Metadata-Version: 2.1
Name: jeteloss
Version: 0.3
Summary: Data-driven extraction of jet energy loss distributions in heavy-ion collisions
Home-page: https://github.com/snowhitiger/jeteloss
Author: Long-Gang Pang, Ya-Yun He and Xin-Nian Wang
Author-email: lgpang@qq.com, heyayun@gmail.com, xnwang@lbl.gov
License: MIT
Keywords: Bayesian,MCMC,Jet energy loss extractor,RAA
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Requires-Dist: numpy (>=1.14)
Requires-Dist: pymc (>=2.3.6)
Requires-Dist: h5py (>=2.8.0)
Requires-Dist: matplotlib (>=2.2.0)
Requires-Dist: scipy (>=1.1.0)

# Data driven extraction of jet energy loss distributions in heavy ion collisions

## Introduction


## Installation

### Method 1: using pip
Step 1: 
> pip install jeteloss

Step 2:
> git clone 

Step 3:
> cd jeteloss/examples
> python example1.py

### Method 2: install from local directory
Step 1: download the code from github
> git clone 

> cd jeteloss

> python setup.py install

### Method 3: using anaconda

Step 1: To create one clean python virtual environment 
> conda create -n test_jeteloss python=3.6

Step 2: To activate this environment, use:
> source activate test_jeteloss

Step 3: Install jeteloss module and its dependences
> pip install jeteloss

Step 4: Run the example code downloaded using:
> git clone *

> cd jeteloss/examples

> python example1.py

Step 5: To deactivate an active environment, use:
> source deactivate

Step 6: Clean up
To see how many environments do you have, use:
> conda env list

To remove one environment, use:
> conda remove --name myenv --all

## Citation



