LC_NUMERIC="en_US.UTF-8"
LC_TIME="en_US.UTF-8"
LC_ALL=while from Windows or Linux may result in:
% locale
LANG=""
LC_COLLATE="C"
LC_CTYPE="C"
LC_MESSAGES="C"
LC_MONETARY="C"
LC_NUMERIC="C"
LC_TIME="C"
LC_ALL=We resolved this issue by creating on the remote macOS computer a file “locale.sh” containing:
export LANG="en_US.UTF-8"
export LC_COLLATE="en_US.UTF-8"
export LC_CTYPE="en_US.UTF-8"
export LC_MESSAGES="en_US.UTF-8"
export LC_MONETARY="en_US.UTF-8"
export LC_NUMERIC="en_US.UTF-8"
export LC_TIME="en_US.UTF-8"then run one time when needed:
source ~/locale.shThis fixed an issue we had with CMake not extracting a .zip file for ExternalProject URL with the error noted at the top of this page.
Another workaround as noted above is to use Remote Desktop over SSH.
Python packaging can be described in pyproject.toml alone per PEP 621. These packages are installable in live developer mode:
python -m pip install -e .Or via PyPI like any other Python package. It can be most effective to put all project configuration, including Python package prerequisites in pyproject.toml alone as a single source of truth. pyproject.toml is human-readable and machine-parseable without first installing the package. Putting all package metadata into pyproject.toml instead of setup.py gives benefits including:
This is an example of a minimal pyproject.toml that works all alone, no other metadata files required, except perhaps MANIFEST.in for advanced cases.
The __version__ is contained in file mypkg/__init__.py as Python code:
__version__ = "1.2.3"pyproject.toml:
[build-system]
requires = ["setuptools>=61.0.0", "wheel"]
build-backend = "setuptools.build_meta"
[project]
name = "mypkg"
description = "really awesome package."
keywords = ["random", "cool"]
classifiers = ["Development Status :: 5 - Production/Stable",
"Environment :: Console",
"Intended Audience :: Science/Research",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
]
requires-python = ">=3.7"
dynamic = ["version", "readme"]
[tool.setuptools.dynamic]
readme = {file = ["README.md"], content-type = "text/markdown"}
version = {attr = "mypkg.__version__"}PEP8 checking via flake8 is configured in .flake8:
[flake8]
max-line-length = 100
exclude = .git,__pycache__,doc/,docs/,build/,dist/,archive/
per-file-ignores =
__init__.py:F401MANIFEST.in is used to specify external files installed.
Classifiers are optional and help projects indexing in PyPI and search engines. Classifiers must be from the official classifier trove or they will fail when uploading a package to PyPI.
Python can easily import Fortran code using f2py. See this f2py example setup.py.
If one is not using Git LFS but has a system with Git LFS installed, it can be useful to uninstall Git LFS to avoid problems like:
Remote "origin" does not support the Git LFS locking API. Consider disabling it with:
$ git config lfs.locksverify falseTo remove Git LFS, simply type:
git lfs uninstall