| import csv | |
| import shutil | |
| from pypdf import PdfReader | |
| # Move PDF files to proceedings format | |
| PAPERS_IN_DIR = "20240201-cr-files" | |
| PAPERS_OUT_DIR = "NIME2023-proceedings" | |
| def convert_csv_to_bibtex(csv_file, bibtex_file, copy_files = False): | |
| """Convert a CSV file to BibTeX format""" |
| #!/usr/bin/env python3 | |
| """FAIS Minus --- a script for handling final marks exported from Wattle Gradebook | |
| OVERVIEW: | |
| This script helps process the final mark output from Gradebook, including: | |
| - generating stats per cohort | |
| - recording and updating special grades (e.g., DA/RP/WD) | |
| - exporting CSV files that are _actually_ SAS-ready. |
| #!/bin/bash | |
| echo "$1" | |
| videoname=${1%%.*} | |
| #echo "$videoname" | |
| ffmpeg -i $1 -c:v libx264 -b:v 2M -minrate 0.5M -preset fast -vf scale=1920:1080 -c:a aac -b:a 128K ${videoname}_1080.mp4 | |
| ffmpeg -i $1 -c:v libx264 -b:v 0.5M -minrate 0.5M -preset fast -vf scale=960:540 -c:a aac -b:a 128K ${videoname}_540.mp4 | |
| ffmpeg -i $1 -y -codec:a libmp3lame -ac 2 -ar 44100 -ab 64k -id3v2_version 3 -metadata title="$videoname" -metadata album_artist="Charles Martin" -metadata album="COMP2300 2020" -metadata genre="Vocal" -metadata date="2020" ${videoname}_audio.mp3 | |
| echo "done, phew." |
| % LaTeX Template for short student reports. | |
| % Citations should be in bibtex format and go in references.bib | |
| \documentclass[a4paper, 11pt]{article} | |
| \usepackage[top=3cm, bottom=3cm, left = 2cm, right = 2cm]{geometry} | |
| \geometry{a4paper} | |
| \usepackage[utf8]{inputenc} | |
| \usepackage{textcomp} | |
| \usepackage{graphicx} | |
| \usepackage{amsmath,amssymb} | |
| \usepackage{bm} |
| from tensorflow.python.client import device_lib | |
| def get_available_gpus(): | |
| local_device_protos = device_lib.list_local_devices() | |
| return [x.name for x in local_device_protos if x.device_type == 'GPU'] | |
| get_available_gpus() |
| from music21 import converter, instrument, note, chord, stream, midi | |
| import numpy as np | |
| import pandas as pd | |
| import os | |
| import time | |
| import h5py | |
| import argparse | |
| import multiprocessing | |
| parser = argparse.ArgumentParser() |
| DOCNAME=nime-paper-input.md | |
| OUTNAME=rendered-output | |
| REFSFILE=references.bib | |
| ARGS=--bibliography $(REFSFILE) | |
| nime: | |
| pandoc --template=nime.latex --self-contained --natbib --filter pandoc-fignos $(DOCNAME) -o $(OUTNAME).tex | |
| sed -i '.bak' 's/\citep{/\cite{/g' $(OUTNAME).tex | |
| pdflatex $(OUTNAME) | |
| bibtex $(OUTNAME) |
title: 'A Very Well-Written NIME Paper' author:
| %% Pandoc template for creating NIME papers | |
| %% Adapted from NIME paper template and Pandoc latex template. | |
| \documentclass[$if(fontsize)$$fontsize$,$endif$$if(lang)$$lang$,$endif$$if(papersize)$$papersize$,$endif$$for(classoption)$$classoption$$sep$,$endfor$]{$documentclass$} | |
| \usepackage{hyperref} | |
| \makeatletter | |
| \def\maxwidth{\ifdim\Gin@nat@width>\linewidth\linewidth\else\Gin@nat@width\fi} | |
| \def\maxheight{\ifdim\Gin@nat@height>\textheight\textheight\else\Gin@nat@height\fi} | |
| \makeatother |