finnolec

#!/usr/bin/env bash
#
# Authors: Axel Huebl, Marco Garten, Klaus Steiniger, Finn-Ole Carstens
#
# last updated: 2021-11-10
# 
# Based on https://gist.github.com/steindev/86df43bef49586e2b33d2fb0a372f09c

# It is advisable to compile libraries on an interactive node
# > srun -p gpu2-interactive --gres=gpu:0 --ntasks=1 --pty --mem=8G -t 2:00:00 bash  # get resources on a node

psychocoderHPC

#last update 05.11.2021

module load craype-accel-amd-gfx908
module load rocm/4.3.0 
module load git/2.31.1
module load cmake/3.20.2
module load boost/1.73.0
module load c-blosc/1.21.0
module load cray-python/3.8.5.1
module load hdf5/1.10.7 # dependency of openpmd-api module (no other possible)

steindev

#!/bin/bash
#
# Script to transfer data from Juelich Supercomputing Center to HZDR.
# Call with
#     > screen # open a screen session first to be able to logout from the
#              # data mover system
#     > exec ssh-agent bash # prepare shell to add ssh key passphrase to ssh-agent
#                           # in order to not type it all the time
#     > ssh-add ~/.ssh/id_ed25519 # add ssh key passphrase to ssh-agent 
#     > xargs -a dirs.list -n 1 -P 5 ~/bin/data-transfer_judac.sh | tee transfer.out

n01r

How to find non-ASCII characters in your bibliography if they make your LaTeX compilation fail

Note: I found the answer here: https://stackoverflow.com/questions/13596531/how-to-search-for-non-ascii-characters-with-bash-tools

1. Noticing that you have this problem

If you find the following error message in your editor it is very likely that you have this problem:

Undefined control sequence. \printbibliography[notkeyword=myPaper, title={References}, heading=bibintoc, resetnumbers=true]

steindev

## Installing the environment
# * Download and install anaconda or miniconda
# * Activate the base environment
#   `conda activate` (which maybe preceeded by `source ~/.bashrc`)
# * Install
#   `conda env create --file picongpu-analysis-environment.yml`
#
# Alternatively, mamba could be used instead of conda which is supposed
# to be faster.
# In the base environment

natowi

Audio Plugin for Audio to MIDI transcription using deep learning (exe, app, vst3, component)

https://github.com/DamRsn/NeuralNote

Recurrent Neural Network for generating piano MIDI-files from audio (MP3, WAV, etc.)

https://github.com/BShakhovsky/PolyphonicPianoTranscription

A python program which performs an FFT on an audio file and produces a MIDI file from the results

https://github.com/NFJones/audio-to-midi

Extract the melody from an audio file and export to MIDI

steindev

#!/usr/bin/env bash
#
# Authors: Axel Huebl, Marco Garten, Klaus Steiniger
#
# last updated: 2019-04-24

PIC_BRANCH="dev"

# get PIConGPU profile
if [ ! -f "$PIC_PROFILE" ]; then

ocoyawale

This page is a curated collection of Jupyter/IPython notebooks that are notable for some reason. Feel free to add new content here, but please try to only include links to notebooks that include interesting visual or technical content; this should not simply be a dump of a Google search on every ipynb file out there.

Important contribution instructions: If you add new content, please ensure that for any notebook you link to, the link is to the rendered version using nbviewer, rather than the raw file. Simply paste the notebook URL in the nbviewer box and copy the resulting URL of the rendered version. This will make it much easier for visitors to be able to immediately access the new content.

Note that Matt Davis has conveniently written a set of bookmarklets and extensions to make it a one-click affair to load a Notebook URL into your browser of choice, directly opening into nbviewer.

MichaelCurrie

PEP-8 is a set of Python style recommendations. pep8 is a module that checks your .py file for violations. To make your Travis-CI build fail if you have any violations, you could add these lines to your .travis.yml:

before_install:
    - pip install pep8
    
script:
    # Run pep8 on all .py files in all subfolders
    # (I ignore "E402: module level import not at top of file"
    # because of use case sys.path.append('..'); import <module>)

sevko

raytracer

A simple Python raytracer that supports spheres with configurable "material" properties (base color and a bunch of light coefficients). To generate a raytraced image of the pre-defined scene, run: python raytracer.py and open image.ppm with a PPM-compatible viewer (eog works fine on Linux):

acknowledgements

I found the following resources extremely helpful: