Matthew Feickert matthewfeickert

I have a Docker image with Python 3.6.8 installed from source on Ubuntu 18.04 and two install scripts that given the stated requirements in the current docs should be able to install iminiuit but fail instead.

install_from_PyPI.sh attempts to install iminuit from PyPI

docker run --rm -it matthewfeickert/docker-python3-ubuntu:3.6.8
git clone https://gist.github.com/matthewfeickert/284cbddc4a60aca2dcda29c354189b35 && cd 284cbddc4a60aca2dcda29c354189b35
source install_from_PyPI.sh

Small From Scratch Demo

This is a very brief demo to show how one could go from an environment with just a Python 3 runtime to pyhf installed and being useful.

Installation

To take advantage of the great tools that uproot provides us let's install pyhf with the xmlio options

pip3 install pyhf[xmlio]

Testing Python Docstrings with `doctest` and `pydocstyle`

Setup

Like any good project, we'll create a virtual environment for this example. You can use your virtual environment tool of choice, but I'm just going to show with Python's venv (and some help from venv-activate)

cd
mkdir venvs
cd venvs

`MG5aMC_PythonMEs` Minimal Working Example

Kyle notified people over email and Twitter that Valentin Hirschi had gotten MadGraph to export matrix element codes for Feynman diagrams into a form that can be used by differentiable machine learning libraries (e.g., TensorFlow and PyTorch). The project is called MG5aMC_PythonMEs. This is a short walkthrough of doing the simplest possible thing: just running the code that MG5aMC_PythonMEs generates. As I don't have much experience with MadGraph5_aMC@NLO and its installation and use instructions are quite bad I threw together a Docker image that at leasts somewhat works. This walkthrough will use that.

To be clear, this is never how you would actually do anything with a Docker image. This is just throwing things against the wall at the moment.

Example of Python classes and the use of `self`

Click this button to run an example in Binder:

As you can see from requirements.txt I've added some fun Python libraries, that are available in the Binder instance so you can use them in this sandbox environment.

	#!/usr/bin/env python3

	import sys
	import json
	import argparse

	import time

	import pyhf

	default: example

	all: example key

	example: example.mmd
	docker run --rm -v "${PWD}":/home/node/data matthewfeickert/mermaid-cli -i example.mmd -o example.svg
	docker run --rm -v "${PWD}":/home/node/data matthewfeickert/mermaid-cli -i example.mmd -o example.pdf
	docker run --rm -v "${PWD}":/home/node/data matthewfeickert/mermaid-cli -i example.mmd -o example.png

	key: gantt_key.mmd

	FROM python:3.8

	MAINTAINER Matthew Feickert <[email protected]>

	USER root
	WORKDIR /root

	SHELL [ "/bin/bash", "-c" ]

	RUN apt-get -qq -y update && \

	# derived from Table 4 (page 58) of http://arxiv.org/pdf/1312.5353v3

	imax 3 number of bins
	jmax 2 number of samples minus one
	kmax * number of nuisance parameters
	--------------------------------------------------------
	bin bin4e bin2e2m bin4m
	observation 4 13 8
	--------------------------------------------------------
	bin bin4e bin2e2m bin4m bin4e bin2e2m bin4m bin4e bin2e2m bin4m

	*.pdf
	HistFactory/
	validation.txt
	.xml2json_output
	roundtrip.json