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"author": "Long-Gang Pang, Ya-Yun He and Xin-Nian Wang",
"author_email": "lgpang@qq.com, heyayun@gmail.com, xnwang@lbl.gov",
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"description": "# Data driven extraction of jet energy loss distributions in heavy ion collisions\nCode Authors: Long-Gang Pang, Ya-Yun He and Xin-Nian Wang\n\n## Introduction\n\nThis python package is a simple tool to extract the pt loss distribution\nand the mean pt loss as a function of jet pt,\nfrom the experimental single jet RAA for AA collisions at a specific beam energy \n(with pt spectra in proton+proton collisions at the same beam energy) or the single hadron/gamma hadron\npt spectra (without pt spectra in proton+proton collisions).\n\nExample:\n```python\nfrom jeteloss import PythiaPP, RAA2Eloss\npp_x, pp_y = PythiaPP(sqrts_in_gev = 2760)\nraa_fname = \"RAA_2760.txt\"\neloss = RAA2Eloss(raa_fname, pp_x, pp_y)\neloss.train()\neloss.save_results()\neloss.plot_mean_ptloss()\neloss.plot_pt_loss_dist()\n```\nThe format of input data \"RAA_2760.txt\":\nThe first row is the comment row start with \"#\" and data description for the following columns,\n\"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.\n\n## Results\n ![](\"examples/figs/RAA.png\")
![](\"examples/figs/mean_pt_loss.png\")
\n\n## Citation\n\nIf you have used this package to produce results for presentation/publications,\nplease cite the following two papers, from where one can find the detailed information of \nthe underlying physics.\n\n\n## Installation\n\n### Method 1: using pip\nStep 1: \n> pip install jeteloss\n\nStep 2:\n> git clone git@github.com:lgpang/jeteloss.git\n\nStep 3:\n> cd jeteloss/examples\n\n> python example1.py\n\n### Method 2: install from local directory\nStep 1: download the code from github\n> git clone git@github.com:lgpang/jeteloss.git\n\nStep 2: install jeteloss and dependences\n> cd jeteloss\n\n> python setup.py install\n\nStep 3: run example code\n> cd examples\n\n> python example1.py\n\n### Method 3: using anaconda\n\nStep 1: To create one clean python virtual environment \n> conda create -n test_jeteloss python=3.6\n\nStep 2: To activate this environment, use:\n> source activate test_jeteloss\n\nStep 3: Install jeteloss module and its dependences\n> pip install jeteloss\n\nStep 4: Run the example code downloaded using:\n> git clone git@github.com:lgpang/jeteloss.git\n\n> cd jeteloss/examples\n\n> python example1.py\n\nStep 5: To deactivate an active environment, use:\n> source deactivate\n\nStep 6: Clean up\nTo see how many environments do you have, use:\n> conda env list\n\nTo remove one environment, use:\n> conda remove --name test_jeteloss --all\n\n\n\n",
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"summary": "Data-driven extraction of jet energy loss distributions in heavy-ion collisions",
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