Just run this from your Mac terminal and it'll drop you in a container with full permissions on the Docker VM. This also works for Docker for Windows for getting in Moby Linux VM (doesn't work for Windows Containers).
docker run -it --rm --privileged --pid=host justincormack/nsenter1
more info: https://github.com/justincormack/nsenter1
This guide was adapted from https://gist.github.com/niw/e4313b9c14e968764a52375da41b4278#running-ubuntu-server-for-arm64
| # -*- coding: utf-8 -*- | |
| '''recorder.py | |
| Provides WAV recording functionality via two approaches: | |
| Blocking mode (record for a set duration): | |
| >>> rec = Recorder(channels=2) | |
| >>> with rec.open('blocking.wav', 'wb') as recfile: | |
| ... recfile.record(duration=5.0) | |
| Non-blocking mode (start and stop recording): |
| // More information: https://danielupshaw.com/openscad-rounded-corners/ | |
| // Set to 0.01 for higher definition curves (renders slower) | |
| $fs = 0.15; | |
| module roundedcube(size = [1, 1, 1], center = false, radius = 0.5, apply_to = "all") { | |
| // If single value, convert to [x, y, z] vector | |
| size = (size[0] == undef) ? [size, size, size] : size; | |
| translate_min = radius; |
First posted in August 2021. This is basically a snapshot of my thinking about pansharpening at that time; I’m not making any substantial updates. Last typo and clarity fixes in February 2023.
This is a collection of notes on how I’ve been approaching convolutional neural networks for pansharpening. It’s an edited version of an e-mail to a friend who had asked about this tweet, so it’s informal and somewhat silly; it’s not as polished as, say, a blog post would be. It’s basically the advice I would give to an image processing hobbyist before they started working on pansharpening.
If you want a more serious introduction, start with the literature review in Learning deep multiresolution representations for pansharpening. Most of the academic work I would recommend is mentioned there.
| #!/bin/sh | |
| if [ ! -z "$*" ]; then | |
| echo "this is ~/bin/df, use /bin/df" | |
| exit 1 | |
| fi | |
| protect=`mount | grep -v "read-only" | grep "protect" | cut -f 3 -w` | |
| nosuid=`mount | grep -v "read-only" | grep "nosuid" | cut -f 3 -w` |
| import pyaudio | |
| import wave | |
| FORMAT = pyaudio.paInt16 | |
| CHANNELS = 2 | |
| RATE = 44100 | |
| CHUNK = 1024 | |
| RECORD_SECONDS = 5 | |
| WAVE_OUTPUT_FILENAME = "file.wav" | |
I am trying to determine if it is possible to build a Swift dynamic library which is itself composed of one of more private modules, without needing to expose to that fact to outside users. My hope was that I could build the private module as a static library, which would be linked into the primary (dynamic) library. The dylib could then be deployed together with its swiftmodule and swiftdoc and be imported, with the private module and its symbols not being exposed at all.
Unfortunately, what I'm currently observing seems to indicate that the private module's swiftmodule also has to be available for the primary library to be successfully imported.
This can be reproduced as follows. I have the following directory structure:
./Greeter/Logger/Logger.swift:
public func log(_ message: String) {