{ "cells": [ { "cell_type": "markdown", "id": "3bc82063-09d6-48b1-93d0-4c7263c7b9da", "metadata": { "tags": [] }, "source": [ "# nnll-fast" ] }, { "cell_type": "code", "execution_count": 1, "id": "b3184bee-8948-4b61-94b5-1a41deccb0f7", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.2.10.6\n", "/home/apn/git/nnll-fast/nnll-fast\n" ] } ], "source": [ "import hepi\n", "print(hepi.__version__)\n", "import smpl\n", "import numpy as np\n", "import hepi.util as util\n", "import matplotlib.pyplot as plt\n", "from hepi.run import nnllfast as nnll\n", "# set the folder where the resummino binary can be found either in either ./{,bin,bin/build}/resummino\n", "nnll.set_path(\"nnll-fast-1.1\")\n", "# By default hepi will run with nice -n5 to prevent overloading the system if more scans than cores are running\n", "#rs.set_pre(\"\") disables any prefixing with nice\n", "print (nnll.get_path())" ] }, { "cell_type": "code", "execution_count": 6, "id": "a922de4c-d019-4b67-bc73-5b062987efce", "metadata": { "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cf5c5e3a062d42a481e99fd39d66048b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Checking input: 0%| | 0/16 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%time\n", "params = [\n", " \"mastercode_with_gm2.in\", # baseline slha file in the relative ./output folder by default unless set_output_dir was used \n", "]\n", "pss = [ \n", " (1000001,1000021), # Final state particles for resummino to run\n", " ]\n", "\n", "for pa,pb in pss:\n", " for param in params:\n", " # All the inputs Order, CMS in GeV, particle 1, particle 2, slha, pdf_lo, pdf_nlo,mu_f, mu_r\n", " i = hepi.Input(hepi.Order.aNNLO_PLUS_NNLL,13000,pa,pb,param,\"PDF4LHC15\",\"PDF4LHC15\",1., 1.,id=1)\n", " li = [i] # li is our list of inputs that we want resummino to run\n", " li = hepi.mass_scan(li,pb, np.linspace(2000,2000,1)) \n", " li = hepi.mass_scan(li,pa, np.linspace(1000,2000,16)) # we scan the slepton mass from 100 to 1000 at 15 equidistant points \n", " rs_dl = nnll.run(li,skip=False,n_jobs=1) # run resummino, skipping if the result already exists.\n", " # rs_dl is a panda table(dataframe) with all inputs and result \n", " _,axs = hepi.init_double_plot()\n", " # hepi has some useful plotting routines, but the results from rs_dl can easily be accessed\n", " # Now plot the mass of PDG id pa from the results at LO ,NLO and aNNLO+NLL\n", " hepi.mass_plot(rs_dl,[\"NLO\",\"aNNLO_PLUS_NNLL\"],pa,axes=axs[0],tight=False)\n", " # Plot K factors vs LO and aNNLO+NLL/NLO\n", " hepi.mass_plot(rs_dl,[\"aNNLO_PLUS_NNLL_OVER_NLO\"],pa, yaxis=None, axes=axs[1],logy=False,tight=False)\n", " # construct a title from the inputs\n", " hepi.title(li[0],axs[0],scenario=\"mastercode\")" ] }, { "cell_type": "code", "execution_count": 3, "id": "595f203c-4255-4b39-8c0c-e36815aa2b37", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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LONLONLO_PLUS_NLLaNNLO_PLUS_NNLLK_LOK_NLOK_NLO_PLUS_NLLNLO_PLUS_NLL_OVER_NLOK_aNNLO_PLUS_NNLLaNNLO_PLUS_NNLL_OVER_NLO...idmodelmumass_1000021mass_1000001runnernf_final_state_innf_squark_massnf_gluino_massnf_deg
0None0.00361+/-0None0.00434+/-0NoneNoneNoneNoneNone1.2022160664819945+/-0...11500.0000002000.01000.000000NLLfastRunner-?sg1000.000000200010
1None0.00304+/-0None0.00368+/-0NoneNoneNoneNoneNone1.2105263157894737+/-0...11533.3333332000.01066.666667NLLfastRunner-?sg1066.666667200010
2None0.00256+/-0None0.00311+/-0NoneNoneNoneNoneNone1.2148437499999998+/-0...11566.6666672000.01133.333333NLLfastRunner-?sg1133.333333200010
3None0.00216+/-0None0.00263+/-0NoneNoneNoneNoneNone1.2175925925925926+/-0...11600.0000002000.01200.000000NLLfastRunner-?sg1200.000000200010
4None0.00183+/-0None0.00224+/-0NoneNoneNoneNoneNone1.2240437158469943+/-0...11633.3333332000.01266.666667NLLfastRunner-?sg1266.666667200010
5None0.0015400000000000001+/-0None0.0019+/-0NoneNoneNoneNoneNone1.2337662337662336+/-0...11666.6666672000.01333.333333NLLfastRunner-?sg1333.333333200010
6None0.00131+/-0None0.00162+/-0NoneNoneNoneNoneNone1.2366412213740459+/-0...11700.0000002000.01400.000000NLLfastRunner-?sg1400.000000200010
7None0.00111+/-0None0.00138+/-0NoneNoneNoneNoneNone1.243243243243243+/-0...11733.3333332000.01466.666667NLLfastRunner-?sg1466.666667200010
8None0.0009440000000000001+/-0None0.00118+/-0NoneNoneNoneNoneNone1.25+/-0...11766.6666672000.01533.333333NLLfastRunner-?sg1533.333333200010
9None0.000802+/-0None0.00101+/-0NoneNoneNoneNoneNone1.259351620947631+/-0...11800.0000002000.01600.000000NLLfastRunner-?sg1600.000000200010
10None0.0006850000000000001+/-0None0.000867+/-0NoneNoneNoneNoneNone1.2656934306569343+/-0...11833.3333332000.01666.666667NLLfastRunner-?sg1666.666667200010
11None0.000584+/-0None0.0007440000000000001+/-0NoneNoneNoneNoneNone1.2739726027397262+/-0...11866.6666672000.01733.333333NLLfastRunner-?sg1733.333333200010
12None0.000498+/-0None0.00064+/-0NoneNoneNoneNoneNone1.2851405622489962+/-0...11900.0000002000.01800.000000NLLfastRunner-?sg1800.000000200010
13None0.000428+/-0None0.000553+/-0NoneNoneNoneNoneNone1.2920560747663552+/-0...11933.3333332000.01866.666667NLLfastRunner-?sg1866.666667200010
14None0.000368+/-0None0.000481+/-0NoneNoneNoneNoneNone1.3070652173913042+/-0...11966.6666672000.01933.333333NLLfastRunner-?sg1933.333333200010
15None0.000319+/-0None0.00042100000000000004+/-0NoneNoneNoneNoneNone1.3197492163009406+/-0...12000.0000002000.02000.000000NLLfastRunner-?sg2000.000000200010
\n", "

16 rows × 43 columns

\n", "
" ], "text/plain": [ " LO NLO NLO_PLUS_NLL aNNLO_PLUS_NNLL \n", "0 None 0.00361+/-0 None 0.00434+/-0 \\\n", "1 None 0.00304+/-0 None 0.00368+/-0 \n", "2 None 0.00256+/-0 None 0.00311+/-0 \n", "3 None 0.00216+/-0 None 0.00263+/-0 \n", "4 None 0.00183+/-0 None 0.00224+/-0 \n", "5 None 0.0015400000000000001+/-0 None 0.0019+/-0 \n", "6 None 0.00131+/-0 None 0.00162+/-0 \n", "7 None 0.00111+/-0 None 0.00138+/-0 \n", "8 None 0.0009440000000000001+/-0 None 0.00118+/-0 \n", "9 None 0.000802+/-0 None 0.00101+/-0 \n", "10 None 0.0006850000000000001+/-0 None 0.000867+/-0 \n", "11 None 0.000584+/-0 None 0.0007440000000000001+/-0 \n", "12 None 0.000498+/-0 None 0.00064+/-0 \n", "13 None 0.000428+/-0 None 0.000553+/-0 \n", "14 None 0.000368+/-0 None 0.000481+/-0 \n", "15 None 0.000319+/-0 None 0.00042100000000000004+/-0 \n", "\n", " K_LO K_NLO K_NLO_PLUS_NLL NLO_PLUS_NLL_OVER_NLO K_aNNLO_PLUS_NNLL \n", "0 None None None None None \\\n", "1 None None None None None \n", "2 None None None None None \n", "3 None None None None None \n", "4 None None None None None \n", "5 None None None None None \n", "6 None None None None None \n", "7 None None None None None \n", "8 None None None None None \n", "9 None None None None None \n", "10 None None None None None \n", "11 None None None None None \n", "12 None None None None None \n", "13 None None None None None \n", "14 None None None None None \n", "15 None None None None None \n", "\n", " aNNLO_PLUS_NNLL_OVER_NLO ... id model mu mass_1000021 \n", "0 1.2022160664819945+/-0 ... 1 1500.000000 2000.0 \\\n", "1 1.2105263157894737+/-0 ... 1 1533.333333 2000.0 \n", "2 1.2148437499999998+/-0 ... 1 1566.666667 2000.0 \n", "3 1.2175925925925926+/-0 ... 1 1600.000000 2000.0 \n", "4 1.2240437158469943+/-0 ... 1 1633.333333 2000.0 \n", "5 1.2337662337662336+/-0 ... 1 1666.666667 2000.0 \n", "6 1.2366412213740459+/-0 ... 1 1700.000000 2000.0 \n", "7 1.243243243243243+/-0 ... 1 1733.333333 2000.0 \n", "8 1.25+/-0 ... 1 1766.666667 2000.0 \n", "9 1.259351620947631+/-0 ... 1 1800.000000 2000.0 \n", "10 1.2656934306569343+/-0 ... 1 1833.333333 2000.0 \n", "11 1.2739726027397262+/-0 ... 1 1866.666667 2000.0 \n", "12 1.2851405622489962+/-0 ... 1 1900.000000 2000.0 \n", "13 1.2920560747663552+/-0 ... 1 1933.333333 2000.0 \n", "14 1.3070652173913042+/-0 ... 1 1966.666667 2000.0 \n", "15 1.3197492163009406+/-0 ... 1 2000.000000 2000.0 \n", "\n", " mass_1000001 runner nf_final_state_in nf_squark_mass \n", "0 1000.000000 NLLfastRunner-? sg 1000.000000 \\\n", "1 1066.666667 NLLfastRunner-? sg 1066.666667 \n", "2 1133.333333 NLLfastRunner-? sg 1133.333333 \n", "3 1200.000000 NLLfastRunner-? sg 1200.000000 \n", "4 1266.666667 NLLfastRunner-? sg 1266.666667 \n", "5 1333.333333 NLLfastRunner-? sg 1333.333333 \n", "6 1400.000000 NLLfastRunner-? sg 1400.000000 \n", "7 1466.666667 NLLfastRunner-? sg 1466.666667 \n", "8 1533.333333 NLLfastRunner-? sg 1533.333333 \n", "9 1600.000000 NLLfastRunner-? sg 1600.000000 \n", "10 1666.666667 NLLfastRunner-? sg 1666.666667 \n", "11 1733.333333 NLLfastRunner-? sg 1733.333333 \n", "12 1800.000000 NLLfastRunner-? sg 1800.000000 \n", "13 1866.666667 NLLfastRunner-? sg 1866.666667 \n", "14 1933.333333 NLLfastRunner-? sg 1933.333333 \n", "15 2000.000000 NLLfastRunner-? sg 2000.000000 \n", "\n", " nf_gluino_mass nf_deg \n", "0 2000 10 \n", "1 2000 10 \n", "2 2000 10 \n", "3 2000 10 \n", "4 2000 10 \n", "5 2000 10 \n", "6 2000 10 \n", "7 2000 10 \n", "8 2000 10 \n", "9 2000 10 \n", "10 2000 10 \n", "11 2000 10 \n", "12 2000 10 \n", "13 2000 10 \n", "14 2000 10 \n", "15 2000 10 \n", "\n", "[16 rows x 43 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rs_dl" ] }, { "cell_type": "code", "execution_count": null, "id": "fff65bb6-66ce-405b-8345-f4e1627d065f", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.3" } }, "nbformat": 4, "nbformat_minor": 5 }