Visual Studio Code is a free open source code editor with numerous extensions supporting most code languages, including for proprietary languages like Matlab. Fortran is well-supported by Visual Studio Code using the Modern Fortran extension, wrapping Gfortran as a linting tool.
Simulations with spatial grids in 3D can be visualized via scatter plots. The grid may have irregular spacing such that each of the x, y, z dimensions is itself a 3-dimensional array. This can be visualized in Matlab or Python by reshaping the 3D arrays to a vector in the plotting command.
Scatter plots are one way to visualize 3D data in Matlab.
scatter3(x(:), y(:), z(:))Python Matplotlib has several 3D visualization methods. Matplotlib scatter() also requires 1D vectors, which can be obtained at O(0) cost by the Numpy ndarray ravel method.
from matplotlib.pyplot import figure, show
ax = figure().gca()
ax.scatter(x.ravel(), y.ravel(), z.ravel())
show()We use the free Poppler tools instead of using Acrobat, despite having a license. Extracting one or more pages from a PDF file can be done with paid Adobe Acrobat. The free Adobe Reader or FoxIt Reader cannot extract pages. Adobe Acrobat is a large and cumbersome program that installs startup daemons that are not trivially disabled.
To extract pages 2 to 3 from in.pdf using Poppler:
pdfseparate -f 2 -l 3 in.pdf out.pdfNote: you must leave a space after -f and -l as shown.
Poppler is installed by:
apt install poppler-utilsbrew install popplerpopplerAn alternative to Poppler is GhostScript can also extract pages from PDF, but it’s a more complicated command:
gs -sDEVICE=pdfwrite -dFirstPage=2 -dLastPage=3 -dNOPAUSE -dSAFER -dBATCH -sOutputFile=out.pdf in.pdfRelated: extract raw full-quality images from PDF
GitHub CodeQL semantically analyzes Python code for security issues. Also, CVE Lists are checked vs. your GitHub repo’s dependency graph. CodeQL can install the Python package for more fidelity.
This approach finally fixes the concerns we had with the previous implementation that simply did CVE scans versus dependency graphs. The prior method of extracting dependencies did not work for modern Python packages. The new CodeQL method is much more robust and useful.
While HDF5 offers support for symbolic links (hard, soft and external), NetCDF4 does NOT support symbolic links.
The Fortran and C HDF5 API uses H5Lcreate_soft to create soft links. In h5fortran, soft links are created by:
call h%softlink(target, link)The Python h5py interface is a popular and robust means to access HDF5 files from Python. h5py.SoftLink allows creating soft links.
The Solaar program manages connections with Logitech Unifying receiver on Linux, including pairing/unpairing. This means wireless keyboards and mice, including wireless trackballs work well on Linux. Logitech wireless firmware updates are provided seamlessly in Linux. The Unifying receiver “just works” on Linux upon plugging in, with trackballs being recognized as an HID device.
Solaar provides a GUI for: pairing/unpairing, configuring buttons, monitoring battery level, and checking firmware version of Unifying receiver and connected devices. Logitech Unifying receivers can be paired with multiple devices. This allows one to carry a laptop from home to office without dragging the wireless keyboard or mouse along.
The 0-based indexing of Python / Numpy versus the 1-based indexing of Matlab is perhaps the most obvious difference when working between the languages. Other, more subtle defaults come into play and may not be immediately caught within functions except by manual checks.
In atmospheric science among other fields, cumulative integration implemented as cumulative trapezoidal numerical integration is a common function named “cumtrapz”.
Distinctive behavior between Matlab
cumtrapz
and
scipy.integrate.cumulative_trapezoid
might not be immediately caught inside a function, although it’s obvious when manually checking.
Specifically, SciPy scipy.integrate.cumulative_trapezoid needs the initial=0 argument to match Matlab.
Let’s show this by example:
Example: given input:
x = [-2.5, 5, 10]Matlab (or GNU Octave) outputs:
y = cumtrapz(x)
[0, 1.25, 8.75]scipy.integrate.cumulative_trapezoid output one element less than the input by default:
scipy.integrate.cumulative_trapezoid(x)
[1.25, 8.75]To match Matlab output from SciPy, add the initial=0 argument:
scipy.integrate.cumulative_trapezoid(x, initial=0)
[0, 1.25, 8.75]CMake is recommended to build HDF5 in general. CMake is required to build HDF5 on Windows. Trying to use Autotools to build HDF5 may encounter needless trouble compared to CMake.
HDF5 builds test executables that don’t pickup CMake environment variables.
Set compiler variables CC and FC in the shell, particularly when using non-system-default compilers.
Avoid this issues by building HDF5 with CMake.
This creates static and dynamic HDF5 libraries under the user install prefix–we don’t show the library suffixes for simplicity. Note: the *stub files may not be present.
hdf5/lib/libhdf5
hdf5/lib/libhdf5_fortran
hdf5/lib/libhdf5_hl
hdf5/lib/libhdf5_hl_fortran
hdf5/lib/libhdf5_hl_f90cstub
hdf5/lib/libhdf5_f90cstubThe Fortran .mod files that need to be included are under
hdf5/include/Tell CMake to use this HDF5 from the user project:
cmake -B build -DHDF5_ROOT=~/.local/hdf5Windows Explorer is available from PowerShell in the desired directory with a simple command like:
explorer $home/code/myprojAutomatic variable $home is the user’s home directory regardless of operating system.
The WSJT-X program enables popular digital modes like FT8 and FT4 among several other modes. WSJT-X is generally easier to use than FLdigi, another popular digital program that handles a wide range of other ham radio data modes. WSJT-X decodes multiple signals at once, and discriminates between overlapping signals. WSJT-X includes multiple high performance digital modes, each tuned for challenging propagation conditions from LF to microwave.
Control transmitter RF output power with the sound card output level, NOT with radio RF power control. Optimize transmitter RF cleanliness by:
If the radio transmitter is driven into ALC by using RF power control to limit transmitter level, the radio will splatter, interfering with adjacent frequencies. While some operators use QRP ≤ 5 watt transmit power for WSJT-X supported modes, many others use 25 .. 100 watts. This accounts for the discrepancy one might experience in a QSO between the received signal strength by each operator. Some use a very minimal amount of RF power for a “kilometers per watt” challenge.
When using CAT control for PTT, RFI (for example, common mode RF interference) can cause the radio to fall out of transmit, even with very low RF transmit power like 100 milliwatts. With the compromise HF antennas enabled by these efficient digital modes, one must be just as mindful of proper grounding and RFI as with a 100 watt SSB or CW station. Diagnosing this problem can be done by setting the transmitter power to as low as measurable, and seeing if the radio still drops out of transmit. If so, then the problem is likely RFI or common mode RF on the USB control cables.
Older radios may have too-narrow receive filters to capture the whole audio passband used by modern data-friendly transceivers. If the radio has “IF shift” or “pass band tuning”, it might receive from 400-2800 Hz. The “split” transmit feature of WSJT-X shifts the radio RF transmit frequency and audio frequency to optimize the transmit audio passband to minimize transmitted audio harmonics.
A radio that receives on multiple HF bands simultaneously greatly increases the data gathering capability for ionospheric studies. SDRs oriented toward “traditional” ham radio use typically receive one or two RF bands at once. Simultaneous multi-band reception can be accomplished with a bank of SDR receivers or a broadband direct-sampling SDR.