Metadata-Version: 2.1
Name: jeteloss
Version: 0.6
Summary: Data-driven extraction of jet energy loss distributions in heavy-ion collisions
Home-page: https://github.com/lgpang/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
Description: # Data driven extraction of jet energy loss distributions in heavy ion collisions
        Code Authors: Long-Gang Pang, Ya-Yun He and Xin-Nian Wang
        
        ## Introduction
        
        This python package is a simple tool to extract the pt loss distribution
        and the mean pt loss as a function of jet pt,
        from the experimental single jet RAA for AA collisions at a specific beam energy 
        (with pt spectra in proton+proton collisions at the same beam energy) or the single hadron/gamma hadron
        pt spectra (without pt spectra in proton+proton collisions).
        
        Example:
        ```python
        from jeteloss import PythiaPP, RAA2Eloss
        pp_x, pp_y = PythiaPP(sqrts_in_gev = 2760)
        raa_fname = "RAA_2760.txt"
        eloss = RAA2Eloss(raa_fname, pp_x, pp_y)
        eloss.train()
        eloss.save_results()
        eloss.plot_mean_ptloss()
        eloss.plot_pt_loss_dist()
        ```
        The format of input data "RAA_2760.txt":
        The first row is the comment row start with "#" and data description for the following columns,
        "RAA_x, RAA_xerr, RAA_y, RAA_yerr" where RAA_x is the pt bins, RAA_xerr is the uncertainties of these pt bins, RAA_y is the RAA value in one A+A collisions, RAA_yerr is the uncertainties of RAA_y.
        
        ## Results
         <img src="examples/figs/RAA.png" width="360"> <img src="examples/figs/mean_pt_loss.png" width="360">
        
        ## Citation
        
        If you have used this package to produce results for presentation/publications,
        please cite the following two papers, from where one can find the detailed information of 
        the underlying physics.
        
        
        ## Installation
        
        ### Method 1: using pip
        Step 1: 
        > pip install jeteloss
        
        Step 2:
        > git clone git@github.com:lgpang/jeteloss.git
        
        Step 3:
        > cd jeteloss/examples
        
        > python example1.py
        
        ### Method 2: install from local directory
        Step 1: download the code from github
        > git clone git@github.com:lgpang/jeteloss.git
        
        Step 2: install jeteloss and dependences
        > cd jeteloss
        
        > python setup.py install
        
        Step 3: run example code
        > cd examples
        
        > python example1.py
        
        ### 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 git@github.com:lgpang/jeteloss.git
        
        > 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 test_jeteloss --all
        
        
Keywords: Bayesian,MCMC,Jet energy loss extractor,RAA
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
