To allow a for loop to make an arbitrary number of overlaid plots in a single axes, we may wish to endlessly cycle colors using
itertools.
This technique only makes sense up to a few dozen cycles depending on the
Matplotlib color palette
but it can be better than just ending a loop after the palette is exhausted.
importitertoolsimportmatplotlib.pyplotaspltimportmatplotlib.colorsasmplcolorscolor_cycle = itertools.cycle(mplcolors.TABLEAU_COLORS)
xy = [(x, x**1.2) for x inrange(20)]
# toy dataax = plt.figure().gca()
for (x, y), color inzip(xy, color_cycle):
ax.scatter(x, y, color=color)
print(xy)
plt.show()
If an external program needs a subdirectory to create and load multiple files, Python
tempfile.TemporaryDirectory()
creates a temporary working directory.
shutil.copytree
is used to recursively copy all files if the call to the external program succeeds.
frompathlibimport Path
importtempfileimportsubprocessimportshutilimportuuidfile = Path(f"~/{uuid.uuid4().hex}.txt").expanduser()
# toy filefile.write_text("Hello World!")
with tempfile.TemporaryDirectory(ignore_cleanup_errors=True) as f:
shutil.copy(file, f)
subprocess.check_call(["cat", Path(f) / file.name])
new_dir = file.parent / f"{file.stem}"print(f"\n{file} Solved, copy to {new_dir}")
shutil.copytree(f, new_dir)
Public Git repo
clone via HTTPS and push via SSH
is fast and generally effective for security.
For private Git repos do Git clone over SSH.
Optionally,
SSH Agent
avoids the need to constantly type the SSH password.
Git clone with
Git over SSH
by simply replacing “https://” in the Git repo URL with “ssh://”.
Be sure to remove the trailing “.git” from the URL if present.
For example:
On Unix-like platforms, CMake variable
CMAKE_DL_LIBS
is populated to link with target_link_libraries(), providing functions like “dlopen” and “dladdr”.
For some libdl functions it’s necessary to also define “_GNU_SOURCE” like:
On Unix-like systems, the concept of
run path
is the search path for libraries used by a binary at runtime.
For Windows there is no separate Rpath, just PATH is used–necessary .dll files must be on PATH environment variable at the time of running a binary.
For Unix-like systems life can be easier since the Rpath is compiled into a binary.
Optionally, using
$ORIGIN in Rpath
allows relocating binary packages.
For CMake, set all the time – no need for “if()” statements.
Exporting symbols for MSVC-based compilers is necessary to generate a “example.lib” corresponding to the “example.dll”.
To have the installed CMake binaries work correctly, it’s necessary to set CMAKE_PREFIX_PATH at configure time.
That is, the configure-build-install sequence for shared library project in CMake is like:
The POSIX C type
ssize_t
is available on Unix-like systems in <sys/types.h>.
Windows Visual Studio
BaseTsd.h
has SSIZE_T.
However, ssize_t is POSIX, but not C standard.
It’s possible to define a signed size type “ssize_t” using “ptrdiff_t” for “ssize_t” in
C
and
C++.
Using ptrdiff_t instead of ssize_t is the practice of major projects like
Emacs.
size_t bit width is guaranteed by
C
and
C++
standards to have bit width not less than 16.
ptrdiff_t bit width is guaranteed by
C
standard to have bit width not less than 16, and
C++
standard to have bit width not less than 17.
This
example
shows how to use ssize_t across computing platforms.
As with HDF5 and h5py, using xarray
to_netcdf()
to write netCDF files can losslessly compress Datasets and DataArrays, but file compression is off by default.
Each data variable must have the compression option set to take effect.
We typically only compress variables of 2-D or higher rank.
Notes:
Specify format="NETCDF4", engine="netcdf4" to allow a broader range of data types.
if “chunksizes” is not set, the data variable will not compress. We arbitrarily made the chunk sizes half of each dimension, but this can be optimized for particular data.
“fletcher32” is a checksum that can be used to detect data corruption.
Setting “.attr” of a data variable will be written to the netCDF file as well. This is useful to note physical units, for example.
frompathlibimport Path
importxarraydefwrite_netcdf(ds: xarray.Dataset, out_file: Path) -> None:
enc = {}
for k in ds.data_vars:
if ds[k].ndim < 2:
continue enc[k] = {
"zlib": True,
"complevel": 3,
"fletcher32": True,
"chunksizes": tuple(map(lambda x: x//2, ds[k].shape))
}
ds.to_netcdf(out_file, format="NETCDF4", engine="netcdf4", encoding=enc)
The Python
imageio
package reads and writes numerous image formats and their metadata.
The time and location of citizen science images are often critical to their interpretation.
Not all cameras have GPS modules.
Not all cameras have sufficiently accurately set clocks (including time zone).
A typical metadata item of interest is “DateTimeOriginal”.
How this is defined and its accuracy is up to the
camera implementation.
We show the reading of image metadata in a few distinct ways.
importimageio.v3asiiofromsysimport argv
frompathlibimport Path
fn = Path(argv[1]).expanduser()
meta = iio.immeta(fn)
for k in ("DateTimeOriginal", "DateTimeDigitized", "DateTime"):
print(k, meta.get(k))
Consider that the timezone may need to be corrected.
By default, the typical directory listing command “ls” does not show paths that start with a dot.
That is, paths that start with a dot are hidden like “.ssh” or “.git” etc.
Most shells will list all paths including those with a leading dot by: