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https://github.com/jrbourbeau/cr-composition
IceCube cosmic-ray composition analysis
https://github.com/jrbourbeau/cr-composition
cosmic-rays data-analysis machine-learning physics python
Last synced: 3 days ago
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IceCube cosmic-ray composition analysis
- Host: GitHub
- URL: https://github.com/jrbourbeau/cr-composition
- Owner: jrbourbeau
- License: mit
- Created: 2016-11-23T22:31:01.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2018-11-01T21:37:01.000Z (over 6 years ago)
- Last Synced: 2024-12-16T08:44:01.870Z (about 2 months ago)
- Topics: cosmic-rays, data-analysis, machine-learning, physics, python
- Language: Jupyter Notebook
- Homepage: https://jrbourbeau.github.io/cr-composition/
- Size: 99.1 MB
- Stars: 1
- Watchers: 3
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Cosmic-ray mass composition analysis
[](https://travis-ci.org/jrbourbeau/cr-composition)
[](https://github.com/jrbourbeau/cr-composition/blob/master/LICENSE)
*NOTE*: This analysis is still under active development
This repository contains the analysis code used to study the cosmic-ray
composition spectrum using data collected by the IceCube South Pole Neutrino
Observatory.
## Repository layout
The layout of this repository is:
```
├── LICENSE
├── Makefile <- Makefile with commands like `make processing` or `make plots`
├── README.md <- Top-level README for developers using this project.
├── config.yml <- Filesystem path configuration file
│
├── models <- Scripts for fitting and saving models
│
├── notebooks <- Jupyter notebooks used for exploratory analysis
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── comptools <- Python package containing analysis-related code
│
├── processing <- Scripts for processing simulation and data
│
└── plotting <- Scripts for making plots
```
While not generated with, this layout for this project was inspired by the
[Cookiecutter Data Science project](https://github.com/drivendata/cookiecutter-data-science).
## Installation
See the [installation and setup](https://jrbourbeau.github.io/cr-composition/setup.html)
section of the documentation.
## Analysis steps
The workflow for this analysis is managed using GNU `make`. Various commands
are stored in `Makefile` and can be executed using the syntax
`make `. The available commands in `Makefile` are shown below
| Name | Command | Description |
|:-------------:|:-------------:| :-----|
| `processing-cluster` | `make processing-cluster` | Submits data and simulation processing cluster jobs |
| `processing` | `make processing` | Runs all other necessary processing (e.g. calculates efficiencies, fits ML models, etc.) |
| `models` | `make models` | Saves machine learning models |
| `livetime` | `make livetime` | Calculates and saves detector livetimes |
| `efficiencies` | `make efficiencies` | Calculates and saves detection efficiencies |
| `plots` | `make plots` | Makes all analysis plots |
*Note*: a `-s` option can be added to `make` to run commands in silent mode (i.e. doesn't print the commands as they are executed).
To reproduce this analysis run:
```bash
make processing-cluster
# Wait for cluster jobs to finish
make processing
make plots
```
## License
[MIT License](LICENSE)
Copyright (c) 2016-2018 James Bourbeau