This is a demo
- Some markdown
I was curious to know what level of precision I could get when using
like, if it made any sense to try and sleep for say
It turns out, it does. On a Windows machine with a 1.6GHz processor, I get a sample rate such:
$ python3 time_sampling.py 6 Average step over 1000000 (100.0%) iterations: 2.77e-07
Two methods - either through WSL, or through SSH with cygwin/git bash/mingw.
Easiest so far to install is VcXsrv through chocolatey
choco install vcxsrv -y
Probably many people have written something like this, for converting those joke images purporting to contain
L3E7 h4xx0R c0deZ.
ascii-bits.txt: taken from a random pic of a mini typewriter with a
message.txt: taken from a music video ad with an AI-type character
A pair of python3 scripts for importing. I wrote these to facilitate writing wrappers external commands, when replacing some shell scripts. There might be better ways to do it (including checking for libraries in lieu of commands) but in absence of that possibility (looking at you,
docker-compose!), these have made things much easier...!
arguments.py script allows loading a parser with some defaults, as well as passing your own
argparse definitions to it. It then returns a usable dicitonary in which to look up items.
runner.sh script provides a convenience set of functions for runnning external commands, as well as a dry run mode predicated on use of
--dry-run from the
arguments.sh script. It also accepts a simple dict mapping extra environment variables into the existing environment
This is a relatively simple JSON object traverser: feed it either a JSON string or a nested set of dicitonaries/lists and use a path notation to access individual items.
/or any separator string you specify
*to iterate over multiple values
It is relatively naive, insomuch as it will create a new array in the output object for each wildcard used along the path. For example, here's an example of getting all subnet definitions on
docker inspect <networks ...>: