tueda

Easily accessible datasets

• Rdatasets (2264)
• UC Irvine Machine Learning Repository (663)
• OpenIntro Data Sets (284)

tueda

#!/bin/bash
#
# Usage:
#   ./x [update]
#       [4.1.0|4.2.0|4.2.1|4.3.0|4.3.1]
#       [check|time [-n <times>]]
#       [coverage|gprof|valgrind [<valgrind options>]|helgrind|drd|sanitize]
#       [form|vorm|tform|tvorm|parform|parvorm]
#       [args...]
#

tueda

* Example:
*   form -D M=10 -D N=15 test11.frm
Symbol x1,...,x`M';
Local F = (x1+...+x`M')^`N';
.end

tueda

Undocumented environment variables

External channels

• FORM_PIPES

ParFORM

• PF_LOG
• PF_RBUFS

tueda

#!/bin/sh
#
# @file auto-cherry-pick.sh
#
# Usage:
#   auto-cherry-pick.sh ref1...ref2:
#
set -eu

output=auto-cherry-pick.log

tueda

• Docker is required.
• NVIDIA Container Toolkit is required to use GPU.

See: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker.

git clone https://gist.github.com/ac4a004de14b34e4e746e6412cf70249.git my-madminer-jupyter-env
cd my-madminer-jupyter-env

tueda

chunkifyString[s_String, n_Integer] :=
  StringJoin @@@ Partition[Characters[s], UpTo[n]];

writeRationalApproximation[
  stream_, x_, n_, width_:72
] := Module[{s, lines, i},
  s = Rationalize[x, Abs[x * 10^(-n)]] // InputForm // ToString;
  lines = chunkifyString[s, width - 1];
  Do[
    s = lines[[i]];

tueda

!pip install git+https://gist.github.com/tueda/ec0181f0f4c8961d49dc659f79cbfd4a

from google.colab import drive
drive.mount('/content/drive')

import colab_cache

cache = colab_cache.Cache('hello-2.12.1')
if cache.exists():

tueda

#!/bin/bash
#
# @file run-mg5_aMC.sh
#
# Runs MadGraph5_aMC@NLO. This script downloads and
# sets up MadGraph5_aMC@NLO if needed.
#
set -eu
set -o pipefail

tueda