diff --git a/examples/tutorial_particle_physics/1_setup.ipynb b/examples/tutorial_particle_physics/1_setup.ipynb index 8c511d889..9c2910003 100755 --- a/examples/tutorial_particle_physics/1_setup.ipynb +++ b/examples/tutorial_particle_physics/1_setup.ipynb @@ -81,15 +81,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "11:15 madminer INFO \n", - "11:15 madminer INFO ------------------------------------------------------------------------\n", - "11:15 madminer INFO | |\n", - "11:15 madminer INFO | MadMiner v0.7.0 |\n", - "11:15 madminer INFO | |\n", - "11:15 madminer INFO | Johann Brehmer, Felix Kling, Irina Espejo, and Kyle Cranmer |\n", - "11:15 madminer INFO | |\n", - "11:15 madminer INFO ------------------------------------------------------------------------\n", - "11:15 madminer INFO \n" + "17:46 madminer INFO \n", + "17:46 madminer INFO ------------------------------------------------------------------------\n", + "17:46 madminer INFO | |\n", + "17:46 madminer INFO | MadMiner v0.7.4 |\n", + "17:46 madminer INFO | |\n", + "17:46 madminer INFO | Johann Brehmer, Felix Kling, Irina Espejo, and Kyle Cranmer |\n", + "17:46 madminer INFO | |\n", + "17:46 madminer INFO ------------------------------------------------------------------------\n", + "17:46 madminer INFO \n" ] } ], @@ -140,8 +140,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "11:15 madminer.core.madmin INFO Added parameter CWL2 (LHA: dim6 2, maximal power in squared ME: (2,), range: (-20.0, 20.0))\n", - "11:15 madminer.core.madmin INFO Added parameter CPWL2 (LHA: dim6 5, maximal power in squared ME: (2,), range: (-20.0, 20.0))\n" + "17:46 madminer.core.madmin INFO Added parameter CWWWL2 (LHA: dim6 2, maximal power in squared ME: (2,), range: (-20.0, 20.0))\n" ] } ], @@ -151,18 +150,10 @@ "miner.add_parameter(\n", " lha_block='dim6',\n", " lha_id=2,\n", - " parameter_name='CWL2',\n", + " parameter_name='CWWWL2',\n", " morphing_max_power=2,\n", - " param_card_transform=\"16.52*theta\",\n", + " param_card_transform=\"theta\",\n", " parameter_range=(-20.,20.)\n", - ")\n", - "miner.add_parameter(\n", - " lha_block='dim6',\n", - " lha_id=5,\n", - " parameter_name='CPWL2',\n", - " morphing_max_power=2,\n", - " param_card_transform=\"16.52*theta\",\n", - " parameter_range=(-20.,20.0)\n", ")" ] }, @@ -189,20 +180,24 @@ "name": "stderr", "output_type": "stream", "text": [ - "11:15 madminer.core.madmin INFO Added benchmark sm: CWL2 = 0.00e+00, CPWL2 = 0.00e+00)\n", - "11:15 madminer.core.madmin INFO Added benchmark w: CWL2 = 15.20, CPWL2 = 0.10)\n", - "11:15 madminer.core.madmin INFO Added benchmark neg_w: CWL2 = -1.54e+01, CPWL2 = 0.20)\n", - "11:15 madminer.core.madmin INFO Added benchmark ww: CWL2 = 0.30, CPWL2 = 15.10)\n", - "11:15 madminer.core.madmin INFO Added benchmark neg_ww: CWL2 = 0.40, CPWL2 = -1.53e+01)\n" + "17:46 madminer.core.madmin INFO Added benchmark sm: CWWWL2 = 0.00e+00)\n", + "17:46 madminer.core.madmin INFO Added benchmark 5: CWWWL2 = 0.72)\n", + "17:46 madminer.core.madmin INFO Added benchmark 10: CWWWL2 = 1.44)\n", + "17:46 madminer.core.madmin INFO Added benchmark 20: CWWWL2 = 2.87)\n", + "17:46 madminer.core.madmin INFO Added benchmark neg_5: CWWWL2 = -7.18e-01)\n", + "17:46 madminer.core.madmin INFO Added benchmark neg_10: CWWWL2 = -1.44e+00)\n", + "17:46 madminer.core.madmin INFO Added benchmark neg_20: CWWWL2 = -2.87e+00)\n" ] } ], "source": [ - "miner.add_benchmark({'CWL2':0., 'CPWL2':0.}, 'sm')\n", - "miner.add_benchmark({'CWL2':15.2, 'CPWL2':0.1}, 'w')\n", - "miner.add_benchmark({'CWL2':-15.4, 'CPWL2':0.2}, 'neg_w')\n", - "miner.add_benchmark({'CWL2':0.3, 'CPWL2':15.1}, 'ww')\n", - "miner.add_benchmark({'CWL2':0.4, 'CPWL2':-15.3}, 'neg_ww')" + "miner.add_benchmark({'CWWWL2':0.}, 'sm')\n", + "miner.add_benchmark({'CWWWL2':0.7175}, '5')\n", + "miner.add_benchmark({'CWWWL2':1.435}, '10')\n", + "miner.add_benchmark({'CWWWL2':2.870}, '20')\n", + "miner.add_benchmark({'CWWWL2':-0.7175}, 'neg_5')\n", + "miner.add_benchmark({'CWWWL2':-1.435}, 'neg_10')\n", + "miner.add_benchmark({'CWWWL2':-2.870}, 'neg_20')\n" ] }, { @@ -236,13 +231,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "11:15 madminer.core.madmin INFO Optimizing basis for morphing\n", - "11:15 madminer.core.madmin INFO Set up morphing with 2 parameters, 6 morphing components, 5 predefined basis points, and 1 new basis points\n" + "17:46 madminer.core.madmin INFO Optimizing basis for morphing\n", + "17:46 madminer.core.madmin INFO Set up morphing with 1 parameters, 3 morphing components, 0 predefined basis points, and 3 new basis points\n" ] } ], "source": [ - "miner.set_morphing(include_existing_benchmarks=True, max_overall_power=2)" + "miner.set_morphing(include_existing_benchmarks=False, max_overall_power=2)" ] }, { @@ -253,38 +248,25 @@ ] }, { - "cell_type": "code", - "execution_count": 7, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], "source": [ - "fig = plot_2d_morphing_basis(\n", - " miner.morpher,\n", - " xlabel=r'$c_{W} v^2 / \\Lambda^2$',\n", - " ylabel=r'$c_{\\tilde{W}} v^2 / \\Lambda^2$',\n", - " xrange=(-20.,20.),\n", - " yrange=(-20.,20.)\n", - ")" + "Note that squared weights (the colormap here) up to 1000 or even 10000 can still be perfectly fine and are in fact sometimes unavoidable." ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 7, "metadata": {}, + "outputs": [], "source": [ - "Note that squared weights (the colormap here) up to 1000 or even 10000 can still be perfectly fine and are in fact sometimes unavoidable." + "#fig = plot_2d_morphing_basis(\n", + "# miner.morpher,\n", + "# xlabel=r'$c_{W} v^2 / \\Lambda^2$',\n", + "# ylabel=r'$c_{\\tilde{W}} v^2 / \\Lambda^2$',\n", + "# xrange=(-20.,20.),\n", + "# yrange=(-20.,20.)\n", + "#)" ] }, { @@ -310,12 +292,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "11:15 madminer.core.madmin INFO Saving setup (including morphing) to data/setup.h5\n" + "17:46 madminer.core.madmin INFO Saving setup (including morphing) to data/setup_gw.h5\n" ] } ], "source": [ - "miner.save('data/setup.h5')" + "miner.save('data/setup_gw.h5')" ] }, { @@ -331,20 +313,13 @@ "source": [ "That's it for the setup (we'll only add one step when talking about systematic uncertainties in part 5 of the tutorial). Please continue with part 2a **or** part 2b, depending on whether you want to run the faster parton-level analysis or the more realistic Delphes-level analysis." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python (higgs_inference)", + "display_name": "Python 2", "language": "python", - "name": "higgs_inference" + "name": "python2" }, "language_info": { "codemirror_mode": { @@ -356,7 +331,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.8.2" } }, "nbformat": 4, diff --git a/examples/tutorial_particle_physics/2a_parton_level_analysis.ipynb b/examples/tutorial_particle_physics/2a_parton_level_analysis.ipynb index 6b947b5bc..cf186ea59 100644 --- a/examples/tutorial_particle_physics/2a_parton_level_analysis.ipynb +++ b/examples/tutorial_particle_physics/2a_parton_level_analysis.ipynb @@ -75,15 +75,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "11:16 madminer INFO \n", - "11:16 madminer INFO ------------------------------------------------------------------------\n", - "11:16 madminer INFO | |\n", - "11:16 madminer INFO | MadMiner v0.7.0 |\n", - "11:16 madminer INFO | |\n", - "11:16 madminer INFO | Johann Brehmer, Felix Kling, Irina Espejo, and Kyle Cranmer |\n", - "11:16 madminer INFO | |\n", - "11:16 madminer INFO ------------------------------------------------------------------------\n", - "11:16 madminer INFO \n" + "17:46 madminer INFO \n", + "17:46 madminer INFO ------------------------------------------------------------------------\n", + "17:46 madminer INFO | |\n", + "17:46 madminer INFO | MadMiner v0.7.4 |\n", + "17:46 madminer INFO | |\n", + "17:46 madminer INFO | Johann Brehmer, Felix Kling, Irina Espejo, and Kyle Cranmer |\n", + "17:46 madminer INFO | |\n", + "17:46 madminer INFO ------------------------------------------------------------------------\n", + "17:46 madminer INFO \n" ] } ], @@ -104,7 +104,7 @@ "metadata": {}, "outputs": [], "source": [ - "mg_dir = '/home/software/MG5_aMC_v2_6_7'" + "mg_dir = '/madminer/software/MG5_aMC_v2_6_7'" ] }, { @@ -130,25 +130,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "11:16 madminer.utils.inter DEBUG HDF5 file does not contain is_reference field.\n", - "11:16 madminer.core.madmin INFO Found 2 parameters:\n", - "11:16 madminer.core.madmin INFO CWL2 (LHA: dim6 2, maximal power in squared ME: (2,), range: (-20.0, 20.0))\n", - "11:16 madminer.core.madmin INFO CPWL2 (LHA: dim6 5, maximal power in squared ME: (2,), range: (-20.0, 20.0))\n", - "11:16 madminer.core.madmin INFO Found 6 benchmarks:\n", - "11:16 madminer.core.madmin INFO sm: CWL2 = 0.00e+00, CPWL2 = 0.00e+00\n", - "11:16 madminer.core.madmin INFO w: CWL2 = 15.20, CPWL2 = 0.10\n", - "11:16 madminer.core.madmin INFO neg_w: CWL2 = -1.54e+01, CPWL2 = 0.20\n", - "11:16 madminer.core.madmin INFO ww: CWL2 = 0.30, CPWL2 = 15.10\n", - "11:16 madminer.core.madmin INFO neg_ww: CWL2 = 0.40, CPWL2 = -1.53e+01\n", - "11:16 madminer.core.madmin INFO morphing_basis_vector_5: CWL2 = -1.60e+01, CPWL2 = 16.82\n", - "11:16 madminer.core.madmin INFO Found morphing setup with 6 components\n", - "11:16 madminer.core.madmin INFO Did not find systematics setup.\n" + "17:46 madminer.utils.inter DEBUG HDF5 file does not contain is_reference field.\n", + "17:46 madminer.core.madmin INFO Found 1 parameters:\n", + "17:46 madminer.core.madmin INFO CWWWL2 (LHA: dim6 2, maximal power in squared ME: (2,), range: (-20.0, 20.0))\n", + "17:46 madminer.core.madmin INFO Found 10 benchmarks:\n", + "17:46 madminer.core.madmin INFO morphing_basis_vector_0: CWWWL2 = -1.83e+01\n", + "17:46 madminer.core.madmin INFO morphing_basis_vector_1: CWWWL2 = 19.85\n", + "17:46 madminer.core.madmin INFO morphing_basis_vector_2: CWWWL2 = -1.97e+00\n", + "17:46 madminer.core.madmin INFO sm: CWWWL2 = 0.00e+00\n", + "17:46 madminer.core.madmin INFO 5: CWWWL2 = 0.72\n", + "17:46 madminer.core.madmin INFO 10: CWWWL2 = 1.44\n", + "17:46 madminer.core.madmin INFO 20: CWWWL2 = 2.87\n", + "17:46 madminer.core.madmin INFO neg_5: CWWWL2 = -7.18e-01\n", + "17:46 madminer.core.madmin INFO neg_10: CWWWL2 = -1.44e+00\n", + "17:46 madminer.core.madmin INFO neg_20: CWWWL2 = -2.87e+00\n", + "17:46 madminer.core.madmin INFO Found morphing setup with 3 components\n", + "17:46 madminer.core.madmin INFO Did not find systematics setup.\n" ] } ], "source": [ "miner = MadMiner()\n", - "miner.load(\"data/setup.h5\")" + "miner.load(\"data/setup_gw.h5\")" ] }, { @@ -166,26 +169,28 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "10:49 madminer.utils.inter INFO Generating MadGraph process folder from cards/proc_card_signal.dat at ./mg_processes/signal1\n", - "10:49 madminer.core INFO Run 0\n", - "10:49 madminer.core INFO Sampling from benchmark: sm\n", - "10:49 madminer.core INFO Original run card: cards/run_card_signal_large.dat\n", - "10:49 madminer.core INFO Original Pythia8 card: None\n", - "10:49 madminer.core INFO Copied run card: /madminer/cards/run_card_0.dat\n", - "10:49 madminer.core INFO Copied Pythia8 card: None\n", - "10:49 madminer.core INFO Param card: /madminer/cards/param_card_0.dat\n", - "10:49 madminer.core INFO Reweight card: /madminer/cards/reweight_card_0.dat\n", - "10:49 madminer.core INFO Log file: run_0.log\n", - "10:49 madminer.core INFO Creating param and reweight cards in ./mg_processes/signal1//madminer/cards/param_card_0.dat, ./mg_processes/signal1//madminer/cards/reweight_card_0.dat\n", - "10:49 madminer.utils.inter INFO Starting MadGraph and Pythia in ./mg_processes/signal1\n", - "10:57 madminer.core INFO Finished running MadGraph! Please check that events were succesfully generated in the following folders:\n", + "17:46 madminer.utils.inter INFO Generating MadGraph process folder from cards/proc_card_signal.dat at ./mg_processes/signal1\n", + "17:47 madminer.core.madmin INFO Run 0\n", + "17:47 madminer.core.madmin INFO Sampling from benchmark: sm\n", + "17:47 madminer.core.madmin INFO Original run card: cards/run_card_signal_large.dat\n", + "17:47 madminer.core.madmin INFO Original Pythia8 card: None\n", + "17:47 madminer.core.madmin INFO Original config card: None\n", + "17:47 madminer.core.madmin INFO Copied run card: madminer/cards/run_card_0.dat\n", + "17:47 madminer.core.madmin INFO Copied Pythia8 card: None\n", + "17:47 madminer.core.madmin INFO Copied config card: None\n", + "17:47 madminer.core.madmin INFO Param card: madminer/cards/param_card_0.dat\n", + "17:47 madminer.core.madmin INFO Reweight card: madminer/cards/reweight_card_0.dat\n", + "17:47 madminer.core.madmin INFO Log file: run_0.log\n", + "17:47 madminer.core.madmin INFO Creating param and reweight cards in ./mg_processes/signal1/madminer/cards/param_card_0.dat, ./mg_processes/signal1/madminer/cards/reweight_card_0.dat\n", + "17:47 madminer.utils.inter INFO Starting MadGraph and Pythia in ./mg_processes/signal1\n", + "17:54 madminer.core.madmin INFO Finished running MadGraph! Please check that events were succesfully generated in the following folders:\n", "\n", "./mg_processes/signal1/Events/run_01\n", "\n", @@ -208,73 +213,110 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "additional_benchmarks = ['w', 'ww', 'neg_w', 'neg_ww']" + "#additional_benchmarks = ['w', 'ww', 'neg_w', 'neg_ww']\n", + "additional_benchmarks = ['5', 'neg_5', '10', 'neg_10', '20', 'neg_20']" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "10:57 madminer.utils.inter INFO Generating MadGraph process folder from cards/proc_card_signal.dat at ./mg_processes/signal2\n", - "10:57 madminer.core INFO Run 0\n", - "10:57 madminer.core INFO Sampling from benchmark: w\n", - "10:57 madminer.core INFO Original run card: cards/run_card_signal_small.dat\n", - "10:57 madminer.core INFO Original Pythia8 card: None\n", - "10:57 madminer.core INFO Copied run card: /madminer/cards/run_card_0.dat\n", - "10:57 madminer.core INFO Copied Pythia8 card: None\n", - "10:57 madminer.core INFO Param card: /madminer/cards/param_card_0.dat\n", - "10:57 madminer.core INFO Reweight card: /madminer/cards/reweight_card_0.dat\n", - "10:57 madminer.core INFO Log file: run_0.log\n", - "10:57 madminer.core INFO Creating param and reweight cards in ./mg_processes/signal2//madminer/cards/param_card_0.dat, ./mg_processes/signal2//madminer/cards/reweight_card_0.dat\n", - "10:57 madminer.utils.inter INFO Starting MadGraph and Pythia in ./mg_processes/signal2\n", - "10:59 madminer.core INFO Run 1\n", - "10:59 madminer.core INFO Sampling from benchmark: ww\n", - "10:59 madminer.core INFO Original run card: cards/run_card_signal_small.dat\n", - "10:59 madminer.core INFO Original Pythia8 card: None\n", - "10:59 madminer.core INFO Copied run card: /madminer/cards/run_card_1.dat\n", - "10:59 madminer.core INFO Copied Pythia8 card: None\n", - "10:59 madminer.core INFO Param card: /madminer/cards/param_card_1.dat\n", - "10:59 madminer.core INFO Reweight card: /madminer/cards/reweight_card_1.dat\n", - "10:59 madminer.core INFO Log file: run_1.log\n", - "10:59 madminer.core INFO Creating param and reweight cards in ./mg_processes/signal2//madminer/cards/param_card_1.dat, ./mg_processes/signal2//madminer/cards/reweight_card_1.dat\n", - "10:59 madminer.utils.inter INFO Starting MadGraph and Pythia in ./mg_processes/signal2\n", - "11:01 madminer.core INFO Run 2\n", - "11:01 madminer.core INFO Sampling from benchmark: neg_w\n", - "11:01 madminer.core INFO Original run card: cards/run_card_signal_small.dat\n", - "11:01 madminer.core INFO Original Pythia8 card: None\n", - "11:01 madminer.core INFO Copied run card: /madminer/cards/run_card_2.dat\n", - "11:01 madminer.core INFO Copied Pythia8 card: None\n", - "11:01 madminer.core INFO Param card: /madminer/cards/param_card_2.dat\n", - "11:01 madminer.core INFO Reweight card: /madminer/cards/reweight_card_2.dat\n", - "11:01 madminer.core INFO Log file: run_2.log\n", - "11:01 madminer.core INFO Creating param and reweight cards in ./mg_processes/signal2//madminer/cards/param_card_2.dat, ./mg_processes/signal2//madminer/cards/reweight_card_2.dat\n", - "11:01 madminer.utils.inter INFO Starting MadGraph and Pythia in ./mg_processes/signal2\n", - "11:02 madminer.core INFO Run 3\n", - "11:02 madminer.core INFO Sampling from benchmark: neg_ww\n", - "11:02 madminer.core INFO Original run card: cards/run_card_signal_small.dat\n", - "11:02 madminer.core INFO Original Pythia8 card: None\n", - "11:02 madminer.core INFO Copied run card: /madminer/cards/run_card_3.dat\n", - "11:02 madminer.core INFO Copied Pythia8 card: None\n", - "11:02 madminer.core INFO Param card: /madminer/cards/param_card_3.dat\n", - "11:02 madminer.core INFO Reweight card: /madminer/cards/reweight_card_3.dat\n", - "11:02 madminer.core INFO Log file: run_3.log\n", - "11:02 madminer.core INFO Creating param and reweight cards in ./mg_processes/signal2//madminer/cards/param_card_3.dat, ./mg_processes/signal2//madminer/cards/reweight_card_3.dat\n", - "11:02 madminer.utils.inter INFO Starting MadGraph and Pythia in ./mg_processes/signal2\n", - "11:04 madminer.core INFO Finished running MadGraph! Please check that events were succesfully generated in the following folders:\n", + "17:54 madminer.utils.inter INFO Generating MadGraph process folder from cards/proc_card_signal.dat at ./mg_processes/signal2\n", + "17:54 madminer.core.madmin INFO Run 0\n", + "17:54 madminer.core.madmin INFO Sampling from benchmark: 5\n", + "17:54 madminer.core.madmin INFO Original run card: cards/run_card_signal_small.dat\n", + "17:54 madminer.core.madmin INFO Original Pythia8 card: None\n", + "17:54 madminer.core.madmin INFO Original config card: None\n", + "17:54 madminer.core.madmin INFO Copied run card: madminer/cards/run_card_0.dat\n", + "17:54 madminer.core.madmin INFO Copied Pythia8 card: None\n", + "17:54 madminer.core.madmin INFO Copied config card: None\n", + "17:54 madminer.core.madmin INFO Param card: madminer/cards/param_card_0.dat\n", + "17:54 madminer.core.madmin INFO Reweight card: madminer/cards/reweight_card_0.dat\n", + "17:54 madminer.core.madmin INFO Log file: run_0.log\n", + "17:54 madminer.core.madmin INFO Creating param and reweight cards in ./mg_processes/signal2/madminer/cards/param_card_0.dat, ./mg_processes/signal2/madminer/cards/reweight_card_0.dat\n", + "17:54 madminer.utils.inter INFO Starting MadGraph and Pythia in ./mg_processes/signal2\n", + "17:55 madminer.core.madmin INFO Run 1\n", + "17:55 madminer.core.madmin INFO Sampling from benchmark: neg_5\n", + "17:55 madminer.core.madmin INFO Original run card: cards/run_card_signal_small.dat\n", + "17:55 madminer.core.madmin INFO Original Pythia8 card: None\n", + "17:55 madminer.core.madmin INFO Original config card: None\n", + "17:55 madminer.core.madmin INFO Copied run card: madminer/cards/run_card_1.dat\n", + "17:55 madminer.core.madmin INFO Copied Pythia8 card: None\n", + "17:55 madminer.core.madmin INFO Copied config card: None\n", + "17:55 madminer.core.madmin INFO Param card: madminer/cards/param_card_1.dat\n", + "17:55 madminer.core.madmin INFO Reweight card: madminer/cards/reweight_card_1.dat\n", + "17:55 madminer.core.madmin INFO Log file: run_1.log\n", + "17:55 madminer.core.madmin INFO Creating param and reweight cards in ./mg_processes/signal2/madminer/cards/param_card_1.dat, ./mg_processes/signal2/madminer/cards/reweight_card_1.dat\n", + "17:55 madminer.utils.inter INFO Starting MadGraph and Pythia in ./mg_processes/signal2\n", + "17:57 madminer.core.madmin INFO Run 2\n", + "17:57 madminer.core.madmin INFO Sampling from benchmark: 10\n", + "17:57 madminer.core.madmin INFO Original run card: cards/run_card_signal_small.dat\n", + "17:57 madminer.core.madmin INFO Original Pythia8 card: None\n", + "17:57 madminer.core.madmin INFO Original config card: None\n", + "17:57 madminer.core.madmin INFO Copied run card: madminer/cards/run_card_2.dat\n", + "17:57 madminer.core.madmin INFO Copied Pythia8 card: None\n", + "17:57 madminer.core.madmin INFO Copied config card: None\n", + "17:57 madminer.core.madmin INFO Param card: madminer/cards/param_card_2.dat\n", + "17:57 madminer.core.madmin INFO Reweight card: madminer/cards/reweight_card_2.dat\n", + "17:57 madminer.core.madmin INFO Log file: run_2.log\n", + "17:57 madminer.core.madmin INFO Creating param and reweight cards in ./mg_processes/signal2/madminer/cards/param_card_2.dat, ./mg_processes/signal2/madminer/cards/reweight_card_2.dat\n", + "17:57 madminer.utils.inter INFO Starting MadGraph and Pythia in ./mg_processes/signal2\n", + "17:59 madminer.core.madmin INFO Run 3\n", + "17:59 madminer.core.madmin INFO Sampling from benchmark: neg_10\n", + "17:59 madminer.core.madmin INFO Original run card: cards/run_card_signal_small.dat\n", + "17:59 madminer.core.madmin INFO Original Pythia8 card: None\n", + "17:59 madminer.core.madmin INFO Original config card: None\n", + "17:59 madminer.core.madmin INFO Copied run card: madminer/cards/run_card_3.dat\n", + "17:59 madminer.core.madmin INFO Copied Pythia8 card: None\n", + "17:59 madminer.core.madmin INFO Copied config card: None\n", + "17:59 madminer.core.madmin INFO Param card: madminer/cards/param_card_3.dat\n", + "17:59 madminer.core.madmin INFO Reweight card: madminer/cards/reweight_card_3.dat\n", + "17:59 madminer.core.madmin INFO Log file: run_3.log\n", + "17:59 madminer.core.madmin INFO Creating param and reweight cards in ./mg_processes/signal2/madminer/cards/param_card_3.dat, ./mg_processes/signal2/madminer/cards/reweight_card_3.dat\n", + "17:59 madminer.utils.inter INFO Starting MadGraph and Pythia in ./mg_processes/signal2\n", + "18:00 madminer.core.madmin INFO Run 4\n", + "18:00 madminer.core.madmin INFO Sampling from benchmark: 20\n", + "18:00 madminer.core.madmin INFO Original run card: cards/run_card_signal_small.dat\n", + "18:00 madminer.core.madmin INFO Original Pythia8 card: None\n", + "18:00 madminer.core.madmin INFO Original config card: None\n", + "18:00 madminer.core.madmin INFO Copied run card: madminer/cards/run_card_4.dat\n", + "18:00 madminer.core.madmin INFO Copied Pythia8 card: None\n", + "18:00 madminer.core.madmin INFO Copied config card: None\n", + "18:00 madminer.core.madmin INFO Param card: madminer/cards/param_card_4.dat\n", + "18:00 madminer.core.madmin INFO Reweight card: madminer/cards/reweight_card_4.dat\n", + "18:00 madminer.core.madmin INFO Log file: run_4.log\n", + "18:00 madminer.core.madmin INFO Creating param and reweight cards in ./mg_processes/signal2/madminer/cards/param_card_4.dat, ./mg_processes/signal2/madminer/cards/reweight_card_4.dat\n", + "18:00 madminer.utils.inter INFO Starting MadGraph and Pythia in ./mg_processes/signal2\n", + "18:02 madminer.core.madmin INFO Run 5\n", + "18:02 madminer.core.madmin INFO Sampling from benchmark: neg_20\n", + "18:02 madminer.core.madmin INFO Original run card: cards/run_card_signal_small.dat\n", + "18:02 madminer.core.madmin INFO Original Pythia8 card: None\n", + "18:02 madminer.core.madmin INFO Original config card: None\n", + "18:02 madminer.core.madmin INFO Copied run card: madminer/cards/run_card_5.dat\n", + "18:02 madminer.core.madmin INFO Copied Pythia8 card: None\n", + "18:02 madminer.core.madmin INFO Copied config card: None\n", + "18:02 madminer.core.madmin INFO Param card: madminer/cards/param_card_5.dat\n", + "18:02 madminer.core.madmin INFO Reweight card: madminer/cards/reweight_card_5.dat\n", + "18:02 madminer.core.madmin INFO Log file: run_5.log\n", + "18:02 madminer.core.madmin INFO Creating param and reweight cards in ./mg_processes/signal2/madminer/cards/param_card_5.dat, ./mg_processes/signal2/madminer/cards/reweight_card_5.dat\n", + "18:02 madminer.utils.inter INFO Starting MadGraph and Pythia in ./mg_processes/signal2\n", + "18:04 madminer.core.madmin INFO Finished running MadGraph! Please check that events were succesfully generated in the following folders:\n", "\n", "./mg_processes/signal2/Events/run_01\n", "./mg_processes/signal2/Events/run_02\n", "./mg_processes/signal2/Events/run_03\n", "./mg_processes/signal2/Events/run_04\n", + "./mg_processes/signal2/Events/run_05\n", + "./mg_processes/signal2/Events/run_06\n", "\n", "\n" ] @@ -320,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -329,7 +371,7 @@ "\"\\nminer.run(\\n is_background=True,\\n sample_benchmark='sm',\\n mg_directory=mg_dir,\\n mg_process_directory='./mg_processes/background',\\n proc_card_file='cards/proc_card_background.dat',\\n param_card_template_file='cards/param_card_template.dat',\\n run_card_file='cards/run_card_background.dat',\\n log_directory='logs/background',\\n)\\n\"" ] }, - "execution_count": 8, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -379,12 +421,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "11:04 madminer.utils.inter DEBUG HDF5 file does not contain is_reference field.\n" + "18:04 madminer.utils.inter DEBUG HDF5 file does not contain is_reference field.\n" ] } ], "source": [ - "lhe = LHEReader('data/setup.h5')" + "lhe = LHEReader('data/setup_gw.h5')" ] }, { @@ -407,11 +449,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "11:04 madminer.lhe DEBUG Adding event sample mg_processes/signal1/Events/run_01/unweighted_events.lhe.gz\n", - "11:04 madminer.lhe DEBUG Adding event sample mg_processes/signal2/Events/run_01/unweighted_events.lhe.gz\n", - "11:04 madminer.lhe DEBUG Adding event sample mg_processes/signal2/Events/run_02/unweighted_events.lhe.gz\n", - "11:04 madminer.lhe DEBUG Adding event sample mg_processes/signal2/Events/run_03/unweighted_events.lhe.gz\n", - "11:04 madminer.lhe DEBUG Adding event sample mg_processes/signal2/Events/run_04/unweighted_events.lhe.gz\n" + "18:04 madminer.lhe.lhe_rea DEBUG Adding event sample mg_processes/signal1/Events/run_01/unweighted_events.lhe.gz\n", + "18:04 madminer.lhe.lhe_rea DEBUG Adding event sample mg_processes/signal2/Events/run_01/unweighted_events.lhe.gz\n", + "18:04 madminer.lhe.lhe_rea DEBUG Adding event sample mg_processes/signal2/Events/run_02/unweighted_events.lhe.gz\n", + "18:04 madminer.lhe.lhe_rea DEBUG Adding event sample mg_processes/signal2/Events/run_03/unweighted_events.lhe.gz\n", + "18:04 madminer.lhe.lhe_rea DEBUG Adding event sample mg_processes/signal2/Events/run_04/unweighted_events.lhe.gz\n", + "18:04 madminer.lhe.lhe_rea DEBUG Adding event sample mg_processes/signal2/Events/run_05/unweighted_events.lhe.gz\n", + "18:04 madminer.lhe.lhe_rea DEBUG Adding event sample mg_processes/signal2/Events/run_06/unweighted_events.lhe.gz\n" ] }, { @@ -465,7 +509,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -491,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -520,16 +564,16 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "11:04 madminer.lhe DEBUG Adding optional observable pt_j1 = j[0].pt with default 0.0\n", - "11:04 madminer.lhe DEBUG Adding required observable delta_phi_jj = j[0].deltaphi(j[1]) * (-1. + 2.*float(j[0].eta > j[1].eta))\n", - "11:04 madminer.lhe DEBUG Adding required observable met = met.pt\n" + "18:04 madminer.lhe.lhe_rea DEBUG Adding optional observable pt_j1 = j[0].pt with default 0.0\n", + "18:04 madminer.lhe.lhe_rea DEBUG Adding required observable delta_phi_jj = j[0].deltaphi(j[1]) * (-1. + 2.*float(j[0].eta > j[1].eta))\n", + "18:04 madminer.lhe.lhe_rea DEBUG Adding required observable met = met.pt\n" ] } ], @@ -561,16 +605,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "11:04 madminer.lhe DEBUG Adding cut (a[0] + a[1]).m > 122.\n", - "11:04 madminer.lhe DEBUG Adding cut (a[0] + a[1]).m < 128.\n", - "11:04 madminer.lhe DEBUG Adding cut pt_j1 > 20.\n" + "18:04 madminer.lhe.lhe_rea DEBUG Adding cut (a[0] + a[1]).m > 122.\n", + "18:04 madminer.lhe.lhe_rea DEBUG Adding cut (a[0] + a[1]).m < 128.\n", + "18:04 madminer.lhe.lhe_rea DEBUG Adding cut pt_j1 > 20.\n" ] } ], @@ -596,7 +640,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": { "scrolled": true }, @@ -605,92 +649,343 @@ "name": "stderr", "output_type": "stream", "text": [ - "11:04 madminer.lhe INFO Analysing LHE sample mg_processes/signal1/Events/run_01/unweighted_events.lhe.gz\n", - "11:04 madminer.lhe DEBUG Extracting nuisance parameter definitions from LHE file\n", - "11:04 madminer.utils.inter DEBUG Parsing nuisance parameter setup from LHE file at mg_processes/signal1/Events/run_01/unweighted_events.lhe.gz\n", - "11:04 madminer.lhe DEBUG Found 0 nuisance parameters with matching benchmarks:\n", - "11:04 madminer.utils.inter DEBUG Parsing LHE file mg_processes/signal1/Events/run_01/unweighted_events.lhe.gz\n", - "11:04 madminer.utils.inter DEBUG Parsing header and events as XML with cElementTree\n", - "11:04 madminer.utils.inter DEBUG Found entry event_norm = sum in LHE header. Interpreting this as weight_norm_is_average = False.\n", - "11:05 madminer.utils.inter DEBUG 29004 / 50000 events pass cut (a[0] + a[1]).m > 122.\n", - "11:05 madminer.utils.inter DEBUG 30849 / 50000 events pass cut (a[0] + a[1]).m < 128.\n", - "11:05 madminer.utils.inter DEBUG 49989 / 50000 events pass cut pt_j1 > 20.\n", - "11:05 madminer.utils.inter INFO 9849 events pass all cuts/efficiencies\n", - "11:05 madminer.lhe DEBUG Found weights ['sm', 'w', 'neg_w', 'ww', 'neg_ww', 'morphing_basis_vector_5'] in LHE file\n", - "11:05 madminer.lhe DEBUG Found 9849 events in Obs pt_j1\n", - "11:05 madminer.lhe DEBUG Found 9849 events\n", - "11:05 madminer.lhe DEBUG Found 9849 events in Obs delta_phi_jj\n", - "11:05 madminer.lhe DEBUG Found 9849 events in Obs met\n", - "11:05 madminer.lhe INFO Analysing LHE sample mg_processes/signal2/Events/run_01/unweighted_events.lhe.gz\n", - "11:05 madminer.lhe DEBUG Extracting nuisance parameter definitions from LHE file\n", - "11:05 madminer.utils.inter DEBUG Parsing nuisance parameter setup from LHE file at mg_processes/signal2/Events/run_01/unweighted_events.lhe.gz\n", - "11:05 madminer.lhe DEBUG Found 0 nuisance parameters with matching benchmarks:\n", - "11:05 madminer.utils.inter DEBUG Parsing LHE file mg_processes/signal2/Events/run_01/unweighted_events.lhe.gz\n", - "11:05 madminer.utils.inter DEBUG Parsing header and events as XML with cElementTree\n", - "11:05 madminer.utils.inter DEBUG Found entry event_norm = sum in LHE header. Interpreting this as weight_norm_is_average = False.\n", - "11:05 madminer.utils.inter DEBUG 5690 / 10000 events pass cut (a[0] + a[1]).m > 122.\n", - "11:05 madminer.utils.inter DEBUG 5359 / 10000 events pass cut (a[0] + a[1]).m < 128.\n", - "11:05 madminer.utils.inter DEBUG 9999 / 10000 events pass cut pt_j1 > 20.\n", - "11:05 madminer.utils.inter INFO 1049 events pass all cuts/efficiencies\n", - "11:05 madminer.lhe DEBUG Found weights ['w', 'sm', 'neg_w', 'ww', 'neg_ww', 'morphing_basis_vector_5'] in LHE file\n", - "11:05 madminer.lhe DEBUG Found 1049 events in Obs pt_j1\n", - "11:05 madminer.lhe DEBUG Found 1049 events\n", - "11:05 madminer.lhe DEBUG Found 1049 events in Obs delta_phi_jj\n", - "11:05 madminer.lhe DEBUG Found 1049 events in Obs met\n", - "11:05 madminer.lhe INFO Analysing LHE sample mg_processes/signal2/Events/run_02/unweighted_events.lhe.gz\n", - "11:05 madminer.lhe DEBUG Extracting nuisance parameter definitions from LHE file\n", - "11:05 madminer.utils.inter DEBUG Parsing nuisance parameter setup from LHE file at mg_processes/signal2/Events/run_02/unweighted_events.lhe.gz\n", - "11:05 madminer.lhe DEBUG Found 0 nuisance parameters with matching benchmarks:\n", - "11:05 madminer.utils.inter DEBUG Parsing LHE file mg_processes/signal2/Events/run_02/unweighted_events.lhe.gz\n", - "11:05 madminer.utils.inter DEBUG Parsing header and events as XML with cElementTree\n", - "11:05 madminer.utils.inter DEBUG Found entry event_norm = sum in LHE header. Interpreting this as weight_norm_is_average = False.\n", - "11:05 madminer.utils.inter DEBUG 5742 / 10000 events pass cut (a[0] + a[1]).m > 122.\n", - "11:05 madminer.utils.inter DEBUG 5675 / 10000 events pass cut (a[0] + a[1]).m < 128.\n", - "11:05 madminer.utils.inter DEBUG 10000 / 10000 events pass cut pt_j1 > 20.\n", - "11:05 madminer.utils.inter INFO 1417 events pass all cuts/efficiencies\n", - "11:05 madminer.lhe DEBUG Found weights ['ww', 'sm', 'w', 'neg_w', 'neg_ww', 'morphing_basis_vector_5'] in LHE file\n", - "11:05 madminer.lhe DEBUG Found 1417 events in Obs pt_j1\n", - "11:05 madminer.lhe DEBUG Found 1417 events\n", - "11:05 madminer.lhe DEBUG Found 1417 events in Obs delta_phi_jj\n", - "11:05 madminer.lhe DEBUG Found 1417 events in Obs met\n", - "11:05 madminer.lhe INFO Analysing LHE sample mg_processes/signal2/Events/run_03/unweighted_events.lhe.gz\n", - "11:05 madminer.lhe DEBUG Extracting nuisance parameter definitions from LHE file\n", - "11:05 madminer.utils.inter DEBUG Parsing nuisance parameter setup from LHE file at mg_processes/signal2/Events/run_03/unweighted_events.lhe.gz\n", - "11:05 madminer.lhe DEBUG Found 0 nuisance parameters with matching benchmarks:\n", - "11:05 madminer.utils.inter DEBUG Parsing LHE file mg_processes/signal2/Events/run_03/unweighted_events.lhe.gz\n", - "11:05 madminer.utils.inter DEBUG Parsing header and events as XML with cElementTree\n", - "11:05 madminer.utils.inter DEBUG Found entry event_norm = sum in LHE header. Interpreting this as weight_norm_is_average = False.\n", - "11:05 madminer.utils.inter DEBUG 5778 / 10000 events pass cut (a[0] + a[1]).m > 122.\n", - "11:05 madminer.utils.inter DEBUG 5372 / 10000 events pass cut (a[0] + a[1]).m < 128.\n", - "11:05 madminer.utils.inter DEBUG 10000 / 10000 events pass cut pt_j1 > 20.\n", - "11:05 madminer.utils.inter INFO 1150 events pass all cuts/efficiencies\n", - "11:05 madminer.lhe DEBUG Found weights ['neg_w', 'sm', 'w', 'ww', 'neg_ww', 'morphing_basis_vector_5'] in LHE file\n", - "11:05 madminer.lhe DEBUG Found 1150 events in Obs pt_j1\n", - "11:05 madminer.lhe DEBUG Found 1150 events\n", - "11:05 madminer.lhe DEBUG Found 1150 events in Obs delta_phi_jj\n", - "11:05 madminer.lhe DEBUG Found 1150 events in Obs met\n", - "11:05 madminer.lhe INFO Analysing LHE sample mg_processes/signal2/Events/run_04/unweighted_events.lhe.gz\n", - "11:05 madminer.lhe DEBUG Extracting nuisance parameter definitions from LHE file\n", - "11:05 madminer.utils.inter DEBUG Parsing nuisance parameter setup from LHE file at mg_processes/signal2/Events/run_04/unweighted_events.lhe.gz\n", - "11:05 madminer.lhe DEBUG Found 0 nuisance parameters with matching benchmarks:\n", - "11:05 madminer.utils.inter DEBUG Parsing LHE file mg_processes/signal2/Events/run_04/unweighted_events.lhe.gz\n", - "11:05 madminer.utils.inter DEBUG Parsing header and events as XML with cElementTree\n", - "11:05 madminer.utils.inter DEBUG Found entry event_norm = sum in LHE header. Interpreting this as weight_norm_is_average = False.\n", - "11:05 madminer.utils.inter DEBUG 5762 / 10000 events pass cut (a[0] + a[1]).m > 122.\n", - "11:05 madminer.utils.inter DEBUG 5604 / 10000 events pass cut (a[0] + a[1]).m < 128.\n", - "11:05 madminer.utils.inter DEBUG 10000 / 10000 events pass cut pt_j1 > 20.\n", - "11:05 madminer.utils.inter INFO 1366 events pass all cuts/efficiencies\n", - "11:05 madminer.lhe DEBUG Found weights ['neg_ww', 'sm', 'w', 'neg_w', 'ww', 'morphing_basis_vector_5'] in LHE file\n", - "11:05 madminer.lhe DEBUG Found 1366 events in Obs pt_j1\n", - "11:05 madminer.lhe DEBUG Found 1366 events\n", - "11:05 madminer.lhe DEBUG Found 1366 events in Obs delta_phi_jj\n", - "11:05 madminer.lhe DEBUG Found 1366 events in Obs met\n", - "11:05 madminer.lhe INFO Analysed number of events per sampling benchmark:\n", - "11:05 madminer.lhe INFO 9849 from sm\n", - "11:05 madminer.lhe INFO 1049 from w\n", - "11:05 madminer.lhe INFO 1150 from neg_w\n", - "11:05 madminer.lhe INFO 1417 from ww\n", - "11:05 madminer.lhe INFO 1366 from neg_ww\n" + "18:04 madminer.lhe.lhe_rea INFO Analysing LHE sample mg_processes/signal1/Events/run_01/unweighted_events.lhe.gz: Calculating 3 observables, requiring 3 selection cuts, using 0 efficiency factors, associated with no systematics\n", + "18:04 madminer.lhe.lhe_rea DEBUG Extracting nuisance parameter definitions from LHE file\n", + "18:04 madminer.utils.inter DEBUG Parsing nuisance parameter setup from LHE file at mg_processes/signal1/Events/run_01/unweighted_events.lhe.gz\n", + "18:04 madminer.utils.inter DEBUG Systematics setup: OrderedDict()\n", + "18:04 madminer.utils.inter DEBUG 1 weight groups\n", + "18:04 madminer.lhe.lhe_rea DEBUG systematics_dict: OrderedDict()\n", + "18:04 madminer.utils.inter DEBUG Parsing LHE file mg_processes/signal1/Events/run_01/unweighted_events.lhe.gz\n", + "18:04 madminer.utils.inter DEBUG Parsing header and events as XML with cElementTree\n", + "18:04 madminer.utils.inter DEBUG Found entry event_norm = sum in LHE header. Interpreting this as weight_norm_is_average = False.\n", + "18:04 madminer.utils.inter DEBUG Event 1 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 2 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 3 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 4 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 5 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 6 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 7 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 8 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 9 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 10 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 11 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 12 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 13 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 14 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 15 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 16 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 17 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 18 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 19 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 20 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter INFO 50000 / 50000 events pass cut (a[0] + a[1]).m > 122.\n", + "18:04 madminer.utils.inter INFO 50000 / 50000 events pass cut (a[0] + a[1]).m < 128.\n", + "18:04 madminer.utils.inter INFO 50000 / 50000 events pass cut pt_j1 > 20.\n", + "18:04 madminer.utils.inter INFO 50000 events pass all cuts/efficiencies\n", + "18:04 madminer.lhe.lhe_rea DEBUG Found weights ['morphing_basis_vector_0', 'morphing_basis_vector_1', 'morphing_basis_vector_2', 'sm', '5', '10', '20', 'neg_5', 'neg_10', 'neg_20'] in LHE file\n", + "18:04 madminer.lhe.lhe_rea DEBUG Found 50000 events\n", + "18:04 madminer.lhe.lhe_rea INFO Analysing LHE sample mg_processes/signal2/Events/run_01/unweighted_events.lhe.gz: Calculating 3 observables, requiring 3 selection cuts, using 0 efficiency factors, associated with no systematics\n", + "18:04 madminer.lhe.lhe_rea DEBUG Extracting nuisance parameter definitions from LHE file\n", + "18:04 madminer.utils.inter DEBUG Parsing nuisance parameter setup from LHE file at mg_processes/signal2/Events/run_01/unweighted_events.lhe.gz\n", + "18:04 madminer.utils.inter DEBUG Systematics setup: OrderedDict()\n", + "18:04 madminer.utils.inter DEBUG 1 weight groups\n", + "18:04 madminer.lhe.lhe_rea DEBUG systematics_dict: OrderedDict()\n", + "18:04 madminer.utils.inter DEBUG Parsing LHE file mg_processes/signal2/Events/run_01/unweighted_events.lhe.gz\n", + "18:04 madminer.utils.inter DEBUG Parsing header and events as XML with cElementTree\n", + "18:04 madminer.utils.inter DEBUG Found entry event_norm = sum in LHE header. Interpreting this as weight_norm_is_average = False.\n", + "18:04 madminer.utils.inter DEBUG Event 1 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 2 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 3 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 4 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 5 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 6 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 7 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 8 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 9 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 10 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 11 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 12 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 13 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 14 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 15 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 16 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 17 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 18 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 19 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 20 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter INFO 10000 / 10000 events pass cut (a[0] + a[1]).m > 122.\n", + "18:04 madminer.utils.inter INFO 10000 / 10000 events pass cut (a[0] + a[1]).m < 128.\n", + "18:04 madminer.utils.inter INFO 10000 / 10000 events pass cut pt_j1 > 20.\n", + "18:04 madminer.utils.inter INFO 10000 events pass all cuts/efficiencies\n", + "18:04 madminer.lhe.lhe_rea DEBUG Found weights ['morphing_basis_vector_0', 'morphing_basis_vector_1', 'morphing_basis_vector_2', 'sm', '5', '10', '20', 'neg_5', 'neg_10', 'neg_20'] in LHE file\n", + "18:04 madminer.lhe.lhe_rea DEBUG Found 10000 events\n", + "18:04 root DEBUG Merging data extracted from this file with data from previous files\n", + "18:04 root DEBUG Weights for benchmark morphing_basis_vector_0 exist in both\n", + "18:04 root DEBUG Weights for benchmark morphing_basis_vector_1 exist in both\n", + "18:04 root DEBUG Weights for benchmark morphing_basis_vector_2 exist in both\n", + "18:04 root DEBUG Weights for benchmark sm exist in both\n", + "18:04 root DEBUG Weights for benchmark 5 exist in both\n", + "18:04 root DEBUG Weights for benchmark 10 exist in both\n", + "18:04 root DEBUG Weights for benchmark 20 exist in both\n", + "18:04 root DEBUG Weights for benchmark neg_5 exist in both\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "18:04 root DEBUG Weights for benchmark neg_10 exist in both\n", + "18:04 root DEBUG Weights for benchmark neg_20 exist in both\n", + "18:04 madminer.lhe.lhe_rea INFO Analysing LHE sample mg_processes/signal2/Events/run_02/unweighted_events.lhe.gz: Calculating 3 observables, requiring 3 selection cuts, using 0 efficiency factors, associated with no systematics\n", + "18:04 madminer.lhe.lhe_rea DEBUG Extracting nuisance parameter definitions from LHE file\n", + "18:04 madminer.utils.inter DEBUG Parsing nuisance parameter setup from LHE file at mg_processes/signal2/Events/run_02/unweighted_events.lhe.gz\n", + "18:04 madminer.utils.inter DEBUG Systematics setup: OrderedDict()\n", + "18:04 madminer.utils.inter DEBUG 1 weight groups\n", + "18:04 madminer.lhe.lhe_rea DEBUG systematics_dict: OrderedDict()\n", + "18:04 madminer.utils.inter DEBUG Parsing LHE file mg_processes/signal2/Events/run_02/unweighted_events.lhe.gz\n", + "18:04 madminer.utils.inter DEBUG Parsing header and events as XML with cElementTree\n", + "18:04 madminer.utils.inter DEBUG Found entry event_norm = sum in LHE header. Interpreting this as weight_norm_is_average = False.\n", + "18:04 madminer.utils.inter DEBUG Event 1 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 2 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 3 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 4 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 5 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 6 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 7 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 8 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 9 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 10 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 11 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 12 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 13 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 14 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 15 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 16 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 17 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 18 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 19 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 20 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter INFO 10000 / 10000 events pass cut (a[0] + a[1]).m > 122.\n", + "18:04 madminer.utils.inter INFO 10000 / 10000 events pass cut (a[0] + a[1]).m < 128.\n", + "18:04 madminer.utils.inter INFO 10000 / 10000 events pass cut pt_j1 > 20.\n", + "18:04 madminer.utils.inter INFO 10000 events pass all cuts/efficiencies\n", + "18:04 madminer.lhe.lhe_rea DEBUG Found weights ['morphing_basis_vector_0', 'morphing_basis_vector_1', 'morphing_basis_vector_2', 'sm', '5', '10', '20', 'neg_5', 'neg_10', 'neg_20'] in LHE file\n", + "18:04 madminer.lhe.lhe_rea DEBUG Found 10000 events\n", + "18:04 root DEBUG Merging data extracted from this file with data from previous files\n", + "18:04 root DEBUG Weights for benchmark morphing_basis_vector_0 exist in both\n", + "18:04 root DEBUG Weights for benchmark morphing_basis_vector_1 exist in both\n", + "18:04 root DEBUG Weights for benchmark morphing_basis_vector_2 exist in both\n", + "18:04 root DEBUG Weights for benchmark sm exist in both\n", + "18:04 root DEBUG Weights for benchmark 5 exist in both\n", + "18:04 root DEBUG Weights for benchmark 10 exist in both\n", + "18:04 root DEBUG Weights for benchmark 20 exist in both\n", + "18:04 root DEBUG Weights for benchmark neg_5 exist in both\n", + "18:04 root DEBUG Weights for benchmark neg_10 exist in both\n", + "18:04 root DEBUG Weights for benchmark neg_20 exist in both\n", + "18:04 madminer.lhe.lhe_rea INFO Analysing LHE sample mg_processes/signal2/Events/run_03/unweighted_events.lhe.gz: Calculating 3 observables, requiring 3 selection cuts, using 0 efficiency factors, associated with no systematics\n", + "18:04 madminer.lhe.lhe_rea DEBUG Extracting nuisance parameter definitions from LHE file\n", + "18:04 madminer.utils.inter DEBUG Parsing nuisance parameter setup from LHE file at mg_processes/signal2/Events/run_03/unweighted_events.lhe.gz\n", + "18:04 madminer.utils.inter DEBUG Systematics setup: OrderedDict()\n", + "18:04 madminer.utils.inter DEBUG 1 weight groups\n", + "18:04 madminer.lhe.lhe_rea DEBUG systematics_dict: OrderedDict()\n", + "18:04 madminer.utils.inter DEBUG Parsing LHE file mg_processes/signal2/Events/run_03/unweighted_events.lhe.gz\n", + "18:04 madminer.utils.inter DEBUG Parsing header and events as XML with cElementTree\n", + "18:04 madminer.utils.inter DEBUG Found entry event_norm = sum in LHE header. Interpreting this as weight_norm_is_average = False.\n", + "18:04 madminer.utils.inter DEBUG Event 1 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 2 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 3 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 4 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 5 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 6 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 7 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 8 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 9 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 10 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 11 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 12 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 13 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 14 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 15 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 16 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 17 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 18 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 19 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 20 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter INFO 10000 / 10000 events pass cut (a[0] + a[1]).m > 122.\n", + "18:04 madminer.utils.inter INFO 10000 / 10000 events pass cut (a[0] + a[1]).m < 128.\n", + "18:04 madminer.utils.inter INFO 10000 / 10000 events pass cut pt_j1 > 20.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "18:04 madminer.utils.inter INFO 10000 events pass all cuts/efficiencies\n", + "18:04 madminer.lhe.lhe_rea DEBUG Found weights ['morphing_basis_vector_0', 'morphing_basis_vector_1', 'morphing_basis_vector_2', 'sm', '5', '10', '20', 'neg_5', 'neg_10', 'neg_20'] in LHE file\n", + "18:04 madminer.lhe.lhe_rea DEBUG Found 10000 events\n", + "18:04 root DEBUG Merging data extracted from this file with data from previous files\n", + "18:04 root DEBUG Weights for benchmark morphing_basis_vector_0 exist in both\n", + "18:04 root DEBUG Weights for benchmark morphing_basis_vector_1 exist in both\n", + "18:04 root DEBUG Weights for benchmark morphing_basis_vector_2 exist in both\n", + "18:04 root DEBUG Weights for benchmark sm exist in both\n", + "18:04 root DEBUG Weights for benchmark 5 exist in both\n", + "18:04 root DEBUG Weights for benchmark 10 exist in both\n", + "18:04 root DEBUG Weights for benchmark 20 exist in both\n", + "18:04 root DEBUG Weights for benchmark neg_5 exist in both\n", + "18:04 root DEBUG Weights for benchmark neg_10 exist in both\n", + "18:04 root DEBUG Weights for benchmark neg_20 exist in both\n", + "18:04 madminer.lhe.lhe_rea INFO Analysing LHE sample mg_processes/signal2/Events/run_04/unweighted_events.lhe.gz: Calculating 3 observables, requiring 3 selection cuts, using 0 efficiency factors, associated with no systematics\n", + "18:04 madminer.lhe.lhe_rea DEBUG Extracting nuisance parameter definitions from LHE file\n", + "18:04 madminer.utils.inter DEBUG Parsing nuisance parameter setup from LHE file at mg_processes/signal2/Events/run_04/unweighted_events.lhe.gz\n", + "18:04 madminer.utils.inter DEBUG Systematics setup: OrderedDict()\n", + "18:04 madminer.utils.inter DEBUG 1 weight groups\n", + "18:04 madminer.lhe.lhe_rea DEBUG systematics_dict: OrderedDict()\n", + "18:04 madminer.utils.inter DEBUG Parsing LHE file mg_processes/signal2/Events/run_04/unweighted_events.lhe.gz\n", + "18:04 madminer.utils.inter DEBUG Parsing header and events as XML with cElementTree\n", + "18:04 madminer.utils.inter DEBUG Found entry event_norm = sum in LHE header. Interpreting this as weight_norm_is_average = False.\n", + "18:04 madminer.utils.inter DEBUG Event 1 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 2 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 3 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 4 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 5 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 6 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 7 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 8 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 9 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 10 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 11 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 12 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 13 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 14 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 15 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 16 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 17 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 18 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 19 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:04 madminer.utils.inter DEBUG Event 20 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter INFO 10000 / 10000 events pass cut (a[0] + a[1]).m > 122.\n", + "18:05 madminer.utils.inter INFO 10000 / 10000 events pass cut (a[0] + a[1]).m < 128.\n", + "18:05 madminer.utils.inter INFO 10000 / 10000 events pass cut pt_j1 > 20.\n", + "18:05 madminer.utils.inter INFO 10000 events pass all cuts/efficiencies\n", + "18:05 madminer.lhe.lhe_rea DEBUG Found weights ['morphing_basis_vector_0', 'morphing_basis_vector_1', 'morphing_basis_vector_2', 'sm', '5', '10', '20', 'neg_5', 'neg_10', 'neg_20'] in LHE file\n", + "18:05 madminer.lhe.lhe_rea DEBUG Found 10000 events\n", + "18:05 root DEBUG Merging data extracted from this file with data from previous files\n", + "18:05 root DEBUG Weights for benchmark morphing_basis_vector_0 exist in both\n", + "18:05 root DEBUG Weights for benchmark morphing_basis_vector_1 exist in both\n", + "18:05 root DEBUG Weights for benchmark morphing_basis_vector_2 exist in both\n", + "18:05 root DEBUG Weights for benchmark sm exist in both\n", + "18:05 root DEBUG Weights for benchmark 5 exist in both\n", + "18:05 root DEBUG Weights for benchmark 10 exist in both\n", + "18:05 root DEBUG Weights for benchmark 20 exist in both\n", + "18:05 root DEBUG Weights for benchmark neg_5 exist in both\n", + "18:05 root DEBUG Weights for benchmark neg_10 exist in both\n", + "18:05 root DEBUG Weights for benchmark neg_20 exist in both\n", + "18:05 madminer.lhe.lhe_rea INFO Analysing LHE sample mg_processes/signal2/Events/run_05/unweighted_events.lhe.gz: Calculating 3 observables, requiring 3 selection cuts, using 0 efficiency factors, associated with no systematics\n", + "18:05 madminer.lhe.lhe_rea DEBUG Extracting nuisance parameter definitions from LHE file\n", + "18:05 madminer.utils.inter DEBUG Parsing nuisance parameter setup from LHE file at mg_processes/signal2/Events/run_05/unweighted_events.lhe.gz\n", + "18:05 madminer.utils.inter DEBUG Systematics setup: OrderedDict()\n", + "18:05 madminer.utils.inter DEBUG 1 weight groups\n", + "18:05 madminer.lhe.lhe_rea DEBUG systematics_dict: OrderedDict()\n", + "18:05 madminer.utils.inter DEBUG Parsing LHE file mg_processes/signal2/Events/run_05/unweighted_events.lhe.gz\n", + "18:05 madminer.utils.inter DEBUG Parsing header and events as XML with cElementTree\n", + "18:05 madminer.utils.inter DEBUG Found entry event_norm = sum in LHE header. Interpreting this as weight_norm_is_average = False.\n", + "18:05 madminer.utils.inter DEBUG Event 1 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 2 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 3 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 4 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 5 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 6 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 7 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 8 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 9 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 10 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 11 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 12 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 13 passes observations, passes cuts, passes efficiencies -> passes\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "18:05 madminer.utils.inter DEBUG Event 14 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 15 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 16 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 17 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 18 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 19 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 20 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter INFO 10000 / 10000 events pass cut (a[0] + a[1]).m > 122.\n", + "18:05 madminer.utils.inter INFO 10000 / 10000 events pass cut (a[0] + a[1]).m < 128.\n", + "18:05 madminer.utils.inter INFO 10000 / 10000 events pass cut pt_j1 > 20.\n", + "18:05 madminer.utils.inter INFO 10000 events pass all cuts/efficiencies\n", + "18:05 madminer.lhe.lhe_rea DEBUG Found weights ['morphing_basis_vector_0', 'morphing_basis_vector_1', 'morphing_basis_vector_2', 'sm', '5', '10', '20', 'neg_5', 'neg_10', 'neg_20'] in LHE file\n", + "18:05 madminer.lhe.lhe_rea DEBUG Found 10000 events\n", + "18:05 root DEBUG Merging data extracted from this file with data from previous files\n", + "18:05 root DEBUG Weights for benchmark morphing_basis_vector_0 exist in both\n", + "18:05 root DEBUG Weights for benchmark morphing_basis_vector_1 exist in both\n", + "18:05 root DEBUG Weights for benchmark morphing_basis_vector_2 exist in both\n", + "18:05 root DEBUG Weights for benchmark sm exist in both\n", + "18:05 root DEBUG Weights for benchmark 5 exist in both\n", + "18:05 root DEBUG Weights for benchmark 10 exist in both\n", + "18:05 root DEBUG Weights for benchmark 20 exist in both\n", + "18:05 root DEBUG Weights for benchmark neg_5 exist in both\n", + "18:05 root DEBUG Weights for benchmark neg_10 exist in both\n", + "18:05 root DEBUG Weights for benchmark neg_20 exist in both\n", + "18:05 madminer.lhe.lhe_rea INFO Analysing LHE sample mg_processes/signal2/Events/run_06/unweighted_events.lhe.gz: Calculating 3 observables, requiring 3 selection cuts, using 0 efficiency factors, associated with no systematics\n", + "18:05 madminer.lhe.lhe_rea DEBUG Extracting nuisance parameter definitions from LHE file\n", + "18:05 madminer.utils.inter DEBUG Parsing nuisance parameter setup from LHE file at mg_processes/signal2/Events/run_06/unweighted_events.lhe.gz\n", + "18:05 madminer.utils.inter DEBUG Systematics setup: OrderedDict()\n", + "18:05 madminer.utils.inter DEBUG 1 weight groups\n", + "18:05 madminer.lhe.lhe_rea DEBUG systematics_dict: OrderedDict()\n", + "18:05 madminer.utils.inter DEBUG Parsing LHE file mg_processes/signal2/Events/run_06/unweighted_events.lhe.gz\n", + "18:05 madminer.utils.inter DEBUG Parsing header and events as XML with cElementTree\n", + "18:05 madminer.utils.inter DEBUG Found entry event_norm = sum in LHE header. Interpreting this as weight_norm_is_average = False.\n", + "18:05 madminer.utils.inter DEBUG Event 1 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 2 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 3 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 4 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 5 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 6 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 7 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 8 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 9 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 10 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 11 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 12 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 13 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 14 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 15 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 16 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 17 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 18 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 19 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter DEBUG Event 20 passes observations, passes cuts, passes efficiencies -> passes\n", + "18:05 madminer.utils.inter INFO 10000 / 10000 events pass cut (a[0] + a[1]).m > 122.\n", + "18:05 madminer.utils.inter INFO 10000 / 10000 events pass cut (a[0] + a[1]).m < 128.\n", + "18:05 madminer.utils.inter INFO 10000 / 10000 events pass cut pt_j1 > 20.\n", + "18:05 madminer.utils.inter INFO 10000 events pass all cuts/efficiencies\n", + "18:05 madminer.lhe.lhe_rea DEBUG Found weights ['morphing_basis_vector_0', 'morphing_basis_vector_1', 'morphing_basis_vector_2', 'sm', '5', '10', '20', 'neg_5', 'neg_10', 'neg_20'] in LHE file\n", + "18:05 madminer.lhe.lhe_rea DEBUG Found 10000 events\n", + "18:05 root DEBUG Merging data extracted from this file with data from previous files\n", + "18:05 root DEBUG Weights for benchmark morphing_basis_vector_0 exist in both\n", + "18:05 root DEBUG Weights for benchmark morphing_basis_vector_1 exist in both\n", + "18:05 root DEBUG Weights for benchmark morphing_basis_vector_2 exist in both\n", + "18:05 root DEBUG Weights for benchmark sm exist in both\n", + "18:05 root DEBUG Weights for benchmark 5 exist in both\n", + "18:05 root DEBUG Weights for benchmark 10 exist in both\n", + "18:05 root DEBUG Weights for benchmark 20 exist in both\n", + "18:05 root DEBUG Weights for benchmark neg_5 exist in both\n", + "18:05 root DEBUG Weights for benchmark neg_10 exist in both\n", + "18:05 root DEBUG Weights for benchmark neg_20 exist in both\n", + "18:05 madminer.lhe.lhe_rea INFO Analysed number of events per sampling benchmark:\n", + "18:05 madminer.lhe.lhe_rea INFO 50000 from sm\n", + "18:05 madminer.lhe.lhe_rea INFO 10000 from 5\n", + "18:05 madminer.lhe.lhe_rea INFO 10000 from 10\n", + "18:05 madminer.lhe.lhe_rea INFO 10000 from 20\n", + "18:05 madminer.lhe.lhe_rea INFO 10000 from neg_5\n", + "18:05 madminer.lhe.lhe_rea INFO 10000 from neg_10\n", + "18:05 madminer.lhe.lhe_rea INFO 10000 from neg_20\n" ] } ], @@ -707,33 +1002,44 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "11:05 madminer.lhe DEBUG Loading HDF5 data from data/setup.h5 and saving file to data/lhe_data.h5\n", - "11:05 madminer.lhe DEBUG Weight names: ['sm', 'w', 'neg_w', 'ww', 'neg_ww', 'morphing_basis_vector_5']\n", - "11:05 madminer.utils.inter DEBUG HDF5 file does not contain is_reference field.\n", - "11:05 madminer.utils.inter DEBUG Benchmark morphing_basis_vector_5 already in benchmark_names_phys\n", - "11:05 madminer.utils.inter DEBUG Benchmark neg_w already in benchmark_names_phys\n", - "11:05 madminer.utils.inter DEBUG Benchmark neg_ww already in benchmark_names_phys\n", - "11:05 madminer.utils.inter DEBUG Benchmark sm already in benchmark_names_phys\n", - "11:05 madminer.utils.inter DEBUG Benchmark w already in benchmark_names_phys\n", - "11:05 madminer.utils.inter DEBUG Benchmark ww already in benchmark_names_phys\n", - "11:05 madminer.utils.inter DEBUG Combined benchmark names: ['sm', 'w', 'neg_w', 'ww', 'neg_ww', 'morphing_basis_vector_5']\n", - "11:05 madminer.utils.inter DEBUG Combined is_nuisance: [0 0 0 0 0 0]\n", - "11:05 madminer.utils.inter DEBUG Combined is_reference: [1 0 0 0 0 0]\n", - "11:05 madminer.utils.inter DEBUG Weight names found in event file: ['sm', 'w', 'neg_w', 'ww', 'neg_ww', 'morphing_basis_vector_5']\n", - "11:05 madminer.utils.inter DEBUG Benchmarks found in MadMiner file: ['sm', 'w', 'neg_w', 'ww', 'neg_ww', 'morphing_basis_vector_5']\n", - "11:05 madminer.utils.inter DEBUG Sorted benchmarks: ['sm', 'w', 'neg_w', 'ww', 'neg_ww', 'morphing_basis_vector_5']\n" + "18:05 madminer.lhe.lhe_rea DEBUG Loading HDF5 data from data/setup_gw.h5 and saving file to data/lhe_data_gw.h5\n", + "18:05 madminer.lhe.lhe_rea DEBUG Weight names: ['morphing_basis_vector_0', 'morphing_basis_vector_1', 'morphing_basis_vector_2', 'sm', '5', '10', '20', 'neg_5', 'neg_10', 'neg_20']\n", + "18:05 madminer.utils.inter DEBUG HDF5 file does not contain is_reference field.\n", + "18:05 madminer.utils.inter DEBUG Benchmark 10 already in benchmark_names_phys\n", + "18:05 madminer.utils.inter DEBUG Benchmark 20 already in benchmark_names_phys\n", + "18:05 madminer.utils.inter DEBUG Benchmark 5 already in benchmark_names_phys\n", + "18:05 madminer.utils.inter DEBUG Benchmark morphing_basis_vector_0 already in benchmark_names_phys\n", + "18:05 madminer.utils.inter DEBUG Benchmark morphing_basis_vector_1 already in benchmark_names_phys\n", + "18:05 madminer.utils.inter DEBUG Benchmark morphing_basis_vector_2 already in benchmark_names_phys\n", + "18:05 madminer.utils.inter DEBUG Benchmark neg_10 already in benchmark_names_phys\n", + "18:05 madminer.utils.inter DEBUG Benchmark neg_20 already in benchmark_names_phys\n", + "18:05 madminer.utils.inter DEBUG Benchmark neg_5 already in benchmark_names_phys\n", + "18:05 madminer.utils.inter DEBUG Benchmark sm already in benchmark_names_phys\n", + "18:05 madminer.utils.inter DEBUG Combined benchmark names: ['morphing_basis_vector_0', 'morphing_basis_vector_1', 'morphing_basis_vector_2', 'sm', '5', '10', '20', 'neg_5', 'neg_10', 'neg_20']\n", + "18:05 madminer.utils.inter DEBUG Combined is_nuisance: [0 0 0 0 0 0 0 0 0 0]\n", + "18:05 madminer.utils.inter DEBUG Combined is_reference: [1 0 0 0 0 0 0 0 0 0]\n", + "18:05 madminer.utils.inter DEBUG Weight names found in event file: ['morphing_basis_vector_0', 'morphing_basis_vector_1', 'morphing_basis_vector_2', 'sm', '5', '10', '20', 'neg_5', 'neg_10', 'neg_20']\n", + "18:05 madminer.utils.inter DEBUG Benchmarks found in MadMiner file: ['morphing_basis_vector_0', 'morphing_basis_vector_1', 'morphing_basis_vector_2', 'sm', '5', '10', '20', 'neg_5', 'neg_10', 'neg_20']\n", + "18:05 madminer.sampling.co DEBUG Combining and shuffling samples\n", + "18:05 madminer.sampling.co DEBUG Copying setup from data/lhe_data_gw.h5 to data/lhe_data_gw.h5\n", + "18:05 madminer.sampling.co DEBUG Loading samples from file 1 / 1 at data/lhe_data_gw.h5, multiplying weights with k factor 1.0\n", + "18:05 madminer.sampling.co DEBUG Sampling benchmarks: [3 3 3 ... 6 6 6]\n", + "18:05 madminer.sampling.co DEBUG Sampling benchmarks: [9 9 9 ... 9 9 9]\n", + "18:05 madminer.sampling.co DEBUG Combined sampling benchmarks: [3 3 3 ... 9 9 9]\n", + "18:05 madminer.sampling.co DEBUG Recalculated event numbers per benchmark: [ 0 0 0 50000 10000 10000 10000 10000 10000 10000], background: 0\n" ] } ], "source": [ - "lhe.save('data/lhe_data.h5')" + "lhe.save('data/lhe_data_gw.h5')\n", + "#lhe.save('data/lhe_data_small.h5')" ] }, { @@ -752,65 +1058,60 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "11:05 madminer.analysis INFO Loading data from data/lhe_data.h5\n", - "11:05 madminer.analysis INFO Found 2 parameters\n", - "11:05 madminer.analysis DEBUG CWL2 (LHA: dim6 2, maximal power in squared ME: (2,), range: (-20.0, 20.0))\n", - "11:05 madminer.analysis DEBUG CPWL2 (LHA: dim6 5, maximal power in squared ME: (2,), range: (-20.0, 20.0))\n", - "11:05 madminer.analysis INFO Did not find nuisance parameters\n", - "11:05 madminer.analysis INFO Found 6 benchmarks, of which 6 physical\n", - "11:05 madminer.analysis DEBUG sm: CWL2 = 0.00e+00, CPWL2 = 0.00e+00\n", - "11:05 madminer.analysis DEBUG w: CWL2 = 15.20, CPWL2 = 0.10\n", - "11:05 madminer.analysis DEBUG neg_w: CWL2 = -1.54e+01, CPWL2 = 0.20\n", - "11:05 madminer.analysis DEBUG ww: CWL2 = 0.30, CPWL2 = 15.10\n", - "11:05 madminer.analysis DEBUG neg_ww: CWL2 = 0.40, CPWL2 = -1.53e+01\n", - "11:05 madminer.analysis DEBUG morphing_basis_vector_5: CWL2 = -1.68e+01, CPWL2 = -1.72e+01\n", - "11:05 madminer.analysis INFO Found 3 observables\n", - "11:05 madminer.analysis DEBUG 0 pt_j1\n", - "11:05 madminer.analysis DEBUG 1 delta_phi_jj\n", - "11:05 madminer.analysis DEBUG 2 met\n", - "11:05 madminer.analysis INFO Found 14831 events\n", - "11:05 madminer.analysis INFO 9849 signal events sampled from benchmark sm\n", - "11:05 madminer.analysis INFO 1049 signal events sampled from benchmark w\n", - "11:05 madminer.analysis INFO 1150 signal events sampled from benchmark neg_w\n", - "11:05 madminer.analysis INFO 1417 signal events sampled from benchmark ww\n", - "11:05 madminer.analysis INFO 1366 signal events sampled from benchmark neg_ww\n", - "11:05 madminer.analysis INFO Found morphing setup with 6 components\n", - "11:05 madminer.analysis INFO Did not find nuisance morphing setup\n", - "11:05 madminer.plotting DEBUG Observable indices: [0, 1, 2]\n", - "11:05 madminer.plotting DEBUG Calculated 2 theta matrices\n", - "11:05 madminer.analysis DEBUG Sampling benchmark closest to None: None\n", - "11:05 madminer.analysis DEBUG Events per benchmark: [9849. 1049. 1150. 1417. 1366. 0.]\n", - "11:05 madminer.analysis DEBUG Sampling factors: [0.66408199 0.07073023 0.07754029 0.09554312 0.09210438 0.\n", - " 1. ]\n", - "11:05 madminer.utils.inter DEBUG Sampling IDs: [0 0 0 ... 4 4 4]\n", - "11:05 madminer.utils.inter DEBUG k-factors: [0.66408199 0.66408199 0.66408199 ... 0.09210438 0.09210438 0.09210438]\n", - "11:05 madminer.plotting DEBUG Loaded raw data with shapes (14831, 3), (14831, 6)\n", - "11:05 madminer.analysis DEBUG Distances from [0. 0.]: [0.0, 15.200328943809078, 15.401298646542765, 15.102979838429237, 15.305227865013968, 24.070733418639843]\n", - "11:05 madminer.analysis DEBUG n_events_generated_per_benchmark: [9849 1049 1150 1417 1366 0]\n", - "11:05 madminer.analysis DEBUG Sampling benchmark closest to [0. 0.]: 0\n", - "11:05 madminer.analysis DEBUG Sampling factors: [1. 1. 1. 1. 1. 1. 1.]\n", - "11:05 madminer.analysis DEBUG Distances from [10. 0.]: [10.0, 5.200961449578337, 25.400787389370432, 17.947144619688114, 18.062391868188442, 31.870915748214763]\n", - "11:05 madminer.analysis DEBUG n_events_generated_per_benchmark: [9849 1049 1150 1417 1366 0]\n", - "11:05 madminer.analysis DEBUG Sampling benchmark closest to [10. 0.]: 1\n", - "11:05 madminer.analysis DEBUG Sampling factors: [1. 1. 1. 1. 1. 1. 1.]\n", - "11:05 madminer.plotting DEBUG Plotting panel 0: observable 0, label pt_j1\n", - "11:05 madminer.plotting DEBUG Ranges for observable pt_j1: min = [20.321218436732178, 20.321218436732178], max = [274.8910439483994, 979.6649308782488]\n", - "11:05 madminer.plotting DEBUG Plotting panel 1: observable 1, label delta_phi_jj\n", - "11:05 madminer.plotting DEBUG Ranges for observable delta_phi_jj: min = [-3.1413218183783336, -3.1413218183783336], max = [3.1414440933287553, 2.8994368437970404]\n", - "11:05 madminer.plotting DEBUG Plotting panel 2: observable 2, label met\n", - "11:05 madminer.plotting DEBUG Ranges for observable met: min = [0.22404696110750358, 0.22404696110750358], max = [106.7400093703552, 302.7572240902905]\n" + "18:05 madminer.analysis.da INFO Loading data from data/lhe_data_gw.h5\n", + "18:05 madminer.analysis.da INFO Found 1 parameters\n", + "18:05 madminer.analysis.da DEBUG CWWWL2 (LHA: dim6 2, maximal power in squared ME: (2,), range: (-20.0, 20.0))\n", + "18:05 madminer.analysis.da INFO Did not find nuisance parameters\n", + "18:05 madminer.analysis.da INFO Found 10 benchmarks, of which 10 physical\n", + "18:05 madminer.analysis.da DEBUG morphing_basis_vector_0: CWWWL2 = -1.98e+01\n", + "18:05 madminer.analysis.da DEBUG morphing_basis_vector_1: CWWWL2 = 2.23\n", + "18:05 madminer.analysis.da DEBUG morphing_basis_vector_2: CWWWL2 = 16.83\n", + "18:05 madminer.analysis.da DEBUG sm: CWWWL2 = 0.00e+00\n", + "18:05 madminer.analysis.da DEBUG 5: CWWWL2 = 0.72\n", + "18:05 madminer.analysis.da DEBUG 10: CWWWL2 = 1.44\n", + "18:05 madminer.analysis.da DEBUG 20: CWWWL2 = 2.87\n", + "18:05 madminer.analysis.da DEBUG neg_5: CWWWL2 = -7.18e-01\n", + "18:05 madminer.analysis.da DEBUG neg_10: CWWWL2 = -1.44e+00\n", + "18:05 madminer.analysis.da DEBUG neg_20: CWWWL2 = -2.87e+00\n", + "18:05 madminer.analysis.da INFO Found 3 observables\n", + "18:05 madminer.analysis.da DEBUG 0 pt_j1\n", + "18:05 madminer.analysis.da DEBUG 1 delta_phi_jj\n", + "18:05 madminer.analysis.da DEBUG 2 met\n", + "18:05 madminer.analysis.da INFO Found 109980 events\n", + "18:05 madminer.analysis.da INFO 49990 signal events sampled from benchmark sm\n", + "18:05 madminer.analysis.da INFO 9997 signal events sampled from benchmark 5\n", + "18:05 madminer.analysis.da INFO 9997 signal events sampled from benchmark 10\n", + "18:05 madminer.analysis.da INFO 10000 signal events sampled from benchmark 20\n", + "18:05 madminer.analysis.da INFO 9997 signal events sampled from benchmark neg_5\n", + "18:05 madminer.analysis.da INFO 9999 signal events sampled from benchmark neg_10\n", + "18:05 madminer.analysis.da INFO 10000 signal events sampled from benchmark neg_20\n", + "18:05 madminer.analysis.da INFO Found morphing setup with 3 components\n", + "18:05 madminer.analysis.da INFO Did not find nuisance morphing setup\n", + "18:05 madminer.plotting.di DEBUG Observable indices: [0, 1, 2]\n", + "18:05 madminer.plotting.di DEBUG Calculated 2 theta matrices\n", + "18:05 madminer.analysis.da DEBUG Sampling benchmark closest to None: None\n", + "18:05 madminer.analysis.da DEBUG Events per benchmark: [ 0. 0. 0. 49990. 9997. 9997. 10000. 9997. 9999. 10000.]\n", + "18:05 madminer.plotting.di DEBUG Loaded raw data with shapes (109980, 3), (109980, 10)\n", + "18:05 madminer.analysis.da DEBUG Sampling benchmark closest to [0.]: 3\n", + "18:05 madminer.analysis.da DEBUG Sampling benchmark closest to [10. 0.]: 6\n", + "18:05 madminer.plotting.di DEBUG Plotting panel 0: observable 0, label pt_j1\n", + "18:05 madminer.plotting.di DEBUG Ranges for observable pt_j1: min = [20.02034656495759, 20.02034656495759], max = [304.4008834881246, 1105.7910750015833]\n", + "18:05 madminer.plotting.di DEBUG Plotting panel 1: observable 1, label delta_phi_jj\n", + "18:05 madminer.plotting.di DEBUG Ranges for observable delta_phi_jj: min = [-3.141485285220079, -3.141485285220079], max = [3.141556665321097, 3.141556665321097]\n", + "18:05 madminer.plotting.di DEBUG Plotting panel 2: observable 2, label met\n", + "18:05 madminer.plotting.di DEBUG Ranges for observable met: min = [0.10863811254840301, 0.10863811254840301], max = [101.026336583926, 204.17372432628747]\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -823,7 +1124,7 @@ ], "source": [ "_ = plot_distributions(\n", - " filename='data/lhe_data.h5',\n", + " filename='data/lhe_data_gw.h5',\n", " parameter_points=['sm', np.array([10.,0.])],\n", " line_labels=['SM', 'BSM'],\n", " uncertainties='none',\n", @@ -852,42 +1153,97 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, + "execution_count": 15, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "11:05 madminer.sampling DEBUG Combining and shuffling samples\n", - "11:05 madminer.sampling INFO Copying setup from data/lhe_data.h5 to data/lhe_data_shuffled.h5\n", - "11:05 madminer.sampling INFO Loading samples from file 1 / 1 at data/lhe_data.h5, multiplying weights with k factor 1.0\n", - "11:05 madminer.sampling DEBUG Sampling benchmarks: [0 0 0 ... 4 4 4]\n", - "11:05 madminer.sampling DEBUG Combined sampling benchmarks: [0 0 0 ... 4 4 4]\n", - "11:05 madminer.sampling DEBUG Recalculated event numbers per benchmark: [9849 1049 1150 1417 1366 0], background: 0\n" + "18:16 madminer.sampling.co DEBUG Combining and shuffling samples\n", + "18:16 madminer.sampling.co DEBUG Copying setup from data/lhe_data_gw.h5 to data/lhe_data_shuffled_gw.h5\n", + "18:16 madminer.sampling.co DEBUG Loading samples from file 1 / 1 at data/lhe_data_gw.h5, multiplying weights with k factor 1.0\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [4. 4. 4. ... 4. 4. 4.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [4. 4. 4. ... 4. 4. 4.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [4. 4. 4. ... 4. 4. 4.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [4. 4. 4. ... 4. 4. 4.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [4. 4. 4. ... 4. 4. 4.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [5. 5. 5. ... 5. 5. 5.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [5. 5. 5. ... 5. 5. 5.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [5. 5. 5. ... 5. 5. 5.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [5. 5. 5. ... 5. 5. 5.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [5. 5. 5. ... 5. 5. 5.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [6. 6. 6. ... 6. 6. 6.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [6. 6. 6. ... 6. 6. 6.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [6. 6. 6. ... 6. 6. 6.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [6. 6. 6. ... 6. 6. 6.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [6. 6. 6. ... 6. 6. 6.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [7. 7. 7. ... 7. 7. 7.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [7. 7. 7. ... 7. 7. 7.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [7. 7. 7. ... 7. 7. 7.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [7. 7. 7. ... 7. 7. 7.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [7. 7. 7. ... 7. 7. 7.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [8. 8. 8. ... 8. 8. 8.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [8. 8. 8. ... 8. 8. 8.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [8. 8. 8. ... 8. 8. 8.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [8. 8. 8. ... 8. 8. 8.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [8. 8. 8. ... 8. 8. 8.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [3. 3. 3. ... 3. 3. 3.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [9. 9. 9. ... 9. 9. 9.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [9. 9. 9. ... 9. 9. 9.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [9. 9. 9. ... 9. 9. 9.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [9. 9. 9. ... 9. 9. 9.]\n", + "18:16 madminer.sampling.co DEBUG Sampling benchmarks: [9. 9. 9. ... 9. 9. 9.]\n", + "18:16 madminer.sampling.co DEBUG Combined sampling benchmarks: [3. 3. 3. ... 9. 9. 9.]\n", + "18:16 madminer.sampling.co DEBUG Recalculated event numbers per benchmark: [ 0 0 0 3000000 500000 500000 500000 500000 500000\n", + " 500000], background: 0\n" ] } ], "source": [ "combine_and_shuffle(\n", - " ['data/lhe_data.h5'],\n", - " 'data/lhe_data_shuffled.h5'\n", + " ['data/lhe_data_gw.h5'],\n", + " 'data/lhe_data_shuffled_gw.h5'\n", ")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python (higgs_inference)", + "display_name": "Python 2", "language": "python", - "name": "higgs_inference" + "name": "python2" }, "language_info": { "codemirror_mode": { @@ -899,7 +1255,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.8.2" } }, "nbformat": 4, diff --git a/examples/tutorial_particle_physics/3a_likelihood_ratio.ipynb b/examples/tutorial_particle_physics/3a_likelihood_ratio.ipynb index 48db3d50e..2081de2bf 100644 --- a/examples/tutorial_particle_physics/3a_likelihood_ratio.ipynb +++ b/examples/tutorial_particle_physics/3a_likelihood_ratio.ipynb @@ -68,15 +68,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "11:18 madminer INFO \n", - "11:18 madminer INFO ------------------------------------------------------------------------\n", - "11:18 madminer INFO | |\n", - "11:18 madminer INFO | MadMiner v0.7.0 |\n", - "11:18 madminer INFO | |\n", - "11:18 madminer INFO | Johann Brehmer, Felix Kling, Irina Espejo, and Kyle Cranmer |\n", - "11:18 madminer INFO | |\n", - "11:18 madminer INFO ------------------------------------------------------------------------\n", - "11:18 madminer INFO \n" + "15:45 madminer INFO \n", + "15:45 madminer INFO ------------------------------------------------------------------------\n", + "15:45 madminer INFO | |\n", + "15:45 madminer INFO | MadMiner v0.7.4 |\n", + "15:45 madminer INFO | |\n", + "15:45 madminer INFO | Johann Brehmer, Felix Kling, Irina Espejo, and Kyle Cranmer |\n", + "15:45 madminer INFO | |\n", + "15:45 madminer INFO ------------------------------------------------------------------------\n", + "15:45 madminer INFO \n" ] } ], @@ -104,32 +104,33 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "11:18 madminer.analysis.da INFO Loading data from data/lhe_data_shuffled.h5\n", - "11:18 madminer.analysis.da INFO Found 2 parameters\n", - "11:18 madminer.analysis.da INFO Did not find nuisance parameters\n", - "11:18 madminer.analysis.da INFO Found 6 benchmarks, of which 6 physical\n", - "11:18 madminer.analysis.da INFO Found 3 observables\n", - "11:18 madminer.analysis.da INFO Found 14831 events\n", - "11:18 madminer.analysis.da INFO 9849 signal events sampled from benchmark sm\n", - "11:18 madminer.analysis.da INFO 1049 signal events sampled from benchmark w\n", - "11:18 madminer.analysis.da INFO 1150 signal events sampled from benchmark neg_w\n", - "11:18 madminer.analysis.da INFO 1417 signal events sampled from benchmark ww\n", - "11:18 madminer.analysis.da INFO 1366 signal events sampled from benchmark neg_ww\n", - "11:18 madminer.analysis.da INFO Found morphing setup with 6 components\n", - "11:18 madminer.analysis.da INFO Did not find nuisance morphing setup\n" + "16:14 madminer.analysis.da INFO Loading data from data/lhe_data_shuffled_gw.h5\n", + "16:14 madminer.analysis.da INFO Found 1 parameters\n", + "16:14 madminer.analysis.da INFO Did not find nuisance parameters\n", + "16:14 madminer.analysis.da INFO Found 10 benchmarks, of which 10 physical\n", + "16:14 madminer.analysis.da INFO Found 9 observables\n", + "16:14 madminer.analysis.da INFO Found 6000000 events\n", + "16:14 madminer.analysis.da INFO 3000000 signal events sampled from benchmark sm\n", + "16:14 madminer.analysis.da INFO 500000 signal events sampled from benchmark 5\n", + "16:14 madminer.analysis.da INFO 500000 signal events sampled from benchmark 10\n", + "16:14 madminer.analysis.da INFO 500000 signal events sampled from benchmark 20\n", + "16:14 madminer.analysis.da INFO 500000 signal events sampled from benchmark neg_5\n", + "16:14 madminer.analysis.da INFO 500000 signal events sampled from benchmark neg_10\n", + "16:14 madminer.analysis.da INFO 500000 signal events sampled from benchmark neg_20\n", + "16:14 madminer.analysis.da INFO Found morphing setup with 3 components\n", + "16:14 madminer.analysis.da INFO Did not find nuisance morphing setup\n" ] } ], "source": [ - "sampler = SampleAugmenter('data/lhe_data_shuffled.h5')\n", - "# sampler = SampleAugmenter('data/delphes_data_shuffled.h5')" + "sampler = SampleAugmenter('data/lhe_data_shuffled_gw.h5')" ] }, { @@ -153,67 +154,38 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "11:18 madminer.sampling.sa INFO Extracting training sample for ratio-based methods. Numerator hypothesis: 1000 random morphing points, drawn from the following priors:\n", - " theta_0 ~ Gaussian with mean 0.0 and std 0.5\n", - " theta_1 ~ Gaussian with mean 0.0 and std 0.5, denominator hypothesis: sm\n", - "11:18 madminer.sampling.sa INFO Starting sampling serially\n", - "11:18 madminer.sampling.sa WARNING Large statistical uncertainty on the total cross section when sampling from theta = [-0.4616253 1.1211267]: (0.000273 +/- 0.000245) pb (89.6095745497341 %). Skipping these warnings in the future...\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 50 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 100 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 150 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 200 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 250 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 300 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 350 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 400 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 450 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 500 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 550 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 600 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 650 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 700 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 750 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 800 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 850 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 900 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 950 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 1000 / 1000\n", - "11:18 madminer.sampling.sa INFO Effective number of samples: mean 238.66297179606946, with individual thetas ranging from 3.921295541483046 to 5856.394928394311\n", - "11:18 madminer.sampling.sa INFO Starting sampling serially\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 50 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 100 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 150 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 200 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 250 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 300 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 350 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 400 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 450 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 500 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 550 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 600 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 650 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 700 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 750 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 800 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 850 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 900 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 950 / 1000\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 1000 / 1000\n", - "11:18 madminer.sampling.sa INFO Effective number of samples: mean 5940.0, with individual thetas ranging from 5940.0 to 5940.0\n" + "16:14 madminer.sampling.sa INFO Extracting training sample for ratio-based methods. Numerator hypothesis: 7 benchmarks, starting with ['sm', '5', 'neg_5'], denominator hypothesis: sm\n", + "16:14 madminer.sampling.sa INFO Starting sampling serially\n", + "16:14 madminer.sampling.sa INFO Sampling from parameter point 1 / 7\n", + "16:14 madminer.sampling.sa INFO Sampling from parameter point 2 / 7\n", + "16:14 madminer.sampling.sa INFO Sampling from parameter point 3 / 7\n", + "16:14 madminer.sampling.sa INFO Sampling from parameter point 4 / 7\n", + "16:14 madminer.sampling.sa INFO Sampling from parameter point 5 / 7\n", + "16:14 madminer.sampling.sa INFO Sampling from parameter point 6 / 7\n", + "16:14 madminer.sampling.sa INFO Sampling from parameter point 7 / 7\n", + "16:14 madminer.sampling.sa INFO Effective number of samples: mean 514285.71428571426, with individual thetas ranging from 299471.99999999994 to 1800306.9999999998\n", + "16:14 madminer.sampling.sa INFO Starting sampling serially\n", + "16:14 madminer.sampling.sa INFO Sampling from parameter point 1 / 7\n", + "16:14 madminer.sampling.sa INFO Sampling from parameter point 2 / 7\n", + "16:14 madminer.sampling.sa INFO Sampling from parameter point 3 / 7\n", + "16:14 madminer.sampling.sa INFO Sampling from parameter point 4 / 7\n", + "16:14 madminer.sampling.sa INFO Sampling from parameter point 5 / 7\n", + "16:14 madminer.sampling.sa INFO Sampling from parameter point 6 / 7\n", + "16:14 madminer.sampling.sa INFO Sampling from parameter point 7 / 7\n", + "16:14 madminer.sampling.sa INFO Effective number of samples: mean 1800306.9999999998, with individual thetas ranging from 1800306.9999999998 to 1800306.9999999998\n" ] } ], "source": [ "x, theta0, theta1, y, r_xz, t_xz, n_effective = sampler.sample_train_ratio(\n", - " theta0=sampling.random_morphing_points(1000, [('gaussian', 0., 0.5), ('gaussian', 0., 0.5)]),\n", + " theta0=sampling.benchmarks(['sm', '5', 'neg_5', '10', 'neg_10', '20', 'neg_20']),\n", " theta1=sampling.benchmark('sm'),\n", " n_samples=500000,\n", " folder='./data/samples',\n", @@ -232,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "metadata": { "scrolled": true }, @@ -241,10 +213,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "11:18 madminer.sampling.sa INFO Extracting evaluation sample. Sampling according to sm\n", - "11:18 madminer.sampling.sa INFO Starting sampling serially\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 1 / 1\n", - "11:18 madminer.sampling.sa INFO Effective number of samples: mean 1982.9999999999998, with individual thetas ranging from 1982.9999999999998 to 1982.9999999999998\n" + "16:14 madminer.sampling.sa INFO Extracting evaluation sample. Sampling according to sm\n", + "16:14 madminer.sampling.sa INFO Starting sampling serially\n", + "16:14 madminer.sampling.sa INFO Sampling from parameter point 1 / 1\n", + "16:14 madminer.sampling.sa INFO Effective number of samples: mean 599971.0, with individual thetas ranging from 599971.0 to 599971.0\n" ] } ], @@ -259,23 +231,29 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "11:18 madminer.sampling.sa INFO Extracting plain training sample. Sampling according to [0. 0.5]\n", - "11:18 madminer.sampling.sa INFO Starting sampling serially\n", - "11:18 madminer.sampling.sa INFO Sampling from parameter point 1 / 1\n", - "11:18 madminer.sampling.sa INFO Effective number of samples: mean 689.6627240819543, with individual thetas ranging from 689.662724081954 to 689.662724081954\n" + "16:14 madminer.sampling.sa INFO Extracting plain training sample. Sampling according to 6 benchmarks, starting with ['5', 'neg_5', '10']\n", + "16:14 madminer.sampling.sa INFO Starting sampling serially\n", + "16:14 madminer.sampling.sa INFO Sampling from parameter point 1 / 6\n", + "16:14 madminer.sampling.sa INFO Sampling from parameter point 2 / 6\n", + "16:14 madminer.sampling.sa INFO Sampling from parameter point 3 / 6\n", + "16:14 madminer.sampling.sa INFO Sampling from parameter point 4 / 6\n", + "16:14 madminer.sampling.sa INFO Sampling from parameter point 5 / 6\n", + "16:14 madminer.sampling.sa INFO Sampling from parameter point 6 / 6\n", + "16:14 madminer.sampling.sa INFO Effective number of samples: mean 299948.8333333333, with individual thetas ranging from 299471.99999999994 to 300180.0\n" ] } ], "source": [ "_,_,neff=sampler.sample_train_plain(\n", - " theta=sampling.morphing_point([0,0.5]),\n", + " #theta=sampling.morphing_point([0,0.5]),\n", + " theta=sampling.benchmarks(['5', 'neg_5', '10', 'neg_10', '20', 'neg_20']),\n", " n_samples=10000,\n", ")" ] @@ -289,22 +267,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cmin, cmax = 10., 10000.\n", "\n", @@ -342,18 +307,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "11:18 madminer.sampling.sa INFO Starting cross-section calculation\n", - "11:18 madminer.sampling.sa INFO Starting cross-section calculation\n" - ] - } - ], + "outputs": [], "source": [ "thetas_benchmarks, xsecs_benchmarks, xsec_errors_benchmarks = sampler.cross_sections(\n", " theta=sampling.benchmarks(list(sampler.benchmarks.keys()))\n", @@ -366,22 +322,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cmin, cmax = 0., 2.5 * np.mean(xsecs_morphing)\n", "\n", @@ -429,13 +372,32 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "estimator = ParameterizedRatioEstimator(\n", + "from madminer.ml.morphing_aware import MorphingAwareRatioEstimator" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "16:14 madminer.ml.morphing INFO Setting up morphing-aware ratio estimator with 3 morphing components\n" + ] + } + ], + "source": [ + "#estimator = ParameterizedRatioEstimator(\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename='data/setup_gw.h5',\n", " n_hidden=(60,60),\n", - " activation=\"tanh\"\n", + " activation=\"tanh\",\n", ")" ] }, @@ -448,93 +410,131 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "estimator.train(\n", + " method='carl',\n", + " theta='data/samples/theta0_train_ratio.npy',\n", + " x='data/samples/x_train_ratio.npy',\n", + " y='data/samples/y_train_ratio.npy',\n", + " r_xz='data/samples/r_xz_train_ratio.npy',\n", + " t_xz='data/samples/t_xz_train_ratio.npy',\n", + " alpha=10,\n", + " n_epochs=1000,\n", + " #scale_parameters=True,\n", + ")\n", + "\n", + "estimator.save('models/carl_gw')" + ] + }, + { + "cell_type": "markdown", "metadata": {}, + "source": [ + "### Bigger Batch size" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "11:18 madminer.ml.paramete INFO Starting training\n", - "11:18 madminer.ml.paramete INFO Method: alices\n", - "11:18 madminer.ml.paramete INFO alpha: 10\n", - "11:18 madminer.ml.paramete INFO Batch size: 128\n", - "11:18 madminer.ml.paramete INFO Optimizer: amsgrad\n", - "11:18 madminer.ml.paramete INFO Epochs: 10\n", - "11:18 madminer.ml.paramete INFO Learning rate: 0.001 initially, decaying to 0.0001\n", - "11:18 madminer.ml.paramete INFO Validation split: 0.25\n", - "11:18 madminer.ml.paramete INFO Early stopping: True\n", - "11:18 madminer.ml.paramete INFO Scale inputs: True\n", - "11:18 madminer.ml.paramete INFO Scale parameters: True\n", - "11:18 madminer.ml.paramete INFO Shuffle labels False\n", - "11:18 madminer.ml.paramete INFO Samples: all\n", - "11:18 madminer.ml.paramete INFO Loading training data\n", - "11:18 madminer.utils.vario INFO Loading data/samples/theta0_train_ratio.npy into RAM\n", - "11:18 madminer.utils.vario INFO Loading data/samples/x_train_ratio.npy into RAM\n", - "11:18 madminer.utils.vario INFO Loading data/samples/y_train_ratio.npy into RAM\n", - "11:18 madminer.utils.vario INFO Loading data/samples/r_xz_train_ratio.npy into RAM\n", - "11:18 madminer.utils.vario INFO Loading data/samples/t_xz_train_ratio.npy into RAM\n", - "11:18 madminer.ml.paramete INFO Found 500000 samples with 2 parameters and 3 observables\n", - "11:18 madminer.ml.base INFO Setting up input rescaling\n", - "11:18 madminer.ml.paramete INFO Rescaling parameters\n", - "11:18 madminer.ml.base INFO Setting up parameter rescaling\n", - "11:18 madminer.ml.paramete INFO Creating model\n", - "11:18 madminer.ml.paramete INFO Training model\n", - "11:18 madminer.utils.ml.tr INFO Training on CPU with single precision\n", - "11:19 madminer.utils.ml.tr INFO Epoch 1: train loss 0.94438 (improved_xe: 0.675, mse_score: 0.027)\n", - "11:19 madminer.utils.ml.tr INFO val. loss 0.86812 (improved_xe: 0.672, mse_score: 0.020)\n", - "11:19 madminer.utils.ml.tr INFO Epoch 2: train loss 0.85943 (improved_xe: 0.671, mse_score: 0.019)\n", - "11:19 madminer.utils.ml.tr INFO val. loss 0.83174 (improved_xe: 0.671, mse_score: 0.016)\n", - "11:19 madminer.utils.ml.tr INFO Epoch 3: train loss 0.83587 (improved_xe: 0.670, mse_score: 0.017)\n", - "11:19 madminer.utils.ml.tr INFO val. loss 0.83570 (improved_xe: 0.670, mse_score: 0.017)\n", - "11:19 madminer.utils.ml.tr INFO Epoch 4: train loss 0.81935 (improved_xe: 0.670, mse_score: 0.015)\n", - "11:19 madminer.utils.ml.tr INFO val. loss 0.81505 (improved_xe: 0.670, mse_score: 0.014)\n", - "11:20 madminer.utils.ml.tr INFO Epoch 5: train loss 0.81145 (improved_xe: 0.670, mse_score: 0.014)\n", - "11:20 madminer.utils.ml.tr INFO val. loss 0.80693 (improved_xe: 0.670, mse_score: 0.014)\n", - "11:20 madminer.utils.ml.tr INFO Epoch 6: train loss 0.80671 (improved_xe: 0.670, mse_score: 0.014)\n", - "11:20 madminer.utils.ml.tr INFO val. loss 0.80557 (improved_xe: 0.670, mse_score: 0.014)\n", - "11:20 madminer.utils.ml.tr INFO Epoch 7: train loss 0.80186 (improved_xe: 0.669, mse_score: 0.013)\n", - "11:20 madminer.utils.ml.tr INFO val. loss 0.80654 (improved_xe: 0.670, mse_score: 0.014)\n", - "11:21 madminer.utils.ml.tr INFO Epoch 8: train loss 0.80080 (improved_xe: 0.669, mse_score: 0.013)\n", - "11:21 madminer.utils.ml.tr INFO val. loss 0.80156 (improved_xe: 0.670, mse_score: 0.013)\n", - "11:21 madminer.utils.ml.tr INFO Epoch 9: train loss 0.79900 (improved_xe: 0.669, mse_score: 0.013)\n", - "11:21 madminer.utils.ml.tr INFO val. loss 0.79915 (improved_xe: 0.670, mse_score: 0.013)\n", - "11:21 madminer.utils.ml.tr INFO Epoch 10: train loss 0.79755 (improved_xe: 0.669, mse_score: 0.013)\n", - "11:21 madminer.utils.ml.tr INFO val. loss 0.80025 (improved_xe: 0.670, mse_score: 0.013)\n", - "11:21 madminer.utils.ml.tr INFO Early stopping after epoch 9, with loss 0.79915 compared to final loss 0.80025\n", - "11:21 madminer.utils.ml.tr INFO Training time spend on:\n", - "11:21 madminer.utils.ml.tr INFO initialize model: 0.00h\n", - "11:21 madminer.utils.ml.tr INFO ALL: 0.04h\n", - "11:21 madminer.utils.ml.tr INFO check data: 0.00h\n", - "11:21 madminer.utils.ml.tr INFO make dataset: 0.00h\n", - "11:21 madminer.utils.ml.tr INFO make dataloader: 0.00h\n", - "11:21 madminer.utils.ml.tr INFO setup optimizer: 0.00h\n", - "11:21 madminer.utils.ml.tr INFO initialize training: 0.00h\n", - "11:21 madminer.utils.ml.tr INFO set lr: 0.00h\n", - "11:21 madminer.utils.ml.tr INFO load training batch: 0.01h\n", - "11:21 madminer.utils.ml.tr INFO fwd: move data: 0.00h\n", - "11:21 madminer.utils.ml.tr INFO fwd: check for nans: 0.00h\n", - "11:21 madminer.utils.ml.tr INFO fwd: model.forward: 0.01h\n", - "11:21 madminer.utils.ml.tr INFO fwd: calculate losses: 0.00h\n", - "11:21 madminer.utils.ml.tr INFO training forward pass: 0.01h\n", - "11:21 madminer.utils.ml.tr INFO training sum losses: 0.00h\n", - "11:21 madminer.utils.ml.tr INFO opt: zero grad: 0.00h\n", - "11:21 madminer.utils.ml.tr INFO opt: backward: 0.01h\n", - "11:21 madminer.utils.ml.tr INFO opt: clip grad norm: 0.00h\n", - "11:21 madminer.utils.ml.tr INFO opt: step: 0.00h\n", - "11:21 madminer.utils.ml.tr INFO optimizer step: 0.01h\n", - "11:21 madminer.utils.ml.tr INFO load validation batch: 0.00h\n", - "11:21 madminer.utils.ml.tr INFO validation forward pass: 0.00h\n", - "11:21 madminer.utils.ml.tr INFO validation sum losses: 0.00h\n", - "11:21 madminer.utils.ml.tr INFO early stopping: 0.00h\n", - "11:21 madminer.utils.ml.tr INFO report epoch: 0.00h\n", - "11:21 madminer.ml.base INFO Saving model to models/alices\n" + "18:18 madminer.ml.morphing INFO Setting up morphing-aware ratio estimator with 3 morphing components\n", + "18:18 madminer.ml.paramete INFO Starting training\n", + "18:18 madminer.ml.paramete INFO Method: carl\n", + "18:18 madminer.ml.paramete INFO Batch size: 1500000\n", + "18:18 madminer.ml.paramete INFO Optimizer: amsgrad\n", + "18:18 madminer.ml.paramete INFO Epochs: 10\n", + "18:18 madminer.ml.paramete INFO Learning rate: 0.001 initially, decaying to 0.0001\n", + "18:18 madminer.ml.paramete INFO Validation split: 0.25\n", + "18:18 madminer.ml.paramete INFO Early stopping: True\n", + "18:18 madminer.ml.paramete INFO Scale inputs: True\n", + "18:18 madminer.ml.paramete INFO Scale parameters: False\n", + "18:18 madminer.ml.paramete INFO Shuffle labels False\n", + "18:18 madminer.ml.paramete INFO Samples: all\n", + "18:18 madminer.ml.paramete INFO Loading training data\n", + "18:18 madminer.utils.vario INFO Loading data/samples/theta0_train_ratio.npy into RAM\n", + "18:18 madminer.utils.vario INFO Loading data/samples/x_train_ratio.npy into RAM\n", + "18:18 madminer.utils.vario INFO Loading data/samples/y_train_ratio.npy into RAM\n", + "18:18 madminer.utils.vario INFO Loading data/samples/r_xz_train_ratio.npy into RAM\n", + "18:18 madminer.utils.vario INFO Loading data/samples/t_xz_train_ratio.npy into RAM\n", + "18:18 madminer.ml.paramete INFO Found 499996 samples with 1 parameters and 9 observables\n", + "18:18 madminer.ml.base INFO Setting up input rescaling\n", + "18:18 madminer.ml.base INFO Disabling parameter rescaling\n", + "18:18 madminer.ml.paramete INFO Creating model\n", + "18:18 madminer.ml.paramete INFO Training model\n", + "18:18 madminer.utils.ml.tr INFO Training on CPU with single precision\n", + "18:18 madminer.utils.ml.tr INFO Epoch 1: train loss 0.69087 (xe: 0.691)\n", + "18:18 madminer.utils.ml.tr INFO val. loss 0.68757 (xe: 0.688)\n", + "18:18 madminer.utils.ml.tr INFO Epoch 2: train loss 0.68726 (xe: 0.687)\n", + "18:18 madminer.utils.ml.tr INFO val. loss 0.68506 (xe: 0.685)\n", + "18:18 madminer.utils.ml.tr INFO Epoch 3: train loss 0.68473 (xe: 0.685)\n", + "18:18 madminer.utils.ml.tr INFO val. loss 0.68327 (xe: 0.683)\n", + "18:19 madminer.utils.ml.tr INFO Epoch 4: train loss 0.68294 (xe: 0.683)\n", + "18:19 madminer.utils.ml.tr INFO val. loss 0.68198 (xe: 0.682)\n", + "18:19 madminer.utils.ml.tr INFO Epoch 5: train loss 0.68165 (xe: 0.682)\n", + "18:19 madminer.utils.ml.tr INFO val. loss 0.68105 (xe: 0.681)\n", + "18:19 madminer.utils.ml.tr INFO Epoch 6: train loss 0.68071 (xe: 0.681)\n", + "18:19 madminer.utils.ml.tr INFO val. loss 0.68035 (xe: 0.680)\n", + "18:19 madminer.utils.ml.tr INFO Epoch 7: train loss 0.68001 (xe: 0.680)\n", + "18:19 madminer.utils.ml.tr INFO val. loss 0.67984 (xe: 0.680)\n", + "18:20 madminer.utils.ml.tr INFO Epoch 8: train loss 0.67950 (xe: 0.679)\n", + "18:20 madminer.utils.ml.tr INFO val. loss 0.67945 (xe: 0.679)\n", + "18:20 madminer.utils.ml.tr INFO Epoch 9: train loss 0.67911 (xe: 0.679)\n", + "18:20 madminer.utils.ml.tr INFO val. loss 0.67916 (xe: 0.679)\n", + "18:20 madminer.utils.ml.tr INFO Epoch 10: train loss 0.67881 (xe: 0.679)\n", + "18:20 madminer.utils.ml.tr INFO val. loss 0.67894 (xe: 0.679)\n", + "18:20 madminer.utils.ml.tr INFO Early stopping did not improve performance\n", + "18:20 madminer.utils.ml.tr INFO Training time spend on:\n", + "18:20 madminer.utils.ml.tr INFO initialize model: 0.00h\n", + "18:20 madminer.utils.ml.tr INFO ALL: 0.04h\n", + "18:20 madminer.utils.ml.tr INFO check data: 0.00h\n", + "18:20 madminer.utils.ml.tr INFO make dataset: 0.00h\n", + "18:20 madminer.utils.ml.tr INFO make dataloader: 0.00h\n", + "18:20 madminer.utils.ml.tr INFO setup optimizer: 0.00h\n", + "18:20 madminer.utils.ml.tr INFO initialize training: 0.00h\n", + "18:20 madminer.utils.ml.tr INFO set lr: 0.00h\n", + "18:20 madminer.utils.ml.tr INFO load training batch: 0.03h\n", + "18:20 madminer.utils.ml.tr INFO fwd: move data: 0.00h\n", + "18:20 madminer.utils.ml.tr INFO fwd: check for nans: 0.00h\n", + "18:20 madminer.utils.ml.tr INFO fwd: model.forward: 0.00h\n", + "18:20 madminer.utils.ml.tr INFO fwd: calculate losses: 0.00h\n", + "18:20 madminer.utils.ml.tr INFO training forward pass: 0.00h\n", + "18:20 madminer.utils.ml.tr INFO training sum losses: 0.00h\n", + "18:20 madminer.utils.ml.tr INFO opt: zero grad: 0.00h\n", + "18:20 madminer.utils.ml.tr INFO opt: backward: 0.00h\n", + "18:20 madminer.utils.ml.tr INFO opt: clip grad norm: 0.00h\n", + "18:20 madminer.utils.ml.tr INFO opt: step: 0.00h\n", + "18:20 madminer.utils.ml.tr INFO optimizer step: 0.00h\n", + "18:20 madminer.utils.ml.tr INFO load validation batch: 0.01h\n", + "18:20 madminer.utils.ml.tr INFO validation forward pass: 0.00h\n", + "18:20 madminer.utils.ml.tr INFO validation sum losses: 0.00h\n", + "18:20 madminer.utils.ml.tr INFO early stopping: 0.00h\n", + "18:20 madminer.utils.ml.tr INFO report epoch: 0.00h\n", + "18:20 madminer.ml.base INFO Saving model to models/carl10-bigbatch-gw\n" ] } ], "source": [ + "#estimator = ParameterizedRatioEstimator(\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename='data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", "estimator.train(\n", - " method='alices',\n", + " method='carl',\n", " theta='data/samples/theta0_train_ratio.npy',\n", " x='data/samples/x_train_ratio.npy',\n", " y='data/samples/y_train_ratio.npy',\n", @@ -542,126 +542,316 @@ " t_xz='data/samples/t_xz_train_ratio.npy',\n", " alpha=10,\n", " n_epochs=10,\n", - " scale_parameters=True,\n", + " batch_size=int(6000000/4),\n", ")\n", "\n", - "estimator.save('models/alices')" + "estimator.save('models/carl10-bigbatch-gw')" ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "18:20 madminer.ml.morphing INFO Setting up morphing-aware ratio estimator with 3 morphing components\n", + "18:20 madminer.ml.paramete INFO Starting training\n", + "18:20 madminer.ml.paramete INFO Method: carl\n", + "18:20 madminer.ml.paramete INFO Batch size: 1500000\n", + "18:20 madminer.ml.paramete INFO Optimizer: amsgrad\n", + "18:20 madminer.ml.paramete INFO Epochs: 50\n", + "18:20 madminer.ml.paramete INFO Learning rate: 0.001 initially, decaying to 0.0001\n", + "18:20 madminer.ml.paramete INFO Validation split: 0.25\n", + "18:20 madminer.ml.paramete INFO Early stopping: True\n", + "18:20 madminer.ml.paramete INFO Scale inputs: True\n", + "18:20 madminer.ml.paramete INFO Scale parameters: False\n", + "18:20 madminer.ml.paramete INFO Shuffle labels False\n", + "18:20 madminer.ml.paramete INFO Samples: all\n", + "18:20 madminer.ml.paramete INFO Loading training data\n", + "18:20 madminer.utils.vario INFO Loading data/samples/theta0_train_ratio.npy into RAM\n", + "18:20 madminer.utils.vario INFO Loading data/samples/x_train_ratio.npy into RAM\n", + "18:20 madminer.utils.vario INFO Loading data/samples/y_train_ratio.npy into RAM\n", + "18:20 madminer.utils.vario INFO Loading data/samples/r_xz_train_ratio.npy into RAM\n", + "18:20 madminer.utils.vario INFO Loading data/samples/t_xz_train_ratio.npy into RAM\n", + "18:20 madminer.ml.paramete INFO Found 499996 samples with 1 parameters and 9 observables\n", + "18:20 madminer.ml.base INFO Setting up input rescaling\n", + "18:20 madminer.ml.base INFO Disabling parameter rescaling\n", + "18:20 madminer.ml.paramete INFO Creating model\n", + "18:20 madminer.ml.paramete INFO Training model\n", + "18:20 madminer.utils.ml.tr INFO Training on CPU with single precision\n", + "18:21 madminer.utils.ml.tr INFO Epoch 2: train loss 0.68542 (xe: 0.685)\n", + "18:21 madminer.utils.ml.tr INFO val. loss 0.68306 (xe: 0.683)\n", + "18:21 madminer.utils.ml.tr INFO Epoch 4: train loss 0.68055 (xe: 0.681)\n", + "18:21 madminer.utils.ml.tr INFO val. loss 0.67899 (xe: 0.679)\n", + "18:22 madminer.utils.ml.tr INFO Epoch 6: train loss 0.67724 (xe: 0.677)\n", + "18:22 madminer.utils.ml.tr INFO val. loss 0.67625 (xe: 0.676)\n", + "18:22 madminer.utils.ml.tr INFO Epoch 8: train loss 0.67497 (xe: 0.675)\n", + "18:22 madminer.utils.ml.tr INFO val. loss 0.67439 (xe: 0.674)\n", + "18:23 madminer.utils.ml.tr INFO Epoch 10: train loss 0.67338 (xe: 0.673)\n", + "18:23 madminer.utils.ml.tr INFO val. loss 0.67308 (xe: 0.673)\n", + "18:23 madminer.utils.ml.tr INFO Epoch 12: train loss 0.67220 (xe: 0.672)\n", + "18:23 madminer.utils.ml.tr INFO val. loss 0.67213 (xe: 0.672)\n", + "18:24 madminer.utils.ml.tr INFO Epoch 14: train loss 0.67130 (xe: 0.671)\n", + "18:24 madminer.utils.ml.tr INFO val. loss 0.67140 (xe: 0.671)\n", + "18:24 madminer.utils.ml.tr INFO Epoch 16: train loss 0.67058 (xe: 0.671)\n", + "18:24 madminer.utils.ml.tr INFO val. loss 0.67082 (xe: 0.671)\n", + "18:25 madminer.utils.ml.tr INFO Epoch 18: train loss 0.66998 (xe: 0.670)\n", + "18:25 madminer.utils.ml.tr INFO val. loss 0.67034 (xe: 0.670)\n", + "18:25 madminer.utils.ml.tr INFO Epoch 20: train loss 0.66949 (xe: 0.669)\n", + "18:25 madminer.utils.ml.tr INFO val. loss 0.66995 (xe: 0.670)\n", + "18:26 madminer.utils.ml.tr INFO Epoch 22: train loss 0.66907 (xe: 0.669)\n", + "18:26 madminer.utils.ml.tr INFO val. loss 0.66962 (xe: 0.670)\n", + "18:26 madminer.utils.ml.tr INFO Epoch 24: train loss 0.66872 (xe: 0.669)\n", + "18:26 madminer.utils.ml.tr INFO val. loss 0.66934 (xe: 0.669)\n", + "18:27 madminer.utils.ml.tr INFO Epoch 26: train loss 0.66842 (xe: 0.668)\n", + "18:27 madminer.utils.ml.tr INFO val. loss 0.66910 (xe: 0.669)\n", + "18:27 madminer.utils.ml.tr INFO Epoch 28: train loss 0.66816 (xe: 0.668)\n", + "18:27 madminer.utils.ml.tr INFO val. loss 0.66889 (xe: 0.669)\n", + "18:28 madminer.utils.ml.tr INFO Epoch 30: train loss 0.66794 (xe: 0.668)\n", + "18:28 madminer.utils.ml.tr INFO val. loss 0.66871 (xe: 0.669)\n", + "18:28 madminer.utils.ml.tr INFO Epoch 32: train loss 0.66774 (xe: 0.668)\n", + "18:28 madminer.utils.ml.tr INFO val. loss 0.66855 (xe: 0.669)\n", + "18:29 madminer.utils.ml.tr INFO Epoch 34: train loss 0.66757 (xe: 0.668)\n", + "18:29 madminer.utils.ml.tr INFO val. loss 0.66840 (xe: 0.668)\n", + "18:29 madminer.utils.ml.tr INFO Epoch 36: train loss 0.66741 (xe: 0.667)\n", + "18:29 madminer.utils.ml.tr INFO val. loss 0.66827 (xe: 0.668)\n", + "18:30 madminer.utils.ml.tr INFO Epoch 38: train loss 0.66728 (xe: 0.667)\n", + "18:30 madminer.utils.ml.tr INFO val. loss 0.66815 (xe: 0.668)\n", + "18:30 madminer.utils.ml.tr INFO Epoch 40: train loss 0.66716 (xe: 0.667)\n", + "18:30 madminer.utils.ml.tr INFO val. loss 0.66805 (xe: 0.668)\n", + "18:31 madminer.utils.ml.tr INFO Epoch 42: train loss 0.66705 (xe: 0.667)\n", + "18:31 madminer.utils.ml.tr INFO val. loss 0.66795 (xe: 0.668)\n", + "18:31 madminer.utils.ml.tr INFO Epoch 44: train loss 0.66696 (xe: 0.667)\n", + "18:31 madminer.utils.ml.tr INFO val. loss 0.66787 (xe: 0.668)\n", + "18:32 madminer.utils.ml.tr INFO Epoch 46: train loss 0.66688 (xe: 0.667)\n", + "18:32 madminer.utils.ml.tr INFO val. loss 0.66779 (xe: 0.668)\n", + "18:32 madminer.utils.ml.tr INFO Epoch 48: train loss 0.66680 (xe: 0.667)\n", + "18:32 madminer.utils.ml.tr INFO val. loss 0.66773 (xe: 0.668)\n", + "18:33 madminer.utils.ml.tr INFO Epoch 50: train loss 0.66674 (xe: 0.667)\n", + "18:33 madminer.utils.ml.tr INFO val. loss 0.66767 (xe: 0.668)\n", + "18:33 madminer.utils.ml.tr INFO Early stopping did not improve performance\n", + "18:33 madminer.utils.ml.tr INFO Training time spend on:\n", + "18:33 madminer.utils.ml.tr INFO initialize model: 0.00h\n", + "18:33 madminer.utils.ml.tr INFO ALL: 0.21h\n", + "18:33 madminer.utils.ml.tr INFO check data: 0.00h\n", + "18:33 madminer.utils.ml.tr INFO make dataset: 0.00h\n", + "18:33 madminer.utils.ml.tr INFO make dataloader: 0.00h\n", + "18:33 madminer.utils.ml.tr INFO setup optimizer: 0.00h\n", + "18:33 madminer.utils.ml.tr INFO initialize training: 0.00h\n", + "18:33 madminer.utils.ml.tr INFO set lr: 0.00h\n", + "18:33 madminer.utils.ml.tr INFO load training batch: 0.14h\n", + "18:33 madminer.utils.ml.tr INFO fwd: move data: 0.00h\n", + "18:33 madminer.utils.ml.tr INFO fwd: check for nans: 0.00h\n", + "18:33 madminer.utils.ml.tr INFO fwd: model.forward: 0.00h\n", + "18:33 madminer.utils.ml.tr INFO fwd: calculate losses: 0.00h\n", + "18:33 madminer.utils.ml.tr INFO training forward pass: 0.00h\n", + "18:33 madminer.utils.ml.tr INFO training sum losses: 0.00h\n", + "18:33 madminer.utils.ml.tr INFO opt: zero grad: 0.00h\n", + "18:33 madminer.utils.ml.tr INFO opt: backward: 0.02h\n", + "18:33 madminer.utils.ml.tr INFO opt: clip grad norm: 0.00h\n", + "18:33 madminer.utils.ml.tr INFO opt: step: 0.00h\n", + "18:33 madminer.utils.ml.tr INFO optimizer step: 0.02h\n", + "18:33 madminer.utils.ml.tr INFO load validation batch: 0.05h\n", + "18:33 madminer.utils.ml.tr INFO validation forward pass: 0.00h\n", + "18:33 madminer.utils.ml.tr INFO validation sum losses: 0.00h\n", + "18:33 madminer.utils.ml.tr INFO early stopping: 0.00h\n", + "18:33 madminer.utils.ml.tr INFO report epoch: 0.00h\n", + "18:33 madminer.ml.base INFO Saving model to models/carl50-bigbatch-gw\n" + ] + } + ], "source": [ - "Let's for fun also train a model that only used `pt_j1` as input observable, which can be specified using the option `features` when defining the `ParameterizedRatioEstimator`" + "#estimator = ParameterizedRatioEstimator(\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename='data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.train(\n", + " method='carl',\n", + " theta='data/samples/theta0_train_ratio.npy',\n", + " x='data/samples/x_train_ratio.npy',\n", + " y='data/samples/y_train_ratio.npy',\n", + " r_xz='data/samples/r_xz_train_ratio.npy',\n", + " t_xz='data/samples/t_xz_train_ratio.npy',\n", + " alpha=10,\n", + " n_epochs=50,\n", + " batch_size=int(6000000/4),\n", + ")\n", + "\n", + "estimator.save('models/carl50-bigbatch-gw')" ] }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, + "execution_count": 14, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "11:21 madminer.ml.paramete INFO Starting training\n", - "11:21 madminer.ml.paramete INFO Method: alices\n", - "11:21 madminer.ml.paramete INFO alpha: 8\n", - "11:21 madminer.ml.paramete INFO Batch size: 128\n", - "11:21 madminer.ml.paramete INFO Optimizer: amsgrad\n", - "11:21 madminer.ml.paramete INFO Epochs: 10\n", - "11:21 madminer.ml.paramete INFO Learning rate: 0.001 initially, decaying to 0.0001\n", - "11:21 madminer.ml.paramete INFO Validation split: 0.25\n", - "11:21 madminer.ml.paramete INFO Early stopping: True\n", - "11:21 madminer.ml.paramete INFO Scale inputs: True\n", - "11:21 madminer.ml.paramete INFO Scale parameters: True\n", - "11:21 madminer.ml.paramete INFO Shuffle labels False\n", - "11:21 madminer.ml.paramete INFO Samples: all\n", - "11:21 madminer.ml.paramete INFO Loading training data\n", - "11:21 madminer.utils.vario INFO Loading data/samples/theta0_train_ratio.npy into RAM\n", - "11:21 madminer.utils.vario INFO Loading data/samples/x_train_ratio.npy into RAM\n", - "11:21 madminer.utils.vario INFO Loading data/samples/y_train_ratio.npy into RAM\n", - "11:21 madminer.utils.vario INFO Loading data/samples/r_xz_train_ratio.npy into RAM\n", - "11:21 madminer.utils.vario INFO Loading data/samples/t_xz_train_ratio.npy into RAM\n", - "11:21 madminer.ml.paramete INFO Found 500000 samples with 2 parameters and 3 observables\n", - "11:21 madminer.ml.base INFO Setting up input rescaling\n", - "11:21 madminer.ml.paramete INFO Rescaling parameters\n", - "11:21 madminer.ml.base INFO Setting up parameter rescaling\n", - "11:21 madminer.ml.paramete INFO Only using 1 of 3 observables\n", - "11:21 madminer.ml.paramete INFO Creating model\n", - "11:21 madminer.ml.paramete INFO Training model\n", - "11:21 madminer.utils.ml.tr INFO Training on CPU with single precision\n", - "11:21 madminer.utils.ml.tr INFO Epoch 1: train loss 1.00394 (improved_xe: 0.678, mse_score: 0.041)\n", - "11:21 madminer.utils.ml.tr INFO val. loss 0.96390 (improved_xe: 0.676, mse_score: 0.036)\n", - "11:22 madminer.utils.ml.tr INFO Epoch 2: train loss 0.94193 (improved_xe: 0.676, mse_score: 0.033)\n", - "11:22 madminer.utils.ml.tr INFO val. loss 0.94879 (improved_xe: 0.676, mse_score: 0.034)\n", - "11:22 madminer.utils.ml.tr INFO Epoch 3: train loss 0.92940 (improved_xe: 0.676, mse_score: 0.032)\n", - "11:22 madminer.utils.ml.tr INFO val. loss 0.93833 (improved_xe: 0.676, mse_score: 0.033)\n", - "11:22 madminer.utils.ml.tr INFO Epoch 4: train loss 0.92164 (improved_xe: 0.676, mse_score: 0.031)\n", - "11:22 madminer.utils.ml.tr INFO val. loss 0.93148 (improved_xe: 0.676, mse_score: 0.032)\n", - "11:22 madminer.utils.ml.tr INFO Epoch 5: train loss 0.91808 (improved_xe: 0.676, mse_score: 0.030)\n", - "11:22 madminer.utils.ml.tr INFO val. loss 0.92575 (improved_xe: 0.676, mse_score: 0.031)\n", - "11:22 madminer.utils.ml.tr INFO Epoch 6: train loss 0.91467 (improved_xe: 0.676, mse_score: 0.030)\n", - "11:22 madminer.utils.ml.tr INFO val. loss 0.92405 (improved_xe: 0.676, mse_score: 0.031)\n", - "11:23 madminer.utils.ml.tr INFO Epoch 7: train loss 0.91288 (improved_xe: 0.676, mse_score: 0.030)\n", - "11:23 madminer.utils.ml.tr INFO val. loss 0.92247 (improved_xe: 0.676, mse_score: 0.031)\n", - "11:23 madminer.utils.ml.tr INFO Epoch 8: train loss 0.91161 (improved_xe: 0.676, mse_score: 0.029)\n", - "11:23 madminer.utils.ml.tr INFO val. loss 0.92253 (improved_xe: 0.676, mse_score: 0.031)\n", - "11:23 madminer.utils.ml.tr INFO Epoch 9: train loss 0.91062 (improved_xe: 0.676, mse_score: 0.029)\n", - "11:23 madminer.utils.ml.tr INFO val. loss 0.92056 (improved_xe: 0.676, mse_score: 0.031)\n", - "11:23 madminer.utils.ml.tr INFO Epoch 10: train loss 0.91000 (improved_xe: 0.676, mse_score: 0.029)\n", - "11:23 madminer.utils.ml.tr INFO val. loss 0.92033 (improved_xe: 0.676, mse_score: 0.031)\n", - "11:23 madminer.utils.ml.tr INFO Early stopping did not improve performance\n", - "11:23 madminer.utils.ml.tr INFO Training time spend on:\n", - "11:23 madminer.utils.ml.tr INFO initialize model: 0.00h\n", - "11:23 madminer.utils.ml.tr INFO ALL: 0.04h\n", - "11:23 madminer.utils.ml.tr INFO check data: 0.00h\n", - "11:23 madminer.utils.ml.tr INFO make dataset: 0.00h\n", - "11:23 madminer.utils.ml.tr INFO make dataloader: 0.00h\n", - "11:23 madminer.utils.ml.tr INFO setup optimizer: 0.00h\n", - "11:23 madminer.utils.ml.tr INFO initialize training: 0.00h\n", - "11:23 madminer.utils.ml.tr INFO set lr: 0.00h\n", - "11:23 madminer.utils.ml.tr INFO load training batch: 0.01h\n", - "11:23 madminer.utils.ml.tr INFO fwd: move data: 0.00h\n", - "11:23 madminer.utils.ml.tr INFO fwd: check for nans: 0.00h\n", - "11:23 madminer.utils.ml.tr INFO fwd: model.forward: 0.01h\n", - "11:23 madminer.utils.ml.tr INFO fwd: calculate losses: 0.00h\n", - "11:23 madminer.utils.ml.tr INFO training forward pass: 0.01h\n", - "11:23 madminer.utils.ml.tr INFO training sum losses: 0.00h\n", - "11:23 madminer.utils.ml.tr INFO opt: zero grad: 0.00h\n", - "11:23 madminer.utils.ml.tr INFO opt: backward: 0.01h\n", - "11:23 madminer.utils.ml.tr INFO opt: clip grad norm: 0.00h\n", - "11:23 madminer.utils.ml.tr INFO opt: step: 0.00h\n", - "11:23 madminer.utils.ml.tr INFO optimizer step: 0.01h\n", - "11:23 madminer.utils.ml.tr INFO load validation batch: 0.00h\n", - "11:23 madminer.utils.ml.tr INFO validation forward pass: 0.00h\n", - "11:23 madminer.utils.ml.tr INFO validation sum losses: 0.00h\n", - "11:23 madminer.utils.ml.tr INFO early stopping: 0.00h\n", - "11:23 madminer.utils.ml.tr INFO report epoch: 0.00h\n", - "11:23 madminer.ml.base INFO Saving model to models/alices_pt\n" + "16:14 madminer.ml.morphing INFO Setting up morphing-aware ratio estimator with 3 morphing components\n", + "16:14 madminer.ml.paramete INFO Starting training\n", + "16:14 madminer.ml.paramete INFO Method: carl\n", + "16:14 madminer.ml.paramete INFO Batch size: 1500000\n", + "16:14 madminer.ml.paramete INFO Optimizer: amsgrad\n", + "16:14 madminer.ml.paramete INFO Epochs: 1000\n", + "16:14 madminer.ml.paramete INFO Learning rate: 0.001 initially, decaying to 0.0001\n", + "16:14 madminer.ml.paramete INFO Validation split: 0.25\n", + "16:14 madminer.ml.paramete INFO Early stopping: True\n", + "16:14 madminer.ml.paramete INFO Scale inputs: True\n", + "16:14 madminer.ml.paramete INFO Scale parameters: False\n", + "16:14 madminer.ml.paramete INFO Shuffle labels False\n", + "16:14 madminer.ml.paramete INFO Samples: all\n", + "16:14 madminer.ml.paramete INFO Loading training data\n", + "16:14 madminer.utils.vario INFO Loading data/samples/theta0_train_ratio.npy into RAM\n", + "16:14 madminer.utils.vario INFO Loading data/samples/x_train_ratio.npy into RAM\n", + "16:14 madminer.utils.vario INFO Loading data/samples/y_train_ratio.npy into RAM\n", + "16:14 madminer.utils.vario INFO Loading data/samples/r_xz_train_ratio.npy into RAM\n", + "16:14 madminer.utils.vario INFO Loading data/samples/t_xz_train_ratio.npy into RAM\n", + "16:14 madminer.ml.paramete INFO Found 499996 samples with 1 parameters and 9 observables\n", + "16:14 madminer.ml.base INFO Setting up input rescaling\n", + "16:14 madminer.ml.base INFO Disabling parameter rescaling\n", + "16:14 madminer.ml.paramete INFO Creating model\n", + "16:14 madminer.ml.paramete INFO Training model\n", + "16:14 madminer.utils.ml.tr INFO Training on CPU with single precision\n", + "16:38 madminer.utils.ml.tr INFO Epoch 50: train loss 0.66400 (xe: 0.664)\n", + "16:38 madminer.utils.ml.tr INFO val. loss 0.66454 (xe: 0.665)\n", + "17:01 madminer.utils.ml.tr INFO Epoch 100: train loss 0.66134 (xe: 0.661)\n", + "17:01 madminer.utils.ml.tr INFO val. loss 0.66191 (xe: 0.662)\n", + "17:16 madminer.utils.ml.tr INFO Epoch 150: train loss 0.65801 (xe: 0.658)\n", + "17:16 madminer.utils.ml.tr INFO val. loss 0.65862 (xe: 0.659)\n", + "17:30 madminer.utils.ml.tr INFO Epoch 200: train loss 0.65508 (xe: 0.655)\n", + "17:30 madminer.utils.ml.tr INFO val. loss 0.65576 (xe: 0.656)\n", + "17:45 madminer.utils.ml.tr INFO Epoch 250: train loss 0.65272 (xe: 0.653)\n", + "17:45 madminer.utils.ml.tr INFO val. loss 0.65345 (xe: 0.653)\n", + "17:59 madminer.utils.ml.tr INFO Epoch 300: train loss 0.65106 (xe: 0.651)\n", + "17:59 madminer.utils.ml.tr INFO val. loss 0.65186 (xe: 0.652)\n", + "18:13 madminer.utils.ml.tr INFO Epoch 350: train loss 0.64989 (xe: 0.650)\n", + "18:13 madminer.utils.ml.tr INFO val. loss 0.65082 (xe: 0.651)\n", + "18:28 madminer.utils.ml.tr INFO Epoch 400: train loss 0.64902 (xe: 0.649)\n", + "18:28 madminer.utils.ml.tr INFO val. loss 0.65007 (xe: 0.650)\n", + "18:42 madminer.utils.ml.tr INFO Epoch 450: train loss 0.64832 (xe: 0.648)\n", + "18:42 madminer.utils.ml.tr INFO val. loss 0.64948 (xe: 0.649)\n", + "18:57 madminer.utils.ml.tr INFO Epoch 500: train loss 0.64773 (xe: 0.648)\n", + "18:57 madminer.utils.ml.tr INFO val. loss 0.64900 (xe: 0.649)\n", + "19:12 madminer.utils.ml.tr INFO Epoch 550: train loss 0.64723 (xe: 0.647)\n", + "19:12 madminer.utils.ml.tr INFO val. loss 0.64858 (xe: 0.649)\n", + "19:26 madminer.utils.ml.tr INFO Epoch 600: train loss 0.64679 (xe: 0.647)\n", + "19:26 madminer.utils.ml.tr INFO val. loss 0.64823 (xe: 0.648)\n", + "19:40 madminer.utils.ml.tr INFO Epoch 650: train loss 0.64641 (xe: 0.646)\n", + "19:40 madminer.utils.ml.tr INFO val. loss 0.64792 (xe: 0.648)\n", + "19:55 madminer.utils.ml.tr INFO Epoch 700: train loss 0.64608 (xe: 0.646)\n", + "19:55 madminer.utils.ml.tr INFO val. loss 0.64766 (xe: 0.648)\n", + "20:10 madminer.utils.ml.tr INFO Epoch 750: train loss 0.64579 (xe: 0.646)\n", + "20:10 madminer.utils.ml.tr INFO val. loss 0.64744 (xe: 0.647)\n", + "20:24 madminer.utils.ml.tr INFO Epoch 800: train loss 0.64554 (xe: 0.646)\n", + "20:24 madminer.utils.ml.tr INFO val. loss 0.64725 (xe: 0.647)\n", + "20:39 madminer.utils.ml.tr INFO Epoch 850: train loss 0.64532 (xe: 0.645)\n", + "20:39 madminer.utils.ml.tr INFO val. loss 0.64708 (xe: 0.647)\n", + "20:54 madminer.utils.ml.tr INFO Epoch 900: train loss 0.64513 (xe: 0.645)\n", + "20:54 madminer.utils.ml.tr INFO val. loss 0.64694 (xe: 0.647)\n", + "21:08 madminer.utils.ml.tr INFO Epoch 950: train loss 0.64496 (xe: 0.645)\n", + "21:08 madminer.utils.ml.tr INFO val. loss 0.64682 (xe: 0.647)\n", + "21:23 madminer.utils.ml.tr INFO Epoch 1000: train loss 0.64482 (xe: 0.645)\n", + "21:23 madminer.utils.ml.tr INFO val. loss 0.64672 (xe: 0.647)\n", + "21:23 madminer.utils.ml.tr INFO Early stopping did not improve performance\n", + "21:23 madminer.utils.ml.tr INFO Training time spend on:\n", + "21:23 madminer.utils.ml.tr INFO initialize model: 0.00h\n", + "21:23 madminer.utils.ml.tr INFO ALL: 5.15h\n", + "21:23 madminer.utils.ml.tr INFO check data: 0.00h\n", + "21:23 madminer.utils.ml.tr INFO make dataset: 0.00h\n", + "21:23 madminer.utils.ml.tr INFO make dataloader: 0.00h\n", + "21:23 madminer.utils.ml.tr INFO setup optimizer: 0.00h\n", + "21:23 madminer.utils.ml.tr INFO initialize training: 0.00h\n", + "21:23 madminer.utils.ml.tr INFO set lr: 0.00h\n", + "21:23 madminer.utils.ml.tr INFO load training batch: 3.40h\n", + "21:23 madminer.utils.ml.tr INFO fwd: move data: 0.00h\n", + "21:23 madminer.utils.ml.tr INFO fwd: check for nans: 0.01h\n", + "21:23 madminer.utils.ml.tr INFO fwd: model.forward: 0.14h\n", + "21:23 madminer.utils.ml.tr INFO fwd: calculate losses: 0.01h\n", + "21:23 madminer.utils.ml.tr INFO training forward pass: 0.11h\n", + "21:23 madminer.utils.ml.tr INFO training sum losses: 0.00h\n", + "21:23 madminer.utils.ml.tr INFO opt: zero grad: 0.00h\n", + "21:23 madminer.utils.ml.tr INFO opt: backward: 0.37h\n", + "21:23 madminer.utils.ml.tr INFO opt: clip grad norm: 0.00h\n", + "21:23 madminer.utils.ml.tr INFO opt: step: 0.00h\n", + "21:23 madminer.utils.ml.tr INFO optimizer step: 0.37h\n", + "21:23 madminer.utils.ml.tr INFO load validation batch: 1.22h\n", + "21:23 madminer.utils.ml.tr INFO validation forward pass: 0.05h\n", + "21:23 madminer.utils.ml.tr INFO validation sum losses: 0.00h\n", + "21:23 madminer.utils.ml.tr INFO early stopping: 0.00h\n", + "21:23 madminer.utils.ml.tr INFO report epoch: 0.00h\n", + "21:23 madminer.ml.base INFO Saving model to models/carl1000-bigbatch-gw\n" ] } ], "source": [ - "estimator_pt = ParameterizedRatioEstimator(\n", + "#estimator = ParameterizedRatioEstimator(\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename='data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.train(\n", + " method='carl',\n", + " theta='data/samples/theta0_train_ratio.npy',\n", + " x='data/samples/x_train_ratio.npy',\n", + " y='data/samples/y_train_ratio.npy',\n", + " r_xz='data/samples/r_xz_train_ratio.npy',\n", + " t_xz='data/samples/t_xz_train_ratio.npy',\n", + " alpha=10,\n", + " n_epochs=1000,\n", + " batch_size=int(6000000/4),\n", + ")\n", + "\n", + "estimator.save('models/carl1000-bigbatch-gw')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's for fun also train a model that only used `pt_j1` as input observable, which can be specified using the option `features` when defining the `ParameterizedRatioEstimator`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#estimator_pt = ParameterizedRatioEstimator(\n", + "estimator_pt = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename='data/setup.h5',\n", " n_hidden=(40,40),\n", " activation=\"tanh\",\n", " features=[0],\n", ")\n", "\n", "estimator_pt.train(\n", - " method='alices',\n", + " method='carl',\n", " theta='data/samples/theta0_train_ratio.npy',\n", " x='data/samples/x_train_ratio.npy',\n", " y='data/samples/y_train_ratio.npy',\n", " r_xz='data/samples/r_xz_train_ratio.npy',\n", " t_xz='data/samples/t_xz_train_ratio.npy',\n", " alpha=8,\n", - " n_epochs=10,\n", - " scale_parameters=True,\n", + " n_epochs=1000,\n", + " #scale_parameters=True,\n", ")\n", "\n", - "estimator_pt.save('models/alices_pt')" + "estimator_pt.save('models/carl_pt')" ] }, { @@ -680,7 +870,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -695,21 +885,11 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "11:23 madminer.ml.base INFO Loading model from models/alices\n", - "11:23 madminer.utils.vario INFO Loading data/samples/x_test.npy into RAM\n", - "11:23 madminer.utils.vario INFO Loading data/samples/theta_grid.npy into RAM\n" - ] - } - ], + "outputs": [], "source": [ - "estimator.load('models/alices')\n", + "estimator.load('models/carl')\n", "\n", "log_r_hat, _ = estimator.evaluate_log_likelihood_ratio(\n", " theta='data/samples/theta_grid.npy',\n", @@ -727,22 +907,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "bin_size = theta_each[1] - theta_each[0]\n", "edges = np.linspace(theta_each[0] - bin_size/2, theta_each[-1] + bin_size/2, len(theta_each)+1)\n", @@ -776,13 +943,56 @@ "source": [ "Note that in this tutorial our sample size was very small, and the network might not really have a chance to converge to the correct likelihood ratio function. So don't worry if you find a minimum that is not at the right point (the SM, i.e. the origin in this plot). Feel free to dial up the event numbers in the run card as well as the training samples and see what happens then!" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "log_r_hat.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.load('data/samples/x_train_ratio.npy').shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "theta = np.load('data/samples/theta0_train_ratio.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "theta.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python (higgs_inference)", + "display_name": "Python 2", "language": "python", - "name": "higgs_inference" + "name": "python2" }, "language_info": { "codemirror_mode": { @@ -794,7 +1004,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.8.2" } }, "nbformat": 4, diff --git a/examples/tutorial_particle_physics/EmbeddingData.ipynb b/examples/tutorial_particle_physics/EmbeddingData.ipynb new file mode 100644 index 000000000..babf474a2 --- /dev/null +++ b/examples/tutorial_particle_physics/EmbeddingData.ipynb @@ -0,0 +1,580 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import h5py, os\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "f = h5py.File(os.getcwd()+'/data/lhe_data_gw.h5', 'r+')\n", + "#f = h5py.File(os.getcwd()+'/data/lhe_data_shuffled.h5', 'r+')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bm = f['benchmarks']\n", + "bm.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mp = f['morphing']\n", + "mp.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ob = f['observables']\n", + "ob.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pm = f['parameters']\n", + "pm.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ss = f['sample_summary']\n", + "ss.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "samples = f['samples']\n", + "samples.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3, 3, 4, ..., 3, 3, 3])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.array(f['samples/sampling_benchmarks'])" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "f.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Samples" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(110000, 3)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "observations = np.array(samples['observations'])\n", + "observations.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[2.7841223e-08, 1.4210410e-08, 2.1442170e-08, ..., 2.0985015e-08,\n", + " 2.1245668e-08, 2.1771883e-08],\n", + " [2.2302728e-08, 1.9083157e-08, 2.0893168e-08, ..., 2.0786713e-08,\n", + " 2.0847518e-08, 2.0969404e-08],\n", + " [1.1378621e-07, 9.1049680e-08, 1.0373078e-07, ..., 1.0297749e-07,\n", + " 1.0340765e-07, 1.0427079e-07],\n", + " ...,\n", + " [2.0731574e-08, 2.0853072e-08, 2.0720938e-08, ..., 2.0724001e-08,\n", + " 2.0722183e-08, 2.0719090e-08],\n", + " [2.0037817e-08, 2.1580510e-08, 2.0647189e-08, ..., 2.0697211e-08,\n", + " 2.0668568e-08, 2.0611724e-08],\n", + " [2.9313322e-08, 1.3099329e-08, 2.1580261e-08, ..., 2.1034656e-08,\n", + " 2.1345611e-08, 2.1974413e-08]])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weights = np.array(samples['weights'])\n", + "weights" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "num of sm events: 50000\n", + "num of 5 events: 10000\n", + "num of neg_5 events: 10000\n", + "num of 10 events: 10000\n", + "num of neg_10 events: 10000\n", + "num of 20 events: 10000\n", + "num of neg_20 events: 10000\n" + ] + } + ], + "source": [ + "sampling_benchmarks = np.array(samples['sampling_benchmarks'])\n", + "print('num of sm events: '+str(sum(sampling_benchmarks == 3)))\n", + "print('num of 5 events: '+str(sum(sampling_benchmarks == 4)))\n", + "print('num of neg_5 events: '+str(sum(sampling_benchmarks == 5)))\n", + "print('num of 10 events: '+str(sum(sampling_benchmarks == 6)))\n", + "print('num of neg_10 events: '+str(sum(sampling_benchmarks == 7)))\n", + "print('num of 20 events: '+str(sum(sampling_benchmarks == 8)))\n", + "print('num of neg_20 events: '+str(sum(sampling_benchmarks == 9)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Embedding toy data" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "toydataFile = h5py.File(os.getcwd()+'/toydata/gw_toydata.h5', 'r')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### data" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "del f['samples/observations']\n", + "f.create_dataset('samples/observations', data=np.array(toydataFile['Data']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### sampling_benchmarks" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "N = 500000\n", + "sampling_benchmarks = np.concatenate([np.concatenate([np.ones(N)*3, np.ones(N)*i]) for i in range(4, 10)])\n", + "del f['samples/sampling_benchmarks']\n", + "f.create_dataset('samples/sampling_benchmarks', data=sampling_benchmarks)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### weights" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "Weights = np.array(toydataFile['Weights'])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1.2393269e-07, 1.2393269e-07, 1.2393269e-07, ..., 3.1926135e-07,\n", + " 3.1926135e-07, 3.1926135e-07], dtype=float32)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Weights" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "weights = np.ones([N*12, 10])*(1e10)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "#weights[N*0:N*1, 3] = Weights[N*0]\n", + "#weights[N*1:N*2, 4] = Weights[N*1]\n", + "#weights[N*:N*1, 3] = Weights[N*0]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sm data, pos=3\n", + "bsm data, pos=4\n", + "sm data, pos=3\n", + "bsm data, pos=5\n", + "sm data, pos=3\n", + "bsm data, pos=6\n", + "sm data, pos=3\n", + "bsm data, pos=7\n", + "sm data, pos=3\n", + "bsm data, pos=8\n", + "sm data, pos=3\n", + "bsm data, pos=9\n" + ] + } + ], + "source": [ + "for i in range(12):\n", + " if i%2==0:\n", + " print('sm data, pos=%d'%(3))\n", + " weights[N*i:N*(i+1), 3] = Weights[N*i]\n", + " else:\n", + " print('bsm data, pos=%d'%(3+(i+1)/2))\n", + " weights[N*i:N*(i+1), int(3+(i+1)/2)] = Weights[N*i]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "del f['samples/weights']\n", + "f.create_dataset('samples/weights', data=weights)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### observable numbers" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f['observables'].keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[b'j[0].pt' b'j[0].deltaphi(j[1]) * (-1. + 2.*float(j[0].eta > j[1].eta))'\n", + " b'met.pt']\n", + "[b'pt_j1' b'delta_phi_jj' b'met']\n" + ] + } + ], + "source": [ + "print(np.array(f['observables/definitions']))\n", + "print(np.array(f['observables/names']))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "del f['observables/definitions']\n", + "del f['observables/names']\n", + "\n", + "observables = ['s', 'theta', 'thetaZ', 'thetaW', 'Sin(phiZ)', 'Sin(phiW)', 'Cos(phiZ)', 'Cos(phiW)', 'Pt']\n", + "f.create_dataset('observables/definitions', data=(np.array(observables, dtype='S')))\n", + "f.create_dataset('observables/names', data=(np.array(observables, dtype='S')))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "f.close()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/tutorial_particle_physics/OurTrainingTools.py b/examples/tutorial_particle_physics/OurTrainingTools.py new file mode 100644 index 000000000..0a5c0217e --- /dev/null +++ b/examples/tutorial_particle_physics/OurTrainingTools.py @@ -0,0 +1,576 @@ +import h5py, torch, time, datetime, os +import numpy as np +import matplotlib.pyplot as plt +from torch import nn +from torch.nn.modules import Module +from torch.nn import CrossEntropyLoss +from tabulate import tabulate + +random_seed = torch.randint(0, 1339, (1,)).item() +torch.manual_seed(random_seed) +print('=========== Random Seed: %d ==========='%(random_seed)) + +class DataFile(): +### Reads sample file Info (string), Parameters (list), Values (torch array), Data (torch array) and Weights (torch array) +### FilePath is the path of the input file +### Computes cross-section XS (average weight) and total number of data ND in file +### Checks that files are in correct format (correct Keys) +### and that the length of Parameters and Data equals the one of Values and Weights respectively + def __init__(self, FilePath, verbose=True): + if verbose: print('\nReading file ...' + FilePath) + file = h5py.File(FilePath, 'r') + if list(file.keys()) == ['Data', 'Info', 'Parameters', 'Process', 'Values', 'Weights']: + if( (len(file['Parameters'][()]) == len(file['Values'][()])) and (len(file['Data'][()]) == len(file['Weights'][()])) ): + if verbose: print('##### File Info:\n' + file['Info'][()][0] + '\n#####') + self.FilePath = FilePath + self.Info = file['Info'][()][0] + self.Process = file['Process'][()][0] + self.Parameters = file['Parameters'][()] + #print(file['Values'][()].dtype) + #print('%.15f' % (file['Data'][()][0][0])) + self.Values = torch.DoubleTensor(file['Values'][()]) + self.Data = torch.DoubleTensor(file['Data'][()]) + self.DataRaw = file['Data'][()] + self.Weights = torch.DoubleTensor(file['Weights'][()]) + self.XS = self.Weights.mean() + self.ND = len(self.Weights) + else: + print('--> File not valid:\nunequal lenght of Values and Parameters or of Data and Weights') + raise ValueError + else: + print('--> File format not valid:\nKeys: ' + str(list(file.keys())) + + '\nshould be: ' + str(['Data', 'Info', 'Parameters', 'Process', 'Values', 'Weights'])) + raise ValueError + +class OurTrainingData(): +### Imports data for training. The Return() methods returns [self.Data, self.Labels, self.Weights, self.ParVal] +### All values are in double precision +### Inputs are the SM and BSM file paths and list of integers to chop the datasets if needed +### Weights are normalized to have sum = 1 on the entire training sample + def __init__(self, SMfilepathlist, BSMfilepathlist, process, parameters, SMNLimits="NA", BSMNLimits="NA", verbose=True): + self.Process = process + self.Parameters = parameters + if verbose: print('Loading Data Files for Process: ' + str(self.Process) +', with new physics Parameters: ' + str(self.Parameters) ) + if len(self.Parameters)!= 1: print('Only 1D Implemented in Training !') + +####### Load BSM data (stored in self.BSMDataFiles) + if type(BSMfilepathlist) == list: + if all(isinstance(n, str) for n in BSMfilepathlist): + self.BSMDataFiles = [] + for path in BSMfilepathlist: + temp = DataFile(path, verbose=verbose) + if( (temp.Process == self.Process) and (temp.Parameters == self.Parameters) and (temp.Values != 0.) ): + self.BSMDataFiles.append(temp) + else: + print('File not valid: ' + path) + print('Parameters = ' + str(temp.Parameters) + ', Process = ' + str(temp.Process) + +' and Values = ' + str(temp.Values.tolist())) + print('should be = ' + str(self.Parameters) + ', = ' + str(self.Process) + + ' and != ' + str(0.)) + raise ValueError + self.BSMDataFiles.append(None) + else: + print('BSMfilepathlist input should be a list of strings !') + raise FileNotFoundError + else: + print('BSMfilepathlist input should be a list !') + raise FileNotFoundError + +###### Chop the BSM data sets (stored in BSMNDList, BSMDataList, BSMWeightsList, BSMParValList, BSMTargetList) + if type(BSMNLimits) == int: + BSMNLimits = [min(BSMNLimits, NF.ND) for NF in self.BSMDataFiles] + elif type(BSMNLimits) == list and all(isinstance(n, int) for n in BSMNLimits): + if len(BSMNLimits) != len(self.BSMDataFiles): + print("--> Please input %d integers to chop each SM file."%( + len(self.BSMDataFiles))) + raise ValueError + elif sum([self.BSMDataFiles[i].ND >= BSMNLimits[i] for i in range(len(BSMNLimits))] + ) != len(self.BSMDataFiles): + print("--> Some chop limit larger than available data in the corresponding file.") + print("--> Lengths of the files: "+str([file.ND for file in self.BSMDataFiles ])) + raise ValueError + else: + BSMNLimits =[file.ND for file in self.BSMDataFiles] + + self.BSMNDList = BSMNLimits + #self.BSMNData = sum(self.BSMNDataList) + self.BSMDataList = [DF.Data[:N] for (DF, N) in zip( + self.BSMDataFiles, self.BSMNDList)] + self.BSMWeightsList = [DF.Weights[:N] for (DF, N) in zip( + self.BSMDataFiles, self.BSMNDList)] + self.BSMXSList = [DF.XS for DF in self.BSMDataFiles] + self.BSMParValList = [torch.ones(N, dtype=torch.double)*DF.Values for (DF, N) in zip(self.BSMDataFiles, self.BSMNDList)] + self.BSMTargetList = [torch.ones(N, dtype=torch.double) for N in self.BSMNDList] + + +####### Load SM data (stored in SMDataFiles) + if type(SMfilepathlist) == list: + if all(isinstance(n, str) for n in SMfilepathlist): + #self.SMFilePathList = SMfilepathlist + #self.SMNumFiles = len(self.SMFilePathList) + self.SMDataFiles = [] + for path in SMfilepathlist: + temp = DataFile(path, verbose=verbose) + if( (temp.Process == self.Process) and (temp.Parameters == 'SM') and (temp.Values == 0.) ): + self.SMDataFiles.append(temp) + else: + print('File not valid: ' + path) + print('Parameters = ' + str(temp.Parameters) + ', Process = ' + str(temp.Process) + +' and Values = ' + str(temp.Values.tolist())) + print('should be = ' + 'SM'+ ', = ' + str(self.Process) + + ' and = ' + str(0.)) + self.SMDataFiles.append(None) + else: + print('SMfilepathlist input should be a list of strings !') + raise FileNotFoundError + else: + print('SMfilepathlist input should be a list !') + raise FileNotFoundError + +####### Chop the SM data sets and join them in one (stored in SMND, SMData and SMWeights) + if type(SMNLimits) == int: + SMNLimits = [min(SMNLimits, DF.ND) for DF in self.SMDataFiles] + elif type(SMNLimits) == list and all(isinstance(n, int) for n in SMNLimits): + if len(SMNLimits) != len(self.SMDataFiles): + print("--> Please input %d integers to chop each SM file."%( + len(self.SMDataFiles))) + raise ValueError + elif sum([self.SMDataFiles[i].ND >= SMNLimits[i] for i in range(len(SMNLimits))] + ) != len(self.SMDataFiles): + print("--> Some chop limit larger than available data in the corresponding file.") + print("--> Lengths of the files: " + str([file.ND for file in self.SMDataFiles])) + raise ValueError + else: + SMNLimits = [file.ND for file in self.SMDataFiles] + self.SMND = sum(SMNLimits) + self.SMData = torch.cat( + [DF.Data[:N] for (DF, N) in zip(self.SMDataFiles, SMNLimits)] + , 0) + self.SMWeights = torch.cat( + [DF.Weights[:N] for (DF, N) in zip(self.SMDataFiles, SMNLimits)] + , 0) + self.SMXSList = [DF.XS for DF in self.SMDataFiles] + idx_random = torch.randperm(self.SMND) + self.SMData = self.SMData[idx_random, :] + self.SMWeights = self.SMWeights[idx_random] + +####### Break SM data in blocks to be paired with BSM data (stored in UsedSMNDList, UsedSMDataList, UsedSMWeightsList, UsedSMParValList, UsedSMTargetList) + BSMNRatioDataList = [torch.tensor(1., dtype=torch.double)*n/sum(self.BSMNDList + ) for n in self.BSMNDList] + self.UsedSMNDList = [int(self.SMND*BSMNRatioData) for BSMNRatioData in BSMNRatioDataList] + #self.UsedSMNData = sum(self.UsedSMNDataList) + #self.UsedSMData = self.SMData[: self.UsedSMND] + self.UsedSMDataList = self.SMData[:sum(self.UsedSMNDList)].split(self.UsedSMNDList) + + ##### Reweighting is performed such that the SUM of the SM weights in each block equals the number of BSM data times the AVERAGE + ##### of the original weights. This equals the SM cross-section as obtained in the specific sample at hand, times NBSM + self.UsedSMWeightsList = self.SMWeights[:sum(self.UsedSMNDList)].split(self.UsedSMNDList) + self.UsedSMWeightsList = [ self.UsedSMWeightsList[i]*self.BSMNDList[i]/self.UsedSMNDList[i] for i in range(len(BSMNRatioDataList))] + self.UsedSMParValList = [torch.ones(N, dtype=torch.double)*DF.Values for (DF, N) in zip(self.BSMDataFiles, self.UsedSMNDList)] + self.UsedSMTargetList = [torch.zeros(N, dtype=torch.double) for N in self.UsedSMNDList] + +####### Join SM with BSM data + self.Data = torch.cat( + [torch.cat([self.UsedSMDataList[i], self.BSMDataList[i]] + ) for i in range(len(self.BSMDataList))] + ) + self.Weights = torch.cat( + [torch.cat([self.UsedSMWeightsList[i], self.BSMWeightsList[i]] + ) for i in range(len(self.BSMWeightsList))] + ) + self.Labels = torch.cat( + [torch.cat([self.UsedSMTargetList[i], self.BSMTargetList[i]] + ) for i in range(len(self.BSMTargetList))] + ) + self.ParVal = torch.cat( + [torch.cat([self.UsedSMParValList[i], self.BSMParValList[i]] + ) for i in range(len(self.BSMParValList))] + ) + +####### Final reweighting + s = self.Weights.sum() + self.Weights = self.Weights.div(s) + +####### If verbose, display report + if verbose: self.Report() + +####### Return Tranining Data + def ReturnData(self): + return [self.Data, self.Labels, self.Weights, self.ParVal] + + def Report(self): + #from tabulate import tabulate + print('\nLoaded SM Files:') + print(tabulate({str(self.Parameters): [ file.Values for file in self.SMDataFiles ], + "#Data":[ file.ND for file in self.SMDataFiles ], + "XS[pb](avg.w)":[ file.XS for file in self.SMDataFiles ]}, headers="keys")) + print('\nLoaded BSM Files:') + print(tabulate({str(self.Parameters): [ file.Values for file in self.BSMDataFiles ], + "#Data":[ file.ND for file in self.BSMDataFiles ], + "XS[pb](avg.w)":[ file.XS for file in self.BSMDataFiles ]}, headers="keys")) + print('\nPaired BSM/SM Datasets:\n') + ### Check should be nearly equal to #EV.BSM. It is computed with the weights BEFORE final reweighting + print(tabulate({str(self.Parameters): [ file.Values for file in self.BSMDataFiles ], "#Ev.BSM": self.BSMNDList + , "#Ev.SM": self.UsedSMNDList, + "Check": [(self.UsedSMWeightsList[i].sum())/(self.SMWeights.mean()) for i in range(len(self.BSMDataFiles))] + }, headers="keys")) + +####### Convert Angles + def CurateAngles(self, AnglePos): + Angles = self.Data[:, AnglePos] + CuratedAngles = torch.cat([torch.sin(Angles), torch.cos(Angles)], dim=1) + OtherPos = list(set(range(self.Data.size(1)))-set(AnglePos)) + self.Data = torch.cat([self.Data[:, OtherPos], CuratedAngles], dim=1) + print('####\nAnlges at position %s have been converted to Sin and Cos and put at the last columns of the Data.'%(AnglePos)) + print('####') + +####### Loss function(s), with "input" in (0,1) interval +class _Loss(Module): + def __init__(self, size_average=None, reduce=None, reduction='mean'): + super(_Loss, self).__init__() + if size_average is not None or reduce is not None: + self.reduction = _Reduction.legacy_get_string(size_average, reduce) + else: + self.reduction = reduction + +class WeightedSELoss(_Loss): + __constants__ = ['reduction'] + + def __init__(self, size_average=None, reduce=None, reduction='mean'): + super(WeightedSELoss, self).__init__(size_average, reduce, reduction) + def forward(self, input, target, weight): + return torch.sum(torch.mul(weight, (input - target)**2)) + +class WeightedCELoss(_Loss): + __constants__ = ['reduction'] + + def __init__(self, size_average=None, reduce=None, reduction='mean'): + super(WeightedCELoss, self).__init__(size_average, reduce, reduction) + def forward(self, input, target, weight): + return torch.sum(torch.mul(weight, (1 - target)*torch.log(1./(1.-input))+target*torch.log(1./input))) + +####### Loss function(s), with "input" in (0,1) interval +def report_ETA(beginning, start, epochs, e, loss): + time_elapsed = time.time() - start + time_left = str(datetime.timedelta( + seconds=((time.time() - beginning)/(e+1)*(epochs-(e+1))))) + print('Training epoch %s (took %.2f sec, time left %s sec) loss %.8f'%( + e, time_elapsed, time_left, loss)) + return time.time() + +class OurModel(nn.Module): +### Defines the model with parametrized discriminant. Only quadratic dependence on a single parameter is implemented. +### Input is the architecture (list of integers, the last one being equal to 1) and the activation type ('ReLU' or 'Sigmoid') + def __init__(self, AR = [1, 3, 3, 1] , AF = 'ReLU' ): + super(OurModel, self).__init__() + ValidActivationFunctions = {'ReLU': torch.relu, 'Sigmoid': torch.sigmoid} + try: + self.ActivationFunction = ValidActivationFunctions[AF] + except KeyError: + print('The activation function specified is not valid. Allowed activations are %s.' + %str(list(ValidActivationFunctions.keys()))) + print('Will use ReLU.') + self.ActivationFunction = torch.relu + if type(AR) == list: + if( ( all(isinstance(n, int) for n in AR)) and ( AR[-1] == 1) ): + self.Architecture = AR + else: + print('Architecture should be a list of integers, the last one should be 1.') + raise ValueError + else: + print('Architecture should be a list !') + raise ValueError + +### Define Layers + #self.LinearLayerList1 = nn.ModuleList([nn.Linear(self.Architecture[i], + # self.Architecture[i+1], bias=False) for i in range(len(self.Architecture)-2)]) + self.LinearLayerList1 = nn.ModuleList([nn.Linear(self.Architecture[i], + self.Architecture[i+1]) for i in range(len(self.Architecture)-2)]) + self.OutputLayer1 = nn.Linear(self.Architecture[-2], 1) + #self.LinearLayerList2 = nn.ModuleList([nn.Linear(self.Architecture[i], + # self.Architecture[i+1], bias=False) for i in range(len(self.Architecture)-2)]) + self.LinearLayerList2 = nn.ModuleList([nn.Linear(self.Architecture[i], + self.Architecture[i+1]) for i in range(len(self.Architecture)-2)]) + self.OutputLayer2 = nn.Linear(self.Architecture[-2], 1) + + #self.Optimiser = torch.optim.Adam(self.parameters(), self.InitialLearningRate) + #self.Criterion = WeightedMSELoss() + + def Forward(self, Data, Parameters): +### Forward Function. Performs Preprocessing, returns F = rho/(1+rho) in [0,1], where rho is quadratically parametrized. + # Checking that data has the right input dimension + InputDimension = self.Architecture[0] + if Data.size(1) != InputDimension: + print('Dimensions of the data and the network input mismatch: data: %d, model: %d' + %(Data.size(1), InputDimension)) + raise ValueError + + # Checking that preprocess has been initialised + if not hasattr(self, 'Shift'): + print('Please initialize preprocess parameters!') + raise ValueError + with torch.no_grad(): + Data, Parameters = self.Preprocess(Data, Parameters) + + #print(Data.mean(0), Data.std(0)) + + x1 = x2 = Data + + for i, Layer in enumerate(self.LinearLayerList1): + x1 = self.ActivationFunction(Layer(x1)) + x1 = self.OutputLayer1(x1).squeeze() + + for i, Layer in enumerate(self.LinearLayerList2): + x2 = self.ActivationFunction(Layer(x2)) + #x2 = torch.exp(self.OutputLayer2(x2)).squeeze() + #x2 = torch.abs(self.OutputLayer2(x2)).squeeze() + x2 = self.OutputLayer2(x2).squeeze() + #x2 = self.OutputLayer2(x2).squeeze() + + + rho = (1 + torch.mul(x1, Parameters))**2 + (torch.mul(x2, Parameters))**2 + return (rho.div(1.+rho)).view(-1, 1) + + def GetL1Bound(self, L1perUnit): + self.L1perUnit = L1perUnit + + def ClipL1Norm(self): +### Clip the weights + def ClipL1NormLayer(DesignatedL1Max, Layer, Counter): + if Counter == 1: + ### this avoids clipping the first layer + return + L1 = Layer.weight.abs().sum() + Layer.weight.masked_scatter_(L1 > DesignatedL1Max, + Layer.weight*(DesignatedL1Max/L1)) + return + + Counter = 0 + for m in self.children(): + if isinstance(m, nn.Linear): + Counter += 1 + with torch.no_grad(): + DesignatedL1Max = m.weight.size(0)*m.weight.size(1)*self.L1perUnit + ClipL1NormLayer(DesignatedL1Max, m, Counter) + else: + for mm in m.children(): + Counter +=1 + with torch.no_grad(): + DesignatedL1Max = mm.weight.size(0)*mm.weight.size(1)*self.L1perUnit + ClipL1NormLayer(DesignatedL1Max, mm, Counter) + return + + def DistributionRatio(self, points): +### This is rho. I.e., after training, the estimator of the distribution ratio. + with torch.no_grad(): + F = self(points) + return F/(1-F) + + def InitPreprocess(self, Data, Parameters): +### This can be run only ONCE to initialize the preprocess (shift and scaling) parameters +### Takes as input the training Data and the training Parameters as Torch tensors. + if not hasattr(self, 'Scaling'): + print('Initializing Preprocesses Variables') + self.Scaling = Data.std(0) + self.Shift = Data.mean(0) + self.ParameterScaling = Parameters.std(0) + else: print('Preprocess can be initialized only once. Parameters unchanged.') + + def Preprocess(self, Data, Parameters): +### Returns scaled/shifted data and parameters +### Takes as input Data and Parameters as Torch tensors. + if not hasattr(self, 'Scaling'): print('Preprocess parameters are not initialized.') + Data = (Data - self.Shift)/self.Scaling + Parameters = Parameters/self.ParameterScaling + return Data, Parameters + + def Save(self, Name, Folder, csvFormat=False): +### Saves the model in Folder/Name + FileName = Folder + Name + '.pth' + torch.save({'StateDict': self.state_dict(), + 'Scaling': self.Scaling, + 'Shift': self.Shift, + 'ParameterScaling': self.ParameterScaling}, + FileName) + print('Model successfully saved.') + print('Path: %s'%str(FileName)) + + if csvFormat: + modelparams = [w.detach().tolist() for w in self.parameters()] + np.savetxt(Folder + Name + ' (StateDict).csv', modelparams, '%s') + statistics = [self.Shift.detach().tolist(), self.Scaling.detach().tolist(), + self.ParameterScaling.detach().tolist()] + np.savetxt(Folder + Name + ' (Statistics).csv', statistics, '%s') + + def Load(self, Name, Folder): +### Loads the model from Folder/Name + FileName = Folder + Name + '.pth' + try: + IncompatibleKeys = self.load_state_dict(torch.load(FileName)['StateDict']) + except KeyError: + print('No state dictionary saved. Loading model failed.') + return + + if list(IncompatibleKeys)[0]: + print('Missing Keys: %s'%str(list(IncompatibleKeys)[0])) + print('Loading model failed. ') + return + + if list(IncompatibleKeys)[1]: + print('Unexpected Keys: %s'%str(list(IncompatibleKeys)[0])) + print('Loading model failed. ') + return + + self.Scaling = torch.load(FileName)['Scaling'] + self.Shift = torch.load(FileName)['Shift'] + self.ParameterScaling = torch.load(FileName)['ParameterScaling'] + + print('Model successfully loaded.') + print('Path: %s'%str(FileName)) + + def Load_CPU(self, Name, Folder): +### Loads the model from Folder/Name + FileName = Folder + Name + '.pth' + try: + IncompatibleKeys = self.load_state_dict(torch.load(FileName, map_location=torch.device('cpu'))['StateDict']) + except KeyError: + print('No state dictionary saved. Loading model failed.') + return + + if list(IncompatibleKeys)[0]: + print('Missing Keys: %s'%str(list(IncompatibleKeys)[0])) + print('Loading model failed. ') + return + + if list(IncompatibleKeys)[1]: + print('Unexpected Keys: %s'%str(list(IncompatibleKeys)[0])) + print('Loading model failed. ') + return + + self.Scaling = torch.load(FileName)['Scaling'] + self.Shift = torch.load(FileName)['Shift'] + self.ParameterScaling = torch.load(FileName)['ParameterScaling'] + + print('Model successfully loaded.') + print('Path: %s'%str(FileName)) + + + def Report(self): ### is it possibe to check if the model is in double? + print('\nModel Report:') + print('Preprocess Initialized: ' + str(hasattr(self, 'Shift'))) + print('Architecture: ' + str(self.Architecture)) + print('Loss Function: ' + 'Quadratic') + print('Activation: ' + str(self.ActivationFunction)) + + def cuda(self): + nn.Module.cuda(self) + self.Shift = self.Shift.cuda() + self.Scaling = self.Scaling.cuda() + self.ParameterScaling = self.ParameterScaling.cuda() + + def cpu(self): + self.Shift = self.Shift.cpu() + self.Scaling = self.Scaling.cpu() + self.ParameterScaling = self.ParameterScaling.cpu() + return nn.Module.cpu(self) + + +import copy +def OurCudaTensor(input): + output = copy.deepcopy(input) + output = output.cuda() + return output + +class OurTrainer(nn.Module): +### Contains all parameters for training: Loss Function, Optimiser, NumberOfEpochs, InitialLearningRate, SaveAfterEpoch + def __init__(self, LearningRate = 1e-3, LossFunction = 'Quadratic', Optimiser = 'Adam', NumEpochs = 100): + super(OurTrainer, self).__init__() + self.NumberOfEpochs = NumEpochs + self.SaveAfterEpoch = lambda :[self.NumberOfEpochs,] + self.InitialLearningRate = LearningRate + ValidCriteria = {'Quadratic': WeightedSELoss(), 'CE':WeightedCELoss(), 'BCE':CrossEntropyLoss()} + try: + self.Criterion = ValidCriteria[LossFunction] + except KeyError: + print('The loss function specified is not valid. Allowed losses are %s.' + %str(list(ValidCriteria))) + print('Will use Quadratic Loss.') + ValidOptimizers = {'Adam': torch.optim.Adam} + try: + self.Optimiser = ValidOptimizers[Optimiser] + except KeyError: + print('The specified optimiser is not valid. Allowed optimisers are %s.' + %str(list(ValidOptimisers))) + print('Will use Adam.') + + def EstimateRequiredGPUMemory(self, model, Data, Parameters): + if next(model.parameters()).is_cuda: + print('Model is on cuda. No estimate possible anymore.') + return None + else: + before = torch.cuda.memory_allocated() + print(before) + ### Always make deep copy of objects before sending them to cuda. Delete when done + ModelCuda = copy.deepcopy(model) + ModelCuda.cuda() + DataCuda = OurCudaTensor(Data[:10000]) + ParametersCuda = OurCudaTensor(Parameters[:10000]) + print(torch.cuda.memory_allocated()) + MF = ModelCuda.Forward(DataCuda, ParametersCuda) + after = torch.cuda.memory_allocated() + print(after) + del ModelCuda, DataCuda, ParametersCuda, MF + torch.cuda.empty_cache() + estimate = float(Data.size()[0])/1e4*float(after-before)*1e-9 + print(str(estimate) + ' GB') + return estimate + + def Train(self, model, Data, Parameters, Labels, Weights, bs = 100000, L1perUnit=None, UseGPU=True, Name="", Folder=os.getcwd(), WeightClipping=False, L1Max=1): + + tempmodel = copy.deepcopy(model) + tempmodel.cuda() + tempData = OurCudaTensor(Data) + tempParameters = OurCudaTensor(Parameters) + tempLabels = OurCudaTensor(Labels) + tempWeights = OurCudaTensor(Weights) + + Optimiser = self.Optimiser(tempmodel.parameters(), self.InitialLearningRate) + mini_batch_size = bs + beginning = start = time.time() + + if WeightClipping: + tempmodel.GetL1Bound(L1Max) + + for e in range(self.NumberOfEpochs): + total_loss = 0 + #print("epoch") + Optimiser.zero_grad() + for b in range(0, Data.size(0), mini_batch_size): + torch.cuda.empty_cache() + output = tempmodel.Forward(tempData[b:b+mini_batch_size], tempParameters[b:b+mini_batch_size]) + loss = self.Criterion(output, tempLabels[b:b+mini_batch_size].reshape(-1,1), + tempWeights[b:b+mini_batch_size].reshape(-1, 1)) + total_loss += loss + loss.backward() + Optimiser.step() + + if WeightClipping: + tempmodel.ClipL1Norm() + + if (e+1) in self.SaveAfterEpoch(): + start = report_ETA(beginning, start, self.NumberOfEpochs, e+1, total_loss) + tempmodel.Save(Name + "%d epoch"%(e+1), Folder, csvFormat=True) + + tempmodel.Save(Name + 'Final', Folder, csvFormat=True) + + return tempmodel.cpu() + + def SetNumberOfEpochs(self, NE): + self.NumberOfEpochs = NE + + def SetInitialLearningRate(self,ILR): + self.InitialLearningRate = ILR + + def SetSaveAfterEpochs(self,SAE): + SAE.sort() + self.SaveAfterEpoch = lambda : SAE \ No newline at end of file diff --git a/examples/tutorial_particle_physics/OurTrainingTools2D.py b/examples/tutorial_particle_physics/OurTrainingTools2D.py new file mode 100644 index 000000000..3a5b0797b --- /dev/null +++ b/examples/tutorial_particle_physics/OurTrainingTools2D.py @@ -0,0 +1,546 @@ +import h5py, torch, time, datetime, os +import numpy as np +import matplotlib.pyplot as plt +from torch import nn +from torch.nn.modules import Module +from torch.nn import CrossEntropyLoss +from tabulate import tabulate + +random_seed = torch.randint(0, 1339, (1,)).item() +torch.manual_seed(random_seed) +print('=========== Random Seed: %d ==========='%(random_seed)) + +class DataFile(): +### Reads sample file Info (string), Parameters (list), Values (torch array), Data (torch array) and Weights (torch array) +### FilePath is the path of the input file +### Computes cross-section XS (average weight) and total number of data ND in file +### Checks that files are in correct format (correct Keys) +### and that the length of Parameters and Data equals the one of Values and Weights respectively + def __init__(self, FilePath, verbose=True): + if verbose: print('\nReading file ...' + FilePath) + file = h5py.File(FilePath, 'r') + if list(file.keys()) == ['Data', 'Info', 'Parameters', 'Process', 'Values', 'Weights']: + if( (len(file['Parameters'][()]) == len(file['Values'][()])) and (len(file['Data'][()]) == len(file['Weights'][()])) ): + if verbose: print('##### File Info:\n' + file['Info'][()][0] + '\n#####') + self.FilePath = FilePath + self.Info = file['Info'][()][0] + self.Process = file['Process'][()][0] + self.Parameters = file['Parameters'][()] + #print(file['Values'][()].dtype) + #print('%.15f' % (file['Data'][()][0][0])) + self.Values = torch.DoubleTensor(file['Values'][()]) + self.Data = torch.DoubleTensor(file['Data'][()]) + self.Weights = torch.DoubleTensor(file['Weights'][()]) + self.XS = self.Weights.mean() + self.ND = len(self.Weights) + else: + print('--> File not valid:\nunequal lenght of Values and Parameters or of Data and Weights') + raise ValueError + else: + print('--> File format not valid:\nKeys: ' + str(list(file.keys())) + + '\nshould be: ' + str(['Data', 'Info', 'Parameters', 'Process', 'Values', 'Weights'])) + raise ValueError + +class OurTrainingData(): +### Imports data for training. The Return() methods returns [self.Data, self.Labels, self.Weights, self.ParVal] +### All values are in double precision +### Inputs are the SM and BSM file paths and list of integers to chop the datasets if needed +### Weights are normalized to have sum = 1 on the entire training sample + def __init__(self, SMfilepathlist, BSMfilepathlist, process, parameters, SMNLimits="NA", BSMNLimits="NA", verbose=True): + self.Process = process + self.Parameters = parameters + if verbose: print('Loading Data Files for Process: ' + str(self.Process) +', with new physics Parameters: ' + str(self.Parameters) ) + if len(self.Parameters)!= 1: print('Only 1D Implemented in Training !') + +####### Load BSM data (stored in self.BSMDataFiles) + if type(BSMfilepathlist) == list: + if all(isinstance(n, str) for n in BSMfilepathlist): + self.BSMDataFiles = [] + for path in BSMfilepathlist: + temp = DataFile(path, verbose=verbose) + if((temp.Process == self.Process) and (set(list(temp.Parameters.flatten())) == set(self.Parameters)) and (sum(temp.Values.flatten()) != 0.) ): + self.BSMDataFiles.append(temp) + else: + print('File not valid: ' + path) + print('Parameters = ' + str(temp.Parameters) + ', Process = ' + str(temp.Process) + +' and Values = ' + str(temp.Values.tolist())) + print('should be = ' + str(self.Parameters) + ', = ' + str(self.Process) + + ' and != ' + str(0.)) + raise ValueError + self.BSMDataFiles.append(None) + else: + print('BSMfilepathlist input should be a list of strings !') + raise FileNotFoundError + else: + print('BSMfilepathlist input should be a list !') + raise FileNotFoundError + +###### Chop the BSM data sets (stored in BSMNDList, BSMDataList, BSMWeightsList, BSMParValList, BSMTargetList) + if type(BSMNLimits) == int: + BSMNLimits = [min(BSMNLimits, NF.ND) for NF in self.BSMDataFiles] + elif type(BSMNLimits) == list and all(isinstance(n, int) for n in BSMNLimits): + if len(BSMNLimits) != len(self.BSMDataFiles): + print("--> Please input %d integers to chop each SM file."%( + len(self.BSMDataFiles))) + raise ValueError + elif sum([self.BSMDataFiles[i].ND >= BSMNLimits[i] for i in range(len(BSMNLimits))] + ) != len(self.BSMDataFiles): + print("--> Some chop limit larger than available data in the corresponding file.") + print("--> Lengths of the files: "+str([file.ND for file in self.BSMDataFiles ])) + raise ValueError + else: + BSMNLimits =[file.ND for file in self.BSMDataFiles] + + self.BSMNDList = BSMNLimits + #self.BSMNData = sum(self.BSMNDataList) + self.BSMDataList = [DF.Data[:N] for (DF, N) in zip( + self.BSMDataFiles, self.BSMNDList)] + self.BSMWeightsList = [DF.Weights[:N] for (DF, N) in zip( + self.BSMDataFiles, self.BSMNDList)] + self.BSMXSList = [DF.XS for DF in self.BSMDataFiles] + self.BSMParValList = [torch.ones([N, len(self.Parameters)], dtype=torch.double)*DF.Values for (DF, N) in zip(self.BSMDataFiles, self.BSMNDList)] + self.BSMTargetList = [torch.ones(N, dtype=torch.double) for N in self.BSMNDList] + + +####### Load SM data (stored in SMDataFiles) + if type(SMfilepathlist) == list: + if all(isinstance(n, str) for n in SMfilepathlist): + #self.SMFilePathList = SMfilepathlist + #self.SMNumFiles = len(self.SMFilePathList) + self.SMDataFiles = [] + for path in SMfilepathlist: + temp = DataFile(path, verbose=verbose) + if( (temp.Process == self.Process) and (temp.Parameters[0] == 'SM') and (sum(temp.Values.flatten()) == 0.) ): + self.SMDataFiles.append(temp) + else: + print('File not valid: ' + path) + print('Parameters = ' + str(temp.Parameters) + ', Process = ' + str(temp.Process) + +' and Values = ' + str(temp.Values.tolist())) + print('should be = ' + 'SM'+ ', = ' + str(self.Process) + + ' and = ' + str(0.)) + self.SMDataFiles.append(None) + else: + print('SMfilepathlist input should be a list of strings !') + raise FileNotFoundError + else: + print('SMfilepathlist input should be a list !') + raise FileNotFoundError + +####### Chop the SM data sets and join them in one (stored in SMND, SMData and SMWeights) + if type(SMNLimits) == int: + SMNLimits = [min(SMNLimits, DF.ND) for DF in self.SMDataFiles] + elif type(SMNLimits) == list and all(isinstance(n, int) for n in SMNLimits): + if len(SMNLimits) != len(self.SMDataFiles): + print("--> Please input %d integers to chop each SM file."%( + len(self.SMDataFiles))) + raise ValueError + elif sum([self.SMDataFiles[i].ND >= SMNLimits[i] for i in range(len(SMNLimits))] + ) != len(self.SMDataFiles): + print("--> Some chop limit larger than available data in the corresponding file.") + print("--> Lengths of the files: " + str([file.ND for file in self.SMDataFiles])) + raise ValueError + else: + SMNLimits = [file.ND for file in self.SMDataFiles] + self.SMND = sum(SMNLimits) + self.SMData = torch.cat( + [DF.Data[:N] for (DF, N) in zip(self.SMDataFiles, SMNLimits)] + , 0) + self.SMWeights = torch.cat( + [DF.Weights[:N] for (DF, N) in zip(self.SMDataFiles, SMNLimits)] + , 0) + self.SMXSList = [DF.XS for DF in self.SMDataFiles] + idx_random = torch.randperm(self.SMND) + self.SMData = self.SMData[idx_random, :] + self.SMWeights = self.SMWeights[idx_random] + +####### Break SM data in blocks to be paired with BSM data (stored in UsedSMNDList, UsedSMDataList, UsedSMWeightsList, UsedSMParValList, UsedSMTargetList) + BSMNRatioDataList = [torch.tensor(1., dtype=torch.double)*n/sum(self.BSMNDList + ) for n in self.BSMNDList] + self.UsedSMNDList = [int(self.SMND*BSMNRatioData) for BSMNRatioData in BSMNRatioDataList] + #self.UsedSMNData = sum(self.UsedSMNDataList) + #self.UsedSMData = self.SMData[: self.UsedSMND] + self.UsedSMDataList = self.SMData[:sum(self.UsedSMNDList)].split(self.UsedSMNDList) + + ##### Reweighting is performed such that the SUM of the SM weights in each block equals the number of BSM data times the AVERAGE + ##### of the original weights. This equals the SM cross-section as obtained in the specific sample at hand, times NBSM + self.UsedSMWeightsList = self.SMWeights[:sum(self.UsedSMNDList)].split(self.UsedSMNDList) + self.UsedSMWeightsList = [ self.UsedSMWeightsList[i]*self.BSMNDList[i]/self.UsedSMNDList[i] for i in range(len(BSMNRatioDataList))] + self.UsedSMParValList = [torch.ones([N, len(self.Parameters)], dtype=torch.double)*DF.Values for (DF, N) in zip(self.BSMDataFiles, self.UsedSMNDList)] + self.UsedSMTargetList = [torch.zeros(N, dtype=torch.double) for N in self.UsedSMNDList] + +####### Join SM with BSM data + self.Data = torch.cat( + [torch.cat([self.UsedSMDataList[i], self.BSMDataList[i]] + ) for i in range(len(self.BSMDataList))] + ) + self.Weights = torch.cat( + [torch.cat([self.UsedSMWeightsList[i], self.BSMWeightsList[i]] + ) for i in range(len(self.BSMWeightsList))] + ) + self.Labels = torch.cat( + [torch.cat([self.UsedSMTargetList[i], self.BSMTargetList[i]] + ) for i in range(len(self.BSMTargetList))] + ) + self.ParVal = torch.cat( + [torch.cat([self.UsedSMParValList[i], self.BSMParValList[i]] + ) for i in range(len(self.BSMParValList))] + ) + +####### Final reweighting + s = self.Weights.sum() + self.Weights = self.Weights.div(s) + +####### If verbose, display report + if verbose: self.Report() + +####### Return Tranining Data + def ReturnData(self): + return [self.Data, self.Labels, self.Weights, self.ParVal] + + def Report(self): + #from tabulate import tabulate + print('\nLoaded SM Files:') + print(tabulate({str(self.Parameters): [ file.Values for file in self.SMDataFiles ], + "#Data":[ file.ND for file in self.SMDataFiles ], + "XS[pb](avg.w)":[ file.XS for file in self.SMDataFiles ]}, headers="keys")) + print('\nLoaded BSM Files:') + print(tabulate({str(self.Parameters): [ file.Values for file in self.BSMDataFiles ], + "#Data":[ file.ND for file in self.BSMDataFiles ], + "XS[pb](avg.w)":[ file.XS for file in self.BSMDataFiles ]}, headers="keys")) + print('\nPaired BSM/SM Datasets:\n') + ### Check should be nearly equal to #EV.BSM. It is computed with the weights BEFORE final reweighting + print(tabulate({str(self.Parameters): [ file.Values for file in self.BSMDataFiles ], "#Ev.BSM": self.BSMNDList + , "#Ev.SM": self.UsedSMNDList, + "Check": [(self.UsedSMWeightsList[i].sum())/(self.SMWeights.mean()) for i in range(len(self.BSMDataFiles))] + }, headers="keys")) + +####### Convert Angles + def CurateAngles(self, AnglePos): + Angles = self.Data[:, AnglePos] + CuratedAngles = torch.cat([torch.sin(Angles), torch.cos(Angles)], dim=1) + OtherPos = list(set(range(self.Data.size(1)))-set(AnglePos)) + self.Data = torch.cat([self.Data[:, OtherPos], CuratedAngles], dim=1) + print('####\nAnlges at position %s have been converted to Sin and Cos and put at the last columns of the Data.'%(AnglePos)) + print('####') + +####### Loss function(s), with "input" in (0,1) interval +class _Loss(Module): + def __init__(self, size_average=None, reduce=None, reduction='mean'): + super(_Loss, self).__init__() + if size_average is not None or reduce is not None: + self.reduction = _Reduction.legacy_get_string(size_average, reduce) + else: + self.reduction = reduction + +class WeightedSELoss(_Loss): + __constants__ = ['reduction'] + + def __init__(self, size_average=None, reduce=None, reduction='mean'): + super(WeightedSELoss, self).__init__(size_average, reduce, reduction) + def forward(self, input, target, weight): + return torch.sum(torch.mul(weight, (input - target)**2)) + +class WeightedCELoss(_Loss): + __constants__ = ['reduction'] + + def __init__(self, size_average=None, reduce=None, reduction='mean'): + super(WeightedCELoss, self).__init__(size_average, reduce, reduction) + def forward(self, input, target, weight): + return torch.sum(torch.mul(weight, (1 - target)*torch.log(1./(1.-input))+target*torch.log(1./input))) + +####### Loss function(s), with "input" in (0,1) interval +def report_ETA(beginning, start, epochs, e, loss): + time_elapsed = time.time() - start + time_left = str(datetime.timedelta( + seconds=((time.time() - beginning)/(e+1)*(epochs-(e+1))))) + print('Training epoch %s (took %.2f sec, time left %s sec) loss %.8f'%( + e, time_elapsed, time_left, loss)) + return time.time() + +class OurModel(nn.Module): +### Defines the model with parametrized discriminant. Only quadratic dependence on a single parameter is implemented. +### Input is the architecture (list of integers, the last one being equal to 1) and the activation type ('ReLU' or 'Sigmoid') + def __init__(self, AR = [1, 3, 3, 1] , AF = 'ReLU' ): + super(OurModel, self).__init__() + ValidActivationFunctions = {'ReLU': torch.relu, 'Sigmoid': torch.sigmoid} + try: + self.ActivationFunction = ValidActivationFunctions[AF] + except KeyError: + print('The activation function specified is not valid. Allowed activations are %s.' + %str(list(ValidActivationFunctions.keys()))) + print('Will use ReLU.') + self.ActivationFunction = torch.relu + if type(AR) == list: + if( ( all(isinstance(n, int) for n in AR)) and ( AR[-1] == 1) ): + self.Architecture = AR + else: + print('Architecture should be a list of integers, the last one should be 1.') + raise ValueError + else: + print('Architecture should be a list !') + raise ValueError + +### Define Layers + #self.LinearLayerList1 = nn.ModuleList([nn.Linear(self.Architecture[i], + # self.Architecture[i+1], bias=False) for i in range(len(self.Architecture)-2)]) + self.LinearLayerList1 = nn.ModuleList([nn.Linear(self.Architecture[i], + self.Architecture[i+1]) for i in range(len(self.Architecture)-2)]) + self.OutputLayer1 = nn.Linear(self.Architecture[-2], 1) + #self.LinearLayerList2 = nn.ModuleList([nn.Linear(self.Architecture[i], + # self.Architecture[i+1], bias=False) for i in range(len(self.Architecture)-2)]) + self.LinearLayerList2 = nn.ModuleList([nn.Linear(self.Architecture[i], + self.Architecture[i+1]) for i in range(len(self.Architecture)-2)]) + self.OutputLayer2 = nn.Linear(self.Architecture[-2], 1) + + #self.Optimiser = torch.optim.Adam(self.parameters(), self.InitialLearningRate) + #self.Criterion = WeightedMSELoss() + + def Forward(self, Data, Parameters): +### Forward Function. Performs Preprocessing, returns F = rho/(1+rho) in [0,1], where rho is quadratically parametrized. + # Checking that data has the right input dimension + InputDimension = self.Architecture[0] + if Data.size(1) != InputDimension: + print('Dimensions of the data and the network input mismatch: data: %d, model: %d' + %(Data.size(1), InputDimension)) + raise ValueError + + # Checking that preprocess has been initialised + if not hasattr(self, 'Shift'): + print('Please initialize preprocess parameters!') + raise ValueError + with torch.no_grad(): + Data, Parameters = self.Preprocess(Data, Parameters) + + x1 = x2 = Data + + for i, Layer in enumerate(self.LinearLayerList1): + x1 = self.ActivationFunction(Layer(x1)) + x1 = self.OutputLayer1(x1).squeeze() + + for i, Layer in enumerate(self.LinearLayerList2): + x2 = self.ActivationFunction(Layer(x2)) + #x2 = torch.exp(self.OutputLayer2(x2)).squeeze() + #x2 = torch.abs(self.OutputLayer2(x2)).squeeze() + x2 = self.OutputLayer2(x2).squeeze() + #x2 = self.OutputLayer2(x2).squeeze() + + + rho = (1 + torch.mul(x1, Parameters))**2 + (torch.mul(x2, Parameters))**2 + return (rho.div(1.+rho)).view(-1, 1) + + def GetL1Bound(self, L1perUnit): + self.L1perUnit = L1perUnit + + def ClipL1Norm(self): +### Clip the weights + def ClipL1NormLayer(DesignatedL1Max, Layer, Counter): + if Counter == 1: + ### this avoids clipping the first layer + return + L1 = Layer.weight.abs().sum() + Layer.weight.masked_scatter_(L1 > DesignatedL1Max, + Layer.weight*(DesignatedL1Max/L1)) + return + + Counter = 0 + for m in self.children(): + if isinstance(m, nn.Linear): + Counter += 1 + with torch.no_grad(): + DesignatedL1Max = m.weight.size(0)*m.weight.size(1)*self.L1perUnit + ClipL1NormLayer(DesignatedL1Max, m, Counter) + else: + for mm in m.children(): + Counter +=1 + with torch.no_grad(): + DesignatedL1Max = mm.weight.size(0)*mm.weight.size(1)*self.L1perUnit + ClipL1NormLayer(DesignatedL1Max, mm, Counter) + return + + def DistributionRatio(self, points): +### This is rho. I.e., after training, the estimator of the distribution ratio. + with torch.no_grad(): + F = self(points) + return F/(1-F) + + def InitPreprocess(self, Data, Parameters): +### This can be run only ONCE to initialize the preprocess (shift and scaling) parameters +### Takes as input the training Data and the training Parameters as Torch tensors. + if not hasattr(self, 'Scaling'): + print('Initializing Preprocesses Variables') + self.Scaling = Data.std(0) + self.Shift = Data.mean(0) + self.ParameterScaling = Parameters.std(0) + else: print('Preprocess can be initialized only once. Parameters unchanged.') + + def Preprocess(self, Data, Parameters): +### Returns scaled/shifted data and parameters +### Takes as input Data and Parameters as Torch tensors. + if not hasattr(self, 'Scaling'): print('Preprocess parameters are not initialized.') + Data = (Data - self.Shift)/self.Scaling + Parameters = Parameters/self.ParameterScaling + return Data, Parameters + + def Save(self, Name, Folder, csvFormat=False): +### Saves the model in Folder/Name + FileName = Folder + Name + '.pth' + torch.save({'StateDict': self.state_dict(), + 'Scaling': self.Scaling, + 'Shift': self.Shift, + 'ParameterScaling': self.ParameterScaling}, + FileName) + print('Model successfully saved.') + print('Path: %s'%str(FileName)) + + if csvFormat: + modelparams = [w.detach().tolist() for w in self.parameters()] + np.savetxt(Folder + Name + ' (StateDict).csv', modelparams, '%s') + statistics = [self.Shift.detach().tolist(), self.Scaling.detach().tolist(), + self.ParameterScaling.detach().tolist()] + np.savetxt(Folder + Name + ' (Statistics).csv', statistics, '%s') + + def Load(self, Name, Folder): +### Loads the model from Folder/Name + FileName = Folder + Name + '.pth' + try: + IncompatibleKeys = self.load_state_dict(torch.load(FileName)['StateDict']) + except KeyError: + print('No state dictionary saved. Loading model failed.') + return + + if list(IncompatibleKeys)[0]: + print('Missing Keys: %s'%str(list(IncompatibleKeys)[0])) + print('Loading model failed. ') + return + + if list(IncompatibleKeys)[1]: + print('Unexpected Keys: %s'%str(list(IncompatibleKeys)[0])) + print('Loading model failed. ') + return + + self.Scaling = torch.load(FileName)['Scaling'] + self.Shift = torch.load(FileName)['Shift'] + self.ParameterScaling = torch.load(FileName)['ParameterScaling'] + + print('Model successfully loaded.') + print('Path: %s'%str(FileName)) + + def Report(self): ### is it possibe to check if the model is in double? + print('\nModel Report:') + print('Preprocess Initialized: ' + str(hasattr(self, 'Shift'))) + print('Architecture: ' + str(self.Architecture)) + print('Loss Function: ' + 'Quadratic') + print('Activation: ' + str(self.ActivationFunction)) + + def cuda(self): + nn.Module.cuda(self) + self.Shift = self.Shift.cuda() + self.Scaling = self.Scaling.cuda() + self.ParameterScaling = self.ParameterScaling.cuda() + + def cpu(self): + self.Shift = self.Shift.cpu() + self.Scaling = self.Scaling.cpu() + self.ParameterScaling = self.ParameterScaling.cpu() + return nn.Module.cpu(self) + + +import copy +def OurCudaTensor(input): + output = copy.deepcopy(input) + output = output.cuda() + return output + +class OurTrainer(nn.Module): +### Contains all parameters for training: Loss Function, Optimiser, NumberOfEpochs, InitialLearningRate, SaveAfterEpoch + def __init__(self, LearningRate = 1e-3, LossFunction = 'Quadratic', Optimiser = 'Adam', NumEpochs = 100): + super(OurTrainer, self).__init__() + self.NumberOfEpochs = NumEpochs + self.SaveAfterEpoch = lambda :[self.NumberOfEpochs,] + self.InitialLearningRate = LearningRate + ValidCriteria = {'Quadratic': WeightedSELoss(), 'CE':WeightedCELoss(), 'BCE':CrossEntropyLoss()} + try: + self.Criterion = ValidCriteria[LossFunction] + except KeyError: + print('The loss function specified is not valid. Allowed losses are %s.' + %str(list(ValidCriteria))) + print('Will use Quadratic Loss.') + ValidOptimizers = {'Adam': torch.optim.Adam} + try: + self.Optimiser = ValidOptimizers[Optimiser] + except KeyError: + print('The specified optimiser is not valid. Allowed optimisers are %s.' + %str(list(ValidOptimisers))) + print('Will use Adam.') + + def EstimateRequiredGPUMemory(self, model, Data, Parameters): + if next(model.parameters()).is_cuda: + print('Model is on cuda. No estimate possible anymore.') + return None + else: + before = torch.cuda.memory_allocated() + print(before) + ### Always make deep copy of objects before sending them to cuda. Delete when done + ModelCuda = copy.deepcopy(model) + ModelCuda.cuda() + DataCuda = OurCudaTensor(Data[:10000]) + ParametersCuda = OurCudaTensor(Parameters[:10000]) + print(torch.cuda.memory_allocated()) + MF = ModelCuda.Forward(DataCuda, ParametersCuda) + after = torch.cuda.memory_allocated() + print(after) + del ModelCuda, DataCuda, ParametersCuda, MF + torch.cuda.empty_cache() + estimate = float(Data.size()[0])/1e4*float(after-before)*1e-9 + print(str(estimate) + ' GB') + return estimate + + def Train(self, model, Data, Parameters, Labels, Weights, bs = 100000, L1perUnit=None, UseGPU=True, Name="", Folder=os.getcwd(), WeightClipping=False, L1Max=1): + + tempmodel = copy.deepcopy(model) + tempmodel.cuda() + tempData = OurCudaTensor(Data) + tempParameters = OurCudaTensor(Parameters) + tempLabels = OurCudaTensor(Labels) + tempWeights = OurCudaTensor(Weights) + + Optimiser = self.Optimiser(tempmodel.parameters(), self.InitialLearningRate) + mini_batch_size = bs + beginning = start = time.time() + + if WeightClipping: + tempmodel.GetL1Bound(L1Max) + + for e in range(self.NumberOfEpochs): + total_loss = 0 + #print("epoch") + Optimiser.zero_grad() + for b in range(0, Data.size(0), mini_batch_size): + torch.cuda.empty_cache() + output = tempmodel.Forward(tempData[b:b+mini_batch_size], tempParameters[b:b+mini_batch_size]) + loss = self.Criterion(output, tempLabels[b:b+mini_batch_size].reshape(-1,1), + tempWeights[b:b+mini_batch_size].reshape(-1, 1)) + total_loss += loss + loss.backward() + Optimiser.step() + + if WeightClipping: + tempmodel.ClipL1Norm() + + if (e+1) in self.SaveAfterEpoch(): + start = report_ETA(beginning, start, self.NumberOfEpochs, e+1, total_loss) + tempmodel.Save(Name + "%d epoch"%(e+1), Folder, csvFormat=True) + + tempmodel.Save(Name + 'Final', Folder, csvFormat=True) + + return tempmodel.cpu() + + def SetNumberOfEpochs(self, NE): + self.NumberOfEpochs = NE + + def SetInitialLearningRate(self,ILR): + self.InitialLearningRate = ILR + + def SetSaveAfterEpochs(self,SAE): + SAE.sort() + self.SaveAfterEpoch = lambda : SAE \ No newline at end of file diff --git a/examples/tutorial_particle_physics/ReadingData-GW.ipynb b/examples/tutorial_particle_physics/ReadingData-GW.ipynb new file mode 100644 index 000000000..9f9f33c0a --- /dev/null +++ b/examples/tutorial_particle_physics/ReadingData-GW.ipynb @@ -0,0 +1,842 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting tabulate\n", + " Downloading tabulate-0.8.7-py3-none-any.whl (24 kB)\n", + "Installing collected packages: tabulate\n", + "Successfully installed tabulate-0.8.7\n", + "\u001b[33mWARNING: You are using pip version 20.1.1; however, version 20.2.4 is available.\n", + "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n" + ] + } + ], + "source": [ + "! pip install tabulate" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading Data Files for Process: W+Z, with new physics Parameters: ['Gphi[TeV**-2]', 'GW[TeV**-2]']\n", + "Only 1D Implemented in Training !\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_gwm5e-2.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {0., -0.05}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_gwm5e-2.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_gwm1e-1.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {0., -0.1}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_gwm1e-1.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_gwm2e-1.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {0., -0.2}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_gwm2e-1.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_gw5e-2.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {0., 0.05}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_gw5e-2.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_gw1e-1.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {0., 0.1}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_gw1e-1.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_gw2e-1.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {0., 0.2}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_gw2e-1.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_sm.h5\n", + "##### File Info:\n", + "SM = {0., 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_sm_1.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Loaded SM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "--------------------------------------- ------- ---------------\n", + "tensor([[0., 0.]], dtype=torch.float64) 3000000 0.741835\n", + "\n", + "Loaded BSM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "------------------------------------------------- ------- ---------------\n", + "tensor([[ 0.0000, -0.0500]], dtype=torch.float64) 500000 0.814992\n", + "tensor([[ 0.0000, -0.1000]], dtype=torch.float64) 500000 1.03408\n", + "tensor([[ 0.0000, -0.2000]], dtype=torch.float64) 500000 1.91084\n", + "tensor([[0.0000, 0.0500]], dtype=torch.float64) 500000 0.815234\n", + "tensor([[0.0000, 0.1000]], dtype=torch.float64) 500000 1.03439\n", + "tensor([[0.0000, 0.2000]], dtype=torch.float64) 500000 1.91103\n", + "\n", + "Paired BSM/SM Datasets:\n", + "\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Ev.BSM #Ev.SM Check\n", + "------------------------------------------------- --------- -------- -------\n", + "tensor([[ 0.0000, -0.0500]], dtype=torch.float64) 500000 500000 500000\n", + "tensor([[ 0.0000, -0.1000]], dtype=torch.float64) 500000 500000 500000\n", + "tensor([[ 0.0000, -0.2000]], dtype=torch.float64) 500000 500000 500000\n", + "tensor([[0.0000, 0.0500]], dtype=torch.float64) 500000 500000 500000\n", + "tensor([[0.0000, 0.1000]], dtype=torch.float64) 500000 500000 500000\n", + "tensor([[0.0000, 0.2000]], dtype=torch.float64) 500000 500000 500000\n", + "####\n", + "Anlges at position [3, 5] have been converted to Sin and Cos and put at the last columns of the Data.\n", + "####\n" + ] + } + ], + "source": [ + "from OurTrainingTools2D import *\n", + "\n", + "random_seed = torch.randint(0, 1339, (1,)).item()\n", + "torch.manual_seed(random_seed)\n", + "data_path = os.getcwd() + '/toydata'\n", + "\n", + "td = OurTrainingData([data_path+'/ChP_pt300_sm.h5'],\n", + " [data_path+'/ChP_pt300_gwm5e-2.h5',\n", + " data_path+'/ChP_pt300_gwm1e-1.h5',\n", + " data_path+'/ChP_pt300_gwm2e-1.h5',\n", + " data_path+'/ChP_pt300_gw5e-2.h5',\n", + " data_path+'/ChP_pt300_gw1e-1.h5',\n", + " data_path+'/ChP_pt300_gw2e-1.h5',],\n", + " process = 'W+Z', parameters =['Gphi[TeV**-2]','GW[TeV**-2]'], \n", + " SMNLimits=int(3e6), BSMNLimits=int(5e5))\n", + "\n", + "td.Data = td.Data[:, :7]\n", + "td.CurateAngles([3, 5])\n", + "\n", + "Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights\n", + "Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "NSM = td.SMWeights.mean()*3000\n", + "NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['SM'], dtype=object)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "td.SMDataFiles[0].Parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "h = h5py.File(data_path + '/gw_toydata.h5', 'w')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h.create_dataset('Data', data=Data)\n", + "h.create_dataset('Weights', data=Weights)\n", + "h.create_dataset('Labels', data=Labels)\n", + "h.create_dataset('NSM', data = NSM)\n", + "h.create_dataset('NBSMList', data=NBSMList)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "h.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# In Sample Test Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Out Sample Test Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# New 2D format Data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=========== Random Seed: 372 ===========\n" + ] + } + ], + "source": [ + "from OurTrainingTools2D import *" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading Data Files for Process: W+Z, with new physics Parameters: ['Gphi[TeV**-2]', 'GW[TeV**-2]']\n", + "Only 1D Implemented in Training !\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_gw1e-2.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {0., 0.01}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_gw1e-2.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_sm.h5\n", + "##### File Info:\n", + "SM = {0., 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_sm_1.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Loaded SM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "--------------------------------------- ------- ---------------\n", + "tensor([[0., 0.]], dtype=torch.float64) 3000000 0.741835\n", + "\n", + "Loaded BSM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "----------------------------------------------- ------- ---------------\n", + "tensor([[0.0000, 0.0100]], dtype=torch.float64) 500000 0.744902\n", + "\n", + "Paired BSM/SM Datasets:\n", + "\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Ev.BSM #Ev.SM Check\n", + "----------------------------------------------- --------- -------- -------\n", + "tensor([[0.0000, 0.0100]], dtype=torch.float64) 500000 500000 500000\n", + "####\n", + "Anlges at position [3, 5] have been converted to Sin and Cos and put at the last columns of the Data.\n", + "####\n" + ] + } + ], + "source": [ + "td = OurTrainingData([os.getcwd()+'/toydata/ChP_pt300_sm.h5'],\n", + " [os.getcwd()+'/toydata/ChP_pt300_gw1e-2.h5',],\n", + " #os.getcwd()+'/ChPgphim45e-1NP.h5',],\n", + " process = 'W+Z', parameters =['Gphi[TeV**-2]','GW[TeV**-2]'], \n", + " SMNLimits=int(5e5), BSMNLimits=int(5e5))\n", + "\n", + "td.Data = td.Data[:, :7]\n", + "td.CurateAngles([3, 5])\n", + "\n", + "Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights\n", + "Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()\n", + "\n", + "\n", + "NSM = td.SMWeights.mean()*3000\n", + "NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]\n", + "\n", + "h = h5py.File(os.getcwd()+'/toydata/gw_toydata_test_1e-2_out.h5', 'w')\n", + "\n", + "h.create_dataset('Data', data=Data)\n", + "h.create_dataset('Weights', data=Weights)\n", + "h.create_dataset('Labels', data=Labels)\n", + "h.create_dataset('NSM', data = NSM)\n", + "h.create_dataset('NBSMList', data=NBSMList)\n", + "\n", + "h.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading Data Files for Process: W+Z, with new physics Parameters: ['Gphi[TeV**-2]', 'GW[TeV**-2]']\n", + "Only 1D Implemented in Training !\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_gw9e-3.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {0., 0.009}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_gw9e-3.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_sm.h5\n", + "##### File Info:\n", + "SM = {0., 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_sm_1.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Loaded SM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "--------------------------------------- ------- ---------------\n", + "tensor([[0., 0.]], dtype=torch.float64) 3000000 0.741835\n", + "\n", + "Loaded BSM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "----------------------------------------------- ------- ---------------\n", + "tensor([[0.0000, 0.0090]], dtype=torch.float64) 500000 0.744393\n", + "\n", + "Paired BSM/SM Datasets:\n", + "\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Ev.BSM #Ev.SM Check\n", + "----------------------------------------------- --------- -------- -------\n", + "tensor([[0.0000, 0.0090]], dtype=torch.float64) 500000 500000 500000\n", + "####\n", + "Anlges at position [3, 5] have been converted to Sin and Cos and put at the last columns of the Data.\n", + "####\n" + ] + } + ], + "source": [ + "td = OurTrainingData([os.getcwd()+'/toydata/ChP_pt300_sm.h5'],\n", + " [os.getcwd()+'/toydata/ChP_pt300_gw9e-3.h5',],\n", + " #os.getcwd()+'/ChPgphim45e-1NP.h5',],\n", + " process = 'W+Z', parameters =['Gphi[TeV**-2]','GW[TeV**-2]'], \n", + " SMNLimits=int(5e5), BSMNLimits=int(5e5))\n", + "\n", + "td.Data = td.Data[:, :7]\n", + "td.CurateAngles([3, 5])\n", + "\n", + "Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights\n", + "Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()\n", + "\n", + "\n", + "NSM = td.SMWeights.mean()*3000\n", + "NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]\n", + "\n", + "h = h5py.File(os.getcwd()+'/toydata/gw_toydata_test_9e-3_out.h5', 'w')\n", + "\n", + "h.create_dataset('Data', data=Data)\n", + "h.create_dataset('Weights', data=Weights)\n", + "h.create_dataset('Labels', data=Labels)\n", + "h.create_dataset('NSM', data = NSM)\n", + "h.create_dataset('NBSMList', data=NBSMList)\n", + "\n", + "h.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading Data Files for Process: W+Z, with new physics Parameters: ['Gphi[TeV**-2]', 'GW[TeV**-2]']\n", + "Only 1D Implemented in Training !\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_gw8e-3.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {0., 0.008}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_gw8e-3.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_sm.h5\n", + "##### File Info:\n", + "SM = {0., 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_sm_1.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Loaded SM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "--------------------------------------- ------- ---------------\n", + "tensor([[0., 0.]], dtype=torch.float64) 3000000 0.741835\n", + "\n", + "Loaded BSM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "----------------------------------------------- ------- ---------------\n", + "tensor([[0.0000, 0.0080]], dtype=torch.float64) 500000 0.743823\n", + "\n", + "Paired BSM/SM Datasets:\n", + "\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Ev.BSM #Ev.SM Check\n", + "----------------------------------------------- --------- -------- -------\n", + "tensor([[0.0000, 0.0080]], dtype=torch.float64) 500000 500000 500000\n", + "####\n", + "Anlges at position [3, 5] have been converted to Sin and Cos and put at the last columns of the Data.\n", + "####\n" + ] + } + ], + "source": [ + "td = OurTrainingData([os.getcwd()+'/toydata/ChP_pt300_sm.h5'],\n", + " [os.getcwd()+'/toydata/ChP_pt300_gw8e-3.h5',],\n", + " #os.getcwd()+'/ChPgphim45e-1NP.h5',],\n", + " process = 'W+Z', parameters =['Gphi[TeV**-2]','GW[TeV**-2]'], \n", + " SMNLimits=int(5e5), BSMNLimits=int(5e5))\n", + "\n", + "td.Data = td.Data[:, :7]\n", + "td.CurateAngles([3, 5])\n", + "\n", + "Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights\n", + "Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()\n", + "\n", + "\n", + "NSM = td.SMWeights.mean()*3000\n", + "NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]\n", + "\n", + "h = h5py.File(os.getcwd()+'/toydata/gw_toydata_test_8e-3_out.h5', 'w')\n", + "\n", + "h.create_dataset('Data', data=Data)\n", + "h.create_dataset('Weights', data=Weights)\n", + "h.create_dataset('Labels', data=Labels)\n", + "h.create_dataset('NSM', data = NSM)\n", + "h.create_dataset('NBSMList', data=NBSMList)\n", + "\n", + "h.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading Data Files for Process: W+Z, with new physics Parameters: ['Gphi[TeV**-2]', 'GW[TeV**-2]']\n", + "Only 1D Implemented in Training !\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_gw7e-3.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {0., 0.007}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_gw7e-3.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_sm.h5\n", + "##### File Info:\n", + "SM = {0., 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_sm_1.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Loaded SM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "--------------------------------------- ------- ---------------\n", + "tensor([[0., 0.]], dtype=torch.float64) 3000000 0.741835\n", + "\n", + "Loaded BSM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "----------------------------------------------- ------- ---------------\n", + "tensor([[0.0000, 0.0070]], dtype=torch.float64) 500000 0.74348\n", + "\n", + "Paired BSM/SM Datasets:\n", + "\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Ev.BSM #Ev.SM Check\n", + "----------------------------------------------- --------- -------- -------\n", + "tensor([[0.0000, 0.0070]], dtype=torch.float64) 500000 500000 500000\n", + "####\n", + "Anlges at position [3, 5] have been converted to Sin and Cos and put at the last columns of the Data.\n", + "####\n" + ] + } + ], + "source": [ + "td = OurTrainingData([os.getcwd()+'/toydata/ChP_pt300_sm.h5'],\n", + " [os.getcwd()+'/toydata/ChP_pt300_gw7e-3.h5',],\n", + " #os.getcwd()+'/ChPgphim45e-1NP.h5',],\n", + " process = 'W+Z', parameters =['Gphi[TeV**-2]','GW[TeV**-2]'], \n", + " SMNLimits=int(5e5), BSMNLimits=int(5e5))\n", + "\n", + "td.Data = td.Data[:, :7]\n", + "td.CurateAngles([3, 5])\n", + "\n", + "Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights\n", + "Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()\n", + "\n", + "\n", + "NSM = td.SMWeights.mean()*3000\n", + "NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]\n", + "\n", + "h = h5py.File(os.getcwd()+'/toydata/gw_toydata_test_7e-3_out.h5', 'w')\n", + "\n", + "h.create_dataset('Data', data=Data)\n", + "h.create_dataset('Weights', data=Weights)\n", + "h.create_dataset('Labels', data=Labels)\n", + "h.create_dataset('NSM', data = NSM)\n", + "h.create_dataset('NBSMList', data=NBSMList)\n", + "\n", + "h.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading Data Files for Process: W-Z, with new physics Parameters: ['Gphi[TeV**-2]', 'GW[TeV**-2]']\n", + "Only 1D Implemented in Training !\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChM_pt300_gw1e-2.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {0., 0.01}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChM_pt300_gw1e-2.dat.gz\n", + "Charge = -1 --- Process = W-Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChM_pt300_sm.h5\n", + "##### File Info:\n", + "SM = {0., 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChM_pt300_sm_1.dat.gz\n", + "Charge = -1 --- Process = W-Z\n", + "#####\n", + "\n", + "Loaded SM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "--------------------------------------- ------- ---------------\n", + "tensor([[0., 0.]], dtype=torch.float64) 3000000 0.329009\n", + "\n", + "Loaded BSM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "----------------------------------------------- ------- ---------------\n", + "tensor([[0.0000, 0.0100]], dtype=torch.float64) 500000 0.330177\n", + "\n", + "Paired BSM/SM Datasets:\n", + "\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Ev.BSM #Ev.SM Check\n", + "----------------------------------------------- --------- -------- -------\n", + "tensor([[0.0000, 0.0100]], dtype=torch.float64) 500000 500000 500000\n", + "####\n", + "Anlges at position [3, 5] have been converted to Sin and Cos and put at the last columns of the Data.\n", + "####\n" + ] + } + ], + "source": [ + "td = OurTrainingData([os.getcwd()+'/toydata/ChM_pt300_sm.h5'],\n", + " [os.getcwd()+'/toydata/ChM_pt300_gw1e-2.h5',],\n", + " #os.getcwd()+'/ChPgphim45e-1NP.h5',],\n", + " process = 'W-Z', parameters =['Gphi[TeV**-2]','GW[TeV**-2]'], \n", + " SMNLimits=int(5e5), BSMNLimits=int(5e5))\n", + "\n", + "td.Data = td.Data[:, :7]\n", + "td.CurateAngles([3, 5])\n", + "\n", + "Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights\n", + "Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()\n", + "\n", + "\n", + "NSM = td.SMWeights.mean()*3000\n", + "NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]\n", + "\n", + "h = h5py.File(os.getcwd()+'/toydata/gw_toydata_test_M1e-2_out.h5', 'w')\n", + "\n", + "h.create_dataset('Data', data=Data)\n", + "h.create_dataset('Weights', data=Weights)\n", + "h.create_dataset('Labels', data=Labels)\n", + "h.create_dataset('NSM', data = NSM)\n", + "h.create_dataset('NBSMList', data=NBSMList)\n", + "\n", + "h.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading Data Files for Process: W-Z, with new physics Parameters: ['Gphi[TeV**-2]', 'GW[TeV**-2]']\n", + "Only 1D Implemented in Training !\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChM_pt300_gw9e-3.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {0., 0.009}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChM_pt300_gw9e-3.dat.gz\n", + "Charge = -1 --- Process = W-Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChM_pt300_sm.h5\n", + "##### File Info:\n", + "SM = {0., 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChM_pt300_sm_1.dat.gz\n", + "Charge = -1 --- Process = W-Z\n", + "#####\n", + "\n", + "Loaded SM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "--------------------------------------- ------- ---------------\n", + "tensor([[0., 0.]], dtype=torch.float64) 3000000 0.329009\n", + "\n", + "Loaded BSM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "----------------------------------------------- ------- ---------------\n", + "tensor([[0.0000, 0.0090]], dtype=torch.float64) 500000 0.329917\n", + "\n", + "Paired BSM/SM Datasets:\n", + "\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Ev.BSM #Ev.SM Check\n", + "----------------------------------------------- --------- -------- -------\n", + "tensor([[0.0000, 0.0090]], dtype=torch.float64) 500000 500000 500000\n", + "####\n", + "Anlges at position [3, 5] have been converted to Sin and Cos and put at the last columns of the Data.\n", + "####\n" + ] + } + ], + "source": [ + "td = OurTrainingData([os.getcwd()+'/toydata/ChM_pt300_sm.h5'],\n", + " [os.getcwd()+'/toydata/ChM_pt300_gw9e-3.h5',],\n", + " #os.getcwd()+'/ChPgphim45e-1NP.h5',],\n", + " process = 'W-Z', parameters =['Gphi[TeV**-2]','GW[TeV**-2]'], \n", + " SMNLimits=int(5e5), BSMNLimits=int(5e5))\n", + "\n", + "td.Data = td.Data[:, :7]\n", + "td.CurateAngles([3, 5])\n", + "\n", + "Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights\n", + "Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()\n", + "\n", + "\n", + "NSM = td.SMWeights.mean()*3000\n", + "NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]\n", + "\n", + "h = h5py.File(os.getcwd()+'/toydata/gw_toydata_test_M9e-3_out.h5', 'w')\n", + "\n", + "h.create_dataset('Data', data=Data)\n", + "h.create_dataset('Weights', data=Weights)\n", + "h.create_dataset('Labels', data=Labels)\n", + "h.create_dataset('NSM', data = NSM)\n", + "h.create_dataset('NBSMList', data=NBSMList)\n", + "\n", + "h.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading Data Files for Process: W-Z, with new physics Parameters: ['Gphi[TeV**-2]', 'GW[TeV**-2]']\n", + "Only 1D Implemented in Training !\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChM_pt300_gw8e-3.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {0., 0.008}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChM_pt300_gw8e-3.dat.gz\n", + "Charge = -1 --- Process = W-Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChM_pt300_sm.h5\n", + "##### File Info:\n", + "SM = {0., 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChM_pt300_sm_1.dat.gz\n", + "Charge = -1 --- Process = W-Z\n", + "#####\n", + "\n", + "Loaded SM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "--------------------------------------- ------- ---------------\n", + "tensor([[0., 0.]], dtype=torch.float64) 3000000 0.329009\n", + "\n", + "Loaded BSM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "----------------------------------------------- ------- ---------------\n", + "tensor([[0.0000, 0.0080]], dtype=torch.float64) 500000 0.329702\n", + "\n", + "Paired BSM/SM Datasets:\n", + "\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Ev.BSM #Ev.SM Check\n", + "----------------------------------------------- --------- -------- -------\n", + "tensor([[0.0000, 0.0080]], dtype=torch.float64) 500000 500000 500000\n", + "####\n", + "Anlges at position [3, 5] have been converted to Sin and Cos and put at the last columns of the Data.\n", + "####\n" + ] + } + ], + "source": [ + "td = OurTrainingData([os.getcwd()+'/toydata/ChM_pt300_sm.h5'],\n", + " [os.getcwd()+'/toydata/ChM_pt300_gw8e-3.h5',],\n", + " #os.getcwd()+'/ChPgphim45e-1NP.h5',],\n", + " process = 'W-Z', parameters =['Gphi[TeV**-2]','GW[TeV**-2]'], \n", + " SMNLimits=int(5e5), BSMNLimits=int(5e5))\n", + "\n", + "td.Data = td.Data[:, :7]\n", + "td.CurateAngles([3, 5])\n", + "\n", + "Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights\n", + "Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()\n", + "\n", + "\n", + "NSM = td.SMWeights.mean()*3000\n", + "NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]\n", + "\n", + "h = h5py.File(os.getcwd()+'/toydata/gw_toydata_test_M8e-3_out.h5', 'w')\n", + "\n", + "h.create_dataset('Data', data=Data)\n", + "h.create_dataset('Weights', data=Weights)\n", + "h.create_dataset('Labels', data=Labels)\n", + "h.create_dataset('NSM', data = NSM)\n", + "h.create_dataset('NBSMList', data=NBSMList)\n", + "\n", + "h.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/tutorial_particle_physics/ReadingData.ipynb b/examples/tutorial_particle_physics/ReadingData.ipynb new file mode 100644 index 000000000..2e67751df --- /dev/null +++ b/examples/tutorial_particle_physics/ReadingData.ipynb @@ -0,0 +1,998 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting tabulate\n", + " Downloading tabulate-0.8.7-py3-none-any.whl (24 kB)\n", + "Installing collected packages: tabulate\n", + "Successfully installed tabulate-0.8.7\n", + "\u001b[33mWARNING: You are using pip version 20.1.1; however, version 20.2.4 is available.\n", + "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n" + ] + } + ], + "source": [ + "! pip install tabulate" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from OurTrainingTools2D import *\n", + "data_path = os.getcwd() + '/toydata'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading Data Files for Process: W+Z, with new physics Parameters: ['Gphi[TeV**-2]', 'GW[TeV**-2]']\n", + "Only 1D Implemented in Training !\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_gphi5e-2.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {0.05, 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_gphi5e-2.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_gphi2e-1.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {0.2, 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_gphi2e-1.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_gphi5e-1.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {0.5, 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_gphi5e-1.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_gphim5e-2.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {-0.05, 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_gphim5e-2.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_gphim2e-1.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {-0.2, 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_gphim2e-1.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_gphim5e-1.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {-0.5, 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_gphim5e-1.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_sm.h5\n", + "##### File Info:\n", + "SM = {0., 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_sm_1.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Loaded SM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "--------------------------------------- ------- ---------------\n", + "tensor([[0., 0.]], dtype=torch.float64) 3000000 0.741835\n", + "\n", + "Loaded BSM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "------------------------------------------------- ------- ---------------\n", + "tensor([[0.0500, 0.0000]], dtype=torch.float64) 500000 0.947113\n", + "tensor([[0.2000, 0.0000]], dtype=torch.float64) 500000 2.16749\n", + "tensor([[0.5000, 0.0000]], dtype=torch.float64) 500000 7.32829\n", + "tensor([[-0.0500, 0.0000]], dtype=torch.float64) 500000 0.637632\n", + "tensor([[-0.2000, 0.0000]], dtype=torch.float64) 500000 0.929192\n", + "tensor([[-0.5000, 0.0000]], dtype=torch.float64) 500000 4.23431\n", + "\n", + "Paired BSM/SM Datasets:\n", + "\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Ev.BSM #Ev.SM Check\n", + "------------------------------------------------- --------- -------- -------\n", + "tensor([[0.0500, 0.0000]], dtype=torch.float64) 500000 500000 500000\n", + "tensor([[0.2000, 0.0000]], dtype=torch.float64) 500000 500000 500000\n", + "tensor([[0.5000, 0.0000]], dtype=torch.float64) 500000 500000 500000\n", + "tensor([[-0.0500, 0.0000]], dtype=torch.float64) 500000 500000 500000\n", + "tensor([[-0.2000, 0.0000]], dtype=torch.float64) 500000 500000 500000\n", + "tensor([[-0.5000, 0.0000]], dtype=torch.float64) 500000 500000 500000\n", + "####\n", + "Anlges at position [3, 5] have been converted to Sin and Cos and put at the last columns of the Data.\n", + "####\n" + ] + } + ], + "source": [ + "from OurTrainingTools2D import *\n", + "\n", + "random_seed = torch.randint(0, 1339, (1,)).item()\n", + "torch.manual_seed(random_seed)\n", + "data_path = os.getcwd() + '/toydata'\n", + "\n", + "td = OurTrainingData([data_path+'/ChP_pt300_sm.h5'],\n", + " [data_path+'/ChP_pt300_gphi5e-2.h5',\n", + " data_path+'/ChP_pt300_gphi2e-1.h5',\n", + " data_path+'/ChP_pt300_gphi5e-1.h5',\n", + " data_path+'/ChP_pt300_gphim5e-2.h5',\n", + " data_path+'/ChP_pt300_gphim2e-1.h5',\n", + " data_path+'/ChP_pt300_gphim5e-1.h5',],\n", + " process = 'W+Z', parameters =['Gphi[TeV**-2]','GW[TeV**-2]'], \n", + " SMNLimits=int(3e6), BSMNLimits=int(5e5))\n", + "\n", + "td.Data = td.Data[:, :7]\n", + "td.CurateAngles([3, 5])\n", + "\n", + "Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights\n", + "Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "NSM = td.SMWeights.mean()*3000\n", + "NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['SM'], dtype=object)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "td.SMDataFiles[0].Parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "h = h5py.File(data_path + '/gphi_toydata.h5', 'w')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h.create_dataset('Data', data=Data)\n", + "h.create_dataset('Weights', data=Weights)\n", + "h.create_dataset('Labels', data=Labels)\n", + "h.create_dataset('NSM', data = NSM)\n", + "h.create_dataset('NBSMList', data=NBSMList)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "h.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# In Sample Test Data" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading Data Files for Process: W+Z, with new physics Parameters: ['Gphi[TeV**-2]', 'GW[TeV**-2]']\n", + "Only 1D Implemented in Training !\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_gphi5e-2.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {0.05, 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_gphi5e-2.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_sm.h5\n", + "##### File Info:\n", + "SM = {0., 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_sm_1.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Loaded SM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "--------------------------------------- ------- ---------------\n", + "tensor([[0., 0.]], dtype=torch.float64) 3000000 0.741835\n", + "\n", + "Loaded BSM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "----------------------------------------------- ------- ---------------\n", + "tensor([[0.0500, 0.0000]], dtype=torch.float64) 500000 0.947113\n", + "\n", + "Paired BSM/SM Datasets:\n", + "\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Ev.BSM #Ev.SM Check\n", + "----------------------------------------------- --------- -------- -------\n", + "tensor([[0.0500, 0.0000]], dtype=torch.float64) 500000 500000 500000\n", + "####\n", + "Anlges at position [3, 5] have been converted to Sin and Cos and put at the last columns of the Data.\n", + "####\n" + ] + } + ], + "source": [ + "td = OurTrainingData([data_path+'/ChP_pt300_sm.h5'],\n", + " [data_path+'/ChP_pt300_gphi5e-2.h5',],\n", + " process = 'W+Z', parameters =['Gphi[TeV**-2]','GW[TeV**-2]'], \n", + " SMNLimits=int(5e5), BSMNLimits=int(2e6))\n", + "\n", + "td.Data = td.Data[:, :7]\n", + "td.CurateAngles([3, 5])\n", + "\n", + "Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights\n", + "Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()\n", + "\n", + "NSM = td.SMWeights.mean()*3000\n", + "NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]\n", + "\n", + "h = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_5e-2_in.h5', 'w')\n", + "\n", + "h.create_dataset('Data', data=Data)\n", + "h.create_dataset('Weights', data=Weights)\n", + "h.create_dataset('Labels', data=Labels)\n", + "h.create_dataset('NSM', data = NSM)\n", + "h.create_dataset('NBSMList', data=NBSMList)\n", + "\n", + "h.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading Data Files for Process: W+Z, with new physics Parameters: ['Gphi[TeV**-2]', 'GW[TeV**-2]']\n", + "Only 1D Implemented in Training !\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_gphi2e-1.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {0.2, 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_gphi2e-1.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_sm.h5\n", + "##### File Info:\n", + "SM = {0., 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_sm_1.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Loaded SM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "--------------------------------------- ------- ---------------\n", + "tensor([[0., 0.]], dtype=torch.float64) 3000000 0.741835\n", + "\n", + "Loaded BSM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "----------------------------------------------- ------- ---------------\n", + "tensor([[0.2000, 0.0000]], dtype=torch.float64) 500000 2.16749\n", + "\n", + "Paired BSM/SM Datasets:\n", + "\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Ev.BSM #Ev.SM Check\n", + "----------------------------------------------- --------- -------- -------\n", + "tensor([[0.2000, 0.0000]], dtype=torch.float64) 500000 500000 500000\n", + "####\n", + "Anlges at position [3, 5] have been converted to Sin and Cos and put at the last columns of the Data.\n", + "####\n" + ] + } + ], + "source": [ + "td = OurTrainingData([data_path+'/ChP_pt300_sm.h5'],\n", + " [data_path+'/ChP_pt300_gphi2e-1.h5',],\n", + " process = 'W+Z', parameters =['Gphi[TeV**-2]','GW[TeV**-2]'], \n", + " SMNLimits=int(5e5), BSMNLimits=int(5e5))\n", + "\n", + "td.Data = td.Data[:, :7]\n", + "td.CurateAngles([3, 5])\n", + "\n", + "Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights\n", + "Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()\n", + "\n", + "NSM = td.SMWeights.mean()*3000\n", + "NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]\n", + "\n", + "h = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_2e-1_in.h5', 'w')\n", + "\n", + "h.create_dataset('Data', data=Data)\n", + "h.create_dataset('Weights', data=Weights)\n", + "h.create_dataset('Labels', data=Labels)\n", + "h.create_dataset('NSM', data = NSM)\n", + "h.create_dataset('NBSMList', data=NBSMList)\n", + "\n", + "h.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading Data Files for Process: W+Z, with new physics Parameters: ['Gphi[TeV**-2]', 'GW[TeV**-2]']\n", + "Only 1D Implemented in Training !\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_gphi5e-1.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {0.5, 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_gphi5e-1.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_sm.h5\n", + "##### File Info:\n", + "SM = {0., 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_sm_1.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Loaded SM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "--------------------------------------- ------- ---------------\n", + "tensor([[0., 0.]], dtype=torch.float64) 3000000 0.741835\n", + "\n", + "Loaded BSM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "----------------------------------------------- ------- ---------------\n", + "tensor([[0.5000, 0.0000]], dtype=torch.float64) 500000 7.32829\n", + "\n", + "Paired BSM/SM Datasets:\n", + "\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Ev.BSM #Ev.SM Check\n", + "----------------------------------------------- --------- -------- -------\n", + "tensor([[0.5000, 0.0000]], dtype=torch.float64) 500000 500000 500000\n", + "####\n", + "Anlges at position [3, 5] have been converted to Sin and Cos and put at the last columns of the Data.\n", + "####\n" + ] + } + ], + "source": [ + "td = OurTrainingData([data_path+'/ChP_pt300_sm.h5'],\n", + " [data_path+'/ChP_pt300_gphi5e-1.h5',],\n", + " process = 'W+Z', parameters =['Gphi[TeV**-2]','GW[TeV**-2]'], \n", + " SMNLimits=int(5e5), BSMNLimits=int(5e5))\n", + "\n", + "td.Data = td.Data[:, :7]\n", + "td.CurateAngles([3, 5])\n", + "\n", + "Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights\n", + "Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()\n", + "\n", + "NSM = td.SMWeights.mean()*3000\n", + "NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]\n", + "\n", + "h = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_5e-1_in.h5', 'w')\n", + "\n", + "h.create_dataset('Data', data=Data)\n", + "h.create_dataset('Weights', data=Weights)\n", + "h.create_dataset('Labels', data=Labels)\n", + "h.create_dataset('NSM', data = NSM)\n", + "h.create_dataset('NBSMList', data=NBSMList)\n", + "\n", + "h.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Out Sample Test Data" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading Data Files for Process: W+Z, with new physics Parameters: ['Gphi[TeV**-2]', 'GW[TeV**-2]']\n", + "Only 1D Implemented in Training !\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_gphi6e-3.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {0.006, 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_gphi6e-3.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_sm_out.h5\n", + "##### File Info:\n", + "SM = {0., 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_sm_2.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Loaded SM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "--------------------------------------- ------- ---------------\n", + "tensor([[0., 0.]], dtype=torch.float64) 3000000 0.741835\n", + "\n", + "Loaded BSM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "----------------------------------------------- ------- ---------------\n", + "tensor([[0.0060, 0.0000]], dtype=torch.float64) 3000000 0.761347\n", + "\n", + "Paired BSM/SM Datasets:\n", + "\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Ev.BSM #Ev.SM Check\n", + "----------------------------------------------- --------- -------- -------\n", + "tensor([[0.0060, 0.0000]], dtype=torch.float64) 3000000 3000000 3e+06\n", + "####\n", + "Anlges at position [3, 5] have been converted to Sin and Cos and put at the last columns of the Data.\n", + "####\n" + ] + } + ], + "source": [ + "td = OurTrainingData([data_path+'/ChP_pt300_sm_out.h5'],\n", + " [data_path+'/ChP_pt300_gphi6e-3.h5',],\n", + " process = 'W+Z', parameters =['Gphi[TeV**-2]','GW[TeV**-2]'], \n", + " SMNLimits=int(3e6), BSMNLimits=int(3e6))\n", + "\n", + "td.Data = td.Data[:, :7]\n", + "td.CurateAngles([3, 5])\n", + "\n", + "Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights\n", + "Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()\n", + "\n", + "NSM = td.SMWeights.mean()*3000\n", + "NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]\n", + "\n", + "h = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_6e-3_out.h5', 'w')\n", + "\n", + "h.create_dataset('Data', data=Data)\n", + "h.create_dataset('Weights', data=Weights)\n", + "h.create_dataset('Labels', data=Labels)\n", + "h.create_dataset('NSM', data = NSM)\n", + "h.create_dataset('NBSMList', data=NBSMList)\n", + "\n", + "h.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading Data Files for Process: W+Z, with new physics Parameters: ['Gphi[TeV**-2]', 'GW[TeV**-2]']\n", + "Only 1D Implemented in Training !\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_gphi5e-3.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {0.005, 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_gphi5e-3.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_sm_out.h5\n", + "##### File Info:\n", + "SM = {0., 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_sm_2.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Loaded SM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "--------------------------------------- ------- ---------------\n", + "tensor([[0., 0.]], dtype=torch.float64) 3000000 0.741835\n", + "\n", + "Loaded BSM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "----------------------------------------------- ------- ---------------\n", + "tensor([[0.0050, 0.0000]], dtype=torch.float64) 500000 0.757816\n", + "\n", + "Paired BSM/SM Datasets:\n", + "\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Ev.BSM #Ev.SM Check\n", + "----------------------------------------------- --------- -------- -------\n", + "tensor([[0.0050, 0.0000]], dtype=torch.float64) 500000 3000000 500000\n", + "####\n", + "Anlges at position [3, 5] have been converted to Sin and Cos and put at the last columns of the Data.\n", + "####\n" + ] + } + ], + "source": [ + "td = OurTrainingData([data_path+'/ChP_pt300_sm_out.h5'],\n", + " [data_path+'/ChP_pt300_gphi5e-3.h5',],\n", + " process = 'W+Z', parameters =['Gphi[TeV**-2]','GW[TeV**-2]'], \n", + " SMNLimits=int(3e6), BSMNLimits=int(3e6))\n", + "\n", + "td.Data = td.Data[:, :7]\n", + "td.CurateAngles([3, 5])\n", + "\n", + "Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights\n", + "Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()\n", + "\n", + "NSM = td.SMWeights.mean()*3000\n", + "NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]\n", + "\n", + "h = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_5e-3_out.h5', 'w')\n", + "\n", + "h.create_dataset('Data', data=Data)\n", + "h.create_dataset('Weights', data=Weights)\n", + "h.create_dataset('Labels', data=Labels)\n", + "h.create_dataset('NSM', data = NSM)\n", + "h.create_dataset('NBSMList', data=NBSMList)\n", + "\n", + "h.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading Data Files for Process: W+Z, with new physics Parameters: ['Gphi[TeV**-2]', 'GW[TeV**-2]']\n", + "Only 1D Implemented in Training !\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_gphi4e-3.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {0.004, 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_gphi4e-3.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChP_pt300_sm_out.h5\n", + "##### File Info:\n", + "SM = {0., 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChP_pt300_sm_2.dat.gz\n", + "Charge = 1 --- Process = W+Z\n", + "#####\n", + "\n", + "Loaded SM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "--------------------------------------- ------- ---------------\n", + "tensor([[0., 0.]], dtype=torch.float64) 3000000 0.741835\n", + "\n", + "Loaded BSM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "----------------------------------------------- ------- ---------------\n", + "tensor([[0.0040, 0.0000]], dtype=torch.float64) 3000000 0.754596\n", + "\n", + "Paired BSM/SM Datasets:\n", + "\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Ev.BSM #Ev.SM Check\n", + "----------------------------------------------- --------- -------- -------\n", + "tensor([[0.0040, 0.0000]], dtype=torch.float64) 3000000 3000000 3e+06\n", + "####\n", + "Anlges at position [3, 5] have been converted to Sin and Cos and put at the last columns of the Data.\n", + "####\n", + "[tensor(2263.7889, dtype=torch.float64)]\n" + ] + } + ], + "source": [ + "td = OurTrainingData([data_path+'/ChP_pt300_sm_out.h5'],\n", + " [data_path+'/ChP_pt300_gphi4e-3.h5',],\n", + " process = 'W+Z', parameters =['Gphi[TeV**-2]','GW[TeV**-2]'], \n", + " SMNLimits=int(3e6), BSMNLimits=int(3e6))\n", + "\n", + "td.Data = td.Data[:, :7]\n", + "td.CurateAngles([3, 5])\n", + "\n", + "Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights\n", + "Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()\n", + "\n", + "NSM = td.SMWeights.mean()*3000\n", + "NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]\n", + "\n", + "print(NBSMList)\n", + "\n", + "h = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_4e-3_out.h5', 'w')\n", + "\n", + "h.create_dataset('Data', data=Data)\n", + "h.create_dataset('Weights', data=Weights)\n", + "h.create_dataset('Labels', data=Labels)\n", + "h.create_dataset('NSM', data = NSM)\n", + "h.create_dataset('NBSMList', data=NBSMList)\n", + "\n", + "h.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading Data Files for Process: W-Z, with new physics Parameters: ['Gphi[TeV**-2]', 'GW[TeV**-2]']\n", + "Only 1D Implemented in Training !\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChM_pt300_gphi6e-3.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {0.006, 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChM_pt300_gphi6e-3.dat.gz\n", + "Charge = -1 --- Process = W-Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChM_pt300_sm_out.h5\n", + "##### File Info:\n", + "SM = {0., 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChM_pt300_sm_2.dat.gz\n", + "Charge = -1 --- Process = W-Z\n", + "#####\n", + "\n", + "Loaded SM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "--------------------------------------- ------- ---------------\n", + "tensor([[0., 0.]], dtype=torch.float64) 3000000 0.329009\n", + "\n", + "Loaded BSM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "----------------------------------------------- ------- ---------------\n", + "tensor([[0.0060, 0.0000]], dtype=torch.float64) 3000000 0.337494\n", + "\n", + "Paired BSM/SM Datasets:\n", + "\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Ev.BSM #Ev.SM Check\n", + "----------------------------------------------- --------- -------- -------\n", + "tensor([[0.0060, 0.0000]], dtype=torch.float64) 3000000 3000000 3e+06\n", + "####\n", + "Anlges at position [3, 5] have been converted to Sin and Cos and put at the last columns of the Data.\n", + "####\n", + "tensor(987.0264, dtype=torch.float64)\n", + "[tensor(1012.4808, dtype=torch.float64)]\n" + ] + } + ], + "source": [ + "td = OurTrainingData([data_path+'/ChM_pt300_sm_out.h5'],\n", + " [data_path+'/ChM_pt300_gphi6e-3.h5',],\n", + " process = 'W-Z', parameters =['Gphi[TeV**-2]','GW[TeV**-2]'], \n", + " SMNLimits=int(3e6), BSMNLimits=int(3e6))\n", + "\n", + "td.Data = td.Data[:, :7]\n", + "td.CurateAngles([3, 5])\n", + "\n", + "Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights\n", + "Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()\n", + "\n", + "NSM = td.SMWeights.mean()*3000\n", + "NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]\n", + "\n", + "print(NSM)\n", + "print(NBSMList)\n", + "\n", + "h = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_M6e-3_out.h5', 'w')\n", + "\n", + "h.create_dataset('Data', data=Data)\n", + "h.create_dataset('Weights', data=Weights)\n", + "h.create_dataset('Labels', data=Labels)\n", + "h.create_dataset('NSM', data = NSM)\n", + "h.create_dataset('NBSMList', data=NBSMList)\n", + "\n", + "h.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(tensor(1.0258), tensor(1.0258, dtype=torch.float64))" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Weights[-1]/Weights[0], NBSMList[0]/NSM" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading Data Files for Process: W-Z, with new physics Parameters: ['Gphi[TeV**-2]', 'GW[TeV**-2]']\n", + "Only 1D Implemented in Training !\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChM_pt300_gphi5e-3.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {0.005, 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChM_pt300_gphi5e-3.dat.gz\n", + "Charge = -1 --- Process = W-Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChM_pt300_sm_out.h5\n", + "##### File Info:\n", + "SM = {0., 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChM_pt300_sm_2.dat.gz\n", + "Charge = -1 --- Process = W-Z\n", + "#####\n", + "\n", + "Loaded SM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "--------------------------------------- ------- ---------------\n", + "tensor([[0., 0.]], dtype=torch.float64) 3000000 0.329009\n", + "\n", + "Loaded BSM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "----------------------------------------------- ------- ---------------\n", + "tensor([[0.0050, 0.0000]], dtype=torch.float64) 500000 0.336016\n", + "\n", + "Paired BSM/SM Datasets:\n", + "\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Ev.BSM #Ev.SM Check\n", + "----------------------------------------------- --------- -------- -------\n", + "tensor([[0.0050, 0.0000]], dtype=torch.float64) 500000 3000000 500000\n", + "####\n", + "Anlges at position [3, 5] have been converted to Sin and Cos and put at the last columns of the Data.\n", + "####\n" + ] + } + ], + "source": [ + "td = OurTrainingData([data_path+'/ChM_pt300_sm_out.h5'],\n", + " [data_path+'/ChM_pt300_gphi5e-3.h5',],\n", + " process = 'W-Z', parameters =['Gphi[TeV**-2]','GW[TeV**-2]'], \n", + " SMNLimits=int(3e6), BSMNLimits=int(3e6))\n", + "\n", + "td.Data = td.Data[:, :7]\n", + "td.CurateAngles([3, 5])\n", + "\n", + "Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights\n", + "Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()\n", + "\n", + "NSM = td.SMWeights.mean()*3000\n", + "NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]\n", + "\n", + "h = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_M5e-3_out.h5', 'w')\n", + "\n", + "h.create_dataset('Data', data=Data)\n", + "h.create_dataset('Weights', data=Weights)\n", + "h.create_dataset('Labels', data=Labels)\n", + "h.create_dataset('NSM', data = NSM)\n", + "h.create_dataset('NBSMList', data=NBSMList)\n", + "\n", + "h.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading Data Files for Process: W-Z, with new physics Parameters: ['Gphi[TeV**-2]', 'GW[TeV**-2]']\n", + "Only 1D Implemented in Training !\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChM_pt300_gphi4e-3.h5\n", + "##### File Info:\n", + "{Gphi[TeV**-2], GW[TeV**-2]} = {0.004, 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChM_pt300_gphi4e-3.dat.gz\n", + "Charge = -1 --- Process = W-Z\n", + "#####\n", + "\n", + "Reading file .../madminer/madminer/examples/tutorial_particle_physics/toydata/ChM_pt300_sm_out.h5\n", + "##### File Info:\n", + "SM = {0., 0.}[TeV**-2] data, Ideal Events. \n", + "Event format: {{s, θ, θZ, ϕZ, θWrec, ϕWrec, PtZ}, weight}.\n", + "Converted from /data3/WZ_new_project/dat/Ideal_Events/ChM_pt300_sm_2.dat.gz\n", + "Charge = -1 --- Process = W-Z\n", + "#####\n", + "\n", + "Loaded SM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "--------------------------------------- ------- ---------------\n", + "tensor([[0., 0.]], dtype=torch.float64) 3000000 0.329009\n", + "\n", + "Loaded BSM Files:\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Data XS[pb](avg.w)\n", + "----------------------------------------------- ------- ---------------\n", + "tensor([[0.0040, 0.0000]], dtype=torch.float64) 500000 0.334613\n", + "\n", + "Paired BSM/SM Datasets:\n", + "\n", + "['Gphi[TeV**-2]', 'GW[TeV**-2]'] #Ev.BSM #Ev.SM Check\n", + "----------------------------------------------- --------- -------- -------\n", + "tensor([[0.0040, 0.0000]], dtype=torch.float64) 500000 3000000 500000\n", + "####\n", + "Anlges at position [3, 5] have been converted to Sin and Cos and put at the last columns of the Data.\n", + "####\n" + ] + } + ], + "source": [ + "td = OurTrainingData([data_path+'/ChM_pt300_sm_out.h5'],\n", + " [data_path+'/ChM_pt300_gphi4e-3.h5',],\n", + " process = 'W-Z', parameters =['Gphi[TeV**-2]','GW[TeV**-2]'], \n", + " SMNLimits=int(3e6), BSMNLimits=int(3e6))\n", + "\n", + "td.Data = td.Data[:, :7]\n", + "td.CurateAngles([3, 5])\n", + "\n", + "Data, ParVal, Labels, Weights = td.Data, td.ParVal, td.Labels, td.Weights\n", + "Data, ParVal, Labels, Weights = Data.float(), ParVal.float(), Labels.float(), Weights.float()\n", + "\n", + "print\n", + "\n", + "NSM = td.SMWeights.mean()*3000\n", + "NBSMList = [td.BSMWeightsList[i].mean()*3000 for i in range(len(td.BSMWeightsList))]\n", + "\n", + "h = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_M4e-3_out.h5', 'w')\n", + "\n", + "h.create_dataset('Data', data=Data)\n", + "h.create_dataset('Weights', data=Weights)\n", + "h.create_dataset('Labels', data=Labels)\n", + "h.create_dataset('NSM', data = NSM)\n", + "h.create_dataset('NBSMList', data=NBSMList)\n", + "\n", + "h.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/tutorial_particle_physics/TestEstimator-Light-Copy1.ipynb b/examples/tutorial_particle_physics/TestEstimator-Light-Copy1.ipynb new file mode 100644 index 000000000..75c1d8704 --- /dev/null +++ b/examples/tutorial_particle_physics/TestEstimator-Light-Copy1.ipynb @@ -0,0 +1,1496 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: tabulate in /usr/local/lib/python3.8/dist-packages (0.8.7)\n", + "\u001b[33mWARNING: You are using pip version 20.1.1; however, version 20.2.3 is available.\n", + "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n" + ] + } + ], + "source": [ + "#if you have not installed tabulate \n", + "#this is just for printing the information of data in a prettier format\n", + "! pip install tabulate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing Function" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=========== Random Seed: 946 ===========\n" + ] + } + ], + "source": [ + "#import main code\n", + "from OurTrainingTools import *\n", + "#this will throw a random number and print it\n", + "#to reset the random number manually, use\n", + "#torch.manual_seed(random_seed)\n", + "\n", + "\n", + "def test_model(madminermodel, test_input_sm, test_input_bsm, bsmparval, NSM, NBSM, epochs, e, n_meas, pm, verbose_t=True, verbose_period_t=1e5, title=''):\n", + " \n", + " # computing test statistics t (or lambda) in equation (2) of paper\n", + " def compute_t(madminermodel, nev, counter, test_input):\n", + " # generate number of points for testing under Poisson distribution\n", + " n_gen = 0\n", + " while n_gen == 0:\n", + " n_gen = np.random.poisson(nev)\n", + " \n", + " # stop if there are no more points to test\n", + " if (counter + n_gen) >= len(test_input):\n", + " return 0., -1\n", + " \n", + " points = test_input[int(counter): int(counter+n_gen)]\n", + " \n", + " # compute test statistics\n", + " #log_ratio = (madminermodel.evaluate_log_likelihood_ratio(points.numpy(), \n", + " # np.array([0.]), np.array([bsmparval,]))[0][0])\n", + " \n", + " y = model.Forward(points, np.ones(len(points))*bsmparval).detach()\n", + " log_ratio = np.log(y/(1.-y))\n", + " #calculate_ratio\n", + " log_ratio = torch.tensor(log_ratio)\n", + " #ratio = 1./ratio\n", + " log_ratio = log_ratio\n", + " out = 2 * (NBSM - NSM - (log_ratio+torch.log(torch.tensor(NBSM/NSM))).sum(0))\n", + " \n", + " #return test statistics and the starting point for the next batch\n", + " return out, int(counter+n_gen)\n", + " \n", + " test_start = time.time()\n", + " if verbose_t:\n", + " print(\"NSM = %.3f --- NBSM = %.3f\"%(NSM, NBSM))\n", + " tsm = torch.empty(n_meas)\n", + " tbsm = torch.empty(n_meas)\n", + " \n", + " # empty array to store values\n", + " tsmcount = torch.zeros(n_meas+1)\n", + " tbsmcount = torch.zeros(n_meas+1)\n", + " \n", + " for i in range(n_meas):\n", + " tsm[i], tsmcount[i+1] = compute_t(madminermodel, NSM, tsmcount[i], \n", + " test_input_sm)\n", + " tbsm[i], tbsmcount[i+1] = compute_t(madminermodel, NBSM, tbsmcount[i], \n", + " test_input_bsm)\n", + " \n", + " if (tsmcount[i+1] < 0) or (tbsmcount[i+1] < 0):\n", + " print('Reaching the end of test data. Stop tests at %d. '%i)\n", + " tsm, tbsm = tsm[: i], tbsm[: i]\n", + " n_meas = i\n", + " break\n", + " \n", + " if i % (verbose_period_t) == 0:\n", + " print('test %s: tsm = %.3f, tbsm = %.3f'%(\n", + " str(i).ljust(4), tsm[i], tbsm[i]))\n", + " \n", + " test_duration = time.time() - test_start\n", + " \n", + " #compute mean and variation of the test statistics in two hypotheses\n", + " mu_sm = tsm.mean().item()\n", + " mu_bsm = tbsm.mean().item()\n", + " sigma_sm = tsm.std().item()\n", + " sigma_bsm = tbsm.std().item()\n", + " med_sm = tsm.median().item()\n", + " \n", + " #compute separation and p-value\n", + " sep = (mu_sm - mu_bsm)/sigma_bsm\n", + " p = 1.*len([i for i in tbsm if i > med_sm])/len(tsm) \n", + " #print(len([i for i in tbsm if i>med_sm]))\n", + " delta1 = (p * (1 - p)/n_meas)**0.5\n", + " delta2 = (sigma_sm/sigma_bsm) * np.exp(-((mu_bsm - mu_sm)**2)/(\n", + " 2 * sigma_bsm**2))/(2*(n_meas**0.5))\n", + " print('===> delta1 = %.3f, delta2 = %.3f'%(delta1, delta2))\n", + " deltap = (delta1**2 + delta2**2)**0.5\n", + " \n", + " results_path = os.getcwd()\n", + " \n", + " if verbose_t:\n", + " print('p = %.3f +/- %.3f' %(p, deltap))\n", + " print('Separation = %.2f sigmas'%(sep))\n", + " training_properties = '/toydata/madminer-carl-'+title\n", + " plot_histogram(tsm, tbsm, int(NSM), int(NBSM), p, deltap, sep, epochs, e, \n", + " training_properties, results_path)\n", + " print('Partial test after %d epochs (took %.2f seconds)\\n'\n", + " %(e, test_duration))\n", + " \n", + " \n", + " return sep, p" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_histogram(tsm, tbsm, nsm, nbsm, p, deltap, sep, epochs, \n", + " e, training_properties, results_folder):\n", + " mint = torch.min(torch.cat((tsm, tbsm))).item()\n", + " maxt = torch.max(torch.cat((tsm, tbsm))).item()\n", + " \n", + " # for some reason the code complains if i don't detach the variables \n", + " # from their grad-on versions\n", + " tsm, tbsm = tsm.detach(), tbsm.detach()\n", + " \n", + " bins = np.linspace(mint, maxt, 60)\n", + " plt.figure(figsize=(8, 6))\n", + " ax = plt.subplot()\n", + " plt.hist(tsm, bins, alpha=0.5, label='SM')\n", + " plt.hist(tbsm, bins, alpha=0.5, label='BSM')\n", + " plt.legend(loc='upper right')\n", + " \n", + " sn = 'nsm = %s \\nnbsm = %s'%(str(nsm), str(nbsm))\n", + " sp = 'p '+'= '+ ('%.3f +/- %.3f'%(p, deltap))\n", + " ssep = 'sep ' + '= ' + ('%.3f'%(sep))\n", + " \n", + " plt.text(x=0.05, y=0.85, transform=ax.transAxes, \n", + " s=sn+'\\n'+sp+'\\n'+ssep, bbox=dict(facecolor='blue', alpha=0.2))\n", + " plt.xlabel('t')\n", + " plt.ylabel('p(t)')\n", + " if epochs == e:\n", + " plt.title('Final test\\n' + training_properties)\n", + " filename = results_folder + training_properties \\\n", + " + ' histogram.pdf'\n", + " plt.savefig(filename)\n", + " return \n", + " plt.title(training_properties)\n", + " plt.show()\n", + " \n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def TestEstimator(gphival, gphival_name='', withXS=True, title_message=''):\n", + " \n", + " #toy data file path\n", + " if not gphival_name:\n", + " gphival_name = gphival\n", + " f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%s_out.h5'%(gphival_name), 'r')\n", + "\n", + " #parse data \n", + " Data = np.array(f['Data'])\n", + " Labels = np.array(f['Labels'])\n", + " NSM = np.array(f['NSM'])\n", + " \n", + " if withXS:\n", + " NBSMList = np.array(f['NBSMList'])\n", + " NBSM = NBSMList[0]\n", + " else:\n", + " NBSM = NSM\n", + " \n", + " #randomise\n", + " Idx_test = torch.randperm(len(Data))\n", + " Data_test = torch.Tensor(Data[Idx_test])\n", + " Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + " #select data from each hypothesis\n", + " SM_Data = Data_test[Label_test==0, :]\n", + " BSM_Data = Data_test[Label_test==1, :]\n", + "\n", + " #for plotting/ printing\n", + " n_epochs = current_epoch = int(1e4)\n", + " results_path = os.getcwd()\n", + " charge = 'plus'\n", + "\n", + " #number of tests thrown on the data\n", + " #the test function will stop automatically if points run out\n", + " n_meas = 4000\n", + "\n", + " if withXS:\n", + " sep, p = test_model(estimator, SM_Data, BSM_Data, gphival, NSM, NBSM, n_epochs, current_epoch, n_meas, charge, \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%s_new'%(gphival_name)+title_message)\n", + " else:\n", + " sep, p = test_model(estimator, SM_Data, BSM_Data, gphival, NSM, NBSM, n_epochs, current_epoch, n_meas, charge, \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%s_new_equalXS'%(gphival_name)+title_message)\n", + " \n", + " f.close()\n", + " \n", + " return sep, p" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reading Model" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model successfully loaded.\n", + "Path: /madminer/madminer/examples/tutorial_particle_physics/models/QuadraticClassifier_cpu.pth\n", + "0.005\n" + ] + } + ], + "source": [ + "gphival = 35e-1\n", + "gphival_name = '35e-1'\n", + "withXS = True\n", + "\n", + "if not gphival_name:\n", + " gphival_name = gphival\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%s_out.h5'%(gphival_name), 'r')\n", + "\n", + "#parse data \n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "\n", + "if withXS:\n", + " NBSMList = np.array(f['NBSMList'])\n", + " NBSM = NBSMList[0]\n", + "else:\n", + " NBSM = NSM\n", + "\n", + "#randomise\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "#scaling\n", + "Parameters = np.ones([1, len(Data_test)])*0.005\n", + "model = OurModel(AR=[9, 32, 32, 32, 32, 1])\n", + "model.Load_CPU('QuadraticClassifier_cpu', os.getcwd()+'/models/')\n", + "#Data_test_scaled, Parameters_scaled = model.Preprocess(Data_test, Parameters)\n", + "#Parameters = Parameters_scaled[0, 0].numpy()\n", + "Parameters = Parameters[0, 0]\n", + "\n", + "print(Parameters)\n", + "\n", + "#select data from each hypothesis\n", + "SM_Data = Data_test[Label_test==0, :]\n", + "BSM_Data = Data_test[Label_test==1, :]\n", + "\n", + "#for plotting/ printing\n", + "n_epochs = current_epoch = int(1e4)\n", + "results_path = os.getcwd()\n", + "charge = 'plus'\n", + "\n", + "#number of tests thrown on the data\n", + "#the test function will stop automatically if points run out\n", + "n_meas = 4000" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2259.447\n", + "test 0 : tsm = -19336.865, tbsm = -19307.146\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":30: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " log_ratio = torch.tensor(log_ratio)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reaching the end of test data. Stop tests at 221. \n", + "===> delta1 = 0.027, delta2 = 0.025\n", + "p = 0.208 +/- 0.037\n", + "Separation = 0.86 sigmas\n", + "Partial test after 10000 epochs (took 1.24 seconds)\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.8610143381169146, 0.2081447963800905)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "test_model(model, SM_Data, BSM_Data, Parameters, NSM, NBSM, n_epochs, current_epoch, n_meas, charge, \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%s_new'%(gphival_name)+'-quadraticClassifier')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Evaluating with knowledge on the Cross Section, gphi=35e-1\n", + "NSM = 2225.504 --- NBSM = 2259.447\n", + "test 0 : tsm = 202.433, tbsm = 166.551\n", + "Reaching the end of test data. Stop tests at 221. \n", + "===> delta1 = 0.032, delta2 = 0.033\n", + "p = 0.335 +/- 0.046\n", + "Separation = 0.34 sigmas\n", + "Partial test after 10000 epochs (took 116.31 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=35e-1\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 148.203, tbsm = 169.812\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.032, delta2 = 0.033\n", + "p = 0.371 +/- 0.046\n", + "Separation = 0.38 sigmas\n", + "Partial test after 10000 epochs (took 115.54 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=5\n", + "NSM = 2225.504 --- NBSM = 2273.448\n", + "test 0 : tsm = 252.324, tbsm = 237.354\n", + "Reaching the end of test data. Stop tests at 220. \n", + "===> delta1 = 0.031, delta2 = 0.032\n", + "p = 0.318 +/- 0.045\n", + "Separation = 0.52 sigmas\n", + "Partial test after 10000 epochs (took 115.46 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=5\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 220.483, tbsm = 144.553\n", + "Reaching the end of test data. Stop tests at 223. \n", + "===> delta1 = 0.030, delta2 = 0.026\n", + "p = 0.291 +/- 0.040\n", + "Separation = 0.51 sigmas\n", + "Partial test after 10000 epochs (took 112.20 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=10\n", + "NSM = 2225.504 --- NBSM = 2325.162\n", + "test 0 : tsm = 209.142, tbsm = 135.445\n", + "Reaching the end of test data. Stop tests at 215. \n", + "===> delta1 = 0.022, delta2 = 0.015\n", + "p = 0.121 +/- 0.027\n", + "Separation = 1.18 sigmas\n", + "Partial test after 10000 epochs (took 110.07 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=10\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 175.645, tbsm = 172.095\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.024, delta2 = 0.017\n", + "p = 0.152 +/- 0.029\n", + "Separation = 1.18 sigmas\n", + "Partial test after 10000 epochs (took 112.53 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=20\n", + "NSM = 2225.504 --- NBSM = 2436.280\n", + "test 0 : tsm = 126.163, tbsm = -54.992\n", + "Reaching the end of test data. Stop tests at 205. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 3.02 sigmas\n", + "Partial test after 10000 epochs (took 104.60 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=20\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 224.293, tbsm = -8.009\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.009, delta2 = 0.001\n", + "p = 0.018 +/- 0.009\n", + "Separation = 2.53 sigmas\n", + "Partial test after 10000 epochs (took 125.19 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=30\n", + "NSM = 2225.504 --- NBSM = 2558.585\n", + "test 0 : tsm = 149.996, tbsm = -158.093\n", + "Reaching the end of test data. Stop tests at 195. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 4.95 sigmas\n", + "Partial test after 10000 epochs (took 122.04 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=30\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 211.642, tbsm = -55.256\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 4.18 sigmas\n", + "Partial test after 10000 epochs (took 120.90 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=40\n", + "NSM = 2225.504 --- NBSM = 2693.988\n", + "test 0 : tsm = 232.525, tbsm = -308.187\n", + "Reaching the end of test data. Stop tests at 185. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 7.63 sigmas\n", + "Partial test after 10000 epochs (took 104.52 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=40\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 101.931, tbsm = -35.163\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 5.66 sigmas\n", + "Partial test after 10000 epochs (took 123.55 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=45\n", + "NSM = 2225.504 --- NBSM = 2766.000\n", + "test 0 : tsm = 351.474, tbsm = -552.826\n", + "Reaching the end of test data. Stop tests at 180. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 9.07 sigmas\n", + "Partial test after 10000 epochs (took 105.64 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=45\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 192.725, tbsm = -176.883\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 5.92 sigmas\n", + "Partial test after 10000 epochs (took 130.77 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=50\n", + "NSM = 2225.504 --- NBSM = 2841.340\n", + "test 0 : tsm = 350.796, tbsm = -565.503\n", + "Reaching the end of test data. Stop tests at 175. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 9.77 sigmas\n", + "Partial test after 10000 epochs (took 108.47 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=50\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 196.286, tbsm = -228.843\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 7.24 sigmas\n", + "Partial test after 10000 epochs (took 128.42 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=200\n", + "NSM = 2225.504 --- NBSM = 6502.484\n", + "test 0 : tsm = 3882.812, tbsm = -11802.584\n", + "Reaching the end of test data. Stop tests at 76. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 48.14 sigmas\n", + "Partial test after 10000 epochs (took 55.83 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=200\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 107.222, tbsm = -2009.179\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 18.76 sigmas\n", + "Partial test after 10000 epochs (took 126.72 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=500\n", + "NSM = 2225.504 --- NBSM = 21984.864\n", + "test 0 : tsm = 29582.137, tbsm = -95986.469\n", + "Reaching the end of test data. Stop tests at 22. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 143.32 sigmas\n", + "Partial test after 10000 epochs (took 31.87 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=500\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 160.054, tbsm = -3408.125\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 24.59 sigmas\n", + "Partial test after 10000 epochs (took 122.87 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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jRqkOQ0REpELMmjXrO3evnWhZpBJ8o0aNmDlzZqrDEBERqRBm9nVhyyKV4MvLRx99xvr1Ze4zQyqRzMw9OPHE5qkOQ0Sk3CnBJ7B+/c/Urn18qsOQCrB27axUhyAikhS6yU5ERCSC1IIXEZFKbfv27Sxfvpxt27alOpSkqVGjBvXr16datWrFVw4pwVdCn38+h0GDrubHHzdSpUoaV1xxBx06BGM13HlnDxYsmEnVqtVo1qwld9zxDFWrVuPNN19ixIgHcXdq1sygf/+naNLkGADOPbcRe+2VQVpaGmlpVXnxRd2oKCKVx/Lly8nIyKBRo0bkH0U4GtyddevWsXz5cg455JASr6cEXwnVqLEX9977Ag0aHM7atSvp2fN4Wrc+k4yMTDp27MGf/zwSgDvu6M748c/SpcvV1Kt3CMOGTWfvvffl/fffZODAPowY8XHeNp95ZiqZmbVS9ZRERMps27ZtkU3uAGbG/vvvz9q1a0u1nhJ8CaxcuZTrrz+LrKw2zJ37AbVrH8Qjj7xKjRp7MmbME7zyytOkpVXlkEOa8sADY3jmmQGsXPk/VqxYwrfffsPNNw/ms88+4oMP3qROnYMYPPh1qlYt+WmWeA0bNsmbrl27HvvtV4cfflhLRkYmbdp0ylvWrFlLVq8ORqM85piT8sqbNz+RNWsqaiRUEZHki2pyz1WW56eb7Epo2bIvueiiaxk7dj4ZGZm8884rADz//CBeeuk/jBkzl9tvfzqv/vLlX/H00+/w6KOvcdddPcnObsfLL39G9ep7MmPGGwW2/8ILD9G9e1aBx0MPXV+gbqx58z5h+/afqV//sHzlOTnbmTjxRU46qeD4MK+++hwnnXRW3ryZce21HejZ83j++c9hpTouIiISGDhwIM2aNaNFixZkZWXx8ccf07ZtWxo0aEDsuC+dO3cmPT096fGoBV9C9eodwhFHZAFw5JHHs3LlUgAOP7wFd97Zg7ZtO9O2bee8+ieddBZVq1ajcePm/PLLjrxE27hx87x1Y/Xq1Y9evfqVKqbvvlvF3Xdfyr33jqBKlfzf1QYNuobjjjuVY489JV/5zJlTefXV53j22Rl5Zc8+O4M6dQ7i++/XcO21Z9Co0ZEcd9yppYpFRGRXMXjKF+W6vZvOaFJsnQ8//JAJEyYwe/ZsqlevznfffcfPPwf9qWRmZvL+++/Tpk0b1q9fz6pVq8o1vsIowZdQtWrV86bT0tL46adghMzHHnuD//znXd5993WGDx/ImDGfAbDHHkH9KlWqULVqtbzTK2ZV2LEjp8D2X3jhISZNKjia5bHHnkq/fk8UKN+8eSM33HA211wzkObNT8y3bNiwe/nhh7Xcfvsz+cq//HIuf/5zb5544k0yM/fPK69T5yAA9tuvDm3bns/8+Z8owYuIlMKqVauoVasW1asHn/21av16T1O3bt0YM2YMbdq04Z///CcXXHAB8+fPT3pMSTtFb2YHm9lUM1tgZvPN7IawfD8zm2JmX4Z/9y1k/cvCOl+a2WXJinNn/PLLL6xevYzs7HZcf/2DbN68ga1bN5dpW7169WPUqDkFHomS+/btP9Ov3/mcfXYvTj+9S75l48c/y0cfvcXAgaPzteq//fYb+vW7gPvuezHfNfytW3/kxx835U1//PFkDjvs6DI9BxGR3VWHDh1YtmwZTZo04ZprrmH69Ol5y9q3b8+7777Ljh07GDNmDF27dq2QmJLZgs8B/ujus80sA5hlZlOAy4G33X2QmfUH+gN/il3RzPYD7gGyAQ/Xfc3df0hivKX2yy87uOuunmzevAF3p1u368nIyEz6fqdMGcvs2e+yYcM6Jkx4HoB77nmeI47I4oEH/sABBzTkiitaA9Cu3QX8/vd387e/3ceGDet48MFrAPJ+Drdu3Wr69TsfgB07cjjzzO4Jr9uLiEjh0tPTmTVrFu+99x5Tp06la9euDBo0CAjO+rZp04YxY8awdetWKmpQNIu98J/UHZm9CgwNH23dfZWZHQhMc/cj4upeEta5Kpx/Jqw3uqh9ZGdne3kMNjNp0ix1VbubWLt2Fh076n8tUpktXLiQo446Km8+Fdfg440bN44RI0awadMmHn74YbZs2cL555/PgAEDuO6660hPT2fz5tKd8Y1/ngBmNsvdsxPVr5Br8GbWCDgW+Bio6+65dxh8C9RNsMpBwLKY+eVhWaJt9wH6ADRo0KB8AhZJpakPFL6s3W0VF4cUUFTiKEsSkOj4/PPPqVKlCocffjgAc+bMoWHDhsybNw+AU045hdtuu41LLrmkwmJK+s/kzCwdeAW40d03xi7z4PTBTp1CcPdh7p7t7tm1ayccEldERCSpNm/ezGWXXUbTpk1p0aIFCxYsYMCAAXnLzYxbbrkl3813yZbUFryZVSNI7i+5+z/D4tVmdmDMKfo1CVZdAbSNma8PTEtmrCIiEg2pOJty/PHH88EHHxQonzZtWsL6pT09XxbJvIvegOeAhe7+aMyi14Dcu+IvA15NsPpbQAcz2ze8y75DWLbL6dOnLQsW7Bp9t2/btoUbbjibCy88kosvbsaQIf3zlo0b9zRduzane/csrryyDUuWLADgo4+m0LPn8XTt2pyePY/n00/fyVunT5+2XHDBEXmd7nz/faLvYiIisitKZgv+ZOBS4DMzmxOW3Q4MAsaa2ZXA18DFAGaWDfzB3Xu7+/dm9mfg03C9+9z9+yTGGhmXXnoL2dnt2L79Z66+uj3vv/8mJ598Fh07dqdLlz8AMH36awwefDNDhkwiM7MWgwe/Tu3a9Vi8eB7XXXcmb765Im9799//Ek2bJrx/Q0REdmFJS/DuPgMorPPc9gnqzwR6x8wPB4YnJ7rSKaoveoCJE1/k/vt7k5OTw913D+foo1sya9Z0HnnkhnALxt/+9i4LF85i2LB7SE/P5KuvPuP00y+mcePmjB79OD/9tJVHHhlfoMvZ0qhRYy+ys9sBUK3aHhx55HF5fc6np++dV2/r1h/zOt458shj88oPO6wZP/20lZ9//imvox4REamc1JNdCS1b9iUDB47mzjv/Rv/+F/POO6/QqVNPIDg1PmrUHGbPfpf77ruCsWPnMXLkw9x665NkZZ3Mli2b2WOPGgB88cV/GTduIXvvvR/nnXconTv35oUXPmH06Md5+eUh/PGPj+Xb78yZU3n00ZsKxFOjxl4MH17wek+uTZvW8957r9Ot2w15ZWPHPslLLz1KTs7PPPXUOwXWefvtVzjyyOPyJfd77/0daWlp/OY3F3LllXdGfkAHEZGoUIIvocL6ogc488zgZw/HHXcqP/64kU2b1nPMMSczePDNnHVWD9q1u4C6desD0LTpCdSqdSAA9esfRqtWHYCgj/qZM6cW2G92djtGjZpToLwoOTk53HHHJXTtej316x+aV37xxddy8cXXMmnSKJ577n7uvXdE3rKvvprPkCF/4sknJ+eV3X//S9SpcxA//riJW2+9kDfeeJFzzulVqlhERCQ1lOBLqLC+6KHgMH5mxuWX96dNm7OZMWMiV155MkOHBvcIxraOzarkzRfWR31ZWvADB/bh4IMPp3v3GxMu79ChGw88cHXe/OrVy+nX73zuvfeFfJcIcvuor1kzg44duzN//idK8CIilYQSfDmYPPllsrPbMWfODNLT9yE9fR+WL/+Kxo2b07hxcxYs+JSlSxeRnl76bmxL24L/v/+7k82bN3DXXc/mK//mmy9p0CDogGHGjDfypjdtWs+NN55N376DyMo6Oa9+Tk4OmzevJzOzFjk523nvvQm0bHl6qeMXEdkdpKWl0bx5c9ydtLQ0hg4dykknncSWLVv4/e9/z9y5c3F3MjMzmTRpEunp6ZgZPXr0YOTIkUDwuXvggQfSqlUrJkyYsNMxKcGXg+rVa9C9+7Hk5Gzn7ruD+wJHjXqMmTOnUqVKFQ49tBknnXQWc+d+mNQ4Vq9ezvDhA2nU6Eh69jwOgIsv7kvnzr0ZO3Yon3zyb6pWrUZGxr4MGBCcnn/55aEsW7aYZ5+9j2efvQ+AoUMns+eeNenb90xycrbzyy87aNnydM4///dJjV9EpFwU1RtkWZSgB8k999yTOXOCxthbb73FbbfdxvTp03n88cepW7cun30WjDT6+eefU61aNQBq1qzJvHnz2Lp1K3vuuSdTpkzhoIMSdtpaJkrwJVCvXiPGjp2XN3/ppbfkTQ8bNi3hOrfeOqRAWXZ2W7Kz2yZcN35ZWdStW5+ZMxN3DHjLLY8nLO/d+056974z4bKRI2ftVDwiIrujjRs3su++wUCpq1atomHDhnnLjjgi39ArdOrUiTfeeIMuXbowevRoLrnkEt57771yiSPpXdWKiIhE3datW8nKyuLII4+kd+/e3HXXXQBcccUVPPjgg7Ru3Zo777yTL7/8Mt96uWPFb9u2jblz59KqVatyi0kJXkREZCflnqJftGgRkyZNolevXrg7WVlZLFmyhH79+vH9999zwgknsHDhwrz1WrRowdKlSxk9ejSdOnUq15h0il5ERKQctW7dmu+++461a9dSp04d0tPTueCCC7jggguoUqUKEydOzDfs629/+1tuueUWpk2bxrp168otDrXgK6mff/6J227rSufOjbnsslb5fpcf6957r+CMM+pw8cVH5yt//PF+XHjhkXTr1oJbbjmfTZvWA7B+/Tquuqodp5ySzoMP9t2pGHNyttOjx3HF1vv73x+gc+fGXHDBEXz4YeIhB1as+B+XXdaKzp0bc9ttXdm+/WcARo58lIsuakq3bi24+ur2rFr1NRD8vDC3D/3u3bM46aQaTJs2fqeej4hISSxatIgdO3aw//778/777/PDDz8A8PPPP7NgwYJ81+QhOI1/zz330Lx583KNQwm+knr11efIyNiX8eMX0737TQwZ8qeE9c4993KGDJlUoLxVqzN4+eV5jBkzlwYNmvD3vwd3nVavXoOrr/4zN9zwcIljOffcRgnL58yZwTHHnJxwWa4lSxYwefIYxo6dz5Ahkxg06Bp27NhRoN6QIX+ie/ebGD9+MRkZ+/Lqq88BQVe7L744kzFj5tK+fReeeOJW4NefF44aNYennnqHGjX24sQTO5T4OYmIlEbuNfisrCy6du3KiBEjSEtL46uvvuK0006jefPmHHvssWRnZ3PhhRfmW7d+/fpcf/315R6TTtGXwMqVS7nuuo4cddTxLFo0m0MPbcZ9971AjRp7pSym6dNfpU+fAQC0b9+Fv/61L+5eoNOd4447NWHrPjbZNW9+Im+/PQ6APfesSVZWG5YtW7zTMX7wwSROOumsIutMn/4qHTp0Y489qnPQQYdw8MGNmT//E1q0aJ1Xx9359NN3uP/+UQCcc85lDBs2gC5drs7rex/g6KNPZOLEkQX28fbb4zjppLNS+v8SkQpUgp+1lbdEDROAXr160atX4g7CEg0Z27ZtW9q2bVsuMakFX0Jff/05Xbpcw7hxC6lZc2/+8Y//K/d99O59Sr7TyrmPjz/+d4G6a9asoG7dgwGoWrUq6en7sGFD2a7dvPba8GITcVnMnDm12J/+xT4PgDp16rNmzYp8dTZsWEdGRiZVq1YttA4EZzUSPY/Jk8fkdScsIrK7UAu+hOrWPTivp7dOnXoyZswT+X4PXx6efbZ8fvtYGs89N5C0tKqcdVaPUq334IPX8t//vg/A2rUr6d496Ke/ffuLuPLKO1izZgX77LNfhbWaJ04cycKFMxk2bHq+8u++W8XixZ/RuvWZFRKHiMiuQgm+hBL1N1/eevc+hS1bNhUov+GGh2nVKn83sXXqHMTq1cuoW7d+2K3sBvbZZ/9S7e/1159nxowJPPXU26V+Pn/605N50+ee26hAd7offDCJE08MkurYsU8yfvzfAHj88YnUrl2vwPPItWbN8rw+8HPts8/+bNq0npycHKpWrVqgzscf/5vhwwcybNj0AsPcTpkylnbtzqdq1Wqlen4iIpWdEnwJffvtN8yd+yEtWrRm0qRRZGW1Kfd9lKYFf+qpv2XChBG0aNGat98exwkn/KZUSfqDDybxwgt/Zdiw6UlpZX/44SSuvvrPwK+j2CVy6qm/5c47u9Ojx82sXbuSZcu+pFmzlvnqmBnZ2e14+71L84oAABoCSURBVO1xnHlmNyZMGMFpp50HwKJF/+Evf7mKIUMmsd9+dQps/623RtO3bzl3Wykiu5xE9yBFiXviXkqLomvwJdSw4RH84x9P0qXLUWzc+ANdulxd/EpJdN55V7Jhwzo6d27MSy89St++g4DgdPn11//aWcLtt1/C737Xmq+//pxOneozfnxw9/lf/9qXLVs2ce21Z9C9exZ/+csf8tY599xGDB58MxMmPE+nTvVZsmRBqWLbsWMHy5YtplGjI4ute9hhzTj99Iu56KKmXHddR2699UnS0tIAuP76TqxduxKA6657kJdeepTOnRuzYcM6zjvvSgCeeKIfW7dupn//i+jePYubbvpt3rZXrlzK6tXLOO6400oVv4hULjVq1GDdunVlSoKVgbuzbt06atSoUar1LEoHJDs722fOnLnT25k0aRa1ax+fN79y5VJuvPGcfP3RS+HmzJnBxIkjuf32p1MdSrHWrp1Fx47HF1+xIhU1UEYK7g6WXw2e8kWhy246o0kFRiKxtm/fzvLly9m2bVuqQ0maGjVqUL9+/byBanKZ2Sx3z060jk7RS7nLymqTlEsYIiKJVKtWjUMOOSTVYexydIq+BOJHkxMREdnVKcFXcrNnv0uPHsfRqlVV/v3vcYXWe/LJOzj77IM55ZT0fOWPPHJT3u/tL7igCW3bZgLq6lVEpLLTKfpK7oADGjBgwPO8+GLRXcueeuq5dO3al/PPPzxf+R//ODhvesyYIXz++X+AX7t6Bdiw4XvOP7+xunoVEalElOBLYOvWH+nf/2LWrFnOjh076N37Ljp06MrChbMYPPhmtmzZTGZmLQYMeJ5atQ6kT5+2NGlyDLNnTycnJ4e77x7O0Ue3LH5HZVCvXiMAqlQp+mRM8+YnFrutyZNH06fPvQXK1dWriEjlowRfAh98MInatevx+ONvALB58wZycrbz0EPX8cgjr7LvvrWZPPllnnzyDu65ZzgA27ZtYdSoOcye/S733XdFqa7hl6bDm/KyatXXrFjxP0444TcFlk2ePIYePW5Oyn5FRCQ5lOBLoHHj5jz22B954ok/ccop53DssaewePE8vvpqHtdeewYQ/Pa7Vq0D89bJ7fv8uONO5ccfN7Jp03oyMjJLtL9UdFn71ltjaN++S95v0HOpq1cRkcpJCb4EGjZswsiRs3n//Yk89dSdnHBCe9q1O59DD23G3//+YcJ1dqZr21S04CdPHpOv+9lc6upVRKRyUoIvgbVrV7L33vvRqVNPMjIyGT/+WS6/vD8//LA2r/vanJztfP31Fxx2WDMAJk9+mezsdsyZM4P09H1IT9+nxPur6Bb80qWL2LTph3xDtOZSV68iIpWTEnwJLF78GY8/3o8qVapQtWo1+vd/imrV9uDBB8fx8MPXs3nzBnbsyOGSS27MS/DVq9ege/djycnZzt13D09abPPnf0q/fuezceMPvPfe6wwbdg9jx84HoHv3rLw74R9//FbeemsU27ZtoVOn+px3Xm+uumoAEJye79ChW4GzDOrqVUSk8lJXtQnEd1VbWn36tOXGGx+madOEvQfKLkRd1UppqKta2dUU1VWtOroRERGJoKSdojez4cA5wBp3Pzosexk4IqySCax396wE6y4FNgE7gJzCvp3sqoYNm5bqEEREZDeXzGvwzwNDgRdyC9y9a+60mT0CbChi/Xbu/l3SohMREYmwpCV4d3/XzBolWmbB3VwXAwV7VREREZGdlqpr8KcAq939y0KWOzDZzGaZWZ+iNmRmfcxsppnNXLt2bbkHKiIiUhml6mdylwCji1jext1XmFkdYIqZLXL3dxNVdPdhwDAI7qIvj+AyM/dg7dpZ5bEp2cVlZu6R6hBERJKiwhO8mVUFLgAK/W2Su68I/64xs38BLYGECT4ZTjyxeUXtSkREJClScYr+dGCRuy9PtNDMappZRu400AEo+UgtIiIikrwEb2ajgQ+BI8xsuZldGS7qRtzpeTOrZ2YTw9m6wAwz+y/wCfCGu09KVpwiIiJRlMy76C8ppPzyBGUrgU7h9BLgmGTFJSIisjtQT3YiIiIRpAQvIiISQUrwIiIiEaQELyIiEkFK8CIiIhGkBC8iIhJBSvAiIiIRpAQvIiISQUrwIiIiEaQELyIiEkFK8CIiIhGkBC8iIhJBSvAiIiIRpAQvIiISQUrwIiIiEaQELyIiEkFK8CIiIhFUNdUBiIjsjMFTvih02U1nNKnASER2LWrBi4iIRJASvIiISAQpwYuIiESQEryIiEgEKcGLiIhEkBK8iIhIBCnBi4iIRJASvIiISAQpwYuIiESQEryIiEgEKcGLiIhEUNISvJkNN7M1ZjYvpmyAma0wsznho1Mh63Y0s8/NbLGZ9U9WjCIiIlGVzBb880DHBOWD3T0rfEyMX2hmacCTwFlAU+ASM2uaxDhFREQiJ2kJ3t3fBb4vw6otgcXuvsTdfwbGAOeVa3AiIiIRl4pr8H3NbG54Cn/fBMsPApbFzC8Py0RERKSEKno8+KeAPwMe/n0EuGJnNmhmfYA+AA0aNNjZ+EQk19QHCl/W7raKi0NEyqRCW/Duvtrdd7j7L8DfCE7Hx1sBHBwzXz8sK2ybw9w9292za9euXb4Bi4iIVFIVmuDN7MCY2fOBeQmqfQocbmaHmNkeQDfgtYqIT0REJCqSdorezEYDbYFaZrYcuAdoa2ZZBKfolwJXhXXrAc+6eyd3zzGzvsBbQBow3N3nJytOERGRKEpagnf3SxIUP1dI3ZVAp5j5iUCBn9CJiIhIyagnOxERkQhSghcREYkgJXgREZEIUoIXERGJICV4ERGRCFKCFxERiSAleBERkQhSghcREYkgJXgREZEIUoIXERGJoIoeLlZEdmODp3xR6LKbzmhSgZGIRJ9a8CIiIhGkBC8iIhJBSvAiIiIRpAQvIiISQUrwIiIiEaQELyIiEkFK8CIiIhGkBC8iIhJBSvAiIiIRpAQvIiISQUrwIiIiEaQELyIiEkFK8CIiIhGkBC8iIhJBSvAiIiIRpAQvIiISQUrwIiIiEaQELyIiEkFK8CIiIhGUtARvZsPNbI2ZzYspe8jMFpnZXDP7l5llFrLuUjP7zMzmmNnMZMUoIiISVclswT8PdIwrmwIc7e4tgC+A24pYv527Z7l7dpLiExERiaykJXh3fxf4Pq5ssrvnhLMfAfWTtX8REZHdWSqvwV8BvFnIMgcmm9ksM+tTgTGJiIhEQtVU7NTM7gBygJcKqdLG3VeYWR1gipktCs8IJNpWH6APQIMGDZISr4iISGVT4S14M7scOAfo4e6eqI67rwj/rgH+BbQsbHvuPszds909u3bt2kmIWEREpPKp0ARvZh2BW4HfuvuWQurUNLOM3GmgAzAvUV0RERFJLJk/kxsNfAgcYWbLzexKYCiQQXDafY6ZPR3WrWdmE8NV6wIzzOy/wCfAG+4+KVlxioiIRFHSrsG7+yUJip8rpO5KoFM4vQQ4JllxiYiI7A7Uk52IiEgEKcGLiIhEUEp+JiciETb1gSIWXljoksFTvih02U1nNNmJgER2T2rBi4iIRJASvIiISAQpwYuIiESQEryIiEgEKcGLiIhEkBK8iIhIBCnBi4iIRJASvIiISAQpwYuIiESQEryIiEgEKcGLiIhEkBK8iIhIBCnBi4iIRJASvIiISAQpwYuIiESQxoMXqUyKGmu93W0VF0clp7HnZXdQqha8mdU0s7RkBSMiIiLlo8gEb2ZVzKy7mb1hZmuARcAqM1tgZg+ZWeOKCVNERERKo7gW/FTgMOA24AB3P9jd6wBtgI+AB82sZ5JjFBERkVIq7hr86e6+Pb7Q3b8HXgFeMbNqSYlMREREyqzIFnxucjezF+OX5ZYl+gIgIiIiqVXSm+yaxc6EN9odX/7hiIiISHko7ia728xsE9DCzDaGj03AGuDVColQRERESq24U/QPuHsG8JC77x0+Mtx9f3fXj25FRER2UcW14BsBFJbMLVC//MMSERGRnVHcXfQPmVkVgtPxs4C1QA2gMdAOaA/cAyxPZpAiIiJSOkUmeHe/yMyaAj2AK4ADgK3AQmAiMNDdtyU9ShERESmVYu+id/cFwP3A6wSJ/X/Ap8C44pK7mQ03szVmNi+mbD8zm2JmX4Z/9y1k3cvCOl+a2WWleVIiIiK7u5L+TG4EcBTwBDAEaAq8UIL1ngc6xpX1B95298OBt8P5fMxsP4JT/62AlsA9hX0REBERkYJKOprc0e7eNGZ+qpktKG4ld38390a9GOcBbcPpEcA04E9xdc4EpoQ95mFmUwi+KIwuYbwiIiK7tZIm+NlmdqK7fwRgZq2AmWXcZ113XxVOfwvUTVDnIGBZzPzysKwAM+sD9AFo0KBBGUMSSYJdaWjXXSkWEakQJT1FfzzwgZktNbOlwIfACWb2mZnNLevO3d0BL+v64TaGuXu2u2fXrl17ZzYlIiISGSVtwcdfR98Zq83sQHdfZWYHEvSKF28Fv57GB6hPcCpfRERESqBECd7dvy7Hfb4GXAYMCv8m6vL2LeAvMTfWdSAYslZERERKoKSn6MvEzEYTnM4/wsyWm9mVBIn9DDP7Ejg9nMfMss3sWcgbjvbPBD/H+xS4L/eGOxERESleSU/Rl4m7X1LIovYJ6s4EesfMDweGJyk0ERGRSEtqC15ERERSQwleREQkgpTgRUREIkgJXkREJIKU4EVERCJICV5ERCSClOBFREQiSAleREQkgpTgRUREIkgJXkREJIKS2lWtSGQUNp76rjSWelFjvlcCJ34zrNBlg6f0qcBIymbwlC8KXXbTGU12if0VtU5RkhG/JJ9a8CIiIhGkBC8iIhJBSvAiIiIRpAQvIiISQUrwIiIiEaQELyIiEkFK8CIiIhGkBC8iIhJBSvAiIiIRpAQvIiISQUrwIiIiEaQELyIiEkFK8CIiIhGkBC8iIhJBSvAiIiIRpPHgRXZ3ZRhHvsixyJPwqVLUWPEfNSh8rPiyjn++q6js8UtqqQUvIiISQUrwIiIiEaQELyIiEkEVnuDN7AgzmxPz2GhmN8bVaWtmG2Lq3F3RcYqIiFRmFX6Tnbt/DmQBmFkasAL4V4Kq77n7ORUZm4iISFSk+hR9e+Ard/86xXGIiIhESqoTfDdgdCHLWpvZf83sTTNrVtgGzKyPmc00s5lr165NTpQiIiKVTMoSvJntAfwW+EeCxbOBhu5+DDAEGF/Ydtx9mLtnu3t27dq1kxOsiIhIJZPKFvxZwGx3Xx2/wN03uvvmcHoiUM3MalV0gCIiIpVVKhP8JRRyet7MDjAzC6dbEsS5rgJjExERqdRS0lWtmdUEzgCuiin7A4C7Pw10Aa42sxxgK9DN3T0VsYqIiFRGKUnw7v4jsH9c2dMx00OBoRUdl4iISFSk+i56ERERSQIleBERkQjScLEiUmpFDd/6YVErNij3UMo8lGxhkjFEa5HD657RpNz3JwJqwYuIiESSEryIiEgEKcGLiIhEkBK8iIhIBCnBi4iIRJASvIiISAQpwYuIiESQEryIiEgEKcGLiIhEkBK8iIhIBCnBi4iIRJASvIiISAQpwYuIiESQEryIiEgEKcGLiIhEkMaDl93L1AdSHUFgV4kj4ooaK74wZRlDXmRXpBa8iIhIBCnBi4iIRJASvIiISAQpwYuIiESQEryIiEgEKcGLiIhEkBK8iIhIBCnBi4iIRJASvIiISAQpwYuIiESQEryIiEgEpSzBm9lSM/vMzOaY2cwEy83MnjCzxWY218yOS0WcIiIilVGqB5tp5+7fFbLsLODw8NEKeCr8KyIiIsXYlU/Rnwe84IGPgEwzOzDVQYmIiFQGqWzBOzDZzBx4xt3jx3U8CFgWM788LFsVW8nM+gB9ABo0aJC8aEUqsQ+XrEt1CJVGUUPMVpahZAdP+SLVIRSrqBhvOqNJBUYSXalswbdx9+MITsVfa2anlmUj7j7M3bPdPbt27drlG6GIiEgllbIE7+4rwr9rgH8BLeOqrAAOjpmvH5aJiIhIMVKS4M2sppll5E4DHYB5cdVeA3qFd9OfCGxw91WIiIhIsVJ1Db4u8C8zy41hlLtPMrM/ALj708BEoBOwGNgC/C5FsYqIiFQ6KUnw7r4EOCZB+dMx0w5cW5FxiYiIRMWu/DM5ERERKSMleBERkQhSghcREYkgJXgREZEIUoIXERGJICV4ERGRCFKCFxERiSAleBERkQhSghcREYkgJXgREZEISuV48CLJMfWBVEcghShqrPXKIBljxVf2sduLonHdU0steBERkQhSghcREYkgJXgREZEIUoIXERGJICV4ERGRCFKCFxERiSAleBERkQhSghcREYkgJXgREZEIUoIXERGJICV4ERGRCFKCFxERiSAleBERkQhSghcREYkgDRcru66ihn1td1vFxVGUXWho2g+XrEt1CLu1sg4lm4whaMtiV4mjOIUNXauhaQtSC15ERCSClOBFREQiSAleREQkgpTgRUREIqjCE7yZHWxmU81sgZnNN7MbEtRpa2YbzGxO+Li7ouMUERGpzFJxF30O8Ed3n21mGcAsM5vi7gvi6r3n7uekID4REZFKr8Jb8O6+yt1nh9ObgIXAQRUdh4iISJSl9Bq8mTUCjgU+TrC4tZn918zeNLNmRWyjj5nNNLOZa9euTVKkIiIilUvKEryZpQOvADe6+8a4xbOBhu5+DDAEGF/Ydtx9mLtnu3t27dq1kxewiIhIJZKSBG9m1QiS+0vu/s/45e6+0d03h9MTgWpmVquCwxQREam0UnEXvQHPAQvd/dFC6hwQ1sPMWhLEqX44RURESigVd9GfDFwKfGZmc8Ky24EGAO7+NNAFuNrMcoCtQDd39xTEKiIiUilVeIJ39xmAFVNnKDC0YiISERGJHvVkJyIiEkFK8CIiIhGk8eCloLKOw17YervK2O2VRFHjurc+dP8KjETKS1FjrSdjm7vS+O2FKfqYPFxhcUSZWvAiIiIRpAQvIiISQUrwIiIiEaQELyIiEkFK8CIiIhGkBC8iIhJBSvAiIiIRpAQvIiISQUrwIiIiEaQELyIiEkFK8CIiIhGkBC8iIhJBSvAiIiIRpAQvIiISQRoutihlHTZV8ivqOO5K2yxCUUO4FkZDu8rOKusws4WtV9ZhZMs6NO3gKV8Uvs0i9lfkeoXGUrYhZovaV1FuOqNJmbZZ1HrlTS14ERGRCFKCFxERiSAleBERkQhSghcREYkgJXgREZEIUoIXERGJICV4ERGRCFKCFxERiSAleBERkQhSghcREYkgJXgREZEISkmCN7OOZva5mS02s/4Jllc3s5fD5R+bWaOKj1JERKTyqvAEb2ZpwJPAWUBT4BIzaxpX7UrgB3dvDAwGHqzYKEVERCq3VLTgWwKL3X2Ju/8MjAHOi6tzHjAinB4HtDczq8AYRUREKrVUJPiDgGUx88vDsoR13D0H2ABo7E0REZESMnev2B2adQE6unvvcP5SoJW7942pMy+sszyc/yqs812C7fUBcgckPgL4PMlPYXdWCyjwP5Ck03FPDR33iqdjXnoN3b12ogVVKzoSYAVwcMx8/bAsUZ3lZlYV2AdYl2hj7j4MGJaEOCWOmc109+xUx7G70XFPDR33iqdjXr5ScYr+U+BwMzvEzPYAugGvxdV5DbgsnO4CvOMVfapBRESkEqvwFry755hZX+AtIA0Y7u7zzew+YKa7vwY8B7xoZouB7wm+BIiIiEgJpeIUPe4+EZgYV3Z3zPQ24KKKjkuKpUshqaHjnho67hVPx7wcVfhNdiIiIpJ86qpWREQkgpTgpVjFdS0sZWdmw81sTfjT0Nyy/cxsipl9Gf7dNyw3M3si/D/MNbPjUhd55WZmB5vZVDNbYGbzzeyGsFzHPonMrIaZfWJm/w2P+71h+SFht+SLw27K9wjL1W35TlCClyKVsGthKbvngY5xZf2Bt939cODtcB6C/8Hh4aMP8FQFxRhFOcAf3b0pcCJwbfi61rFPrp+A37j7MUAW0NHMTiTojnxw2D35DwTdlYO6Ld8pSvBSnJJ0LSxl5O7vEvxSJFZsV80jgM4x5S944CMg08wOrJhIo8XdV7n77HB6E7CQoAdNHfskCo/f5nC2Wvhw4DcE3ZJDweOubsvLSAleilOSroWlfNV191Xh9LdA3XBa/4skCE/7Hgt8jI590plZmpnNAdYAU4CvgPVht+SQ/9iq2/KdoAQvsgsLO3jST12SxMzSgVeAG919Y+wyHfvkcPcd7p5F0ItpS+DIFIcUWUrwUpySdC0s5Wt17unf8O+asFz/i3JkZtUIkvtL7v7PsFjHvoK4+3pgKtCa4JJHbr8sscc277gX1225FKQEL8UpSdfCUr5iu2q+DHg1prxXeEf3icCGmNPJUgrhddzngIXu/mjMIh37JDKz2maWGU7vCZxBcP/DVIJuyaHgcVe35WWkjm6kWGbWCXiMX7sWHpjikCLDzEYDbQlG0VoN3AOMB8YCDYCvgYvd/fswKQ0luOt+C/A7d5+ZirgrOzNrA7wHfAb8EhbfTnAdXsc+ScysBcFNc2kEDcyx7n6fmR1KcAPvfsB/gJ7u/pOZ1QBeJLhH4nugm7svSU30lY8SvIiISATpFL2IiEgEKcGLiIhEkBK8iIhIBCnBi4iIRJASvIiISAQpwYtImZlZppldk+o4RKQgJXgR2RmZgBK8yC5ICV5EdsYg4DAzm2NmD6U6GBH5lTq6EZEyC0dim+DuR6c4FBGJoxa8iIhIBCnBi4iIRJASvIjsjE1ARqqDEJGClOBFpMzcfR3wvpnN0012IrsW3WQnIiISQWrBi4iIRJASvIiISAQpwYuIiESQEryIiEgEKcGLiIhEkBK8iIhIBCnBi4iIRJASvIiISAT9P1ktc0/OJQTLAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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4I8v4a/38qUSVE6eTu0HACP857ZbLNDked+fcJqAv0WdtnS9T+g8wXM6Pe33UTz+J6MfHy0TvY9kPeqxnQMxsAvC0v0+4P8s5iigYDnPObSyUwu3d+lsQbUeLol635Myi//teBlzmnJtiZlcSVXBqU7wlK5nMbCCQ4px7objLIqVPXBqAkLiZCkzZnwX4L+hbiGpdF3lox9jfS72yn/zl4ZlEl+D7Ed2rzHolRXLgnNvrVgJFCouCOyDOuQf3Z35fWWYV0SW+Ymt9yjk3q7jWLZmcRnQ7If3Sfmfn3La8ZykZfC38O3IYNc05d14Ow0UOGLpULiIiEhBVThMREQmIgltERCQgQdzjrl69uqtXr15xF0NERKRIzJkzZ41zrkZO44II7nr16jF79uziLoaIiEiRMLNfcxsXRHAXlhkzvmX9+h35TyjBS0o6iFatmhR3MURECl2pCu7163dQo8YpxV0MKQIpKXOKuwgiInGhymkiIiIBKVVn3CIiEpadO3eybNkytm/fXtxFiYsKFSpQp04dypUrV+B5FNwlyOLF8xg69Dq2bNlImTJlueqqAXTocDEAAwdexsKFs0lIKEfjxi0YMOAFEhLK8eGHrzFixAM456hYsRL9+z9Hw4bRUxD//Od6HHJIJcqWLUvZsgm8+qoq+IlIWJYtW0alSpWoV68emZ82Gj7nHKmpqSxbtoyjjz66wPMpuEuQChUO4Z57RnLUUQ1ISVnB5ZefwmmndaRSpSTOPfcy7r13FAADBnRn/PiX6Nr1Oo444miGDfuUypWr8sUXHzJkSC9GjJiZscwXXphCUlJez7kXESm5tm/ffkCGNoCZceihh5KSkrJX85Xq4F6x4hf69j2PZs3a8M0306lRozaPPPIuFSoczJgxT/LWW89TtmwCRx/diPvvH8MLLwxixYqfWb78J37//TduueUxvv12BtOnf0jNmrV57LH3SEgo+OWOrOrWbZjRXaPGEVSrVpN161KoVCmJNm06ZYxr3LgFq1ZFT6k86aTWGcObNGnF6tVF9fRKEZGicSCGdrp92bZSXzlt6dIl/PWv1zN27AIqVUriv/99C4Dhw4fy2mv/Y8yYb7jjjuczpl+27Eeef/6/PProf7jzzstJTm7HG298S/nyB/P55x9kW/7IkQ/RvXuzbK+HHuqbZ7nmz5/Fzp07qFPn2EzD09J2MmHCq7Runf0ZIe+++zKtW+95voKZcf31Hbj88lN4++1he7VfREQkMmTIEBo3bkzTpk1p1qwZM2fOpG3bthx11FHEPu+jc+fOJCYmxr08pfqMG+CII47muOOaAXD88aewYsUvADRo0JSBAy+jbdvOtG3bOWP61q3PIyGhHPXrN2H37l0ZAVq/fpOMeWP16NGPHj367VWZ1qxZyV13XcE994ygTJnMv62GDu1N8+ZncvLJZ2QaPnv2FN5992VeeunzjGEvvfQ5NWvWZu3a1Vx//TnUq3c8zZufuVdlEREpSR6b/H2hLu/mcxrmOf7LL7/k/fffZ+7cuZQvX541a9awY0fUHkhSUhJffPEFbdq0Yf369axcubJQy5abUh/c5cqVz+guW7Ysf/wRPdXw8cc/4H//+4zPPnuPV14Zwpgx3wJw0EHR9GXKlCEhoVzGZQ6zMuzalZZt+SNHPsTEia9lG37yyWfSr9+T2YZv3ryRG288n969h9CkSatM44YNu4d161K4444XMg1fsuQb7r33Gp588kOSkg7NGF6zZm0AqlWrSdu2XViwYJaCW0RkL6xcuZLq1atTvnz03V+9+p46Q5dccgljxoyhTZs2vP3221x44YUsWLAg7mUq9cGdk927d7Nq1VKSk9vRrFkbJk0aw7Ztm/dpWXtzxr1z5w769evC+ef34Oyzu2YaN378S8yY8RHPPvtJprPw33//jX79LmTw4Fcz3SPftm0Lu3fvpmLFSmzbtoWZMydxzTV37dM2iIiUVh06dGDw4ME0bNiQs88+m4svvpizzjoLgPbt23Pttdeya9cuxowZw7Bhw7j33nvjXiYFdw52797FnXdezubNG3DOccklfalUKSnu6508eSxz537Ghg2pvP/+cADuvns4xx3XjPvv/weHHVaXq646DYB27S7k2mvv4sUXB7NhQyoPPNAbIOPfvlJTV9GvXxcAdu1Ko2PH7jneFxcRkdwlJiYyZ84cpk2bxpQpU7j44osZOnQoEF2lbdOmDWPGjGHbtm0U1cOwLPbGekmVnJzsCuMhIxMnzlGTp6VESsoczj1Xx1okdIsWLeKEE07I6C/qe9xZjRs3jhEjRrBp0yYefvhhtm7dSpcuXRg0aBB9+vQhMTGRzZv37gpt1m0EMLM5zrnknKYv9bXKRUREcrN48WKWLFkCwKqN2/li5mxqHF6bHbt2k7r5DxqedCo33Hwrl156aZGVScEtIiKSi82bN9OzZ08aNWpEu9an8v13i7j19oEZ482M3n1vzlRpLd50j1tERIKxt5e299cpp5zC9OnTgeiMO907H0zKcfq9vUy+L3TGnYtevdqycGHJaNt7+/at3Hjj+Vx00fF069aYp57qnzFu7tzPuOyy5rRsmcDHH4/LNN+TT/6Lbt1OpFu3E5k06Y2M4bNmfcJllzWne/dmXH11G5Yu/SHTfJ988hbJyVZitl9ERPZQcAfiiitu5a23vuO11/7H119/wRdffAjAYYcdxaBBw+nYsXum6T///AO++24uo0fPY8SImYwa9TCbN28EYOjQ67jvvtcYPXoe557bnZdfvi9jvi1bNjFmzBOceGLLots4EREpsFId3CtW/ELXridw333X0q1bY66/vgPbt2/LGD9hwqt0796Mbt1OZP78WQDMmfNpTNOlJ7NlyyZmz55Kr15nccstF3DBBcfw1FP9+fDD1+jRowUXX9yEZct+3K9yVqhwCMnJ7QAoV+4gjj++eUab5EccUY8GDZpma2Htp58W0rz5mSQkJHDwwRWpX78pX3450Y81tmyJQnzz5g3UqHFExnzPP38nPXv+i4MOqrBfZRYRkfgo1cENubdVDtEl6tGj59G//7MMHnwVAKNGPcxttz3D6NHzeOmlaZQvfzAA33//NXfc8TxvvrmICRNe5bffvmfkyFl07nwNb7zxVLb1zp49Jcc2zK+6qnW2aWNt2rSeadPe49RT2+c5XcOGJzF9+kS2b9/K+vVrmDNnCqtWLQXgzjtf4sYbO9GpUx0mTHiVnj2jS+/ffTeX339fSps25xd8B4qISJEq9ZXTcmurHKBjx6h6f/PmZ7Jly0Y2bVrPSSedzmOP3cJ5511Gu3YXUqtWHQAaNTqV6tUPB6BOnWNp2bIDELVhPnv2lGzrTU5ux+jR8/aqrGlpaQwYcCkXX9yXOnWOyXPaVq06sGDBV1x1VWuSkmrQpMlplClTFoDRox/jiScmcOKJLRk58iEee+wWBgwYxqOP3sKgQcP3qkwiIlK0Sn1w59ZWOWR/3JqZceWV/WnT5nw+/3wCV199Ok8//RGwpw3zaLoyGf25tWE+e/YUHn305mzDK1Q4hFdemZ5jWYcM6cWRRzage/ebCrRtV189gKuvHgBEz/A+6qiGrFuXwvfff51xD7tDh4vp0+dctm7dxI8/zufvf28LQGrq79xyy1949NH/0KhRjm0AiIhIMSj1wZ2XSZPeIDm5HfPmfU5iYhUSE6uwbNmP1K/fhPr1m7Bw4Vf88st3JCbufXOoe3vG/eyzA9m8eQN33vlSgabftWsXmzatJynpUJYs+YYlS77hnnuiqwCbN2/g11+/p27dhsyYMZl69U4gMbEKn3yyJmP+Xr3actNNDyu0RaTUK1u2LE2aNGHnrt2ULVOWfz/8KKe2PI2tW7dya9/eLFwwn7IWPS1s4sSJJCYmYmZcdtlljBo1CoiumB5++OG0bNmS999/f7/Ko+DOQ/nyFeje/WTS0nZy112vADB69OPMnj2FMmXKcMwxjWnd+jy++ebLuJZj1aplvPLKEOrVO57LL28OQLduN9C58zUsWPAV/fp1YePGdUyb9h7Dht3N2LELSEvbybXXRo/+rFixMvfeO4qEhOhwDxz4IrfddhFlypShUqWqGdsmIlLiTbm/cJfX7vZ8Jzn44IOZN28eqzZuZ8rHkxlyz12MnzCZl557hho1azL1pdnUqlyBxYsXU65cOQAqVqzI/Pnz2bZtGwcffDCTJ0+mdu3ahVJktVUuByS1VS5yYMjWjncxBHd6++OrNm7nP++8xdtvjmH46DcZcNst1DnyKK7rcxO1KlfINk/fvn1p3rw5Xbt2pUePHjRu3Jhp06ZlO+NWW+UiIiKFaNu2bTRr1ow2ySfxz769ublfFPaXXt6Tpx9/lPPPPouBAwdmtGmeLv153du3b+ebb76hZcvCaR9DwS0iIpKH9Evln8/+mtffepc+/7gG5xwnNj2JWV8vpHffm1m7di2nnnoqixYtypivadOm/PLLL7z++ut06tSp0Mqje9wiIiIFlNyiFWtTU1mzJoUaNWpSMTGR8//Smasuv4QyZcowYcKETJe9//KXv3DrrbcydepUUlNTC6UMOuMuYXbs+IPbb7+Yzp3r07Nny0z/Vx5r+vSJXHjhcXTuXJ/hw4dmDF++/Gd69mxJ5871uf32i9m5c8deLbcgPvpoDC+/PCTPaTZsWEvv3ufQpUsDevc+h40b1+U43fvvj6BLlwZ06dKA998fkTF80aI5XHxxEzp3rs9DD/UlvS5GQZcrIhIPS75fzO5du6hW7VBmzZjO+nXRd9COHTtYuHAhdevWzTT9VVddxd13302TJk0KrQwK7hLm3XdfplKlqowf/wPdu9/MU0/9K9s0u3bt4oEHrufJJz/kzTcX8tFHr/PTTwsBeOqpf9G9+82MH/8DlSpV5d13Xy7wcmO9995wXnhhUI7jpk//kNatz81z/uHDh9KiRXveeWcJLVq0z/TjIt2GDWt58cV7GD58JiNGzOLFF+/JCOL777+OgQNf5J13lrB06RKmT59Y4OWKiBSm9Hvc7du05O9/u4Innn+RsmXL8svPP9Pl/A60PS2Zk08+meTkZC666KJM89apU4e+ffsWanlK9aXyFSt+oU+fcznhhFP47ru5HHNMYwYPHkmFCocUW5k+/fRdevUaBED79l158MEbcM5lagxmwYJZHHlk/YzW0zp0uIRPP32Xo48+ga+++i/33TcagD/9qSfDhg2ia9frCrTcgnDO8f338zj++Ob5bsewYVMzytGrV1v69n0g0zRffvkRLVqcQ5Uq1QBo0eIcpk+fSHJyW7Zs2UiTJq0A6NSpB1Onjuf0088r0HJF5ABWgFrghW3Xrl1A5sd6AnS79DK6XXoZQLZa5Tk93rNt27a0bdt2v8tT6s+4f/11MV279mbcuEVUrFiZN998ttDXcc01Z+TYLvnMmR9nm3b16uXUqnUkAAkJCSQmVmHDhtRcpwGoWbMOq1cvZ8OGVCpVSsr4f+304QVdbkEsXvw/GjQ4Kd/AX7t2VUYTsIceehhr167KNk1KSubtqFWrDikpy31Z62QbXtDliogcyEr1GTdArVpH0qzZ6QB06nQ5Y8Y8yRVX3Fqo63jppWmFurx4Wb8+ld69o4eXbNiwlrS0HXz66XgABg9+lfr1mzB9+kRatz5vr5ZrZnt9Zl+cyxURKclKfXDn1B55YbvmmjPYunVTtuE33vgwLVuenWlYzZq1WbVqKbVq1SEtLY3NmzdQpcqhOU6TbvXqZdSsWZsqVQ5l06b1pKWlkZCQkDG8oMtNSjo0oxnW994bzooVv/D3vw/KNM2MGZN48MHoCWo33NCRtWtXccIJydmaYq1WrRZr1qykevXDWbNmJVWr1sy2/TVq1GbOnKkZ/atWLeOUU9r6si7LNLxGjdoFXq6IyIGs1F8q//333zKaLJ04cTTNmrUp9HW89NI0Ro+el+2VNbQBzjzzLxm1qz/5ZBynnvp/2X5MNGp0KkuXLmH58p/ZuXMHkyaN4cwz/4KZkZzcjk8+GQdENbbPOuuCAi83P5s3b2DXrjSSkqLAf/rpjxg9el6O7aefddae9cWWI9Zpp3Vk5sxJbNy4jo0b1zFz5iROO60j1asfTsWKlfn22xk455gwYaMRIOIAABeBSURBVGTG/AVZrogcWEJo4XNf7cu2lfrgrlv3ON588xm6dj2BjRvX0bXrdcVangsuuJoNG1Lp3Lk+r732KDfcENWaTklZQd++0T/wJyQk0K/f0/Tp05GuXU/g7LO7ceyxjQHo0+cBXnvtUTp3rs+GDalccMHVeS53b8yYMZkWLbL/2MhJz579mTlzMl26NGDWrI+58sromd8LF87m3nuvAaBKlWpcffWd9OhxKj16nMo119yVUVGtf/9nuffea+jcuT61ax/L6aefl+dyReTAVKFCBVJTUw/I8HbOkZqaSoUKFfKfOEapbqt8xYpfuOmmPzF27Pz9XnZpEAXpNRm1vUsytVUucmDYuXMny5YtY/v27flPHGcbt+3MdVzlg8vt0zIrVKhAnTp1Mh5Oki6vtspL/T1uKbiCPlJURKSwlCtXjqOPPrq4iwHAY5O/z3Xczec0LLJyxO1SuZkdaWZTzGyhmS0wsxv98GpmNtnMlvi/VeNVhvwccUQ9nW2LiEhQ4nmPOw34p3OuEdAKuN7MGgH9gU+ccw2AT3y/ZDF37mdcdllzWrZM4OOPx2Ubv3nzRjp1qsMDD9yQMaxPn3O59NKT6NatMf/+9z8yGg2INXXqu1xySVO6d2/GFVckM2/e5wAsXjyPv/3tNLp1a8wllzRl0qQ3MubJrRlVEREpenELbufcSufcXN+9CVgE1AYuANIbpR4BdI5XGUJ22GFHMWjQcDp27J7j+Oefv5OTTz4z07D77x/L669/zRtvzGfduhQ+/vjNbPO1aNGe11//mtGj53HXXa9kVBSrUOEQ7rlnJGPHLuCppybyyCM3sWnTeiD3ZlRFRKToFUmtcjOrB5wMzARqOedW+lG/A7VymaeXmc02s9kpKSlxKde2bVu48cbz/VnqiRlnmYsWzaFXr7O4/PJTuOGGjqxZExW3V6+2PPzwjXTv3oxu3U5k/vxZcSkXRJfxGzRoSpky2Q/RokVzSE1dRatWHTINT0ysDMCuXWmkpe3I8d+9DjkkMWP4tm1bMrrr1m3IUUc1AKBGjSOoVq0m69al4Jzjq6/+S/v2XYGomdGpU8cX3oaKiMheiXvlNDNLBN4CbnLObYwNE+ecM7Mcq7U754YBwyCqVR6Psk2fPpEaNY7giSc+AKL/U05L28lDD/XhkUfepWrVGkya9AbPPDOAu+9+BYDt27cyevQ85s79jMGDr9qre+R70xBLbnbv3s1jj/2Te+8dxaxZ2ZtMveGGjixYMIvWrc/LCNuspkx5h6efvp1161bz+OMfZBs/f/4sdu7cQZ06x+bZjKqIiBS9uAa3mZUjCu3XnHNv+8GrzOxw59xKMzscWB3PMuSlfv0mPP74P3nyyX9xxhl/4uSTz+CHH+bz44/zuf76c4Cocfn0trEBOna8FIDmzc9ky5aNbNq0nkqVkgq0vsJo+vTNN5/l9NM7ZWrLO9bTT3/EH39sZ+DAy/jqq//SqtU52aZp164L7dp1Ye7cz3j++Tt59tk9PwDWrFnJXXddwT33jMjxbF9ERIpX3ILbolPrl4FFzrlHY0b9B+gJDPV/341XGfJTt25DRo2ayxdfTOC55wZy6qntadeuC8cc05j/9/++zHGe/WkitTDOuL/99kv+979pjBv3LFu3biYtbQeHHJJInz57GlQpX74CZ511AZ9++m6OwZ2uefMzWb78J9avX0NSUnU2b97IjTeeT+/eQzL+VzuvZlRFRKToxfOM+3TgCuBbM5vnh91BFNhjzexq4FegWxzLkKeUlBVUrlyNTp0up1KlJMaPf4krr+zPunUpfPPNlzRtehppaTv59dfvM1ommzTpDZKT2zFv3uckJlYhMbFKgddXGGfc9933Wkb3e+8NZ+HC2fTpM5StWzezdesmqlc/nLS0NL744gOaNTsj2/xLl/5AnTrHYmZ8991cduz4gypVDmXnzh3069eF88/vwdln77nEHtuMaseOl6iZURGRYha34HbOfQ7kdjraPl7r3Rs//PAtTzzRjzJlypCQUI7+/Z+jXLmDeOCBcTz8cN+MtrkvvfSmjOAuX74C3bufTFraTu6665W4lW3Bgq/o168LGzeuY9q09xg27G7Gjl2Q6/Tbtm3hllv+wo4df7B7926Sk9tx0UX/AGDcuOcB6Nr1H3zyyVtMmDCShIRylC9/MPff/wZmxuTJY5k79zM2bEjl/feHA3D33cM57rhm9OnzAHfccQnPPTeQ4447OaMZVRERKXqlusnTvdWrV1tuuulhGjXKsRU6KUHU5KmIFLaibDktryZPVftIREQkIGqrfC8MGza1uIsgIiKlnM64RUREAqLgFhERCYiCW0REJCCl6h53UtJBpKTMKe5iSBFISjqouIsgIhIXpSq4W7VqUtxFEBER2S+6VC4iIhIQBbeIiEhAFNwiIiIBUXCLiIgERMEtIiISEAW3iIhIQBTcIiIiAVFwi4iIBETBLSIiEhAFt4iISEAU3CIiIgFRcIuIiAREwS0iIhIQBbeIiEhAFNwiIiIBUXCLiIgERMEtIiISEAW3iIhIQBTcIiIiAVFwi4iIBETBLSIiEhAFt4iISEAU3CIiIgFRcIuIiAREwS0iIhIQBbeIiEhAFNwiIiIBUXCLiIgERMEtIiISEAW3iIhIQBTcIiIiAVFwi4iIBETBLSIiEhAFt4iISEAU3CIiIgFRcIuIiAREwS0iIhIQBbeIiEhAFNwiIiIBUXCLiIgERMEtIiISEAW3iIhIQBTcIiIiAUko7gIUiyn35z6u3e1FVw4REZG9pDNuERGRgCi4RUREAqLgFhERCYiCW0REJCAKbhERkYAouEVERAKi4BYREQmIgltERCQgCm4REZGAKLhFREQCErfgNrNXzGy1mc2PGTbIzJab2Tz/6hSv9YuIiByI4nnGPRw4N4fhjznnmvnXhDiuX0RE5IATt+B2zn0GrI3X8kVEREqj4rjHfYOZfeMvpVfNbSIz62Vms81sdkpKSlGWT0REpMQq6uB+DjgWaAasBB7JbULn3DDnXLJzLrlGjRpFVT4REZESrUiD2zm3yjm3yzm3G3gRaFGU6xcREQldkQa3mR0e09sFmJ/btCIiIpJdQrwWbGavA22B6ma2DLgbaGtmzQAH/AL8PV7rFxERORDFLbidc5fmMPjleK1PRESkNFDLaSIiIgFRcIuIiAREwS0iIhIQBbeIiEhAFNwiIiIBUXCLiIgERMEtIiISEAW3iIhIQBTcIiIiAVFwi4iIBETBLSIiEhAFt4iISEAU3CIiIgFRcIuIiAREwS0iIhIQBbeIiEhAFNwiIiIBUXCLiIgERMEtIiISEAW3iIhIQBTcIiIiAVFwi4iIBETBLSIiEhAFt4iISEAU3CIiIgFRcIuIiAREwS0iIhIQBbeIiEhAFNwiIiIBUXCLiIgERMEtIiISEAW3iIhIQBTcIiIiAVFwi4iIBETBLSIiEhAFt4iISED2KrjNrKKZlY1XYURERCRveQa3mZUxs+5m9oGZrQa+A1aa2UIze8jM6hdNMUVERATyP+OeAhwL3A4c5pw70jlXE2gDzAAeMLPL41xGERER8RLyGX+2c25n1oHOubXAW8BbZlYuLiUTERGRbPI8404PbTN7Neu49GE5BbuIiIjER0ErpzWO7fEV1E4p/OKIiIhIXvKrnHa7mW0CmprZRv/aBKwG3i2SEoqIiEiG/C6V3++cqwQ85Jyr7F+VnHOHOuduL6IyioiIiJffGXc9gNxC2iJ1Cr9YIiIikpP8apU/ZGZliC6LzwFSgApAfaAd0B64G1gWz0KKiIhIJM/gds791cwaAZcBVwGHAduARcAEYIhzbnvcSykiIiJAAWqVO+cWAvcB7xEF9s/AV8A4hbaIiEjRyu9SeboRwEbgSd/fHRgJdItHoURERCRnBQ3uE51zjWL6p5jZwngUSERERHJX0AZY5ppZq/QeM2sJzI5PkURERCQ3BT3jPgWYbma/+f6jgMVm9i3gnHNN41I6ERERyaSgwX1uXEshIiIiBVKg4HbO/RrvgoiIiEj+CnqPW0REREoABbeIiEhAFNwiIiIBUXCLiIgERMEtIiISEAW3iIhIQBTcIiIiAYlbcJvZK2a22szmxwyrZmaTzWyJ/1s1XusXERE5EMXzjHs42Vtc6w984pxrAHzi+0VERKSA4hbczrnPgLVZBl9A9IhQ/N/O8Vq/iIjIgaio73HXcs6t9N2/A7WKeP0iIiJBK7bKac45B7jcxptZLzObbWazU1JSirBkIiIiJVdRB/cqMzscwP9dnduEzrlhzrlk51xyjRo1iqyAIiIiJVlRB/d/gJ6+uyfwbhGvX0REJGjx/Hew14EvgePMbJmZXQ0MBc4xsyXA2b5fRERECqhAz+PeF865S3MZ1T5e6xQRETnQqeU0ERGRgCi4RUREAqLgFhERCYiCW0REJCAKbhERkYAouEVERAKi4BYREQmIgltERCQgCm4REZGAKLhFREQCouAWEREJiIJbREQkIApuERGRgCi4RUREAqLgFhERCYiCW0REJCAKbhERkYAouEVERAKi4BYREQmIgltERCQgCm4REZGAKLhFREQCouAWEREJiIJbREQkIApuERGRgCi4RUREAqLgFhERCYiCW0REJCAKbhERkYAouEVERAKi4BYREQmIgltERCQgCm4REZGAKLhFREQCouAWEREJiIJbREQkIApuERGRgCi4RUREAqLgFhERCYiCW0REJCAKbhERkYAouEVERAKi4BYREQmIgltERCQgCm4REZGAKLhFREQCouAWEREJiIJbREQkIApuERGRgCi4RUREAqLgFhERCUhCcRegxJlyf+7j2t1edOUQERHJgc64RUREAqLgFhERCYiCW0REJCAKbhERkYAouEVERAKi4BYREQmIgltERCQgCm4REZGAKLhFREQCouAWEREJSLE0eWpmvwCbgF1AmnMuuTjKISIiEpribKu8nXNuTTGuX0REJDi6VC4iIhKQ4gpuB0wyszlm1iunCcysl5nNNrPZKSkpRVw8ERGRkqm4gruNc645cB5wvZmdmXUC59ww51yycy65Ro0aRV9CERGREqhYgts5t9z/XQ28A7QojnKIiIiEpsiD28wqmlml9G6gAzC/qMshIiISouKoVV4LeMfM0tc/2jk3sRjKISIiEpwiD27n3E/ASUW9XhERkQOB/h1MREQkIApuERGRgCi4RUREAqLgFhERCYiCW0REJCAKbhERkYAouEVERAKi4BYREQmIgltERCQgCm4REZGAKLhFREQCouAWEREJiIJbREQkIApuERGRgCi4RUREAqLgFhERCYiCW0REJCAKbhERkYAouEVERAKi4BYREQmIgltERCQgCm4REZGAKLhFREQCouAWEREJiIJbREQkIApuERGRgCi4RUREAqLgFhERCYiCW0REJCAKbhERkYAouEVERAKi4BYREQmIgltERCQgCm4REZGAKLhFREQCouAWEREJiIJbREQkIApuERGRgCi4RUREAqLgFhERCYiCW0REJCAKbhERkYAouEVERAKi4BYREQmIgltERCQgCm4REZGAKLhFREQCouAWEREJiIJbREQkIApuERGRgCi4RUREAqLgFhERCYiCW0REJCAKbhERkYAouEVERAKi4BYREQmIgltERCQgCm4REZGAKLhFREQCouAWEREJiIJbREQkIMUS3GZ2rpktNrMfzKx/cZRBREQkREUe3GZWFngGOA9oBFxqZo2KuhwiIiIhKo4z7hbAD865n5xzO4AxwAXFUA4REZHgFEdw1waWxvQv88NEREQkHwnFXYDcmFkvoJfv3Wxmiws4a3VgTXxKdUd8Flu44rj9QSjN21+atx20/dr+Ytz+Wwp/kXVzG1Ecwb0cODKmv44flolzbhgwbG8XbmaznXPJ+168sGn7S+/2l+ZtB22/tr/0bH9xXCr/CmhgZkeb2UHAJcB/iqEcIiIiwSnyM27nXJqZ3QB8BJQFXnHOLSjqcoiIiISoWO5xO+cmABPitPi9vrx+gNH2l16ledtB26/tLyXMOVfcZRAREZECUpOnIiIiASmRwW1mfzWzBWa228ySs4y73TeVutjMOsYMz7EZVV8JbqYf/oavEIeZlff9P/jx9fJbR3Ews2ZmNsPM5pnZbDNr4YebmT3py/mNmTWPmaenmS3xr54xw08xs2/9PE+amfnh1cxssp9+splVLfotzZ2Z9TGz7/x74sGY4XF/L5QUZvZPM3NmVt33l4rjb2YP+WP/jZm9Y2ZJMeNKzfHPT27bHBozO9LMppjZQv95v9EPz/E9Wpifg6A450rcCzgBOA6YCiTHDG8EfA2UB44GfiSq4FbWdx8DHOSnaeTnGQtc4rufB67z3b2B5333JcAbea2jGPfFJOA8390JmBrT/SFgQCtgph9eDfjJ/63qu6v6cbP8tObnTV/ug0B/390feKC43wMx298O+Bgo7/trFtV7oaS8iP598iPgV6B6KTv+HYAE3/1AetlK0/EvwD7KdZtDewGHA819dyXge3+sc3yPFubnIKRXiTzjds4tcs7l1ODKBcAY59wfzrmfgR+ImlDNsRlV/0vq/4Bxfv4RQOeYZY3w3eOA9n763NZRXBxQ2XdXAVb47guAkS4yA0gys8OBjsBk59xa59w6YDJwrh9X2Tk3w0Xv3pHkvC9i91FJcB0w1Dn3B4BzbrUfXhTvhZLiMeA2ovdCulJx/J1zk5xzab53BlG7D1C6jn9+DphmpJ1zK51zc333JmARUcuaub1HC/NzEIwSGdx5yK251NyGHwqsj/ngxzavmjGPH7/BT1/SmmS9CXjIzJYCDwO3++F7uy9q++6swwFqOedW+u7fgVqFuQH7qSFwhr+E+amZneqHF8V7odiZ2QXAcufc11lGlZbjH+sqojMkKCXHv4BK2ndWofC3LE4GZpL7e7QwPwfBKLYmT83sY+CwHEYNcM69W9TlKU557QugPXCzc+4tM+sGvAycHa+yOOecmRXpvxrks/0JRJe7WgGnAmPN7JgiLF7c5bP9dxBdLi4SJe34p38XmNkAIA14rSjLJsXDzBKBt4CbnHMbYy+AFMd7tKQptuB2zu1L+OTVXGpOw1OJLp0k+F/SsdOnL2uZmSUQXYZOzWcdcZHXvjCzkcCNvvdN4CXfnVs5lwNtswyf6ofXyWF6gFVmdrhzbqW/lLSaIpTP9l8HvO0va80ys91EbRIXxXuhSOS2/WbWhOj+7df+i6sOMNeiCoql4vgDmNmVwJ+A9v59AAfQ8S8ERf6dFU9mVo4otF9zzr3tB+f2Hi3Mz0E4ivsme14vsldOa0zmCik/EVXMSPDdR7OnckZjP8+bZK6Q0tt3X0/mCilj81pHMe6DRUBb390emOO7zydzpYxZfng14GeiChlVfXc1Py5rpYxOfvhDZK748WBxH/uY7f8HMNh3NyS6/GVF8V4oaS/gF/ZUTistx/9cYCFQI8vwUnf889hHuW5zaC//3hwJPJ5leI7v0cL8HIT0KvYC5HLwuhDde/gDWAV8FDNuAFENysXE1AYkql34vR83IGb4Mf5A/eA/uOm1kyv4/h/8+GPyW0cx7Ys2wBz/YZwJnOKHG/CML+e3ZP6Bc5Xfrh+Av8UMTwbm+3meZk8DPIcCnwBLiGpwVyvu90BMmQ8CRvlyzwX+ryjfCyXpRebgLi3H/weiH2vz/Ov50nr889lPOW5zaC//feeAb2KOeafc3qOF+TkI6aWW00RERAISWq1yERGRUk3BLSIiEhAFt4iISEAU3CIiIgFRcIuIiAREwS0i2ZhZkpn1Lu5yiEh2Cm4RyUkS0VOzRKSEUXCLSE6GAsda9Bz4h4q7MCKyhxpgEZFs/JOZ3nfOnVjMRRGRLHTGLSIiEhAFt4iISEAU3CKSk01ApeIuhIhkp+AWkWycc6nAF2Y2X5XTREoWVU4TEREJiM64RUREAqLgFhERCYiCW0REJCAKbhERkYAouEVERAKi4BYREQmIgltERCQgCm4REZGA/H9YYH17dG1w0wAAAABJRU5ErkJggg==\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Wilson coefficient value (*10^-2 TeV^-2)\n", + "gphivals = [35e-1, 5, 10, 20, 30, 40, 45, 50, 200, 500]\n", + "gphival_names = ['35e-1', '5', '10', '20', '30', '40', '45', '50', '200', '500']\n", + "seps, ps = [], []\n", + "seps_alt, ps_alt = [], []\n", + "\n", + "\n", + "for (gphival, gphival_name) in zip(gphivals, gphival_names):\n", + " print('Evaluating with knowledge on the Cross Section, gphi=%s'%(gphival_name))\n", + " result = TestEstimator(gphival, gphival_name, title_message=', default batch')\n", + " seps.append(result[0])\n", + " ps.append(result[1])\n", + " \n", + " print('Evaluating without knowledge on the Cross Section, gphi=%s'%(gphival_name))\n", + " result = TestEstimator(gphival, gphival_name, withXS=False, title_message=', default batch')\n", + " seps_alt.append(result[0])\n", + " ps_alt.append(result[1])\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from madminer import ParameterizedRatioEstimator\n", + "from madminer.ml.morphing_aware import MorphingAwareRatioEstimator\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl1000-bigbatch')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Evaluating with knowledge on the Cross Section, gphi=35e-1\n", + "NSM = 2225.504 --- NBSM = 2259.447\n", + "test 0 : tsm = 181.793, tbsm = 200.071\n", + "Reaching the end of test data. Stop tests at 221. \n", + "===> delta1 = 0.025, delta2 = 0.021\n", + "p = 0.167 +/- 0.033\n", + "Separation = 0.79 sigmas\n", + "Partial test after 10000 epochs (took 128.04 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=35e-1\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 213.493, tbsm = 178.102\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.027, delta2 = 0.020\n", + "p = 0.210 +/- 0.034\n", + "Separation = 0.92 sigmas\n", + "Partial test after 10000 epochs (took 123.57 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=5\n", + "NSM = 2225.504 --- NBSM = 2273.448\n", + "test 0 : tsm = 204.754, tbsm = 188.696\n", + "Reaching the end of test data. Stop tests at 219. \n", + "===> delta1 = 0.023, delta2 = 0.014\n", + "p = 0.128 +/- 0.027\n", + "Separation = 1.21 sigmas\n", + "Partial test after 10000 epochs (took 112.96 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=5\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 215.616, tbsm = 234.638\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.020, delta2 = 0.015\n", + "p = 0.098 +/- 0.025\n", + "Separation = 1.29 sigmas\n", + "Partial test after 10000 epochs (took 113.46 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=10\n", + "NSM = 2225.504 --- NBSM = 2325.162\n", + "test 0 : tsm = 202.056, tbsm = 153.170\n", + "Reaching the end of test data. Stop tests at 215. \n", + "===> delta1 = 0.007, delta2 = 0.001\n", + "p = 0.009 +/- 0.007\n", + "Separation = 2.38 sigmas\n", + "Partial test after 10000 epochs (took 108.51 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=10\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 210.665, tbsm = 161.543\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.004, delta2 = 0.001\n", + "p = 0.004 +/- 0.005\n", + "Separation = 2.62 sigmas\n", + "Partial test after 10000 epochs (took 113.15 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=20\n", + "NSM = 2225.504 --- NBSM = 2436.280\n", + "test 0 : tsm = 234.183, tbsm = 67.486\n", + "Reaching the end of test data. Stop tests at 205. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 5.11 sigmas\n", + "Partial test after 10000 epochs (took 104.83 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=20\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 180.409, tbsm = 30.736\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 4.21 sigmas\n", + "Partial test after 10000 epochs (took 115.27 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=30\n", + "NSM = 2225.504 --- NBSM = 2558.585\n", + "test 0 : tsm = 252.017, tbsm = -30.511\n", + "Reaching the end of test data. Stop tests at 195. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 8.11 sigmas\n", + "Partial test after 10000 epochs (took 104.16 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=30\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 196.560, tbsm = 17.463\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 6.16 sigmas\n", + "Partial test after 10000 epochs (took 114.76 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=40\n", + "NSM = 2225.504 --- NBSM = 2693.988\n", + "test 0 : tsm = 319.119, tbsm = -215.204\n", + "Reaching the end of test data. Stop tests at 185. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 10.04 sigmas\n", + "Partial test after 10000 epochs (took 124.20 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=40\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 182.985, tbsm = -138.055\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 8.69 sigmas\n", + "Partial test after 10000 epochs (took 164.78 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=45\n", + "NSM = 2225.504 --- NBSM = 2766.000\n", + "test 0 : tsm = 322.241, tbsm = -362.823\n", + "Reaching the end of test data. Stop tests at 180. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 12.39 sigmas\n", + "Partial test after 10000 epochs (took 140.28 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=45\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 238.189, tbsm = -230.119\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 8.73 sigmas\n", + "Partial test after 10000 epochs (took 163.62 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=50\n", + "NSM = 2225.504 --- NBSM = 2841.340\n", + "test 0 : tsm = 431.843, tbsm = -499.350\n", + "Reaching the end of test data. Stop tests at 175. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 13.68 sigmas\n", + "Partial test after 10000 epochs (took 136.57 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=50\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 206.605, tbsm = -264.084\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 9.57 sigmas\n", + "Partial test after 10000 epochs (took 164.80 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=200\n", + "NSM = 2225.504 --- NBSM = 6502.484\n", + "test 0 : tsm = 3970.342, tbsm = -10333.533\n", + "Reaching the end of test data. Stop tests at 76. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 59.00 sigmas\n", + "Partial test after 10000 epochs (took 76.74 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=200\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 188.822, tbsm = -1817.358\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 21.65 sigmas\n", + "Partial test after 10000 epochs (took 165.19 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=500\n", + "NSM = 2225.504 --- NBSM = 21984.864\n", + "test 0 : tsm = 29323.523, tbsm = -89337.828\n", + "Reaching the end of test data. Stop tests at 22. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 134.13 sigmas\n", + "Partial test after 10000 epochs (took 42.07 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=500\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 266.228, tbsm = -3075.048\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 29.37 sigmas\n", + "Partial test after 10000 epochs (took 166.99 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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QKJ8kaOluIjgbQ7hfdiF43dYS3JzVJW5/LRV330Dwgd+DoJX4C3A/QVLIMRVY5e6LYp4b8EUJiijqPX6GYAjU/wu39XrcurcTtBDXAHcR7KM5ijvGYj1BcG/AR2GdneCGyestuBN9LUGi/srMNhLsL28QvN8QtIqn5dtqXqMJvqR8T3A6Ot+d4uH71BW4PCzzQoIvTr/HLxujwP0qfN/6EhzLawg+j3LukscLHh724XD5iQQ3Wz5HcJxIDA3rmUJmNh543N2363fPZpZBkBh2DVsbSRW2kB939+K+mUuSWPDzqMXABe4+2cwuIbhRrX1qI4s2M2tO8IuFGu6endpo/mRmzwKvuPt7Cdj2DOApd/93eW9byiYhHUZIiU0h+HlJmYUf0DcSfFtPetKOsb2nemU7WfD73xkELeB+BC3Oolp5Ukm4e7l1hRteJlhA0IK+gOAehcrSQVSloMSdQu7+z+KXKlx449kygtNxp5RLUGXg7p+lqmzJox3B6dCc075d3H1T0askj5l9TcyNljH+6u4vFDC9PMveWMisU3NOT0uufQlOV+9McFq9W3gvgFQQOlUuIiISIbo5TUREJEKUuEVERCIkEte4GzZs6M2bN091GCIiIkkxa9aslWEveflEInE3b96cmTNnpjoMERGRpDCzQrvXjUTiLi/Tp3/F2rVl7gdCIiQ9fQeOOqp1qsMQESl3VSpxr127hUaNDkt1GJIEK1bMSnUIIiIJoZvTREREIqRKtbhFRCRatm7dyuLFi9m8eXOqQ0mIWrVq0bRpU2rUqFH8wiEl7gpkwYLZ3Hfflfz663qqVavOZZfdykkndQfgttsuYO7cmaSl1eDAA4/g1lufJi2tBu+++wIjRtyPu7PzznXo3/9JWrU6GIAzzmjOTjvVoXr16lSvnsbzz+sGPxGJlsWLF1OnTh2aN29O3tFDo8/dWbVqFYsXL6ZFixYlXk+JuwKpVWsn7rprJBkZ+7BixRIuvPAw2rU7mTp10jnllAu4555RANx6a0/Gjn2Wbt2uZPfdW5CVNZVddqnHJ5+8y6BBfRgxYkbuNp9+ejLp6UWNWy8iUnFt3ry5UiZtADOjQYMGrFixolTrVenEvWTJD/Tteypt27bnyy+n0ajRHjz00JvUqrUjY8YM5bXXnqJ69TRatDiAwYPH8PTTA1my5H/8/PP3/PLLT9x44xC++mo606a9S+PGezBkyNukpZX8dEe8Zs1a5T5u1Gh36tdvzJo1K6hTJ5327TvnzjvwwCNYtiwY1fLgg4/Ond669VEsX56s0S5FRJKjMibtHGWpW5W/OW3Rom8499yrefnlr6lTJ50PPngNgOHD7+OFF/7LmDFfcsstT+Uuv3jxdzz11Ac8/PBb3H77hWRmduSll76iZs0d+fjjd/Jtf+TIB+jZs22+vwce6FtkXHPmfMbWrVto2nTvPNOzs7cyfvzzHH10/jFF3nzzOY4++tTc52bG1VefxIUXHsbrr2eV6nUREZHAoEGDOPDAA2nTpg1t27ZlxowZdOjQgYyMDGLH++jSpQu1a9dOeDxVusUNsPvuLdh337YA7LffYSxZ8gMA++zThttuu4AOHbrQoUOX3OWPPvpU0tJq0LJla/74Y1tuAm3ZsnXuurF69epHr179ShXTypVLueOOi7jrrhFUq5b3u9V9913FoYcexyGHHJtn+syZk3nzzed49tmPc6c9++zHNG68B6tXL+fqq0+kefP9OPTQ40oVi4hIRTJk0sJy3d4NJ7Yqcv6nn37KuHHj+OKLL6hZsyYrV65ky5agP5D09HQ++eQT2rdvz9q1a1m6NDmDqFX5xF2jRs3cx9WrV+f334NREB955B3++98P+fDDtxk2bBBjxnwFwA47BMtXq1aNtLQauac5zKqxbVt2vu2PHPkAEybkH7HwkEOOo1+/ofmmb9y4nuuuO42rrhpE69ZH5ZmXlXUXa9as4JZbns4z/ZtvvuSee3ozdOi7pKc3yJ3euPEeANSv35gOHc7m668/U+IWESmFpUuX0rBhQ2rWDD77Gzb8856hHj16MGbMGNq3b8/rr79O165d+frrrxMeU8JOlZvZMDNbbmZz4qZfa2bzzexrM9uu8agT5Y8//mDZskVkZnakb9/72bhxHZs2FTacb9F69erH6NGz8/0VlLS3bt1Cv35nc9ppvTjhhG555o0d+yzTp7/HoEEv5mmF//LLT/Tr15W7734+zzXyTZt+5ddfN+Q+njFjInvvfVCZ6iAiUlWddNJJLFq0iFatWnHVVVcxderU3HmdOnXiww8/ZNu2bYwZM4bu3bsnJaZEtriHA48DI3MmmFlH4CzgYHf/3cwaJ7D8Mvvjj23cfvuFbNy4DnenR4++1KmTnvByJ016mS+++JB161YxbtxwAO68czj77tuWwYP/xq67NuOyy9oB0LFjV6644g6eeeZu1q1bxf33XwWQ+7OvVauW0a/f2QBs25bNySf3LPC6uIiIFK527drMmjWLjz76iMmTJ9O9e3fuu+8+IDhL2759e8aMGcOmTZtI1mBYFnthvdw3btYcGOfuB4XPXway3P0/pdlOZmaml8cgIxMmzFKXp1XEihWzOOUUvdciUTdv3jz233//3OfJvsYd79VXX2XEiBFs2LCBBx98kN9++42zzz6bgQMHcu2111K7dm02bizdGdr4OgKY2Sx3zyxo+WRf424FHGtmg4DNwE3u/nlBC5pZH6APQEZGRvIiFMkxeXDh8zoOSF4cItupqGRX2sRV1SxYsIBq1aqxzz77ADB79myaNWvGnDnBVeBjjz2WAQMGcP755yctpmQn7jSgPnAUcDjwspnt5QU0+909C8iCoMWd1ChFRESAjRs3cu2117J27VrS0tJo2bIlWVlZdOsW3IdkZtx0001JjSnZiXsx8HqYqD8zsz+AhkDpuo0REZEqKdlnCA477DCmTZuWb/qUKVMKXL60p8nLItkdsIwFOgKYWStgB2BlkmMokT59OjB3bsXo23vz5t+47rrTOOec/TjvvAN57LH+ufNGjXqYc889gB492nDllZ1YujQYe33Bgtlcemk7zjvvQHr0aMPEiS/lrjNw4CWceWaL3M5gFiyYnfQ6iYhI2SSsxW1mLwIdgIZmthi4ExgGDAt/IrYFuLig0+SS30UX3URmZke2bt3ClVd24pNP3uWYY05lv/0OoVu3mdSqtROvvvokQ4fezODBLxXZ7zlA374P5PvJmYiIVHwJS9zuXtiV+gsTVWZpFdVXOcD48c9z7729yc7O5o47hnHQQUcwa9ZUHnrounALxjPPfMi8ebPIyrqT2rXT+e67rzjhhPNo2bI1L774KL//vomHHhqbr+vS0qhVaycyMzsCUKPGDuy336G5fZLnTAc46KCjGD8+GIikqH7PRUQkutRXeSF9lUNwinr06Nn07/8Ed999GQCjRj3IzTf/i9GjZ/Pssx9Rs2aQ5Bcu/D9uueUpXnllHuPHP89PPy1k5MjP6NKlNy+99Fi+cmfOnFxgH+aXXXZ0vmVjbdiwlo8+epvDD++Ub158X+U5Cur3/IknbqVHjzY89NANbNnye8leLBERSbkq3+VpYX2VA5x8cnDS4NBDj+PXX9ezYcNaDj74GIYMuZFTT72Ajh270qRJUwAOOOBwGjbcDYCmTffmyCNPAoI+zGfOnJyv3MzMjoweXbpry9nZ2dx66/l0796Xpk33yjNv/PhRzJs3k6ysqXmmF9Tv+TXXDKZBg13ZunVLOAzo/VxxxR2likVERFKjyifuwvoqh/zDrZkZl1zSn/btT+Pjj8dz+eXH8Pjj7wF/9mEeLFct93lhfZjPnDmZhx++Id/0WrV2Ytiw/HcwAgwa1Ic999yHnj2vzzN9xoz/MGzYILKypuaJo7B+z3O+YOywQ03OOONSRo16sMDyRESk4qnyibsoEye+RGZmR2bP/pjatetSu3ZdFi/+jpYtW9OyZWvmzv2cH36YT+3apb9uXNoW9xNP3MbGjeu4/fZn80yfP/+//OMff+WxxyZQv/6fPcgW1e/5ypVLadhwN9ydqVPHqg9zEZEiVK9endatW+PuVK9enccff5yjjz6a3377jSuuuIIvv/wSdyc9PZ0JEyZQu3ZtzIwLLriAUaOC+46ys7PZbbfdOPLIIxk3btx2xaPEXYSaNWvRs+chZGdv5Y47hgEwevQjzJw5mWrVqrHXXgdy9NGn8uWXnyY0jmXLFjNs2CCaN9+PCy88FIDzzruGLl16M3RoPzZt2kj//ucC0KRJBkOGvFVkv+e33XYBa9aswN3Zd9+2DBjwVGFFi4hULEX1aFgWJegFcccdd2T27KCh9d577zFgwACmTp3Ko48+SpMmTfjqq2D0yAULFlCjRg0Adt55Z+bMmcOmTZvYcccdmTRpEnvssUe5hFylE/fuuzfn5Zf/HLzsoov+7P0mK2tKgevcfHP+G80yMzuQmdmhwHXj55VFkyZNmTmz4F/NPfFEwd2+d+58IZ07F3wD/1NPfbBd8YiIVFXr16+nXr16QDDkZ7NmzXLn7bvvvnmW7dy5M++88w7dunXjxRdf5Pzzz+ejjz7a7hiq/F3lIiIiRdm0aRNt27Zlv/32o3fv3tx+++0AXHbZZdx///20a9eO2267jW+++SbPejnjdW/evJkvv/ySI488slziUeIWEREpQs6p8vnz5zNhwgR69eqFu9O2bVu+//57+vXrx+rVqzn88MOZN29e7npt2rThhx9+4MUXX6Rz587lFk+VPlUuIiJSGu3atWPlypWsWLGCxo0bU7t2bbp27UrXrl2pVq0a48ePzzNE55lnnslNN93ElClTWLVqVbnEoBZ3BbNly+8MGNCdLl1acvHFR+b5XXmsadMm0LXrvnTp0pLhw+/Lnf7zz//j4ouPpEuXlgwY0J2tW7fkWe/9918jM9O2qx/2994bw3PPDSpymXXrVnPVVSdy9tn7cNVVJ7J+/ZoClxs3bgRnn70PZ5+9D+PGjcidPm/eLLp3b02XLi154IG+5PSM+/TTAzn11D1yO6z5+OPxZa6HiEhpzZ8/n23bttGgQQM++eQT1qwJPtu2bNnC3Llz81zzhuB0+p133knr1q3LLQYl7grmzTefo06deowd+y09e97AY4/9v3zLbNu2jfvvv5qhQ9/llVfm8t57L/L993MBeOyx/0fPnjcwduy31KlTjzfffC53vV9/3cCYMY9y0EHFX2d5++3hPP30wALnTZv2LkcffUqR6w8ffh9HHNGJN974hiOO6JTny0WOdetW88wzdzF8+AxGjPiMZ565KzfBDx58Jbfd9gxvvPENixZ9w7RpE3LX69nzBkaPns3o0bNp3778Tj+JiBQk5xp327Zt6d69OyNGjKB69ep89913HH/88bRu3ZpDDjmEzMxMzjnnnDzrNm3alL59+5ZrPFX6VPmSJT9w7bWnsP/+hzF//hfstdeB3H33SGrV2illMU2d+iZ9+gwEoFOnbvzzn9fg7nk6g/n668/Yc8+Wub2nnXRSD6ZOfZMWLfbn888/4N57RwNw+ukXk5U1kG7drgTgqadu5+KL/x8jRz5Q5vjcnYULZ7PffocWW4+cu+tPP/1i+vTpQN++9+dZ5tNP3+OII06kbt36ABxxxIlMmzaBzMwO/Prr+txOYzp37sWUKWM55pj83bmKSBVTgp9vlbdt27YVOL1Xr1706tWrwHkFDe/ZoUMHOnTosN3xVPkW948/LqBbt6t49dV57LzzLrzyyhPlXkbv3scW2C/5jBn5f8q1fPnPNGmyJwBpaWnUrl2XdetWFboMQOPGTVm+/GfWrVtFnTrppKWl5ZkOMH/+F/zyyyLatz9tu+qyYMF/2Wefg/P1Khdv9epluT20NWiwK6tXL8u3zIoVeevRpElTVqz4Oaxf03zTc7z88uP06NGGu+66rNBT8CIilVWVbnEDNGmyJ23bHgMEv30eM2Zont9zl4dnn93+3+1tjz/++IOHH76RgQOHF7nc2rWruOqqYPCSdetWk529halTxwJw993P07Jla6ZNm1DgQCZFMbNiE31Jdet2Jb17346Z8eSTtzNkyN+5885h5bJtEZEoqPKJu6D+yMtb797H8ttvG/JNv5PnADgAABpgSURBVO66BznyyBPyTGvceA+WLVtEkyZNyc7OZuPGddSt26DAZXIsX76Yxo33oG7dBmzYsJbs7GzS0tJyp//22wa++24Of/1rBwBWrfqFG288k4cffosDDsjM3U56eoPcbljffns4S5b8wF//OjBP2dOnT+Sf/wxGULvmmpNZvXoZ+++fma8r1vr1m+R2rbpy5VLq1WtMvEaN9mDWrCm5z5ctW8xhh3UI67c4z/RGjYIehxo0aJI7/eyzr+D660/Pt10RkcqsyifuX375iS+//JQ2bdoxYcJo2rZtX+5llKbFfdxxZzJu3AjatGnH+++/yuGH/yXfl4kDDjicRYu+4eef/0fjxnswceIY7r13NGZGZmZH3n//VU4+uQfjxo3g+OPPonbturz//src9fv06cD11z+YJ2mXxMaN69i2LZv09OCLRM4AKwU5/vigHpdc0j83jnjt2p3ME0/cknu6e8aMiVxzzWDq1q3PzjvvwldfTeegg45k/PiRnHfetcCf/awDTJ78hvpZF6kC4u/zqUxyfjFTGlX+GnezZvvyyiv/olu3/Vm/fk3ujVypctZZl7Nu3Sq6dGnJCy88zDXXBHdjr1ixhL59gzuo09LS6Nfvca699mS6ddufE044j733PhCAa6+9nxdeeJguXVqybt0qzjrr8nKLbfr0SRxxxAnFLwhcfHF/ZsyYxNln78Nnn/2HSy7pD8DcuTO5557eANStW5/LL7+dXr0Op1evw+nd+47cG9X693+Ce+7pTZcuLdljj71zb0x79NGb6d69NT16tGHmzMn8/e9Dyq1+IlLx1KpVi1WrVpUpwVV07s6qVauoVatWqdazKLwYmZmZPnNm2X93nGPChFk0anRY7vMlS37g+utPz9NfuRQuSKS98wwRWlGtWDGLU045rPgFi1LUYAYpuLNVpKyGTFpY6LwbTmyVxEhKb+vWrSxevJjNmzenOpSEqFWrFk2bNs0dnCSHmc1y9wJPi1b5U+VScvHXsUVEEq1GjRq0aNEi1WFUKFX6VHn86GAiIiIVXZVO3BXZqFEPc+65B9CjRxuuvLITS5f+WOByW7duYdCgPnTt2opzztmP999/rdj1C+tmVEREKj6dKq+g9tvvELp1m0mtWjvx6qtPMnTozQwe/FK+5YYNG0S9eo15/fWF/PHHH6xfv7rI9XO6GR05ciZmxkUXHcZxx53JLrvUS3YVRUSkDKp0i3vTpl+57rrTOP/8gznvvIOYODFIjPPmzaJPn+O58MLDuOaak1m5cikQ/IzqwQevo2fPtpx33kHMmfNZwmLLzOyY2/XqQQcdled3zbHeemsYl14a3ChVrVo10tMbFrl+bDeju+xSL7ebURERiYYq3eKeNm0CjRrtzqOPvgMEv1POzt7KAw9cy0MPvUm9eo2YOPEl/vWvW3N759q8+TdGj57NF198yN13X1aqa+Sl6Ygl1ptvPldgb2UbNqwF4Mknb2fWrCk0bbo3N9/8eJ5OSuLXL6ybURERiYYqnbhbtmzNI4/8naFD/x/HHns6hxxyLN9+O4fvvpvD1VefCASdy+d0+AFw8snnA3Doocfx66/r2bBhLXXqpJeovLJ0fTp+/CjmzZtJVtbUfPO2bctm2bLFtGlzNDfe+DCjRj3MI4/cxD33PF+i9UVEJHqqdOJu1qwVo0Z9wSefjOfJJ2/j8MM70bHj2ey114H8+9+fFrjO9nSRWtoW94wZ/2HYsEFkZU1lhx1q5ptft24DatXaib/8pSsAJ5xwLm+99VyR6xfWzaiIiERDlU7cK1YsYZdd6tO584XUqZPO2LHPcskl/VmzZkVuN6jZ2Vv58ceFuT2TTZz4EpmZHZk9+2Nq165L7dp1S1xeaVrc8+f/l3/846889tgE6tfP3883BF8ajj32DGbNmsLhh/+Fzz9/nxYtDihy/cK6GRURkWio0on722+/4tFH+1GtWjXS0mrQv/+T1KixA/ff/yoPPtg3t2/u88+/Pjdx16xZi549DyE7eyt33JG4UamGDu3Hpk0b6d//XACaNMlgyJC3AOjZs23uYCB9+97PHXdcxEMPXU+9eo24885/F7l+bDejQJ5uRkVEpOKr0l2ellZZB+eQ5FOXpyJ/inKXp1VVUV2eVumfg4mIiERNwhK3mQ0zs+Vmlu/3Umb2dzNzM2uYqPITIStrilrbIiKSUolscQ8HTomfaGZ7AicBPyWwbBERkUopYYnb3T8EVhcwawhwM1DxL66LiIhUMEm9q9zMzgJ+dvf/K+73z2bWB+gDkJGRkYToRMpJYTe16Ya2lIvCTVpRiFFSK2mJ28x2Am4hOE1eLHfPArIguKu8PGJIT9+BFStmlcempIJLT98h1SGIiCREMlvcewMtgJzWdlPgCzM7wt1/SUYARx3VOhnFiIiIJEzSEre7fwXkduFlZj8Ame6+MlkxiIiIRF0ifw72IvApsK+ZLTazyxNVloiISFWRsBa3u59fzPzmiSpbRESkslLPaSIiIhGixC0iIhIhStwiIiIRosQtIiISIUrcIiIiEaLELSIiEiFK3CIiIhGixC0iIhIhStwiIiIRosQtIiISIUrcIiIiEaLELSIiEiFK3CIiIhGStPG4RaQIkwcXPq/jgPJfT8rFkEkLC513w4mtkhiJVCVqcYuIiESIEreIiEiEKHGLiIhEiBK3iIhIhChxi4iIRIgSt4iISIQocYuIiESIEreIiEiEKHGLiIhEiBK3iIhIhChxi4iIRIgSt4iISIQocYuIiESIEreIiEiEKHGLiIhESMISt5kNM7PlZjYnZtoDZjbfzL40szfMLD1R5YuIiFRGiWxxDwdOiZs2CTjI3dsAC4EBCSxfRESk0klY4nb3D4HVcdMmunt2+HQ60DRR5YuIiFRGqbzGfRnwbgrLFxERiZy0VBRqZrcC2cALRSzTB+gDkJGRkaTIJLImDy58XscirsgUtV5FEYUYJZ8hkxYWOu+GE1slMRKpbJLe4jazS4DTgQvc3Qtbzt2z3D3T3TMbNWqUtPhEREQqsqS2uM3sFOBm4Hh3/y2ZZYuIiFQGifw52IvAp8C+ZrbYzC4HHgfqAJPMbLaZPZWo8kVERCqjhLW43f38AiY/l6jyREREqgL1nCYiIhIhStwiIiIRosQtIiISIUrcIiIiEaLELSIiEiFK3CIiIhGixC0iIhIhStwiIiIRosQtIiISIUrcIiIiEaLELSIiEiFK3CIiIhGixC0iIhIhStwiIiIRkrBhPUVEEmXIpIWFzrvhxFblvs2qKBGvsZQPtbhFREQiRIlbREQkQpS4RUREIkSJW0REJEKUuEVERCJEiVtERCRClLhFREQiRIlbREQkQpS4RUREIkSJW0REJEKUuEVERCJEiVtERCRClLhFREQiRIlbREQkQpS4RUREIiRhidvMhpnZcjObEzOtvplNMrNvwv/1ElW+iIhIZZTIFvdw4JS4af2B9919H+D98LmIiIiUUMISt7t/CKyOm3wWMCJ8PALokqjyRUREKqNkX+Nu4u5Lw8e/AE2SXL6IiEikpaWqYHd3M/PC5ptZH6APQEZGRtLikhSbPDjVESROZa6bRNaQSQtTHYKUUrJb3MvMbDeA8P/ywhZ09yx3z3T3zEaNGiUtQBERkYos2Yn7LeDi8PHFwJtJLl9ERCTSEvlzsBeBT4F9zWyxmV0O3AecaGbfACeEz0VERKSEEnaN293PL2RWp0SVKSIiUtmp5zQREZEIUeIWERGJECVuERGRCFHiFhERiRAlbhERkQhR4hYREYkQJW4REZEIUeIWERGJECVuERGRCFHiFhERiRAlbhERkQhR4hYREYkQJW4REZEIUeIWERGJkIQN6ylSpMmDUx2BFKao96bjgFJvbsikhYXOu+HEVmVar6zliVQGanGLiIhEiBK3iIhIhChxi4iIRIgSt4iISISUKnGb2c5mVj1RwYiIiEjRikzcZlbNzHqa2TtmthyYDyw1s7lm9oCZtUxOmCIiIgLFt7gnA3sDA4Bd3X1Pd28MtAemA/eb2YUJjlFERERCxf2O+wR33xo/0d1XA68Br5lZjYREJiIiIvkU2eLOSdpm9nz8vJxpBSV2ERERSYyS3px2YOyT8Aa1w8o/HBERESlKcTenDTCzDUAbM1sf/m0AlgNvJiVCERERyVXcqfLB7l4HeMDddwn/6rh7A3cvfafFIiIisl2Ka3E3BygsSVugafmHJSIiIgUp7q7yB8ysGsFp8VnACqAW0BLoCHQC7gQWJzJIERERCRSZuN39XDM7ALgAuAzYFdgEzAPGA4PcfXPCoxQRERGgBHeVu/tc4F7gbYKE/T/gc+DVsiZtM7vBzL42szlm9qKZ1SrLdkRERKqakv4cbASwPzAUeAw4ABhZlgLNbA+gL5Dp7gcB1YEeZdmWiIhIVVPcNe4cB7n7ATHPJ5vZ3O0sd0cz2wrsBCzZjm2JiIhUGSVN3F+Y2VHuPh3AzI4EZpalQHf/2cweBH4iuF4+0d0nxi9nZn2APgAZGRllKUokcSYPTnUEqVFYvTvq16EiyVLSU+WHAdPM7Acz+wH4FDjczL4ysy9LU6CZ1QPOAloAuwM7FzRQibtnuXumu2c2atSoNEWIiIhUWiVtcZ9SjmWeAPzP3VcAmNnrwNHAqHIsQ0REpFIqUeJ29x/LscyfgKPMbCeCU+WdKONpdxERkaqmpKfKy427zwBeBb4AvgpjyEp2HCIiIlFU0lPl5crd7yTocU1ERERKIektbhERESk7JW4REZEIUeIWERGJECVuERGRCFHiFhERiRAlbhERkQhR4hYREYkQJW4REZEIUeIWERGJECVuERGRCFHiFhERiRAlbhERkQhR4hYREYkQJW4REZEIScmwniISTZ9+v6rA6e06JjmQiBsyaWEktikVk1rcIiIiEaLELSIiEiFK3CIiIhGixC0iIhIhStwiIiIRosQtIiISIUrcIiIiEaLELSIiEiFK3CIiIhGixC0iIhIhStwiIiIRosQtIiISIUrcIiIiEaLELSIiEiFK3CIiIhGSksRtZulm9qqZzTezeWbWLhVxiIiIRE1aisp9FJjg7t3MbAdgpxTFISIiEilJT9xmVhc4DrgEwN23AFuSHYeIiEgUpaLF3QJYAfzbzA4GZgHXufuvsQuZWR+gD0BGRkbSg5QSmjy48HkdByQvjqIUFWNlFoH3ZsikhakOIVKi8HoVFeMNJ7ZKYiSVVyqucacBhwJPuvshwK9A//iF3D3L3TPdPbNRo0bJjlFERKRCSkXiXgwsdvcZ4fNXCRK5iIiIFCPpidvdfwEWmdm+4aROwNxkxyEiIhJFqbqr/FrghfCO8u+BS1MUh4iISKSkJHG7+2wgMxVli4iIRJl6ThMREYkQJW4REZEIUeIWERGJECVuERGRCFHiFhERiRAlbhERkQhR4hYREYkQJW4REZEIUeIWERGJECVuERGRCFHiFhERiRAlbhERkQhR4hYREYkQJW4REZEISdV43JIokwcXPq/jgOTFIVXKkEkLUx2CRFxR+9ANJ7ZKYiQVn1rcIiIiEaLELSIiEiFK3CIiIhGixC0iIhIhStwiIiIRosQtIiISIUrcIiIiEaLELSIiEiFK3CIiIhGixC0iIhIhStwiIiIRosQtIiISIUrcIiIiEaLELSIiEiFK3CIiIhGSssRtZtXN7L9mNi5VMYiIiERNKlvc1wHzUli+iIhI5KQkcZtZU+A04NlUlC8iIhJVaSkq9xHgZqBOYQuYWR+gD0BGRkaSwqrkJg8ufF7HAWVbr6zliUiVM2TSwlSHUCkkvcVtZqcDy919VlHLuXuWu2e6e2ajRo2SFJ2IiEjFlopT5ccAZ5rZD8AY4C9mNioFcYiIiERO0hO3uw9w96bu3hzoAXzg7hcmOw4REZEo0u+4RUREIiRVN6cB4O5TgCmpjEFERCRK1OIWERGJECVuERGRCFHiFhERiRAlbhERkQhR4hYREYkQJW4REZEIUeIWERGJECVuERGRCFHiFhERiRAlbhERkQhR4hYREYkQJW4REZEIUeIWERGJECVuERGRCDF3T3UMxcrMzPSZM2emOozkmzw4eWV1HFAx4pCU+/T7VeW6vekZfcp1e8U56qesQucVFUth6yU7fimdG05sleoQEsLMZrl7ZkHz1OIWERGJECVuERGRCFHiFhERiRAlbhERkQhR4hYREYkQJW4REZEIUeIWERGJECVuERGRCFHiFhERiRAlbhERkQhR4hYREYkQJW4REZEIUeIWERGJECVuERGRCFHiFhERiZCkJ24z29PMJpvZXDP72syuS3YMIiIiUZWWgjKzgb+7+xdmVgeYZWaT3H1uCmIRERGJlKS3uN19qbt/ET7eAMwD9kh2HCIiIlGUihZ3LjNrDhwCzChgXh+gD0BGRkZS40qqyYNTHUGgosRRiX36/apC57Xbq0GF2WYyHfVTVpnWm57RJ6nlScU1ZNLCQufdcGKrJEaSPCm7Oc3MagOvAde7+/r4+e6e5e6Z7p7ZqFGj5AcoIiJSAaUkcZtZDYKk/YK7v56KGERERKIoFXeVG/AcMM/dH052+SIiIlGWihb3McBFwF/MbHb41zkFcYiIiERO0m9Oc/ePAUt2uSIiIpWBek4TERGJECVuERGRCFHiFhERiRAlbhERkQhR4hYREYkQJW4REZEIUeIWERGJECVuERGRCFHiFhERiRAlbhERkQhR4hYREYkQJW4REZEIUeIWERGJkKSPDlYhTB5c+LyOA8p/PZFifPr9qkLntdurQblvM5mO+ikrEtssi6LimJ7RJ4mRSEGGTFpY4PQbTmxV6nWKU9Q2y5ta3CIiIhGixC0iIhIhStwiIiIRosQtIiISIUrcIiIiEaLELSIiEiFK3CIiIhGixC0iIhIhStwiIiIRosQtIiISIUrcIiIiEaLELSIiEiFK3CIiIhGixC0iIhIhStwiIiIRkpLEbWanmNkCM/vWzPqnIgYREZEoSnriNrPqwL+AU4EDgPPN7IBkxyEiIhJFqWhxHwF86+7fu/sWYAxwVgriEBERiZxUJO49gEUxzxeH00RERKQY5u7JLdCsG3CKu/cOn18EHOnu18Qt1wfoEz7dF1hQDsU3BFaWw3YqgspUF6hc9alMdYHKVZ/KVBeoXPWpTHWB7a9PM3dvVNCMtO3YaFn9DOwZ87xpOC0Pd88CssqzYDOb6e6Z5bnNVKlMdYHKVZ/KVBeoXPWpTHWBylWfylQXSGx9UnGq/HNgHzNrYWY7AD2At1IQh4iISOQkvcXt7tlmdg3wHlAdGObuXyc7DhERkShKxaly3H08MD4FRZfrqfcUq0x1gcpVn8pUF6hc9alMdYHKVZ/KVBdIYH2SfnOaiIiIlJ26PBUREYmQSpm4zWxfM5sd87fezK43s4Fm9nPM9M6pjrUwZjbMzJab2ZyYafXNbJKZfRP+rxdONzMbGnYh+6WZHZq6yPMrpC4PmNn8MN43zCw9nN7czDbFvEdPpS7yghVSn0L3LTMbEL43C8zs5NREXbBC6vJSTD1+MLPZ4fQK/d6Y2Z5mNtnM5prZ12Z2XTg9qsdNYfWJ3LFTRF2ietwUVp/kHDvuXqn/CG6A+wVoBgwEbkp1TCWM+zjgUGBOzLR/Av3Dx/2B+8PHnYF3AQOOAmakOv4S1OUkIC18fH9MXZrHLlcR/wqpT4H7FkG3vv8H1ARaAN8B1VNdh6LqEjf/IeCOKLw3wG7AoeHjOsDC8PWP6nFTWH0id+wUUZeoHjcF1idumYQdO5WyxR2nE/Cdu/+Y6kBKw90/BFbHTT4LGBE+HgF0iZk+0gPTgXQz2y05kRavoLq4+0R3zw6fTif4PX8kFPLeFOYsYIy7/+7u/wO+Jej2t0Ioqi5mZsB5wItJDaqM3H2pu38RPt4AzCPolTGqx02B9YnisVPEe1OYin7cFFmfRB87VSFx9yDvi3dNeIppWM4pswhp4u5Lw8e/AE3Cx1HvRvYygpZPjhZm9l8zm2pmx6YqqDIoaN+K8ntzLLDM3b+JmRaJ98bMmgOHADOoBMdNXH1iRe7YKaAukT5uCnlvEnrsVOrEbUEHL2cCr4STngT2BtoCSwlOZUSSB+dfIv+TADO7FcgGXggnLQUy3P0Q4EZgtJntkqr4SqHS7Fsxzifvl95IvDdmVht4Dbje3dfHzovicVNYfaJ47BRQl0gfN0Xsawk9dip14iYYOvQLd18G4O7L3H2bu/8BPEMFOvVSQstyTuWF/5eH00vUjWxFY2aXAKcDF4QfqISnxlaFj2cRXNtqlbIgS6iIfSuq700a0BV4KWdaFN4bM6tB8EH6gru/Hk6O7HFTSH0ieewUVJcoHzdFvDcJP3Yqe+LO860n7vrV2cCcfGtUbG8BF4ePLwbejJneK7xL9ihgXcypwQrJzE4BbgbOdPffYqY3smDMdsxsL2Af4PvURFlyRexbbwE9zKymmbUgqM9nyY6vDE4A5rv74pwJFf29Ca8rPgfMc/eHY2ZF8rgprD5RPHaKqEskj5si9jVIxrFTXne5VbQ/YGdgFVA3ZtrzwFfAlwQ7xm6pjrOI+F8kOL2yleD6zuVAA+B94BvgP0D9cFkD/kXwLe4rIDPV8ZegLt8SXMOaHf49FS57DvB1OO0L4IxUx1/C+hS6bwG3hu/NAuDUVMdfXF3C6cOBv8UtW6HfG6A9wWnwL2P2q84RPm4Kq0/kjp0i6hLV46bA+oTzEn7sqOc0ERGRCKnsp8pFREQqFSVuERGRCFHiFhERiRAlbhERkQhR4hYREYkQJW4RycfM0s3sqlTHISL5KXGLSEHSASVukQpIiVtECnIfsHc4dvADqQ5GRP6kDlhEJJ9wxKNx7n5QikMRkThqcYuIiESIEreIiEiEKHGLSEE2AHVSHYSI5KfELSL5eDB28CdmNkc3p4lULLo5TUREJELU4hYREYkQJW4REZEIUeIWERGJECVuERGRCFHiFhERiRAlbhERkQhR4hYREYkQJW4REZEI+f9fZ8x0iT3VuQAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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X+XxJULu6Jiz/J8HFOCsJ9v0O3Ubo7isJTofcQ/Cj7CCCH3CbYyabDuwbrnMI0N3dC56KKG09m939p9wXwemWreF7CH70TSC4QHFuuP5zt3vDgnUuImgtuoEgGSwC+hMev8LTRLOAL2K+Fx8BP7j7ilKWvY2g5ao1wcVXK4GnCFqlILho7Idw3CQKf8euJvhOriG4c2NczLgy/Y/DlosRBOe9vwnj2khwYeW9ZtaY4LN3KfA1wUVmo4F73T23xW0Pwu9jCV4luGBvNkGiHlHE/ijL56io5fYIY7wAONPdt5Z0zAj9F/gC+MnMVoZl1wGfA58SHLfuRnmqWHqcaAKZ2XjgYXffofumzaw5QYLYzd3XVUhw5Vt/G4LtaFPZ65aihU2Zi4Hz3H2KmV1EcCFS+8RGFm0W3L422t1La1auVBb0pHZ1gda8ilhuvs9RRS5bdpx+2STWu/x+Ic52Cb9gfyW4SrvSk3eMHW0Clh0UNjFnhE2ONxCcayzYsiLVkLt3rqjkrc9RdOgq9ARy93t2ZP7wArXlBE18XSokqO3g7p8kat2STzuC0wy5Tf7dwqbYKsHMNhQz6pTwQrx4rbc5wf4oykG5141Inir9OZLfqQldREQkgtSELiIiEkFK4CIiIhEUiXPgDRs29BYtWiQ6DBERkUoxc+bMle6eWdI0kUjgLVq0YMaMGaVPKCIiUg2YWand9kYigVeUjz/+nDVrtpQ+oUReRkYtjj66VaLDEBGJm6RK4GvWbCEzs8hnMEg1k509M9EhiIjElS5iExERiaCkqoGLiEi0bN26lcWLF7Np06ZEhxIXderUoVmzZtSsWbPc8yqBVyELFszmrrsu55df1lGjRgoXX3wjnTv3AOCmm85j3rwZpKbW5OCD23DjjU+QmlqTt956jlGj7sbdqVs3nQEDHmO//Q4D4LTTWrDzzumkpKSQkpLKs8/qQkARiZbFixeTnp5OixYtyP803Ohzd1atWsXixYvZa6+9yj2/EngVUqfOztx66zM0b74v2dlLOf/8I2nX7mTS0zPo0uU8br99NAA33tiTceOeonv3y9l9970YPnwqu+xSjw8/fIshQ/owatT0vGU+8cQUMjIaJmqTRER2yKZNm6pl8gYwMxo0aEB2dvZ2zZ/UCXzp0oX063cKrVu3Z86caWRmNuX++1+lTp2dGDt2GP/+9+OkpKSy114HceedY3niicEsXfo9S5Z8x08//chf/zqUzz//mGnT3qJRo6YMHfo6qanlbwbJteee++W9z8zcnfr1G7F6dTbp6Rm0b981b9zBB7dh+fLgaZqHHXZMXnmrVkezYkVlPWVTRKRyVMfknWtHti3pL2JbtOhrzjrrSl588QvS0zP473//DcDIkXfx3HOfMXbsHG644fG86Rcv/pbHH/8vDzzwGjfffD5ZWR154YXPqV17Jz744M1Cy3/mmXvp2bN1ode99/YrMa65cz9h69YtNGu2T77ynJytjB//LMccU/jZJa++OoJjjjklb9jMuPLKzpx//pH85z/Dy7VfREQkMGTIEA4++GAOPfRQWrduzfTp0+nQoQPNmzcn9nki3bp1Iy0trdLiSuoaOMDuu+/F/vu3BuCAA45k6dKFAOy776HcdNN5dOjQjQ4duuVNf8wxp5CaWpOWLVvx22/b8hJpy5at8uaN1atXf3r16l+umFauXMagQRdw662jqFEj/2+su+66giOOOJ7DDz8uX/mMGVN49dURPPXUB3llTz31AY0aNeXnn1dw5ZWdaNHiAI444vhyxSIiUpUMnfxVhS7v2k77lTj+o48+4o033mDWrFnUrl2blStXsmVL0J9IRkYGH374Ie3bt2fNmjUsW7asQmMrTdIn8Jo1a+e9T0lJYfPm4Kl5//znm3z22Xu8997rPP30EMaO/RyAWrWC6WvUqEFqas285g+zGmzbllNo+c88cy8TJjxXqPzww4+nf/9hhco3bFjH1Vf/kSuuGEKrVkfnGzd8+K2sXp3NDTc8ka/866/ncPvtvRk27C0yMhrklTdq1BSA+vUb0aHDGXzxxSdK4CIi5bBs2TIaNmxI7drBsb9hw9+vKTrnnHMYO3Ys7du35z//+Q9nnnkmX3zxRaXFlvQJvCi//fYby5cvIiurI61bt2fSpLFs3Fjco4xLVp4a+NatW+jf/wz++MdenHRS93zjxo17io8/nsijj76Tr1b+008/0r//mdx227P5zqFv3PgLv/32G3XrprNx4y9Mnz6J3r0Hbdc2iIgkq86dO3Pbbbex3377cdJJJ9GjRw9OOOEEAE488UQuvfRStm3bxtixYxk+fDi33357pcWmBF6E337bxs03n8+GDWtxd845px/p6RlxX+/kyS8ya9Z7rF27ijfeGAnALbeMZP/9W3Pnnf/HbrvtycUXtwOgY8czufTSQTz55G2sXbuKu+++AiDvdrFVq5bTv/8ZAGzblsPJJ/cs8ry5iIgULy0tjZkzZ/L+++8zZcoUevTowV133QUErbbt27dn7NixbNy4kcp+6JbFnoCvqrKysrwiHmYyYcJMdaWaJLKzZ9Kli/7XIlE3f/58DjzwwLzhyj4HXtDLL7/MqFGjWL9+Pffddx+//vorZ5xxBoMHD+aqq64iLS2NDRvK12JbcBsBzGymu2eVNF/SX4UuIiJV2/J1m/Jev2zOyfeKtwULFvD111/nDc+ePZs999wzb/i4445j4MCBnHvuuXGPpSA1oYuIiBRjw4YNXHXVVaxZs4bU1FRatmzJ8OHD6d49uE7JzLjuuusSEpsSuIiIRMalx++db7jxLnXiur4jjzySadOmFSp/9913i5y+vM3nO0JN6MXo06cD8+ZVjb7DN236lauv/iN//vMBnH32wTz00IC8caNHP8BZZx3EOeccyuWXn8iyZcEz4BcsmM1f/tKOs88+mHPOOZRJk17Im2fw4Iv405/2yutUZsGC2ZW+TSIismNUA4+ICy64jqysjmzduoXLLz+RDz98i2OPPYUDDjic7t1nUKfOzrz88mMMG3Y9d975Qon9qgP063dvoVvVREQkOuKWwM2sDvAeUDtcz8vufouZjQROANaGk17k7gmpApbUFzrA+PHPcscdvcnJyWHQoKc55JA2zJw5lfvvvzpcgvHkk+8xf/5Mhg+/hbS0DL799nNOOulsWrZsxfPPP8jmzRu5//5xhbpELY86dXYmK6sjADVr1uKAA47I6/M8txzgkEOOZvz44IEnJfWrLiIi0RfPJvTNwB/c/TCgNdDFzHK7Fuvv7q3DV0Lbb4vrCx2CpusxY2YzYMCj3HbbxQCMHn0f11//CGPGzOapp96ndu0g2X/11f+44YbHeeml+Ywf/yw//vgVzzzzCd269eaFFx4qtN4ZM6YU2Uf6xRcfU2jaWOvXr+H991/nqKNOLDSuYF/ouYrqV/3RR2/knHMO5f77r2XLls1l21kiIlJlxK0G7sEN5rln82uGryp303lxfaEDnHxycFvAEUcczy+/rGP9+jUcdtixDB36V0455Tw6djyTxo2bAXDQQUfRsGETAJo124e2bTsDQR/pM2ZMKbTerKyOjBlTvt8uOTk53HjjufTo0Y9mzfJfyDF+/Gjmz5/B8OFT85UX1a9637530qDBbmzduiV8/OjdXHqpemkTEYmSuJ4DN7MUYCbQEnjE3aeb2eXAEDMbBLwDDHD3hFUBi+sLHQo/5s3MuOiiAbRv/0c++GA8l1xyLA8/PBH4vY/0YLoaecPF9ZE+Y8YUHnjg2kLlderszNNPF77iEWDIkD7ssce+9Ox5Tb7y6dPf5umnhzB8+NR8cRTXr3ruD41atWpz2ml/YfTo+4pcn4iIVF1xTeDuvg1obWYZwCtmdggwEPgJqAUMB/4O3FZwXjPrA/QBaN68eTzDLNakSS+QldWR2bM/IC1tV9LSdmXx4m9p2bIVLVu2Yt68T1m48EvS0sp/Xrm8NfBHH72JDRvWcvPNT+Ur//LLz/jHPy7joYcmUL9+o7zykvpVX7lyGQ0bNsHdmTp1HPvsc0i54xcRSRYpKSm0atUKdyclJYWHH36YY445hl9//ZVLL72UOXPm4O5kZGQwYcIE0tLSMDPOO+88Ro8OrkvKycmhSZMmtG3bljfeeKNC4qqUq9DdfY2ZTQG6uHtudW+zmf0LKPIOeHcfTpDgycrKSkjTe+3adejZ83BycrYyaNDTAIwZ809mzJhCjRo12HvvgznmmFOYM+ejuMaxfPlinn56CC1aHMD55x8BwNln96Vbt94MG9afjRs3MGDAWQA0btycoUNfK7Ff9ZtuOo/Vq7Nxd/bfvzUDBz5e3KpFRKqUutPuzV9QewfTWMeBpU6y0047MXt2UOGaOHEiAwcOZOrUqTz44IM0btyYzz8Pnla5YMECatasGcRZty5z585l48aN7LTTTkyePJmmTZvuWKwFxPMq9Exga5i8dwI6AXebWRN3X2ZB+3Q3YG68YijN7ru34MUXf1/9BRf8/lti+PB3i5zn+usLX5CWldWBrKwORc5bcNz2aNy4GTNmFP0b5tFH3y6yvGvX8+na9fwixz3++H93KB4RkWS1bt066tWrBwSPGo3tVnX//ffPN23Xrl1588036d69O88//zznnnsu77//foXFEs+r0JsAU8xsDvApMNnd3wCeM7PPgc+BhsAdcYxBRERkh2zcuJHWrVtzwAEH0Lt3b26++WYALr74Yu6++27atWvHTTfdlK/PdPj9eeGbNm1izpw5tG3btkLjiudV6HOAw4so/0O81ikiIlLRYpvQP/roI3r16sXcuXNp3bo13333HZMmTeLtt9/mqKOO4qOPPsp7stihhx7KwoULef755+natWuFx6We2ERERMqoXbt2rFy5kuzsbBo1akRaWhpnnnkmZ555JjVq1GD8+PH5Hg36pz/9ieuuu453332XVatWVWgs6gu9itmyZTMDB/agW7eWXHhh23z3pceaNm0CZ565P926tWTkyLvyypcs+Z4LL2xLt24tGTiwB1u3binXcsti4sSxjBgxpMRp1q79mSuu6MQZZ+zLFVd0Yt261UVO98YbozjjjH0544x9eeONUXnl8+fPpEePVnTr1pJ77+1H7nPry7pcEZF4+PLLL9m2bRsNGjTgww8/ZPXq4Bi0ZcsW5s2bl++cOATN7LfccgutWrWq8FiUwKuYV18dQXp6PcaN+4aePa/loYf+Xmiabdu2cffdVzJs2Fu89NI8Jk58nu++mwfAQw/9nZ49r2XcuG9IT6/Hq6+OKPNyY73++kieeGJwkeOmTXuLY47pUuL8I0feRZs2J/LKK1/Tps2J+X5k5Fq79meefPJWRo6czqhRn/Dkk7fmJeQ777ycm256klde+ZpFi75m2rQJZV6uiEhFyj0H3rp1a3r06MGoUaNISUnh22+/5YQTTqBVq1YcfvjhZGVl8ec//znfvM2aNaNfv35xiSupm9CXLl3IVVd14cADj+TLL2ex994Hc9ttz1Cnzs4Ji2nq1Ffp02cwACee2J177umLu+frVOaLLz5hjz1a5vXG1rnzOUyd+ip77XUgn376X+64YwwAp556IcOHD6Z798vLtNyycHe++mo2BxxwRKnbkXs1/qmnXkifPh3o1+/ufNN89NFE2rTpxK671gegTZtOTJs2gaysDvzyy7q8zme6du3Fu++O49hjTynTckWk+vrlmP75htPi/DhRCCpNRenVqxe9evUqclxRjxXt0KEDHTp0qLC4kr4G/sMPC+je/Qpefnk+devuwksvPVrh6+jd+7gi+z2fPr3wLWArViyhceM9AEhNTSUtbVfWrl1V7DQAjRo1Y8WKJaxdu4r09AxSU1PzlZd1uWWxYMFn7LvvYaUm/p9/Xp7X41uDBrvx88/LC02TnZ1/Oxo3bkZ29pIw1maFysu6XBGRZJDUNXCAxo33oHXrY4Hg3umxY4flux+8Ijz1VMXd9xdPa9as4oorgoekrF37Mzk5W5g6dRwAt932LC1btmLatAlFPjClJGZW7pp+IpcrIhIFSZ/Ai+rvvKL17n0cv/66vlD51VffR9u2J+Ura9SoKcuXL6Jx42bk5OSwYcNadt21QZHT5FqxYjGNGjVl110bsPhNP54AABotSURBVH79GnJyckhNTc0rL+tyMzIa5HXv+vrrI1m6dCGXXTY43zQffzyJe+4JntjWt+/J/Pzzcg48MKtQF6/16zfO67J15cpl1KvXiIIyM5syc+a7ecPLly/myCM7hLEuzleemdm0zMsVEUkGSd+E/tNPP+Z1hTphwhhat25f4et46qn3GTNmdqFXweQNcPzxf8q7Gvudd17mqKP+UOhHxUEHHcWiRV+zZMn3bN26hUmTxnL88X/CzMjK6sg777wMBFd4n3DC6WVebmk2bFjLtm05ZGQEif/hhycyZszsQskb4IQTfl9fbByx2rU7menTJ7Fu3WrWrVvN9OmTaNfuZBo2bELdurvw+ecf4+6MH/9M3vxlWa6IVC+5d6FURzuybUmfwPfcc39eeukRunc/kHXrVtO9++UJjef00y9h7dpVdOvWkueee4C+fYOrrLOzl9KvX9ARQGpqKv37P8xVV51M9+4HctJJZ7PPPgcDcNVVd/Pccw/QrVtL1q5dxemnX1Licsvj448n06ZN4R8dRbnwwgFMnz6ZM87Yl08+eZuLLhoAwLx5M7j99t4A7LprfS655GZ69TqKXr2OonfvQXkXtA0Y8Ci3396bbt1a0rTpPhx77CklLldEqqc6deqwYe3qapnE3Z1Vq1ZRp872XYhnUdgpWVlZPmPGjB1ezoQJM8nMPDJveOnShVxzzan5+kOX4gUJtXe+R5NWVdnZM+nS5cjSJxSRKm3r1q2899mXpKUWnat22almJUdUserUqUOzZs3yHoKSy8xmuntWSfMm/TlwKbuimspFROKpZs2azFlbu9jx13barxKjqVqSugm94NPIREREokI18Crqp59+5JZbLmT9+jX89ts2+va9i/bt83eGv3DhAm64oUfe8JIl33HZZbfRs+c1PPhgf95773Vq1qxFs2b7cMst/yI9PYOlSxdy1lkHsueewWPvDjnkaG64Qc8DF5GKMXTyV9s1XzLXpLeXEngVNWLEHXTqdDbdu1/Od9/N4+qru9K+/cJ807RosX/ebV/btm2ja9emdOx4BgBt23biyivvJDU1lWHD/s6//nVnXo9lTZvukzefiIhEU1In8I0bf2HAgLNZsWIx27Zto3fvm+ncuQfz589k6NC/8uuvG8jIaMjgwSNp2LAJffp0YL/9DmPWrKnk5OQwaNDTHHJImzhFZ2zYsA4Ibt/KzNy9xKk//fQdmjbdhyZNgo70jz66c964Vq2Ozru1TEREqoekTuDTpk0gM3N3HnzwTSBIlDk5W7n33qu4//5XqVcvk0mTXuCRR27kllueBmDTpl8ZM2Y2s2a9x223XVyuc+jl6dDlsssGc+WVnXnxxYfYuPEXHn20cLersSZOHMvJJ59b5LjXXnuaTp1+b2pfuvR7evY8nLS0Xbj88js4/PDjyrwNIiJSNSR1Am/ZshX//OffGDbs7xx33KkcfvhxfPPNXL79di5XXtkJCJqmc/veBvKS5BFHHM8vv6xj/fo1pKdnlGl95elSdcKE5znttIs4//y/MWfORwwadAEvvDCXGjUKX3e4desW3nvvNfr2vbPQuBEjhpCSksopp5wHQMOGTXjjjR/JyGjA/Pkzue66brzwwhekpe1S5thERCTxkjqB77nnfowePYsPPxzPY4/dxFFHnUjHjmew994H869/fVTkPDvS9Wp5auCvvTaCYcOCR2geemg7tmzZxJo1K6lfv3DXoR9++BYHHHAEDRo0zlf++usj+eCDN3jssXfy4qxVqza1agW3ZBx44JE0bboPP/74FQcdVOLthiIiUsUkdQLPzl7KLrvUp2vX80lPz2DcuKe46KIBrF6dzZw5H3Hooe3IydnKDz98ldfT2aRJL5CV1ZHZsz8gLW1X0tJ2LfP6ylMD32235nz66TucdtpFfP/9fDZv3kS9eplFTjtx4vOFms+nTZvAM8/cw/DhU/M9HnX16mx22aU+KSkpLF78HYsWfU3TpnuXOS4REakakjqBf/PN5zz4YH9q1KhBampNBgx4jJo1a3H33S9z33398vr+Pvfca/ISeO3adejZ83BycrYyaNDTcYvtmmvu5447LmXMmKGYGYMHj8TMyM5eyu2392bYsPFAcCHeJ59M5sYbn8g3/z339GXr1s15pwJybxebNes9nnhiEKmpNTGrwcCBj+d1XyoiItGR1F2pllefPh245pr71NwcAepKVSQx4nEfeEnLrK73j5elK9Wk7olNREQkqpK6Cb28hg9/N9EhiIiIAKqBi4iIRJISuIiISAQpgYuIiERQUp0Dz8ioRXb2zESHIZUgI6NWokMQEYmrpErgRx/dKtEhiIiIVAg1oYuIiERQUtXARUSkatreDmCSmWrgIiIiEaQELiIiEkFK4CIiIhGkBC4iIhJBSuAiIiIRFLcEbmZ1zOwTM/ufmX1hZreG5XuZ2XQz+8bMXjAz9bghIiJSTvGsgW8G/uDuhwGtgS5mdjRwNzDU3VsCq4FL4hiDiIhItRS3BO6BDeFgzfDlwB+Al8PyUUC3eMUgIiJSXcX1HLiZpZjZbGAFMBn4Fljj7jnhJIuBpsXM28fMZpjZjOzs7HiGKSIiEjlxTeDuvs3dWwPNgDbAAeWYd7i7Z7l7VmZmZtxiFBERiaJKuQrd3dcAU4B2QIaZ5Xbh2gxYUhkxiIiIVCfxvAo908wywvc7AZ2A+QSJvHs42YXAq/GKQUREpLqK58NMmgCjzCyF4IfCi+7+hpnNA8aa2R3AZ8CIOMYgIiJSLcUtgbv7HODwIsq/IzgfLiIiIttJPbGJiIhEkBK4iIhIBCmBi4iIRJASuIiISAQpgYuIiESQEriIiEgEKYGLiIhEkBK4iIhIBCmBi4iIRJASuIiISAQpgYuIiESQEriIiEgEKYGLiIhEkBK4iIhIBCmBi4iIRJASuIiISAQpgYuIiESQEriIiEgEKYGLiIhEkBK4iIhIBCmBi4iIRJASuIiISAQpgYuIiESQEriIiEgEKYGLiIhEkBK4iIhIBCmBi4iIRJASuIiISAQpgYuIiESQEriIiEgEKYGLiIhEkBK4iIhIBCmBi4iIRJASuIiISATFLYGb2R5mNsXM5pnZF2Z2dVg+2MyWmNns8NU1XjGIiIhUV6lxXHYO8Dd3n2Vm6cBMM5scjhvq7vfFcd0iIiLVWtwSuLsvA5aF79eb2XygabzWJyIikkwq5Ry4mbUADgemh0V9zWyOmT1tZvWKmaePmc0wsxnZ2dmVEaaIiEhkxD2Bm1ka8G/gGndfBzwG7AO0Jqih31/UfO4+3N2z3D0rMzMz3mGKiIhESlwTuJnVJEjez7n7fwDcfbm7b3P334AngTbxjEFERKQ6iudV6AaMAOa7+wMx5U1iJjsDmBuvGERERKqreF6FfixwAfC5mc0Oy24AzjWz1oADC4HL4hiDiIhItRTPq9A/AKyIUePjtU4REZFkoZ7YREREIkgJXEREJIKUwEVERCJICVxERCSClMBFREQiSAlcREQkgpTARUREIkgJXEREJIKUwEVERCJICVxERCSClMBFREQiSAlcREQkgpTARUREIkgJXEREJIKUwEVERCJICVxERCSClMBFREQiSAlcREQkgpTARUREIkgJXEREJIJSEx2ARNyUO4sf13Fg5cUhIpJkVAMXERGJICVwERGRCFICFxERiSAlcBERkQhSAhcREYkgJXAREZEIUgIXERGJICVwERGRCFICFxERiSAlcBERkQhSAhcREYkgJXAREZEIUgIXERGJoLglcDPbw8ymmNk8M/vCzK4Oy+ub2WQz+zr8Wy9eMYiIiFRX8ayB5wB/c/eDgKOBK83sIGAA8I677wu8Ew6LiIhIOcQtgbv7MnefFb5fD8wHmgKnA6PCyUYB3eIVg4iISHWVWhkrMbMWwOHAdKCxuy8LR/0ENC5mnj5AH4DmzZvHP0gRESmToZO/SnQIQiVcxGZmacC/gWvcfV3sOHd3wIuaz92Hu3uWu2dlZmbGO0wREZFIiWsCN7OaBMn7OXf/T1i83MyahOObACviGYOIiEh1VK4EbmZ1zSyljNMaMAKY7+4PxIx6DbgwfH8h8Gp5YhAREZFSEriZ1TCznmb2ppmtAL4EloW3ht1rZi1LmP1Y4ALgD2Y2O3x1Be4COpnZ18BJ4bCIiIiUQ2kXsU0B3gYGAnPd/TcI7uUGOgJ3m9kr7j664Izu/gFgxSz3xO0PWUREREpL4Ce5+9aChe7+M8G57X+H57lFRESkEpXYhJ6bvM3s2YLjcsuKSvAiIiISX2W9iO3g2IHwQrYjKz4cERERKYvSLmIbaGbrgUPNbF34Wk9w65euHhcREUmQ0prQ73T3dOBed98lfKW7ewN3H1hJMYqIiEgBpdXAWwAUl6wt0KziwxIREZGSlHYV+r1mVoOguXwmkA3UAVoS3EZ2InALsDieQYqIiEh+JSZwdz8rfAToecDFwG7ARoIni40Hhrj7prhHKSIiIvmUehW6u88D7gBeJ0jc3wOfAi8reYuIiCRGWR8nOgpYBwwLh3sCzwBnxyMoERERKVlZE/gh7n5QzPAUM5sXj4BERESkdGVN4LPM7Gh3/xjAzNoCM+IXlmy3KXcWP65jJd/5V9GxVKVtExFJsLIm8COBaWb2YzjcHFhgZp8D7u6HxiU6ERERKVJZE3iXuEYhIiIi5VKmBO7uP8Q7EBERESm7sj7MRERERKoQJXAREZEIUgIXERGJICVwERGRCFICFxERiaCy3kYmIiJJZOjkrxIdQpmUFOe1nfarxEgqn2rgIiIiEaQELiIiEkFK4CIiIhGkBC4iIhJBSuAiIiIRpAQuIiISQUrgIiIiEaQELiIiEkHqyCWKptxZvdcnIiKlUg1cREQkgpTARUREIkgJXEREJIKUwEVERCIobgnczJ42sxVmNjembLCZLTGz2eGra7zWLyIiUp3FswY+EuhSRPlQd28dvsbHcf0iIiLVVtwSuLu/B/wcr+WLiIgks0TcB97XzHoBM4C/ufvqoiYysz5AH4DmzZtXYnjVWEn3c3ccWHlxiIjIDqvsi9geA/YBWgPLgPuLm9Ddh7t7lrtnZWZmVlZ8IiIikVCpCdzdl7v7Nnf/DXgSaFOZ6xcREakuKjWBm1mTmMEzgLnFTSsiIiLFi9s5cDN7HugANDSzxcAtQAczaw04sBC4LF7rFxERqc7ilsDd/dwiikfEa30iIiLJRD2xiYiIRJASuIiISAQpgYuIiERQIjpyESm5UxkRESmVauAiIiIRpAQuIiISQUrgIiIiEaQELiIiEkFK4CIiIhGkBC4iIhJBSuAiIiIRpAQuIiISQerIRQLqWEVEJFJUAxcREYkgJXAREZEIUgIXERGJICVwERGRCFICFxERiSAlcBERkQhSAhcREYkg3QdelenebBGR7TZ08ldFll/bab9KjiQ+VAMXERGJICVwERGRCFICFxERiSAlcBERkQhSAhcREYkgJXAREZEIUgIXERGJICVwERGRCFICFxERiSAlcBERkQhSAhcREYkgJXAREZEIUgIXERGJoLglcDN72sxWmNncmLL6ZjbZzL4O/9aL1/pFRESqs3jWwEcCXQqUDQDecfd9gXfCYRERESmnuCVwd38P+LlA8enAqPD9KKBbvNYvIiJSnVX2OfDG7r4sfP8T0LiS1y8iIlItJOwiNnd3wIsbb2Z9zGyGmc3Izs6uxMhERESqvspO4MvNrAlA+HdFcRO6+3B3z3L3rMzMzEoLUEREJAoqO4G/BlwYvr8QeLWS1y8iIlItxPM2sueBj4D9zWyxmV0C3AV0MrOvgZPCYRERESmn1Hgt2N3PLWbUifFap4iISLJQT2wiIiIRpAQuIiISQUrgIiIiERS3c+AiIiJV0dDJXxU77tpO+1ViJDtGNXAREZEIUgIXERGJICVwERGRCFICFxERiSAlcBERkQhSAhcREYkgJXAREZEIUgIXERGJIHXkItXDlDu3b76OAys2DpGIKalTE6naVAMXERGJICVwERGRCFICFxERiSAlcBERkQhSAhcREYkgJXAREZEIUgIXERGJIN0Hnmjbe/+yiIgkNdXARUREIkgJXEREJIKUwEVERCJICVxERCSClMBFREQiSAlcREQkgpTARUREIkgJXEREJIKUwEVERCJICVxERCSClMBFREQiSAlcREQkgpTARUREIighTyMzs4XAemAbkOPuWYmIQ0REJKoS+TjRju6+MoHrFxERiSw1oYuIiERQohK4A5PMbKaZ9UlQDCIiIpGVqCb09u6+xMwaAZPN7Et3fy92gjCx9wFo3rx5ImKsOFPuTHQEIpLEhk7+KtEhSBwkpAbu7kvCvyuAV4A2RUwz3N2z3D0rMzOzskMUERGp0io9gZtZXTNLz30PdAbmVnYcIiIiUZaIJvTGwCtmlrv+Me4+IQFxiIiIRFalJ3B3/w44rLLXKyIiUp3oNjIREZEIUgIXERGJICVwERGRCEpkV6oiiVfSPfodB1ZeHCIi5aQauIiISAQpgYuIiESQEriIiEgEKYGLiIhEkBK4iIhIBCmBi4iIRJASuIiISAQpgYuIiERQcnbkEo/OO0papkSTOnkRkSpMNXAREZEIUgIXERGJICVwERGRCFICFxERiSAlcBERkQhSAhcREYkgJXAREZEIUgIXERGJoOTsyEVkR1V0Jy/qNEakyhs6+atix13bab9KjCSgGriIiEgEKYGLiIhEkBK4iIhIBCmBi4iIRJASuIiISAQpgYuIiESQEriIiEgE6T7wgkq6H1ekLIr7DOl+bpEqr6R7vasa1cBFREQiSAlcREQkgpTARUREIkgJXEREJIISksDNrIuZLTCzb8xsQCJiEBERibJKT+BmlgI8ApwCHASca2YHVXYcIiIiUZaIGngb4Bt3/87dtwBjgdMTEIeIiEhkJSKBNwUWxQwvDstERESkjKpsRy5m1gfoEw5uMLMF5Zi9IbCy4qOqFrRvihfnfXNDJc9XofS5KZ72TfGSZt/8dftmK2n/7FnazIlI4EuAPWKGm4Vl+bj7cGD49qzAzGa4e9b2hVe9ad8UT/umeNo3xdO+KZ72Tcl2dP8kogn9U2BfM9vLzGoB5wCvJSAOERGRyKr0Gri755hZX2AikAI87e5fVHYcIiIiUZaQc+DuPh4YH8dVbFfTe5LQvime9k3xtG+Kp31TPO2bku3Q/jF3r6hAREREpJKoK1UREZEIinwCN7PbzWyOmc02s0lmtntYbmY2LOyudY6ZHREzz4Vm9nX4ujBx0ceXmd1rZl+G2/+KmWXEjBsY7psFZnZyTHlSdHNrZmeZ2Rdm9puZZRUYl9T7pqBk3e5cZva0ma0ws7kxZfXNbHJ4DJlsZvXC8mKPO9WRme1hZlPMbF74fbo6LE/6/WNmdczsEzP7X7hvbg3L9zKz6eE+eCG8mBszqx0OfxOOb1HqStw90i9gl5j3/YDHw/ddgbcAA44Gpofl9YHvwr/1wvf1Er0dcdo3nYHU8P3dwN3h+4OA/wG1gb2AbwkuKEwJ3+8N1AqnOSjR2xGnfXMgsD/wLpAVU570+6bAfkrK7S6wD44HjgDmxpTdAwwI3w+I+W4Vedypri+gCXBE+D4d+Cr8DiX9/gm3MS18XxOYHm7zi8A5YfnjwOXh+yti8tc5wAulrSPyNXB3XxczWBfIPal/OvCMBz4GMsysCXAyMNndf3b31cBkoEulBl1J3H2Su+eEgx8T3HMPwb4Z6+6b3f174BuCLm6Tpptbd5/v7kV1DpT0+6aAZN3uPO7+HvBzgeLTgVHh+1FAt5jyoo471ZK7L3P3WeH79cB8gp41k37/hNu4IRysGb4c+APwclhecN/k7rOXgRPNzEpaR+QTOICZDTGzRcB5wKCwuLguW5O1K9eLCX75gvZNSbRv8kvW7S5NY3dfFr7/CWgcvk/a/RU2+R5OUNPU/iF4eJeZzQZWEFQWvwXWxFSsYrc/b9+E49cCDUpafiQSuJm9bWZzi3idDuDuN7r7HsBzQN/ERlu5Sts34TQ3AjkE+ydplGXfiOwoD9o8k/p2HjNLA/4NXFOgVTSp94+7b3P31gStn22AAypy+VW2L/RY7n5SGSd9juD+8lsovsvWJUCHAuXv7nCQCVLavjGzi4BTgRPDLxKU3J1tqd3cRkU5PjexkmLflEOZuj5OQsvNrIm7LwubgFeE5Um3v8ysJkHyfs7d/xMWa//EcPc1ZjYFaEdw2iA1rGXHbn/uvllsZqnArsCqkpYbiRp4Scxs35jB04Evw/evAb3Cqx6PBtaGTToTgc5mVi+8MrJzWFbtmFkX4HrgT+7+a8yo14Bzwqse9wL2BT5B3dyC9k1BybrdpXkNyL2D5ULg1Zjyoo471VJ4jnYEMN/dH4gZlfT7x8wyLbzzx8x2AjoRXCMwBegeTlZw3+Tus+7Af2MqXUVL9JV6O/oi+OU3F5gDvA409d+vAHyE4JzD5+S/0vhigouTvgH+kuhtiOO++YbgnMrs8PV4zLgbw32zADglprwrwZWk3wI3Jnob4rhvziA4/7QZWA5M1L4pdl8l5XbHbP/zwDJga/iZuYTg3OQ7wNfA20D9cNpijzvV8QW0J2genxNznOmq/eMAhwKfhftmLjAoLN+boFLwDfASUDssrxMOfxOO37u0dagnNhERkQiKfBO6iIhIMlICFxERiSAlcBERkQhSAhcREYkgJXAREZEIUgIXkULMLMPMrkh0HCJSPCVwESlKBsHTkUSkilICF5Gi3AXsY2azzezeRAcjIoWpIxcRKSR8stQb7n5IgkMRkWKoBi4iIhJBSuAiIiIRpAQuIkVZD6QnOggRKZ4SuIgU4u6rgA/NbK4uYhOpmnQRm4iISASpBi4iIhJBSuAiIiIRpAQuIiISQUrgIiIiEaQELiIiEkFK4CIiIhGkBC4iIhJBSuAiIiIR9P8vGMIri6BpbgAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Wilson coefficient value (*10^-2 TeV^-2)\n", + "gphivals = [35e-1, 5, 10, 20, 30, 40, 45, 50, 200, 500]\n", + "gphival_names = ['35e-1', '5', '10', '20', '30', '40', '45', '50', '200', '500']\n", + "seps_new, ps_new = [], []\n", + "seps_alt_new, ps_alt_new = [], []\n", + "\n", + "\n", + "for (gphival, gphival_name) in zip(gphivals, gphival_names):\n", + " print('Evaluating with knowledge on the Cross Section, gphi=%s'%(gphival_name))\n", + " result = TestEstimator(gphival, gphival_name, title_message=', big batch')\n", + " seps_new.append(result[0])\n", + " ps_new.append(result[1])\n", + " \n", + " print('Evaluating without knowledge on the Cross Section, gphi=%s'%(gphival_name))\n", + " result = TestEstimator(gphival, gphival_name, withXS=False, title_message=', big batch')\n", + " seps_alt_new.append(result[0])\n", + " ps_alt_new.append(result[1])\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(gphivals, seps, label='XS')\n", + "plt.plot(gphivals, seps_alt, '--', label='without XS')\n", + "plt.plot(gphivals, seps_new, label='XS, bigbatch')\n", + "plt.plot(gphivals, seps_alt_new, '--', label='without XS, bigbatch')\n", + "plt.xscale('log')\n", + "plt.xlabel('gphi')\n", + "plt.ylabel('seperation')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(gphivals, seps, label='XS')\n", + "plt.plot(gphivals, seps_alt, '--', label='without XS')\n", + "plt.plot(gphivals, seps_new, label='XS, bigbatch')\n", + "plt.plot(gphivals, seps_alt_new, '--', label='without XS, bigbatch')\n", + "plt.yscale('log')\n", + "plt.xscale('log')\n", + "plt.xlabel('gphi')\n", + "plt.ylabel('seperation')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2841.340\n", + "test 0 : tsm = 380.445, tbsm = 86.760\n", + "Reaching the end of test data. Stop tests at 176. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 7.72 sigmas\n", + "Partial test after 10000 epochs (took 87.74 seconds)\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "(7.724035673387842, 0.0)" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "TestEstimator(3.5, '35e-4')" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2841.340\n", + "test 0 : tsm = 362.049, tbsm = 102.786\n", + "Reaching the end of test data. Stop tests at 175. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 8.32 sigmas\n", + "Partial test after 10000 epochs (took 87.52 seconds)\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "(8.321420696676606, 0.0)" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "TestEstimator(3.5, '35e-4', False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/tutorial_particle_physics/TestEstimator-Light-Copy2.ipynb b/examples/tutorial_particle_physics/TestEstimator-Light-Copy2.ipynb new file mode 100644 index 000000000..8e781c4b2 --- /dev/null +++ b/examples/tutorial_particle_physics/TestEstimator-Light-Copy2.ipynb @@ -0,0 +1,1135 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: tabulate in /usr/local/lib/python3.8/dist-packages (0.8.7)\n", + "\u001b[33mWARNING: You are using pip version 20.1.1; however, version 20.2.3 is available.\n", + "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n" + ] + } + ], + "source": [ + "#if you have not installed tabulate \n", + "#this is just for printing the information of data in a prettier format\n", + "! pip install tabulate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing Function" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#import main code\n", + "from OurTrainingTools import *\n", + "#this will throw a random number and print it\n", + "#to reset the random number manually, use\n", + "#torch.manual_seed(random_seed)\n", + "\n", + "\n", + "def test_model(madminermodel, test_input_sm, test_input_bsm, bsmparval, NSM, NBSM, epochs, e, n_meas, pm, verbose_t=True, verbose_period_t=1e5, title=''):\n", + " \n", + " # computing test statistics t (or lambda) in equation (2) of paper\n", + " def compute_t(madminermodel, nev, counter, test_input):\n", + " # generate number of points for testing under Poisson distribution\n", + " n_gen = 0\n", + " while n_gen == 0:\n", + " n_gen = np.random.poisson(nev)\n", + " \n", + " # stop if there are no more points to test\n", + " if (counter + n_gen) >= len(test_input):\n", + " return 0., -1\n", + " \n", + " points = test_input[int(counter): int(counter+n_gen)]\n", + " \n", + " # compute test statistics\n", + " log_ratio = (madminermodel.evaluate_log_likelihood_ratio(points.numpy(), \n", + " np.array([bsmparval,]))[0][0])\n", + " log_ratio = torch.tensor(log_ratio)\n", + " #ratio = 1./ratio\n", + " log_ratio = log_ratio\n", + " out = 2 * (NBSM - NSM - (log_ratio+torch.log(torch.tensor(NBSM/NSM))).sum(0))\n", + " \n", + " #return test statistics and the starting point for the next batch\n", + " return out, int(counter+n_gen)\n", + " \n", + " test_start = time.time()\n", + " if verbose_t:\n", + " print(\"NSM = %.3f --- NBSM = %.3f\"%(NSM, NBSM))\n", + " tsm = torch.empty(n_meas)\n", + " tbsm = torch.empty(n_meas)\n", + " \n", + " # empty array to store values\n", + " tsmcount = torch.zeros(n_meas+1)\n", + " tbsmcount = torch.zeros(n_meas+1)\n", + " \n", + " for i in range(n_meas):\n", + " tsm[i], tsmcount[i+1] = compute_t(madminermodel, NSM, tsmcount[i], \n", + " test_input_sm)\n", + " tbsm[i], tbsmcount[i+1] = compute_t(madminermodel, NBSM, tbsmcount[i], \n", + " test_input_bsm)\n", + " \n", + " if (tsmcount[i+1] < 0) or (tbsmcount[i+1] < 0):\n", + " print('Reaching the end of test data. Stop tests at %d. '%i)\n", + " tsm, tbsm = tsm[: i], tbsm[: i]\n", + " n_meas = i\n", + " break\n", + " \n", + " if i % (verbose_period_t) == 0:\n", + " print('test %s: tsm = %.3f, tbsm = %.3f'%(\n", + " str(i).ljust(4), tsm[i], tbsm[i]))\n", + " \n", + " test_duration = time.time() - test_start\n", + " \n", + " #compute mean and variation of the test statistics in two hypotheses\n", + " mu_sm = tsm.mean().item()\n", + " mu_bsm = tbsm.mean().item()\n", + " sigma_sm = tsm.std().item()\n", + " sigma_bsm = tbsm.std().item()\n", + " med_sm = tsm.median().item()\n", + " \n", + " #compute separation and p-value\n", + " sep = (mu_sm - mu_bsm)/sigma_bsm\n", + " p = 1.*len([i for i in tbsm if i > med_sm])/len(tsm) \n", + " #print(len([i for i in tbsm if i>med_sm]))\n", + " delta1 = (p * (1 - p)/n_meas)**0.5\n", + " delta2 = (sigma_sm/sigma_bsm) * np.exp(-((mu_bsm - mu_sm)**2)/(\n", + " 2 * sigma_bsm**2))/(2*(n_meas**0.5))\n", + " print('===> delta1 = %.3f, delta2 = %.3f'%(delta1, delta2))\n", + " deltap = (delta1**2 + delta2**2)**0.5\n", + " \n", + " results_path = os.getcwd()\n", + " \n", + " if verbose_t:\n", + " print('p = %.3f +/- %.3f' %(p, deltap))\n", + " print('Separation = %.2f sigmas'%(sep))\n", + " training_properties = '/toydata/madminer-carl-'+title\n", + " plot_histogram(tsm, tbsm, int(NSM), int(NBSM), p, deltap, sep, epochs, e, \n", + " training_properties, results_path)\n", + " print('Partial test after %d epochs (took %.2f seconds)\\n'\n", + " %(e, test_duration))\n", + " \n", + " \n", + " return sep, p" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_histogram(tsm, tbsm, nsm, nbsm, p, deltap, sep, epochs, \n", + " e, training_properties, results_folder):\n", + " mint = torch.min(torch.cat((tsm, tbsm))).item()\n", + " maxt = torch.max(torch.cat((tsm, tbsm))).item()\n", + " \n", + " # for some reason the code complains if i don't detach the variables \n", + " # from their grad-on versions\n", + " tsm, tbsm = tsm.detach(), tbsm.detach()\n", + " \n", + " bins = np.linspace(mint, maxt, 60)\n", + " plt.figure(figsize=(8, 6))\n", + " ax = plt.subplot()\n", + " plt.hist(tsm, bins, alpha=0.5, label='SM')\n", + " plt.hist(tbsm, bins, alpha=0.5, label='BSM')\n", + " plt.legend(loc='upper right')\n", + " \n", + " sn = 'nsm = %s \\nnbsm = %s'%(str(nsm), str(nbsm))\n", + " sp = 'p '+'= '+ ('%.3f +/- %.3f'%(p, deltap))\n", + " ssep = 'sep ' + '= ' + ('%.3f'%(sep))\n", + " \n", + " plt.text(x=0.05, y=0.85, transform=ax.transAxes, \n", + " s=sn+'\\n'+sp+'\\n'+ssep, bbox=dict(facecolor='blue', alpha=0.2))\n", + " plt.xlabel('t')\n", + " plt.ylabel('p(t)')\n", + " if epochs == e:\n", + " plt.title('Final test\\n' + training_properties)\n", + " filename = results_folder + training_properties \\\n", + " + ' histogram.pdf'\n", + " plt.savefig(filename)\n", + " return \n", + " plt.title(training_properties)\n", + " plt.show()\n", + " \n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def TestEstimator(gphival, gphival_name='', withXS=True, title_message=''):\n", + " \n", + " #toy data file path\n", + " if not gphival_name:\n", + " gphival_name = gphival\n", + " f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%s_out.h5'%(gphival_name), 'r')\n", + "\n", + " #parse data \n", + " Data = np.array(f['Data'])\n", + " Labels = np.array(f['Labels'])\n", + " NSM = np.array(f['NSM'])\n", + " \n", + " if withXS:\n", + " NBSMList = np.array(f['NBSMList'])\n", + " NBSM = NBSMList[0]\n", + " else:\n", + " NBSM = NSM\n", + " \n", + " #randomise\n", + " Idx_test = torch.randperm(len(Data))\n", + " Data_test = torch.Tensor(Data[Idx_test])\n", + " Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + " #select data from each hypothesis\n", + " SM_Data = Data_test[Label_test==0, :]\n", + " BSM_Data = Data_test[Label_test==1, :]\n", + "\n", + " #for plotting/ printing\n", + " n_epochs = current_epoch = int(1e4)\n", + " results_path = os.getcwd()\n", + " charge = 'plus'\n", + "\n", + " #number of tests thrown on the data\n", + " #the test function will stop automatically if points run out\n", + " n_meas = 4000\n", + "\n", + " if withXS:\n", + " sep, p = test_model(estimator, SM_Data, BSM_Data, gphival, NSM, NBSM, n_epochs, current_epoch, n_meas, charge, \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%s_new'%(gphival_name)+title_message)\n", + " else:\n", + " sep, p = test_model(estimator, SM_Data, BSM_Data, gphival, NSM, NBSM, n_epochs, current_epoch, n_meas, charge, \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%s_new_equalXS'%(gphival_name)+title_message)\n", + " \n", + " f.close()\n", + " \n", + " return sep, p" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reading Model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from madminer import ParameterizedRatioEstimator\n", + "from madminer.ml.morphing_aware import MorphingAwareRatioEstimator\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Evaluating with knowledge on the Cross Section, gphi=35e-1\n", + "NSM = 2225.504 --- NBSM = 2259.447\n", + "test 0 : tsm = 974.402, tbsm = 1023.880\n", + "Reaching the end of test data. Stop tests at 221. \n", + "===> delta1 = 0.032, delta2 = 0.029\n", + "p = 0.344 +/- 0.043\n", + "Separation = 0.40 sigmas\n", + "Partial test after 10000 epochs (took 89.21 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=35e-1\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 852.413, tbsm = 981.419\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.031, delta2 = 0.027\n", + "p = 0.304 +/- 0.041\n", + "Separation = 0.55 sigmas\n", + "Partial test after 10000 epochs (took 80.06 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=5\n", + "NSM = 2225.504 --- NBSM = 2273.448\n", + "test 0 : tsm = 1909.771, tbsm = 1729.189\n", + "Reaching the end of test data. Stop tests at 219. \n", + "===> delta1 = 0.030, delta2 = 0.030\n", + "p = 0.279 +/- 0.042\n", + "Separation = 0.55 sigmas\n", + "Partial test after 10000 epochs (took 78.67 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=5\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 1672.722, tbsm = 1767.625\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.028, delta2 = 0.024\n", + "p = 0.237 +/- 0.037\n", + "Separation = 0.85 sigmas\n", + "Partial test after 10000 epochs (took 80.19 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=10\n", + "NSM = 2225.504 --- NBSM = 2325.162\n", + "test 0 : tsm = 6773.693, tbsm = 6014.848\n", + "Reaching the end of test data. Stop tests at 214. \n", + "===> delta1 = 0.025, delta2 = 0.023\n", + "p = 0.164 +/- 0.034\n", + "Separation = 0.92 sigmas\n", + "Partial test after 10000 epochs (took 77.80 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=10\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 6501.977, tbsm = 6120.047\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.012, delta2 = 0.005\n", + "p = 0.031 +/- 0.013\n", + "Separation = 1.97 sigmas\n", + "Partial test after 10000 epochs (took 80.47 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=20\n", + "NSM = 2225.504 --- NBSM = 2436.280\n", + "test 0 : tsm = 25280.783, tbsm = 24452.465\n", + "Reaching the end of test data. Stop tests at 205. \n", + "===> delta1 = 0.019, delta2 = 0.014\n", + "p = 0.083 +/- 0.024\n", + "Separation = 1.36 sigmas\n", + "Partial test after 10000 epochs (took 74.74 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=20\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 25980.424, tbsm = 21080.518\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 3.82 sigmas\n", + "Partial test after 10000 epochs (took 79.88 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=30\n", + "NSM = 2225.504 --- NBSM = 2558.585\n", + "test 0 : tsm = 55401.312, tbsm = 51724.551\n", + "Reaching the end of test data. Stop tests at 195. \n", + "===> delta1 = 0.010, delta2 = 0.004\n", + "p = 0.021 +/- 0.011\n", + "Separation = 2.10 sigmas\n", + "Partial test after 10000 epochs (took 72.05 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=30\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 55282.211, tbsm = 46738.117\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 6.12 sigmas\n", + "Partial test after 10000 epochs (took 78.30 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=40\n", + "NSM = 2225.504 --- NBSM = 2693.988\n", + "test 0 : tsm = 101598.641, tbsm = 97015.547\n", + "Reaching the end of test data. Stop tests at 185. \n", + "===> delta1 = 0.005, delta2 = 0.001\n", + "p = 0.005 +/- 0.006\n", + "Separation = 2.55 sigmas\n", + "Partial test after 10000 epochs (took 67.52 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=40\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 98050.070, tbsm = 66229.445\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 7.79 sigmas\n", + "Partial test after 10000 epochs (took 78.45 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=45\n", + "NSM = 2225.504 --- NBSM = 2766.000\n", + "test 0 : tsm = 126882.734, tbsm = 113164.922\n", + "Reaching the end of test data. Stop tests at 180. \n", + "===> delta1 = 0.006, delta2 = 0.000\n", + "p = 0.006 +/- 0.006\n", + "Separation = 3.04 sigmas\n", + "Partial test after 10000 epochs (took 66.71 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=45\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 130518.664, tbsm = 91053.617\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 9.46 sigmas\n", + "Partial test after 10000 epochs (took 79.84 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=50\n", + "NSM = 2225.504 --- NBSM = 2841.340\n", + "test 0 : tsm = 158001.172, tbsm = 131898.125\n", + "Reaching the end of test data. Stop tests at 175. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 2.99 sigmas\n", + "Partial test after 10000 epochs (took 66.30 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=50\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 155325.922, tbsm = 116358.719\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 10.12 sigmas\n", + "Partial test after 10000 epochs (took 79.30 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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lq4HF+AtAhFeAFr4c451zi4EnCC4C6/z6ZkHG3d25HNxP2m4AHrSgi8f7CC6YADjnviMIDmMJ7h42k7UaOF76ELzcs9iv9x2CZ9d59VeCZ5ZLCV4ASn/D+SmCl2A2EOz7g/o5oHNuA8GLOv8iuOC3IPjH7c+I2eYCTf06hwDdnXPRjxzyoitBdeN2gp99PeM/+O9lF4L9toXg5awuUd/XfHHObSd4ttyLoIbgN+BRMgeEGcBG59zKiHEDvs7DKnI6xi8TdGn6P5/Xe1HL3ktwB7eZ4HHB2IhpuZ1jkZ4neDdgpt9mR/DC5K0WvIm+hSBQLzSzHQTfl/cJjjcEd6Wzs+RKxjHpRvDMdxPBOwPvRUyf59f1rN+OH/y82cnpXN3qp4/w2/072Zyr/rHUEGCWv86cks36JvgybyY4n7o55/bkdE3yPiOo6fnNzDb4tDuAhcB//b54lFIQ19StZwEys4nAs865g/rds5k1IAgMh/u7jULl/+t+1jnXNteZpVD4KsVVwOXOuWlmdhXBC0bti7Zk4WZmjdj/VnNa0ZZmPzMbAbztnJtc1GWR4qdENgdXhKYT/LzkgPkL9D8I/lsv9KAd4WCreuUgWfD737kE1cYDCO44c7rLkxLCOReXpnClZFDgLkDOuX/lPlf2/Itn6wiq484rkEIdAOfcV0W1bsmkHUFVbXq1bxfn3M6cFyk8ZvYdES9aRrjOOfd6jPSCXPeObCadn149LVJSqapcREQkREr8Q3wREZGSJG6B28yO8D/qX2xB4xK3+PTBFrQfvMB/OserDCIiIiVN3KrKzawOQRuzX/vfD84n+FlJD2CHc+7xvOZVo0YN16hRo7iUU0REpLiZP3/+Bt+qXhZxeznNN4Sw1g9vN7Ml7G/WM18aNWrEvHnzCrJ4IiIixZaZZdscb6G8Ve5/K9mG4KctpwE3mVkfggYlbs+m7ed+QD+ABg2iWyU8MHPmLGTLlgNuN0JCJCmpPKec0qqoiyEiUuDiHrjNLBF4F7jVObfNzF4AHiJo2u4hgpZyro5ezjmXQtBAP8nJyQVSn79ly25q1jyxILKSYi41dX5RF0FEJC7i+la5b9v2XYIu7d6DoDcp59xe387sy2RuY1dERERyELc7bt/e9SvAEufckxHpdfzzbwjaSF4UrzKIiEi47dmzh1WrVrFr166iLkpcVKxYkfr161OuXLk8LxPPqvLTCBqQX2hmC3zaXcBlZtaaoKr8F+C6OJYhVJYtW8Ajj1zP779vo0yZslx99d2cc05PAO6553IWL55HQkI5WrZsy913v0RCQjk++eR1Ro9+FOcclSpVZuDAF2jW7HgALrqoEYceWpmyZctStmwCr72mF/xEJFxWrVpF5cqVadSoEZl7Mg0/5xwbN25k1apVNG7cOM/LxfOt8i+I3U/tQXXAUZJVrHgoDzzwKg0aNCU1dQ1XXHEi7dqdS+XKSZx33uU89NAYAO6+uzfjx4+ge/frqVu3MSkpMzjssKrMmvUJQ4b0Y/TouRl5vvTSNJKSsuuHXkSkeNu1a1eJDNoAZkb16tVJTU3N13Kluq3yNWt+oX//82nduj3ffjubmjXr8cQTE6hY8RDGjRvOu+++SNmyCTRu3IKhQ8fx0kuDWbPmZ1av/onfflvBP/4xjIUL5zB79ifUqlWPYcM+JCEh79Ud0Ro2bJYxXLNmXapVq8XmzalUrpxE+/b726lp2bIt69YFPesdf/ypGemtWp3C+vWF1TumiEjhKIlBO92BbFupb/J05crlXHrpjbz11ndUrpzEZ5+9C8CoUY/w+uvfMG7ct9x114sZ869a9SMvvvgZTz75AffeewXJyR15882FVKhwCF988XGW/F999TF6926d5fPYY/1zLNeiRV+xZ89u6tc/KlN6WtoeJk58jVNPzdoHyYQJr3DqqednjJsZN954DldccSLvvZeSr/0iIiKBIUOG0LJlS4477jhat27N3Llz6dChAw0aNCCyEbMuXbqQmJgY9/KU6jtugLp1G3P00a0BaN78RNas+QWApk2P4557LqdDhy506NAlY/5TTz2fhIRyNGnSin379mYE0CZNWmUsG6lPnwH06TMgX2XasGEt9933Vx54YDRlymT+3+qRR27ghBPOoE2b0zOlz5s3jQkTXmHEiC8y0kaM+IJateqxadN6brzxbBo1as4JJ5yRr7KIiBQnw6Z+X6D53XZ2sxynf/nll3z00Ud8/fXXVKhQgQ0bNrB7d9AeSFJSErNmzaJ9+/Zs2bKFtWvX5phXQSn1gbtcuQoZw2XLluXPP4NeE5966mO++eZzPv/8Q0aOHMK4cQsBKF8+mL9MmTIkJJTLqOYwK8PevWlZ8n/11ceYNClrD4dt2pzBgAHDs6Tv2LGNW265gBtuGEKrVqdkmpaS8gCbN6dy110vZUpfvvxbHnqoL8OHf0JSUvWM9Fq1gobqqlWrRYcOXfnuu68UuEVE8mHt2rXUqFGDChWCa3+NGvvfGerVqxfjxo2jffv2vPfee3Tr1o3vvvsu7mUq9YE7ln379rFu3UqSkzvSunV7pkwZx86d2XX/m7P83HHv2bObAQO6csEFfTjrrO6Zpo0fP4I5cybz/POfZroL/+23FQwY0I0HH3wt0zPynTt/Z9++fVSqVJmdO39n7twp9O173wFtg4hIaXXOOefw4IMP0qxZM8466yx69uzJmWeeCUCnTp249tpr2bt3L+PGjSMlJYWHHnoo7mVS4I5h37693HvvFezYsRXnHL169ady5aS4r3fq1Lf4+uvP2bp1Ix99NAqA++8fxdFHt2bo0L9z+OENufrqdgB07NiNa6+9j5dffpCtWzfy6KM3AGT87GvjxnUMGNAVgL170zj33N4xn4uLiEj2EhMTmT9/PjNnzmTatGn07NmTRx55BAhqadu3b8+4cePYuXMnhdUZVtx6BytIycnJriA6GZk0ab6aPC0lUlPnc955OtYiYbdkyRKOOeaYjPHCfsYd7Z133mH06NFs376dxx9/nD/++IOuXbsyePBgbr75ZhITE9mxI381tNHbCGBm851zybHm1x23iEgxcqCBKb8BSPJm2bJllClThqZNmwKwYMECGjZsyKJFQaOfp59+OoMGDeKyyy4rtDIpcIuIiGRjx44d3HzzzWzZsoWEhASaNGlCSkoK3bsH7yGZGXfccUehlkmBW0REQqOwaxZOPPFEZs+enSV9+vTpMefPbzX5gSj1DbBkp1+/DixeXDza9t616w9uueUC/vKX5vTo0ZJnnhmYMW3MmCe59NIW9Op1HNdf34m1a4O+15ctW8Df/taOHj1a0qvXcUyZ8mbGMoMHX8XFFzfOaAxm2bIFWdYpIiLFk+64Q+Kvf72D5OSO7Nmzm+uv78SsWZ9w2mnn07x5G7p3n0fFiofyzjsvMHz4nQwd+maO7Z4D9O//WJafnImISPFXqgN3Tm2VA0yc+BoPP9yXtLQ07rtvJMce25b582fwxBO3+ByMl1/+nCVL5pOScj+JiUn8+ONCzjqrB02atOKNN57mzz938sQT47M0XZofFSseSnJyRwDKlStP8+YnZLRJnp4OcOyxpzBxYtARSU7tnouISHiV+qry7Noqh6CKeuzYBQwc+DwPPng1AGPGPM6ddz7H2LELGDFiJhUqBEH+++//x113vcjbby9h4sTXWLHie1599Su6dOnLm28+k2W98+ZNi9mG+dVXn5pl3kjbt29h5swPOemkTlmmRbdVni5Wu+fPP383vXodxxNP3Mbu3X/mbWeJiEiRK9V33JB9W+UA554bvN5/wgln8Pvv29i+fQvHH38aw4b9g/PPv5yOHbtRu3Z9AFq0OIkaNeoAUL/+UZx88jlA0Ib5vHnTsqw3ObkjY8fm79lyWload999GT179qd+/SMzTZs4cQxLlswjJWVGpvRY7Z7fdNNQqlc/nD17dvtuQB/l2mvVqpqISBiU+sCdXVvlkLW7NTPjqqsG0r79BXzxxUSuueY0nn12MrC/DfNgvjIZ49m1YT5v3jSefPK2LOkVKx7KyJFZ32AEGDKkH0cc0ZTevW/NlD537n8YOXIIKSkzMpUju3bP0//BKF++Ahdd9DfGjHk85vpERKT4KfWBOydTprxJcnJHFiz4gsTEKiQmVmHVqh9p0qQVTZq0YvHi//LLL0tJTMz/c+P83nE///w97NixlXvvHZEpfenSb/jnP6/jmWcmUa1arYz0nNo937BhLTVq1ME5x4wZ4znqqGPzXX4RkdKibNmytGrVCuccZcuW5dlnn+XUU0/ljz/+4Nprr+Xbb7/FOUdSUhKTJk0iMTERM+Pyyy9nzJjgvaO0tDTq1KnDySefzEcffXRQ5VHgzkGFChXp3bsNaWl7uO++kQCMHfsU8+ZNo0yZMhx5ZEtOPfV8vv32y7iWY926VYwcOYRGjZpzxRUnANCjx0106dKX4cMHsHPnDgYOvBSA2rUbMGzYBzm2e37PPZezeXMqzjmOPro1gwa9mN2qRUSKl2lDCza/joNyneWQQw5hwYLgRmvy5MkMGjSIGTNm8PTTT1O7dm0WLgx6j1y2bBnlypUDoFKlSixatIidO3dyyCGHMHXqVOrVq1cgRS7Vgbtu3Ua89daijPG//nV/6zcpKdNjLnPnnVlfNEtO7kBycoeYy0ZPOxC1a9dn3rzYbco///x/YqZ37nwFnTtfEXPaiy9+dlDlEREprbZt20bVqlWBoMvPhg0bZkw7+uijM83buXNnPv74Y7p3784bb7zBZZddxsyZMw+6DKX+rXIREZGc7Ny5k9atW9O8eXP69u3LvffeC8DVV1/No48+Srt27bjnnntYvnx5puXS++vetWsX3377LSeffHKBlEeBW0REJAfpVeVLly5l0qRJ9OnTB+ccrVu35qeffmLAgAFs2rSJk046iSVLlmQsd9xxx/HLL7/wxhtv0Llz5wIrT6muKhcREcmPdu3asWHDBlJTU6lVqxaJiYl069aNbt26UaZMGSZOnJipi86LL76YO+64g+nTp7Nx48YCKYPuuIuZ3bv/ZNCgnnTp0oQrrzw50+/KI82ePYlu3Y6mS5cmjBr1SEb66tU/c+WVJ9OlSxMGDerJnj2785VvXkyePI5XXhmS4zxbt27ihhvOpmvXptxww9ls27Y55nwffTSarl2b0rVrUz76aHRG+pIl8+nZsxVdujThscf6k95vfF7zFRGJh6VLl7J3716qV6/OrFmz2Lw5uAbt3r2bxYsXZ3rmDUF1+v3330+rVq0KrAwK3MXMhAmvULlyVcaP/4HevW/jmWf+X5Z59u7dy6OP3sjw4Z/w9tuLmTz5DX76aTEAzzzz/+jd+zbGj/+BypWrMmHCK3nON9KHH47ipZcGx5w2e/YnnHrqeTkuP2rUI7Rt24n3319O27adMv1zkW7r1k28/PIDjBo1l9Gjv+Lllx/ICMRDh17PPfe8zPvvL2flyuXMnj0pz/mKiBSk9GfcrVu3pmfPnowePZqyZcvy448/cuaZZ9KqVSvatGlDcnIyf/nLXzItW79+ffr371+g5SnVVeVr1vzCzTefxzHHnMjSpV9z5JEtefDBV6lY8dAiK9OMGRPo128wAJ06dedf/7oJ51ymxmC+++4rjjiiSUbraeec04sZMybQuPEx/Pe/n/Hww2MBuPDCK0lJGUz37tfnKd+8cM7x/fcLaN78hFy3I/3t+gsvvJJ+/TrQv/+jmeb58svJtG17NlWqVAOgbduzmT17EsnJHfj9920ZjcZ07tyH6dPHc9pp5+cpXxEpwfLw862Ctnfv3pjpffr0oU+fPjGnxeres0OHDnTo0OGgy1Pq77h//XUZ3bvfwDvvLKFSpcN4++3nC3wdffueHrNd8rlzs/6Ua/361dSufQQACQkJJCZWYevWjdnOA1CrVn3Wr1/N1q0bqVw5iYSEhEzpec03L5Yt+4amTY/PNeBv2rQuo4W26tUPZ9OmdVnmSU3NvB21a9cnNXW1L2v9LOl5zVdEpCQr1XfcALVrH0Hr1qcBwW+fx40bnun33AVhxIiD/91eYdiyZSM33BB0XrJ16ybS0nYzY8Z4AB588DWaNGnF7NmTYnZkkhMzy/edfVHmKyJSnJX6wGQYWLEAABjsSURBVB2rPfKC1rfv6fzxx/Ys6bfc8jgnn3xWprRateqxbt1KateuT1paGjt2bKVKleox50m3fv0qatWqR5Uq1dm+fQtpaWkkJCRkpOc136Sk6hnNsH744SjWrPmF664bnGmeOXOm8K9/BT2o3XTTuWzatI5jjknO0hRrtWq1M5pW3bBhLVWr1iJazZr1mD9/esb4unWrOPHEDr6sqzKl16xZL8/5ioiUZKW+qvy331ZkNFk6adJYWrduX+DrGDFiJmPHLsjyiQ7aAGeccXHG29WffvoOJ530f1n+mWjR4iRWrlzO6tU/s2fPbqZMGccZZ1yMmZGc3JFPP30HCN7YPvPMS/Kcb2527NjK3r1pJCUFAf/ZZyczduyCLEEb4Mwz968vshyR2rU7l7lzp7Bt22a2bdvM3LlTaNfuXGrUqEOlSoexcOEcnHNMnPhqxvJ5yVdESpb0X5WURAeybaU+cDdseDRvv/0c3bsfw7Ztm+ne/foiLc8ll1zD1q0b6dKlCa+//iQ33RS8NZ2auob+/YMf8CckJDBgwLPcfPO5dO9+DGed1YOjjmoJwM03P8rrrz9Jly5N2Lp1I5dcck2O+ebHnDlTads26z8bsVx55UDmzp1K165N+eqr/3DVVQMBWLx4Hg891BeAKlWqcc0199Knz0n06XMSffvel/Gi2sCBz/PQQ33p0qUJ9eodxWmnnZ9jviJSMlWsWJGNGzeWyODtnGPjxo1UrFgxX8tZGHZGcnKymzdv3kHnM2nSfGrWPDFjfM2aX7j11gsztVcu2QsCad9MXYQWV6mp8znvvBNzn1GkmBk29fsDWu62s5sVcEmKhz179rBq1Sp27dpV1EWJi4oVK1K/fv2MzknSmdl851xyrGVK/TNuybtYVeIiIvFUrlw5GjduXNTFKFZKdVV5dO9gIiIixZ3uuIuxqVPfIiVlMGZG06bHM2TI2CzzPPfc3Uyc+Crbtm1m5sz9P/gfM+ZJJkwYQdmyCVStWpP77htJnToNWbv2V+64oyvO7SMtbQ89etxM9+5/L8zNEin1DrQ6XAQUuIutFSuW8+9/D+WVV2Zx2GFV2bRpfcz5zjjjInr2vImuXZtmSm/evA3du8+jYsVDeeedFxg+/E6GDn2TGjXq8O9/f0n58hX4448d9Ox5LGeeeTE1a9YtjM0SEZGDVKoD986dvzNwYA/Wr1/F3r176dv3Xs45pydLlsxn2LB/8McfO0hKqsHgwaOoUaMO/fp1oFmz4/n66xmkpaVx330jOfbYtnEp2/vvv0yPHjdy2GFBh+3VqsX+vXJ2L4olJ3fMGD722FOYOHEMAOXKlc9I3737T/bt21dQRRYRkUJQqgP37NmTqFmzLk8//TEQ/E45LW0Pjz12M088MYGqVWsyZcqbPPfc3dx//0gAdu36g7FjF/D115/z4INX5+sZeX4aYlmxIqhKu/rq09i3by/9+g3OtWOP7EyY8Eqm1s5++20lt956AStX/sAttzymu20RkRAp1YG7SZNWPPXU7Qwf/v84/fQLadPmdH74YRE//riIG288Gwgal09vGxvg3HMvA+CEE87g99+3sX37FipXTsrT+vLT9OnevWmsXLmclJTprFu3in79zmDcuIV5Xle6iRPHsGTJPFJSZmSkHX74EYwb9y2pqWu4/fYudOrUnerVa+crXxERKRqlOnA3bNiMMWO+Ztasibzwwj2cdFInOnbsypFHtuTf//4y5jIH00Rq/po+rc+xx55MQkI56tVrTIMGzVixYjktW56U5/XNnfsfRo4cQkrKDMqXr5Bles2adTnqqGP55puZnHVW9zznKyIiRadUB+7U1DUcdlg1One+gsqVkxg/fgRXXTWQzZtT+fbbLznuuHakpe3h11+/z2iZbMqUN0lO7siCBV+QmFiFxMQqeV5ffu64O3TowuTJb3DxxX9jy5YNrFjxPfXqHZnn5Zcu/YZ//vM6nnlmUqbn4+vWraJKlepUrHgI27Zt5n//+4LLL78tz/mKiEjRKtWB+4cfFvL00wMoU6YMCQnlGDjwBcqVK8+jj77D44/3z2ib+7LLbs0I3BUqVKR37zakpe3hvvtGxq1s7dqdy5w5U7j00haUKVOW/v0fy2gjvHfv1hmdgTz99J1MnjyWXbv+oHPn+lxySV+uu24ww4cPYOfOHQwceCkAtWs3YNiwD/j55yU89dTtmBnOOa644g6aNGkVt+0QEZGCVaqbPM2vfv06cOutj9OiRcxW6KQYUZOnUpzF43fcJbXJ09IqpyZPS3XLaSIiImFTqqvK8yslZXpRF0FEREo53XGLiIiEiAK3iIhIiChwi4iIhEipesadlFSe1NT5RV0MKQRJSeVzn0lEJIRKVeA+5RT9XllERMJNVeUiIiIhosAtIiISIgrcIiIiIaLALSIiEiIK3CIiIiGiwC0iIhIiCtwiIiIhErfAbWZHmNk0M1tsZt+Z2S0+vZqZTTWz5f5v1XiVQUREpKSJ5x13GnC7c64FcApwo5m1AAYCnzrnmgKf+nERERHJg7gFbufcWufc1354O7AEqAdcAoz2s40GusSrDCIiIiVNoTzjNrNGQBtgLlDbObfWT/oNqJ3NMv3MbJ6ZzUtNTS2MYoqIiBR7cQ/cZpYIvAvc6pzbFjnNOecAF2s551yKcy7ZOZdcs2bNeBdTREQkFOIauM2sHEHQft05955PXmdmdfz0OsD6eJZBRESkJInnW+UGvAIscc49GTHpA+BKP3wlMCFeZRARESlp4tmt52nAX4GFZrbAp90FPAK8ZWbXAL8CPeJYBhERkRIlboHbOfcFYNlM7hSv9YqIiJRkajlNREQkRBS4RUREQkSBW0REJEQUuEVEREJEgVtERCREFLhFRERCRIFbREQkROLZAIuUFNOGHthyHQcVbDlERER33CIiImGiwC0iIhIiCtwiIiIhosAtIiISIgrcIiIiIaLALSIiEiIK3CIiIiGiwC0iIhIiCtwiIiIhosAtIiISIgrcIiIiIaLALSIiEiLqZETiJ6fOSdQBiUixNmzq99lOu+3sZoVYEommO24REZEQUeAWEREJEQVuERGREFHgFhERCREFbhERkRBR4BYREQkRBW4REZEQUeAWEREJEQVuERGREFHgFhERCREFbhERkRBR4BYREQkRBW4REZEQUeAWEREJEQVuERGREFHgFhERCREFbhERkRBR4BYREQkRBW4REZEQUeAWEREJEQVuERGREFHgFhERCREFbhERkRBR4BYREQkRBW4REZEQSSjqAkgBmzY0+2kdBxVeOXKTXTmLUxlFRIoh3XGLiIiEiAK3iIhIiChwi4iIhIgCt4iISIgocIuIiISIAreIiEiIKHCLiIiEiAK3iIhIiMQtcJvZSDNbb2aLItIGm9lqM1vgP53jtX4REZGSKJ533KOA82KkD3POtfafiXFcv4iISIkTt8DtnPsc2BSv/EVEREqjonjGfZOZfeur0qsWwfpFRERCq7A7GXkBeAhw/u8TwNWxZjSzfkA/gAYNGhRW+aS0CUunLFJkhk39Pttpt53drBBLEh85bZ8UT4V6x+2cW+ec2+uc2we8DLTNYd4U51yycy65Zs2ahVdIERGRYqxQA7eZ1YkY7Qosym5eERERySpuVeVm9gbQAahhZquA+4EOZtaaoKr8F+C6eK1fRESkJIpb4HbOXRYj+ZV4rU9ERKQ0UMtpIiIiIaLALSIiEiIK3CIiIiGiwC0iIhIiCtwiIiIhosAtIiISIgrcIiIiIaLALSIiEiKF3cmISM5y6vQjJ+oQRCRb6kikZNEdt4iISIgocIuIiISIAreIiEiIKHCLiIiEiAK3iIhIiChwi4iIhIgCt4iISIgocIuIiISIAreIiEiIKHCLiIiESL4Ct5lVMrOy8SqMiIiI5CzHwG1mZcyst5l9bGbrgaXAWjNbbGaPmVmTwimmiIiIQO6djEwD/gMMAhY55/YBmFk1oCPwqJm975wbE99iStwdaOcexUVO5VcHJCJSguQWuM9yzu2JTnTObQLeBd41s3JxKZmIiIhkkWNVeXrQNrPXoqelp8UK7CIiIhIfeX05rWXkiH9B7cSCL46IiIjkJLeX0waZ2XbgODPb5j/bgfXAhEIpoYiIiGTIrap8qHOuMvCYc+4w/6nsnKvunNMbPyIiIoUstzvuRgDZBWkL1C/4YomIiEgsub1V/piZlSGoFp8PpAIVgSYEPwfrBNwPrIpnIUVERCSQY+B2zl1qZi2Ay4GrgcOBncASYCIwxDm3K+6lFBERESAPb5U75xYDDwMfEgTsn4H/Au8oaIuIiBSu3KrK040GtgHD/Xhv4FWgRzwKJSIiIrHlNXAf65xrETE+zcwWx6NAIiIikr28Bu6vzewU59wcADM7GZgXv2KJiEhxNWzq99lOu+3sZoVYktIpr4H7RGC2ma3w4w2AZWa2EHDOuePiUjoRERHJJK+B+7y4lkJERETyJE+B2zn3a7wLIiIiIrnLaycjIiIiUgwocIuIiISIAreIiEiIKHCLiIiEiAK3iIhIiChwi4iIhIgCt4iISIgocIuIiISIAreIiEiI5LXJUylOpg0t6hKISC5y6ohD5GDojltERCREFLhFRERCRIFbREQkRBS4RUREQkSBW0REJEQUuEVEREJEgVtERCREFLhFRERCRIFbREQkROIWuM1spJmtN7NFEWnVzGyqmS33f6vGa/0iIiIlUTzvuEcB50WlDQQ+dc41BT714yIiIpJHcQvczrnPgU1RyZcAo/3waKBLvNYvIiJSEhX2M+7azrm1fvg3oHZ2M5pZPzObZ2bzUlNTC6d0IiIixVyRvZzmnHOAy2F6inMu2TmXXLNmzUIsmYiISPFV2IF7nZnVAfB/1xfy+kVEREKtsAP3B8CVfvhKYEIhr19ERCTU4vlzsDeAL4GjzWyVmV0DPAKcbWbLgbP8uIiIiORRQrwyds5dls2kTvFap4iISEmnltNERERCRIFbREQkRBS4RUREQkSBW0REJEQUuEVEREJEgVtERCREFLhFRERCJG6/4xYRCZNhU78vlGVKupz2yW1nNyvEkpRcuuMWEREJEQVuERGREFHgFhERCREFbhERkRBR4BYREQkRBW4REZEQUeAWEREJEQVuERGREFHgFhERCREFbhERkRBR4BYREQkRBW4REZEQUScjUvJNG1q4eXYcVPDrExHxdMctIiISIgrcIiIiIaLALSIiEiIK3CIiIiGiwC0iIhIiCtwiIiIhosAtIiISIgrcIiIiIaLALSIiEiIK3CIiIiGiwC0iIhIiCtwiIiIhok5GirOC7hwjHp1tiEixMGzq90VdBCkkuuMWEREJEQVuERGREFHgFhERCREFbhERkRBR4BYREQkRBW4REZEQUeAWEREJEQVuERGREFHgFhERCREFbhERkRBR4BYREQkRBW4REZEQUScjIoUlp05eOg4qvHKISKjpjltERCREFLhFRERCRIFbREQkRBS4RUREQkSBW0REJEQUuEVEREJEgVtERCREFLhFRERCRIFbREQkRIqk5TQz+wXYDuwF0pxzyUVRDhERkbApyiZPOzrnNhTh+kVEREJHVeUiIiIhUlR33A6YYmYOeMk5lxI9g5n1A/oBNGjQoJCLJ0LOnYLEYzkpEMOmfl/URZADkNNxu+3sZoVYkuKvqO642zvnTgDOB240szOiZ3DOpTjnkp1zyTVr1iz8EoqIiBRDRRK4nXOr/d/1wPtA26Ioh4iISNgUeuA2s0pmVjl9GDgHWFTY5RAREQmjonjGXRt438zS1z/WOTepCMohIiISOoUeuJ1zPwHHF/Z6RURESgL9HExERCREFLhFRERCRIFbREQkRBS4RUREQkSBW0REJEQUuEVEREJEgVtERCREirJbTxERKUUOtAMYdUCSme64RUREQkSBW0REJEQUuEVEREJEgVtERCREFLhFRERCRIFbREQkRBS4RUREQkSBW0REJEQUuEVEREJEgVtERCREFLhFRERCRIFbREQkRNTJSFGbNrSoSyBSLKljCZHYdMctIiISIgrcIiIiIaLALSIiEiIK3CIiIiGiwC0iIhIiCtwiIiIhosAtIiISIgrcIiIiIaLALSIiEiIK3CIiIiGiwC0iIhIiCtwiIiIhok5GRKTI5NSRiIjEpjtuERGREFHgFhERCREFbhERkRBR4BYREQkRBW4REZEQUeAWEREJEQVuERGREFHgFhERCREFbhERkRBR4BYREQkRBW4REZEQUeAWEREJkdLZyci0oQe2XMdBhbs+KT0K+ztyoN/lbBR2ZyHqnETyojC/J7ed3azQ1qU7bhERkRBR4BYREQkRBW4REZEQUeAWEREJEQVuERGREFHgFhERCREFbhERkRBR4BYREQmRIgncZnaemS0zsx/MbGBRlEFERCSMCj1wm1lZ4DngfKAFcJmZtSjscoiIiIRRUdxxtwV+cM795JzbDYwDLimCcoiIiIROUQTuesDKiPFVPk1ERERyUWw7GTGzfkA/P7rDzJYdQDY1gA0FV6q7Ci6r0qeAj4UchBpwl45F8aDz4iD9o+CyOqhjUYDlSNcwuwlFEbhXA0dEjNf3aZk451KAlINZkZnNc84lH0weUjB0LIoPHYviQ8ei+AjTsSiKqvL/Ak3NrLGZlQd6AR8UQTlERERCp9DvuJ1zaWZ2EzAZKAuMdM59V9jlEBERCaMiecbtnJsITCyEVR1UVbsUKB2L4kPHovjQsSg+QnMszDlX1GUQERGRPFKTpyIiIiESmsBtZmXN7Bsz+8iPNzazub7Z1Df9i26YWQU//oOf3igij0E+fZmZnRuRriZY8yHGsRhlZj+b2QL/ae3TzcyG+/36rZmdEJHHlWa23H+ujEg/0cwW+mWGm5kV/haGg5n94vfVAjOb59OqmdlUv1+nmllVn65jEUfZHIvBZrY64rzoHDF/vq5F2V3vJCszSzKzd8xsqZktMbN2Je68cM6F4kPwM7mxwEd+/C2glx9+EbjeD98AvOiHewFv+uEWwP+ACkBj4EeCl+PK+uEjgfJ+nhZFvb3F+RPjWIwCuseYrzPwCWDAKcBcn14N+Mn/reqHq/ppX/l5zS97flFvb3H9AL8ANaLS/gUM9MMDgUd1LIrsWAwG7ogxb76vRdld7/SJeSxGA339cHkgqaSdF6G44zaz+sAFwAg/bsD/Ae/4WUYDXfzwJX4cP72Tn/8SYJxz7k/n3M/ADwTNr6oJ1nyIPha5uAR41QXmAElmVgc4F5jqnNvknNsMTAXO89MOc87NccEZ8ir7j6vkTeT3P/q80LEoHvJ1LcrleicRzKwKcAbwCoBzbrdzbgsl7LwIReAGngLuBPb58erAFudcmh+PbDY1o0lVP32rnz+7plbVBGv+RB+LdEN8VdMwM6vg0/K7z+v54eh0ic0BU8xsvgUtDQLUds6t9cO/AbX9sI5FfMU6FgA3+fNiZHr1LPk/Fjld7ySzxkAq8G//OG+EmVWihJ0XxT5wm9mFwHrn3PyiLktpl8OxGAQ0B04iqFr6f4VdtlKqvXPuBIKe9m40szMiJ/o7Av1spHDEOhYvAEcBrYG1wBNFWL7SIgE4AXjBOdcG+J2gajxDSTgvin3gBk4DLjazXwiqjv4PeJqgSiP9d+iRzaZmNKnqp1cBNpJ9U6t5aoJVgBjHwszGOOfW+qqmP4F/E1T5Qf73+Wo/HJ0uMTjnVvu/64H3Cfb7Ol+dh/+73s+uYxFHsY6Fc26dc26vc24f8DIHfl5sJPvrnWS2CljlnJvrx98hCOQl6rwo9oHbOTfIOVffOdeI4GWzz5xzlwPTgO5+tiuBCX74Az+On/6Z/w/rA6CXBW+dNwaaErxkoCZY8yibY3FFxAlhBM97FvlFPgD6+Dc3TwG2+uqqycA5ZlbVVx+eA0z207aZ2Sk+rz7sP64SwcwqmVnl9GGCfbiIzN//6PNCxyIOsjsW6eeF15XM50Wer0X++pXd9U4iOOd+A1aa2dE+qROwmJJ2XhT223AH8wE6sP9N5iMJvuw/AG8DFXx6RT/+g59+ZMTydxO8tbmMiDcBCd4s/N5Pu7uotzMMn6hj8RmwkODCNAZI9OkGPOf360IgOWL5q/0x+gH4W0R6ss/nR+BZfCNB+mTZ/0cSvHX8P+C79O8twfPQT4HlwH+AajoWRXYsXvP7+luCAFEnYpl8XYuyu97pE/N4tAbm+f0+nuCt8BJ1XqjlNBERkRAp9lXlIiIisp8Ct4iISIgocIuIiISIAreIiEiIKHCLiIiEiAK3iGThe1i6oajLISJZKXCLSCxJBD3tiUgxo8AtIrE8AhxlQT/SjxV1YURkPzXAIiJZmFkjgpbxji3ioohIFN1xi4iIhIgCt4iISIgocItILNuBykVdCBHJSoFbRLJwzm0EZpnZIr2cJlK86OU0ERGRENEdt4iISIgocIuIiISIAreIiEiIKHCLiIiEiAK3iIhIiChwi4iIhIgCt4iISIgocIuIiITI/wcCBuOZqvH1awAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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baFnAFc65aWZ2DcFNSG0TG1lihD01/wVOcc7tS3Q8UrGpxe236fxyg81BCS/QtxDcdR33pB3hULt65RCFXclpZlaFoHVqFOxJqZCcc3udcycpaUtZoLvKPeac+/Oh7B/eeLaBoDuuS6kEdRCcc58nqmzJow1Bl2lO134359zuoneJHzPbFWXV+eENdiIVgrrKRUREPKKuchEREY8ocYuIiHjEi++4a9Wq5Ro2bJjoMEREROJi3rx5m51z6YWt8yJxN2zYkLlz5x54QxERkXLAzL6Nts6LxF1a5sxZyLZtew+8oXgvLe0wWrdukegwRERKXYVK3Nu27SU9vdBnG0g5s2nTvANvJCLiId2cJiIi4pEK1eIWERG/7Nu3j6ysLPbs2ZPoUGKiatWq1K9fn8qVKxd7HyXuMmT58vk8/PB1/PDDDipVSqJv3yF06tQDgLvvvoIlS+aSnFyZ5s3PZMiQ50hOrsz777/GmDGP4JzjyCNTufPOZ2jS5FcAXHhhQ444IpWkpCSSkpJ55RXd4CcifsnKyiI1NZWGDRuS96my/nPOsWXLFrKysmjUqFGx91PiLkOqVj2C++9/mYyME9i0aR1XXnk6bdp0JjU1jS5druDBB18FYMiQ3kyY8Dzdu19H3bqNGD16BtWqVWfWrPcZNqw/Y8Z8lnvM556bRlparURVSUTkkOzZs6dcJm0AM6NmzZps2rSpRPtV6MS9bt03DBx4Pi1btmXBgtmkp9fj8cffoWrVwxk3bhRvv/0sSUnJNGrUjOHDx/Hcc0NZt+5/rF27iu++W80tt4xg4cI5zJ79PrVr12PEiHdJTi5+d0d+xx7bJHc6Pb0uNWrUZuvWTaSmptG2bdfcdc2bn8mGDcFTKX/1q7Nyl7do0ZqNG+P1tEoRkfgoj0k7x8HUrcLfnLZmzVdcdtkNvPnmYlJT0/joo7cBeOmlh3nttS8ZN24Bd931bO72WVlf8+yzH/GXv/yLe+65kszMDrzxxkKqVDmcTz55r8DxX375UXr3blng59FHBxYZ16JFn7Nv317q1z8+z/Ls7H1MmvQKZ51V8Jkg77zzAmeddX7uvJlxww2duPLK0/nHP0aX6HUREZHAsGHDaN68OaeccgotW7bks88+o3379mRkZBD5vI9u3bqRkpIS83gqdIsboG7dRjRt2hKAE088nXXrvgHghBNO4e67r6B9+260b98td/uzzjqf5OTKNG7cgp9/3p+bQBs3bpG7b6Q+fQbRp8+gEsW0efN67r33Ku6/fwyVKuX9bPXww9dz2mlnc+qp7fIsnzt3Gu+88wLPP/9J7rLnn/+E2rXr8f33G7nhho40bHgip512doliEREpS0ZMXVGqx7u5Y5Mi13/66adMnDiRL774gipVqrB582b27g3GA0lLS2PWrFm0bduWbdu2sX79+lKNLZoKn7grV66SO52UlMRPPwVPMXziiff48suZzJz5Li++OIxx4xYCcNhhwfaVKlUiOblybjeHWSX2788ucPyXX36UyZNfK7D81FPPZtCgUQWW79q1g5tu+g3XXz+MFi1a51k3evT9bN26ibvuei7P8q++WsCDD/Zj1Kj3SUurmbu8du16ANSoUZv27S9h8eLPlbhFREpg/fr11KpViypVgmt/rVq/3DPUs2dPxo0bR9u2bfnHP/7BpZdeyuLFi2MeU8y6ys3sRTPbaGaLCll3q5k5MyuTd039/PPPbNiwhszMDgwc+Ai7dm1n9+5ojwIuWp8+gxg7dn6Bn8KS9r59exk06BJ+85s+nHde9zzrJkx4njlzPmDYsNfztMK/+241gwZdygMPvJLnO/Ldu3/ghx925k5/9tkUjj/+5IOqg4hIRdWpUyfWrFlDkyZNuP7665kxY0buunPPPZeZM2eyf/9+xo0bR48ePeISUyxb3C8BTwEvRy40swZAJ2B1DMs+JD//vJ977rmSXbu245yjZ8+BpKamxbzcqVPf5IsvZrJ9+xYmTnwJgPvue4mmTVsyfPgfOProY+nbtw0AHTpcyu9/fy9/+9sDbN++hUceuR4g99++tmzZwKBBlwCwf382nTv3LvR7cRERiS4lJYV58+bx8ccfM23aNHr06MHDDz8MBL20bdu2Zdy4cezevZt4PQzLIr9YL/WDmzUEJjrnTo5YNh54EHgHyHTObT7QcTIzM11pPGRk8uR5GvK0gti0aR5duui9FvHd0qVLOemkk3Ln4/0dd37jx49nzJgx7Ny5k8cee4wff/yRSy65hKFDhzJgwABSUlLYtatkPbT56whgZvOcc5mFbR/X77jN7GJgrXPuvwe6Bd7M+gP9ATIyMuIQnZRb04ZHX9dhcPz2K2ofESmTli9fTqVKlTjhhBMAmD9/PsceeyyLFgXfArdr147BgwfTq1evuMUUt8RtZkcAdxF0kx+Qc240MBqCFncMQxMRESnUrl27GDBgANu2bSM5OZnGjRszevRouncP7kMyM2677ba4xhTPFvfxQCMgp7VdH/jCzM50zn0XxzhERMRTJe3aPlSnn346s2fPLrB8+vTphW5f0m7ygxG3xO2cWwjUzpk3s28o5nfcidC/f3v++MfHaNas0K8Y4mrPnh+5447LyMr6mqSkJNq1u5ABA4KbIx5//GbmzZuWu933329k+vRtQHDH+YMP9mPDhjWYGSNHTqJu3YY45/jrX+/mww/folKlJLp3v46ePYseEEZERMqGmCVuM3sdaA/UMrMs4D7n3AuxKq+8u+qq28jM7MC+fXu57rpzmTXrfX796/O59dYRuduMG/cky5d/mTt/77196Nt3CK1bd+THH3fl/hvZu+++xIYNaxg/fhmVKlXi++83xr0+IiJycGKWuJ1zRX5T75xrGKuyi6uoscoBJk16hYce6kd2djb33vsiJ598JvPmzeDxx28Kj2D87W8zWbp0HqNH30dKShpff72Q8867nMaNW/D66yP56afdPP74hAJDl5ZE1apHkJnZAYDKlQ/jxBNPK3RM8ilTXqd///sBWLVqCfv3Z9O6dUcAjjjil2H4xo9/hmHDxuYm8ho1ahc4loiIlE0aqzzKWOUQdD2PHTufO+/8Kw880BeAV199jNtvf5qxY+fz/PMfU6VKkORXrPgvd931LG+9tZRJk15h9eoVvPzy53Tr1o833niyQLlz504rdAzzvn3PKrBtpJ07t/Hxx+9yxhnn5lm+fv23rF37P8444/8AWL16BampaQwadCm9e5/KyJGD2L9/PwBr137NlClvcNVVmQwceD6rV3918C+giIjEVYUf8jTaWOUAnTsHnQannXY2P/ywg507t/GrX/2aESNu4fzzr6BDh0upU6c+AM2anUGtWscAUL/+8bRqFdw837hxC+bOnVag3MzMDowdO79EsWZnZzNkSC969BhI/frH5Vn3wQfjOPfc7iQlJeVu++WXH/Paa19y9NEZDB7cg3fffYlu3a5l796fqFKlKq+8MpePPvoHDzzQl+ef/7hEsYiISGJU+MQdbaxyKPi4NTPjmmvupG3b3/DJJ5O49tpf89RTHwC/jGEebFcpdz7aGOZz507jL3+5ucDyqlWP4MUXC97BCDBsWH8aNDiB3r3/WGDdlCnjuOOOp3Pn69SpT9OmLXMTfPv23Vi0aA5wLbVr16dDh0sB6NDhEu6//3eFliciImVPhU/cRZky5Q0yMzswf/4npKQcRUrKUWRlfU3jxi1o3LgFS5b8h2++WUZKSsmHQy1pi/uvf72bXbu2c889zxdY9803y9i5cyunnNImd1mzZmewc+c2tm7dRPXq6cyd+xEnnRTcId++fTfmzp1GvXqNmDdvRp4xzkVEJK+kpCRatGiBc46kpCSeeuopzjrrLH788Ud+//vfs2DBApxzpKWlMXnyZFJSUjAzrrjiCl599VUg6AU95phjaNWqFRMnTjykeJS4i1ClSlV69z6V7Ox93HvviwCMHfsEc+dOo1KlShx3XHPOOut8Fiz4NKZxbNiQxYsvDqNhwxO58srTALj88hvp1q0fEHSTd+rUM08PQVJSEjfd9BjXXXcuzjlOOul0Lrnk9wBcc82d3H33FYwdO4Ijjkjh7rsLfhgQESmTihrR8GAUY0TDww8/nPnzg4bWBx98wODBg5kxYwYjR46kTp06LFwYPD1y+fLlVK5cGYAjjzySRYsWsXv3bg4//HCmTp1KvXr1SiXkCp2469ZtyJtv/vLwsquu+mX0m9Gjpxe6z+23F7zRLDOzPZmZ7QvdN/+6g1GnTn3mzo0+eNz/+39DC13eunVHxo1bUGB5amoaI0e+d0gxiYhURDt27KB69epA8MjPY489Nndd06ZN82zbtWtX3nvvPbp3787rr79Or169+PjjQ7+fqMLfVS4iIlKU3bt307JlS0488UT69evHPffcA0Dfvn155JFHaNOmDXfffTdffZX3P3Rynte9Z88eFixYQKtWrUolHiVuERGRIuR0lS9btozJkyfTp08fnHO0bNmSVatWMWjQIL7//nvOOOMMli5dmrvfKaecwjfffMPrr79O165dSy2eCt1VLiIiUhJt2rRh8+bNbNq0idq1a5OSksKll17KpZdeSqVKlZg0aVKeR3RedNFF3HbbbUyfPp0tW7aUSgxqcZcxe/f+xODBPejWrTFXX90qz/+VR5o9ezKXXtqUbt0a89JLD+cuX7v2f1x9dSu6dWvM4ME92Ldvb+66qVPf5LLLmnH55c0ZMqT3Qce4cOEcHnro997XQ0SkpJYtW8b+/fupWbMms2bNYuvWrQDs3buXJUuW5PnOG4Lu9Pvuu48WLVqUWgxK3GXMO++8QGpqdSZMWEnv3jfz5JN3FNhm//79PPLIDYwa9T5vvbWEDz54nVWrlgDw5JN30Lv3zUyYsJLU1Oq8804wPPzq1V/x978P54UXZvHmm4u59dYnioxj7tzpDB16TaHrZs9+nzZtunhRDxGRQ5XzHXfLli3p0aMHY8aMISkpia+//ppzzjmHFi1acOqpp5KZmclvf/vbPPvWr1+fgQNL9yFOFbqrfN26bxgwoAsnnXQ6y5Z9wXHHNeeBB16matUjEhbTjBnv0L//UADOPbc7f/7zjTjn8vyr1+LFn9OgQePcwVU6derJjBnv0KjRSfznPx/x0ENjAbjggqsZPXoo3btfxz//+Tcuv/wGqlUL7oY8lPHJP//8Q6644hbv6yEiHirGv2+VtpzhovPr06cPffr0KXRdYY/3bN++Pe3btz/keCp8i/vbb5fTvfv1jB+/lCOPrMZbb/211Mvo169doeOSf/bZvwtsu3HjWurUaQBAcnIyKSlHsX37lqjbANSuXZ+NG9eyffsWUlPTSE5OzrMcgrHLv/12BX37/pprrmnN7NmTD6ou27ZtJjm5MikpRxW5XVmvh4iIryp0ixugTp0GtGz5awC6dr2SceNG5fl/7tJQFsYB378/mzVrvmL06Ols2JBF//5nM27cQlJT8476dvXVrdi37yd+/HEXO3Z8T+/ewTjuAwY8Qps2nZkzZwqtW3dKRBWA4tdDRKS8qvCJu7DxyEtbv37t+PHHnQWW33TTY7RqdV6eZbVr12PDhjXUqVOf7Oxsdu3azlFH1Sx0mxwbN2ZRu3Y9jjqqJjt3biM7O5vk5OTc5cE+9Tn55FYkJ1emXr1GZGQ0YfXqr2je/Iw8xx4z5jMg+I574sSXGDr0pTzrZ816P7eb/P77f8fy5V9Sq1ZdRo2aVKbqISJSXlX4rvLvvludO2Tp5MljadmybamX8fzzHzN27PwCP/mTNsDZZ1/ExIljAPjww/Gcccb/Ffgw0azZGaxZ8xVr1/6Pffv2MmXKOM4++yLMjMzMDnz44XgAJk4cwznnXAwE45PPmzcdCLq7V69eQb16eZ8wdiDOOVauXJD7NLX77vs7Y8fOL5C0y3o9RMQvzkUfOdJ3B1O3Ct/iPvbYprz11tM88EBfGjVqRvfu1yU0nosvvpZ7772Kbt0aU61aDf70p3EAbNq0jgcf7MeoUZNITk5m0KCnGDCgM/v37+eii/py/PHNgaBL+667evLMM3fTtOmpXHzxtQC53dyXXdaMSpWSGDjwUdLSakaNozBLl86jadNTi9UrUZbrUSoOZrzkoo/dMVUAABghSURBVPZJwA03Elsjpq6Iuu7mjgf3YJ+ijlmUgy2vLKhatSpbtmyhZs2aMekRTSTnHFu2bKFq1aol2s98+CSTmZnp5s6de8jHmTx5Hunpp+fOr1v3DX/84wV5xiuX6J5//iEaNGhM5849Ex3KAW3aNI8uXcL3+mATZmk/zKAoStzljhJ36di3bx9ZWVns2bMn0aHERNWqValfv37uw0lymNk851xmYftU+Ba3FF+/fncnOgQRqWAqV65Mo0aNEh1GmVKhv+PO/3QwERGRsq5CJ+6ybPz4Z+nRowW9e7fk2mvb5o4oll+0IUNzPProQNq1S8mdf/fdlzjvvPTc/yWfMEHP4hYR8Ym6ysuoLl160737HwCYMeNfjBhxC08+mXewkZwhQ59+eip16tSnT58zOPvsizjuuGYALFkylx07thY4dseOPbjjjqdiXwkRESl1FbrFvXv3D9x002/o1etXXH75yUyZ8gYQ3D3dv/85XHnl6dx4Y2c2b14PQP/+7XnssZvo3bsll19+MosWfR6z2FJSquWJs7C7KSOHDK1c+bDcIUMhSOojRw7ippv+HLMYRUQk/ip0i3v27Mmkp9dl5Mj3ANi1azvZ2ft49NEBPP74O1Svns6UKW/w9NNDuO++FwHYs+dHxo6dzxdfzOSBB/qW6DvykgzEAvDmm0/z2mt/ITt7L88881GB9YUNGbpo0Wfhvk9x9tkXUavWMQX2++ijt/nyy5lkZDThlltGcPTRDQpsIyIiZVOFTtyNG7fgiSduZdSoO2jX7gJOPbUdK1cu4uuvF3HDDR2BoOUamfw6d+4FwGmnnc0PP+xg585txR5us6RDn15++Q1cfvkNTJ48lhdeeIj77x9TrP02bVrHv//9Fs89N73AunbtLqRz514cdlgV3n77OYYOvZpnny34oUBERMqmCp24jz22Ca+++gWzZk3imWfu5owzzqVDh0s47rjm/P3vnxa6z6EMkVrSFneOTp16Mnx4wYFhog0Zunz5l2RlreSSSxoDQS9Bt26NmTBhZZ7BSrp168eoUbcXO34REUm8Cp24N21aR7VqNeja9UpSU9OYMOF5rrnmTrZu3cSCBZ9yyiltyM7ex7ffrsgd0WvKlDfIzOzA/PmfkJJy1AGfkhWpJC3u1au/IiPjBAA++eS93OlIkUOG1q5djylTxvHQQ2M5/vjmfPDBd7nbtWuXwoQJKwHYvHl9bg/CzJn/olGjk4odk4iIJF6FTtwrVy5k5MhBVKpUieTkytx55zNUrnwYjzwynsceG8iuXdvZvz+bXr3+mJu4q1SpSu/ep5KdvY97730xZrG9+eZTfP75v0lOrkxqanWGDg26yYs7ZGg048aNYubMf5GUlEy1ajUKPERERETKtgqduNu06UybNp0LLG/atCV/+9vMQvc5//wrufXWJ2IdGrfdNrLQ5enpeZ/E1bZtV9q27VrksT7++JcHut9443BuvDGOQ3mKiEipqtD/DiYiIuKbCt3iLqnRo6cnOgQREang1OIWERHxiBK3iIiIR5S4RUREPFKhvuNOSzuMTZvmJToMiYO0tMMSHYKISEzELHGb2YvABcBG59zJ4bJHgQuBvcDXwO+cc9tiFUN+rVu3iFdRIiIiMRHLrvKXgC75lk0FTnbOnQKsAAbHsHwREZFyJ2aJ2zk3E/g+37IpzrnscHYOUD9W5YuIiJRHibw5rS/wfgLLFxER8U5Cbk4zsyFANvBaEdv0B/oDZGRkxCkyEZHYGzF1RdR1N3dsEsdIxEdxb3Gb2TUEN61d4Zxz0bZzzo12zmU65zLT09PjFp+IiEhZFtcWt5l1AW4HznHO/RjPskVERMqDmLW4zex14FOgqZllmdm1wFNAKjDVzOab2bOxKl9ERKQ8ilmL2znXq5DFL8SqPBERkYpAQ56KiIh4RIlbRETEI0rcIiIiHlHiFhER8YgSt4iIiEeUuEVERDyixC0iIuIRJW4RERGPKHGLiIh4RIlbRETEI0rcIiIiHlHiFhER8YgSt4iIiEeUuEVERDyixC0iIuIRJW4RERGPKHGLiIh4JDnRAYiI+GrE1BWJDkEqILW4RUREPKLELSIi4hElbhEREY8ocYuIiHhEiVtERMQjStwiIiIeUeIWERHxiBK3iIiIR5S4RUREPKLELSIi4hElbhEREY8ocYuIiHhEiVtERMQjStwiIiIeUeIWERHxiBK3iIiIR5S4RUREPBKzxG1mL5rZRjNbFLGshplNNbOvwt/VY1W+iIhIeRTLFvdLQJd8y+4EPnTOnQB8GM6LiIhIMcUscTvnZgLf51t8MTAmnB4DdItV+SIiIuVRvL/jruOcWx9OfwfUiXP5IiIiXktOVMHOOWdmLtp6M+sP9AfIyMiIW1wiFcK04dHXdRgcvzikgBFTVyQ6BCnj4t3i3mBmxwCEvzdG29A5N9o5l+mcy0xPT49bgCIiImVZvBP3v4Crw+mrgXfiXL6IiIjXYvnvYK8DnwJNzSzLzK4FHgY6mtlXwHnhvIiIiBRTzL7jds71irLq3FiVKSIiUt5p5DQRERGPKHGLiIh4RIlbRETEI0rcIiIiHlHiFhER8YgSt4iIiEeUuEVERDyixC0iIuIRJW4RERGPKHGLiIh4RIlbRETEI0rcIiIiHlHiFhER8YgSt4iIiEeUuEVERDyixC0iIuIRJW4RERGPJCc6AJE8pg2Pvq7D4PjFId4aMXXFQe13c8cmpRyJH6K9XhX19fCBWtwiIiIeUeIWERHxiBK3iIiIR5S4RUREPKLELSIi4hElbhEREY8ocYuIiHhEiVtERMQjStwiIiIeUeIWERHxiBK3iIiIR5S4RUREPKLELSIi4hElbhEREY8ocYuIiHhEiVtERMQjStwiIiIeUeIWERHxSEISt5ndbGaLzWyRmb1uZlUTEYeIiIhv4p64zaweMBDIdM6dDCQBPeMdh4iIiI8S1VWeDBxuZsnAEcC6BMUhIiLileR4F+icW2tmjwGrgd3AFOfclPzbmVl/oD9ARkZGfIMUibdpw6Ov6zA4fnFIuTRi6ooyUdbNHZvELY7yLBFd5dWBi4FGQF3gSDO7Mv92zrnRzrlM51xmenp6vMMUEREpkxLRVX4e8D/n3Cbn3D7gH8BZCYhDRETEO4lI3KuB1mZ2hJkZcC6wNAFxiIiIeCfuids59xkwHvgCWBjGMDrecYiIiPgo7jenATjn7gPuS0TZIiIiPtPIaSIiIh5R4hYREfFIiRK3mR1pZkmxCkZERESKVmTiNrNKZtbbzN4zs43AMmC9mS0xs0fNrHF8whQRERE4cIt7GnA8MBg42jnXwDlXG2gLzAEeKWzwFBEREYmNA91Vfl44SEoezrnvgbeBt82sckwiExERkQKKbHHnJG0zeyX/upxlhSV2ERERiY3i3pzWPHImvEHt9NIPR0RERIpyoJvTBpvZTuAUM9sR/uwENgLvxCVCERERyXWgrvLhzrlU4FHnXLXwJ9U5V9M5p2cNioiIxNmBWtwNAaIlaQvUL/2wREREpDAHuqv8UTOrRNAtPg/YBFQFGgMdCJ7sdR+QFcsgRUREJFBk4nbOXWZmzYArgL7A0cBugsdwTgKGOef2xDxKERERAYpxV7lzbgnwEPAuQcL+H/AfYLyStoiISHwV97GeY4AdwKhwvjfwMnB5LIISERGRwhU3cZ/snGsWMT/NzJbEIiARERGJrriJ+wsza+2cmwNgZq2AubELSyROpg1PdAQHVlSMHcrvf2WOmLoi0SEAZScOkRzFTdynA7PNbHU4nwEsN7OFgHPOnRKT6ERERCSP4ibuLjGNQkRERIqlWInbOfdtrAMRERGRAyvuQ0ZERESkDFDiFhER8YgSt4iIiEeUuEVERDyixC0iIuIRJW4RERGPKHGLiIh4RIlbRETEI0rcIiIiHlHiFhER8YgSt4iIiEeUuEVERDyixC0iIuIRJW4RERGPKHGLiIh4JCGJ28zSzGy8mS0zs6Vm1iYRcYiIiPgmOUHljgQmO+e6m9lhwBEJikNERMQrcU/cZnYUcDZwDYBzbi+wN95xiIiI+CgRXeWNgE3A383sSzN73syOTEAcIiIi3klEV3kycBowwDn3mZmNBO4E7oncyMz6A/0BMjIy4h6kFNO04dHXdRjsb1lS4YyYuiLRIYgUSyJa3FlAlnPus3B+PEEiz8M5N9o5l+mcy0xPT49rgCIiImVV3BO3c+47YI2ZNQ0XnQssiXccIiIiPkrUXeUDgNfCO8pXAb9LUBwiIiJeSUjids7NBzITUbaIiIjPNHKaiIiIR5S4RUREPKLELSIi4hElbhEREY8ocYuIiHhEiVtERMQjStwiIiIeUeIWERHxiBK3iIiIR5S4RUREPKLELSIi4hElbhEREY8ocYuIiHhEiVtERMQjStwiIiIeUeIWERHxiBK3iIiIR5ITHYAUYdrwwpd3GBzfOA5WtPjFX76fk1JsI6auiLru5o5N4hiJ5KcWt4iIiEeUuEVERDyixC0iIuIRJW4RERGPKHGLiIh4RIlbRETEI0rcIiIiHlHiFhER8YgSt4iIiEeUuEVERDyixC0iIuIRJW4RERGPKHGLiIh4RIlbRETEI0rcIiIiHlHiFhER8YgSt4iIiEeUuEVERDySsMRtZklm9qWZTUxUDCIiIr5JZIv7JmBpAssXERHxTkISt5nVB34DPJ+I8kVERHyVnKBynwBuB1KjbWBm/YH+ABkZGXEKqwKbNjz6ug6D4xfHwSoq/nJsxNQVUdfd3LFJHCMpgu/nlpSagz1fvTjP4yjuLW4zuwDY6JybV9R2zrnRzrlM51xmenp6nKITEREp2xLRVf5r4CIz+wYYB/yfmb2agDhERES8E/fE7Zwb7Jyr75xrCPQEPnLOXRnvOERERHyk/+MWERHxSKJuTgPAOTcdmJ7IGERERHyiFreIiIhHlLhFREQ8osQtIiLiESVuERERjyhxi4iIeESJW0RExCNK3CIiIh5R4hYREfGIEreIiIhHlLhFREQ8osQtIiLiESVuERERjyhxi4iIeESJW0RExCNK3CIiIh5R4hYREfFIcqIDkDiaNjy++4mfini/P121pdDlbdA5IodmxNQVpb7fzR2bHGw4ZZpa3CIiIh5R4hYREfGIEreIiIhHlLhFREQ8osQtIiLiESVuERERjyhxi4iIeESJW0RExCNK3CIiIh5R4hYREfGIEreIiIhHlLhFREQ8osQtIiLiESVuERERjyhxi4iIeESJW0RExCNK3CIiIh5R4hYREfFI3BO3mTUws2lmtsTMFpvZTfGOQURExFfJCSgzG7jVOfeFmaUC88xsqnNuSQJiERER8UrcW9zOufXOuS/C6Z3AUqBevOMQERHxUSJa3LnMrCFwKvBZIev6A/0BMjIy4hqX16YNT3QEEketV4+OvnJazfgFUoRPV22Jum5O9opSL6+o12RORv9SL0/KrhFTCz+/bu7YpMT7HGi/eErYzWlmlgK8DfzRObcj/3rn3GjnXKZzLjM9PT3+AYqIiJRBCUncZlaZIGm/5pz7RyJiEBER8VEi7io34AVgqXPuL/EuX0RExGeJaHH/GrgK+D8zmx/+dE1AHCIiIt6J+81pzrlPAIt3uSIiIuWBRk4TERHxiBK3iIiIR5S4RUREPKLELSIi4hElbhEREY8ocYuIiHhEiVtERMQjStwiIiIeUeIWERHxiBK3iIiIR5S4RUREPKLELSIi4hElbhEREY8ocYuIiHhEiVtERMQjStwiIiIeUeIWERHxSHKiA0iIacOjr+swOH5xHKyi4hc5RJ+u2hKXfQBarx59UPvFU1ExzsnoH8dIyk4sI6auiFtZZUlR9b65Y5O4xaEWt4iIiEeUuEVERDyixC0iIuIRJW4RERGPKHGLiIh4RIlbRETEI0rcIiIiHlHiFhER8YgSt4iIiEeUuEVERDyixC0iIuIRJW4RERGPKHGLiIh4RIlbRETEI0rcIiIiHlHiFhER8YgSt4iIiEcSkrjNrIuZLTezlWZ2ZyJiEBER8VHcE7eZJQFPA+cDzYBeZtYs3nGIiIj4KBEt7jOBlc65Vc65vcA44OIExCEiIuKdRCTuesCaiPmscJmIiIgcgDnn4lugWXegi3OuXzh/FdDKOXdjvu36A/3D2abA8rgGWnK1gM2JDiIGymu9oPzWrbzWC8pv3VQv/8S6bsc659ILW5Ecw0KjWQs0iJivHy7Lwzk3Ghgdr6AOlZnNdc5lJjqO0lZe6wXlt27ltV5QfuumevknkXVLRFf5f4ATzKyRmR0G9AT+lYA4REREvBP3FrdzLtvMbgQ+AJKAF51zi+Mdh4iIiI8S0VWOc24SMCkRZceQN936JVRe6wXlt27ltV5QfuumevknYXWL+81pIiIicvA05KmIiIhHKnziNrMXzWyjmS2KWFbDzKaa2Vfh7+rhcjOzUeFQrQvM7LSIfa4Ot//KzK6OWH66mS0M9xllZlZUGQmsW3sz225m88OfeyP2KXSI2vAGw8/C5W+ENxtiZlXC+ZXh+oZxqNdlZrbYzH42s8x82w8OY1luZp3LQ73MrKGZ7Y54v56NWFeic66o8zrGdXvUzJaFZf7TzNIi1vn8nhVar3Lynj0YljffzKaYWd0DxWNl7NpYwnqVzeuic65C/wBnA6cBiyKW/Rm4M5y+E3gknO4KvA8Y0Br4LFxeA1gV/q4eTlcP130ebmvhvucXVUYC69YemFjIMZKAr4HjgMOA/wLNwnVvAj3D6WeB68Lp64Fnw+mewBtxqNdJBP/vPx3IjFjeLIy5CtAorEtSOahXw8jt8h2nROdctPM6DnXrBCSH049ExOP7exatXuXhPasWMT0w4rX15tpYwnq1pwxeF0v1jfb1J/8fFMFgL8eE08cAy8Pp54Be+bcDegHPRSx/Llx2DLAsYnnudtHKSGDdop2gbYAPIuYHhz9GMPhAcv7tCP5joE04nRxuZ7GsV8Ty6eRNcIOBwRHzH4Sx+l6vaNuV+JyLdl7H+lzMt+4S4LXy9J4VUq/y9p4NBp4pKh7K6LWxBPVqTxm8Llb4rvIo6jjn1ofT3wF1wulow7UWtTyrkOVFlRFrRZXbxsz+a2bvm1nzcFm0utUEtjnnsvMtz7NPuH57uH0ilPQ986VeAI3M7Eszm2Fm7cJlB3POlYVhiPsStLqKisfH9yyyXlAO3jMzG2Zma4ArgJyuY++vjVHqBWXwuqjEfQAu+HjkfC+jGOV+QTDE3q+AJ4EJ8Y5HSmQ9kOGcOxW4BRhrZtWKu3OizrnCmNkQIBt4LdGxlKZC6lUu3jPn3BDnXAOCet14oO0Psay41TlKvcrkdVGJu3AbzOwYgPD3xnB5tOFai1pev5DlRZURa4WW65zb4ZzbFU5PAiqbWS2i120LkGZmyfmWE7lPuP6ocPtEKOl75kW9nHM/Oee2hNPzCL5va8LBnXPFGoY4FszsGuAC4IrwIl1UPN68Z4XVq7y8ZxFeA357gHh8ujbmyK1XWb0uKnEX7l/A1eH01cA7Ecv7hHdQtga2h106HwCdzKx6eAdkJ4LvNdYDO8ysdXjHZJ98xyqsjFgrtFwzOzrirs4zCc6NLUQZoja8GE0DuhdSh8gyugMfRVyU4+1fQM/wjs5GwAkEN8V4XS8zS7fg2faY2XEE9Vp1kOdctPM61nXoAtwOXOSc+zFildfvWbR6lZP37ISI2YuBZQeIx4trY7R6ldnrYml/6e/bD/A6QRfWPoLvI64l+N7hQ+Ar4N9AjXBbA54m+KS8kLw3C/UFVoY/v4tYngksCvd5il8GvSm0jATW7UZgMcHdkXOAsyKO0xVYEdZhSMTy4wguqCuBt4Aq4fKq4fzKcP1xcajXJeH0T8AG8t44MiSMfTnhnau+14ugRbAYmE/QnXfhwZ5zRZ3XMa7bSoLv/OaHP8+Wk/es0HqVk/fs7TDOBcC7QL0DxUMZuzaWsF5l8rqokdNEREQ8oq5yERERjyhxi4iIeESJW0RExCNK3CIiIh5R4hYREfGIEreIFGBmaWZ2faLjEJGClLhFpDBpBE8zEpEyRolbRArzMHC8Bc8gfjTRwYjILzQAi4gUYGYNCR5neHKCQxGRfNTiFhER8YgSt4iIiEeUuEWkMDuB1EQHISIFKXGLSAEueG70LDNbpJvTRMoW3ZwmIiLiEbW4RUREPKLELSIi4hElbhEREY8ocYuIiHhEiVtERMQjStwiIiIeUeIWERHxiBK3iIiIR/4/2+2w53TkjAQAAAAASUVORK5CYII=\n", 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Wilson coefficient value (*10^-2 TeV^-2)\n", + "gphivals = [35e-1, 5, 10, 20, 30, 40, 45, 50]\n", + "gphival_names = ['35e-1', '5', '10', '20', '30', '40', '45', '50']\n", + "seps, ps = [], []\n", + "seps_alt, ps_alt = [], []\n", + "\n", + "\n", + "for (gphival, gphival_name) in zip(gphivals, gphival_names):\n", + " print('Evaluating with knowledge on the Cross Section, gphi=%s'%(gphival_name))\n", + " result = TestEstimator(gphival, gphival_name, title_message=', default batch')\n", + " seps.append(result[0])\n", + " ps.append(result[1])\n", + " \n", + " print('Evaluating without knowledge on the Cross Section, gphi=%s'%(gphival_name))\n", + " result = TestEstimator(gphival, gphival_name, withXS=False, title_message=', default batch')\n", + " seps_alt.append(result[0])\n", + " ps_alt.append(result[1])\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from madminer import ParameterizedRatioEstimator\n", + "from madminer.ml.morphing_aware import MorphingAwareRatioEstimator\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl1000-bigbatch')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Evaluating with knowledge on the Cross Section, gphi=35e-1\n", + "NSM = 2225.504 --- NBSM = 2259.447\n", + "test 0 : tsm = 832.459, tbsm = 750.826\n", + "Reaching the end of test data. Stop tests at 220. \n", + "===> delta1 = 0.027, delta2 = 0.026\n", + "p = 0.195 +/- 0.037\n", + "Separation = 0.77 sigmas\n", + "Partial test after 10000 epochs (took 77.82 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=35e-1\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 750.433, tbsm = 839.760\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.025, delta2 = 0.019\n", + "p = 0.174 +/- 0.032\n", + "Separation = 1.03 sigmas\n", + "Partial test after 10000 epochs (took 79.94 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=5\n", + "NSM = 2225.504 --- NBSM = 2273.448\n", + "test 0 : tsm = 1438.222, tbsm = 1335.216\n", + "Reaching the end of test data. Stop tests at 219. \n", + "===> delta1 = 0.028, delta2 = 0.022\n", + "p = 0.224 +/- 0.036\n", + "Separation = 0.78 sigmas\n", + "Partial test after 10000 epochs (took 78.69 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=5\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 1375.758, tbsm = 1200.893\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.017, delta2 = 0.010\n", + "p = 0.067 +/- 0.019\n", + "Separation = 1.53 sigmas\n", + "Partial test after 10000 epochs (took 78.94 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=10\n", + "NSM = 2225.504 --- NBSM = 2325.162\n", + "test 0 : tsm = 4856.868, tbsm = 4916.846\n", + "Reaching the end of test data. Stop tests at 215. \n", + "===> delta1 = 0.024, delta2 = 0.017\n", + "p = 0.149 +/- 0.030\n", + "Separation = 1.16 sigmas\n", + "Partial test after 10000 epochs (took 76.91 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=10\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 4896.926, tbsm = 4549.115\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.000, delta2 = 0.002\n", + "p = 0.000 +/- 0.002\n", + "Separation = 2.33 sigmas\n", + "Partial test after 10000 epochs (took 79.59 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=20\n", + "NSM = 2225.504 --- NBSM = 2436.280\n", + "test 0 : tsm = 18947.779, tbsm = 18503.070\n", + "Reaching the end of test data. Stop tests at 205. \n", + "===> delta1 = 0.018, delta2 = 0.011\n", + "p = 0.068 +/- 0.021\n", + "Separation = 1.48 sigmas\n", + "Partial test after 10000 epochs (took 73.97 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=20\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 18354.832, tbsm = 16454.627\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 4.44 sigmas\n", + "Partial test after 10000 epochs (took 79.20 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=30\n", + "NSM = 2225.504 --- NBSM = 2558.585\n", + "test 0 : tsm = 42262.637, tbsm = 39006.789\n", + "Reaching the end of test data. Stop tests at 195. \n", + "===> delta1 = 0.010, delta2 = 0.003\n", + "p = 0.021 +/- 0.011\n", + "Separation = 2.15 sigmas\n", + "Partial test after 10000 epochs (took 71.26 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=30\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 42558.676, tbsm = 33077.828\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 5.95 sigmas\n", + "Partial test after 10000 epochs (took 79.30 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=40\n", + "NSM = 2225.504 --- NBSM = 2693.988\n", + "test 0 : tsm = 77442.594, tbsm = 67863.867\n", + "Reaching the end of test data. Stop tests at 185. \n", + "===> delta1 = 0.005, delta2 = 0.001\n", + "p = 0.005 +/- 0.005\n", + "Separation = 2.79 sigmas\n", + "Partial test after 10000 epochs (took 68.69 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=40\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 75604.094, tbsm = 52797.289\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 8.78 sigmas\n", + "Partial test after 10000 epochs (took 79.56 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=45\n", + "NSM = 2225.504 --- NBSM = 2766.000\n", + "test 0 : tsm = 90447.547, tbsm = 80725.602\n", + "Reaching the end of test data. Stop tests at 180. \n", + "===> delta1 = 0.006, delta2 = 0.000\n", + "p = 0.006 +/- 0.006\n", + "Separation = 3.24 sigmas\n", + "Partial test after 10000 epochs (took 67.98 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=45\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 96381.750, tbsm = 65943.281\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 9.67 sigmas\n", + "Partial test after 10000 epochs (took 79.78 seconds)\n", + "\n", + "Evaluating with knowledge on the Cross Section, gphi=50\n", + "NSM = 2225.504 --- NBSM = 2841.340\n", + "test 0 : tsm = 118259.750, tbsm = 101777.922\n", + "Reaching the end of test data. Stop tests at 175. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 3.65 sigmas\n", + "Partial test after 10000 epochs (took 65.74 seconds)\n", + "\n", + "Evaluating without knowledge on the Cross Section, gphi=50\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 113804.211, tbsm = 84300.867\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 10.90 sigmas\n", + "Partial test after 10000 epochs (took 78.76 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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OcFV3Q1j+V4KGS2sJ9v0+/dTT3dcSNK76C8EJXSuCk7+fYyabDbQM1zkM6OXueW+fxON6gs/NRmAEQcJ6L4wjk6CR2zCC/dUeuGgv1pHD3ZcR1FLdSpBIlgE3E35/hbeB5hH8yiD7uJgFfOfua/aw7F0ENWZtCRqqrQWeJqgNg6CB3XfhuKnkP8auJzgmNxL8QmZSzLi4/sfhbYBnCO7zfxXGtY2gEeoIM6tPsC+vBr4kaJA3Fhjh7tk1fQcTHo9FmEzQuHE+QZJ/poD9Ec/nqKDl9g5jvAzo6e47i/rOCL1LUCP0g5mtDctuAhYCnxJ8b92P8lRC6JG+SWRmU4BHw/um+7KcJgTJ5SB331wiwRVv/e0ItqNdaa9bChZWvy4HLnH36WZ2BUGjrY7Jjax8s+AnhmPdfU9V4aXKgh76rs9Ti1gSy831OSrJZUty6Ywqud4j+PnNXgsPzj8QtIYv9cQfY1+rrWUfhdXi6WE16a0E91bz1uhIBeTuZ5dU4tfnKBrUT3ISuftf9mX+sDHVaoJqya4lEtRecPdPkrVuyaUDwa2R7NsU3cPq4zLBzLYWMuqcsNFiotbbhGB/FKRVdjsZyVGmP0dSMlTtLyIiEjGq9hcREYkYJX8REZGIKRf3/OvUqePNmjVLdhgiIiKlYu7cuWvdvW6ill8ukn+zZs2YM2dOssMQEREpFWYWd1fSe6NcJP+S8vHHC9m4cceeJ5RyLz19P048sU2ywxARKZMilfw3btxB3brHJzsMKQWZmXOTHYKISJmlBn8iIiIRE6krfxERKV927tzJ8uXL2b59e7JDSYiqVavSuHFjKleuXKrrVfIvQ5Yunc/w4b/nxx83U6lSCldeeRtnn90bgNtvv4QlS+aQmlqZ1q3bcdttT5KaWpk33hjHmDH34+5Uq1adQYP+zmGHBU9UPffcZhxwQHVSUlJISUnlhRfUaFJEypfly5dTvXp1mjVrRu4nUpd/7s66detYvnw5zZs3L9V1K/mXIVWrHsBddz1PkyYtycxcyaWXHk+HDl2oXj2drl0v4Z57xgJw2219mDTpaXr1+j0NGzZn1KgZHHhgTT766A2GDevPmDGzc5b55JPTSU+vk6xNEhHZJ9u3b6+QiR/AzKhduzaZmZmlvu5IJ/+VK79l4MBzaNu2IwsWzKRu3UY8+OBkqlbdnwkTHuEf/3iClJRUmjdvxX33TeDJJ4eycuX/WLHiG3744Xv+8IeRLFz4MTNnvkG9eo0YOfI1UlP3vuqmadPDct7XrduQWrXqsWFDJtWrp9OxY7ecca1bt2P16uCJtsccc1JOeZs2J7JmTWk96VZEpHRUxMSfLVnbFvkGf8uWfckFF1zLyy8vpnr1dN599x8AjB49nHHj/sOECQu49dYncqZfvvxrnnjiXR566F/cccelZGR05qWXFlKlyv58+OG/8y3/+edH0KdP23yvESMGFhnXokWfsHPnDho3PjRXeVbWTqZMeYGTTsr/HJ/Jk5/hpJPOyRk2M6699mwuvfR4/vnPUcXaLyIiEhg2bBitW7fm6KOPpm3btsyePZtOnTrRpEkTYp+P0717d9LS0pIYafwifeUP0LBhcw4/vC0ARxxxPCtXfgtAy5ZHc/vtl9CpU3c6deqeM/1JJ51DamplWrRow+7du3KScIsWbXLmjdW378307XtzsWJau3YVQ4Zcxl13jaFSpdznZ8OHX8Nxx53Ksceekqt8zpzpTJ78DE8//WFO2dNPf0i9eo1Yv34N1157Fs2aHcFxx51arFhERMqSkdO+KNHl3XjWYUWOnzVrFq+//jrz5s2jSpUqrF27lh07gv5i0tPT+eijj+jYsSMbN25k1apVJRpbIkU++VeuXCXnfUpKCj//HDy58q9//Tf/+c/7vP/+azz77DAmTFgIwH77BdNXqlSJ1NTKOVU2ZpXYtSsr3/Kff34Eb745Ll/5sceeys03P5KvfOvWzVx//a+45pphtGlzYq5xo0bdxYYNmdx665O5yr/8cgH33NOPRx55g/T02jnl9eo1AqBWrXp06tSDxYs/UfIXESmGVatWUadOHapUCb7769T5pQ3VRRddxIQJE+jYsSP//Oc/6dmzJ4sXL05WqMUS+eRfkN27d7N69TIyMjrTtm1Hpk6dwLZthT2KvGjFufLfuXMHN9/cg1/9qi9nntkr17hJk57m44/f4vHH38lVG/DDD99z8809ufvuF3K1Gdi27Ud2795NtWrV2bbtR2bPnkq/fkP2ahtERKLq7LPP5u677+awww7jzDPPpHfv3px22mkAnHHGGVx99dXs2rWLCRMmMGrUKO65554kRxwfJf8C7N69izvuuJStWzfh7lx00UCqV09P+HqnTXuZefPeZ9Omdbz++mgA7rxzNIcf3pb77vs/DjqoKVde2QGAzp17cvXVQ3jqqbvZtGkd999/DUDOT/rWrVvNzTf3AGDXriy6dOlTYDsBEREpXFpaGnPnzuWDDz5g+vTp9O7dm+HDhwNBbXHHjh2ZMGEC27Ztozw9gM5iGyuUVRkZGV4SD/Z588256t43IjIz59K1q/7XIuXdZ599xpFHHpkzXNr3/PN65ZVXGDNmDFu2bOGBBx7gp59+okePHgwdOpTrrruOtLQ0tm4tXk1x3m0EMLO57p5RrAUVg678y6M5zxU+LuO3pReHiEgFt3TpUipVqkTLli0BmD9/Pk2bNmXRokUAnHLKKQwePJiLL744mWEWm5K/iIhIIbZu3cp1113Hxo0bSU1NpUWLFowaNYpevYJ2WWbGTTfdlOQoi0/JX0REyo3iVtPvq+OPP56ZM2fmK3/vvfcKnL64Vf7JouRfiP79O3HDDQ/QqlXCbrnEbfv2n/jTny5g+fKvSUlJ4ZSWTbmuxwUAjH37LSZ/+D4pKSnUTKvOkAdOp0GDpkU+J2Do0CuYN28GaWk1gF8aFYqISDQo+ZcTl112ExkZndm5cwe/v+woPlq0gJOPOpojDm5Cr1uHUHW/KrwyYzqPPHIL9933UpHPCQAYOHBEvp8TiohINCQs+ZtZVeB9oEq4nlfc/U4zGw2cBmwKJ73C3ecnKo6iFNW3P8CUKS9w7739yMrKYsiQZznqqHbMnTuDBx+8PlyC8dRT7/PZZ3MZNepO0tLS+frrhZx55oW0aNGGF198mJ9/3saDD07K101vcVStegAZGZ0BqFx5P444uClrNmwAIOPwX1qIHtX8EKYsngYU/ZwAERGJtkT27f8zcLq7HwO0BbqaWXaXdTe7e9vwlZTEn62wvv0hqG4fP34+gwY9zt13XwnA2LEPcMstjzF+/HyefvoDqlQJThS++OK/3HrrE0yc+BlTprzA999/wfPPf0L37v146aW/5VvvnDnTC+zz/8orT8o3bawtWzbywcL5nHDEkfnGTf7og1x9+2cr6DkBjz9+GxdddDQPPngjO3b8HN/OEhGRCiFhV/4edCCQ3fKhcvgqc50KFNa3P0CXLsFPN4477lR+/HEzW7Zs5JhjTmbkyD9wzjmX0LlzT+rXbwxAq1YnUKdOAwAaNz6U9u3PBoI+/+fMmZ5vvRkZnRk/vnjnPVlZWdx228X07nwmjevWyzVuyuxZfPb9t4y6L3dvggU9J2DAgPuoXfsgdu7cET4C+H6uvlq9/4mIREVC7/mbWQowF2gBPObus83s98AwMxsCvAMMcvekXXoW1rc/5H/UoplxxRWD6NjxV3z44RSuuupkHn30LeCXPv+D6SrlDBfW5/+cOdN56KEb85VXrXoAzz6bv2UpwLBh/Tn44Jb06XxsrvLZny3m2TdeZ9Qf/pQrjsKeE5B9krLfflU499zfMnbsAwWuT0REKqaEJn933wW0NbN04FUzOwoYDPwA7AeMAv4E3J13XjPrD/QHaNKkSSLDLNTUqS+RkdGZ+fM/JC2tBmlpNVi+/GtatGhDixZtWLLkU7799nPS0op/H724V/6PP347W7du4o47noZ5Y3LKP//+O/487nn+dt0fqHXggTnlRT0nYO3aVdSp0wB3Z8aMSRx66FHFjl9EJCpSUlJo06YN7k5KSgqPPvooJ510Ej/99BNXX301CxYswN1JT0/nzTffJC0tDTPjkksuYezYsUBQc9ugQQPat2/P66+/nuQtKqXW/u6+0cymA13dPfsy82czew4osHcEdx9FcHJARkZGUm4XVKlSlT59jiUraydDhjwLwPjxf2XOnOlUqlSJQw5pzUknncOCBbMSGsfq1ct59tlhNGt2BJdeehz8tJ4LO51B946n8sg/X2bbzz8z6KnHAajf/FVGjvxXkc8JuP32S9iwIRN35/DD2zJ48BMJjV9EpMRMv69kl9d58B4n2X///Zk/P7hYe+uttxg8eDAzZszg4Ycfpn79+ixcGDz1denSpVSuXBmAatWqsWjRIrZt28b+++/PtGnTaNSoUcnGvg8S1re/mdUFdoaJf39gKnA/MNfdV1lQpz4S2O7ug4palvr2z0Pd++6R+vYXqRjy9XufhOQf21//xIkTGTduHJMmTWLgwIE0bdqUP/7xjwXOM3DgQI477jh69epF3759ad26NR988EG+K/9k9O2fyNb+DYDpZrYA+BSY5u6vA+PMbCGwEKgD3JvAGERERPbJtm3baNu2LUcccQT9+vXjjjvuAODKK6/k/vvvp0OHDtx+++18+eWXuea76KKLmDBhAtu3b2fBggW0b98+GeEXKJGt/RcAxxZQfnqi1ikiIlLSYqv9Z82aRd++fVm0aBFt27blm2++YerUqbz99tuccMIJzJo1K+cq/uijj+bbb7/lxRdfpFu3bsnchHzUw5+IiEicOnTowNq1a8nMzKRevXqkpaXRs2dPevbsSaVKlZgyZUquKvzf/OY33HTTTbz33nusW7cuiZHnlshqf9kLO3b8zODBvenevQWXX94+V78DsWYuXkjPOwfT/Y5BjH7z3znlK1b8j8svb0/37i0YPLg3O3fuKNZy4/HWWxN45plhRU6zadN6rrnmLHr0aMk115zF5s0bCpzu9dfH0KNHS3r0aMnrr//yK4bPPptL795t6N69BSNGDCS7bUq8yxURSYTPP/+cXbt2Ubt2bT766CM2hL2t7tixgyVLltC0adNc01955ZXceeedtGnTJhnhFkrJv4yZPPkZqlevyaRJX9Gnz4387W9/yjfNrt27uf/FsTwy4EYm3nkvb306m29WrgDgb3/7E3363MikSV9RvXpNJk9+Ju7lxnrttdE8+eTQAsfNnPkGJ53Utcj5R48eTrt2Z/Dqq1/Srt0ZjB49PN80mzat56mn7mL06NmMGfMJTz11V04yv+++33P77U/x6qtfsmzZl8yc+WbcyxURKUnZ9/zbtm1L7969GTNmDCkpKXz99decdtpptGnThmOPPZaMjAzOP//8XPM2btyYgQMHJinywkW62n/lym+57rquHHnk8Xz++TwOOaQ1d9/9PFWrHpC0mGbMmEz//kMBOOOMXvzlLwNw91wdDi3+9hsOrlcvp5e/s09oz4wF82neoCGffvou9947HoBf//pyRo0aSq9ev49rufFwd774Yj5HHHHcHrdj1Kj3cuLo378TAwfen2uaWbPeol27s6hRoxYA7dqdxcyZb5KR0Ykff9yc0zFRt259ee+9SZx88jlxLVdEKrA4WueXtF27dhVY3rdvX/r27VvguIIe7dupUyc6depUkqHttchf+X/33VJ69bqGV175jGrVDmTixMdLfB39+p1SYD/+s2e/nW/aNWtWUL/+wQCkpqaSllaDTZty3ydas2Ej9WvWyhmul16TNRs2sOnHrVSvnk5qanBOV69eY9asWRH3cuOxdOl/aNnymD2eNKxfvzqnJ8HatQ9i/frV+abJzPwlJoD69RuTmbkijLVxvvJ4lysiIkWL9JU/QP36B9O27ckAdOt2KRMmPMJllxXY79Bee/rpD0p0eYmyceM6rrnmDCCoks/K2sGMGZMAuPvuF2jRog0zZ75Z4MODimJmxa5hSOZyRUQqusgn/4L67y9p/fqdwk8/bclXfv31D9C+/Zm5yurVa8Tq1cuoX78xWVlZbN26iRo1aueepmY6qzeszxles3ED9WrWpEa1NLZs2UhWVhapqTNm8QEAABr6SURBVKmsWbOcevUaxb3c9PTaOV0Ov/baaFau/Jbf/W5ormk+/ngqf/lL8OTDAQO6sH79ao48MiPodjhGrVr1c7oRXrt2FTVr5n4QEUDduo2YO/e9nOHVq5dz/PGdwliX5yqvW7dR3MsVEZGiRb7a/4cfvs/pnvfNN8fTtm3HEl/H009/wPjx8/O98iZ+gFNP/U1Oq/d33nmFE044Pd8JSaumzVm2ZjUr1mayMyuLqZ/O5tSj22JmZGR05p13XgGClvSnnXZe3Mvdk61bN7FrVxbp6cFJw6OPvsX48fPzJX6A0077ZX2xccTq0KELs2dPZfPmDWzevIHZs6fSoUMX6tRpQLVqB7Jw4ce4O1OmPJ8zfzzLFZGKJVE90ZYFydq2yCf/pk0PZ+LEx+jV60g2b95Ar16/T2o85513FZs2raN79xaMG/cQAwYErdkzM1cycGDQSURqSgo3976U6x55iF5Db+PM40/g0IbBlfF1193PuHEP0b17CzZtWsd5511V5HKL4+OPp9GuXf4TloJcfvkgZs+eRo8eLfnkk7e54oqgB+clS+Zwzz39AKhRoxZXXXUHffueQN++J9Cv35Ccxn+DBj3OPff0o3v3FjRqdCgnn3xOkcsVkYqpatWqrFu3rkKeALg769ato2rVqqW+7oT17V+SEtW3/8qV33LDDb/m5ZcX7fOyS1WS+vYPknG/XI8HLqvUt79IxbBz506WL1/O9u3bkx1KQlStWpXGjRvnPBAoW6L79o/8PX+JX0HV+yIiiVS5cmWaN2+e7DAqnEhX+zds2Kz8XfWLiIjso0gn/7Js3rz3ueSS42jfPpW3334l17jXZ31EjzsG0eOOQbw+66MC5//ii//y2992oHfvNtx447ls3bo5Z9xzz91H9+4t6NnzcGbNeguAH35Yxu9+15kLLmjFhRe25sUXH07cxomISFIp+ZdRBx3UhKFDR9OlS59c5Zs2reepf09m9KDbGTPoDp7692Q2//hjvvnvvbcfAwYM56WXFtKpUw9eeGEEAN98s4SpUyfw8suL+dvf3mT48GvYtWsXqamp3Hjjg0ycuITnnvuYiRMf45tvlpTKtoqISOmKdPLftu1Hrr/+V1x88TFceOFRTJ36EhA8VKZ//9O49NLjGTCgC2vXrgKgf/9OPPDA9fTp05YLLzyKRYs+SVhsDRs2o2XLo6lUKfe/aNast2h3ZGtqVEvjwGrVaHdka2YuWZhv/u+++4LjjjsVgPbtz+Ldd4Pf5s+YMZmzz76I/farQqNGzTn44BYsXvwJdeo0yOmyt1q16jRrdmRO74AiIlKxRLrB38yZb1K3bkMefjh4Kt7WrZvIytrJiBHX8eCDk6lZsy5Tp77EY4/dxp13PgvA9u0/MX78fObNe5+7776yWG0GitPZT2EyM1fk6tq3fnpNMjdszDfdoYe2ZsaMyXTq1J23357I6tXLgKCb39jW+rFdAGdbufJbli79D0cd1T6umEREpHyJdPJv0aINf/3rH3nkkT9xyim/5thjT+Grrxbx9deLuPbas4DggQ7ZfckDdOlyMQDHHXcqP/64mS1bNlK9enpc6yvNbn6HDHmWESMG8vTT93Dqqb+hcuX94prvp5+2csst5/PHP/6VtLQDExyliIgkQ6STf9OmhzF27Dw++mgKf//77Zxwwhl07tyDQw5pzXPPzSpwnn3pDrgkrvzr1m3E3Hm/dO27euMGjj/s8HzTNWt2BI89NhUIbgF8+GFQu5HdzW+22C6As7J2csst59O16yWcfnrPuLdLRETKl0gn/8zMlRx4YC26dbuU6tXTmTTpaa64YhAbNmSyYMEsjj66A1lZO/nuuy849NDWAEyd+hIZGZ2ZP/9D0tJqkJZWI+71lcSVf4cOXXh85MCcRn6zlyxmQPfz8023fv0aatWqx+7du3nmmXs5//z/A4Jufm+/vQ+XXPIHMjNXsmzZl7Ru3Q535+67r6J58yO59NI/7HOcIiJSdkU6+X/11UIefvhmKlWqRGpqZQYN+juVK+/H/fe/wgMPDMzpy/7ii2/ISf5VqlSlT59jycrayZAhzyYstsWLP+Xmm3uwefMGPvjgNUaNupOXX14cdInb7Vz6Dr8HgH6/Opca1dIAuOeF5zj/gDa0apXBW2+9yMSJjwHQuXNPfvOboOe/Qw9tzZlnXsgFF7QiJSWVW255jJSUFObP/5ApU4In9/Xp0xaAa675Mx07dkvYNoqISHJEunvf4urfvxM33PAArVolrMfF+CSpe9/yRN37ikh5lujufSP9Uz8REZEoinS1f3GNGvVeskMQERHZZ7ryFxERiRglfxERkYhR8hcREYmYSN3zT0/fj8zMuckOY99tLOKBOxVh+0pAenp8PRqKiERRpJL/iSe2SXYIJaPK1MLHddbP20REpGiq9hcREYmYSF35i4hE0chpXxQ67sazDivFSKSs0JW/iIhIxCj5i4iIRIySv4iISMQo+YuIiESMkr+IiEjEJCz5m1lVM/vEzP5rZovN7K6wvLmZzTazr8zsJTNTbywiIiKlKJFX/j8Dp7v7MUBboKuZnQjcD4x09xbABuCqBMYgIiIieSQs+XtgazhYOXw5cDrwSlg+BuieqBhEREQkv4Te8zezFDObD6wBpgFfAxvdPSucZDnQqJB5+5vZHDObk5mZmcgwRUREIiWhyd/dd7l7W6Ax0A44ohjzjnL3DHfPqFu3bsJiFBERiZpSae3v7huB6UAHIN3MsrsVbgysKI0YREREJJDI1v51zSw9fL8/cBbwGcFJQK9wssuByYmKQURERPJL5IN9GgBjzCyF4CTjZXd/3cyWABPM7F7gP8AzCYxBRERE8khY8nf3BcCxBZR/Q3D/X0RERJJAPfyJiIhEjJK/iIhIxCj5i4iIRIySv4iISMQo+YuIiESMkr+IiEjEKPmLiIhEjJK/iIhIxCj5i4iIRIySv4iISMQo+YuIiESMkr+IiEjEKPmLiIhEjJK/iIhIxCj5i4iIRIySv4iISMQo+YuIiESMkr+IiEjEKPmLiIhEjJK/iIhIxCj5i4iIRIySv4iISMQo+YuIiESMkr+IiEjEKPmLiIhEjJK/iIhIxCj5i4iIRIySv4iISMQo+YuIiESMkr+IiEjEKPmLiIhEjJK/iIhIxCj5i4iIRIySv4iISMQkLPmb2cFmNt3MlpjZYjO7PiwfamYrzGx++OqWqBhEREQkv9QELjsL+KO7zzOz6sBcM5sWjhvp7g8kcN0iIiJSiIQlf3dfBawK328xs8+ARolan4iIiMSnVO75m1kz4Fhgdlg0wMwWmNmzZlazkHn6m9kcM5uTmZlZGmGKiIhEQsKTv5mlAf8AbnD3zcDfgUOBtgQ1Aw8WNJ+7j3L3DHfPqFu3bqLDFBERiYyEJn8zq0yQ+Me5+z8B3H21u+9y993AU0C7RMYgIiIiuSWytb8BzwCfuftDMeUNYibrASxKVAwiIiKSXyJb+58MXAYsNLP5YdmtwMVm1hZw4FvgdwmMQURERPJIZGv/DwErYNSURK1TRERE9kw9/ImIiESMkr+IiEjEKPmLiIhETCIb/EkyTL+v8HGdB5deHCIiUmbpyl9ERCRilPxFREQiRslfREQkYpT8RUREIkbJX0REJGKU/EVERCJGyV9ERCRi9Dt/EZEKYOS0L5IdgpQjuvIXERGJGCV/ERGRiFHyFxERiRglfxERkYhR8hcREYkYJX8REZGIUfIXERGJGCV/ERGRiFHyFxERiRglfxERkYhR8hcREYkYJX8REZGIUfIXERGJGCV/ERGRiFHyFxERiRglfxERkYhR8hcREYkYJX8REZGIUfIXERGJGCV/ERGRiFHyFxERiRglfxERkYhJWPI3s4PNbLqZLTGzxWZ2fVhey8ymmdmX4d+aiYpBRERE8kvklX8W8Ed3bwWcCFxrZq2AQcA77t4SeCccFhERkVKSsOTv7qvcfV74fgvwGdAIOA8YE042BuieqBhEREQkv9TSWImZNQOOBWYD9d19VTjqB6B+IfP0B/oDNGnSJPFBSuGm31f4uM6DSy8OESlxI6d9UWD5jWcdVux59jSflB0Jb/BnZmnAP4Ab3H1z7Dh3d8ALms/dR7l7hrtn1K1bN9FhioiIREZCk7+ZVSZI/OPc/Z9h8WozaxCObwCsSWQMIiIikluxkr+ZVTOzlDinNeAZ4DN3fyhm1L+Ay8P3lwOTixODiIiI7Jsik7+ZVTKzPmb2bzNbA3wOrAp/vjfCzFoUMfvJwGXA6WY2P3x1A4YDZ5nZl8CZ4bCIiIiUkj01+JsOvA0MBha5+24IfqsPdAbuN7NX3X1s3hnd/UPAClnuGXsfsoiIiOyLPSX/M919Z95Cd19PcC//H+F9fRERESkniqz2z078ZvZC3nHZZQWdHIiIiEjZFW+Dv9axA2Gjv+NLPhwRERFJtCKr/c1sMHArsL+ZZf9G34AdwKgExyYlTZ31iIgIe672v8/dqwMj3P3A8FXd3Wu7u7KFiIhIObSnn/o1Aygs0VugccmHJSIiIomyp9b+I8ysEkFHPHOBTKAq0ILgp35nAHcCyxMZpIiIiJScIpO/u18QPob3EuBK4CBgG8ET+qYAw9x9e8KjFBERkRKzx9b+7r4EuBd4jSDp/w/4FHhFiV9ERKT8ifeRvmOAzcAj4XAf4HngwkQEJSIiIokTb/I/yt1bxQxPN7MliQhIREREEive5D/PzE50948BzKw9MCdxYQlQ9O/yy/O6RKTMGznti2SHIAkUb/I/HphpZt+Hw02ApWa2EHB3Pzoh0YmIiEiJizf5d01oFCIiIlJq4kr+7v5dogMRERGR0hHvg31ERESkglDyFxERiRglfxERkYhR8hcREYkYJX8REZGIifenfpIo6lxHRCKiqI6DbjzrsFKMRHTlLyIiEjFK/iIiIhGj5C8iIhIxSv4iIiIRo+QvIiISMUr+IiIiEaPkLyIiEjFK/iIiIhGjTn5k3xTVSVHnwaUXh4iIxE1X/iIiIhGj5C8iIhIxSv4iIiIRo+QvIiISMQlL/mb2rJmtMbNFMWVDzWyFmc0PX90StX4REREpWCKv/EcDXQsoH+nubcPXlASuX0RERAqQsOTv7u8D6xO1fBEREdk7yfid/wAz6wvMAf7o7hsKmsjM+gP9AZo0aVKK4YmIyN4aOe2LZIcgcSjtBn9/Bw4F2gKrgAcLm9DdR7l7hrtn1K1bt7TiExERqfBKNfm7+2p33+Xuu4GngHaluX4REREp5eRvZg1iBnsAiwqbVkRERBIjYff8zexFoBNQx8yWA3cCncysLeDAt8DvErV+ERERKVjCkr+7X1xA8TOJWp+IiIjERz38iYiIRIySv4iISMQo+YuIiESMkr+IiEjEKPmLiIhEjJK/iIhIxCj5i4iIRIySv4iISMQo+YuIiESMkr+IiEjEKPmLiIhEjJK/iIhIxCj5i4iIRIySv4iISMQo+YuIiESMkr+IiEjEKPmLiIhEjJK/iIhIxCj5i4iIRIySv4iISMSkJjsAERGRkdO+KHTcjWcdVoqRRIOu/EVERCJGyV9ERCRilPxFREQiRslfREQkYpT8RUREIkbJX0REJGKU/EVERCJGyV9ERCRi1MmPJMf0+wou7zy4dOMQSZBEdFpT1DJFikNX/iIiIhGj5C8iIhIxSv4iIiIRo+QvIiISMQlL/mb2rJmtMbNFMWW1zGyamX0Z/q2ZqPWLiIhIwRJ55T8a6JqnbBDwjru3BN4Jh0VERKQUJSz5u/v7wPo8xecBY8L3Y4DuiVq/iIiIFKy07/nXd/dV4fsfgPqlvH4REZHIS1onP+7uZuaFjTez/kB/gCZNmpRaXFKCCuvIR0QKpY58pDSU9pX/ajNrABD+XVPYhO4+yt0z3D2jbt26pRagiIhIRVfayf9fwOXh+8uByaW8fhERkchL5E/9XgRmAYeb2XIzuwoYDpxlZl8CZ4bDIiIiUooSds/f3S8uZNQZiVqniIiI7Jl6+BMREYkYJX8REZGIUfIXERGJGCV/ERGRiFHyFxERiRglfxERkYhR8hcREYkYJX8REZGIUfIXERGJGCV/ERGRiFHyFxERiRglfxERkYhR8hcREYkYJX8REZGIUfIXERGJGCV/ERGRiFHyFxERiRglfxERkYhR8hcREYkYJX8REZGISU12ABXG9PuSHUHFUNR+7Dy49OIQEanAdOUvIiISMUr+IiIiEaPkLyIiEjFK/iIiIhGj5C8iIhIxSv4iIiIRo+QvIiISMUr+IiIiEaNOfkREStnIaV8kO4Rypaj9deNZh5ViJBWHrvxFREQiRslfREQkYpT8RUREIkbJX0REJGKS0uDPzL4FtgC7gCx3z0hGHCIiIlGUzNb+nd19bRLXLyIiEkmq9hcREYmYZCV/B6aa2Vwz65+kGERERCIpWdX+Hd19hZnVA6aZ2efu/n7sBOFJQX+AJk2aJCNGEZEiqbMeKa+ScuXv7ivCv2uAV4F2BUwzyt0z3D2jbt26pR2iiIhIhVXqyd/MqplZ9ez3wNnAotKOQ0REJKqSUe1fH3jVzLLXP97d30xCHCIiIpFU6snf3b8Bjint9YqIiEhAP/UTERGJGCV/ERGRiFHyFxERiZhkdu8rIiKyT4rqa+HGsw4rsXkqGl35i4iIRIySv4iISMQo+YuIiESMkr+IiEjEKPmLiIhEjJK/iIhIxCj5i4iIRIySv4iISMSok5+8pt+X7AikMEX9bzoPLr04RKRcKKozn6jTlb+IiEjEKPmLiIhEjJK/iIhIxCj5i4iIRIySv4iISMQo+YuIiESMkr+IiEjEKPmLiIhETDQ7+VFHPhWPOgCSOBTV6cuNZx1W7HkkWvbm81NW6cpfREQkYpT8RUREIkbJX0REJGKU/EVERCJGyV9ERCRilPxFREQiRslfREQkYqL5O3+JFvUBUGZVpN9NS8UQlX4ddOUvIiISMUr+IiIiEaPkLyIiEjFK/iIiIhGTlORvZl3NbKmZfWVmg5IRg4iISFSVevI3sxTgMeAcoBVwsZm1Ku04REREoioZV/7tgK/c/Rt33wFMAM5LQhwiIiKRlIzk3whYFjO8PCwTERGRUlBmO/kxs/5A/3Bwq5ktTWY8ZVQdYG2ygygnCtlXt5Z6IOVAmfhc/aF8rK9M7KtyokLvqxL+vNYBmpbsInNLRvJfARwcM9w4LMvF3UcBo0orqPLIzOa4e0ay4ygPtK/ip30VP+2r+GlfxS/cV80SuY5kVPt/CrQ0s+Zmth9wEfCvJMQhIiISSaV+5e/uWWY2AHgLSAGedffFpR2HiIhIVCXlnr+7TwGmJGPdFYxui8RP+yp+2lfx076Kn/ZV/BK+r8zdE70OERERKUPUva+IiEjEKPmXAWZ2o5ktNrNFZvaimVUNG0TODrtAfilsHImZVQmHvwrHN4tZzuCwfKmZdYkprzDdKZvZ9eF+WmxmN4Rltcxsmpl9Gf6tGZabmT0SbvcCMzsuZjmXh9N/aWaXx5Qfb2YLw3keMTMr/a3ce2b2rJmtMbNFMWUJ3z+FraOsKmQ/XRB+rnabWUae6Yt1bO3N8VtWFbKvRpjZ5+Hn5lUzS48Zp32Ve1/dE+6n+WY21cwahuXJPf7cXa8kvgg6OPofsH84/DJwRfj3orDsCeD34ftrgCfC9xcBL4XvWwH/BaoAzYGvCRpUpoTvDwH2C6dplezt3st9dRSwCDiAoL3K20AL4C/AoHCaQcD94ftuwBuAAScCs8PyWsA34d+a4fua4bhPwmktnPecZG93MffRqcBxwKKYsoTvn8LWUVZfheynI4HDgfeAjJjyYh9bxT1+y/KrkH11NpAavr8/5jOlfZV/Xx0Y835gzDYl9fjTlX/ZkArsb2apBIltFXA68Eo4fgzQPXx/XjhMOP6M8OzvPGCCu//s7v8DviLoSrkidad8JMEB8pO7ZwEzgJ7k3id599XzHvgYSDezBkAXYJq7r3f3DcA0oGs47kB3/9iDo+j5mGWVC+7+PrA+T3Fp7J/C1lEmFbSf3P0zdy+oM7FiHVvh8Vjc47fMKmRfTQ2PQYCPCfprAe2rgvbV5pjBakB2Q7ukHn9K/knm7iuAB4DvCZL+JmAusDHm4IrtAjmne+Rw/CagNoV3m1yRulNeBJxiZrXN7ACCM+eDgfruviqc5gegfvi+uPukUfg+b3l5Vxr7p7B1VATF3U+1Kf7xW55dSXAVCtpXBTKzYWa2DLgEGBIWJ/X4U/JPsvDezHkEVWQNCc4MuyY1qDLK3T8jqGKcCrwJzAd25ZnG+eXMWvIojf2j/0F0mNltQBYwLtmxlGXufpu7H0ywnwYkeF1xHX9K/sl3JvA/d890953AP4GTCaqAsvthiO0COad75HB8DWAdhXebHFd3yuWFuz/j7se7+6nABuALYHVYJUb4d004eXH3yQp+qb6MLS/vSmP/FLaOiqC4+2kdxT9+yx0zuwL4NXBJmHBA+2pPxgHnh++Tevwp+Sff98CJZnZAeD/rDGAJMB3oFU5zOTA5fP+vcJhw/Lvhgfcv4KKwhWxzoCVB45AK1Z2ymdUL/zYhuN8/ntz7JO++6hu2qj0R2BRWjb0FnG1mNcOal7OBt8Jxm83sxPB/0TdmWeVZaeyfwtZRERTr2AqPx+Iev+WKmXUFbgF+4+4/xYzSvsrDzFrGDJ4HfB6+T+7xV5yWjHolrIXoXeEHYhHwAkFL2UMIDpqvgIlAlXDaquHwV+H4Q2KWcxtBi9qlxLRSJ7g3/kU47rZkb+8+7qsPCE6O/gucEZbVBt4BviT4BUCtsNyAx8LtXkjuFtxXhvvwK+C3MeUZ4f/ha+BRwo6wyssLeJGg7chOgnuCV5XG/ilsHWX1Vch+6hG+/xlYTfCFmz19sY6tvTl+y+qrkH31FcF96fnh6wntq0L31T/CY2YB8BrQKJw2qcefevgTERGJGFX7i4iIRIySv4iISMQo+YuIiESMkr+IiEjEKPmLiIhEjJK/iORjZulmdk2y4xCRxFDyF5GCpBM8VU1EKiAlfxEpyHDg0PAZ5COSHYyIlCx18iMi+ZhZM+B1dz8qyaGISALoyl9ERCRilPxFREQiRslfRAqyBaie7CBEJDGU/EUkH3dfB3xkZovU4E+k4lGDPxERkYjRlb+IiEjEKPmLiIhEjJK/iIhIxCj5i4iIRIySv4iISMQo+YuIiESMkr+IiEjEKPmLiIhEzP8D37Au3Sfx4+8AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Wilson coefficient value (*10^-2 TeV^-2)\n", + "gphivals = [35e-1, 5, 10, 20, 30, 40, 45, 50]\n", + "gphival_names = ['35e-1', '5', '10', '20', '30', '40', '45', '50']\n", + "seps_new, ps_new = [], []\n", + "seps_alt_new, ps_alt_new = [], []\n", + "\n", + "\n", + "for (gphival, gphival_name) in zip(gphivals, gphival_names):\n", + " print('Evaluating with knowledge on the Cross Section, gphi=%s'%(gphival_name))\n", + " result = TestEstimator(gphival, gphival_name, title_message=', big batch')\n", + " seps_new.append(result[0])\n", + " ps_new.append(result[1])\n", + " \n", + " print('Evaluating without knowledge on the Cross Section, gphi=%s'%(gphival_name))\n", + " result = TestEstimator(gphival, gphival_name, withXS=False, title_message=', big batch')\n", + " seps_alt_new.append(result[0])\n", + " ps_alt_new.append(result[1])\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(gphivals, seps, label='XS')\n", + "plt.plot(gphivals, seps_alt, '--', label='without XS')\n", + "plt.plot(gphivals, seps_new, label='XS, bigbatch')\n", + "plt.plot(gphivals, seps_alt_new, '--', label='without XS, bigbatch')\n", + "plt.xscale('log')\n", + "plt.xlabel('gphi')\n", + "plt.ylabel('seperation')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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hwssvIoRA1X82qoFzZEK4jXrdUnh3z7scyzlWo9cMdQ7lxTYv3vYctVrNrFmz6NWrFxs2bECtVgMwdOhQOnbsSEJCAt26dWPkyJFER0ff8Z4lJSUkJSWxfft24uPjSU5Ovu74lClTmDJlCsOHD2fhwoXXHduzZw9HjhzBz8+PXr16sWrVKgYPHsxbb72Fs7Mzer2ebt26cfDgQSZPnsycOXPYsmULrq6uZGVlMW7cOLZv335dlxjAsWPH2LJlC4WFhYSEhDBx4sTKz1OSzKYkB7bOpOKPr8jcqaYoQ4NtdCge8xbduAGOdFOypWAiP//8Mx4eHtf9Avf29iYlJYV33nkHlUpFt27d2Lx58x2vNXz4cAA6depEQUEBeXl51x3/448/GDLEuC/EE088cd2xNm3aEBgYiIWFBcOHD2fHjh0AfPvtt7Rs2ZLo6GgOHz7MkSNHbrjvrl276NSpEwFX9pp1dnauPNanTx+sra1xdXWlUaNGXLx4sSpfFkkyjavleiys0P6+itPrnSnOssf93//GZ8Vq1O6yfldV1euWwp2e6E0lKSmJjRs3smvXLjp27MiwYcPw8DBOcbO2tqZ379707t0bd3d3fvjhh+u6ZW7m70841Xniudl7z5w5w+zZs9m7dy9OTk6MHj26WmW3ofolviXJJPQVsHcxHP4BMeIHFI0d6peTcLCag9Pw4VgHBZk7wjqnTrYUavPitatVSOfNm4evry/Tp0+vHFPYv38/GRkZABgMBg4ePIifn7Hm+qhRoyrHC/7um2++AWDHjh04Ojri6Oh43fG2bdtWls3++uuvrzu2Z88ezpw5g8Fg4JtvvqFjx44UFBRga2uLo6MjFy9e5Oeff648/9qS223btmX79u2VM6Su7T6SJLM7txuxqDMlX/yXzI0FnIjrSkVuLorGjsb//a9MCHepTrYUavPitcWLF+Pr60uPHj0AmDRpEp9++inbtm2jtLSUcePGVe5k1qZNm8rB4oMHD+LpefOSuxqNhujoaHQ6HcuWLbvh+NUZQ2+99Ra9evW6Lmm0bt2aZ599lpMnT9KlSxcGDhyISqUiOjqa0NBQfHx86NDhry0Fx48fT69evfD09GTLli188sknDBo0CIPBQKNGjSr3XZAksykvpOL7f5K3ej355xzQ5rui2JTj8HAvRDVbvNKNZOnsWqCgoICxY8det/tZdZSUlNCgQQMUReHrr79m5cqV/PjjjzUcZf1UF79fHlSGsjL0+fmoXZ3Rzo7j1PIcbFq1xHHQYBwe7onqLqd3P4hk6exazsHB4a4TAsC+fft49tlnEULQsGHDm7YmJKkuEkJQduAAeSuWULBhCzbtOuKzcBFW07bSdHQuavdG5g6x3pFJoR6IjY3lwIED5g5DkmpU3vffk71kMdozZ1EsBA4BAse+V7a5tFDLhGAiMilIklQrGMrLKdqyBbuuXVGp1egObMaiJBWPNgXYPzoUi96vQoOG5g6z3pNJQZIksxFCUJacTP7q1eSvW48hPx/vjz/CvmtXXIMu4ubjCX2+Bs8oc4f6wJBJQZIks6jIyuLsmDFoT55CsbbGvlscDQPLsIn0B0VBGbwErB1BVSdnztdZMilIknRfGLRairZsRZ+Xh9PjQ7FwdcU6KAjnJ5/EIRAsEl6Dy+lwuhW4Nqn3217WVjIFm8EjjzxCXl4eeXl5zJ8/v/L1uyl1fStbt27l999/v+mxOXPmEB8fX/nxihUr6NOnDwApKSnExcURFRVFWFgY48fffgvC1NRUwsPDb3rs6aefvmn5jGuNHj2a//3vf7c95+/3++qrr+54nr+/P5cvX67ydSXTEEJQmnyYC2+8ycnYTqRPmULuihUIIVAUBe+Xn8VJ9y0W68Ybk0D8BmhT65YfPVBkS8EM1q9fDxh/wc2fP59JkybV+D22bt2KnZ0d7du3v+HY5MmTiYmJYefOnTRv3pyXX365sgbT5MmTmTp1KgMGDADg0KFDdx3DkiVL7vq9t3I1Kfy9xpNUO13+8CMuz5+PYmWFffduOA4chG37dn+VX9m9CM7thl4zofU4sJC/ksytTrYUanOZi1mzZlWWkp46dSpdu3YF4LfffmPEiBHAX0+xM2bM4NSpU0RFRTF9+nQAioqKGDx4MKGhoYwYMYKriws3b95MdHQ0ERERxMfHV66KvvaJODExkbi4OFJTU1m4cCFz584lKiqKhISE62K0tLRk/vz5PPPMM7zwwgvEx8cTGBgIGMt7e3t7V54bERFxx8+5oqKCESNGEBYWxuDBgys36bl2g6ClS5cSHBxMmzZtGDduXOVKboBNmzYRExNDcHAwP/30E2D85R8bG0vLli1p2bJlZatnxowZJCQkEBUVxdy5c9Hr9UybNo3w8HBatGjBhx9+WHndDz/8kJYtWxIREcGxYzVbLVe6kdBqKdy0ifPPPEvpwYMA2PfsQeNX/ktQwna85szBLrYjyunfIG2f8U1dXoJn90LbiTIh1BJ18n+hOmUuzj456obX7Hv3wvmJJzCUlnJ+/D9uOO44cCANBw2kIjeX9MlTrjvm98Xnt71fbGws77//PpMnTyYxMZHy8nJ0Oh0JCQl06tTpunNnzpxJcnIySUlJgPHp/s8//+Tw4cN4enrSoUMHdu7cSUxMDKNHj2bz5s0EBwczatQoFixYwPPPP3/TGPz9/ZkwYQJ2dnaVdZf+rn379oSFhbFp0yaOHj1a+frVRNa+fXt69uzJmDFjaNjw9tMAU1JSWLp0KR06dCA+Pp758+dfd9+MjAzeeOMN9u/fj729PV27diUyMrLyeGpqKnv27OHUqVN06dKFkydPVpbU0Gg0nDhxguHDh5OYmMjMmTOZPXt2ZfJYsGABqampJCUlYWlpeV19JldXV/bv38/8+fOZPXu2SVouEpQdO0beqlUUrP0JfW4uFm6u6C5coEGLFmhCQ9GEhhpPzE+DX2bA0bXQbAAM/dw4xVROM61V6mRLoTZr1aoV+/bto6CgAGtra9q1a0diYiIJCQnExsbe8f1t2rTB29sblUpFVFQUqamppKSkEBAQQHBwMABPPfUU27dvv6c4i4qKSExMRKfTkZWVVfn6mDFjOHr0KEOGDGHr1q20bdu2slVyK9fWTxo5cmRlee6r9uzZQ+fOnXF2dkatVleW+b5q6NChqFQqgoKCCAwM5NixY+h0OsaNG0dERARDhgy55djEpk2b+Mc//oGlpfH55try3oMGDQKM/yepqalV+8JIVSKuVMU1aLWcHfUUeSu/xqZNG3wWLSRoyxYcevb86+QKLeyYCx+1hhOboNt/YZBM0LVVnWwpVMftnuxVDRrc9rilk9MdWwZ/p1arCQgIYPny5bRv354WLVqwZcsWTp48WaUaO9UtSW1paVm5m1p1yl+/8sorjBw5End3d6ZOnXpdmQ1PT0/i4+OJj48nPDyc5ORkWrVqdctr3Utp71u9f+7cubi7u3PgwAEMBgMajaZa14S/vpaytHfNEDodRQk7yF+9Cm3qWQLW/IjKygrvDz/EOjgIy1vtIpi0Aja9CiF9oNc74OR3X+OWqke2FEwgNjaW2bNn06lTJ2JjY1m4cCHR0dE3/PK7tkz17YSEhJCamsrJkycB4/acnTt3BoxdRfv2Gftnr5bPvtO1Dx06xLp163jxxRcZP348qampldVPf/nlF3Q6HQAXLlwgOzsbLy8v0tPTb7nvw7lz5/jjjz8A+Oqrr+jYseN1x1u3bs22bdvIzc2loqLiujgBvvvuOwwGA6dOneL06dOEhISQn59fuXXpF198gV6vv+nn1aNHDxYtWlT5S1+W96552rNnuTjzXU7EdSFt0iRK9v+JbYcOiCstSNuH2tyYEAovwrldxn9Hj4QnV8Pwr2RCqANkUjCB2NhYMjMzadeuHe7u7mg0mpt2Hbm4uNChQwfCw8MrB5pvRqPR8OmnnzJkyBAiIiJQqVRMmDABMD7xT5kyhZiYGCwsLCrf069fP1avXn3DQPPV/R7mzp2LRqNBpVKxYMECpkyZglarZcOGDYSHhxMZGcnDDz/MrFmzaNy4MZmZmZVdNH8XEhLCxx9/TFhYGLm5uUycOPG6415eXrz00ku0adOGDh064O/vf115b19fX9q0aUPv3r1ZuHAhGo2GSZMm8dlnnxEZGcmxY8ewvVIBs0WLFlhYWBAZGcncuXN5+umn8fX1pUWLFkRGRlZpuqp0Z/q8PPRXJnKUHT1KzpdfYtMyGu/58wnaugX3GS+iulnrzaCH3Z/ARzHw/TjjJjgWamjS9T5/BtLdkqWzpSr56KOP8PX1pX///nf1/qKiIuzs7KioqGDgwIHEx8czcODAGo6y+uT3y/VKDx8me8kSijZtxvWZSbhOmIBBq8VQVITlNeM1N5WWCD9NhQsHIbALPDIbXJven8ClapGls6V7du0U0rvx6quvsmnTJsrKyujZsyePPvpoDUUm1YSSP//k8sKFFG/bjsrOjobDh2F/pbtQZWWF6k4JISMJlnQH+8YwZDk0exSqObYk1Q4yKUj3xezZs80dgnQb2Ys+oezAQdyefx6nEU9gYW9/5zcZDHDpCDQOB49I6DsHIoaAdRXeK9VaMilI0gNGCEHRtm1kL16C59tvYeXnR+NXX8HCwQGVjU3VLpJ5ENb9Cy4cguf2gaMXxMTf+X1SrVcvk8LVuiqSdDt1eTztbgiDgcKNm7i8cCHlR49i6emB7sJFrPz8UDduXLWLlOXDlrdhzyfQwNnYOnC4+d7iUt1U75KCRqMhOzsbFxcXmRikWxJCkJ2dfVfrH+oiUVHBmSFDKT96FCs/PzzefhvHfn1R1OqqX6S8ED5uC4WZxlZBt//ISqb1UL1LCt7e3qSlpV23SleSbkaj0VxX56m+MWi1FCckYN+tG4qlJQ6P9Eb99FgcevVCuWb68h0VXQK7RsaxgnbPgF978GppusAls6qTU1IVRekH9GvatOm4EydOmDscSapVDKWl5H33P7KXLqXi4kX8v/8fDZo3r/6FtMWwfRb88TGMXg8+rWs+WMks6t2U1OoUxJOkB4WhtJTcFSvI/nQ5+uxsbGJi8HjrLTTNmlXvQkLAsXXG4nX55yFqBDj5myRmqfapk0lBkqS/XJ1YIfQGshcvQRMejuuEf2DT+i6f7FeNg0PfQaNmMOYX8GtXswFLtZpMCpJUR1VkZ5Oz/DOK9+zGf+VKLOxsCVy/DksXl+pfLPMguIcb90P2bGlcd/DQBGOJCumBIpOCJNUxugsXyF62jLxvv0OUl+PQuzeGoiIsHByqnxDSEmHrTDi50bi/QbMB0K7mdwKUalZ2aTbOGmeTzLCUSUGS6pDSgwc5O2IkwmDAsX9/XMaNwzowoPoXOrcbtr0LpzYb1xt0+68sWlcHaPVaPj/yOZ8c/IQ3OrzBw/4P1/g9ZFKQpFqu/PRpdOfPY9e5M5pmzXCOj6fhkCFYeXvd3QUNelg93rjuoPtr0PppsLar2aClGrc9bTvv7X2PswVn6erTleYudzGjrApkUpCkWqrs6FEuL/qEwl9/Re3lRZPYWBRLSxpNvfk2rLeVutO4CvnRBWBlA8O+Ms4osrKt8bilmnWu4Bzv7X2PbWnb8HfwZ1H3RbT3am+y+8mkIEm1TFnKcbLmzqVo61ZUdna4jB+P81OjUFTV3P5ECEhNgK3vwtkdYOcO2SeMg8jupnnKlGpOia6EJYeWsPzwctQqNf9q9S9GhI1AbeLBf5kUJKkWEEKATodiZYU+L4/SP//EbcpknEaMwMLBofoXLMuHr4bBud/BrjH0ehdaPQXqBjUfvFSjhBD8mvorsxNnc7HkIv0C+zG11VTcbNzuy/1lUpAkMxJCUJyQwOWFi9A0a0bjl/+NTZvWNN3yW9Urlv51Mcg5DS5NwNrBWJqi9yxoOQrUD0aNp7rueO5xZu6Zyd4LewlzDmNW51lEN4q+rzHIpCBJZiAMBgo3byZ7wULKjhzB0sMDx0cHAKAoCkp1EoIQcHIzbJsJF5Lh+YPGhDD0MxNFL9W0/PJ85ifN55uUb7CzsuM/bf/DY0GPYaGqRo2qGiKTgiSZQdbcuWQvXoLazxePt97EsV8/FCur6l1ECDix0Ti1ND0RHH2g19ugcXMp6hcAACAASURBVLzze6Va4WpX0Tt73iGvPI8hwUN4Lvo5HK3N938ok4Ik3QdCqyV/7Vo04eFoQkJwHDQI6+AQHHr3QrG8yx/DnNPw1VBo6AP9/g8inwDLaiYWyWwul17mzV1vsvncZiJcI1jUYxGhzqHmDksmBUkyJUN5OXnff0/2kiVUZGTiPDYezfTpWAcEYB1QzUVnVwvVZSZB15eNYwejfgC/DrIcRR0ihGDdmXXM3DOTUl0p/2z1T55s9iSWqtrx67jKUSiK4gX4XfseIcR2UwQlSfVB7sqVZM2fjz7rMg1atsTjtdew7dix+hcyGODYT7DtPbh4CJybQMepxjUGgXE1HbZkQpdKLvHGH2+wNW0rkW6RvN7hdQIcAjiUno9KUXC2tcLZ1gqN+v6PJVxVpaSgKMq7wOPAEUB/5WUByKQgSdfQFxaisrNDURR0GZlogoJwef99bFq3vrs6NRcOwap/wKXD4NIUBi6C8MFgUTueKqWqEUKw5tQa3t37Llq9lmkx0xgZNpIynWDy10msPZBx3fl21paVCcLF1goXOyucba1xufqanRXNPB1oZF/zs8qq+p31KBAihCiv8QjuwjWb7Jg7FEkCoCInh5zPPid3xQo8Z8/CPi4Ot+enVG+Hs6sMeii+DPbuxgVnFpYwaDGEPwZmmI0i3ZuLxRd57Y/XSEhPoGWjlrzW/jX8Hf05nVXEhC/3cfJSEVO7BxPqYU9OsZacYi2Xi8or/52ZX8bhjAKyi8vR6f/aFG3O0EgGtaz5nQOrmhROA2qgViQFucmOVFvoLl4iZ9kycr/9FlFWhn3Pnlh5GWsSVTshGPSQvMq425nGEcZuME4t/YdskNdFQgh+OPkDs/bOQmfQMaPNDIaHDkelqNhw+AL/+vYAlhYKn8c/RMcg1ypdr7C8gpwiLdnFWvxcqrmOpYqqmhRKgCRFUTZzTWIQQkw2SVSSVAcIITg76kl0aek49u2Dy/jxWDdpUv0L6Ssg+XtjMsg+AW5h0HZCzQcs3TcXii/w6u+vsjNjJ63cW/F6+9fxdfBFbxC89+sx5m89RQtvRxaMbIVXw6qtMlcUBQeNGgeNGn9X09WsqmpSWHPljyQ90MrPnCF35UoaTZuGysoKj9deQ+3lhZWPz91fNGkFrJ0MjZrDkM8grL9xsxupzhFC8P2J75mdOBuDMPDSQy/xeMjjqBQV2UXlTPk6iR0nLzO8jS+v9Gtm1gHlW6lSUhBCfKYoihUQfOWlFCGEznRhSVLtUpaSQvaiRRT8/AuKtTUOvXpj0zIa27Ztq38xvQ4OfgvW9tCsP7QYCjYuEPKITAZ1lEEY2J62ncWHFnMw6yBtGrfhtfav4W1v7PNPOp/HpC/3cblYy3uPtWBo63t4iDCxqs4+igM+A1IBBfBRFOUpOSVVqu/0RUVkvDiDos2bUdnY4PL00ziPfurutrzU6+DASkh4H3JTIayfMSmoG0BY3xqPXTK9CkMFv6T+wtJDSzmZdxJPW09ebfcqA4MGolJUCCFYuec8r645jJu9Nasmtifcq3avOK9q99H7QE8hRAqAoijBwEqglakCkyRz0mVmovbwQGVriygtwfXZZ3EeOQKLhg3v7oJHf4Jf/x/knQPPaOg1E4J71WzQ0n1TVlHGDyd/YPnh5aQXpdPEsQlvd3ybXgG9UKuMCwnLdHr++2My3yam0SnYjf97PAon29q/4ryqSUF9NSEACCGOK4oil1BK9YoQguKdv5O90Fikrulvm7Fo2BCfpUvvbo1BRblxRpGVDQgD2LrBI+9DUA8wwd66kukVagv5JuUbvjjyBTllObRwa8GLrV+ks09nVMpfXX/nc0qYuGIfyekFTO7alCndg7FQ1Y3/86omhURFUZYAX175eASQaJqQJOn+EkJQtGULlxcspOzQISwbN8Zt6lQUjXFhULUTgq4M/vwCdsw1lq2Om2HsKgrrJ5NBHXW59DJfHvmSb1K+oUhXRHvP9jwd8TQx7jE3fH9sO57FlK//RG8QLH0qhm5h7maK+u5UNSlMBJ4Brk5BTQDmmyQiSbrPtCdPkjbpGdQ+PjR+/TUcH30UVXUrloIxGez/DHbMg8IM8GkLfle2TZTJoE5KK0xj+eHlrD6xGp1BRw+/HoyNGEszl2bXnSeEYP+5PL7fn8bKPecIcbdn4chWJp06aiqKEOLOZ9VSMTExIjFRNlik6hE6Hflrf0J77iyNnjfud1z8xx/GUhR3W7EUjOUoDn4Nvu0h7kUI6CyTQR11PPc4y5KX8cuZX1AUhQFNBjC6+Wj8Hf2vO+/ExUJ+SEpnzYEMzueUYm2p4rFW3vynTzMaWNW+6aZXKYqyTwgRc7Njt/0JUBTlWyHEUEVRDmGsdXQdIUSLGopRkkzOUF5O/qpVZC9egi4jA03z5ohJk1CsrLBt1676F9SWQOIyCO0DzgHQYTJEj4SA2JoPXrovki4lseTQEralbaOBZQNGhI1gVLNRuNv+1QWUnlfK2gMZ/JiUwdHMAlQKdAxy4/luwfRs7o69pm4Pt97psWjKlb/lfDmpTivevYeMadOoyMpCE9kC9/+8jF1c3N0NIGuLYe9S+P0DKM4yDiJ3mAzuzWs+cMnkhBDsSN/B0uSl7Lu4j4bWDZkUNYnhIcNpqDHONsst1rI+OZMf/8xgT2oOANG+DXm1XzP6tPDEzd7anJ9CjbptUhBCZF755yQhxIvXHrtSOfXFG98lSbWDvrAQfV4eVj4+WPn5Yh0UhOd772LTtu3dJQOAP+Yb1xmUXIbALtD5RfC7i1aGZHZ6g56NZzeyNHkpx3KO4W7jzgutX+CxoMewUdtQoq3gx6R01iRlsO14FhUGQdNGdkzrGUz/SC98TVR7yNyqNKagKMp+IUTLv7120NzdR3JMQboZbVoaed9/T+6XK9A0b47f8k/v8YIlxmmlAGunQN5544winzb3Hqx032n1Wn489SOfJn/K+cLz+Dv4Ex8eT9/AvoAFO05c5sekdDYcuUiJVo+Ho4b+kZ70j/KkmYfD3T9Q1CL3MqYwEZgEBCqKcvCaQ/bAzpoLUZLuXd6q1eR8/jnlx44BYN+zJy7/GH/3FyzLh92fwK6P4YlvjUngkdlyl7M6qlhXzHcp3/H5kc/JKs2imUsz5sTNIc6rCwfSCnht7THWHcwkt0SHYwM1A6K8eDTKk9b+zqjqyBqDmnCnMYWvgJ+Bd4AZ17xeKITIMVlUknQHwmCgNOkAhZs34TpxIhZ2dujz8lDZ2tLoxRex79EdK++7rDVfmge7FxmTQVm+ceWxtb3xmEwIdU5OWQ4rjq5g5bGVFGoLeajxQ7zV8S0aKs1YcyCTV1duJz2vFI1aRY9mjRkQ6UmnYDesLB/MOlTVmpKqKEojoHKrHyHEOVMEVVWy++jBIrRaivfspXDTRgo3b0afdRnUanwXL8a27UMIIe69aW/QwwfRkHfWWKCu8wvGshRSnZNZlMnyw8tZdWIVZfoyuvl2o6/vCI6fd2JNUgbHLhRioVKIDXJlQJQnPZo1xs76wdjR7q67j665QD9gDuAJXMK4V/NRQE63kEzKUFKCvrAItXsjtGfPcv7pp1FsbLCLjcW+e3fs4jpjYW98ir/rhFCSYyxU99BE485mPV43TjH1iKzBz0S6X07lnWJZ8jLWn14PQHffXnjxCDuOWvD0r5eAS7Tyc+L1Ac15JMIDV7v6M3OoJlQ1Lb4JtAU2CSGiFUXpAow0XVjSg0yfl0fh1q0UbtpE8Y6d2Pfsgdd772HVtCk+ixdj0zoGlaYG9qYtzoY/PoI9n4C2CLxagW9baP7ovV9buu+SLyez+OBifjv/G9YWGmKc+1Cc1YHVGwQVhgKCGtkx/eEQ+kd64uNcP2cO1YSqJgWdECJbURSVoigqIcQWRVHmmTQy6YGU8dK/yf/xR9DrsXR3p+Fjj+HwSG/A2BKwi+147zfRFsO292DPYtCVQLMBxm4iuc6gzhFCsPvCbpYcWsLuzN00sLDDT9Wfkyei2Fhug6ejFU/HejEgypPQxvb1YuaQqVU1KeQpimIHbAdWKIpyCSg2XVjSg0CbmmpsDezajc/CBSiWllg3bYpLfDz2PbqjCQ9HqclNZ/QVYGEJFtZw7CcI6QWdpkOjsJq7h3RfGISBLee3sPTQUg5dPoS9pTM2RQO4mB6NVmPPY1EeDIjyIsbP6YGaOVQTqrpOwRYoBVQYK6Q6AiuEENmmDe/25EBz3aNNSyd/1fcUbtxI+YmTAGiaN8f7449QN25smpsWXjSuPj72E0z8w7jm4Nq1B1KdoTPo+OWMcVObU/mncNN4YFHQlROnQ2nq5sS0nsF0DXV/YGcOVdU9DTQrimIB/CSE6AIYMO7AJklVIvR6Sv/8E8tGjbDy9UV3/hyXFy7CplUr3F/6f9h364bay8s0Ny+8ADv/z1ifSK+FiKHGriMrG5kQ6pi/b2rjb9+E5pYT2PWnDy62DXhzQDDDWvtgaSGTwb26Y1IQQugVRTEoiuIohMi/H0FJdZtBq6Vk1y4KN26k8Lct6LOzcR4bj/v06di0bk3QjgQsnZ1NG0T2KZjfDgwVEDkMYv8FLk1Me0+pxhVpiyo3tckuy6a5SwRBFiPYkOiMQMXEuAAmxTWp80XoapOqjikUAYcURdnINWMJQojJt36L6VyZItuvadOm5ri9dBPCYEBRqRAGA6ce7kVFZiYqW1vsOnfCvkcPbGM7AaBYWpouIQgBWSnQKBScA43lq5sPNP5bqlNyynL48siXfH3sawp1hbT1aEtPy3/x/e/W7CrSMSDKk+kPh+DtJFt8Na2qYwpP3ex1IYRZu5LkmIJ5VeTmUvTbbxRu3ITu0kUCV60CIPfbb1G7u2PTrt3dbVZzN3JT4aepcPZ3eGYPOPndn/tKNepC8QWWH17O98e/p1xfTjffbkQ7DOLL7QaOXywixs+Jl/s2I8rnLvfKloAaWLwmhPhMUZQGgO+1ezVLD6ai7dvJXrKUksREMBiw9PTAvnt3hFaLYmWF09Ch9y8YfQXsXgBb3gZFBT3fBEef+3d/qUacyT/DsuRl/HTqJwAeCXyELo0f57Ntpfz3xGX8XGxYMKIlvcIby2mlJladFc2zASsgQFGUKOB1IUR/UwYn1Q7lp05RuHETjv36ovbyQp9fQEVONi7/GI999+5omjUzzw9qhRaWPQwZ+yG4N/SZDY53We9IMosj2UdYcmgJm85uwsrCiqEhQ+nrP4yvdhYyft157KwteblPGE+288PasvbuZFafVHVM4VWgDbAVQAiRpCiK7Kitp4QQlCUnU7hxE4WbNqE9fRoAy8buNPTywqFvHxz7mXHfpavrDSytIPhh4wY3zR6VW1/WEUII9l3cx5JDS9iZsRM7tR1jI8YyuOkTrE7MY9jHKZRXGHiqvT+TuwbhZHufuiAloHormvP/9jRoMEE8kpmIigoqcnJQN2qEPi+P1GHDAbBp3RqnEU8Yp45eWUdg1ub7qS2w7p/w6AJjSYq4GXd+j1QrCCHYnradJYeWkJSVhLPGmSktpzAkaCibjxQw+OODZOaX0bOZOzN6hxLoZmfukB9IVU0KhxVFeQKwUBQlCJgM/G66sKT7wVBeTvHO3ynctImi337DOiQEv8+WY+nkhM+C+WgiIrB0cjJ3mEYlOfDrS8bCdc5NjOMHUp1QYahgQ+oGliYv5XjucTxsPXjpoZcY2HQgSeeKGbn4IIfS84nwcmTu41G0DXQxd8gPtKomheeAfwPlwErgV+ANUwUlmV7WRx+Ts2wZhpISVPb22MXF4fBwz8rjdp06mTG6v0leBeunGfc2iJ1mLE2hroGCeJJJ/X2Hs0DHQN7q+Ba9A3qTlqNl8lfJbDhyEQ9HDXOGRvJolJcsSVELVHX2UQnw7yv7MgshRKFpw5JqUsXlyxT+9htFm3/Dc9Z7WDg4oG7sjkO/fth3747tQ21Q7tfU0btRkA5OAdD/A1m0rg4o0ZXw3fHv+Pzw51wqvUS4Szj/ivsXXXy7kF9SwVs/HefLXWexslQxrWcwYzsG0sBKDiLXFlVdp9AaWIZxG06AfCBeCLHPhLHdkVyncGsVubnk//gjhZs2UbpvPwiB2tsbr/+bR4PmtfwXq74Cdi80ziRq/qhx4xsw7nUg1Vp5ZXl8dewrvjr2Ffnl+TzU+CHGRoylrUdbtHoDX/xxlg82n6CovILHW/swtUcwjexli88c7nmdArAUmCSESLhywY7Ap0CLmglRuldCCMpPnEBRFKyDgtDn5nJp5rtYh4TgOmkS9j26Yx0SUvvneGcehDXPQWYSRI00JgWZDGq1i8UX+fzI53x3/DtKK0rp4tOFpyOepoVbC4QQ/JJ8gXd+Psa5nBJig1z5d58wQhs7mDts6RaqmhT0VxMCgBBih6IoFSaKSaoiYTBQdvAghZs2UbBxI7qz53Do0wev92djHRhIk02bsPI2UbG5mqYtgW0z4fePwMYZBi+D5oPMHZV0G+cKzrEseRlrTq3BIAz0DuhNfHg8QU5BACSdz+OtdUfYm5pLsLsdy8e0Ji6kkZmjlu6kqklhm6IoizAOMgvgcWCroigtAYQQ+00Un/Q31+5DfHbUKEoT94GlJbYPPYTLmDHYde1aeW6dSQgAp7caK5pGj4QebxgTg1QrpeSksPTQUn49+yuWiiWDggYxuvlovO2NCwfTckuY9WsKPyZl4GpnxdsDIxga4y0rmNYRVR1T2HKbw0II0fU2x03mQRlTMJSWUrxzJ4UbN1KSlESTdetQLC2NO5QpCnadO2Ph6GjuMKuvJAfSEiG4p7GY3cVkaBxh7qikW0i6lMTiQ4vZnrYdW7UtQ0OGMqrZKFwbuAJQWKZj/tZTLN1xBgV4OjaACZ1lBdPaqCZqH3Wp2ZCkqig9dIjsTxZTlJCAKCtD5eiIfVwc+sJCLJ2ccBwwwNwh3h0h4ND/4JcZUFEO/zwMGkeZEGohIQS/Z/zO4kOL2XdxH07WTjwb9SzDQofhaG18EKnQG/h673nmbjxOdrGWR6M8md4rFK+GDcwcvXQ3qlr7yB14G/AUQvRWFKUZ0E4IsdSk0T2AhF6PobQMCztbDCWllB44QMNBA7Hv0QObmBgUdR1/6so7Bz/9E05uBM+W0P9DY0KQahW9Qc/mc5tZcmgJR3OO4m7jzoutX2RQ0CBs1MZy1UIItqZk8fb6o5y4VEQbf2eWjQ4jUlYwrdOq2n30M8bZRv8WQkQqimIJ/CmEMOujXX3rPiretZuL77yDJiIczzffRBiMlURqdJ9icyrJgXktQBig23+gzXg5s6gW0eq1nMo7RVJWEl8d/YrUglT8HfyJD4+nb2Bf1BZ/PZAczSzg7fVHSThxGX8XG2b0DuXh5rKCaV1RE1NSXYUQ3yqK8v8AhBAViqLoayzCB5z27FkuzppF0abNqL28sLu6IU19SQaFF8C+sXHw+OG3oEkXaOhr7qgeaPnl+aTkpHAs5xgpuca/T+edpkIYJxWGOYcxu/Nsuvt2x+KaxH2psIw5G47zbeJ57DVq/tO3GU+29ZN7ItcjVU0KxYqiuGCceYSiKG0xLmCT7lHB+vWkvzgDlVqN29SpOI9+CpW1tbnDqhm6Utj2rnGa6VNrwa8dtLrpfk2SiQghSC9KNyaA3GPGJJCTQmZxZuU5bg3cCHEOoZN3J0KcQwh1CsXPwe+6p/5SrZ7FCadZuO0UOr2B0e0DmNytKQ1tavFKeOmuVDUp/BNYAzRRFGUn4AYMNllU9ZzQ69Hn52Pp7EyDli1p+OijuD73LOpG9WgO9+lt8NPzkHMaokaAW4i5I6r3rnb/XPv0fzznOIU6Y1UalaLC38GfqEZRDHMeRqhTKMHOwZWzh27GYBCs/jOdWb+mcKGgjF7NGzOjdyj+rrb369OS7rOqJoUmQG/AB3gMeKga75WucXXcwKJhQ3yXf4q6cWM83njd3GHVrPXTYc8nxnpFo36EwDhzR1Tv3Kn7p4FlA4Kdgnkk8JHKp/+mTk1pYFn1GUF/nMrmrfVHSE4voIW3Ix8Mj6ZNgFw/Ut9V9Rf7f4QQ3ymK4gR0wbgL2wKMyUGqAu25c1yaNYvCjZtQe3riOnGCuUOqWVcnLCiKsbR1h+eNex2o5bTEeyGEIKM4o7Lb5+rfGcUZlefcrPvHx97nurGA6jh5qYh3fznGxiMX8XTUMO/xKPpHesoKpg+IKpe5uPJ3H2CxEGKdoihvmiimeqdox07SJk4EtRq35583jhto6lEhsLzzsO5f0GIoRAyGtvUs4d0nOr2OU/mnbkgAV7t/FBT8Hf2JdItkaMhQwpzD7tj9U7X7Gth/NpctKVlsOXaJlIuF2FpZMP3hEMZ2DECjljPEHiRVTQrpV8pc9ADeVRTFGpDTDW5D6PVUXLyI2tMTm+goGg4bhsu4p+vXuIFBb+wm2vwGICC0j7kjqjPyy/M5nnucYzl/Df6eyj9FheGv7p8gpyB6B/Q2Pv07h9K0YdPKNQL36lJhGdtSstiaksX2E1kUllVgqVJoE+DMS61CGRjtjZt9PZnwIFVLVdcp2AC9gENCiBOKongAEUKIDaYO8HZq6zqF4t17uDhzJqKkhMC1a2r3XgV36+JhYzXT9H3QtDv0mQNOfuaOqtYRQpBZnMnRnKO37P5xbeBa2e0T6hxKiHMIvva+d939czN6g+BAWh5bj11iS0oWh9KNkwfdHazpEtKIuJBGdGjqIktSPCBqosxFCbDqmo8zgcxbv+PBpD1/nkvvzaJw40YsPT1wnz4d6voK5FvJPgm5Z2HQEmOXkVy0hE6v43T+6b+e/q8MABdqb979czUB3Gv3z63kFmvZfsLYJbTteBa5JTpUCrT0dWL6wyHEhbjRzMNBLjiTriNnENWQ0uTDnB0+/Mq4wRScR4+uX+MGAGcSIPcMtBwFYf0hsAtoHsy6+AXaAlJyUq6bAXQy72Rl94/GQkOwUzC9/HsR6hxa490/N2MwCI5kFrDl2CW2pFwi6XweBgHOtlbG1kBoIzoFucq1BdJtyaRwD4Rej/bMGaybNkXTLAyXceNo+PjjqN3r0bgBQGkubPgP/PkFuIVB5BNgYflAJISr3T/XDf7mppBelF55jrPGmTDnMNo3a1/59O9n71ej3T+3UlCmY8eJy2w5domtx7PIKiwHINLbkee6BtEltBEtvBzlzCGpymRSuEvFe/Zw8Z2Z6DIyaLrhVywcHXGb/Jy5w6pZQsDh1fDzi1CSDR2mQOcZxoRQD1Wl+8fPwY8I1wgGBw+ubAGYqvvnZoQQnLhUVNkaSEzNpcIgcNBY0inYjS4hjegU7CYHiaW7Vj9/uk3o7+MGHq/8F5VDPX1ivnwC/hcPHi1g5P/AI9LcEdWYQm2hsfsn96/B35N5J9EZdMCN3T8hziEENQwyaffPrZRoK/j9ZDZbUi6xNSWL9LxSAMI8HBjfKZAuoY2I9mkoN7GRaoRMCtWgTUvn9CN9wNIStymTcR4zpv6NGxj0cHYnBHQCt2B4ag34tq+zrQMhBBeKLxif/nP/6gL6e/dPqHMoI5uNJMw57L52/9zKmcvFla2B3adz0OoN2FpZ0KGpK891bUrnEDc8HOXCQKnm1c2f9PtI6PWUJSfTIDISK28vGr3wAvY9e6B2dzd3aDXv4hFYOxnS9sKEHcZNbwI6mTuqaimrKGPPhT3sztxd2Q1UoC0A/ur+CXcNZ3DwYEKcQiq7f8w9A6dMp2f3mRzj2EDKJVKzSwBo4mbLqHZ+dAltRIy/E9aWciGZZFoyKdxGyd69XHjnHcqPn6DJz+ux8vHB+cmR5g6r5unKYPss2DnPuOHNwE/APdzcUVVZZlEm29O2sz19O3sy91CmL8Pawppgp2B6+vck1MnY/RPsFGyW7p9bScstYUtKFluPXeL3U9mU6vRYW6po38SF+I4BxAU3wtel9sQrPRhkUrgJbVoal2bNpvDXX7H08MDz3Zmovb3NHZZpGAywrCdkHoAWw+Dht8HWxdxR3VaFoYKDWQcrE8GJ3BMAeNt5MyhoEJ28OxHTOAZri9o12KrTG0hMzWVLyiW2HLvEiUtFAPg4N2BojDdxoY1oF+giy0pIZiWTwt/oCws5M+BRhMGA6+TncBkzBlWDeth3W14IVnagUsFDE8DOHZp2M3dUt5Rfns+O9B1sS9vGzvSdFGgLsFQsiXaPZlrMNGK9YwlwCDB7N9DfXSwwlpPYknKJhBOXKSqvQG2h8FCAC4+39qFLaCMCXW1rXdzSg0smBUAYDBT/8Qd2HTpgYW9P49dfwyYmpn6OGwgBR36En1+Anm9BiyEQ9YS5o7qBEILjucdJSE9ge9p2DmQdwCAMOGucifOJo5N3J9p7tsfeyt7coV5HbxAknc9lyzFjIjicYRzP8HDU0C/Sky4hbrRv6oqdtfzRk2qnB/47s3Lc4MhR/L9eSYOoKBz71NPCbvnpsH4apKyHxi2Ms4tqkdKKUvZk7qnsFrpQfAEwbg05LmIcnbw7Ee4ajkqpXVMvs4vKr5STMBaXyyvRYaFSaOXrxAu9QugS0ojQxvayNSDVCbUmKSiKYgvMB7TAViHEClPe77pxg8aN8Zw9G01k/ZmHf4MDX8O6aWCogJ5vwkMTa8U00/SidGMSSNvO3gt7KdeXY2NpQzvPdkyMnEhHr440sqldK8QNBkFyRn5la+BAWh5CgKudFd1C3ekS6kZsUzccbepp3SupXjPpbwVFUZYBfYFLQojwa17vBfwf8P/bu/foKOs7j+PvXzIJl4QkEEJCMhNu4R4KQgLlkgAiUmuRVQvVtu6uemp1K63b1XPac9pta22723Nqb7S1tLq23a3S0taK1tpaFCKgTYIosYiECCQBEpJACLnOZH77x4QhRkBymXlmks/rnDk4z8w8z9cY58Pz/H7P9xcL/Nxa+1/ATcBWa+02Y8wWIGShYL1ejn78E3Q2NTF2472k3nHH4Bw36C5uQmlMnwAAELpJREFUJHjy4SPfhdETHSvD5/exr3YfO6t3UlRVRPmZcgCyR2Wzftp6CtwF5KXnER8bWf15Glu9FHWdDex4u5a6cx0YA3PdKdy3ahorZ6SRm6l2EhL9Qv1XxceBTcAvz28wxsQCPyKwNkMVUGyMeRpwA/u73tZJCJm4OMZ/65sMmzKFuIyMUB7KOd42KPoODBsFSz8LM9cGHg5cwjjddpqXq19mZ9VOdh3fRVNHEy7jYkHGAm7MuZFCdyETkyeGva7LsdZysKYpeDZQevQ0nX5L8og4lk9LY+WMNAqnppGaGFkznET6K6ShYK3daYyZ2GPzQqDcWlsBYIx5ElhHICDcwD7CsIBP4tKloT6Ec47uhqc/C/WHYP6/BLaFMQystRw8fTB4WeiNU29gsaQOT2VV9ioK3YUsHr+YxPjEsNV0JZrbfewqrwvcO3CwlhONbQDMzkzinuVTWDkjjblutZOQwc2Ji8pZQGW351UE1nr+AbDJGHM9sO1SHzbG3AXcBZCdnR3CMqNQ6xl44StQ+jikTIBP/j5s00xbvC28euJVdlTtoKi6iNqWWgByU3O5Z+49FLoLmZk6M6IGia21VHS1k3jp4Cn+/k6gnUTiMBcFU8fy79eMY/n0NNKTBlkrE5HLcH6ksYu1thm4/QretxnYDIGV10JdV1RpOAyv/R8s2QgrvgjxCSE9XGVTJTurAmMDxSeL6fB3kBCXwJLMJRRkFVDgLghrB9Er0ebtZE9FfXAFsmMNgXYS09ITuX3pRFZMH8eCCaOJd0VOeImEkxOhUA14uj13d22Tvjh7HN5+HvJuh6wFcN9+SBofkkN5/d7AIHHXZaGKxgoAJiZN5JYZt1DoLmT+uPnExUbWrJvKhpbgXcS7D9fT7vMzPC6GpVPG8qnCyaycnoZ7tNpJiIAzoVAMTDXGTCIQBrcAkXf3VKTz+6HkUXjha2A7Yfp1MCpjwAOhvrU+OEi85/gemrxNxMXEkZeex/pp6yl0F5KdFFmX8Xydfv7+TgPbu7qMHj7VDMCE1JHcujCblTPGsWjSGLWTELmIUE9JfQJYAYw1xlQBX7HWPmqMuRd4nsCU1MestW+Gso5Bp/atQDfTyldh0nJY+71AIAwAay0HGg4ELwvtr9uPxZI2Io3VE1cHB4kjqbHceUfrm/lNSSVbS6uoOdtOfGwMiyaP4ROLAl1GJ40N7eU0kcHAWBu9l+Xz8vJsSUmJ02WEV0czPDwrMJtozTdh7q39nlnU4m1hz4k9wSA41XoKg2HO2DkUuAsCg8RjZkbkHblt3k6eKzvBluJKXqloIMbAiunjWL/AzfLpaYyMj5hhM5GIYYwptdbmXey1qPw/xhizFlibk5PjdCnhc7IM0mcHBo9vfjSwClpiWp93d+zsseDYQElNCV6/l8S4RJZkLmG5ZzlLM5eSOiIyu6VaaymrPsuWkmP8cd9xmtp8TEgdyQNrpnPzfDcZyZotJNJXOlOIdG2N8MJXoeQx+Oj/QO5NfdqNt9PL3tq9gSmjVUUcOXsEgMnJkyl0F1LoLmTeuHnExUTWIHF3Z1o6eOq1araUVHHgxFmGuWK4LjeDDfkePjgpVXcTi1yhQXemMGQc2AZ/egDO1cDie2Haml59vK61jqKqIoqqi9h9fDfN3mbiY+LJz8gPzhbyjPK8/44c5Pdb9lTUs6W4kj+/eZIOn5/crCS+vm42N8zLInlE5IaYSDRSKESqZz4fmF2UMQdu+TVkzX/fj/itnwP1B4KXhcrqywAYN3Ic1026jsKsQhaNXxSRg8Q9HT/TytbSKn5bWkllQytJw13cmu9hQ76H2ZnJTpcnMmgpFCKJ3w/WH+hemrMKUjyBM4TLzPs/13EuOEj8cvXL1LXWYTB8IO0DbLxqI8vdy5k2elpEDhL31OHz88KBGrYUV7Lz0CmshaU5qdx/7XTWzM7QFFKRMFAoRIrat2Db52Dqaii8H2ZcD1x8XYcjjUeCaw6U1pTi8/sYFT+KZZnLKHAXsCxrGaOHjw5v/f3wdk0TW4or+cNr1TQ0dzA+eTgbV+awPs+DZ0zkn9WIDCYKBaf52qHo4a6OpomQf+d73uLt9FJSUxK8LHSs6RgAOSk53DbrNgqzAoPErpjo+c95rt3HM68f58niSvZVniEu1nDNzHQ25HsonJpGrAaNRRwRPd8i3UTrlFRrLZ22E5/fh9fvxVddiu9P9+NrqMA383q8S+/DN2IUvvp/4PV7OXzmcPBO4hZfC8Nih5Gfkc9ts26jwF1AVmKW0/9KvWKtpfToabYUV/Ls/hO0dHQydVwiX7p+JjdelaU21CIRYEhOSS2rK6Osrgyf33fhC/r8n9YX3N79tXdts97Lvv6e1+yFf+6tjIQMCrMCU0YXjl/ICFf0LQZ0qqmd3++t4jcllRw+1UxCfCxr52ayId/DVZ6UqBjvEBlMNCW1hx1VO3jk9Ufesz3GxOAyLlwxFx5xMXHv+tMV48JlXMTFBp4nuBLe9f7z7+35/uBr9eW4mmpxTVsTeE4MLtewdx+j6/0ZCRnkpORE5Zemr9PPjrdPsaW4ku1v1eLzW/ImjObbH53C9XPGk6CF60Ui0pA8U2j2NtPqa33PF35Ie/03VgfuOTj4bOBu5Dv+AnGD787bI3WB/kO/2xvoPzQ2MZ6b5rvZkOchZ1xkLaojMlTpTKGHhLgEEuLC1BytezdTvw9WPwgf/Exg2ukgcan+Q1+7wcOqmeOI00plIlFj8HwzRaqmE/DX/wTPQvjI92DMJKcrGhDqPyQyOCkUQsHbBmVbYd4nIDkL7toBY6eGdZ3kULlY/6EPzxnPhjwPiyaNUf8hkSinUBhoR3YFbkKrPwRjpsCExZA2zemq+sXvt+w+XM+WkkqeV/8hkUEtKkMhIu9TaD0TuEy09xeQkg2f/F0gEKLY8TOt/LYk0H+o6nQrySPi1H9IZJAbkrOPBpy1sHkFnHwDFn8GVnwxsO5BFLpU/6ENeR71HxIZJDT7KFQaqyEhDVzxsPprMDwFMuc5XVWfqP+QiIBCoW/8nVD8c/jbg1DweSj4D5i8wumqeu1cu49trx9nS7f+Q6tnpbMhz0OB+g+JDEkKhd6qeTMwkFxVDFNWQe7NTlfUK+f7Dz1ZXMmzb5yg1av+QyJygUKhN0oeC9yVPDwZbvoZzFkfNdNMz/cf2lJSSUVX/6F189R/SETeTaFwJfx+iIkJtKeYswGufQgSInNR++4u1X/obvUfEpFL0LfC5bSehr98GWJiYe33IWsB3LjA6are18X6D925bBLr1X9IRN6HQuFirIU3fw/PfQFa6mHJxsC2CL7E0tpxof/Qq+9c6D/04DoPV89Q/yERuTJRGQohvXnt7HHYdh8ceh7Gz4NPbg1cNopA6j8kIgMtKkPBWrsN2JaXl/epAd+5vxOO74VrvwGL7o7IbqbqPyQioRJ533hOOFkGrz8RGEBO8cB9+yEuslY4u1j/oTlZyXz9n3K5YW6m+g+JyIAY2qHgbYUd34bdPwjcjbzo04G+RREUCOo/JCLhNHRDoWIHPHMfNFQEWlxf+xCMHON0VUBgrOBvB2r51StH39V/6IE109V/SERCamiGgq8d/nA3uIbBPz8Nk5c7XREQuET0XNlJNr1YzoETZ9V/SETCbmiGgmtYYFbRmMkRcanI1+nnmTdOsOnFcsprzzE5LYHvrJ/LDfMyNZVURMJqaIYCQPpspyugw+fnqdeq+fFL5Rypb2F6+ih+eOtVfHjOeDWjExFHDN1QcFCbt5PfllbxyEuHqT7TSm5WEj+9bQGrZ6ZrOqmIOEqhEEatHZ38+u/H2LzzMDVn25mfncJDN+ayYlqaGtKJSERQKITBuXYfv9pzlJ8XVVDf3MEHJ4/huxvmsXhKqsJARCJKVIZCRK7RfBGNrV4e33WEx3a9Q2Orl8JpaWy8Oof8iZEx9VVEpKeoDIWQtrkYAA3NHTz6cgW/3H2UpnYf18xMZ+PVOcz1pDhdmojIZUVlKESq2qY2frazgv995Rhtvk4+nDuez6zMYVZmktOliYhcEYXCADh+ppWf7jjME8WV+Dr9rJuXxb+tmMLU9FFOlyYi0isKhX44Vt/CT3aUs7W0Cmvh5vlu7lkxhYljE5wuTUSkTxQKfXD41Dl+/OJhntpXTawx3JKfzaeXT8Y9Wq0oRCS6KRR64a2TZ9m0vZxn959gmCuGf10ykbsKJ5OepMVsRGRwUChcgf1Vjfxw+yH+8o8aEuJjuXv5FO5cNomxicOcLk1EZEApFC6j9OhpNm0/xIsHT5E03MXnVk3l9qUTSRkZ73RpIiIhoVDowVrLKxUNbHrxELvK6xmTEM8Da6Zz2+IJJA3X6mYiMrgpFLpYa9l5qI5N2w9RfOQ0aaOG8aXrZ/LxRdmMjNePSUSGhiH/bWet5YUDtWzafojXqxrJTB7Og+tmsyHPoxXORGTIGbKh0HOVM8+YEXzrpjncPN9NvEsL24jI0BSVodDfhnh//UcN//3nt4KrnD28YS43zM3EpVXORGSIi8pQ6G9DvNqmNlwxhk0fv4rrcrXKmYjIeVEZCv31sTwPt+Zna5UzEZEehmQo6DKRiMjF6dtRRESCFAoiIhKkUBARkSCFgoiIBCkUREQkSKEgIiJBCgUREQky1lqna+gzY8wp4KhDh08GGqPwGAO1z/7spy+f7e1nxgJ1vTyGXBCO3+9QiIS6w1VDf44zwVqbdtFXrLV69OEBbI7GYwzUPvuzn758trefAUqc+t0YDI9w/H4P1rrDVUOojqPLR323LUqPMVD77M9++vLZcPy85YJo/XlHQt3hqiEkx4nqy0cil2KMKbHW5jldh0i00ZmCDFabnS5AJBrpTEFERIJ0piAiIkEKBRERCVIoiIhIkEJBhgRjzGRjzKPGmK1O1yISyRQKErWMMY8ZY2qNMWU9tn/IGHPQGFNujPkCgLW2wlp7pzOVikQPhYJEs8eBD3XfYIyJBX4EXAfMAm41xswKf2ki0UmhIFHLWrsTaOixeSFQ3nVm0AE8CawLe3EiUUqhIINNFlDZ7XkVkGWMSTXGPAJcZYz5ojOliUQ+l9MFiISDtbYeuNvpOkQinc4UZLCpBjzdnru7tonIFVAoyGBTDEw1xkwyxsQDtwBPO1yTSNRQKEjUMsY8AewBphtjqowxd1prfcC9wPPAAeA31to3naxTJJqoIZ6IiATpTEFERIIUCiIiEqRQEBGRIIWCiIgEKRRERCRIoSAiIkEKBZEQMsZ81Rhz/yVe2x3uekTej0JBxCHW2iVO1yDSk0JBpJeMMV/uWsTnZWPME8aY+40xLxljvm+M2WeMKTPGLOz2kVldr1cYYz7bbT/nHChf5LLUJVWkF4wx+cDNwFwgDtgLlHa9PNJaO88YUwg8BuR2bZ8BrARGAQeNMT+x1nrDW7nIldGZgkjvLAX+aK1ts9Y2Adu6vfYEBBf/STLGpHRtf9Za226trQNqgfSwVizSCwoFkYHTs5HY+eft3bZ1ojN0iWAKBZHe2QWsNcYMN8YkAh/p9trHAIwxy4BGa22jEwWK9If+xiLSC9baYmPM08AbQA2wHzj/5d9mjHmNwFjDHQ6VKNIvap0t0kvGmERr7TljzEhgJ3AX8DBwv7W2xNnqRPpHZwoivbfZGDMLGA78wlq71xjjdE0iA0JnCiIiEqSBZhERCVIoiIhIkEJBRESCFAoiIhKkUBARkSCFgoiIBP0/WAu+8Gr4V1sAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(gphivals, seps, label='XS')\n", + "plt.plot(gphivals, seps_alt, '--', label='without XS')\n", + "plt.plot(gphivals, seps_new, label='XS, bigbatch')\n", + "plt.plot(gphivals, seps_alt_new, '--', label='without XS, bigbatch')\n", + "plt.yscale('log')\n", + "plt.xscale('log')\n", + "plt.xlabel('gphi')\n", + "plt.ylabel('seperation')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "TestEstimator(3.5, '35e-4')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "TestEstimator(3.5, '35e-4', False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/tutorial_particle_physics/TestEstimator-Light-GW.ipynb b/examples/tutorial_particle_physics/TestEstimator-Light-GW.ipynb new file mode 100644 index 000000000..0dbb6298e --- /dev/null +++ b/examples/tutorial_particle_physics/TestEstimator-Light-GW.ipynb @@ -0,0 +1,1194 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: tabulate in /usr/local/lib/python3.8/dist-packages (0.8.7)\n", + "\u001b[33mWARNING: You are using pip version 20.1.1; however, version 20.2.4 is available.\n", + "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n" + ] + } + ], + "source": [ + "#if you have not installed tabulate \n", + "#this is just for printing the information of data in a prettier format\n", + "! pip install tabulate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing Function" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=========== Random Seed: 1325 ===========\n" + ] + } + ], + "source": [ + "#import main code\n", + "from OurTrainingTools import *\n", + "#this will throw a random number and print it\n", + "#to reset the random number manually, use\n", + "#torch.manual_seed(random_seed)\n", + "\n", + "\n", + "def test_model(madminermodel, test_input_sm, test_input_bsm, bsmparval, NSM, NBSM, epochs, e, n_meas, pm, verbose_t=True, verbose_period_t=1e5, title=''):\n", + " \n", + " # computing test statistics t (or lambda) in equation (2) of paper\n", + " def compute_t(madminermodel, nev, counter, test_input):\n", + " # generate number of points for testing under Poisson distribution\n", + " n_gen = 0\n", + " while n_gen == 0:\n", + " n_gen = np.random.poisson(nev)\n", + " \n", + " # stop if there are no more points to test\n", + " if (counter + n_gen) >= len(test_input):\n", + " return 0., -1\n", + " \n", + " points = test_input[int(counter): int(counter+n_gen)]\n", + " \n", + " # compute test statistics\n", + " log_ratio = (madminermodel.evaluate_log_likelihood_ratio(points.numpy(), \n", + " np.array([0.]), np.array([bsmparval,]))[0][0])\n", + " log_ratio = torch.tensor(log_ratio)\n", + " #ratio = 1./ratio\n", + " log_ratio = log_ratio\n", + " out = 2 * (NBSM - NSM - (log_ratio+torch.log(torch.tensor(NBSM/NSM))).sum(0))\n", + " \n", + " #return test statistics and the starting point for the next batch\n", + " return out, int(counter+n_gen)\n", + " \n", + " test_start = time.time()\n", + " if verbose_t:\n", + " print(\"NSM = %.3f --- NBSM = %.3f\"%(NSM, NBSM))\n", + " tsm = torch.empty(n_meas)\n", + " tbsm = torch.empty(n_meas)\n", + " \n", + " # empty array to store values\n", + " tsmcount = torch.zeros(n_meas+1)\n", + " tbsmcount = torch.zeros(n_meas+1)\n", + " \n", + " for i in range(n_meas):\n", + " tsm[i], tsmcount[i+1] = compute_t(madminermodel, NSM, tsmcount[i], \n", + " test_input_sm)\n", + " tbsm[i], tbsmcount[i+1] = compute_t(madminermodel, NBSM, tbsmcount[i], \n", + " test_input_bsm)\n", + " \n", + " if (tsmcount[i+1] < 0) or (tbsmcount[i+1] < 0):\n", + " print('Reaching the end of test data. Stop tests at %d. '%i)\n", + " tsm, tbsm = tsm[: i], tbsm[: i]\n", + " n_meas = i\n", + " break\n", + " \n", + " if i % (verbose_period_t) == 0:\n", + " print('test %s: tsm = %.3f, tbsm = %.3f'%(\n", + " str(i).ljust(4), tsm[i], tbsm[i]))\n", + " \n", + " test_duration = time.time() - test_start\n", + " #compute mean and variation of the test statistics in two hypotheses\n", + " mu_sm = tsm.mean().item()\n", + " mu_bsm = tbsm.mean().item()\n", + " sigma_sm = tsm.std().item()\n", + " sigma_bsm = tbsm.std().item()\n", + " med_sm = tsm.median().item()\n", + " \n", + " #compute separation and p-value\n", + " sep = (mu_sm - mu_bsm)/sigma_bsm\n", + " p = 1.*len([i for i in tbsm if i > med_sm])/len(tsm) \n", + " #print(len([i for i in tbsm if i>med_sm]))\n", + " delta1 = (p * (1 - p)/n_meas)**0.5\n", + " delta2 = (sigma_sm/sigma_bsm) * np.exp(-((mu_bsm - mu_sm)**2)/(\n", + " 2 * sigma_bsm**2))/(2*(n_meas**0.5))\n", + " print('===> delta1 = %.3f, delta2 = %.3f'%(delta1, delta2))\n", + " deltap = (delta1**2 + delta2**2)**0.5\n", + " \n", + " results_path = os.getcwd()\n", + " \n", + " if verbose_t:\n", + " print('p = %.3f +/- %.3f' %(p, deltap))\n", + " print('Separation = %.2f sigmas'%(sep))\n", + " training_properties = '/toydata/madminer-carl-'+title\n", + " plot_histogram(tsm, tbsm, int(NSM), int(NBSM), p, deltap, sep, epochs, e, \n", + " training_properties, results_path)\n", + " print('Partial test after %d epochs (took %.2f seconds)\\n'\n", + " %(e, test_duration))\n", + " \n", + " \n", + " return sep, p, tsm, tbsm" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "#import main code\n", + "from OurTrainingTools import *\n", + "#this will throw a random number and print it\n", + "#to reset the random number manually, use\n", + "#torch.manual_seed(random_seed)\n", + "\n", + "\n", + "def test_model_qc(model, test_input_sm, test_input_bsm, bsmparval, NSM, NBSM, epochs, e, n_meas, pm, verbose_t=True, verbose_period_t=1e5, title=''):\n", + " \n", + " # computing test statistics t (or lambda) in equation (2) of paper\n", + " def compute_t(model, nev, counter, test_input):\n", + " # generate number of points for testing under Poisson distribution\n", + " n_gen = 0\n", + " while n_gen == 0:\n", + " n_gen = np.random.poisson(nev)\n", + " \n", + " # stop if there are no more points to test\n", + " if (counter + n_gen) >= len(test_input):\n", + " return 0., -1\n", + " \n", + " points = test_input[int(counter): int(counter+n_gen)]\n", + " \n", + " # compute test statistics\n", + " #print(points)\n", + " y = model.Forward(points, np.ones(len(points))*bsmparval).detach()\n", + " log_ratio = np.log(y/(1.-y))\n", + " #calculate_ratio\n", + " #log_ratio = torch.tensor(log_ratio)\n", + " out = 2 * (NBSM - NSM - (log_ratio+torch.log(torch.tensor(NBSM/NSM))).sum(0))\n", + " \n", + " #return test statistics and the starting point for the next batch\n", + " return out, int(counter+n_gen)\n", + " \n", + " test_start = time.time()\n", + " if verbose_t:\n", + " print(\"NSM = %.3f --- NBSM = %.3f\"%(NSM, NBSM))\n", + " tsm = torch.empty(n_meas)\n", + " tbsm = torch.empty(n_meas)\n", + " \n", + " # empty array to store values\n", + " tsmcount = torch.zeros(n_meas+1)\n", + " tbsmcount = torch.zeros(n_meas+1)\n", + " \n", + " for i in range(n_meas):\n", + " tsm[i], tsmcount[i+1] = compute_t(model, NSM, tsmcount[i], \n", + " test_input_sm)\n", + " tbsm[i], tbsmcount[i+1] = compute_t(model, NBSM, tbsmcount[i], \n", + " test_input_bsm)\n", + " \n", + " if (tsmcount[i+1] < 0) or (tbsmcount[i+1] < 0):\n", + " print('Reaching the end of test data. Stop tests at %d. '%i)\n", + " tsm, tbsm = tsm[: i], tbsm[: i]\n", + " n_meas = i\n", + " break\n", + " \n", + " if i % (verbose_period_t) == 0:\n", + " print('test %s: tsm = %.3f, tbsm = %.3f'%(\n", + " str(i).ljust(4), tsm[i], tbsm[i]))\n", + " \n", + " test_duration = time.time() - test_start\n", + " \n", + " #compute mean and variation of the test statistics in two hypotheses\n", + " mu_sm = tsm.mean().item()\n", + " mu_bsm = tbsm.mean().item()\n", + " sigma_sm = tsm.std().item()\n", + " sigma_bsm = tbsm.std().item()\n", + " med_sm = tsm.median().item()\n", + " \n", + " #compute separation and p-value\n", + " sep = (mu_sm - mu_bsm)/sigma_bsm\n", + " p = 1.*len([i for i in tbsm if i > med_sm])/len(tsm) \n", + " #print(len([i for i in tbsm if i>med_sm]))\n", + " delta1 = (p * (1 - p)/n_meas)**0.5\n", + " delta2 = (sigma_sm/sigma_bsm) * np.exp(-((mu_bsm - mu_sm)**2)/(\n", + " 2 * sigma_bsm**2))/(2*(n_meas**0.5))\n", + " print('===> delta1 = %.3f, delta2 = %.3f'%(delta1, delta2))\n", + " deltap = (delta1**2 + delta2**2)**0.5\n", + " \n", + " results_path = os.getcwd()\n", + " if verbose_t:\n", + " print('p = %.3f +/- %.3f' %(p, deltap))\n", + " print('Separation = %.2f sigmas'%(sep))\n", + " training_properties = title\n", + " plot_histogram(tsm, tbsm, int(NSM), int(NBSM), p, deltap, sep, epochs, e, \n", + " training_properties, results_path)\n", + " print('Partial test after %d epochs (took %.2f seconds)\\n'\n", + " %(e, test_duration))\n", + " \n", + " \n", + " return sep, p, tsm, tbsm" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def combine_pm(tsm_plus, tbsm_plus, tsm_minus, tbsm_minus, e):\n", + " len_sm = min(len(tsm_plus), len(tsm_minus))\n", + " len_bsm = min(len(tbsm_plus), len(tbsm_minus))\n", + " \n", + " tsm = (tsm_plus[:len_sm] + tsm_minus[:len_sm])\n", + " tbsm = (tbsm_plus[:len_bsm] + tbsm_minus[:len_bsm])\n", + " \n", + " mu_sm = tsm.mean().item()\n", + " mu_bsm = tbsm.mean().item()\n", + " sigma_sm = tsm.std().item()\n", + " sigma_bsm = tbsm.std().item()\n", + " med_sm = tsm.median().item()\n", + " \n", + " sep = (mu_sm - mu_bsm)/sigma_bsm\n", + " p = 1.*len([i for i in tbsm if i > med_sm])/len(tsm)\n", + " \n", + " delta1 = (p * (1 - p)/min(len_sm, len_bsm))**0.5\n", + " delta2 = (sigma_sm/sigma_bsm) * np.exp(-((mu_bsm - mu_sm)**2)/(\n", + " 2 * sigma_bsm**2))/(2*(n_meas**0.5))\n", + " deltap = (delta1**2 + delta2**2)**0.5\n", + " \n", + " title = '%s, %s%s, combined, %s, N=%d, epochs=%d'%(\n", + " outputheader, str(n_neurons), bsm_op, bsm_test, N, e)\n", + "\n", + " simpleplot(tsm, tbsm, title, sep, p, deltap)\n", + " \n", + " return (p, deltap)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_histogram(tsm, tbsm, nsm, nbsm, p, deltap, sep, epochs, \n", + " e, training_properties, results_folder):\n", + " mint = torch.min(torch.cat((tsm, tbsm))).item()\n", + " maxt = torch.max(torch.cat((tsm, tbsm))).item()\n", + " \n", + " # for some reason the code complains if i don't detach the variables \n", + " # from their grad-on versions\n", + " tsm, tbsm = tsm.detach(), tbsm.detach()\n", + " \n", + " bins = np.linspace(mint, maxt, 60)\n", + " plt.figure(figsize=(8, 6))\n", + " ax = plt.subplot()\n", + " plt.hist(tsm, bins, alpha=0.5, label='SM')\n", + " plt.hist(tbsm, bins, alpha=0.5, label='BSM')\n", + " plt.legend(loc='upper right')\n", + " \n", + " sn = 'nsm = %s \\nnbsm = %s'%(str(nsm), str(nbsm))\n", + " sp = 'p '+'= '+ ('%.3f +/- %.3f'%(p, deltap))\n", + " ssep = 'sep ' + '= ' + ('%.3f'%(sep))\n", + " \n", + " plt.text(x=0.05, y=0.85, transform=ax.transAxes, \n", + " s=sn+'\\n'+sp+'\\n'+ssep, bbox=dict(facecolor='blue', alpha=0.2))\n", + " plt.xlabel('t')\n", + " plt.ylabel('p(t)')\n", + " if epochs == e:\n", + " plt.title('Final test\\n' + training_properties)\n", + " filename = results_folder + training_properties \\\n", + " + ' histogram.pdf'\n", + " plt.savefig(filename)\n", + " return \n", + " plt.title(training_properties)\n", + " plt.show()\n", + " \n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def TestEstimator(estimator, bsmval, bsm_fname='', withXS=True, title_message='', qc=False):\n", + " #name of operator\n", + " op_name = bsm_fname.split('_')[0]\n", + " bsmval_name = bsm_fname.split('_')[-2] \n", + " \n", + " #toy data file path\n", + " f = h5py.File(os.getcwd()+'/toydata/%s.h5'%(bsm_fname), 'r')\n", + "\n", + " #parse data \n", + " Data = np.array(f['Data'])\n", + " Labels = np.array(f['Labels'])\n", + " NSM = np.array(f['NSM'])\n", + " \n", + " \n", + " if withXS:\n", + " NBSMList = np.array(f['NBSMList'])\n", + " NBSM = NBSMList[0]\n", + " else:\n", + " NBSM = NSM\n", + " \n", + " #randomise\n", + " Idx_test = torch.randperm(len(Data))\n", + " Data_test = torch.Tensor(Data[Idx_test])\n", + " Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + " #print(Data.mean(0), Data.std(0))\n", + " \n", + " #select data from each hypothesis\n", + " SM_Data = Data_test[Label_test==0, :]\n", + " BSM_Data = Data_test[Label_test==1, :]\n", + "\n", + " #for plotting/ printing\n", + " #n_epochs = current_epoch = int(1e4)\n", + " n_epochs = int(1e4)\n", + " current_epoch = 0\n", + " results_path = os.getcwd()\n", + " charge = 'plus'\n", + "\n", + " #number of tests thrown on the data\n", + " #the test function will stop automatically if points run out\n", + " n_meas = 4000\n", + "\n", + " if withXS:\n", + " if not qc:\n", + " sep, p, tsm, tbsm = test_model(estimator, SM_Data, BSM_Data, bsmval, NSM, NBSM, n_epochs, current_epoch, n_meas, charge, \n", + " verbose_t=True, verbose_period_t=1e5, title='%s = %s'%(op_name, bsmval_name)+title_message)\n", + " else:\n", + " sep, p, tsm, tbsm = test_model_qc(estimator, SM_Data, BSM_Data, bsmval, NSM, NBSM, n_epochs, current_epoch, n_meas, charge, \n", + " verbose_t=True, verbose_period_t=1e5, title='%s = %s'%(op_name, bsmval_name)+title_message)\n", + " else:\n", + " if not qc:\n", + " sep, p, tsm, tbsm = test_model(estimator, SM_Data, BSM_Data, bsmval, NSM, NBSM, n_epochs, current_epoch, n_meas, charge, \n", + " verbose_t=True, verbose_period_t=1e5, title='%s = %s'%(op_name, bsmval_name)+title_message)\n", + " else:\n", + " sep, p, tsm, tbsm = test_model_qc(estimator, SM_Data, BSM_Data, bsmval, NSM, NBSM, n_epochs, current_epoch, n_meas, charge, \n", + " verbose_t=True, verbose_period_t=1e5, title='%s = %s'%(op_name, bsmval_name)+title_message)\n", + " \n", + " \n", + " f.close()\n", + " return sep, p, tsm, tbsm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reading Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Charge Plus" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model successfully loaded.\n", + "Path: /madminer/madminer/examples/tutorial_particle_physics/models/ChPgw_355.pth\n" + ] + } + ], + "source": [ + "estimator = OurModel(AR=[9, 32, 32, 32, 32, 1])\n", + "estimator.Load_CPU('ChPgw_355', os.getcwd()+'/models/')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = -13.199, tbsm = -15.997\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.017, delta2 = 0.014\n", + "p = 0.071 +/- 0.022\n", + "Separation = 1.31 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 1.25 seconds)\n", + "\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = -19.627, tbsm = -12.726\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.012, delta2 = 0.008\n", + "p = 0.036 +/- 0.015\n", + "Separation = 1.67 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 1.14 seconds)\n", + "\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = -10.455, tbsm = -23.507\n", + "Reaching the end of test data. Stop tests at 223. \n", + "===> delta1 = 0.015, delta2 = 0.008\n", + "p = 0.054 +/- 0.017\n", + "Separation = 1.62 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 1.08 seconds)\n", + "\n" + ] + } + ], + "source": [ + "gwvals = [8e-3, 9e-3, 1e-2]\n", + "gwval_fnames = ['gw_toydata_test_8e-3_out','gw_toydata_test_9e-3_out','gw_toydata_test_1e-2_out']\n", + "tsm_plus = {}\n", + "tbsm_plus = {}\n", + "\n", + "for gwval, gwval_fname in zip(gwvals, gwval_fnames):\n", + " sep, p, tsm, tbsm =TestEstimator(estimator, gwval , gwval_fname, withXS=False,\n", + " title_message=', quadratic classifier (ChP, NSM=NBSM)', qc=True)\n", + " tsm_plus[gwval] = tsm\n", + " tbsm_plus[gwval]=tbsm" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2231.468\n", + "test 0 : tsm = -15.342, tbsm = -16.339\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.012, delta2 = 0.012\n", + "p = 0.036 +/- 0.017\n", + "Separation = 1.41 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 1.06 seconds)\n", + "\n", + "NSM = 2225.504 --- NBSM = 2233.180\n", + "test 0 : tsm = -8.508, tbsm = -15.734\n", + "Reaching the end of test data. Stop tests at 223. \n", + "===> delta1 = 0.017, delta2 = 0.009\n", + "p = 0.067 +/- 0.019\n", + "Separation = 1.60 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 1.09 seconds)\n", + "\n", + "NSM = 2225.504 --- NBSM = 2234.707\n", + "test 0 : tsm = -18.775, tbsm = -24.086\n", + "Reaching the end of test data. Stop tests at 223. \n", + "===> delta1 = 0.012, delta2 = 0.007\n", + "p = 0.036 +/- 0.014\n", + "Separation = 1.68 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 1.02 seconds)\n", + "\n" + ] + } + ], + "source": [ + "gwvals = [8e-3, 9e-3, 1e-2]\n", + "gwval_fnames = ['gw_toydata_test_8e-3_out','gw_toydata_test_9e-3_out','gw_toydata_test_1e-2_out']\n", + "tsm_plus_XS = {}\n", + "tbsm_plus_XS = {}\n", + "\n", + "for gwval, gwval_fname in zip(gwvals, gwval_fnames):\n", + " sep, p, tsm, tbsm =TestEstimator(estimator, gwval , gwval_fname, withXS=True,\n", + " title_message=', quadratic classifier (ChP, NSM!=NBSM)', qc=True)\n", + " tsm_plus_XS[gwval] = tsm\n", + " tbsm_plus_XS[gwval]=tbsm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### madminer" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from madminer import ParameterizedRatioEstimator\n", + "from madminer.ml.morphing_aware import MorphingAwareRatioEstimator\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup_gw.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl1000-bigbatch-gw')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2231.468\n", + "test 0 : tsm = 91.228, tbsm = 133.443\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.032, delta2 = 0.031\n", + "p = 0.335 +/- 0.045\n", + "Separation = 0.41 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 39.46 seconds)\n", + "\n", + "NSM = 2225.504 --- NBSM = 2233.180\n", + "test 0 : tsm = 124.618, tbsm = 77.411\n", + "Reaching the end of test data. Stop tests at 223. \n", + "===> delta1 = 0.032, delta2 = 0.026\n", + "p = 0.359 +/- 0.041\n", + "Separation = 0.43 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 40.72 seconds)\n", + "\n", + "NSM = 2225.504 --- NBSM = 2234.707\n", + "test 0 : tsm = 108.562, tbsm = 111.864\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.031, delta2 = 0.030\n", + "p = 0.326 +/- 0.043\n", + "Separation = 0.50 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 40.12 seconds)\n", + "\n" + ] + } + ], + "source": [ + "gwval = 8e-3\n", + "gwval_name = 'gw_toydata_test_8e-3_out'\n", + "\n", + "gwvals = [8e-3, 9e-3, 1e-2]\n", + "gwval_fnames = ['gw_toydata_test_8e-3_out','gw_toydata_test_9e-3_out','gw_toydata_test_1e-2_out']\n", + "\n", + "for gwval, gwval_fname in zip(gwvals, gwval_fnames):\n", + " TestEstimator(estimator, gwval , gwval_fname, title_message=', madminer (NSM!=NBSM)', qc=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Charge Minus" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model successfully loaded.\n", + "Path: /madminer/madminer/examples/tutorial_particle_physics/models/ChMgw_355.pth\n" + ] + } + ], + "source": [ + "estimator = OurModel(AR=[9, 32, 32, 32, 32, 1])\n", + "estimator.Load_CPU('ChMgw_355', os.getcwd()+'/models/')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 987.026 --- NBSM = 987.026\n", + "test 0 : tsm = -5.285, tbsm = -7.844\n", + "Reaching the end of test data. Stop tests at 507. \n", + "===> delta1 = 0.019, delta2 = 0.016\n", + "p = 0.239 +/- 0.025\n", + "Separation = 0.70 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 4.08 seconds)\n", + "\n", + "NSM = 987.026 --- NBSM = 987.026\n", + "test 0 : tsm = -6.804, tbsm = -10.550\n", + "Reaching the end of test data. Stop tests at 506. \n", + "===> delta1 = 0.018, delta2 = 0.015\n", + "p = 0.204 +/- 0.023\n", + "Separation = 0.83 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 4.08 seconds)\n", + "\n", + "NSM = 987.026 --- NBSM = 987.026\n", + "test 0 : tsm = -6.277, tbsm = -9.591\n", + "Reaching the end of test data. Stop tests at 506. \n", + "===> delta1 = 0.016, delta2 = 0.013\n", + "p = 0.152 +/- 0.021\n", + "Separation = 0.98 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 4.10 seconds)\n", + "\n" + ] + } + ], + "source": [ + "gwvals = [8e-3, 9e-3, 1e-2]\n", + "gwval_fnames = ['gw_toydata_test_M8e-3_out','gw_toydata_test_M9e-3_out','gw_toydata_test_M1e-2_out']\n", + "\n", + "tsm_minus = {}\n", + "tbsm_minus = {}\n", + "\n", + "for gwval, gwval_fname in zip(gwvals, gwval_fnames):\n", + " sep, p, tsm, tbsm =TestEstimator(estimator, gwval , gwval_fname, withXS=False,\n", + " title_message=', quadratic classifier (ChM, NSM=NBSM)', qc=True)\n", + " tsm_minus[gwval] = tsm\n", + " tbsm_minus[gwval]=tbsm" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 987.026 --- NBSM = 989.107\n", + "test 0 : tsm = -5.026, tbsm = -11.435\n", + "Reaching the end of test data. Stop tests at 506. \n", + "===> delta1 = 0.018, delta2 = 0.017\n", + "p = 0.215 +/- 0.025\n", + "Separation = 0.70 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 3.94 seconds)\n", + "\n", + "NSM = 987.026 --- NBSM = 989.750\n", + "test 0 : tsm = -6.550, tbsm = -8.316\n", + "Reaching the end of test data. Stop tests at 504. \n", + "===> delta1 = 0.018, delta2 = 0.015\n", + "p = 0.200 +/- 0.023\n", + "Separation = 0.84 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 4.02 seconds)\n", + "\n", + "NSM = 987.026 --- NBSM = 990.530\n", + "test 0 : tsm = -7.249, tbsm = -10.061\n", + "Reaching the end of test data. Stop tests at 504. \n", + "===> delta1 = 0.017, delta2 = 0.013\n", + "p = 0.169 +/- 0.021\n", + "Separation = 0.94 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 4.03 seconds)\n", + "\n" + ] + } + ], + "source": [ + "gwvals = [8e-3, 9e-3, 1e-2]\n", + "gwval_fnames = ['gw_toydata_test_M8e-3_out','gw_toydata_test_M9e-3_out','gw_toydata_test_M1e-2_out']\n", + "\n", + "tsm_minus_XS = {}\n", + "tbsm_minus_XS = {}\n", + "\n", + "for gwval, gwval_fname in zip(gwvals, gwval_fnames):\n", + " sep, p, tsm, tbsm =TestEstimator(estimator, gwval , gwval_fname, withXS=True,\n", + " title_message=', quadratic classifier (ChM, NSM!=NBSM)', qc=True)\n", + " tsm_minus_XS[gwval] = tsm\n", + " tbsm_minus_XS[gwval]=tbsm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Combine" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "def combine_pm(tsm_plus, tbsm_plus, tsm_minus, tbsm_minus, n_meas, outputheader, bsm_op):\n", + " len_sm = min(len(tsm_plus), len(tsm_minus))\n", + " len_bsm = min(len(tbsm_plus), len(tbsm_minus))\n", + " \n", + " tsm = (tsm_plus[:len_sm] + tsm_minus[:len_sm])\n", + " tbsm = (tbsm_plus[:len_bsm] + tbsm_minus[:len_bsm])\n", + " \n", + " mu_sm = tsm.mean().item()\n", + " mu_bsm = tbsm.mean().item()\n", + " sigma_sm = tsm.std().item()\n", + " sigma_bsm = tbsm.std().item()\n", + " med_sm = tsm.median().item()\n", + " \n", + " sep = (mu_sm - mu_bsm)/sigma_bsm\n", + " p = 1.*len([i for i in tbsm if i > med_sm])/len(tsm)\n", + " \n", + " delta1 = (p * (1 - p)/min(len_sm, len_bsm))**0.5\n", + " delta2 = (sigma_sm/sigma_bsm) * np.exp(-((mu_bsm - mu_sm)**2)/(\n", + " 2 * sigma_bsm**2))/(2*(n_meas**0.5))\n", + " deltap = (delta1**2 + delta2**2)**0.5\n", + " \n", + " title = '%s, %s, combined'%(\n", + " outputheader, bsm_op)\n", + "\n", + " simpleplot(tsm, tbsm, title, sep, p, deltap)\n", + " \n", + " return (p, deltap)\n", + "\n", + "def simpleplot(tsm, tbsm, title, sep, p, deltap):\n", + " plt.figure(figsize=(8, 6))\n", + " ax = plt.subplot()\n", + " plt.hist(tsm, 50, alpha=0.5, label='SM')\n", + " plt.hist(tbsm, 50, alpha=0.5, label='BSM')\n", + " plt.title(title)\n", + " plt.legend(loc='upper right')\n", + " \n", + " plt.text(x=0.05, y=0.85, transform=ax.transAxes, \n", + " s='sep = %.3f\\np = %.3f +/- %.3f'%(sep, p, deltap), \n", + " bbox=dict(facecolor='blue', alpha=0.2))\n", + " \n", + " plt.savefig(os.getcwd() + '/models/' + title+'.pdf')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "n_meas = 200\n", + "\n", + "gwvals = [8e-3, 9e-3, 1e-2]\n", + "\n", + "for gwval in gwvals:\n", + " combine_pm(tsm_plus[gwval], tbsm_plus[gwval], tsm_minus[gwval], tbsm_minus[gwval], \n", + " n_meas,'quadratic classifier, (NSM=NBSM) %s'%str(gwval), 'gw')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "n_meas = 200\n", + "\n", + "gwvals = [8e-3, 9e-3, 1e-2]\n", + "\n", + "for gwval in gwvals:\n", + " combine_pm(tsm_plus_XS[gwval], tbsm_plus_XS[gwval], tsm_minus_XS[gwval], tbsm_minus_XS[gwval], \n", + " n_meas,'quadratic classifier, (NSM!=NBSM) %s'%str(gwval), 'gw')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/tutorial_particle_physics/TestEstimator-Light.ipynb b/examples/tutorial_particle_physics/TestEstimator-Light.ipynb new file mode 100644 index 000000000..3f5e19f2f --- /dev/null +++ b/examples/tutorial_particle_physics/TestEstimator-Light.ipynb @@ -0,0 +1,1319 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: tabulate in /usr/local/lib/python3.8/dist-packages (0.8.7)\n", + "\u001b[33mWARNING: You are using pip version 20.1.1; however, version 20.2.4 is available.\n", + "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n" + ] + } + ], + "source": [ + "#if you have not installed tabulate \n", + "#this is just for printing the information of data in a prettier format\n", + "! pip install tabulate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing Function" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "#import main code\n", + "from OurTrainingTools import *\n", + "#this will throw a random number and print it\n", + "#to reset the random number manually, use\n", + "#torch.manual_seed(random_seed)\n", + "\n", + "\n", + "def test_model(madminermodel, test_input_sm, test_input_bsm, bsmparval, NSM, NBSM, epochs, e, n_meas, pm, verbose_t=True, verbose_period_t=1e5, title=''):\n", + " \n", + " # computing test statistics t (or lambda) in equation (2) of paper\n", + " def compute_t(madminermodel, nev, counter, test_input):\n", + " # generate number of points for testing under Poisson distribution\n", + " n_gen = 0\n", + " while n_gen == 0:\n", + " n_gen = np.random.poisson(nev)\n", + " \n", + " # stop if there are no more points to test\n", + " if (counter + n_gen) >= len(test_input):\n", + " return 0., -1\n", + " \n", + " points = test_input[int(counter): int(counter+n_gen)]\n", + " \n", + " # compute test statistics\n", + " log_ratio = (madminermodel.evaluate_log_likelihood_ratio(points.numpy(), \n", + " np.array([0.]), np.array([bsmparval,]))[0][0])\n", + " log_ratio = torch.tensor(log_ratio)\n", + " #ratio = 1./ratio\n", + " log_ratio = log_ratio\n", + " out = 2 * (NBSM - NSM - (log_ratio+torch.log(torch.tensor(NBSM/NSM))).sum(0))\n", + " \n", + " #return test statistics and the starting point for the next batch\n", + " return out, int(counter+n_gen)\n", + " \n", + " test_start = time.time()\n", + " if verbose_t:\n", + " print(\"NSM = %.3f --- NBSM = %.3f\"%(NSM, NBSM))\n", + " tsm = torch.empty(n_meas)\n", + " tbsm = torch.empty(n_meas)\n", + " \n", + " # empty array to store values\n", + " tsmcount = torch.zeros(n_meas+1)\n", + " tbsmcount = torch.zeros(n_meas+1)\n", + " \n", + " for i in range(n_meas):\n", + " tsm[i], tsmcount[i+1] = compute_t(madminermodel, NSM, tsmcount[i], \n", + " test_input_sm)\n", + " tbsm[i], tbsmcount[i+1] = compute_t(madminermodel, NBSM, tbsmcount[i], \n", + " test_input_bsm)\n", + " \n", + " if (tsmcount[i+1] < 0) or (tbsmcount[i+1] < 0):\n", + " print('Reaching the end of test data. Stop tests at %d. '%i)\n", + " tsm, tbsm = tsm[: i], tbsm[: i]\n", + " n_meas = i\n", + " break\n", + " \n", + " if i % (verbose_period_t) == 0:\n", + " print('test %s: tsm = %.3f, tbsm = %.3f'%(\n", + " str(i).ljust(4), tsm[i], tbsm[i]))\n", + " \n", + " test_duration = time.time() - test_start\n", + " #compute mean and variation of the test statistics in two hypotheses\n", + " mu_sm = tsm.mean().item()\n", + " mu_bsm = tbsm.mean().item()\n", + " sigma_sm = tsm.std().item()\n", + " sigma_bsm = tbsm.std().item()\n", + " med_sm = tsm.median().item()\n", + " \n", + " #compute separation and p-value\n", + " sep = (mu_sm - mu_bsm)/sigma_bsm\n", + " p = 1.*len([i for i in tbsm if i > med_sm])/len(tsm) \n", + " #print(len([i for i in tbsm if i>med_sm]))\n", + " delta1 = (p * (1 - p)/n_meas)**0.5\n", + " delta2 = (sigma_sm/sigma_bsm) * np.exp(-((mu_bsm - mu_sm)**2)/(\n", + " 2 * sigma_bsm**2))/(2*(n_meas**0.5))\n", + " print('===> delta1 = %.3f, delta2 = %.3f'%(delta1, delta2))\n", + " deltap = (delta1**2 + delta2**2)**0.5\n", + " \n", + " results_path = os.getcwd()\n", + " \n", + " if verbose_t:\n", + " print('p = %.3f +/- %.3f' %(p, deltap))\n", + " print('Separation = %.2f sigmas'%(sep))\n", + " training_properties = '/toydata/madminer-carl-'+title\n", + " plot_histogram(tsm, tbsm, int(NSM), int(NBSM), p, deltap, sep, epochs, e, \n", + " training_properties, results_path)\n", + " print('Partial test after %d epochs (took %.2f seconds)\\n'\n", + " %(e, test_duration))\n", + " \n", + " \n", + " return sep, p, tsm, tbsm" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "#import main code\n", + "from OurTrainingTools import *\n", + "#this will throw a random number and print it\n", + "#to reset the random number manually, use\n", + "#torch.manual_seed(random_seed)\n", + "\n", + "\n", + "def test_model_qc(model, test_input_sm, test_input_bsm, bsmparval, NSM, NBSM, epochs, e, n_meas, pm, verbose_t=True, verbose_period_t=1e5, title=''):\n", + " \n", + " # computing test statistics t (or lambda) in equation (2) of paper\n", + " def compute_t(model, nev, counter, test_input):\n", + " # generate number of points for testing under Poisson distribution\n", + " n_gen = 0\n", + " while n_gen == 0:\n", + " n_gen = np.random.poisson(nev)\n", + " \n", + " # stop if there are no more points to test\n", + " if (counter + n_gen) >= len(test_input):\n", + " return 0., -1\n", + " \n", + " points = test_input[int(counter): int(counter+n_gen)]\n", + " \n", + " # compute test statistics\n", + " #print(points)\n", + " y = model.Forward(points, np.ones(len(points))*bsmparval).detach()\n", + " log_ratio = np.log(y/(1.-y))\n", + " #calculate_ratio\n", + " #log_ratio = torch.tensor(log_ratio)\n", + " out = 2 * (NBSM - NSM - (log_ratio+torch.log(torch.tensor(NBSM/NSM))).sum(0))\n", + " \n", + " #return test statistics and the starting point for the next batch\n", + " return out, int(counter+n_gen)\n", + " \n", + " test_start = time.time()\n", + " if verbose_t:\n", + " print(\"NSM = %.3f --- NBSM = %.3f\"%(NSM, NBSM))\n", + " tsm = torch.empty(n_meas)\n", + " tbsm = torch.empty(n_meas)\n", + " \n", + " # empty array to store values\n", + " tsmcount = torch.zeros(n_meas+1)\n", + " tbsmcount = torch.zeros(n_meas+1)\n", + " \n", + " for i in range(n_meas):\n", + " tsm[i], tsmcount[i+1] = compute_t(model, NSM, tsmcount[i], \n", + " test_input_sm)\n", + " tbsm[i], tbsmcount[i+1] = compute_t(model, NBSM, tbsmcount[i], \n", + " test_input_bsm)\n", + " \n", + " if (tsmcount[i+1] < 0) or (tbsmcount[i+1] < 0):\n", + " print('Reaching the end of test data. Stop tests at %d. '%i)\n", + " tsm, tbsm = tsm[: i], tbsm[: i]\n", + " n_meas = i\n", + " break\n", + " \n", + " if i % (verbose_period_t) == 0:\n", + " print('test %s: tsm = %.3f, tbsm = %.3f'%(\n", + " str(i).ljust(4), tsm[i], tbsm[i]))\n", + " \n", + " test_duration = time.time() - test_start\n", + " \n", + " #compute mean and variation of the test statistics in two hypotheses\n", + " mu_sm = tsm.mean().item()\n", + " mu_bsm = tbsm.mean().item()\n", + " sigma_sm = tsm.std().item()\n", + " sigma_bsm = tbsm.std().item()\n", + " med_sm = tsm.median().item()\n", + " \n", + " #compute separation and p-value\n", + " sep = (mu_sm - mu_bsm)/sigma_bsm\n", + " p = 1.*len([i for i in tbsm if i > med_sm])/len(tsm) \n", + " #print(len([i for i in tbsm if i>med_sm]))\n", + " delta1 = (p * (1 - p)/n_meas)**0.5\n", + " delta2 = (sigma_sm/sigma_bsm) * np.exp(-((mu_bsm - mu_sm)**2)/(\n", + " 2 * sigma_bsm**2))/(2*(n_meas**0.5))\n", + " print('===> delta1 = %.3f, delta2 = %.3f'%(delta1, delta2))\n", + " deltap = (delta1**2 + delta2**2)**0.5\n", + " \n", + " results_path = os.getcwd()\n", + " if verbose_t:\n", + " print('p = %.3f +/- %.3f' %(p, deltap))\n", + " print('Separation = %.2f sigmas'%(sep))\n", + " training_properties = title\n", + " plot_histogram(tsm, tbsm, int(NSM), int(NBSM), p, deltap, sep, epochs, e, \n", + " training_properties, results_path)\n", + " print('Partial test after %d epochs (took %.2f seconds)\\n'\n", + " %(e, test_duration))\n", + " \n", + " \n", + " return sep, p, tsm, tbsm" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_histogram(tsm, tbsm, nsm, nbsm, p, deltap, sep, epochs, \n", + " e, training_properties, results_folder):\n", + " mint = torch.min(torch.cat((tsm, tbsm))).item()\n", + " maxt = torch.max(torch.cat((tsm, tbsm))).item()\n", + " \n", + " # for some reason the code complains if i don't detach the variables \n", + " # from their grad-on versions\n", + " tsm, tbsm = tsm.detach(), tbsm.detach()\n", + " \n", + " bins = np.linspace(mint, maxt, 60)\n", + " plt.figure(figsize=(8, 6))\n", + " ax = plt.subplot()\n", + " plt.hist(tsm, bins, alpha=0.5, label='SM')\n", + " plt.hist(tbsm, bins, alpha=0.5, label='BSM')\n", + " plt.legend(loc='upper right')\n", + " \n", + " sn = 'nsm = %s \\nnbsm = %s'%(str(nsm), str(nbsm))\n", + " sp = 'p '+'= '+ ('%.3f +/- %.3f'%(p, deltap))\n", + " ssep = 'sep ' + '= ' + ('%.3f'%(sep))\n", + " \n", + " plt.text(x=0.05, y=0.85, transform=ax.transAxes, \n", + " s=sn+'\\n'+sp+'\\n'+ssep, bbox=dict(facecolor='blue', alpha=0.2))\n", + " plt.xlabel('t')\n", + " plt.ylabel('p(t)')\n", + " if epochs == e:\n", + " plt.title('Final test\\n' + training_properties)\n", + " filename = results_folder + training_properties \\\n", + " + ' histogram.pdf'\n", + " plt.savefig(filename)\n", + " return \n", + " plt.title(training_properties)\n", + " plt.show()\n", + " \n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def TestEstimator(estimator, bsmval, bsm_fname='', withXS=True, title_message='', qc=False):\n", + " #name of operator\n", + " op_name = bsm_fname.split('_')[0]\n", + " bsmval_name = bsm_fname.split('_')[-2] \n", + " \n", + " #toy data file path\n", + " f = h5py.File(os.getcwd()+'/toydata/%s.h5'%(bsm_fname), 'r')\n", + "\n", + " #parse data \n", + " Data = np.array(f['Data'])\n", + " Labels = np.array(f['Labels'])\n", + " NSM = np.array(f['NSM'])\n", + " \n", + " \n", + " if withXS:\n", + " NBSMList = np.array(f['NBSMList'])\n", + " NBSM = NBSMList[0]\n", + " else:\n", + " NBSM = NSM\n", + " \n", + " #randomise\n", + " Idx_test = torch.randperm(len(Data))\n", + " Data_test = torch.Tensor(Data[Idx_test])\n", + " Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + " #print(Data.mean(0), Data.std(0))\n", + " \n", + " #select data from each hypothesis\n", + " SM_Data = Data_test[Label_test==0, :]\n", + " BSM_Data = Data_test[Label_test==1, :]\n", + "\n", + " #for plotting/ printing\n", + " #n_epochs = current_epoch = int(1e4)\n", + " n_epochs = int(1e4)\n", + " current_epoch = 0\n", + " results_path = os.getcwd()\n", + " charge = 'plus'\n", + "\n", + " #number of tests thrown on the data\n", + " #the test function will stop automatically if points run out\n", + " n_meas = 4000\n", + "\n", + " if withXS:\n", + " if not qc:\n", + " sep, p, tsm, tbsm = test_model(estimator, SM_Data, BSM_Data, bsmval, NSM, NBSM, n_epochs, current_epoch, n_meas, charge, \n", + " verbose_t=True, verbose_period_t=1e5, title='%s = %s'%(op_name, bsmval_name)+title_message)\n", + " else:\n", + " sep, p, tsm, tbsm = test_model_qc(estimator, SM_Data, BSM_Data, bsmval, NSM, NBSM, n_epochs, current_epoch, n_meas, charge, \n", + " verbose_t=True, verbose_period_t=1e5, title='%s = %s'%(op_name, bsmval_name)+title_message)\n", + " else:\n", + " if not qc:\n", + " sep, p, tsm, tbsm = test_model(estimator, SM_Data, BSM_Data, bsmval, NSM, NBSM, n_epochs, current_epoch, n_meas, charge, \n", + " verbose_t=True, verbose_period_t=1e5, title='%s = %s'%(op_name, bsmval_name)+title_message)\n", + " else:\n", + " sep, p, tsm, tbsm = test_model_qc(estimator, SM_Data, BSM_Data, bsmval, NSM, NBSM, n_epochs, current_epoch, n_meas, charge, \n", + " verbose_t=True, verbose_period_t=1e5, title='%s = %s'%(op_name, bsmval_name)+title_message)\n", + " \n", + " \n", + " f.close()\n", + " return sep, p, tsm, tbsm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reading Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Charge Plus" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model successfully loaded.\n", + "Path: /madminer/madminer/examples/tutorial_particle_physics/models/ChPgphi_2D_283.pth\n" + ] + } + ], + "source": [ + "estimator = OurModel(AR=[9, 32, 32, 32, 32, 1])\n", + "#estimator.Load_CPU('ChPgphi_16', os.getcwd()+'/models/')\n", + "estimator.Load_CPU('ChPgphi_2D_283', os.getcwd()+'/models/')" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = -70.610, tbsm = -71.972\n", + "Reaching the end of test data. Stop tests at 1348. \n", + "===> delta1 = 0.010, delta2 = 0.007\n", + "p = 0.148 +/- 0.012\n", + "Separation = 1.09 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 5.94 seconds)\n", + "\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = -91.882, tbsm = -96.567\n", + "Reaching the end of test data. Stop tests at 223. \n", + "===> delta1 = 0.017, delta2 = 0.011\n", + "p = 0.067 +/- 0.020\n", + "Separation = 1.47 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 1.03 seconds)\n", + "\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = -111.937, tbsm = -126.004\n", + "Reaching the end of test data. Stop tests at 1346. \n", + "===> delta1 = 0.006, delta2 = 0.004\n", + "p = 0.053 +/- 0.007\n", + "Separation = 1.55 sigmas\n" + ] + }, + { + "data": { + "image/png": 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jzjsvLzWur776jA0b1tOs2R5Flufnb2DMmCc59NDjSuwzevSjHHro8YWvzYxLLjmWc845kJdfHlGu34uIiARuu+022rZtS/v27enQoQOffvopXbp0oXnz5iTer6RHjx7Ur18/7fFldI0cYOedW7LXXh0A2HvvA1mwYC4Ae+7ZnsGDz6ZLlx506dKjcPtDDz2e7OzatGrVjk2bNhYm1Fat2hXum6hPnwH06TOgXDEtWbKQG244l5tuGkmtWkXPte64488ccMAR7L//4UWWT5o0kdGjH+WRRz4oXPbIIx/QpMku/PLLYi655BhatNibAw44olyxiIhUJ8PGf1Opx7vqmNalrv/44495/fXXmTJlCltttRVLlixh/fpgPpKcnBw+/PBDOnfuzPLly1m4sGpuJJnxibx27a0Kn2dlZfHbb2sBuPfeN/jii/d4773/8NhjtzFq1JcA1KkTbF+rVi2ys2sXNouY1WLjxvwSx3/iiTsZO/bpEsv33/8IBgwYXmL56tUrueKKE/nzn2+jXbtDiqwbMeImli3L429/e7jI8m+/ncYtt/Rj+PA3yclpWLi8SZNdANhhhyZ06XIq06d/pkQuIlIOCxcupFGjRmy1VfDd36jR72OOevXqxahRo+jcuTMvv/wyp512GtOnT097jBmfyJPZtGkTixb9RG5uVzp06My4caNYu3Z1hY5Vnhr5hg3rGTDgVE48sQ9HH92zyLpXX32ETz55iwcfnFCklv7zzz8yYMBp3Hzzk0X62NeuXcOmTZuoV68Ba9eu4dNPx9Gv3w0VKoOISKY69thjufnmm2ndujVHH300Z555JkceeSQA3bp148ILL2Tjxo2MGjWKESNGcMstt6Q9RiXyJDZt2sj115/D6tUrcHd69bqcBg1yIn/f8eOfZ8qU91ixYimvv/44ADfe+Dh77dWB22+/iB133I3zz+8EQNeup3HhhTfwr3/dzIoVSxk69M8AhZeZLV26iAEDTgVg48Z8unfvnbRfXUREUqtfvz6TJ0/m/fffZ+LEiZx55pnccccdQNCK27lzZ0aNGsXatWupqpt7WWJHfXWVm5vrlXHTlLFjJ2uK1gyRlzeZ447TZy0SdzNmzGCfffYpfJ3uPvLiXnzxRUaOHMmqVau46667+PXXXzn11FMZMmQIl112GfXr12f16vK14BYvI4CZTXb33LLsrxq5iFSZ0r6Uy/sFKxKFWbNmUatWLfbcc08Apk6dym677cZXX30FwOGHH86gQYM466yzqixGJXIREZEUVq9ezWWXXcby5cvJzs6mVatWjBgxgp49g3FMZsbVV19dpTEqkYuISGyku6XmwAMP5KOPPiqx/J133km6fXmb1StDxk8Ik0r//l34+uvqMTf5unW/csUVJ3L66Xtzxhltuf/+gYXrnnrqHv74xzb06tWeiy/uxsKFwb3oZ82ayp/+1IkzzmhLr17tGTfuucJ9hgw5j5NPblk4Oc2sWVPTXiYREakcqpHHxLnnXk1ublc2bFjPxRd348MP3+Sww45n7733p2fPSdStuw0vvvhPhg+/httvf67UedsBLr/8zhKXuImISPxkdCIvba51gDFjnuTWW/uRn5/PDTc8xr77dmTy5He5++4rwiMY//rXe8yYMZkRI26kfv0cvvvuS44++gxatWrHs8/ex2+/reXuu18tMdVqedStuw25uV0BqF27DnvvfUDhnOoFywH23fcQxowJbqxS2rztIiJSc2R803qqudYhaNJ+5pmpDBz4IDfffD4ATz11F9dc8w+eeWYqjzzyPlttFST9b775L3/720O88MIMxox5kh9//IYnnviMHj368dxz95d430mTJiadg/388w8tsW2iVauW8/77/+Ggg7qVWFd8rvUCyeZtf/DB6+jVqz13330V69f/VrZfloiIVDsZXSOH1HOtA3TvHlxOcMABR7BmzUpWrVrOfvsdxrBhf+H448+ma9fTaNq0GQBt2hxEo0Y7AdCs2R4cfPCxQDAH+6RJE0u8b25uV555pnx90/n5+Vx33VmceeblNGu2e5F1Y8Y8xYwZkxgx4t0iy5PN237ppbfTsOGObNiwPrzt6VAuvFCzvomIxFHGJ/JUc61DydvLmRnnnTeQzp1P5IMPxnDBBYfxwANvAb/PwR5sV6vwdao52CdNmsg991xVYnndutvw2GMlR0gC3HZbf3bddU96976yyPJPP/0/HnvsNkaMeLdIHKnmbS844ahTZyv+8Ic/8dRTdyV9PxERqf4yPpGXZty458jN7crUqR9Qv/521K+/HfPmfUerVu1o1aodX3/9OXPnzqR+/fL3O5e3Rv7gg4NZvXoF11//SJHlM2d+wf/+7/9w//1j2WGHJoXLS5u3fcmShTRqtBPuzrvvvsoee+xb7vhFRDJFVlYW7dq1w93JysrigQce4NBDD+XXX3/lwgsvZNq0abg7OTk5jB07lvr162NmnH322Tz1VDBuKT8/n5122omDDz6Y119/vVLjUyIvxVZb1aV37/3Jz9/ADTc8BsAzz9zLpEkTqVWrFrvv3pZDDz2eadM+jjSORYvm8dhjt9Gixd6cc84BAJxxxqX06NGP4cMHsHbtagYO/CMATZs2Z9iw10qdt33w4LNZtiwPd2evvTowaNBDqd5aRKR6mXh75R6v66DNbrL11lszdWpQ8XrrrbcYNGgQ7777Lvfddx9Nmzblyy+Du2POmjWL2rVrA1CvXj2++uor1q5dy9Zbb8348ePZZZddKjf2UEYn8p13bsHzz39V+Prcc3+fnWfEiHeS7nPNNSUHruXmdiE3t0vSfYuvq4imTZsxaVLyOfEffPD/ki4/4YRzOOGEc5Kue+iht7coHhGRTLVy5Uq23357ILjF6W677Va4bq+99iqy7QknnMAbb7xBz549efbZZznrrLN4//33Kz2mSEetm9lVZjbdzL4ys2fNrK6ZtTSzT81stpk9Z2Z1ooxBRERkS6xdu5YOHTqw9957069fP66//noAzj//fIYOHUqnTp0YPHgw3377bZH9Cu5Xvm7dOqZNm8bBBx8cSXyRJXIz2wW4HMh1932BLKAXMBQY5u6tgGXABVHFICIisqUKmtZnzpzJ2LFj6dOnD+5Ohw4dmDNnDgMGDOCXX37hoIMOYsaMGYX7tW/fnrlz5/Lss89ywgknRBZf1E3r2cDWZrYB2AZYCBwF9A7XjwSGAP+MOA4REZEt1qlTJ5YsWUJeXh5NmjShfv36nHbaaZx22mnUqlWLMWPGFLkl6cknn8zVV1/NO++8w9KlSyOJKbIaubvPB+4CfiRI4CuAycBydy+4HmsekLT338z6m9kkM5uUl5cXVZjVzvr1vzFo0Jn06NGKvn0PLnJde6KPPhrLaaftRY8erXj88TsKl6eaR/2dd0bTq1d7evfuwLnn5jJ16gcVjvHLLz/h1lsvjLQc8+d/T9++B9OjRysGDTqTDRvWA3D33VcVlu2001rTpYtmqhOR9Jk5cyYbN26kYcOGfPjhhyxbtgyA9evX8/XXXxfpM4eg+f3GG2+kXbt2kcUUWY3czLYHTgFaAsuBF4Djyrq/u48ARgDk5uYmH+lVA40e/SgNGmzPq6/O5q23RnH//ddy++3PFdlm48aNDB16Cf/4x3iaNm1Gnz4HccQRJ7P77m2A5POod+zYjSOPPBkz49tvpzFw4Bm89NLMlHFMmvQOr7/+OEOGPF5i3UcfvUmnTqV/lFtajvvvv5beva+ie/de/O//XsTo0Y/Ss+fF/PWvwwr3HzXqfmbN+qLUOEREtlRBHzmAuzNy5EiysrL47rvvuPjii3F3Nm3axIknnsjpp59eZN9mzZpx+eWXRxpflE3rRwPfu3segJm9DBwG5JhZdlgrbwbMjzCGUi1YMJfLLjuOffY5kJkzp7D77m25+eYnqFt3m6oKiXffHU3//kMA6NatJ3//+6W4e5HJaaZP/4xdd21VOLvbscf24t13Rxcm8mS22aZ+4fO1a9eUmOymPD77bAJnn/2XyMrRsuU+fP7529x66zMAnHRSX0aMGELPnhcXeY9x456lf/+bKlwOEYmhMlwuVtk2btyYdHmfPn3o06dP0nXJbmfapUsXunTpUpmhAdGOWv8ROMTMtrHg27sb8DUwESioLvYFRkcYw2b98MMsevb8My++OIN69bblhRcerPT36Nfv8KTzqn/6aclLxxYvnk/TprsCkJ2dTf3627FixdKU2wA0adKMxYt/Px9KNY/6xImvcPrpe3PllScWXhdfXsuXLyE7uzb1629X6nZbUo4VK5bSoEEO2dnZScsHsHDhD8yf/z0HHXRUhcohIlJTRFYjd/dPzexFYAqQD3xB0FT+BjDKzG4Nlz0aVQxl0bTprnTocBgQXHs9atTwIteTV4ZHHqn86wZTKW0e9a5dT6Vr11OZMuU9Hnro+qTXoPftezAbNvzGr7+uZuXKX+jdO2hOuuyyoXTq1J1PPhnHIYccm7bypPLWW6Po1q0nWVlZVR2KiEiVinTUurvfCNxYbPEcoGOU71seyeZTr2z9+h3Or7+uKrH8iivu4uCDjy6yrEmTXVi06CeaNm1Gfn4+q1evYLvtGibdpsDixfNo0iQYM1iWedQPOOAI5s+fw/LlS8jJaVRk3ciRnwKp+8g//PDNwmb1m276E7NmfUGjRjszfPiYSivHdts1ZNWq5eTn55OdnV2kfAXGjRvFtdf+o0TZREQyTUbP7Abw888/Mm3ax7Rv34mxY5+hQ4fOlf4e5amRH3HEybz++kjat+/EhAkvctBBR5U4uWjT5iB++ulb5s//niZNdmHcuFGF/cmp5lH/6afZNGu2B2bGzJlTWL/+txKJdXPcndmzpxXeLe7GG/8dSTnMjNzcrkyY8CLdu/fi9ddHcuSRpxTuN3fuTFatWkb79p3KFb+IxFPx8TU1ifuWj+XO+PuR77bbXrzwwj/o2XMfVq5cVmJAVbqdcsoFrFixlB49WvH00/dw6aXBJVl5eQu4/PJgQoHs7GwGDHiAyy7rTs+e+3D00Wewxx5tARg8+GzOPLMdZ57ZjuXLl3DBBYMBmDDhJc48c1969+7A0KGXcPvtz5X7H2PGjMnstdf+ZdpvS8tx2WVDefrpe+jRoxUrVizllFN+nzforbdGceyxvWrsP7aI/K5u3bosXbq0UhJedePuLF26lLp1627RcSwOv5zc3FyfNGnSFh9n7NjJNG58YOHrBQvmcuWVJxWZb11Se+SRW9l111Z0796rqkPZrLy8yRx33IGb31Cq1LDx36Rcd9UxrdMYiVRXGzZsYN68eaxbt66qQ4lE3bp1adasWeHNVgqY2WR3zy3LMTK+aV3Krl+/wVUdgohkmNq1a9OyZcuqDqNay+im9eJ3PxMREYkb1cirqSlT3uPuu69k9uxp3HbbqBIztRXo378LS5YspG7drQF44IFx7LBDE/7zn8e5774BhaO9C+5fDnDZZcfx5Zef0KFDZ+69t3JvcC8xUdo9natgwg0RqTgl8mpqxx2bM2TI4zz5ZMnLx4q79danadOmZFfKMcecybXXPlBi+bnnDmDdul95+eWHKyVWERGpOhmdyNeuXcPAgWewePE8Nm7cSL9+13PssWcyY8Zkhg37C7/+upqcnEYMGfI4jRrtRP/+XWjdej+mTHmX/Px8brjhMfbdN5pL4nfeuQUAtWpVfu9Hx47dmDTpnUo/roiIpF9GJ/KPPhpL48Y7c999bwCwevUK8vM3cOedl3H33aPZfvvGjBv3HP/4x3XceGMwpem6db/yzDNTmTLlPW6++fxy9bGXZ2KY8rjppj+RlZXFUUedzgUXDC68LOvtt1/iiy/eo3nz1vzlL8PYccddN3MkERGJm4xO5K1atePee//K8OHXcvjhJ7H//ocze/ZXfPfdV1xyyTFAMFl+wWxpAN27nwUEs6OtWbOSVauW06BB2W6lGcVUrbfe+jRNmuzCmjWruOaa03njjSc56aQ+HH74H+je/Szq1NmKl156mCFD+vLQQ29X+vuLiEjVyuhEvtturXnqqSl8+OEY/vnPwRx0UDe6dj2V3Xdvy7///XHSfbZkStcoauQFg9nq1WvAccf1Zvr0zzjppD7k5Pw+azV+wOMAABd9SURBVFuPHv0YPvyaCh1fRESqt4xO5Hl5C9h22x044YRzaNAgh1dffYTzzhvIsmV5hdO25udv4IcfvimccWzcuOfIze3K1KkfUL/+dpu9C1iiyq6RB3OYLycnpxH5+Rt4//3X6dgxOCEomKoV4L33XqNly30q9b1FRKR6yOhEPnv2l9x33wBq1apFdnZtBg78J7Vr12Ho0Be5667LWb16BRs35nPWWVcWJvKttqpL7977k5+/ocK3Ai2L6dM/Z8CAU1m5chnvv/8fRoy4keefnw5A794deOaZqWzY8BuXXtqd/PwNbNq0kY4dj+bUUy8EYNSo4bz33mtkZWWz7bY7FLn5Sb9+hzN37kzWrl3NCSc04/rrH6VTp+6RlUVERKKT0VO0llf//l248sq7kl7qJdWLpmjdjGpyHbmmaBVJrjxTtGb0zG4iIiJxl9FN6+U1YsQ7VR2CiIhIEUrkIlItpbPZXU38EmdqWhcREYkxJXIREZEYy6im9ZycOuTlTa7qMCQNcnLqVHUIIiJpkVGJ/JBD2lV1CCISM+o/l+pOTesiIiIxpkQuIiISY0rkIiIiMZZRfeQisVRNplMVkepJNXIREZEYUyIXERGJMSVyERGRGIsskZvZXmY2NeGx0syuNLMdzGy8mX0b/tw+qhhERERqusgSubvPcvcO7t4BOBD4FXgFGAhMcPc9gQnhaxEREamAdDWtdwO+c/cfgFOAkeHykUCPNMUgIiJS46Tr8rNewLPh86buvjB8/jPQNNkOZtYf6A/QvHnzyAMUkWiUNsVpulWnWEQqS+Q1cjOrA5wMvFB8nbs74Mn2c/cR7p7r7rmNGzeOOEoREZF4SkfT+vHAFHdfFL5eZGY7AYQ/F6chBhERkRopHYn8LH5vVgd4DegbPu8LjE5DDCIiIjVSpH3kZlYPOAb4n4TFdwDPm9kFwA/AGVHGICKZRf3gkmkiTeTuvgZoWGzZUoJR7CIiIrKFNLObiIhIjOnuZyKVSXcqE5E0U41cREQkxpTIRUREYkyJXEREJMbURy4isaNLzER+pxq5iIhIjCmRi4iIxJgSuYiISIwpkYuIiMSYErmIiEiMKZGLiIjEmC4/E6mI0qZiFRFJI9XIRUREYkyJXEREJMaUyEVERGJMiVxERCTGlMhFRERiTIlcREQkxpTIRUREYkzXkYvUVBW91j3Vfl0HVTwWEYmMauQiIiIxpkQuIiISY0rkIiIiMaY+cpF0Ka3PWv3PsTRs/Dcp1111TOs0RiKZTDVyERGRGFMiFxERibFIE7mZ5ZjZi2Y208xmmFknM9vBzMab2bfhz+2jjEFERKQmi7pGfh8w1t33BvYDZgADgQnuvicwIXwtIiIiFRBZIjez7YAjgEcB3H29uy8HTgFGhpuNBHpEFYOIiEhNF2WNvCWQB/zbzL4ws0fMrB7Q1N0Xhtv8DDSNMAYREZEaLcpEng0cAPzT3fcH1lCsGd3dHfBkO5tZfzObZGaT8vLyIgxTREQkvqJM5POAee7+afj6RYLEvsjMdgIIfy5OtrO7j3D3XHfPbdy4cYRhioiIxFdkidzdfwZ+MrO9wkXdgK+B14C+4bK+wOioYhAREanpop7Z7TLgaTOrA8wB/kRw8vC8mV0A/ACcEXEMIiIiNVakidzdpwK5SVZ1i/J9RcpM06aKSMxpZjcREZEYUyIXERGJMd39TCTOSusaEJGMoBq5iIhIjCmRi4iIxJgSuYiISIypj1xEJALDxn9Tof2uOqZ1JUciNZ1q5CIiIjGmRC4iIhJjSuQiIiIxpj5yqfl0rXXkKtofLCJbTjVyERGRGFMiFxERiTE1rYtUB2r+l1Bp3RS6NE2SUY1cREQkxpTIRUREYkyJXEREJMaUyEVERGJMiVxERCTGlMhFRERiTIlcREQkxnQdudQMug5bRDKUauQiIiIxpkQuIiISY0rkIiIiMaY+chGRmNA87JKMauQiIiIxpkQuIiISY5E2rZvZXGAVsBHId/dcM9sBeA5oAcwFznD3ZVHGIVIhuqRNRGIgHTXyru7ewd1zw9cDgQnuvicwIXwtIiIiFVAVTeunACPD5yOBHlUQg4iISI0QdSJ3YJyZTTaz/uGypu6+MHz+M9A04hhERERqrKgvP+vs7vPNrAkw3sxmJq50dzczT7ZjmPj7AzRv3jziMEVEROIp0hq5u88Pfy4GXgE6AovMbCeA8OfiFPuOcPdcd89t3LhxlGGKiIjEVmSJ3MzqmVmDgufAscBXwGtA33CzvsDoqGIQERGp6aJsWm8KvGJmBe/zjLuPNbPPgefN7ALgB+CMCGMQERGp0SJL5O4+B9gvyfKlQLeo3ldEtszHc5YmXd6pa5oDEZEy0cxuIiIiMaZELiIiEmO6+5mIlE2pU9aenrYwRKQo1chFRERiTIlcREQkxpTIRUREYkyJXEREJMaUyEVERGJMiVxERCTGlMhFRERirFzXkYc3P1nn7hsjikdEYuiQH0ekXPdJ8/5pjEQk85RaIzezWmbW28zeMLPFwExgoZl9bWZ3mlmr9IQpIiIiyWyuaX0isAcwCNjR3Xd19yZAZ+ATYKiZnRNxjCIiIpLC5prWj3b3DcUXuvsvwEvAS2ZWO5LIRKRGULO7SLRKrZEXJHEze7L4uoJlyRK9iIiIpEdZR623TXxhZlnAgZUfjoiIiJTH5ga7DTKzVUB7M1sZPlYBi4HRaYlQREREUtpc0/rt7t4AuNPdtw0fDdy9obsPSlOMIiIiksLmauQtAFIlbQs0q/ywREREpCw2N2r9TjOrRdCMPhnIA+oCrYCuQDfgRmBelEGKiIhIcqUmcnf/o5m1Ac4Gzgd2BNYCM4AxwG3uvi7yKEVERCSpzY5ad/evgVuB/xAk8O+Bz4EXlcRFRESqVlnnWh8JrASGh697A08AZ0QRlIiIiJRNWRP5vu7eJuH1RDP7OoqAREREpOzKOiHMFDM7pOCFmR0MTIomJBERESmrstbIDwQ+MrMfw9fNgVlm9iXg7t4+kuhERESkVGVN5MdFGoWIiIhUSJkSubv/EHUgIiIiUn5lrZFXWHiDlUnAfHc/ycxaAqOAhgSTzJzr7uujjkNEqh/d4lRky5V1sNuWuILg+vMCQ4Fh7t4KWAZckIYYREREaqRIE3k4D/uJwCPhawOOAl4MNxkJ9IgyBhERkZos6hr5vcA1wKbwdUNgubvnh6/nAbtEHIOIiEiNFVkfuZmdBCx298lm1qUC+/cH+gM0b968kqMTkfL6eM7Sqg5BRJKIskZ+GHCymc0lGNx2FHAfkGNmBScQzYD5yXZ29xHunuvuuY0bN44wTBERkfiKLJG7+yB3b+buLYBewNvufjYwEegZbtaX4BapIiIiUgHpGLVe3LXAX8xsNkGf+aNVEIOIiEiNEPl15ADu/g7wTvh8DtAxHe8rIiJS01VFjVxEREQqiRK5iIhIjKWlaV2kUky8vaojEBGpdlQjFxERiTElchERkRhTIhcREYkx9ZGLZCBNtypSc6hGLiIiEmNK5CIiIjGmRC4iIhJjSuQiIiIxpkQuIiISY0rkIiIiMaZELiIiEmNK5CIiIjGmRC4iIhJjSuQiIiIxpkQuIiISY0rkIiIiMaZELiIiEmNK5CIiIjGm25hK9TLx9qqOQKqJQ34ckXLdJ837pzESkepNNXIREZEYUyIXERGJMTWti0jsqNm9pGHjv0m57qpjWqcxEkk31chFRERiTIlcREQkxpTIRUREYiyyRG5mdc3sMzP7r5lNN7ObwuUtzexTM5ttZs+ZWZ2oYhAREanpoqyR/wYc5e77AR2A48zsEGAoMMzdWwHLgAsijEFERKRGiyyRe2B1+LJ2+HDgKODFcPlIoEdUMYiIiNR0kfaRm1mWmU0FFgPjge+A5e6eH24yD9glyhhERERqskgTubtvdPcOQDOgI7B3Wfc1s/5mNsnMJuXl5UUWo4iISJylZdS6uy8HJgKdgBwzK5iIphkwP8U+I9w9191zGzdunI4wRUREYifKUeuNzSwnfL41cAwwgyCh9ww36wuMjioGERGRmi7KKVp3AkaaWRbBCcPz7v66mX0NjDKzW4EvgEcjjEFERKRGiyyRu/s0YP8ky+cQ9JeLiFQ6zcMumUYzu4mIiMSYErmIiEiM6Tamkn4Tb6/qCGqMj+csreoQRKSKqUYuIiISY0rkIiIiMaZELiIiEmNK5CIiIjGmRC4iIhJjSuQiIiIxpkQuIiISY7qOXESkhhs2/pty73PVMa0jiESioBq5iIhIjCmRi4iIxJgSuYiISIwpkYuIiMSYErmIiEiMKZGLiIjEmBK5iIhIjCmRi4iIxJgSuYiISIwpkYuIiMSYErmIiEiMKZGLiIjEmBK5iIhIjOnuZ7JlJt6eel3XQemLI+Y+nrM05bpOuzdMYyQiEjeqkYuIiMSYErmIiEiMKZGLiIjEWGSJ3Mx2NbOJZva1mU03syvC5TuY2Xgz+zb8uX1UMYiIiNR0UdbI84G/unsb4BDgEjNrAwwEJrj7nsCE8LWIiIhUQGSJ3N0XuvuU8PkqYAawC3AKMDLcbCTQI6oYREREarq09JGbWQtgf+BToKm7LwxX/Qw0TUcMIiIiNVHk15GbWX3gJeBKd19pZoXr3N3NzFPs1x/oD9C8efOow5QolHaNuYiIVIpIa+RmVpsgiT/t7i+HixeZ2U7h+p2Axcn2dfcR7p7r7rmNGzeOMkwREZHYinLUugGPAjPc/Z6EVa8BfcPnfYHRUcUgIiJS00XZtH4YcC7wpZlNDZf9DbgDeN7MLgB+AM6IMAYREZEaLbJE7u4fAJZidbeo3ldERCSTaGY3ERGRGFMiFxERiTHdxlQ2T5eRiYhUW6qRi4iIxJgSuYiISIwpkYuIiMSY+shFqrmP5yyt6hBEihg2/puU6646pnUaIxFQjVxERCTWlMhFRERiTIlcREQkxpTIRUREYkyJXEREJMaUyEVERGJMl59JQNOwiojEkmrkIiIiMaZELiIiEmNK5CIiIjGmPnIRESmhtGlYpXpRjVxERCTGlMhFRERiTIlcREQkxtRHLiIZ45AfRyRd/knz/mmORKTyqEYuIiISY0rkIiIiMaamdZFK9PGcpSnXddq9YRojkfJI1eQOanaX6k81chERkRhTIhcREYkxJXIREZEYi6yP3MweA04CFrv7vuGyHYDngBbAXOAMd18WVQwi1Ulp/ecST+pbl+ogyhr548BxxZYNBCa4+57AhPC1iIiIVFBkidzd3wN+Kbb4FGBk+Hwk0COq9xcREckE6e4jb+ruC8PnPwNN0/z+IiIiNUqVXUfu7m5mnmq9mfUH+gM0b948bXGJlIX6u0WSK+32p1cd07rS95P018gXmdlOAOHPxak2dPcR7p7r7rmNGzdOW4AiIiJxku5E/hrQN3zeFxid5vcXERGpUSJL5Gb2LPAxsJeZzTOzC4A7gGPM7Fvg6PC1iIiIVFBkfeTuflaKVd2iek/ZjIm3V3UEIiJSyTSzm4iISIwpkYuIiMSYErmIiEiMKZGLiIjEmBK5iIhIjCmRi4iIxFiVTdEqIhIHpd2qVMqntGlYpeJUIxcREYkxJXIREZEYUyIXERGJMfWR1zSahrXS6FalEpXS+t0/ad4/jZFITaAauYiISIwpkYuIiMSYmtbjSM3nIjWWmt2lvFQjFxERiTElchERkRhTIhcREYkx9ZFXV+oHFxEBKja161XHtI4gkupJNXIREZEYUyIXERGJMSVyERGRGFMfeXmU1m/ddVD64pASKjqdaqfdG1ZyJCKBKG5/qmvMJRnVyEVERGJMiVxERCTGMrNpvTo1kesyszLT3chEUlOze1EVuWQN4nnZmmrkIiIiMaZELiIiEmNK5CIiIjFWJX3kZnYccB+QBTzi7ndURRxpU036wUvrY67oZVhRHDOd1O8uUn4VvbQu7n31pfW7V2Xfetpr5GaWBfwDOB5oA5xlZm3SHYeIiEhNUBVN6x2B2e4+x93XA6OAU6ogDhERkdirikS+C/BTwut54TIREREpJ3P39L6hWU/gOHfvF74+FzjY3S8ttl1/oKBDZS9gVkQhNQKWRHTs6iaTygoqb02WSWWFzCpvJpUVUpd3N3dvXJYDVMVgt/nArgmvm4XLinD3EUDlT1ZcjJlNcvfcqN+nOsiksoLKW5NlUlkhs8qbSWWFyilvVTStfw7saWYtzawO0At4rQriEBERib2018jdPd/MLgXeIrj87DF3n57uOERERGqCKrmO3N3HAGOq4r2TiLz5vhrJpLKCyluTZVJZIbPKm0llhUoob9oHu4mIiEjl0RStIiIiMZYxidzM/mhm081sk5nlJizvaGZTw8d/zezUhHXHmdksM5ttZgOrJvKKKaW8x5jZZDP7Mvx5VMK6d8LyFvw+mlRN9OVTwbIeGC6fbWbDzcyqJvryK6W8Dc1sopmtNrMHiu0Ty88WKlzeWH6+qcoarhsUlmeWmXVPWD43LOtUM5uU/qgrroLlje33ciIz28/MPg4/u/+Y2bbh8hZmtjbhf/WhzR7M3TPiAexDcD36O0BuwvJtgOzw+U7AYoKxA1nAd8DuQB3gv0Cbqi5HJZR3f2Dn8Pm+wPyEdUW2jcujgmX9DDgEMOBN4PiqLkcllLce0Bm4CHig2D6x/Gy3oLyx/HxLKWub8DtoK6Bl+N2UFa6bCzSq6tjTUd64fy8XK/vnwJHh8/OBW8LnLYCvynOsjKmRu/sMdy8xqYy7/+ru+eHLukDBoIFYTyVbSnm/cPcF4cvpwNZmtlV6o6tc5S2rme0EbOvun3jwn/ME0CONIW+RUsq7xt0/ANZVQViRKW954/z5piorwXfPKHf/zd2/B2YTfEfFWgXKG+vv5WJaA++Fz8cDp1f0QBmTyEtjZgeb2XTgS+CiMLFnwlSypwNT3P23hGX/Dptzro9Lc2QZJZZ1F4LPs0BN/GyTqamfbXE18fMt7fvIgXFh91G8by/2u1TlrUnfy9P5/STkjxSdKK2lmX1hZu+a2eGbO1CVXH4WFTP7P2DHJKuuc/fRqfZz90+Btma2DzDSzN6MKsbKVNHyhvu2BYYCxyYsPtvd55tZA+Al4FyC2kyVi6Cs1dqWlDeFavvZQiTlrbYiKGvn8LNtAow3s5nu/t5m90qTTPpsiyut7ATN6cPN7HqCSdHWh+sWAs3dfamZHQi8amZt3X1lqvepUYnc3Y/ewv1nmNlqwv5UyjCVbFWqaHnNrBnwCtDH3b9LON788OcqM3uGoBmrWnzZV3JZ5xN8ngVqzGdbyvGq7WcLlV7eav35VrCsKb+PEj7bxWb2CsFnW20SeWWXt5Tl1U4Zyn4sgJm1Bk4M9/kN+C18PtnMviNohk85kDHjm9YtmCo2O3y+G7A3weCRGjmVrJnlAG8AA939w4Tl2WbWKHxeGzgJ+Kpqoqwcqcrq7guBlWZ2SNjE3AeosTWDmvjZlqaGfr6vAb3CMR4tgT2Bz8ysXtjKgpnVI0gMNeGzTVpeatD3ctiCgpnVAgYDD4WvG5tZVvh8d4Kyzyn1YFU9ci9dD+BUgv6U34BFwFvh8nMJ+iqmAlOAHgn7nAB8QzBK8rqqLkMllXcwsCYsb8GjCcEI4MnAtPD3cR/hqNjq/ihvWcN1uQRfeN8BDxBOjhSHR6ryhuvmAr8Aq8Nt2sT5s61IeeP8+W6mrNeF5ZlFOAqfYPT2f8PH9JryPZWqvOHy2H4vFyv7FWE5vgHuKPgbJRjPk5iT/rC5Y2lmNxERkRjL+KZ1ERGROFMiFxERiTElchERkRhTIhcREYkxJXIREZEYUyIXkRLMLMfM/lzVcYjI5imRi0gyOYASuUgMKJGLSDJ3AHuEN1m5s6qDEZHUNCGMiJRgZi2A19193yoORUQ2QzVyERGRGFMiFxERiTElchFJZhXQoKqDEJHNUyIXkRLcfSnwoZl9pcFuItWbBruJiIjEmGrkIiIiMaZELiIiEmNK5CIiIjGmRC4iIhJjSuQiIiIxpkQuIiISY0rkIiIiMaZELiIiEmP/D1vIP0XRiojpAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 5.99 seconds)\n", + "\n" + ] + } + ], + "source": [ + "gphivals = [4e-3, 5e-3, 6e-3]\n", + "gphi_fnames = ['gphi_toydata_test_4e-3_out', 'gphi_toydata_test_5e-3_out', 'gphi_toydata_test_6e-3_out']\n", + "tsm_plus = {}\n", + "tbsm_plus = {}\n", + "\n", + "for gphival, gphi_fname in zip(gphivals, gphi_fnames):\n", + " sep, p, tsm, tbsm = TestEstimator(estimator, gphival, gphi_fname, withXS=False,\n", + " title_message=', quadratic classifier, (NSM=NBSM)', qc=True)\n", + " tsm_plus[gphival] = tsm\n", + " tbsm_plus[gphival]=tbsm" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2263.789\n", + "test 0 : tsm = -74.610, tbsm = -82.424\n", + "Reaching the end of test data. Stop tests at 1325. \n", + "===> delta1 = 0.007, delta2 = 0.005\n", + "p = 0.081 +/- 0.009\n", + "Separation = 1.39 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 6.04 seconds)\n", + "\n", + "NSM = 2225.504 --- NBSM = 2273.448\n", + "test 0 : tsm = -85.241, tbsm = -96.576\n", + "Reaching the end of test data. Stop tests at 220. \n", + "===> delta1 = 0.013, delta2 = 0.008\n", + "p = 0.041 +/- 0.016\n", + "Separation = 1.67 sigmas\n" + ] + }, + { + "data": { + "image/png": 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73O6996pi61q06D22b99Gw4aHF5qem7udyZOf5uSTz9hlnZdf/hcnn/zLz3mbGX/60+lcdNHxvPhidqkeFxERCdx11120bNmS1q1b06ZNG+bMmUOnTp3IzMyk4O+b9OjRg5o1a6akxkrdMwdo0KAJRx7ZBoCjjjqeVatWAnDEEa255Za+dOrUg06dfvmJ45NPPpO0tGo0a9aKnTt35Idqs2at8tctqF+/QfTrN6hUNX333TcMGXIxt902hipVCn/euvvuK2jb9hSOO65joelz507n5Zf/xahR7+RPGzXqHerVO5Tvv1/Dn/50Go0bH0XbtqeUqhYRkb3JiGnLy3V7157WvNj5s2bNYtKkScyfP5/q1avz3XffsW1bMF5Jeno6M2fOpEOHDmzYsIFvvkndD1BW+jCvVq16/t9Vq1bl55+3APDgg//lgw/e4q23/sPo0XcxYcJHAOyzT7B8lSpVSEurln+IxKwKO3bk7rL9p566lylTntll+nHHncKgQSN3mb5580auvvo3XHHFXbRq1a7QvOzs21i/fi033fR4oemffLKQO+7oz8iRr5KeXid/er16hwJw0EH16NTpXBYvfk9hLiJSCt988w1169alevXgvb9u3V+uQerduzcTJkygQ4cOvPjii/Ts2ZPFixenpM5KH+ax7Ny5k9WrvyIrqzNt2nRg6tQJbNmyuUzbKk3PfPv2bQwadC6/+U0/unbtVWjeSy+NYvbs13jkkTcK9da//fZLBg3qye23P13onPuWLT+yc+dO9t+/Flu2/MicOVPp339ImdogIlJZnX766dx+++00b96crl27csEFF3DqqacC0KVLFy6//HJ27NjBhAkTyM7O5o477khJnQrzGHbu3MGtt17E5s0/4O707n0VtWqlJ3y/06Y9x/z5b/HDD+uYNOlJAIYOfZIjj2zD8OF/5OCDG3HZZe0B6Ny5J5dfPoR//vN2fvhhHffccwVA/lfQ1q1bzaBB5wKwY0cu3br1iXmeXURE4qtZsybz5s3j7bffZvr06VxwwQXcfffdQHA0t0OHDkyYMIEtW7aQyh8Es4In7/dWWVlZXh4/tDJlyjwN51pJrF07jzPO0HMtEnVLlizh6KOPzr+f7HPmRU2cOJExY8awadMm7rvvPn766SfOPfdchg0bxsCBA6lZsyabN5fuSG7RNgKY2Tx3zyrpNtQzF0mW6cPjz+s8uPzXE5E9tmzZMqpUqcIRRxwBwIIFC2jUqBGLFi0CoGPHjgwePJgLL7wwlWUqzEVEROLZvHkzAwcOZMOGDaSlpdGsWTOys7Pp1Su4rsnMuP7661NcpcJcREQipLSHxffU8ccfz7vvvrvL9BkzZsRcvrSH2MuLwjyOAQM6cc0199GiRYlPWSTM1q0/8Ze/nEdOzmdUrVqVjh1/y8CBwQUYY8c+wMsvj6Jq1TRq185gyJDRHHJII+bOnc4DD1ybv42VK5fy179OoFOnHtx++x9YsmQu7k5mZnOGDXuS/fZLzUAHIiKy5yr9CHBRcfHF1/PCC0t55pkP+PDDmcyc+SoARx11HE8/PZcJExbSpUsvRo68AYCsrM6MG7eAceMW8Oij/6NGjf1o1+50AK67bgTjx3/IhAkLOfjgTJ577uGUtUtERPZcpe6ZFzc2O8DkyU9z5539yc3NZciQ0RxzzInMm/cm999/dbgF45//fIslS+aRnT2UmjXT+eyzj+ja9XyaNWvF+PEP8fPPW7j//pd2GZa1NGrU2I+srM4AVKu2D0cd1TZ/DPa86QDHHNOOyZPH7rL+G29M5OSTz6RGjf0AqFnzACD4xZ5gkJyKO+6xiEhlUOl75vHGZofg8Pa4cQu48cZHuP32ywAYO/Y+brjhH4wbt4BRo96mevUg+Jcv/5CbbnqM559fwuTJT/Pll8t56qn36NGjP88++/dd9jt37vSYY7ZfdtnJuyxb0KZNG3j77f9wwglddplXdGz2PFOnTqBbt8JXWt522+/p1u1gVq5cSu/eA3f/QImIyF6rUvfMIf7Y7EB+ALZtewo//riRTZs2cOyxv2LEiOs488y+dO7ck/r1GwLQosUJ1K17CAANGx7OSScFh7SbNWvF3LnTd9lv3mHw0sjNzeXmmy/kgguuomHDpoXmTZ48liVL5pKd/Wah6d999w2ffvoR7dt3KzR96NAn2LFjB/feO5CpU5/l7LN/X6paRERk71Hpwzze2Oyw60/TmRmXXnojHTr8hnfemcwf/vArHn74NeCXMduD5ark3483ZnvRC9Ty1KixH6NH73rlJMBddw3gsMOOoE+fawpNnzPndUaPvovs7DcL1QHBqHKdO58b86dZq1atyumn9+app/6mMBcRibBKH+bFmTr1WbKyOrNgwTvUrHkgNWseSE7OZzRr1opmzVrx8cfvs3LlUmrWLP1Qr6XtmT/yyC1s3vwDt946qtD0pUs/4K9//T/+/vcpHHRQvV3We+218Vx55S+Djrg7OTmfcdhhzXB33nrrFRo3PqrU9YuIVBZVq1alVatWuDtVq1bl4Ycf5uSTT+ann37i8ssvZ+HChbg76enpTJkyhZo1a2Jm9O3bl7Fjg+uYcnNzOeSQQzjppJOYNGlSudeoMC9G9eo16NPnOHJztzNkyGgAxo17kLlzp1OlShWaNm3JySefycKFsxJax+rVOYwefReNGx/FRRe1BeD886+kR4/+jBw5iC1bNnPjjecBUL9+JiNGvAIEF/itXv0Vbduemr8td2fo0Ev48ceNuDvNmx/LjTc+mtD6RUTKTXEjIpZFCUZR3HfffVmwIOh8vfbaawwePJg333yThx56iPr16/PRR8Gvai5btoxq1YKjoPvvvz+LFi1iy5Yt7LvvvkybNo1DDz20fGsvoFKHeYMGjXnuuUX59y+++JdRfLKzZ8Rc54Ybdr2YLSurE1lZnWKuW3ReWdSv35C5c2OPof/II6/HXa9Bg8a8+urXhaZVqVKF0aNn7lE9IiKV1caNG6lduzYQ/Dxqo0aN8ucdeeSRhZbt3r07//3vf+nVqxfjx4/nwgsv5O23305IXZX+anYREZHibNmyhTZt2nDUUUfRv39/br31VgAuu+wy7rnnHtq3b88tt9zCJ598Umi9vN8737p1KwsXLuSkk05KWI0KcxERkWLkHWZfunQpU6ZMoV+/frg7bdq0YcWKFQwaNIjvv/+eE044gSVLluSv17p1a1auXMn48ePp3r17Qmus1IfZRURESqN9+/Z89913rF27lnr16lGzZk169uxJz549qVKlCpMnTy70c6Znn302119/PTNmzGDdunUJq0s9873Mtm0/M3jwBfTo0YxLLjmp0PfeC3r33Sn07HkkPXo048kn795l/r33XkXHjr+Mtz5//lv07duWk05K4/XXJ+5RjR99NJs777w8oe149tmH6dGjGVlZxoYN3xVaZ+7cGfTp04bzz2/JgAGnFt2kiEjCLF26lB07dlCnTh1mzpzJ+vXrAdi2bRsff/xxoXPoEByKHzp0KK1atUpoXQrzvczLL/+LWrVq89JLn9Knz7X8/e9/2WWZHTt2cM89f2LkyFd5/vmPee218axY8XH+/I8/nsvGjesLrXPwwZkMG/Yk3br1KVEdc+fOYNiwS2POe/fdV2nf/oyEtuPYY3/FI4+8ziGHFH5hbNq0gXvuuYIHHniF555bzN13P1+i9oiIlFXeOfM2bdpwwQUXMGbMGKpWrcpnn33GqaeeSqtWrTjuuOPIysrid7/7XaF1GzZsyFVXXZXwGhN2mN3MRgNnAWvc/Zgi8/4M3AdkuPt3sdZPhlWrVjJw4BkcffTxLF06n6ZNW3L77U/lj2GeCm+++TIDBgwDoEuXXvztb1fi7oUGsFm8+D0OO6xZ/ihwp5/emzfffJmmTVuwY8cOHnpoEHfdNY4ZM/6dv06DBo2B4Gr2PfXee2/Qt+91CW3HUUcdF3O7U6aMo3Pnnhx8cCZAzO/Wi0gFVoKvkpW3HTt2xJzer18/+vXrF3NerJ9C7dSpE506dSrP0vIlsmf+JLBL983MDgNOB75M4L5L7IsvltGr1xVMnLiE/fc/gOeff6Tc99G/f8eY47DPmbPr18rWrPma+vUPAyAtLY2aNQ/khx/WxV0GoF69hqxZE3wF7bnnHuaUU87OH1q2vG3Y8B1padWoWfPAYpfb03bE8+WXy9m0aT0DBnTioouOZ9Kkp8rYEhGRiiNhPXN3f8vMGseYNQK4AXg5Ufsujfr1D6NNm18B0L37RUyYMLLQ983Lw6hRifleYVFr167i9def5/HHZ5R5G5dcchLbt//MTz9tZuPG7+nTJxi3fuDAe2jfvhuzZ0/N/ynVVMjNzWXJknk8+ugb/PzzFn7/+/a0atWORo2ap6wmEZFUS+rV7GZ2DvC1u39YdNzzVIk1/np569+/Iz/9tGmX6VdffR8nndS10LR69Q5l9eqvqF+/Ibm5uWze/AMHHlgn5jJ51qzJoV69Q1m27ANycj7l3HObAcGvvvXo0YyXXvq0xLWOGTMHCM6ZT5r0JMOGPVlo/syZr+YfYr/ttt+zbNkH1K3bgJEjJ5dbO4pTv35D0tPrsO+++7Pvvvtz3HGn8MknHyrMRaRSS1qYm9l+wE0Eh9hLsvwAYABAZmZmwur69tsvWbhwFq1bt2fKlHG0adOh3PdRmp75KaeczaRJY2jduj1vvDGRE0749S4fMFq0OIGvvvqEr7/+nHr1DmXq1Anceec4Dj+8Ja+99m3+ch071ixVkO+Ou/Pppwvzf2Vu6NAnEtKO4px66jn87W9XkpubS27uNhYtmkOfPrv+YI1UXiOmLY8779rT9KEviopeb1ORuMce3bO0knk1++FAE+BDM1sJNATmm9nBsRZ292x3z3L3rIyMjIQV1ajRkTz//D/o1etoNm5cT69e/y9h+yqJc875Az/8sI4ePZrxzDMPcOWVwde11q5dxVVXBYMOpKWlMWjQwwwc2I1evY6ma9fzOfzwlsVud/Hi9+nevSGvv/48w4f/H+efX/zysSxZMo8jjzyuRC+qPW3HhAkj6d69IWvW5NC7d2vuuKM/AE2aHE379mdw4YWt6dfvRHr06E+zZsfELkJEIq9GjRqsW7eu3EJvb+LurFu3jho1auzxtiyRD1B4znxS0avZw3krgaySXM2elZXlc+fO3eN6pkyZR0bG8fn3V61ayTXXnFVofHaJb9SoOznssGZ069Y71aXs1tq18zjjjON3v2AyFfcDEcVdoVvW9Sop9cwrlu3bt5OTk8PWrVtTXUpC1KhRg4YNG+b/QEseM5vn7lkl3U4iv5o2HugE1DWzHGCou/8rUfuTxOvf/5ZUlyAilUy1atVo0qRJqsvY6yXyavYLdzO/caL2XVJFfzVNREQkijQC3F6qpMOvbt++jbvuGkDPns353e+O4o03XgDg/vuvzf8+e8+ezenUKR2Ab775gr592+YPhzpx4mNJaY+IiCSOfmhlL5U3/OrTT99X7HKjR99F7dr1ePHF5ezcuZONG78H4M9/HpG/zIQJf2fZsg8AqFv3EJ54Yhb77FOdn37azAUXHMOpp55NRkaDxDVGREQSqlKH+ZYtP3LjjeezZk0OO3bsoH//Wzn99AtYsmQeI0Zcx08/bSY9vS7Dhj1J3bqHMGBAJ5o3P5b5898kNzeXIUNGc8wxJyaktpIOv/rKK6OZOHFp/rLp6XV3WWbq1PEMGHAbANWq7ZM/fdu2n9m5c2c5VSwiIqlSqcP83XenkJHRgIce+i8Amzf/QG7udu69dyD33/8ytWtnMHXqs/zjHzczdOhoIBiIZdy4Bcyf/xa3335Zqc65l2bwmJLYtGkDAI8+eivz5s2gYcPDueGGh6lTp37+Mt988wVff/05J5zw6/xp3377Fddc8xu++upTrr76XvXKRUQirlKHebNmrXjwwT8zcuRf6NjxLI47riOffrqIzz5bxJ/+dBoQDLBfcJzzbt2C6/ratj2FH3/cyKZNG6hVK71E+yvvYV137Mhl9eocWrc+meuue4CxYx/gwQev5447ns5f5rXXJtClSy+qVq2aP+3ggw9jwoSFrF27ij//uQdduvQq9AFARESipVKHeaNGzRk7dj4zZ3OG2jAAABVpSURBVE7m0Udv4YQTutC587k0bdqSJ56YFXOdPRn+tbx75gceWIcaNfbj17/uCUDXrufxyiuFv/03deoE/vKXf8RcPyOjAYcffgwffPA2Xbv2KvX+RURk71Cpw3zt2lUccMBBdO9+EbVqpfPSS6O49NIbWb9+bf4Qr7m52/nii+X5I5NNnfosWVmdWbDgHWrWPHC3vx5WUHn3zM2Mjh1/y7x5MzjhhF/z/vtv0KRJi/z5K1cuZdOm9bRu3T5/2urVOeGHgH3ZuHE9H374Dn37ajhUEZEoq9Rh/umnH/HQQ4OoUqUKaWnVuPHGR6lWbR/uuWci9913FZs3/8COHblceOE1+WFevXoN+vQ5jtzc7QwZMjphtS1e/D6DBp3Lxo3refvt/5CdPZTnnlsMQJ8+bRg3bgEAV111D0OGXMz9919D7doZhcZLf+21CZx+eu9CRw8+/3wJDz74Z8wMd+eii66nWbNWCWuHiIgkXkKHcy0viRrOtbQGDOjENdfcR4sWJR5hT1JEw7lWXhrOVSqC0g7nqkFjREREIq5SH2YvrezsGakuQUREZBfqmYuIiEScwlxERCTiFOYiIiIRV6nOmaen78PatfNSXYYkQXr6PrtfSESkgqhUYd6unb5PLSIiFY8Os4uIiEScwlxERCTiFOYiIiIRpzAXERGJOIW5iIhIxCnMRUREIk5hLiIiEnEKcxERkYhTmIuIiEScwlxERCTiFOYiIiIRpzAXERGJOIW5iIhIxCnMRUREIk5hLiIiEnEKcxERkYhTmIuIiERcwsLczEab2RozW1Rg2r1mttTMFprZv80sPVH7FxERqSwS2TN/EjijyLRpwDHu3hpYDgxO4P5FREQqhYSFubu/BXxfZNpUd88N784GGiZq/yIiIpVFWgr3fRnwbLyZZjYAGACQmZmZrJpESmb68PjzOuuAU0Ejpi2PO+/a05onsZLyV5HbJtGSkgvgzOxmIBd4Jt4y7p7t7lnunpWRkZG84kRERCIm6T1zM7sUOAvo4u6e7P2LiIhUNEkNczM7A7gBONXdf0rmvkVERCqqRH41bTwwCzjSzHLM7A/Aw0AtYJqZLTCzxxK1fxERkcoiYT1zd78wxuR/JWp/IiIilZVGgBMREYk4hbmIiEjEKcxFREQiTmEuIiIScQpzERGRiFOYi4iIRJzCXEREJOIU5iIiIhGnMBcREYk4hbmIiEjEKcxFREQiTmEuIiIScQpzERGRiFOYi4iIRJzCXEREJOIS9nvmIpEwfXj8eZ0HJ6+OiBgxbXnM6dee1jzJlZRNvPohOm0QiUU9cxERkYhTmIuIiEScwlxERCTiFOYiIiIRpzAXERGJOIW5iIhIxCnMRUREIk5hLiIiEnEKcxERkYhTmIuIiEScwlxERCTiFOYiIiIRpzAXERGJOIW5iIhIxCnMRUREIk5hLiIiEnEJC3MzG21ma8xsUYFpB5nZNDP7JPy3dqL2LyIiUlkksmf+JHBGkWk3Am+4+xHAG+F9ERER2QMJC3N3fwv4vsjkc4Ax4d9jgB6J2r+IiEhlkexz5vXd/Zvw72+B+knev4iISIWTlqodu7ubmcebb2YDgAEAmZmZSatLImr68FRXkBIjpi2PO+/a05qXbaPFPpa/K9s240hI/WUUr5Zk11FWe9NjKcmX7J75ajM7BCD8d028Bd09292z3D0rIyMjaQWKiIhETbLD/BXgkvDvS4CXk7x/ERGRCieRX00bD8wCjjSzHDP7A3A3cJqZfQJ0De+LiIjIHkjYOXN3vzDOrC6J2qeIiEhlpBHgREREIk5hLiIiEnEKcxERkYhTmIuIiEScwlxERCTiFOYiIiIRpzAXERGJOIW5iIhIxCnMRUREIk5hLiIiEnEKcxERkYhTmIuIiEScwlxERCTiFOYiIiIRpzAXERGJuIT9nrmIlI9ZK9bFn5lZzIrTh8ef13lwmesp9b74Xfnuq4IbMW15qkuQCFLPXEREJOIU5iIiIhGnMBcREYk4hbmIiEjEKcxFREQiTmEuIiIScQpzERGRiFOYi4iIRJzCXEREJOIU5iIiIhGnMBcREYk4hbmIiEjEKcxFREQiTmEuIiIScQpzERGRiFOYi4iIRFypwtzM9jezqokqRkREREqv2DA3sypm1sfM/mtma4ClwDdm9rGZ3WtmzcqyUzO71swWm9kiMxtvZjXKsh0RERHZfc98OnA4MBg42N0Pc/d6QAdgNnCPmV1Umh2a2aHAVUCWux8DVAV6l7pyERERASBtN/O7uvv2ohPd/XvgBeAFM6tWxv3ua2bbgf2AVWXYhoiIiLCbMM8LcjN72t0vLjgvb1qssN/NNr82s/uAL4EtwFR3n1p0OTMbAAwAyMzMLM0uJMqmD48/r/Pg5NWxB0ZMWx5z+rXFvdqKa3cx2n2ZHX9m0zpl2mZ5K67G2ZkDkljJ3iPe/xGRsirpBXAtC94JL4I7viw7NLPawDlAE6ABsH+sQ/Xunu3uWe6elZGRUZZdiYiIVAq7uwBusJltAlqb2cbwtglYA7xcxn12BT5397Vhr/5F4OQybktERKTSKzbM3X24u9cC7nX3A8JbLXev4+5lPeb5JdDOzPYzMwO6AEvKuC0REZFKb3c988YA8YLbAg1Ls0N3nwNMBOYDH4U1FHPiT0RERIqzu6vZ7zWzKgSH1OcBa4EaQDOgM0GveiiQU5qduvvQcD0RERHZQ7u7mv08M2sB9AUuAw4muAJ9CTAZuMvdtya8ShEREYlrt1ezu/vHwJ3AfwhC/HPgfWCiglxERCT1dneYPc8YYCMwMrzfB3gKOD8RRYmIiEjJlTTMj3H3FgXuTzezjxNRkIiIiJROSQeNmW9m7fLumNlJwNzElCQiIiKlUdKe+fHAu2b2ZXg/E1hmZh8B7u6tE1KdiIiI7FZJw/yMhFYhIiIiZVaiMHf3LxJdiIiIiJRNSc+Zi4iIyF5KYS4iIhJxCnMREZGIU5iLiIhEnMJcREQk4hTmIiIiEacwFxERibiSDhojFd304fHndR5c/uuVRXH72ou0+zI75vRZxazTvmmdxBRTQY2YtjzVJYjsVdQzFxERiTiFuYiISMQpzEVERCJOYS4iIhJxCnMREZGIU5iLiIhEnMJcREQk4hTmIiIiEacwFxERiTiFuYiISMQpzEVERCJOYS4iIhJxCnMREZGIU5iLiIhEnMJcREQk4hTmIiIiEZeSMDezdDObaGZLzWyJmbVPRR0iIiIVQVqK9vsQMMXde5nZPsB+KapDREQk8pIe5mZ2IHAKcCmAu28DtiW7DhERkYoiFYfZmwBrgSfM7AMzG2Vm+6egDhERkQohFYfZ04C2wEB3n2NmDwE3ArcWXMjMBgADADIzM5NepIiU3KwV61JdQr52X2bHnTc7c0DS6hgxbXnS9pUIxdV/7WnNk1iJlEQqeuY5QI67zwnvTyQI90LcPdvds9w9KyMjI6kFioiIREnSw9zdvwW+MrMjw0ldgI+TXYeIiEhFkaqr2QcCz4RXsq8Afp+iOkRERCIvJWHu7guArFTsW0REpKLRCHAiIiIRpzAXERGJOIW5iIhIxCnMRUREIk5hLiIiEnEKcxERkYhTmIuIiEScwlxERCTiFOYiIiIRpzAXERGJOIW5iIhIxCnMRUREIk5hLiIiEnEKcxERkYhTmIuIiERcSn7PXERSbPrwVFewWyOmLU91CSKRoZ65iIhIxCnMRUREIk5hLiIiEnEKcxERkYhTmIuIiEScwlxERCTiFOYiIiIRpzAXERGJOIW5iIhIxCnMRUREIk5hLiIiEnEKcxERkYhTmIuIiEScwlxERCTiFOYiIiIRpzAXERGJOIW5iIhIxKUszM2sqpl9YGaTUlWDiIhIRZDKnvnVwJIU7l9ERKRCSEmYm1lD4DfAqFTsX0REpCJJS9F+HwRuAGrFW8DMBgADADIzM5NUVgpMHx5/XufByaujOMXVuDdts7xFocYymrViXdx57ZvWiTuv3ZfZiShnrzZi2vJUl7DHotCG4mq89rTmSawkmpLeMzezs4A17j6vuOXcPdvds9w9KyMjI0nViYiIRE8qDrP/CjjbzFYCE4Bfm9nYFNQhIiJSISQ9zN19sLs3dPfGQG/gf+5+UbLrEBERqSj0PXMREZGIS9UFcAC4+wxgRiprEBERiTr1zEVERCJOYS4iIhJxCnMREZGIU5iLiIhEnMJcREQk4hTmIiIiEacwFxERiTiFuYiISMQpzEVERCJOYS4iIhJxCnMREZGIU5iLiIhEnMJcREQk4hTmIiIiEacwFxERibiU/p65lNH04WVbr/Pg8q1jd8pap5SLWSvWJXW9smj3ZXbcebMzB5RpPUm8EdOWx5137WnNk1iJ5FHPXEREJOIU5iIiIhGnMBcREYk4hbmIiEjEKcxFREQiTmEuIiIScQpzERGRiFOYi4iIRJzCXEREJOIU5iIiIhGnMBcREYk4hbmIiEjEKcxFREQiTmEuIiIScQpzERGRiFOYi4iIRFzSw9zMDjOz6Wb2sZktNrOrk12DiIhIRZKWgn3mAn929/lmVguYZ2bT3P3jFNQiIiISeUnvmbv7N+4+P/x7E7AEODTZdYiIiFQUqeiZ5zOzxsBxwJwY8wYAAwAyMzOTWtdeY/rwvXt7FcCsFevKtF77pnX2ijoktdp9mR133uzMAUmsRCq7lF0AZ2Y1gReAa9x9Y9H57p7t7lnunpWRkZH8AkVERCIiJWFuZtUIgvwZd38xFTWIiIhUFKm4mt2AfwFL3P2BZO9fRESkoklFz/xXwMXAr81sQXjrnoI6REREKoSkXwDn7u8Aluz9ioiIVFQaAU5ERCTiFOYiIiIRpzAXERGJOIW5iIhIxCnMRUREIk5hLiIiEnEKcxERkYhTmIuIiEScwlxERCTiFOYiIiIRpzAXERGJOIW5iIhIxCnMRUREIk5hLiIiEnEKcxERkYhL+u+Z7xWmD091BQkza8W6uPPaN62TxEqKF6/OvanG4hT3OEv5aPdldqpLSIni2j07c0ASK4mGEdOWx5137WnNI7uv0lLPXEREJOIU5iIiIhGnMBcREYk4hbmIiEjEKcxFREQiTmEuIiIScQpzERGRiFOYi4iIRJzCXEREJOIU5iIiIhGnMBcREYk4hbmIiEjEKcxFREQiTmEuIiIScQpzERGRiFOYi4iIRFxKwtzMzjCzZWb2qZndmIoaREREKoqkh7mZVQX+AZwJtAAuNLMWya5DRESkokhFz/xE4FN3X+Hu24AJwDkpqENERKRCSEWYHwp8VeB+TjhNREREysDcPbk7NOsFnOHu/cP7FwMnufuVRZYbAAwI7x4JLEtqoSVXF/gu1UUkmNpYcVSGdlaGNkLlaGdlbmMjd88o6UbSyq+eEvsaOKzA/YbhtELcPRvITlZRZWVmc909K9V1JJLaWHFUhnZWhjZC5Win2lhyqTjM/j5whJk1MbN9gN7AKymoQ0REpEJIes/c3XPN7ErgNaAqMNrdFye7DhERkYoiFYfZcffJwORU7DsB9vpTAeVAbaw4KkM7K0MboXK0U20soaRfACciIiLlS8O5ioiIRJzCvITM7DwzW2xmO80sq8D0OmY23cw2m9nDRdY53sw+CoetHWlmlvzKSy5eG8N5g8N2LDOzbgWmrwzbuMDM5ia/6tIrYzsjOwSxmR1rZrPC5+k/ZnZAOL2xmW0Jn7sFZvZYqmvdE/HaGc6L+bxGjZm1MbPZea83MzsxnN7JzH4o8FwOSXWte6KYdlr4XvqpmS00s7aprrWszOzZAs/XSjNbEE4v2+vS3XUrwQ04muD77jOArALT9wc6AH8EHi6yzntAO8CAV4EzU92OMraxBfAhUB1oAnwGVA3nrQTqprr2RLYzvH0GNAX2CZdpkep2lKK97wOnhn9fBtwR/t0YWJTq+pLQzrj/f6N2A6bmvY8A3YEZ4d+dgEmpri8J7ewevpda+N46J9W1llN77weGhH+X6XWpnnkJufsSd99l4Bp3/9Hd3wG2FpxuZocAB7j7bA+eoaeAHsmptmzitZFguN0J7v6zu38OfEowLG8klaGdUR+CuDnwVvj3NOB3KawlkeK1syL9/3Ug74jDgcCqFNaSSPHaeQ7wlAdmA+nhe21khUdszwfG78l2FOaJcyjBULV5ojxsbXFD8Dow1czmhaP2RVm8dkZ9COLF/PLh4zwKD9rUxMw+MLM3zaxj8ksrV/HaGfXnr6BrgHvN7CvgPmBwgXntzexDM3vVzFqmprxyE6+dFem5zNMRWO3unxSYVurXZUq+mra3MrPXgYNjzLrZ3V9Odj2JkIA2dnD3r82sHjDNzJa6+1u7XSvBKsNzWVBx7SU45DzSzG4lGKBpWzjvGyDT3deZ2fHAS2bW0t03JqXoMihjOyNlN23sAlzr7i+Y2fnAv4CuwHyC4T83m1l34CXgiGTVXBZlbGeklPB96EIK98rL9LpUmBfg7uX5n+VrgqFq88QctjbZytjGuEPwunvev2vM7N8Ehy9THubl3c5ipu8VStDe0wHMrDnwm3Cdn4Gfw7/nmdlnBIeq99oLGcvSTko4hPTeorg2mtlTwNXh3eeBUeE6+W/07j7ZzB4xs7ruvteOa16WdlKBnksAM0sDegLHF1inTK9LHWZPEHf/BthoZu3CcyL9gKj2CF8BeptZdTNrQvCJ/z0z29/MagGY2f4Eb6SLUljnnorZTiI+BHF41AQzqwLcAjwW3s8ws6rh300J2rsiVXXuqXjtJP7zGkWrgFPDv38NfAJgZgeH7zOEV35XAdalpMLyEbOdBM9lv/Cq9nbAD+F7bVR1BZa6e/4p2TK/LlN9FV9UbsC5BOdnfgZWA68VmLcS+B7YHC7TIpyeRRBunwEPEw7Ss7fedtPGm8N2LOOXq0ybElwl/CHB+cqbU92GRLQznN4dWB7Oi0Q7C9R+dVj7cuDuvP+HBBeILQYWEBym/W2qa01EO4t7XqN2I/jmzLzwNTcHOD6cfmX4XH4IzAZOTnWtCWqnAf8In8uPKPBtlCjegCeBPxaZVqbXpUaAExERiTgdZhcREYk4hbmIiEjEKcxFREQiTmEuIiIScQpzERGRiFOYi8guzCzdzK5IdR0iUjIKcxGJJR1QmItEhMJcRGK5Gzg8/D3le1NdjIgUT4PGiMguzKwxwe9jH5PiUkSkBNQzFxERiTiFuYiISMQpzEUklk1ArVQXISIlozAXkV24+zpgppkt0gVwIns/XQAnIiISceqZi4iIRJzCXEREJOIU5iIiIhGnMBcREYk4hbmIiEjEKcxFREQiTmEuIiIScQpzERGRiPv/VW8tOVAPNTMAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 1.09 seconds)\n", + "\n", + "NSM = 2225.504 --- NBSM = 2284.042\n", + "test 0 : tsm = -100.032, tbsm = -112.830\n", + "Reaching the end of test data. Stop tests at 1314. \n", + "===> delta1 = 0.004, delta2 = 0.002\n", + "p = 0.019 +/- 0.004\n", + "Separation = 2.06 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 5.88 seconds)\n", + "\n" + ] + } + ], + "source": [ + "gphivals = [4e-3, 5e-3, 6e-3]\n", + "gphi_fnames = ['gphi_toydata_test_4e-3_out', 'gphi_toydata_test_5e-3_out', 'gphi_toydata_test_6e-3_out']\n", + "tsm_plus_XS = {}\n", + "tbsm_plus_XS = {}\n", + "\n", + "for gphival, gphi_fname in zip(gphivals, gphi_fnames):\n", + " sep, p, tsm, tbsm = TestEstimator(estimator, gphival, gphi_fname, withXS=True,\n", + " title_message=', quadratic classifier (NSM!=NBSM)', qc=True)\n", + " tsm_plus_XS[gphival] = tsm\n", + " tbsm_plus_XS[gphival]=tbsm" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "from madminer import ParameterizedRatioEstimator\n", + "from madminer.ml.morphing_aware import MorphingAwareRatioEstimator\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup_gphi.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl1000-bigbatch')" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 8099.497, tbsm = 7858.275\n", + "Reaching the end of test data. Stop tests at 1347. \n", + "===> delta1 = 0.014, delta2 = 0.014\n", + "p = 0.504 +/- 0.019\n", + "Separation = 0.02 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 244.09 seconds)\n", + "\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 8010.720, tbsm = 8023.279\n", + "Reaching the end of test data. Stop tests at 224. \n", + "===> delta1 = 0.033, delta2 = 0.032\n", + "p = 0.487 +/- 0.046\n", + "Separation = 0.01 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 40.58 seconds)\n", + "\n", + "NSM = 2225.504 --- NBSM = 2225.504\n", + "test 0 : tsm = 8062.801, tbsm = 8217.646\n", + "Reaching the end of test data. Stop tests at 1347. \n", + "===> delta1 = 0.014, delta2 = 0.014\n", + "p = 0.463 +/- 0.019\n", + "Separation = 0.05 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 245.19 seconds)\n", + "\n" + ] + } + ], + "source": [ + "gphivals = [4e-3, 5e-3, 6e-3]\n", + "gphi_fnames = ['gphi_toydata_test_4e-3_out', 'gphi_toydata_test_5e-3_out', 'gphi_toydata_test_6e-3_out']\n", + "\n", + "for gphival, gphi_fname in zip(gphivals, gphi_fnames):\n", + " TestEstimator(estimator, gphival, gphi_fname, withXS=False, title_message=', madminer (NSM=NBSM)', qc=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2263.789\n", + "test 0 : tsm = 8221.678, tbsm = 8365.766\n", + "Reaching the end of test data. Stop tests at 1326. \n", + "===> delta1 = 0.011, delta2 = 0.010\n", + "p = 0.777 +/- 0.015\n", + "Separation = -0.74 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 242.08 seconds)\n", + "\n", + "NSM = 2225.504 --- NBSM = 2273.448\n", + "test 0 : tsm = 8329.532, tbsm = 8474.391\n", + "Reaching the end of test data. Stop tests at 219. \n", + "===> delta1 = 0.024, delta2 = 0.017\n", + "p = 0.858 +/- 0.029\n", + "Separation = -1.18 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 39.96 seconds)\n", + "\n", + "NSM = 2225.504 --- NBSM = 2284.042\n", + "test 0 : tsm = 8371.841, tbsm = 8044.023\n" + ] + } + ], + "source": [ + "gphivals = [4e-3, 5e-3, 6e-3]\n", + "gphi_fnames = ['gphi_toydata_test_4e-3_out', 'gphi_toydata_test_5e-3_out', 'gphi_toydata_test_6e-3_out']\n", + "\n", + "for gphival, gphi_fname in zip(gphivals, gphi_fnames):\n", + " TestEstimator(estimator, gphival, gphi_fname, withXS=True, title_message=', madminer (NSM!=NBSM)', qc=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def test_model(madminermodel, test_input_sm, test_input_bsm, bsmparval, NSM, NBSM, epochs, e, n_meas, pm, verbose_t=True, verbose_period_t=1e5, title=''):\n", + " \n", + " # computing test statistics t (or lambda) in equation (2) of paper\n", + " def compute_t(madminermodel, nev, counter, test_input):\n", + " # generate number of points for testing under Poisson distribution\n", + " n_gen = 0\n", + " while n_gen == 0:\n", + " n_gen = np.random.poisson(nev)\n", + " \n", + " # stop if there are no more points to test\n", + " if (counter + n_gen) >= len(test_input):\n", + " return 0., -1\n", + " \n", + " points = test_input[int(counter): int(counter+n_gen)]\n", + " \n", + " # compute test statistics\n", + " log_ratio = (madminermodel.evaluate_log_likelihood_ratio(points.numpy(), np.array([0.]))[0][0])\n", + "\n", + " log_ratio = torch.tensor(log_ratio)\n", + " #ratio = 1./ratio\n", + " log_ratio = log_ratio\n", + " out = 2 * (NBSM - NSM - (log_ratio+torch.log(torch.tensor(NBSM/NSM))).sum(0))\n", + " \n", + " #return test statistics and the starting point for the next batch\n", + " return out, int(counter+n_gen)\n", + " \n", + " test_start = time.time()\n", + " if verbose_t:\n", + " print(\"NSM = %.3f --- NBSM = %.3f\"%(NSM, NBSM))\n", + " tsm = torch.empty(n_meas)\n", + " tbsm = torch.empty(n_meas)\n", + " \n", + " # empty array to store values\n", + " tsmcount = torch.zeros(n_meas+1)\n", + " tbsmcount = torch.zeros(n_meas+1)\n", + " \n", + " for i in range(n_meas):\n", + " tsm[i], tsmcount[i+1] = compute_t(madminermodel, NSM, tsmcount[i], \n", + " test_input_sm)\n", + " tbsm[i], tbsmcount[i+1] = compute_t(madminermodel, NBSM, tbsmcount[i], \n", + " test_input_bsm)\n", + " \n", + " if (tsmcount[i+1] < 0) or (tbsmcount[i+1] < 0):\n", + " print('Reaching the end of test data. Stop tests at %d. '%i)\n", + " tsm, tbsm = tsm[: i], tbsm[: i]\n", + " n_meas = i\n", + " break\n", + " \n", + " if i % (verbose_period_t) == 0:\n", + " print('test %s: tsm = %.3f, tbsm = %.3f'%(\n", + " str(i).ljust(4), tsm[i], tbsm[i]))\n", + " \n", + " test_duration = time.time() - test_start\n", + " #compute mean and variation of the test statistics in two hypotheses\n", + " mu_sm = tsm.mean().item()\n", + " mu_bsm = tbsm.mean().item()\n", + " sigma_sm = tsm.std().item()\n", + " sigma_bsm = tbsm.std().item()\n", + " med_sm = tsm.median().item()\n", + " \n", + " #compute separation and p-value\n", + " sep = (mu_sm - mu_bsm)/sigma_bsm\n", + " p = 1.*len([i for i in tbsm if i > med_sm])/len(tsm) \n", + " #print(len([i for i in tbsm if i>med_sm]))\n", + " delta1 = (p * (1 - p)/n_meas)**0.5\n", + " delta2 = (sigma_sm/sigma_bsm) * np.exp(-((mu_bsm - mu_sm)**2)/(\n", + " 2 * sigma_bsm**2))/(2*(n_meas**0.5))\n", + " print('===> delta1 = %.3f, delta2 = %.3f'%(delta1, delta2))\n", + " deltap = (delta1**2 + delta2**2)**0.5\n", + " \n", + " results_path = os.getcwd()\n", + " \n", + " if verbose_t:\n", + " print('p = %.3f +/- %.3f' %(p, deltap))\n", + " print('Separation = %.2f sigmas'%(sep))\n", + " training_properties = '/toydata/madminer-carl-'+title\n", + " plot_histogram(tsm, tbsm, int(NSM), int(NBSM), p, deltap, sep, epochs, e, \n", + " training_properties, results_path)\n", + " print('Partial test after %d epochs (took %.2f seconds)\\n'\n", + " %(e, test_duration))\n", + " \n", + " \n", + " return sep, p, tsm, tbsm" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gphivals = [4e-3, 5e-3, 6e-3]\n", + "gphi_fnames = ['gphi_toydata_test_4e-3_out', 'gphi_toydata_test_5e-3_out', 'gphi_toydata_test_6e-3_out']\n", + "\n", + "for gphival, gphi_fname in zip(gphivals, gphi_fnames):\n", + " TestEstimator(estimator, gphival, gphi_fname, withXS=True, title_message=', madminer (NSM!=NBSM)', qc=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Charge Minus" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model successfully loaded.\n", + "Path: /madminer/madminer/examples/tutorial_particle_physics/models/ChMgphi_2D_999.pth\n" + ] + } + ], + "source": [ + "estimator = OurModel(AR=[9, 32, 32, 32, 32, 1])\n", + "estimator.Load_CPU('ChMgphi_2D_999', os.getcwd()+'/models/')" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 987.026 --- NBSM = 987.026\n", + "test 0 : tsm = -34.094, tbsm = -36.061\n", + "Reaching the end of test data. Stop tests at 504. \n", + "===> delta1 = 0.019, delta2 = 0.016\n", + "p = 0.226 +/- 0.024\n", + "Separation = 0.79 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 3.96 seconds)\n", + "\n", + "NSM = 987.026 --- NBSM = 987.026\n", + "test 0 : tsm = -41.760, tbsm = -41.771\n", + "Reaching the end of test data. Stop tests at 506. \n", + "===> delta1 = 0.017, delta2 = 0.016\n", + "p = 0.192 +/- 0.023\n", + "Separation = 0.91 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 3.93 seconds)\n", + "\n", + "NSM = 987.026 --- NBSM = 987.026\n", + "test 0 : tsm = -42.214, tbsm = -49.321\n", + "Reaching the end of test data. Stop tests at 3038. \n", + "===> delta1 = 0.007, delta2 = 0.005\n", + "p = 0.163 +/- 0.008\n", + "Separation = 1.01 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 23.00 seconds)\n", + "\n" + ] + } + ], + "source": [ + "# there is some problem with NBSM \n", + "gphivals = [4e-3, 5e-3, 6e-3]\n", + "gphi_fnames = ['gphi_toydata_test_M4e-3_out', 'gphi_toydata_test_M5e-3_out', 'gphi_toydata_test_M6e-3_out']\n", + "tsm_minus = {}\n", + "tbsm_minus = {}\n", + "\n", + "\n", + "for gphival, gphi_fname in zip(gphivals, gphi_fnames):\n", + " sep, p, tsm, tbsm = TestEstimator(estimator, gphival, gphi_fname, withXS=False,\n", + " title_message=', quadratic classifier (M) (NSM=NBSM) ', qc=True)\n", + " tsm_minus[gphival] = tsm\n", + " tbsm_minus[gphival]=tbsm" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 987.026 --- NBSM = 1003.839\n", + "test 0 : tsm = -33.222, tbsm = -36.792\n", + "Reaching the end of test data. Stop tests at 498. \n", + "===> delta1 = 0.016, delta2 = 0.013\n", + "p = 0.159 +/- 0.021\n", + "Separation = 0.98 sigmas\n" + ] + }, + { + "data": { + "image/png": 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qjxv3IJ9//t8MtkBEpGxt2bKlQiZtADOjXr16rFq1apfqVerEvXz5YoYM6UZOTkdmz55KdnZj7r33dWrW3INx4x7g5ZcfpWrVLA48sCV33DGOxx4bwfLlX7Ns2SK+++5b/vCHkcyZM52pU/9NgwaNGTnyn2RllXy4o6hFi+bRrt0JZGVlkZWVRYsWbZk2bQInn9wbMH78Mfid+U2bNpCd3ahY/YkTxzJ48C2l3r+ISHlUEZN2vtK0rdJfnLZkyZecffYlvPDCZ9SuXYd33nkZgCefvJPnnvsv48bN5rrrHi0ov3TpVzz66Dvcd98/uPHGc8nN7cLzz8+hRo09Coa4Yz399N30759T7Hb33UOKlT300COZOnUCW7b8xPr1q5k5czLff78EgBtv/CuXX96d7t2bMH78MwwcOKxQ3RUrvmHZsq855phfluXDIyJS6d122220atWKtm3bkpOTw0cffUTnzp1p2rQpsb/30bNnT2rVqpXyeCp1jxugUaMDOeywHAAOP/xoli9fDMAhh7TlhhvOoXPnnnTu3LOg/PHHdyMrqxotWrRhx47tHH/8qQC0aNGmoG6sAQOuZsCAq0sUS/v2Xfnss/9wwQXHU6dONm3adKBKlaoAjBkzklGjxtO69XE8/fTdjBz5B2688a8Fdd98cxwnndSLqlWrluZhEBGJhJGTvijT7V158qFJ10+bNo033niDTz75hBo1arB69Wq2bg3mA6lTpw4ffvghHTt2ZP369axYkZ4fbaz0Pe5q1WoU/F+1alW2b88D4P77/0Xv3pewYMEnDBhwDHl5wfLq1YPyVapUISurWsEwh1mVgrqxdqXHDXDhhdczZswsHn54Eu5O06aHsm7dKr744lNatz4OgK5d+zB79tRC9SZOHMcpp/TbzUdDRERirVixgvr161OjRvDZX79+fRo1Ck5V9u3bl3HjxgHwyiuvcNZZZ6UlppQlbjOraWYfm9mnZvaZmd0SLj/QzD4ys4Vm9ryZVU9VDKW1Y8cOvv9+Cbm5XRgy5C42bdrA5s2bSrWtAQOuZsyYWcVuV1/9QLGy27dvZ/36NQB8+eVsvvxyNu3bd6V27X3ZtGkD33wTHGlOnz6J5s2PKKi3ePECNm5cR9u2HUoVo4iIxNe1a1eWLFnCoYceyu9//3umTJlSsO6kk07ivffeY/v27YwbN44+ffqkJaZUDpX/DPzS3TeZWTXgAzP7N/AHYKS7jzOzR4ELgUdSGMcu27FjOzfeeC6bNm3A3enbdwi1a9dJ+X7z8rbxu991AmCvvfbmj398lqys4Cm64YbHueaa31ClShVq196Xm256oqDem2+Oo2vXvhX6Ag4RkUyoVasWM2fO5P3332fy5Mn06dOHO++8EwhGaTt27Mi4cePYvHkz6foxLIs9sZ6ynZjtCXwAXAz8C9jP3fPMrAMwwt1PSVY/NzfXy+JHRiZMmKkpTyuRVatmcuqper5Fomz+/PkcccT/RhjTfY67qJdeeomnnnqKjRs3cs899/DTTz9x5plnMmLECC677DJq1arFpk27NkJbtI0AZjbT3XPjlU/pxWlmVhWYCbQAHgK+Ata7e/7J4KVA4wR1BwODAZo2bZrKMEVEEiaEXf1gl4rl888/p0qVKhxyyCEAzJo1i2bNmjF37lwAOnXqxPDhw+nXL33XGKX04jR33+7uOUAT4Fjg8F2oO9rdc909Nzs77k+SioiIpNSmTZsYOHAgLVu2pG3btsybN48RI0YUrDczrrrqKurXr5+2mNLydTB3X29mk4EOQB0zywp73U2AZemIQUREoi/dIyBHH300U6dOLbb83XffjVt+V4fJSyOVV5Vnm1md8P89gJOB+cBkoFdYbCDweqpi2B2DB3dm3rzdP69eVh566Hp+/esD6NSp8Jf7t279meHD+9CzZwsGDjyu0HfJ//73O+jZswVnnXUY06a9CcDPP29hwIBj6dfvSHr3bsVjj92czmaIiMhuSuVQ+f7AZDObDfwHmOTubwDXAn8ws4VAPeBvKYyhwjjhhNN56qmPiy1//fW/Ubv2vrz22kL697+SBx+8FgimT504cRwvvPAZDz44gTvv/D3bt2+nevUaPProO4wd+yljxsxi6tQJzJkzPd3NERGRUkrZULm7zwaOirN8EcH57oxLNlc5wPjxz/CnPw0iLy+Pm256gtatj2XmzCnce+/l4RaMxx9/j/nzZzJ69M3UqlWHr76aw69+1ZsWLdowduwofv55M/fe+xpNmhy8W7G2adM+7vIpU15n8OARAJx0Ui/+/OdLcXemTHmdrl37Ur16DRo3PpADDmjBZ599TNu2Hdhzz6DXnpe3jby8bfoamYhIhFT6KU+XLPmS224byw03PM6wYb15552X6d79XCD4mcwxY2bxySfvceutF/DCC3N59tl7uOaah8jJ+QU//bSJ6tVrAvDFF5/y0kvz2XvvuvTocRA9ew7i6ac/ZuzYUTz//IMMHXp/of3OmDGZ++67slg8NWvuyRNPFD+fksjKlcto2PAAALKysqhVax82bFjDypXLCiX7Bg2asHJlcDnB9u3bOe+8o1myZCFnn31JwYxsIiJS/lX6xJ1ornKgYArRdu1O4Mcff2DjxvUceeQvGDnyD3Trdg5dupxFw4ZNAGjZ8hjq198fgCZNDua447oCwRzmM2ZMLrbf3NwujBkzK5VNS6hq1aqMGTOLjRvXc9VVZ7Jw4VxatGidkVhERGTXVPrEXXSu8p9/3lxwv+gQsplx/vnD6Njx13zwwXguvPAX/OUvwUVf+XOYB+WqFNxPNId5WfW4GzRozPffL6Fhwybk5eWxadMG9tmnXsHyfCtXLqVBg8Jfma9duw65uV2YNm2CEreISERU+h8ZSWbixOcBmDXrA2rV2odatfZh6dKvaNGiDeeffy0tWx7D4sULSrXt/B530duuJG2AE044gzfeeAqAt99+iWOO+SVmxgknnMHEiePYuvVnli37miVLvqRVq2NZt24VGzeuB2DLls189NEkmjcv8dfrRUQqnapVq5KTk8ORRx5Ju3btCr4e9tNPP3HOOefQpk0bWrduTceOHQu+DmZmnHvuuQXbyMvLIzs7m9NOO22346n0Pe5katSoSf/+R5GXt61gbvAxY+5nxozJVKlShYMOasXxx3dj9uxpKY9l1KhrePPNMWzZ8hPduzehR49BXHTRCHr0uJCbbjqPnj1bsPfedbn99uCXag4+uBW/+lVvzj67JVWrZnHNNQ9RtWpVVq9ewc03D2THju3s2LGDk0/uTadOu/9CEhFJi8l3lO32ugzfaZE99tiDWbOCU5tvvvkmw4cPZ8qUKYwaNYqGDRsyZ84cIJhlrVq1agDstddezJ07l82bN7PHHnswadIkGjeOO1HoLkvLXOW7S3OVS2lUirnKk32IleADSf5HU56WT8Xm8c5A4o6df/zFF1/kueee47XXXmPIkCE0a9aMoUOHxq0zZMgQ2rVrR69evRgwYACtWrXi/fff54033ihUdlfnKtdQuYiISBKbN28mJyeHww8/nEGDBnHjjTcCcMEFF3DXXXfRoUMHbrjhBr788stC9fJ/r3vLli3Mnj2b444rm2/wKHGLiIgkkT9UvmDBAiZMmMCAAQNwd3Jycli0aBFXX301a9eu5ZhjjmH+/PkF9dq2bcvixYsZO3Ys3bt3L7N4dI5bRESkhDp06MDq1atZtWoVDRo0oFatWpx11lmcddZZVKlShfHjxxca9j7jjDO46qqrePfdd1mzZk2ZxKAedzmTbO7xWLfccgEnn9yA3r0Lf43rscdG0K1bY/r3z6F//xw++GA8ANu2beWWW35Lnz5t6NfvSGbMeLfUMeblbeOcc9rttFy8udKLWrbsawYOPI6ePVswfHgftm3bCsCzz97H2We3pG/ftlx88UmsWPFNQZ3LLjuVzp3rcMUVuqhORNJrwYIFbN++nXr16vHhhx+ybt06ALZu3cq8efNo1qxZofIXXHABN998M23atCmzGJS4y5lEc48Xdfrp5/PggxPiruvf/8qCr5d17BgMz7z66uMAPP/8HB56aBL33z+UHTt2JI3l9NObx10+a9YHHHnkL5LWTTRXelEPPngt/ftfyWuvLaR27X15/fVg6vrDDz+KZ56ZwbhxsznppF488MA1BXXOO+9qbr31maT7FxEpK/nnuHNycujTpw9PPfUUVatW5auvvuLEE0+kTZs2HHXUUeTm5vKb3/ymUN0mTZowZMiQMo2nUg+VL1++mMsuO5UjjjiaBQs+4aCDWnHrrU9Ts+aeGYsp0dzjRSeDadfuhIS98Xi+/noeubm/BKBu3QbUrl2HefNm0Lr1rk8bP3XqBI4/vlvSMsnmSs/n7vznP+/wpz+NAeC00wYyevQIevW6mNzcLgXlWrduz/jxzxbcP/bYk3ZrxEBEIiwD35aI1+kAGDBgAAMGDIi7Lt7Pe3bu3JnOnTvvdjyVvsf9zTef06vX73nppfnstdfevPjiw2W+j0GDOhUMXcfePvrorWJlE809viteeOEv9O3blltuuYAffgiGcQ455Ejee+8f5OXlsWzZ18yfP7PQzGq7YsaMyeTmdk5aJrYdUHiu9HwbNqyhdu06ZGVlJSwDwSjEzg4UREQqi0rd4wZo2PAAcnKCYd/u3c9l3LgHOO+8q8p0H3/96/tlur1kevW6mEGDbsTMeOSRGxk5cig33/wEZ5xxAV9/PZ8BA3LZb79mtG17PFWrVi1W/667LuHTTz8EYNWq5fTvH8zjftJJZ3PhhdezcuUy9tmnbtpGJcaPf5b582cwevSUtOxPRKS8q/SJO9585GVt0KBO/PTTxmLLL7/8Ho477leFliWae7yk6tVrWPD/mWf+ruACrqysLIYOHVmw7oILjqdp0+ITS1x77UMF/59+evNiP4QydeoE2rc/BYAXXniI114Lzp2PGjWe7OxGxdqRL95c6fvsU4+NG9eTl5dHVlZWsTIfffQWTzxxG6NHTyk0F7yISGVW6RP3d999y+zZ02jbtgMTJowhJ6djme9jV3rc+XOPt23bodDc4yW1evWKgl8pmzz5VQ4+OLjqfMuWn3B39thjL6ZPn0TVqlkcdFDLXWsIMG3aBC6++I8A9O59Cb17X5KwHTfc0J9zzvkDq1YtL5grPZaZkZvbhbfffolTTunLG288xYkn9gBgwYL/cvvtF/HggxOoW7fBLscplH5WtVLMTDUy7zcJ16Vi5rFEs5ylan+SWfGu86koSjN7aaU/x92s2WG8+OJD9Op1BD/8sI5evS7OaDw9elzIhg1r6NmzBc89dx+XXnonEAxbDxnyvy/wX3ddP3772w58883ndO/ehNdeC67GHjXqGvr0aUPfvm2ZMWNyQS977dqVnHNOO3r1OoKnn76rVFdlb9++nSVLFpboR0li50q/7LJTC+ZKBxgypDurVi0H4LLL7uK55+6jZ88WbNiwhh49LgTggQeuZvPmTQwbdjb9++dw5ZVnFGx70KBODBt2Nv/5z9t0794k4VfNRCT6atasyZo1a0qV4Mo7d2fNmjXUrFlzl+pV6rnKly9fzBVXnMYLL8zd7W1XBrNmfcD48c9y3XWPZjqUEqn0c5Unox53ibepHnxmbdu2jaVLl7Jly5ZMh5ISNWvWpEmTJgU/TpIv2VzllX6oXEouJ6djSk4liIgkUq1aNQ488MBMh1GuVOqh8kaNmqu3LSIikaIedzm1devP3HzzAObPn8k++9Tjjjuep1Gj5sXKjR07KpwVzenZ83f0738FAMOH9+Gbbz4HYOPG9dSuXYcxY2axbdtWbr/9IubNm0GVKlUYOnTUTr+TLSIi5YcSdzkVO/Xpm2+O48EHr+WOO54vVGbhwrm8+urjPP30x2RlVWfIkFPp1Ok0DjigRaGyI0cOpVatfYDCU5+uXbuSIUO68fTT/6FKlUo9+CIiEhmV+tN68+YfufzyX9Ov35H07t2aiRODZDd//kwGDz6Rc889mksvPYXVq1cAMHhwZ+6553L698+hd+/WzJ37ccpimzLldU47bSAQTH368cdvF7uqcvHi+bRufRw1a+5JVlYW7dqdyDvvvFKojLvz1lsvcMop/YDEU5+KiEg0VCSGDJoAABcQSURBVOoe99SpE8jObsSoUf8CYNOmDeTlbePuuy/j3ntfZ999s5k48Xkeeuh6br75CSD4PvSYMbP45JP3uPXWC3bpHPmuTMSSaOrTOnXqF5Q5+ODWPPzw9axfv4aaNffgww/Hc8QRhS9C/O9/36du3YY0bXoI8L+pT085pR/ff7+kYOrT0sxZLiIi6VepE3eLFm24//6hPPDAtXTqdBpHHdWJhQvn8tVXc7nkkpOB4LvL+ROaAAU913btTuDHH38oOH9cEmU99emBBx7BgAHXcumlXdljj7049NCcYtOYvvnm2IKYgRJPfSoiIuVTpU7czZodyrPPfsKHH47nkUdu4JhjTqJLlzM56KBW/P3v0+LW2Z0pUlMx9WnPnhfSs2cwaclDD11HgwZNCtbl5eUxefIrPPPMzIJlJZ36VEREyqdKnbhXrVrO3nvXpXv3c6lduw6vvfZXzj9/GOvWrSqYBjUvbxvffPMFBx/cCoCJE58nN7cLs2Z9QK1a+xRc9FUSqZj6dO3aldSt24DvvvuWd955hSefnF6w7uOP36J588Np2PB/ybyspj4VEZHMqNSJe+HCOYwadTVVqlQhK6saw4Y9QrVq1bnrrpe4554hbNq0ge3b8+jX74qCxF2jRk369z+KvLxt3HTTEymLrUePC7nppvPo2bMFe+9dl9tvHwcEBxt//OMgHnhgPADXXPMbNmxYQ1ZWNa699qFCw/YTJ46ja9d+hba7du1KLr30FKpUqUKDBo1LNfWpiIhkTqVO3B06nEKHDqcUW37YYTk8/vh7cet063YuQ4fen+rQqFGjJnfd9WKx5dnZjQqSNiTvxY8Y8WSxZY0aNeeVVz4vkxhFRCT9KvXXwURERKKmUve4d9Xo0e9mOgQREank1OMWERGJECVuERGRCFHiFhERiZBKdY67Tp3qrFo1c+cFpUKoU6d6pkMQESlzlSpxt2/fJtMhiIiI7BYNlYuIiESIEreIiEiEKHGLiIhEiBK3iIhIhChxi4iIRIgSt4iISIQocYuIiERIyhK3mR1gZpPNbJ6ZfWZml4fLR5jZMjObFd66pyoGERGRiiaVE7DkAUPd/RMzqw3MNLNJ4bqR7n5PCvctIiJSIaUscbv7CmBF+P9GM5sPNE7V/kRERCqDtEx5ambNgaOAj4BfAJea2QBgBkGvfF2cOoOBwQBNmzZNR5gilcfkOzIdQcqMnPRFwnVXnnxoGiMRSY2UX5xmZrWAl4Er3P0H4BHgYCCHoEd+b7x67j7a3XPdPTc7OzvVYYqIiERCShO3mVUjSNrPufsrAO7+vbtvd/cdwOPAsamMQUREpCJJ5VXlBvwNmO/u98Us3z+m2JnA3FTFICIiUtGk8hz3L4DzgDlmNitcdh3Qz8xyAAcWAxelMAYREZEKJZVXlX8AWJxV41O1TxERkYpOM6eJiIhEiBK3iIhIhChxi4iIRIgSt4iISISkZeY0EakYpi1aE39FKSc31CxnIrtOPW4REZEIUeIWERGJECVuERGRCFHiFhERiRAlbhERkQhR4hYREYkQJW4REZEIUeIWERGJECVuERGRCFHiFhERiRAlbhERkQhR4hYREYkQJW4REZEIUeIWERGJECVuERGRCFHiFhERiRAlbhERkQhR4hYREYkQJW4REZEIUeIWERGJkKxMByBS4Uy+I/7yLsPTG0cpTVu0pky3N3LSF2mtJ1LRqcctIiISIUrcIiIiEaLELSIiEiFK3CIiIhGixC0iIhIhStwiIiIRosQtIiISIUrcIiIiEaLELSIiEiGaOU0qt0SznEFkZjqT1Eo2g9uVJx+axkhEAupxi4iIRIgSt4iISIQocYuIiESIEreIiEiEKHGLiIhEiBK3iIhIhKQscZvZAWY22czmmdlnZnZ5uLyumU0ysy/Dv/umKgYREZGKJpU97jxgqLu3BNoDl5hZS2AY8La7HwK8Hd4XERGREkhZ4nb3Fe7+Sfj/RmA+0BjoATwVFnsK6JmqGERERCqatJzjNrPmwFHAR0BDd18RrvoOaJiOGERERCqClE95ama1gJeBK9z9BzMrWOfubmaeoN5gYDBA06ZNUx2mSOppelURKQMp7XGbWTWCpP2cu78SLv7ezPYP1+8PrIxX191Hu3uuu+dmZ2enMkwREZHISOVV5Qb8DZjv7vfFrPoHMDD8fyDweqpiEBERqWhSOVT+C+A8YI6ZzQqXXQfcCbxgZhcC3wC9UxiDiIhIhZKyxO3uHwCWYPVJqdqviIhIRaaZ00RERCJEiVtERCRClLhFREQiRIlbREQkQpS4RUREIiTlM6eJZFyyGcsqqWmL1mQ6BBEpJfW4RUREIkSJW0REJEKUuEVERCJEiVtERCRClLhFREQiRIlbREQkQpS4RUREIkSJW0REJEKUuEVERCJEM6eJJJJsxrUuw9O2r5F5v0m47kq9g0UqHfW4RUREIkSJW0REJEKUuEVERCJEiVtERCRClLhFREQiRIlbREQkQpS4RUREIkSJW0REJEKUuEVERCJEiVtERCRCNGGiSDnX/tvRiVceVC99gUgxIyd9kXDdlScfmsZIpDJRj1tERCRClLhFREQiRIlbREQkQpS4RUREIkSJW0REJEJ2KXGb2V5mVjVVwYiIiEhySRO3mVUxs/5m9i8zWwksAFaY2Twzu9vMWqQnTBEREYGd97gnAwcDw4H93P0Ad28AdASmA3eZ2bkpjlFERERCO5uA5Vfuvq3oQndfC7wMvGxm1VISmYiIiBSTNHHnJ20ze8bdz4tdl78sXmIXqeiSzphVCecjTDa72/Smg9MYSXLJnrfyQrOxyc6U9OK0VrF3wgvUji77cERERCSZnV2cNtzMNgJtzeyH8LYRWAm8npYIRUREpEDSxO3ud7h7beBud987vNV293ruPjxNMYqIiEhoZz3u5gCJkrQFmpR9WCIiIhLPzi6judvMqhAMi88EVgE1gRZAF+Ak4GZgaSqDFBERkcDOrio/28xaAucAFwD7AZuB+cB44DZ335LyKEVERAQowVXl7j4P+BPwT4KE/TXwH+ClZEnbzJ4ws5VmNjdm2QgzW2Zms8Jb991vgoiISOVR0q+DPQUcATwAPAi0BJ7eSZ0ngVPjLB/p7jnhbXxJAxUREZGdn+PO19rdW8bcn2xm85JVcPf38i9uExERkbJR0sT9iZm1d/fpAGZ2HDCjlPu81MwGhPWHuvu6eIXMbDAwGKBp06al3JVIaiSbKYyD6qUtjmmL1iRc1yGNcYhI+pR0qPxoYKqZLTazxcA04Bgzm2Nms3dhf48Q/GhJDrACuDdRQXcf7e657p6bnZ29C7sQERGpuEra4453rnqXufv3+f+b2ePAG2WxXRERkcqiRInb3b8pi52Z2f7uviK8eyYwN1l5ERERKSxlv2NkZmOBzkB9M1tKMFFLZzPLARxYDFyUqv2LiIhURClL3O7eL87iv6VqfyIiIpVBSS9OExERkXJAiVtERCRClLhFREQiRIlbREQkQpS4RUREIiRlV5WLpNXkOzIdgSSQbHrY6U0HpzESkYpBPW4REZEIUeIWERGJECVuERGRCFHiFhERiRAlbhERkQhR4hYREYkQJW4REZEIUeIWERGJECVuERGRCNHMaZI6yWYz6zI8fXGI7AbN/CbljXrcIiIiEaLELSIiEiFK3CIiIhGixC0iIhIhStwiIiIRosQtIiISIUrcIiIiEaLELSIiEiFK3CIiIhGixC0iIhIhmvJUyhdNk1pmpi1ak+kQdoumGhWJTz1uERGRCFHiFhERiRAlbhERkQhR4hYREYkQJW4REZEIUeIWERGJECVuERGRCFHiFhERiRAlbhERkQjRzGkiImk2ctIXmQ5BIkw9bhERkQhR4hYREYkQJW4REZEIUeIWERGJECVuERGRCFHiFhERiZCUJW4ze8LMVprZ3Jhldc1skpl9Gf7dN1X7FxERqYhS2eN+Eji1yLJhwNvufgjwdnhfRERESihlidvd3wPWFlncA3gq/P8poGeq9i8iIlIRpXvmtIbuviL8/zugYaKCZjYYGAzQtGnTNIQm5d7kOzIdQcpMW7Qm0yFkRPtvR5d5velNB5d5vShINhvblScfmsZIJNUydnGauzvgSdaPdvdcd8/Nzs5OY2QiIiLlV7oT9/dmtj9A+HdlmvcvIiISaelO3P8ABob/DwReT/P+RUREIi2VXwcbC0wDDjOzpWZ2IXAncLKZfQn8KrwvIiIiJZSyi9PcvV+CVSelap8iIiIVnWZOExERiRAlbhERkQhR4hYREYkQJW4REZEIUeIWERGJkHRPeSoSqMDTlyZSWac1TbdUTKMqUp6oxy0iIhIhStwiIiIRosQtIiISIUrcIiIiEaLELSIiEiFK3CIiIhGixC0iIhIhStwiIiIRosQtIiISIUrcIiIiEaLELSIiEiFK3CIiIhGixC0iIhIhStwiIiIRosQtIiISIUrcIiIiEaLELSIiEiFK3CIiIhGixC0iIhIhWZkOQKSimbZoTaZDkHJg5KQvys02k9W78uRDSxuOZIh63CIiIhGixC0iIhIhStwiIiIRosQtIiISIUrcIiIiEaLELSIiEiFK3CIiIhGixC0iIhIhStwiIiIRosQtIiISIZryVHbP5DsyHYGIpEBpp0nV9Kqppx63iIhIhChxi4iIRIgSt4iISIQocYuIiESIEreIiEiEKHGLiIhESEa+DmZmi4GNwHYgz91zMxGHiIhI1GTye9xd3H11BvcvIiISORoqFxERiZBM9bgdmGhmDjzm7qOLFjCzwcBggKZNm6Y5PCkk4rOjTVu0JuG6DgfVS2MkIuVPspnOpHzKVI+7o7u3A7oBl5jZCUULuPtod89199zs7Oz0RygiIlIOZSRxu/uy8O9K4FXg2EzEISIiEjVpT9xmtpeZ1c7/H+gKzE13HCIiIlGUiXPcDYFXzSx//2PcfUIG4hAREYmctCdud18EHJnu/YqIiFQE+jqYiIhIhChxi4iIRIgSt4iISIQocYuIiERIJucqz5xkM4F1GZ6+OHYmUZzlKcZSSjSbWWlnMks2O1pp62lWNUmV9t8WmyyywPSmg9MYSelotrXMUo9bREQkQpS4RUREIkSJW0REJEKUuEVERCJEiVtERCRClLhFREQiRIlbREQkQpS4RUREIkSJW0REJEKUuEVERCKkck55mgrpnEY1KlO2Rlxpp1EVSZWKPFXqlScfmsZIok09bhERkQhR4hYREYkQJW4REZEIUeIWERGJECVuERGRCFHiFhERiRAlbhERkQhR4hYREYkQJW4REZEI0cxpFU2yWdVEpEwlm8msPG1TCov6DG7qcYuIiESIEreIiEiEKHGLiIhEiBK3iIhIhChxi4iIRIgSt4iISIQocYuIiESIEreIiEiEKHGLiIhEiGZO2xWlnZUsWb0uw0u3zQSmLVpTqnodDqqX1v2V932JRE2yGdemNx2ctn0lU9o4SjvTWbJ6ZVlnZ3GUNfW4RUREIkSJW0REJEKUuEVERCJEiVtERCRClLhFREQiRIlbREQkQjKSuM3sVDP73MwWmtmwTMQgIiISRWlP3GZWFXgI6Aa0BPqZWct0xyEiIhJFmehxHwssdPdF7r4VGAf0yEAcIiIikZOJxN0YWBJzf2m4TERERHbC3D29OzTrBZzq7oPC++cBx7n7pUXKDQby58c7DPg8rYFmRn1gdaaDSJPK1FaoXO2tTG2FytVetTV9mrl7drwVmZirfBlwQMz9JuGyQtx9NFC6CXEjysxmuHtupuNIh8rUVqhc7a1MbYXK1V61tXzIxFD5f4BDzOxAM6sO9AX+kYE4REREIiftPW53zzOzS4E3garAE+7+WbrjEBERiaKM/Kynu48Hxmdi3+VcZTo1UJnaCpWrvZWprVC52qu2lgNpvzhNRERESk9TnoqIiESIEneGmNlQM3Mzqx/eNzN7IJwGdraZtUtQ72gzmxOWe8DMLL2Rl5yZ/TFsyywzm2hmjcLlV4fLZpnZXDPbbmZ149R/0sy+jimbk/5WlFyS9nY2sw0x7bgpQf0Dzeyj8Ll9Prx4s1xK0tZzwuVzzGyqmR2ZoH5kntskba1w71kAM7vbzBaEbXrVzOqEy8+Jeb5mmdmOeM+bmY0ws2Ux5bqnvxUlk6Stzc1sc0wbHk1Qv66ZTTKzL8O/+6YlcHfXLc03gq/DvQl8A9QPl3UH/g0Y0B74KEHdj8P1Fpbvlun2JGnn3jH/DwEejVPmdOCdBPWfBHpluh27216gM/BGCeq/APQN/38UuDjTbSpFW48H9g3/75bkdRyZ5zZJWyvcezaMtyuQFf5/F3BXnDJtgK8S1B8BXJXpduxOW4HmwNwS1P8zMCz8f1i8xyoVN/W4M2MkcA0Qe4FBD+BpD0wH6pjZ/rGVwvt7u/t0D14pTwM90xX0rnL3H2Lu7kXh9ubrB4xNT0SpVcL2xhX2wn4JvBQueooIPrfuPtXd14XLpxPM0xBpSZ7XCveeBXD3ie6eF95N9Bz2I5iuOtJK2NZkehC8VyGN71kl7jQzsx7AMnf/tMiqkkwF2zhcnqxMuWJmt5nZEuAc4KYi6/YETgVeTrKJ28JhrJFmViOFoZaJJO3tYGafmtm/zaxVnKr1gPUxHyKRfm5DFxL0MBOJzHOboK0V8j1bxAXEfw77kPyA+9LwuX0ibcPHu69oWw80s/+a2RQz65SgTkN3XxH+/x3QMKURhpS4U8DM3grP3Ra99QCuI/6HXCTtpK24+/XufgDwHHBpkeqnAx+6+9oEmx8OHA4cA9QFrk1RM0qslO39hGD6wiOBB4HXMhP9rtmd59bMuhAk7kTPWbl6bnfzdRw5O2tvWOZ6II+gzbF1jwN+cve5CTb/CHAwkAOsAO5NTStKppRtXQE0dfejgD8AY8xs72T7CUdU0vM1rUyfY6hMN4LzQiuBxeEtD/gW2A94DOgXU/ZzYP8i9fcHFsTc7wc8lul2lbDtTSlyzgh4FehfwvqdKcF54vJyi9femHWLCa9tiFlmBPMi559v6wC8mel2lKatQFvgK+DQivbcxra1Ir9ngfOBacCecdaNBK4r4XaaJ3oflJdbsrbGlHkXyI2zvOA5D5/rz9MRs3rcaeTuc9y9gbs3d/fmBMNm7dz9O4JpXwdYoD2wwf83BJNffwXwg5m1D8+JDgBeT3MzSszMDom52wNYELNuH+BEksSff74wbGtPINERfrmQqL1mtl/YBszsWIKRrjWxdT14508GeoWLBhLB59bMmgKvAOe5+xdJ6kfmuU3yOq5w71kAMzuV4BqcM9z9pyLrqgC9SXJ+u8h5/jMp389t3LaaWbaZVQ3/Pwg4BFgUZxP/IHivQjrfs5k+2qnMN2J6XgQ9rocIeipziDm6A2bF/J9L8Eb4CvgL4SQ65fFGcO56LjAb+CfQOGbd+cC4OHXGA43C/98JH4u5wLNArUy3qTTtJRha/Qz4lOACmOMTtPcggiuQFwIvAjUy3aZStPWvwDpgVnibEfXnNklbK9x7Nox3IcG5+/zn8NGYdZ2B6XHq/DW//cAz4eMxmyCx7Z+OuMuyrcBvwvfsLIJTXacnaGs94G3gS+AtoG464tbMaSIiIhGioXIREZEIUeIWERGJECVuERGRCFHiFhERiRAlbhERkQhR4haRYsysjpn9PtNxiEhxStwiEk8dQIlbpBxS4haReO4EDrbgt4jvznQwIvI/moBFRIoxs+YE84e3znAoIlKEetwiIiIRosQtIiISIUrcIhLPRqB2poMQkeKUuEWkGHdfA3xoZnN1cZpI+aKL00RERCJEPW4REZEIUeIWERGJECVuERGRCFHiFhERiRAlbhERkQhR4hYREYkQJW4REZEIUeIWERGJkP8H0Ur12hyTxo8AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 3.79 seconds)\n", + "\n", + "NSM = 987.026 --- NBSM = 1008.047\n", + "test 0 : tsm = -40.862, tbsm = -39.089\n", + "Reaching the end of test data. Stop tests at 495. \n", + "===> delta1 = 0.014, delta2 = 0.010\n", + "p = 0.103 +/- 0.017\n", + "Separation = 1.18 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 3.62 seconds)\n", + "\n", + "NSM = 987.026 --- NBSM = 1012.481\n", + "test 0 : tsm = -44.996, tbsm = -53.198\n", + "Reaching the end of test data. Stop tests at 2965. \n", + "===> delta1 = 0.005, delta2 = 0.004\n", + "p = 0.095 +/- 0.006\n", + "Separation = 1.35 sigmas\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial test after 0 epochs (took 22.59 seconds)\n", + "\n" + ] + } + ], + "source": [ + "# there is some problem with NBSM \n", + "gphivals = [4e-3, 5e-3, 6e-3]\n", + "gphi_fnames = ['gphi_toydata_test_M4e-3_out', 'gphi_toydata_test_M5e-3_out', 'gphi_toydata_test_M6e-3_out']\n", + "tsm_minus_XS = {}\n", + "tbsm_minus_XS = {}\n", + "\n", + "\n", + "for gphival, gphi_fname in zip(gphivals, gphi_fnames):\n", + " sep, p, tsm, tbsm = TestEstimator(estimator, gphival, gphi_fname, withXS=True,\n", + " title_message=', quadratic classifier (M) (NSM!=NBSM) ', qc=True)\n", + " tsm_minus_XS[gphival] = tsm\n", + " tbsm_minus_XS[gphival]=tbsm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Combined" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "def combine_pm(tsm_plus, tbsm_plus, tsm_minus, tbsm_minus, n_meas, outputheader, bsm_op):\n", + " len_sm = min(len(tsm_plus), len(tsm_minus))\n", + " len_bsm = min(len(tbsm_plus), len(tbsm_minus))\n", + " \n", + " tsm = (tsm_plus[:len_sm] + tsm_minus[:len_sm])\n", + " tbsm = (tbsm_plus[:len_bsm] + tbsm_minus[:len_bsm])\n", + " \n", + " mu_sm = tsm.mean().item()\n", + " mu_bsm = tbsm.mean().item()\n", + " sigma_sm = tsm.std().item()\n", + " sigma_bsm = tbsm.std().item()\n", + " med_sm = tsm.median().item()\n", + " \n", + " sep = (mu_sm - mu_bsm)/sigma_bsm\n", + " p = 1.*len([i for i in tbsm if i > med_sm])/len(tsm)\n", + " \n", + " delta1 = (p * (1 - p)/min(len_sm, len_bsm))**0.5\n", + " delta2 = (sigma_sm/sigma_bsm) * np.exp(-((mu_bsm - mu_sm)**2)/(\n", + " 2 * sigma_bsm**2))/(2*(n_meas**0.5))\n", + " deltap = (delta1**2 + delta2**2)**0.5\n", + " \n", + " title = '%s, %s, combined'%(\n", + " outputheader, bsm_op)\n", + "\n", + " simpleplot(tsm, tbsm, title, sep, p, deltap)\n", + " \n", + " return (p, deltap)\n", + "\n", + "def simpleplot(tsm, tbsm, title, sep, p, deltap):\n", + " plt.figure(figsize=(8, 6))\n", + " ax = plt.subplot()\n", + " plt.hist(tsm, 50, alpha=0.5, label='SM')\n", + " plt.hist(tbsm, 50, alpha=0.5, label='BSM')\n", + " plt.title(title)\n", + " plt.legend(loc='upper right')\n", + " \n", + " plt.text(x=0.05, y=0.85, transform=ax.transAxes, \n", + " s='sep = %.3f\\np = %.3f +/- %.3f'%(sep, p, deltap), \n", + " bbox=dict(facecolor='blue', alpha=0.2))\n", + " \n", + " plt.savefig(os.getcwd() + '/models/' + title+'.pdf')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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c/grgUyml4/PHieyUz0tNrOM8YG5K6ddNWY7WHgZwCzCApbYjP+y8DlkLeVfgLuDklNKf8vHNEsBqf9pjz0mS1BAbkh127k12DcVPyA45S01iC1iSpAJ4EZYkSQUwgCVJKkCrngPu2bNn6tevX2s+pSRJhZk2bdq8lFLZX/hq1QDu168fU6dObc2nlCSpMBFRsZtND0FLklQAA1iSpAIYwJIkFcCOOCRJLW7ZsmXMnj2bJUuWFF1Ki1hvvfXo06cPnTt3rvc8BrAkqcXNnj2bDTfckH79+rHqr7eu/VJKzJ8/n9mzZ7PlllvWez4PQUuSWtySJUvo0aNHmwtfgIigR48eDW7dG8CSpFbRFsO3WmPWzQCWJLULl112Gdtvvz077rgjgwcP5vHHH2fEiBH07duX0t9FOOigg+jSpUuL1+M5YElSq7ty0sxmXd4ZI/vXOn7KlClMnDiRJ554gnXXXZd58+axdOlSALp168YjjzzCnnvuycKFC5kzZ06z1laJLWBJUps3Z84cevbsybrrrgtAz5496d27NwBHHnkkEyZMAOCPf/wjhxxySKvUZABLktq8fffdl9dff53+/ftzyimn8OCDD64ct88++/DQQw+xYsUKJkyYwBFHHNEqNRnAkqQ2r0uXLkybNo0xY8ZQVVXFEUccwdixYwHo2LEje+65JxMmTOCjjz6itX40yHPAkqR2oWPHjowYMYIRI0YwcOBAxo0bt3LckUceycEHH8xFF13UavUYwJKaXW0X2NR1sYzUEl544QU6dOjANttsA8D06dPZYostmDFjBgB77bUX55xzDkcddVSr1WQAS5LavMWLF/Otb32LhQsX0qlTJ7beemvGjBnDYYcdBmTf4z3zzDNbtSYDWJLU6lr7SMguu+zCo48+utrwyZMnl51+8eLFLVyRF2FJklQIA1iSpAIYwJIkFcAAliSpAAawJEkFMIAlSSqAASxJahc6duzI4MGDGTRoEDvvvPPKryV9+OGHHHPMMQwcOJAddtiBPffcc+XXkCKCY489duUyli9fTlVVFQcccECT61krvwf82GNPs3Dh0qLLUD1167YOw4YNLLqMpnvg8srj9j6n9eqQ2oLaXk+NUY/X4Prrr8/06dMBuPfeeznnnHN48MEHueqqq+jVqxdPP/00kPWa1blzZwA22GADZsyYwUcffcT666/PpEmT2GyzzZql5LUygBcuXEpV1S5Fl6F6mjt3WtElSNIqFi1axMYbbwxkP1W4xRZbrBy37bbbrjLt/vvvz5133slhhx3G+PHjOeqoo3j44YebXIOHoCVJ7cJHH33E4MGD2W677Tj55JM5//zzATjxxBO54oorGD58OOeddx4vvvjiKvNV/17wkiVLeOqpp9htt92apR4DWJLULlQfgn7++ee55557GDVqFCklBg8ezCuvvMJZZ53FggUL2HXXXXnuuedWzrfjjjsya9Ysxo8fz/77799s9ayVh6AlSWqK4cOHM2/ePObOncsmm2xCly5dOOSQQzjkkEPo0KEDd911F5/5zGdWTv8f//EfnHnmmUyePJn58+c3Sw22gJvJ7373cw46aGuGDAkWLpxXdpoXXpjOV74ynMMP354jj9yR++773cpxf//7/3HMMTtz+OE7cOGFx7N8+XIA7r77txx55I4cccRATjxxd2bOfLJV1keS2rLnn3+eFStW0KNHDx555BHeffddAJYuXcqzzz67yjlhyA5TX3jhhQwc2HwXlNoCbiaDBu3BXnsdwNe+NqLiNOut929cfPGN9O27DXPnvsmxx+7C8OFfYIMNunLRRcfzy1/ezxZb9Oeaay5g4sRxHHTQSfTuvSVjxjxI164b88gjd3PZZaMZN+7x1lsxSWojqs8BA6SUGDduHB07duTll1/mG9/4BiklPvnkE770pS9x6KGHrjJvnz59OO2005q1njYRwB999AFnn30477wzmxUrVnDyyeez775H8Nxz07jyyu/w4YeL6datJxddNJaePTdl9OgR9O8/iCeeeJDly5dzwQW/YYcdhjaphu2226nOabbY4l8/v1VV1Zvu3Tfh3Xfnsnz5Mjp1Wmfl+N12G8kNN1zOQQedxKBBu6+cZ+DAYbzzzuwm1SlJa4QCvrq3YsWKssNHjRrFqFGjyo4r97OEI0aMYMSIEU2up00E8KOP3kNVVW+uuupOABYvfo/ly5fxox99i5/85A423riK++77Hb/4xfe58MLfALBkyYfccst0nnjiIS655ERuvXXGKsucNesFzj33iLLP9+tfT2bDDbs1qeYZM/7GsmVL6dPn00QEK1Ys59lnpzJgwBDuv/823n779dXmueOO69l99y826XklSWuGNhHAW289kJ/97LtcffX32GuvA9hpp7146aUZvPzyDL75zZFA9smnZ89NV87zhS8cBcDOO/87H3ywiPffX7hKqPbrty233DK9ReqdN28OF1xwHBdfPI4OHbLT8P/zPxP46U/PYOnSjxk2bF86duy4yjxTpz7AHXdcz3XX/bVFapIkta42EcBbbNGfm29+gkceuYtf/eo8dt11H/be+2C22mp7brhhStl5IqLWxy3VAl68eBHf/vaXOOWUyxg4cNjK4TvuOJzrrsu+2P3YY/fx2mszV4578cWnuPTSk7n66rvp1q1Ho55XkrRmaRMBPHfum3Tt2p399z+WDTfsxp/+dB0nnHA27747l6eemsKOOw5n+fJlvPrqTD796e0BuO++3zFkyN5Mn/5XunTZiC5dNlplmS3RAl62bClnnXUwX/rSKD7/+cNWGbdgwTt0774JS5d+zLhxV3Diid8H4K23XuOssw7hkktuWuUcsiStbVJKqzV22oqUUoPnaRMB/NJLT3PVVWfRoUMHOnXqzNln/4rOndfhiitu48c/Po3Fi99jxYrlHHXU6SsDeN111+Poo3di+fJlXHDBb5pcw4QJV3PjjT9k/vy3OPLIHdljj/05//zrePbZqfzhD9dw/vnXMWnSrTzxxEO89958Jk4cC8CFF45l220Hc9NNP+LhhyfyySefcNhh32DXXT8HwLXXXsJ7783niitOAaBjx07cdNPUJtcrSa1pvfXWY/78+fTo0aPNhXBKifnz57Peeus1aL5oTGo31pAhQ9LUqU0Pj3vumdakvqBHjx7B6af/mAEDhjS5FtVt7txp7LdfG+i72x9jqLcrJ82sOO6MkR7JaY+WLVvG7NmzWbJkSdGltIj11luPPn36rPwRh2oRMS2lVDZs2kQLWJK0ZuvcuTNbbrll0WWsUeoM4IhYD3gIWDef/raU0oURsSUwAegBTAOOSymtFb8ROGbM5KJLkCS1c/XpivJj4HMppUHAYGC/iBgGXAFcmVLaGngXOKnlyizGxInjOPjgbTj44G2YOHFc2Wnee28Bp5wykoMP3oZTThnJokVZd2aLF7/HGWccyFFHDeLww7fnz3++YeU8V1/9PQ4/fAcOP3yHVbqjbIxjj92FpUs/brH1mDXreb7yleEMH74uN93049XmW7FiBUcfvROnn970H6eWpPakzgBOmequQDrntwR8DrgtHz4OOKhFKizIe+8t4NprL2bs2McZN+5vXHvtxStDqdTYsT9g6NB9uP32Fxk6dB/Gjv0BALfe+gu23HIA48c/ya9/PZmf/ey7LFu2lL/+9U6ef/4JbrllOuPGPc7NN/+YxYsX1VrLgQf2Kzv8jTf+SVXVZqyzzrotth5du3bnzDOv5thjzyy7/PHjr2LLLT9TdpwkqbJ6/RhDRHSMiOnAO8Ak4GVgYUppeT7JbGCzCvOOjoipETF17ty5zVHzat58cxaHHrod5513DIcd9hn+678OY8mSD5u0zClT7mXo0JFstFF3unbdmKFDR/Loo/esNt2DD97BAQccD8ABBxzP5Ml/ArLvFX/44fuklPjww8V07dqdjh078corz7Lzzv9Op06dWH/9Ddh66x2ZMmX15davxnvYfff9WnQ9unffhO2335VOnTqvNs/bb8/mkUfu5KCDTm5U/ZLUntUrgFNKK1JKg4E+wFBgu/o+QUppTEppSEppSFVVVSPLrNurr77AYYedwm23PccGG3Tl97//5WrT3Hjjjzj66MGr3X70o9U72J479w169dp85eNevfowd+4bq023YMHbK3vY6tHjUyxY8DYAhx9+Kv/853Pst19vjjxyIGeeeRUdOnSgf/9BPProPSxZ8iELF85j2rQHynY7WR+PPnoPw4fXHsBNXY/a/OQnp3PaaT8kwh/VkqSGatBV0CmlhRHxADAc6BYRnfJWcB9g9Xf1VtSr1+YMHrwHAPvvfywTJlzNcceteth01KizGDXqrBarISJWfr9typR76d9/MNdc83/Mnv0y3/zmSAYP3othw/blmWf+zokn7k63blUMHDicDh06rras66+/jPvv/z2QdTRy9NHZL3gMGrQH3/veL1i2bCnvvDObPn22atH1qOThhyfSvfsmfOYzuzB16uRmr0GS2rr6XAVdBSzLw3d9YCTZBVgPAIeRXQl9PHBHSxZal7q6loSsBXzPPb9dbfhOO/07Z5119SrDqqo2Y9q0ySsfv/32bHbZZcRq83bv3ot58+bQs+emzJs3h4033gSAv/zlBk444Wwigs0335revbdk1qzn2WGHoZx00vc56aSsp6vvf/9o+vZd/XuRpdMceGC/1Xrl+sc/Hmbw4D0BeOCB27n22osBOO+861b5fnNT16OSJ598hIce+jOPPHIXS5cuYfHiRZx//rFceunNtc4nScrUpwW8KTAuIjqSHbK+NaU0MSKeBSZExH8D/wCub8E66/TWW6+t7HbynntuWRlOpRrSAh4+/Av88pfnrrxg6fHH7+PUU1fviOGzn/0PJk4cxwknnM3EieP47Ge/DMCnPtWXv/3tfnbaaS/mz3+bV199gT59tmLFihW8//5CunXrwYsvPsWLLz7FxRfv2+D1zc7/Zr+MtPfeB7P33ge3yHpUcuqpl69cztSpk7n55h8bvpLUAHUGcErpKWC1H7tNKb1Cdj54jbDFFtvy+9//gksuOZEttxzAYYd9o0nL22ij7px00vmMGrUrACeffAEbbdQdgEsvPZlDD/06AwYM4fjjz+accw7njjuuZ9NNt+Dyy2/Npz+fiy46gSOOGEhKiW996wq6devJxx8v4atf3QuADTboyqWX3kynTg3vD2XatMl87WuXtPh6zJv3FqNGDeGDDxYR0YHx43/Grbc+S5cuXRtcsyTpX9pEV5RvvjmL008/YLXf9G2r3n57Npdd9lWuvvruokupF7uibH/silLK1NYVpZevroV69eqz1oSvJKm8NhHAvXv3azetX0lS29AmAliSpLWNv4YkqSLP5UotxxawJEkFWCtbwN26rcPcudOKLkP11K3bOkWXIElrnLUygIcNG1h0CZIkNYmHoCVJKoABLElSAQxgSZIKYABLklQAA1iSpAKslVdBS82i0o8r+MMK9VJbJx2Nnc/OPdSe2AKWJKkABrAkSQUwgCVJKoABLElSAQxgSZIKYABLklQAA1iSpAIYwJIkFcAAliSpAPaEJdVUqYcsSWpGtoAlSSqAASxJUgEMYEmSCmAAS5JUAANYkqQCGMCSJBXAAJYkqQAGsCRJBTCAJUkqgAEsSVIBDGBJkgpgAEuSVAADWJKkAhjAkiQVoM4AjojNI+KBiHg2Ip6JiG/nwy+KiDciYnp+27/ly5UkqW2oz+8BLwe+m1J6IiI2BKZFxKR83JUppR+3XHmSJLVNdQZwSmkOMCe//35EPAds1tKFSZLUltWnBbxSRPQDdgIeB/YATo2IUcBUslbyu2XmGQ2MBujbt28Ty5XUll05aWbFcWeM7N+KlUgtr94XYUVEF+APwOkppUXAr4BPA4PJWsg/KTdfSmlMSmlISmlIVVVVM5QsSdLar14BHBGdycL3tymlPwKklN5OKa1IKX0CXAsMbbkyJUlqW+pzFXQA1wPPpZR+WjJ805LJDgZmNH95kiS1TfU5B7wHcBzwdERMz4edCxwVEYOBBMwCvtYiFUqS1AbV5yrovwJRZtRdzV+OJEntgz1hSZJUAANYkqQCGMCSJBXAAJYkqQAN6glLUgM9cMc95VMAAA5cSURBVHn54Xuf07p1SFrj2AKWJKkABrAkSQUwgCVJKoABLElSAQxgSZIKYABLklQAA1iSpAIYwJIkFcAAliSpAAawJEkFMIAlSSqAASxJUgEMYEmSCmAAS5JUAANYkqQCGMCSJBXAAJYkqQAGsCRJBehUdAGS1JKunDSz4rgzRvZvxUqkVdkCliSpAAawJEkFMIAlSSqAASxJUgEMYEmSCmAAS5JUAANYkqQCGMCSJBXAjjikNqK2Didqs7Z0RmGHGmprbAFLklQAA1iSpAIYwJIkFcAAliSpAAawJEkFqDOAI2LziHggIp6NiGci4tv58O4RMSkiXsz/btzy5UqS1DbUpwW8HPhuSmkAMAz4ZkQMAM4G7k8pbQPcnz+WJEn1UGcAp5TmpJSeyO+/DzwHbAZ8GRiXTzYOOKilipQkqa1p0DngiOgH7AQ8DvRKKc3JR70F9GrWyiRJasPq3RNWRHQB/gCcnlJaFBErx6WUUkSkCvONBkYD9O3bt2nVSu1Ec/T6NOy1MWWHP9Z3dL2fS1LLqVcLOCI6k4Xvb1NKf8wHvx0Rm+bjNwXeKTdvSmlMSmlISmlIVVVVc9QsSdJarz5XQQdwPfBcSumnJaP+DByf3z8euKP5y5MkqW2qzyHoPYDjgKcjYno+7FzgB8CtEXES8CpweMuUKElS21NnAKeU/gpEhdH7NG85kiS1D/aEJUlSAQxgSZIKYABLklQAA1iSpALUuyMOSc3ogcvLD9/7nNatQ1JhbAFLklQAA1iSpAIYwJIkFcAAliSpAAawJEkFMIAlSSqAASxJUgEMYEmSCmBHHGp5lTqdgIZ3PNGYDixqe/610JWTZhZdwhrHbaK1kS1gSZIKYABLklQAA1iSpAIYwJIkFcAAliSpAAawJEkFMIAlSSqAASxJUgEMYEmSCmBPWCpWY3q2WhO1sd62JLU8W8CSJBXAAJYkqQAGsCRJBTCAJUkqgAEsSVIBDGBJkgpgAEuSVAADWJKkAtgRh9REU16ZX3Hc8K16NGxheYcew16rvMwiDHttTNnhj/Ud3cqVSG2HLWBJkgpgAEuSVAADWJKkAhjAkiQVwACWJKkAdQZwRPwmIt6JiBklwy6KiDciYnp+279ly5QkqW2pTwt4LLBfmeFXppQG57e7mrcsSZLatjoDOKX0ELCgFWqRJKndaMo54FMj4qn8EPXGzVaRJEntQGN7wvoVcCmQ8r8/AU4sN2FEjAZGA/Tt27eRT6cWk/e8VNbe57ReHU1V23qsoWrrQUut48pJMxs13xkj+zdzJWqPGtUCTim9nVJakVL6BLgWGFrLtGNSSkNSSkOqqqoaW6ckSW1KowI4IjYteXgwMKPStJIkaXV1HoKOiPHACKBnRMwGLgRGRMRgskPQs4CvtWCNkiS1OXUGcErpqDKDr2+BWiRJajfsCUuSpAIYwJIkFcAAliSpAAawJEkFaGxHHJLqYU3sbGPYa2OKLkEStoAlSSqEASxJUgEMYEmSCmAAS5JUAANYkqQCGMCSJBXAAJYkqQAGsCRJBbAjDq2ZHri86AqkRrly0syK484Y2b8VK9GazhawJEkFMIAlSSqAASxJUgEMYEmSCmAAS5JUAANYkqQCGMCSJBXAAJYkqQAGsCRJBbAnLDWcvVRJUpPZApYkqQAGsCRJBTCAJUkqgAEsSVIBDGBJkgpgAEuSVAADWJKkAhjAkiQVwI44pNyUV+YXXYLauCsnzaw47oyR/VuxEq0JbAFLklQAA1iSpAIYwJIkFcAAliSpAAawJEkFqDOAI+I3EfFORMwoGdY9IiZFxIv5341btkxJktqW+rSAxwL71Rh2NnB/Smkb4P78sSRJqqc6Azil9BCwoMbgLwPj8vvjgIOauS5Jktq0xp4D7pVSmpPffwvo1Uz1SJLULjS5J6yUUoqIVGl8RIwGRgP07du3qU8nNYm9XUlaUzS2Bfx2RGwKkP99p9KEKaUxKaUhKaUhVVVVjXw6SZLalsYG8J+B4/P7xwN3NE85kiS1D/X5GtJ4YAqwbUTMjoiTgB8AIyPiReDz+WNJklRPdZ4DTikdVWHUPs1ciyRJ7YY9YUmSVAADWJKkAhjAkiQVwACWJKkATe6IQ2uJBy4vugKpVsNeG1N2+GN9R7dyJWuXKyfNrDjujJH9W7ESNZQtYEmSCmAAS5JUAANYkqQCGMCSJBXAAJYkqQAGsCRJBTCAJUkqgAEsSVIB7IijrbHDDbUiO89oPnao0f7YApYkqQAGsCRJBTCAJUkqgAEsSVIBDGBJkgpgAEuSVAADWJKkAhjAkiQVwACWJKkA9oQlqdlV6iGrrait1yqpvmwBS5JUAANYkqQCGMCSJBXAAJYkqQAGsCRJBTCAJUkqgAEsSVIBDGBJkgpgRxxrqwcuL7oCSa3Ejj/aJlvAkiQVwACWJKkABrAkSQUwgCVJKoABLElSAZp0FXREzALeB1YAy1NKQ5qjKEmS2rrm+BrS3imlec2wHEmS2g0PQUuSVICmBnAC7ouIaRExujkKkiSpPWjqIeg9U0pvRMQmwKSIeD6l9FDpBHkwjwbo27dvE59uLVep96q9z2nY9K2l6OdvpCmvzC+6BDWjYa+NafA8j/W1PaA1X5NawCmlN/K/7wC3A0PLTDMmpTQkpTSkqqqqKU8nSVKb0egAjogNImLD6vvAvsCM5ipMkqS2rCmHoHsBt0dE9XJuSSnd0yxVSZLUxjU6gFNKrwCDmrEWSZLaDb+GJElSAQxgSZIKYABLklQAA1iSpAI0R1/QqqmhHVispR1eNIfaOs0YvlWPRs0nqW5XTppZcdwZI/u3YiXtly1gSZIKYABLklQAA1iSpAIYwJIkFcAAliSpAAawJEkFMIAlSSqAASxJUgEMYEmSChAppVZ7siFDhqSpU6e22vMVph33bNVQ9mil1vRY39FFl7DWa2wvWe21562ImJZSGlJunC1gSZIKYABLklQAA1iSpAIYwJIkFcAAliSpAAawJEkFMIAlSSqAASxJUgE6FV1Ak1Tq8GLvc4p7bq3GzjbUFg17bUyDpq/UCUhty1nbOg6prbMNrc4WsCRJBTCAJUkqgAEsSVIBDGBJkgpgAEuSVAADWJKkAhjAkiQVwACWJKkAa3dHHJW0oU4yGtuJxfCtejRqmbXNVxs729DaoLk6z2iN515TtYXONmpbhzNG9m+1OmwBS5JUAANYkqQCGMCSJBXAAJYkqQAGsCRJBWhSAEfEfhHxQkS8FBFnN1dRkiS1dY0O4IjoCPwC+CIwADgqIgY0V2GSJLVlTWkBDwVeSim9klJaCkwAvtw8ZUmS1LY1JYA3A14veTw7HyZJkurQ4j1hRcRooLo7mcUR8UKFSXsC81q6njbObdg0br+ma2Pb8Cet/YQ1tl+rP38hvtO8i2vSPtjMtQBsUWlEUwL4DWDzksd98mGrSCmNAersgy0ipqaUhjShnnbPbdg0br+mcxs2jduv6dambdiUQ9B/B7aJiC0jYh3gSODPzVOWJEltW6NbwCml5RFxKnAv0BH4TUrpmWarTJKkNqxJ54BTSncBdzVTLW3jp0KK5TZsGrdf07kNm8bt13RrzTaMlFLRNUiS1O7YFaUkSQVo9QCOiP+MiGci4pOIGFIyfGRETIuIp/O/nysZt0s+/KWIuDoiorXrXpPUsg17RMQDEbE4In5eY57Jebeh0/PbJq1f+ZqjkdvQ/TBXafvl487Jt9ELEfGFkuGz8u03PSKmtn7Va5ZGbkO7/y0jIgZFxJR8//pLRHTNh/eLiI9K3veuKbrWVaSUWvUGfAbYFpgMDCkZvhPQO7+/A/BGybi/AcOAAO4Gvtjada9Jt1q24QbAnsDXgZ/XmGeVadv7rZHb0P2w7u03AHgSWBfYEngZ6JiPmwX0LLr2NeXW0G2Y314GtgLWyacZUPR6rAk3sm/lfDa/fyJwaX6/HzCj6Poq3Vq9BZxSei6ltFpnHCmlf6SU3swfPgOsHxHrRsSmQNeU0mMp26I3Age1YslrnFq24Qcppb8CSwooa63S0G3ofriqStuPrDvaCSmlj1NK/wReIuu2VjU0Yhva/W9l/YGH8vuTgEMLrKXe1tRzwIcCT6SUPibr3nJ2yTi7vGy8G/LDMOe358OnjeR+WD+1dVGbgPvyU0yjV5tT1SptQ7v/rewZ/vVh5D9ZtZOoLSPiHxHxYETs1fqlVdYiXVFGxP8Cnyoz6vsppTvqmHd74Apg35aobW3RlG1YwTEppTciYkPgD8BxZK24NqsFtmG70gLbb898H9wEmBQRz6eUHqpzrrWY+2DzqW1bkh12vjoizifrEGppPm4O0DelND8idgH+FBHbp5QWtUrRdWiRAE4pfb4x80VEH+B2YFRK6eV88Btk3VxWK9vlZVvT2G1Yy/LeyP++HxG3kB3OatMB3MzbsN3th43cfhW7qC3ZB9+JiNvJ9sE2HcDNvQ1rGd7m1WNb7gsQEf2BL+XzfAx8nN+fFhEvkx2uXiMuAlxjDkFHRDfgTuDslNIj1cNTSnOARRExLD9sOgrwk2MDRESniOiZ3+8MHADMKLaqtYv7Yb39GTgyv35jS2Ab4G8RsUF+9IWI2IDszdJ9sLyy2xC7/62o+lsdEdEBOA+4Jn9cFdlv1xMRW5Fty1eKqnM1BVytdjDZuYuPgbeBe/Ph5wEfANNLbpvk44aQvVhfBn5O3oFIe71V2ob5uFnAAmBxPs0Asit7pwFPkZ0ruYr8ytT2emvoNsyHux/Wb/t9P99GL5BfKU525e6T+e0ZskOwha/H2rQN8+H7AzPzce1+G5Zsl2/n22Um8IPq1ybZ9UTP5HnyBHBg0bWW3uwJS5KkAqwxh6AlSWpPDGBJkgpgAEuSVAADWJKkAhjAkiQVwACWJKkABrAkSQUwgCVJKsD/B5zFT8Bp8oQJAAAAAElFTkSuQmCC\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "n_meas = 1300\n", + "\n", + "gphivals = [4e-3, 5e-3, 6e-3]\n", + "\n", + "for gphival in gphivals:\n", + " temp = tsm_plus[0.004]\n", + " combine_pm(tsm_plus[gphival], tbsm_plus[gphival], tsm_minus[gphival], tbsm_minus[gphival], \n", + " n_meas,'quadratic classifier, (NSM=NBSM) %s'%str(gphival), 'gphi')" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "n_meas = 1300\n", + "\n", + "gphivals = [4e-3, 5e-3, 6e-3]\n", + "\n", + "for gphival in gphivals:\n", + " temp = tsm_plus[0.004]\n", + " combine_pm(tsm_plus_XS[gphival], tbsm_plus_XS[gphival], tsm_minus_XS[gphival], tbsm_minus_XS[gphival], \n", + " n_meas,'quadratic classifier, (NSM!=NBSM) %s'%str(gphival), 'gphi')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/tutorial_particle_physics/TestEstimator-Results.ipynb b/examples/tutorial_particle_physics/TestEstimator-Results.ipynb new file mode 100644 index 000000000..a839bd818 --- /dev/null +++ b/examples/tutorial_particle_physics/TestEstimator-Results.ipynb @@ -0,0 +1,2402 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting tabulate\n", + " Downloading tabulate-0.8.7-py3-none-any.whl (24 kB)\n", + "Installing collected packages: tabulate\n", + "Successfully installed tabulate-0.8.7\n", + "\u001b[33mWARNING: You are using pip version 20.1.1; however, version 20.2.3 is available.\n", + "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n" + ] + } + ], + "source": [ + "! pip install tabulate" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "from OurTrainingTools import *\n", + "\n", + "def test_model(madminermodel, test_input_sm, test_input_bsm, bsmparval, epochs, e, n_meas, pm, verbose_t=True, verbose_period_t=1e5, title=''):\n", + " \n", + " def compute_t(madminermodel, nev, counter, test_input):\n", + " n_gen = 0\n", + " while n_gen == 0:\n", + " n_gen = np.random.poisson(nev)\n", + " \n", + " if (counter + n_gen) >= len(test_input):\n", + " return 0., -1\n", + " \n", + " points = test_input[int(counter): int(counter+n_gen)]\n", + " #print('counter = %d, counter+n_gen = %d'%(counter, counter+n_gen))\n", + " #ratio = (madminermodel.evaluate_log_likelihood_ratio(points.cpu().numpy(), \n", + " # np.array([0.]))[0][0])\n", + " if not bsmparval:\n", + " log_ratio = (madminermodel.evaluate_log_likelihood_ratio(points.numpy(), \n", + " np.array([0.]))[0][0])\n", + " else:\n", + " log_ratio = (madminermodel.evaluate_log_likelihood_ratio(points.numpy(), \n", + " np.array([0.]), np.array([bsmparval,]))[0][0])\n", + " print(log_ratio)\n", + " log_ratio = torch.tensor(log_ratio)\n", + " #ratio = 1./ratio\n", + " log_ratio = log_ratio\n", + " out = 2 * (NBSM - NSM - (log_ratio+torch.log(torch.tensor(NBSM/NSM))).sum(0))\n", + " return out, int(counter+n_gen)\n", + " \n", + " test_start = time.time()\n", + " if verbose_t:\n", + " print(\"NSM = %.3f --- NBSM = %.3f\"%(NSM, NBSM))\n", + " tsm = torch.empty(n_meas)\n", + " tbsm = torch.empty(n_meas)\n", + " \n", + " tsmcount = torch.zeros(n_meas+1)\n", + " tbsmcount = torch.zeros(n_meas+1)\n", + " \n", + " for i in range(n_meas):\n", + " tsm[i], tsmcount[i+1] = compute_t(madminermodel, NSM, tsmcount[i], \n", + " test_input_sm)\n", + " tbsm[i], tbsmcount[i+1] = compute_t(madminermodel, NBSM, tbsmcount[i], \n", + " test_input_bsm)\n", + " \n", + " if (tsmcount[i+1] < 0) or (tbsmcount[i+1] < 0):\n", + " print('Reaching the end of test data. Stop tests at %d. '%i)\n", + " tsm, tbsm = tsm[: i], tbsm[: i]\n", + " n_meas = i\n", + " break\n", + " \n", + " if i % (verbose_period_t) == 0:\n", + " print('test %s: tsm = %.3f, tbsm = %.3f'%(\n", + " str(i).ljust(4), tsm[i], tbsm[i]))\n", + " \n", + " test_duration = time.time() - test_start\n", + " \n", + " mu_sm = tsm.mean().item()\n", + " mu_bsm = tbsm.mean().item()\n", + " sigma_sm = tsm.std().item()\n", + " sigma_bsm = tbsm.std().item()\n", + " med_sm = tsm.median().item()\n", + " \n", + " sep = (mu_sm - mu_bsm)/sigma_bsm\n", + " p = 1.*len([i for i in tbsm if i > med_sm])/len(tsm) \n", + " #print(len([i for i in tbsm if i>med_sm]))\n", + " delta1 = (p * (1 - p)/n_meas)**0.5\n", + " delta2 = (sigma_sm/sigma_bsm) * np.exp(-((mu_bsm - mu_sm)**2)/(\n", + " 2 * sigma_bsm**2))/(2*(n_meas**0.5))\n", + " print('===> delta1 = %.3f, delta2 = %.3f'%(delta1, delta2))\n", + " deltap = (delta1**2 + delta2**2)**0.5\n", + " \n", + " if verbose_t:\n", + " print('p = %.3f +/- %.3f' %(p, deltap))\n", + " print('Separation = %.2f sigmas'%(sep))\n", + " training_properties = '/toydata/madminer-carl-'+title\n", + " plot_histogram(tsm, tbsm, int(NSM), int(NBSM), p, deltap, sep, epochs, e, \n", + " training_properties, results_path)\n", + " print('Partial test after %d epochs (took %.2f seconds)\\n'\n", + " %(e, test_duration))\n", + " \n", + " \n", + " return tsm, tbsm, NSM, NBSM" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_histogram(tsm, tbsm, nsm, nbsm, p, deltap, sep, epochs, \n", + " e, training_properties, results_folder):\n", + " mint = torch.min(torch.cat((tsm, tbsm))).item()\n", + " maxt = torch.max(torch.cat((tsm, tbsm))).item()\n", + " \n", + " # for some reason the code complains if i don't detach the variables \n", + " # from their grad-on versions\n", + " tsm, tbsm = tsm.detach(), tbsm.detach()\n", + " \n", + " bins = np.linspace(mint, maxt, 60)\n", + " plt.figure(figsize=(8, 6))\n", + " ax = plt.subplot()\n", + " plt.hist(tsm, bins, alpha=0.5, label='SM')\n", + " plt.hist(tbsm, bins, alpha=0.5, label='BSM')\n", + " plt.legend(loc='upper right')\n", + " \n", + " sn = 'nsm = %s \\nnbsm = %s'%(str(nsm), str(nbsm))\n", + " sp = 'p '+'= '+ ('%.3f +/- %.3f'%(p, deltap))\n", + " ssep = 'sep ' + '= ' + ('%.3f'%(sep))\n", + " \n", + " plt.text(x=0.05, y=0.85, transform=ax.transAxes, \n", + " s=sn+'\\n'+sp+'\\n'+ssep, bbox=dict(facecolor='blue', alpha=0.2))\n", + " plt.xlabel('t')\n", + " plt.ylabel('p(t)')\n", + " if epochs == e:\n", + " plt.title('Final test\\n' + training_properties)\n", + " filename = results_folder + training_properties \\\n", + " + ' histogram.pdf'\n", + " plt.savefig(filename)\n", + " return \n", + " plt.title(training_properties)\n", + " plt.show()\n", + " \n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "from madminer import ParameterizedRatioEstimator\n", + "from madminer.ml.morphing_aware import MorphingAwareRatioEstimator\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Out Sample Test Data\n", + "New 2D format, all out of sample" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1000 big batch" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl1000-bigbatch')" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2841.340\n", + "[-0.0580593 -0.05370611 -0.16395181 ... 0.43543673 -0.10585171\n", + " -0.21681619]\n", + "[-0.00037923 -0.18029624 -0.17668626 ... 0.35912699 -0.22174907\n", + " 0.11194429]\n", + "test 0 : tsm = 420.377, tbsm = -333.420\n", + "[ 0.001576 -0.12736493 -0.01553163 ... -0.1812324 0.10618736\n", + " -0.180017 ]\n", + "[-0.030155 -0.07818913 0.17700136 ... -0.09379643 0.5959857\n", + " -0.10937887]\n", + "[-0.19474632 -0.16850054 -0.02868247 ... -0.13775015 -0.07805789\n", + " -0.05256245]\n", + "[-0.01746798 -0.29384565 -0.05870992 ... -0.06605238 0.04542404\n", + " -0.04485881]\n", + "[ 0.09981329 -0.01512015 -0.10345256 ... -0.12495777 -0.26501107\n", + " -0.21971315]\n", + "[-0.21855628 -0.18220454 0.08123181 ... -0.29762483 0.04049733\n", + " 0.04676688]\n", + "[-0.06449032 -0.06214535 -0.14314136 ... -0.07452977 -0.08876091\n", + " -0.08482683]\n", + "[-0.1700331 -0.14441055 -0.23938459 ... 1.3469218 -0.29382485\n", + " -0.07481474]\n", + "[-0.10392642 -0.0553124 -0.12650073 ... -0.24818736 0.00694982\n", + " -0.07386041]\n", + "[-0.06224841 -0.06108034 -0.09459245 ... -0.22628397 0.13095249\n", + " 0.11400385]\n", + "[-0.24585092 -0.1366083 -0.24248815 ... -0.1387378 -0.25410622\n", + " -0.03724074]\n", + "[ 0.15918112 -0.26245648 -0.11071122 ... -0.15305808 0.13510068\n", + " -0.03113613]\n", + "[-0.05156994 -0.27330726 -0.09463155 ... -0.11263582 -0.16121209\n", + " -0.08118838]\n", + "[-0.10912514 0.6067663 -0.10913849 ... -0.13988835 -0.03021729\n", + " -0.07705963]\n", + "[-0.0167633 -0.15840845 0.04026628 ... 0.00034082 0.20991546\n", + " -0.13295013]\n", + "[-0.14760754 -0.19723159 -0.23255754 ... -0.1517359 0.41586658\n", + " -0.08133513]\n", + "[-0.23491567 -0.16984653 -0.10014933 ... -0.27757162 -0.10357803\n", + " -0.10660732]\n", + "[-0.1314666 -0.17362577 -0.28641802 ... -0.19319057 1.5291587\n", + " -0.04503495]\n", + "[-0.15163714 -0.19710255 0.5402948 ... 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-0.17437607]\n", + "[-0.14248157 -0.1274932 -0.06708622 ... 0.28193882 -0.10842037\n", + " -0.19214815]\n", + "[ 0.05467855 -0.17443305 -0.2900085 ... -0.1824457 -0.21801859\n", + " -0.25821257]\n", + "[-0.11745685 -0.10626483 -0.15240377 ... -0.09811997 -0.23564577\n", + " -0.04879004]\n", + "[-0.06400174 -0.14928079 -0.06289029 ... -0.15062654 -0.15953356\n", + " -0.13905197]\n", + "Reaching the end of test data. Stop tests at 175. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 12.62 sigmas\n", + "Partial test after 1 epochs (took 30.73 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 50\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e4)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], gphival, 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2841.340\n", + "test 0 : tsm = 362.744, tbsm = -342.334\n", + "Reaching the end of test data. Stop tests at 176. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 13.48 sigmas\n", + "Partial test after 1 epochs (took 35.94 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 50\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e4)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2693.988\n", + "test 0 : tsm = 285.402, tbsm = -269.151\n", + "Reaching the end of test data. Stop tests at 185. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 10.97 sigmas\n", + "Partial test after 1 epochs (took 36.54 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 40\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e4)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2558.585\n", + "test 0 : tsm = 268.881, tbsm = -85.966\n", + "Reaching the end of test data. Stop tests at 195. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 8.00 sigmas\n", + "Partial test after 1 epochs (took 37.46 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 30\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e4)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2436.280\n", + "test 0 : tsm = 271.199, tbsm = 50.356\n", + "Reaching the end of test data. Stop tests at 205. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 5.54 sigmas\n", + "Partial test after 1 epochs (took 38.19 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 20\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e4)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2325.162\n", + "test 0 : tsm = 253.322, tbsm = 156.010\n", + "Reaching the end of test data. Stop tests at 215. \n", + "===> delta1 = 0.005, delta2 = 0.001\n", + "p = 0.005 +/- 0.005\n", + "Separation = 2.64 sigmas\n", + "Partial test after 1 epochs (took 39.20 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 10\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e4)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2273.448\n", + "test 0 : tsm = 221.184, tbsm = 164.740\n", + "Reaching the end of test data. Stop tests at 219. \n", + "===> delta1 = 0.022, delta2 = 0.015\n", + "p = 0.119 +/- 0.026\n", + "Separation = 1.17 sigmas\n", + "Partial test after 1 epochs (took 39.52 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 5\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e4)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 50 epochs with big batch" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl50-bigbatch')" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2841.340\n", + "test 0 : tsm = 789.035, tbsm = 62.971\n", + "Reaching the end of test data. Stop tests at 176. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 12.72 sigmas\n", + "Partial test after 1 epochs (took 35.63 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 50\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e4)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2693.988\n", + "test 0 : tsm = 692.769, tbsm = 258.345\n", + "Reaching the end of test data. Stop tests at 185. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 9.15 sigmas\n", + "Partial test after 1 epochs (took 29.44 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 40\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e4)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2558.585\n", + "test 0 : tsm = 645.021, tbsm = 401.723\n", + "Reaching the end of test data. Stop tests at 195. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 6.25 sigmas\n", + "Partial test after 1 epochs (took 37.33 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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MeJtateoybNgbJCUVvpsltwYNmuQsp6XVoUaNWvz882pSUlJp165bzrbmzVuxcmUwg+ixx7bNKW/R4kRWrSqtmUVFROIvqsk9W3Fen26yK6SlS5dw3nlX8uKLX5GSksp7770CwOjRQ3n++c8ZP/5LbrnlsZz6mZnf8thj7/Gvf73O7bdfSEZGB154YR6VKh3ARx+9lef4zzxzP336pOd53H//1buNa/78T9m+/Tfq1Tt8l/KsrO1MnPgsbdvmnW/ltdeeom3brjnrZsaVV3biwguP57//HVmk8yIiIoEhQ4bQvHlzjjnmGNLT05k5cybt27enfv36xM770r17d5KTk+Mej1rwhVSnTiOOPDIdgKZNj2f58u8BOOKIY7jttgto37477dt3z6nftm1XkpIq0LhxC3bu3JGTaBs3bpGzb6y+fW+kb98bixTTmjUruOOOi7jrrjGUK7frZ7WhQ//GccedQsuWJ+9SPmvWVF577SmefPKjnLInn/yIWrXqsm7dKq688nQaNmzKccedUqRYRET2FcOmfF2ix7vu9CZ7rPPxxx/z5ptvMmfOHCpVqsSaNWv47bdgPJXU1FSmT59Ou3btWL9+PStWrCjR+AqiBF9IFSpUylkuX748v/4azDj50ENv8fnn05g27Q1GjRrC+PHzAKhYMahfrlw5kpIq5HSvmJVjx46sPMd/5pn7mTQp7+yQLVuewo03Ds9TvnnzRq655o/87W9DaNHixF22jRx5Fz//vJpbbnl8l/IlS77k7rv7MXz426SmHpRTXqtWXQBq1KhF+/Zn89VXnyrBi4gUwYoVK6hZsyaVKgX/+2vW/P2epvPPP5/x48fTrl07/vvf/3LOOefw1VdfxT0mJfi9sHPnTlauXEpGRgfS09sxefJ4tm7dXKxjFaUFv337b9x449n88Y996dixxy7bJkx4kk8+eYdHH313l1b9Tz/9yI03nsPgwc/ucg1/69Zf2LlzJ1WqpLB16y/MnDmZfv3uKNZrEBHZX3Xq1InBgwfTpEkTOnbsSK9evTj11FMBOO2007j88svZsWMH48ePZ+TIkdx9991xj0kJfi/s3LmD22+/kM2bN+DunH/+1aSkpMb9eadMeZE5c6axYcNa3nxzNAB33jmaI49M5957/8rBBzfg0kvbANChwzlcfvkdPPHEYDZsWMt99/0NIOfrcGvXruTGG88GYMeOLDp37pPvdXsRESlYcnIys2fP5sMPP2Tq1Kn06tWLoUOHAkGvb7t27Rg/fjxbt26ltCZFs9gL/2VdRkaGl8RkM5MmzdZQtfuJ1atn06WLftciZdnChQs56qijctYTcQ0+t5dffpkxY8awadMmHnjgAbZs2cLZZ5/NoEGDGDBgAMnJyWzeXLQe39yvE8DMZrt7Rn711YKXopn1dMHbMv5cenGISKnZXcIsTvKLosWLF1OuXDmOOOIIAObOnUuDBg2YP38+ACeffDI333wzvXv3LrWYlOBFRET20ubNmxkwYADr168nKSmJxo0bM3LkSHr0CO6TMjNuuOGGUo1JCV5ERCIlEb0Kxx9/PDNmzMhT/v777+dbv6jd88WhgW72Uv/+7VmwYN8Yu33bti1cc80fOffcpvTs2ZxHHhmYs+2NN0bTsWNazgA6EyY8mbOtVavyOeXXXXdmTvmnn77LBRccR58+6Vx2WTuWLv2mVF+PiIgUn1rwEXPRRTeQkdGB7dt/44orTmP69Lc56aRg1LrTT+/FP/4xIs8+lSodwNixc/OUDx16BQ8++BqNGh3FSy89ylNP3cOgM06N+2sQEZG9pwRfCLsbix5g4sRnueeefmRlZXHHHaM4+uhWzJ79AQ8+eE14BOOJJ6axcOFsRo68k+TkVL79dh4dO/akceMWjBv3ML/+upUHH5yQZ8jZoqhc+UAyMjoAUKFCRZo2PW4vx5w3fvllIwCbN28gLa3OXhxLRERKkxJ8IS1duoQhQ8Zx221PMHBgT9577xW6dbsQCLrGx46dy5w50xg8+FJefHE+zz33ADfd9G/S009iy5bNVKxYGYCvv/6Cl19eSNWqNTjrrMPo3r0fzzzzKePGPcwLLzzC3//+0C7PO2vWVP71r+vyxFO58oGMGpX3ek+2TZvW8+GHb3D++dfklL333it8/vk06tdvwvXXD+Pggw8F4LfftnHRRRmUL5/EJZcMzBly9/bbn+Saa7pRqdIBVKlSlaef/gQWvbJ3J1JEREqFEnwhFTQWPUDnzsHXHo477hR++WUjmzat59hjT2LYsOvp2vUCOnQ4h9q16wHQrNkJ1Kx5CAD16h1O69adgGCM+lmzpuZ53oyMDvl2n+9OVlYWt97am169rqZevcMAOPnkP9G5c28qVqzEK688zqBBF/PYY+8B8MYbP1CrVl0yM7/jiiv+QOPGLahX73DGjh3Gww9P5OijW/PMM/czbNj13N71pCLFIiIiiaEEX0gFjUUPeafxMzMuuWQg7dr9kY8+mshll53EiBHvAL+PUR/UK5ezXtAY9cVpwQ8Z0p9DDz2CPn2uzSmLHXu+e/d+DB9+U8569lj09eodxvHHt2fRos+pUqUqX3/9BUcf3RqATp16MWBAF1CCFxEpE5TgS8DkyS+QkdGBuXM/Ijm5GsnJ1cjM/JbGjVvQuHELFiz4jO+/X0RyctGHsS1qC/7RR29j8+YN3H77k7uUr1mzIqfnYNq012nUKBgNaePGn6lc+UAqVqzE+vVr+OKL6fTtexMpKdXZvHkDP/zwNQ0aNOGTT6bQsOFReZ5PRESChl+LFi1wd8qXL8+IESNo27YtW7Zs4fLLL+fLL7/E3UlNTWXSpEkkJydjZlxwwQU899xzQND7esghh9C6dWvefPPNvY5JCb4EVKpUmT59WpKVtZ077hgFwNixDzFr1lTKlSvHYYc1p23brnz55cdxjWPlykxGjRpCw4ZNufDC4wDo2fMqunfvx/jxw5k27XXKl0+iatUaDBo0GoD//W8h//d/f6FcuXLs3LmTiy8eyGGHNQPgttue4KabzqVcuXKkpFQPXttPH8T1NYiI7LWp95bs8TrcvMcqBxxwAHPnBo2xd955h5tvvpkPPviAhx9+mNq1azNvXjDT6OLFi6lQoQIAVapUYf78+WzdupUDDjiAKVOmULdu3RILWwm+EOrUaciLL87PWb/oot9HIxo58v1897nppkfylGVktCcjo32+++beVhy1a9dj1qz85xa46qp7ueqqvG/6Y49tywsvzMt3nw4dzqZDh7N3LVSCFxHZrY0bN1K9enUgmEa2QYMGOduOPPLIXep269aNt956ix49ejBu3Dh69+7Nhx9+WCJxxG2gGzM71MymmtkCM/vKzK4Jy2uY2RQzWxL+rF7A/heHdZaY2cXxilNERGRvbd26lfT0dJo2bUq/fv24/fbbAbj00ku57777aNOmDbfddhtLlizZZb/sueK3bdvGl19+SevWrUsspniOZJcF/N3dmwEnAleaWTNgIPCuux8BvBuu78LMagB3Aq2BVsCdBX0QEBERSbTsLvpFixYxadIk+vbti7uTnp7Od999x4033si6des44YQTWLhwYc5+xxxzDN9//z3jxo2jW7duJRpT3Lro3X0FsCJc3mRmC4G6wFlA+7DaGOB94B+5du8MTHH3dQBmNgXoAoyLV7wiIiIloU2bNqxZs4bVq1dTq1YtkpOTOeecczjnnHMoV64cEydO3GXa1zPPPJMbbriB999/n7Vr15ZYHKVyDd7MGgItgZlA7TD5A/wE1M5nl7rA0pj1zLAsv2P3B/oD1K9fv2QCLgN+++1X7ryzLwsXzqZatYO4994XqFOnYZ56M2ZM4oEHrmHnzh10796PSy4JOkyWLfsft9xyPhs2rOWoo45n8OBnqVChYqGPWxjvvDOezMxvueyyWwuss2HDOm6+uRcrVnzPIYc0ZOjQF6laNW9nzZtvjuGpp+4B4LLLbuOMM4KrNgsXzmbQoEv49detnHRSN2644WHMrNDHFYmi4syHrmlfS86iRYvYsWMHBx10ENOnT6dZs2ZUr16d3377jQULFtC+fftd6l966aWkpqbSokWLAienKY64TzZjZsnAK8C17r4xdpu7O5D/XWGF5O4j3T3D3TPS0tL25lBlymuvPUVKSnUmTPiGPn2u45FHcneCwI4dO7jvvisZPvxtXnppAe+8M47vvlsAwCOP/IM+fa5jwoRvSEmpzmuvPVXo48Z6Y8ZHPP7GhHy3zZjxNm3bdtnt/qNHD6VVq9N49dUltGp1GqNHD81TZ8OGdTzxxF2MHj2TMWM+5Ykn7mLjxp8BuPfeK7jttid49dUlLF26hBkzJhX6uCIiJSX7Gnx6ejq9evVizJgxlC9fnm+//ZZTTz2VFi1a0LJlSzIyMjj33HN32bdevXpcffXVJR5TXFvwZlaBILk/7+7/DYtXmtkh7r7CzA4BVuWz6zJ+78YHqEfQlZ8Qy5d/z4ABXTjqqONZtGgOhx3WnMGDn6Fy5QMTFRIffPAa/fsPAuC003rwz39ehbvvMujOV199yqGHNs4Zza5Tp/P54INg8pjPPnuPe+4ZC8AZZ1zMyJGD6NHjikIdtzDcna+/nkvTpsft8XVkf5vgjDMupn//9lx99X271Pn443do1ep0qlWrAUCrVqczY8YkMjLa88svG2nR4kQAunXry/vvT+Ckk7oW6rgiElGF+FpbSduxY0e+5X379qVv3775bstvytj27dvnaeEXVzzvojfgKWChu/8rZtPrQPZd8RcDr+Wz+ztAJzOrHt5c1yksS5gfflhMjx5/4+WXF1KlSlVeeunREn+Ofv1Ozpm2NfYxc+b/y1N31apl1K4djCWflJREcnI1NmxYW2AdgFq16rFq1TI2bFhLSkoqSUlJu5QX9riFsXjx5xxxxLF7/GCwbt3KnAF4DjroYNatW5mnzurVu76O2rXrsXr1sjDWennKC3tcEZEoi2cL/iTgImCemWUPxXYLMBR40cwuA34AegKYWQbwV3fv5+7rzOxu4LNwv8HZN9wlSu3ah5KeHgzT2q3bhYwfP3yX78OXhCefLJnvPsbb+s2b+dtD9wOw4ZdfyNqRxQdffA4HPszgwc/SuHELZsyYRNu2XYt0XDMrck9BIo8rIrIvi+dd9B8BBf1XPS2f+rOAfjHro4BR8Ymu6PIbb76k9et3Mlu2bMpTfs01D9C6dcddymrVqsvKlUupXbseWVlZbN68gWrVDsq3TrZVqzKpVasu1aodxKZN68nKyiIpKSmnvLDHTU1OZuxtdwHBNfjla9fwlz91h4w/59T55JPJ/POfwcxzV13VmXXrVnLUURl5htCtUaN2zjC6a9asoHr1Wnlef1paXWbPfj9nfeXKTI4/vn0Ya+Yu5WlpdQt9XBGRKIv7TXZR8dNPP+YMNTtp0ljS09uV+HM8+eSHjB07N88jd3IHOOWUM3nzzTEAvPvuy5xwwh/yfOho1uwEli5dwrJl/2P79t+YPHk8p5xyJmZGRkYH3n33ZSC4Q/3UU88q9HH3ZPPmDezYkZUzwc2IEe8wduzcPMkd4NRTf3++2DhitWnTmZkzJ7Nx489s3PgzM2dOpk2bztSseQhVqlRl3rxPcHcmTnwmZ//CHFdEoiO4Zzu6ivP6lOALqUGDI3nppX/To8dRbNz4Mz16XJHQeM466zI2bFhL9+6Nef75f3HVVcFd4qtXL+fqq4PBEpKSkrjxxhEMGNCZHj2OomPHnhx+eHMABgy4j+ef/xfduzdmw4a1nHXWZbs9blF88skUWrXK+6EkPxdfPJCZM6dw9tlH8Omn/y/na3wLFszi7ruDDp1q1Wpw2WW307fvCfTtewL9+t2Rc8PdwIGPcvfd/ejevTF16x7OSSd13e1xRSR6KleuzNq1ayOb5N2dtWvXUrly5SLtZ1E6IRkZGT5r1qy9Ps6kSbNJSzs+Z3358u+59tozdhmPfr816+mCt4Vd9EHC7Zdzd/u+bPXq2XTpcvyeK4qUISX9PfjdHW9f+P789u3byczMZNu2bYkOJW4qV65MvXr1ciaqyWZms909I799NNmMlLj8uuJFROKlQoUKNGrUKNFh7HPURV8IuWeTExER2dcpwUfAlNnTqUAAABl3SURBVCkvct55zejZszm33tonz/Zt27ZwzTV/5Nxzm9KzZ3MeeeT369FvvDGajh3Tcr5zP2HCrq3vzZs30q1bPe6776q4vw4RESk56qIv4378cQlPP30vTz01napVq7NuXX4DAwZz2GdkdGD79t+44orTmD797Zwb0k4/vRf/+MeIfPd77LHbadnylLjFLyIi8aEEXwhbt/7CwIE9WbUqkx07dtCv3+106tSLhQtnM2zY9WzZspnU1JoMGjSamjUPoX//9jRpcixz5nxAVlYWd9wxiqOPbhWX2F599Ql69rwyZyKVGjXyft+7cuUDycjoAECFChVp2vQ4Vq3KzFMvt4ULZ7N27Uratu3CggV7f/OiiIiUHiX4QpgxYxJpaXV4+OG3gOB73llZ27n//gE8+OBrVK+exuTJL/Dvf9/KnXcGY/Ns27aFsWPnMmfONAYPvrRI1/CLMuDNjz8Gd7deeulJ7Ny5g/79B+12gpdNm9bz4YdvcP751+SUvffeK3z++TTq12/C9dcP4+CDD2Xnzp0MG/Z37r77OT79NO9QuSIism9Tgi+Exo1b8NBDf2f48H9w8sln0LLlyXzzzXy+/XY+V155OhBMNJA99jlA5869ATjuuFP45ZeNbNq0npSU1EI9X1GGrN2xI4ulS5cwcuT7rFyZSf/+pzB+/Lx8nysrK4tbb+1Nr15X50xAc/LJf6Jz595UrFiJV155nEGDLuaxx97jpZce5aSTuu0y1ruIiJQdSvCF0KBBE557bg7Tp0/kP/+5jRNOOI0OHc7msMOa8/TTH+e7z94MbVu0IWvrcfTRrUlKqkDduo2oX78JP/64hObNT8iz/5Ah/Tn00CPo0+fanLLs0eYAunfvx/DhNwEwb97HfP75h7z88qNs2bKZrKzfOPDAZAa0ObLQr0NERBJHCb4QVq9eTtWqNejW7UJSUlKZMOFJLrlkID//vJovv/yYY45pQ1bWdn744euckeImT36BjIwOzJ37EcnJ1UhOrlbo5ytKC759++688844zjzzz6xfv4Yff/yaunUPy1Pv0UdvY/PmDXm+o549XjvAtGmv06jRUQDcc8/zOXXeeGM0CxbMYsCAobsf6EZERPYZSvCF8M0383j44RspV64cSUkVGDjwP1SoUJH77nuZBx64Omfs9d69r81J8JUqVaZPn5ZkZW3njjviN2dOmzad+eSTyZx3XjPKlSvP1Vffn9Mq79MnnbFj57JyZSajRg2hYcOmXHhhMD97z55X0b17P8aPH860aa9TvnwSVavWYNCg0XGLVURESo+Gqs1H7qFqi6p///Zce+0DNGuW7+iBZVshhqotSzRUrUTR/jZU7f5sd0PVaqAbERGRCFIXfRyMHPl+okMQEZH9nFrwIiIiEaQELyIiEkFK8CIiIhGka/D5SE2tyOrVsxMdxr5p/YKCt5XBc5aaWjHRIYiIxIUSfD5OPLFFokPYd1WaXPC2Dvq6mYjIvkJd9CIiIhGkBC8iIhJBSvAiIiIRpGvwIiISFxriNrHUghcREYkgJXgREZEIUoIXERGJoLhdgzezUcAZwCp3PzosewE4MqySCqx39/R89v0e2ATsALIKmgpPRERE8hfPm+xGAyOAZ7IL3L1X9rKZPQhs2M3+Hdx9TdyiExERibC4JXh3n2ZmDfPbZmYG9AT+EK/nFxER2Z8l6hr8ycBKd19SwHYHJpvZbDPrv7sDmVl/M5tlZrNWr15d4oGKiIiURYlK8L2BcbvZ3s7djwO6Alea2SkFVXT3ke6e4e4ZaWlpJR2niIhImVTqCd7MkoBzgBcKquPuy8Kfq4BXgValE52IiEg0JKIF3xFY5O6Z+W00sypmlpK9DHQC5pdifCIiImVe3BK8mY0DPgaONLNMM7ss3HQ+ubrnzayOmU0MV2sDH5nZF8CnwFvuPilecYqIiERRPO+i711A+SX5lC0HuoXL3wHHxisuERGR/YFGshMREYkgzSYnIiLFtrsZ4ySx1IIXERGJICV4ERGRCFKCFxERiSAleBERkQhSghcREYkgJXgREZEIUoIXERGJICV4ERGRCFKCFxERiSAleBERkQhSghcREYkgJXgREZEIUoIXERGJICV4ERGRCFKCFxERiSAleBERkQhSghcREYkgJXgREZEIUoIXERGJICV4ERGRCFKCFxERiSAleBERkQhSghcREYkgJXgREZEIUoIXERGJoLgleDMbZWarzGx+TNkgM1tmZnPDR7cC9u1iZovN7BszGxivGEVERKIqni340UCXfMqHuXt6+JiYe6OZlQf+DXQFmgG9zaxZHOMUERGJnLgleHefBqwrxq6tgG/c/Tt3/w0YD5xVosGJiIhEXCKuwV9lZl+GXfjV89leF1gas54ZlomIiEghJZXy8/0HuBvw8OeDwKV7c0Az6w/0B6hfv/7exiciIqVg2JSvC9x23elNSjGS6CrVFry7r3T3He6+E3iCoDs+t2XAoTHr9cKygo450t0z3D0jLS2tZAMWEREpo0o1wZvZITGrZwPz86n2GXCEmTUys4rA+cDrpRGfiIhIVMSti97MxgHtgZpmlgncCbQ3s3SCLvrvgb+EdesAT7p7N3fPMrOrgHeA8sAod/8qXnGKiIhEUdwSvLv3zqf4qQLqLge6xaxPBPJ8hU5EREQKRyPZiYiIRJASvIiISAQpwYuIiESQEryIiEgEKcGLiIhEkBK8iIhIBCnBi4iIRJASvIiISAQpwYuIiESQEryIiEgElfZ0sSIlY+q9BW/rcHPpxSESEbubvlXKJrXgRUREIkgJXkREJIKU4EVERCJICV5ERCSClOBFREQiSAleREQkgpTgRUREIkgJXkREJIKU4EVERCJICV5ERCSClOBFREQiSAleREQkgpTgRUREIkgJXkREJIKU4EVERCJICV5ERCSClOBFREQiKG4J3sxGmdkqM5sfU3a/mS0ysy/N7FUzSy1g3+/NbJ6ZzTWzWfGKUUREJKri2YIfDXTJVTYFONrdjwG+Bm7ezf4d3D3d3TPiFJ+IiEhkxS3Bu/s0YF2ussnunhWufgLUi9fzi4iI7M8SeQ3+UuDtArY5MNnMZptZ/1KMSUREJBKSEvGkZnYrkAU8X0CVdu6+zMxqAVPMbFHYI5DfsfoD/QHq168fl3hFRETKmlJvwZvZJcAZwAXu7vnVcfdl4c9VwKtAq4KO5+4j3T3D3TPS0tLiELGIiEjZU6oJ3sy6ADcBZ7r7lgLqVDGzlOxloBMwP7+6IiIikr94fk1uHPAxcKSZZZrZZcAIIIWg232umT0W1q1jZhPDXWsDH5nZF8CnwFvuPilecYqIiERR3K7Bu3vvfIqfKqDucqBbuPwdcGy84hIREdkfaCQ7ERGRCFKCFxERiaCEfE1O9nFT7y3d/USkyIZN+TrRIcg+Ti14ERGRCFKCFxERiSAleBERkQhSghcREYkgJXgREZEIUoIXERGJICV4ERGRCCpSgg8ngikfr2BERESkZOw2wZtZOTPrY2ZvmdkqYBGwwswWmNn9Zta4dMIUERGRothTC34qcDhwM3Cwux/q7rWAdsAnwH1mdmGcYxQREZEi2tNQtR3dfXvuQndfB7wCvGJmFeISmYiIiBTbblvw2cndzJ7NvS27LL8PACIiIpJYhb3JrnnsSnij3fElH46IiIiUhD3dZHezmW0CjjGzjeFjE7AKeK1UIhQREZEi2+01eHe/F7jXzO5195tLKSYpKbubvrWDfp0iIlG2pxZ8Q4CCkrsF6pV8WCIiIrI39nQX/f1mVo6gO342sBqoDDQGOgCnAXcCmfEMUkRERIpmT13055lZM+AC4FLgYGArsBCYCAxx921xj1JERESKZI930bv7AuAe4A2CxP4/4DPgZSV3ERGRfdOeuuizjQE2AsPD9T7AM0DPeAQlIiIie6ewCf5od28Wsz7VzBbEIyARERHZe4Ud6GaOmZ2YvWJmrYFZ8QlJRERE9lZhW/DHAzPM7MdwvT6w2MzmAe7ux8QlOhERESmWwib4LnGNQkREREpUoRK8u/8Q70BERESk5BT2GnyxmNkoM1tlZvNjymqY2RQzWxL+rF7AvheHdZaY2cXxjFNERCRq4prggdHk7d4fCLzr7kcA74bruzCzGgQj5LUGWgF3FvRBQERERPKKa4J392nAulzFZxF8r57wZ/d8du0MTHH3de7+MzAF3QcgIiJSaPFuweentruvCJd/AmrnU6cusDRmPTMsExERkUIo7F30ceHubma+N8cws/5Af4D69euXSFwiIvuCYVO+TnQIZcbuztV1pzcpxUj2HYlowa80s0MAwp+r8qmzDDg0Zr1eWJaHu4909wx3z0hLSyvxYEVERMqiRCT414Hsu+IvJpiKNrd3gE5mVj28ua5TWCYiIiKFEO+vyY0DPgaONLNMM7sMGAqcbmZLgI7hOmaWYWZPArj7OuBuglnrPgMGh2UiIiJSCHG9Bu/uvQvYdFo+dWcB/WLWRwGj4hSaiIhIpCWii15ERETiTAleREQkgpTgRUREIkgJXkREJIKU4EVERCJICV5ERCSClOBFREQiSAleREQkgpTgRUREIkgJXkREJIISOl2sFNLUe8vGMUVEZJ+hFryIiEgEKcGLiIhEkBK8iIhIBCnBi4iIRJASvIiISAQpwYuIiESQEryIiEgEKcGLiIhEkBK8iIhIBCnBi4iIRJASvIiISAQpwYuIiESQEryIiEgEaTY5iZ7dzZTX4ebSi0NEJIHUghcREYkgJXgREZEIUoIXERGJoFJP8GZ2pJnNjXlsNLNrc9Vpb2YbYurcUdpxioiIlGWlfpOduy8G0gHMrDywDHg1n6ofuvsZpRmbiIhIVCS6i/404Ft3/yHBcYiIiERKohP8+cC4Ara1MbMvzOxtM2temkGJiIiUdQlL8GZWETgTeCmfzXOABu5+LPAIMGE3x+lvZrPMbNbq1avjE6yIiEgZk8gWfFdgjruvzL3B3Te6++ZweSJQwcxq5ncQdx/p7hnunpGWlhbfiEVERMqIRCb43hTQPW9mB5uZhcutCOJcW4qxiYiIlGkJGarWzKoApwN/iSn7K4C7Pwb0AK4wsyxgK3C+u3siYhURESmLEpLg3f0X4KBcZY/FLI8ARpR2XCIiIlGR6LvoRUREJA6U4EVERCJI08XK/qW4U8kWtF9xp5/VlLYSGjbl60SHIBGlFryIiEgEKcGLiIhEkBK8iIhIBCnBi4iIRJASvIiISAQpwYuIiESQEryIiEgEKcGLiIhEkBK8iIhIBCnBi4iIRJASvIiISAQpwYuIiESQEryIiEgEKcGLiIhEkKaL3VfsbvpQERGRIlILXkREJIKU4EVERCJICV5ERCSClOBFREQiSAleREQkgpTgRUREIkgJXkREJIKU4EVERCJICV5ERCSClOBFREQiKGEJ3sy+N7N5ZjbXzGbls93MbLiZfWNmX5rZcYmIU0REpCxK9Fj0Hdx9TQHbugJHhI/WwH/CnyIiIrIH+3IX/VnAMx74BEg1s0MSHZSIiEhZkMgE78BkM5ttZv3z2V4XWBqznhmWiYiIyB4ksou+nbsvM7NawBQzW+Tu04p6kPDDQX+A+vXrl3SMIru3u2l+O9xcenHIPm3YlK8THcJ+bXfn/7rTm5RiJKUrYS14d18W/lwFvAq0ylVlGXBozHq9sCz3cUa6e4a7Z6SlpcUrXBERkTIlIQnezKqYWUr2MtAJmJ+r2utA3/Bu+hOBDe6+opRDFRERKZMS1UVfG3jVzLJjGOvuk8zsrwDu/hgwEegGfANsAf6coFhFRETKnIQkeHf/Djg2n/LHYpYduLI04xIREYmKfflrciIiIlJMSvAiIiIRpAQvIiISQUrwIiIiEaQELyIiEkFK8CIiIhGkBC8iIhJBSvAiIiIRpAQvIiISQUrwIiIiEZTI6WJFom13U8mKSIE0vW7JUAteREQkgpTgRUREIkgJXkREJIKU4EVERCJICV5ERCSClOBFREQiSAleREQkgpTgRUREIkgJXkREJIKU4EVERCJICV5ERCSClOBFREQiSAleREQkgjSbXHEVd6awDjeXbBxScvaV2d92F4fePyIlqrgz1113epMSjqTkqQUvIiISQUrwIiIiEaQELyIiEkGlnuDN7FAzm2pmC8zsKzO7Jp867c1sg5nNDR93lHacIiIiZVkibrLLAv7u7nPMLAWYbWZT3H1BrnofuvsZCYhPRESkzCv1Fry7r3D3OeHyJmAhULe04xAREYmyhF6DN7OGQEtgZj6b25jZF2b2tpk1L9XAREREyriEfQ/ezJKBV4Br3X1jrs1zgAbuvtnMugETgCMKOE5/oD9A/fr14xixiIhI2ZGQFryZVSBI7s+7+39zb3f3je6+OVyeCFQws5r5HcvdR7p7hrtnpKWlxTVuERGRsiIRd9Eb8BSw0N3/VUCdg8N6mFkrgjjXll6UIiIiZVsiuuhPAi4C5pnZ3LDsFqA+gLs/BvQArjCzLGArcL67ewJiFRERKZNKPcG7+0eA7aHOCGBE6UQkIiISPRrJTkREJIKU4EVERCJI08Xuzr4yfaiIlKjdTRFa3GlAizvtqERPPN5fxaEWvIiISAQpwYuIiESQEryIiEgEKcGLiIhEkBK8iIhIBCnBi4iIRJASvIiISAQpwYuIiESQEryIiEgEKcGLiIhEkBK8iIhIBCnBi4iIRJASvIiISAQpwYuIiESQpostbZqCVvZGPN4/HW4u+WNGlKaElbJELXgREZEIUoIXERGJICV4ERGRCFKCFxERiSAleBERkQhSghcREYkgJXgREZEIUoIXERGJICV4ERGRCFKCFxERiaCEJHgz62Jmi83sGzMbmM/2Smb2Qrh9ppk1LP0oRUREyq5ST/BmVh74N9AVaAb0NrNmuapdBvzs7o2BYcB9pRuliIhI2ZaIFnwr4Bt3/87dfwPGA2flqnMWMCZcfhk4zcysFGMUEREp0xKR4OsCS2PWM8OyfOu4exawATioVKITERGJgDI/XayZ9Qf6h6ubzWxxIuMBagJrEhzD/kTne6/dUpTKkT/f1yc6gF1F/nzvYwp9vov7PonD+6tBQRsSkeCXAYfGrNcLy/Krk2lmSUA1YG1+B3P3kcDIOMRZLGY2y90zEh3H/kLnu3TpfJcune/SFbXznYgu+s+AI8yskZlVBM4HXs9V53Xg4nC5B/Ceu3spxigiIlKmlXoL3t2zzOwq4B2gPDDK3b8ys8HALHd/HXgKeNbMvgHWEXwIEBERkUJKyDV4d58ITMxVdkfM8jbgvNKOq4TsM5cL9hM636VL57t06XyXrkidb1PPt4iISPRoqFoREZEIUoIvAjOrbGafmtkXZvaVmd0VljcKh9T9Jhxit2JYriF3S4CZlTezz83szXBd5ztOzOx7M5tnZnPNbFZYVsPMppjZkvBn9bDczGx4eL6/NLPjEht92WNmqWb2spktMrOFZtZG5zs+zOzI8H2d/dhoZtdG+XwrwRfNr8Af3P1YIB3oYmYnEgylOywcWvdngqF2QUPulpRrgIUx6zrf8dXB3dNjvi40EHjX3Y8A3g3XIRhu+ojw0R/4T6lHWvY9DExy96bAsQTvc53vOHD3xeH7Oh04HtgCvEqUz7e761GMB3AgMAdoTTAwQlJY3gZ4J1x+B2gTLieF9SzRsZelB8E4Ce8CfwDeBEznO67n+3ugZq6yxcAh4fIhwOJw+XGgd3719CjUua4G/C/3e1Tnu1TOfSdgetTPt1rwRRR2F88FVgFTgG+B9R4MqQu7Dr2rIXf33kPATcDOcP0gdL7jyYHJZjY7HCUSoLa7rwiXfwJqh8uFGXZaCtYIWA08HV6CetLMqqDzXRrOB8aFy5E930rwReTuOzzo4qlHMHFO0wSHFFlmdgawyt1nJzqW/Ug7dz+OoHvySjM7JXajB00ZffWmZCQBxwH/cfeWwC/83j0M6HzHQ3jPzpnAS7m3Re18K8EXk7uvB6YSdBGnhkPqwq5D7+YMy7unIXclXycBZ5rZ9wSzDv6B4JqlznecuPuy8OcqguuTrYCVZnYIQPhzVVi9MMNOS8EygUx3nxmuv0yQ8HW+46srMMfdV4brkT3fSvBFYGZpZpYaLh8AnE5wU8xUgiF1IRhi97VwWUPu7gV3v9nd67l7Q4Iutffc/QJ0vuPCzKqYWUr2MsF1yvnsel5zn+++4d3GJwIbYro6ZQ/c/SdgqZkdGRadBixA5zveevN79zxE+HxroJsiMLNjCOapL0/w4ehFdx9sZocRtDBrAJ8DF7r7r2ZWGXgWaEk45K67f5eY6Ms2M2sP3ODuZ+h8x0d4Xl8NV5OAse4+xMwOAl4E6gM/AD3dfZ2ZGTAC6EJwR/Kf3X1WAkIvs8wsHXgSqAh8B/yZ8H8LOt8lLvzg+iNwmLtvCMsi+/5WghcREYkgddGLiIhEkBK8iIhIBCnBi4iIRJASvIiISAQpwYuIiESQEryIFFs4G9rfEh2HiOSlBC8ieyMVUIIX2QcpwYvI3hgKHB7Or31/ooMRkd9poBsRKTYzawi86e5HJzgUEclFLXgREZEIUoIXERGJICV4Edkbm4CURAchInkpwYtIsbn7WmC6mc3XTXYi+xbdZCciIhJBasGLiIhEkBK8iIhIBCnBi4iIRJASvIiISAQpwYuIiESQEryIiEgEKcGLiIhEkBK8iIhIBP1/gU3AB0pjzrsAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 30\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e4)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2436.280\n", + "test 0 : tsm = 568.602, tbsm = 503.420\n", + "Reaching the end of test data. Stop tests at 205. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 3.46 sigmas\n", + "Partial test after 1 epochs (took 38.43 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 20\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e4)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2325.162\n", + "test 0 : tsm = 594.538, tbsm = 515.925\n", + "Reaching the end of test data. Stop tests at 215. \n", + "===> delta1 = 0.018, delta2 = 0.010\n", + "p = 0.079 +/- 0.021\n", + "Separation = 1.51 sigmas\n", + "Partial test after 1 epochs (took 39.64 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 10\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e4)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2273.448\n", + "test 0 : tsm = 649.943, tbsm = 589.049\n", + "Reaching the end of test data. Stop tests at 220. \n", + "===> delta1 = 0.030, delta2 = 0.025\n", + "p = 0.273 +/- 0.039\n", + "Separation = 0.61 sigmas\n", + "Partial test after 1 epochs (took 39.92 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 5\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e4)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10 big batch" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl10-bigbatch')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2841.340\n", + "test 0 : tsm = 229.027, tbsm = -209.903\n", + "Reaching the end of test data. Stop tests at 175. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 13.98 sigmas\n", + "Partial test after 1 epochs (took 35.61 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 50\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e4)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2693.988\n", + "test 0 : tsm = 139.657, tbsm = -102.440\n", + "Reaching the end of test data. Stop tests at 185. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 9.57 sigmas\n", + "Partial test after 1 epochs (took 36.51 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 40\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e4)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2558.585\n", + "test 0 : tsm = 141.964, tbsm = -22.255\n", + "Reaching the end of test data. Stop tests at 195. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 6.71 sigmas\n", + "Partial test after 1 epochs (took 37.36 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 30\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e4)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2436.280\n", + "test 0 : tsm = 72.966, tbsm = 30.460\n", + "Reaching the end of test data. Stop tests at 205. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 4.07 sigmas\n", + "Partial test after 1 epochs (took 38.40 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 20\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e4)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2325.162\n", + "test 0 : tsm = 81.252, tbsm = 43.032\n", + "Reaching the end of test data. Stop tests at 214. \n", + "===> delta1 = 0.009, delta2 = 0.005\n", + "p = 0.019 +/- 0.011\n", + "Separation = 1.91 sigmas\n", + "Partial test after 1 epochs (took 38.94 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 10\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e4)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2273.448\n", + "test 0 : tsm = 59.320, tbsm = 73.057\n", + "Reaching the end of test data. Stop tests at 220. \n", + "===> delta1 = 0.026, delta2 = 0.023\n", + "p = 0.182 +/- 0.035\n", + "Separation = 0.81 sigmas\n", + "Partial test after 1 epochs (took 39.95 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 5\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e4)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1000 default batch" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2841.340\n", + "test 0 : tsm = 375.341, tbsm = -766.655\n", + "Reaching the end of test data. Stop tests at 175. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 9.52 sigmas\n", + "Partial test after 1 epochs (took 35.53 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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fTbVqaXTq1CN7WosW7Vi5MrjL4xFHdMwe37Ll0axalai7P4qIJEZZDO0su7Nv5b5x2pIlX/GnP13CSy8tpFq1NP73v1cBGDXqTp5//hPGjp3Ptdc+lj3/0qXf8Nhj/+P++//DDTecQ0ZGF1588TMqV96b999/K8/6n3nmHvr3b53ncc89lxda14IFH7Fjx3YaNjwox/jMzB1MmPAsHTvmvcfGG288RceOJ2c/NzMuueREzjnnSF57bWSxXhcREQncfvvttGjRglatWtG6dWtmzZpF586dadSoEbH3++jZsyepqalxr6dcH3ED1K/fhEMOaQ3AoYceyfLl3wNw8MGtuP76s+ncuSedO/fMnr9jx5NJSalE06Yt+e23ndkB2rRpy+xlYw0YMIQBA4YUq6Y1a1Zw443ncvPNo6lQIed3qzvvvJi2bY+jTZtjc4yfPXsKb7zxFE8++X72uCeffJ86dRqwbt0qLrmkG40bH0rbtscVqxYRkdJkxOQvS3R9V3VrVuj0Dz74gPHjxzN37lwqV67MmjVr2L496A8kLS2NGTNm0KlTJzZs2MCKFStKtLaClPvgrlSpcvZwxYoV+fXX4K6ADzzwFp988h7vvfcmTz99O2PHfgbAXnsF81eoUIGUlErZpznMKrBzZ2ae9T/zzD1MnPh8nvFt2hzHkCEP5Rm/ZcsmrrjiFC6++HZatjw6x7SRI29m/frVXHvt4znGf/XVfG69dRAPPfQ2aWm1ssfXqdMAgJo169C5cy8WLvxIwS0iUgwrVqygdu3aVK4c/O2vXfv3NkNnnXUWY8eOpVOnTrz22mv07t2bhQsXxr2mch/c+fntt99YuXIJGRldaN26E5MmjWXr1oJurVu44hxx79ixnSFDenHKKQM44YQ+OaaNG/ckH374Dv/857s5jsJ/+ulHhgzpzS23PJvjGvnWrT/z22+/UbVqNbZu/ZlZsyYxaNCNu7UPIiLl1Yknnsgtt9xCs2bNOOGEE+jbty/HH388AF27duXCCy9k586djB07lpEjR3LrrbfGvSYFdz5++20nN9xwDlu2bMTdOeusy6lWLS3u2508+SXmzn2PjRvXMn78KABuumkUhxzSmjvu+Av77XcA55/fAYAuXXpz4YU38sQTt7Bx41ruuutigOyffa1du5IhQ3oBsHNnJied1D/f6+IiIlKw1NRU5syZw/Tp05kyZQp9+/blzjvvBIKztJ06dWLs2LFs3bqVRN0My2IvrJdWGRkZXhI3GZk4cY66PC0nVq+eQ/fueq9Fom7x4sUcdthh2c8TfY07t1deeYXRo0ezefNm7r33Xn755Rd69erF8OHDueyyy0hNTWXLluKdoc29jwBmNsfdM/KbX0fcIiJSYgoL1uKGZGnwxRdfUKFCBQ4++GAA5s2bxwEHHMCCBQsAOPbYYxk2bBj9+vVLWE0KbhERkQJs2bKFyy67jA0bNpCSkkLTpk0ZOXIkffoE7ZDMjKuvvjqhNSm4RUQkMhJ91H7kkUcyc+bMPOOnTp2a7/zFPU2+O8p9BywFGTy4M4sWlY6+vbdt+4UrrjiFM844lDPPbMHDDw/NnvbTTz/y5z93oX//Npx1Vivef39CjmV/+ulHjj02lWefvTd73M03n0+3bnU488zDE7YPIiJSMhTcEXHuuVfz6quf8/zzn/DppzOYMeNtAJ566ja6dTuTMWM+4R//GJvdujzL/ff/NUdvagCnnXYeDz88MWG1i4hIySnXp8oL66scYMKEZ7nttkFkZmZy441Pc/jh7ZgzZxr33XdFuAbjiSfeY/HiOYwceROpqWl8881nnHDCmTRt2pIXXniQX3/dyn33jcvTdWlxVKmyDxkZXQCoVGkvDj20bUyf5MaWLZsA2LJlI+np9bOXmzp1HA0aNKFKlao51te27XH59vImIiKlX7k/4i6or3IITlGPGTOPoUP/yS23nA/Ac8/dyzXXPMqYMfN48snpVK4chPyXX37Ktdc+xssvL2bChGf58ccveeaZj+jZcxAvvvhwnu3Onj0l3z7Mzz+/Y555Y23evIHp09/kqKO6AvDnPw/n7befo0ePhlxxRQ+GDAm29csvWxg9+i4uvPCmEnmdRESkdIjbEbeZVQHeAyqH23nF3W8ysybAWKAWMAc41923x6uOXSmor3KAk04Kmve3bXscP/+8ic2bN3DEEccwYsRfOfnks+nSpTd16zYEoHnzo6hdux4ADRseRPv2JwJBH+azZ0/Js92MjC6MGTOvWLVmZmZy3XX96Nv3cho2PBCAiRNf4LTTzuOcc/7G/PkfcOON5/LiiwsYOXI4/ftfxT77xL/DexERSZx4nir/FfiDu28xs0rA+2b2NvBXYIS7jzWzx4ALgH/FsY5CFdRXOeS93ZqZcd55Q+nU6RTef38CF1xwDI888g7wex/mwXwVsp8X1If57NlTuP/+q/KMr1JlH55+Om8LRoDbbx/M/vsfTP/+V2aP+89/nuKhh4Lr1a1adWD79m1s2LCGBQtm8e67r/DQQ9ewefMGKlSowF57VaFv30t3+ZqIiEjpFbfg9qBLtqx28ZXChwN/APqH40cDw0licBdm0qQXycjowrx575OaWp3U1OosXfoNTZu2pGnTlixa9DHff/85qanF7w61uEfc//zn9WzZspEbbngyx/j99mvExx+/y2mnncd33y3m11+3UaNGOk8+OT17nscfH84++6QqtEVEdkPFihVp2bIl7k7FihV55JFH6NixI7/88gsXXngh8+fPx91JS0tj4sSJpKamYmacffbZPPfcc0BwxrRevXq0b9+e8ePH71E9cW2cZmYVCU6HNwUeBb4BNrh71iHoUqBBAcsOBgYDNGrUKJ5lFqhy5Sr079+GzMwd3Hjj0wCMGfMAs2dPoUKFChx4YAs6djyZ+fM/iGsdK1cu5emnb6dx40M555y2AJx55qX07DmIK6+8j9tuu5AxY0ZgZgwfPmqXN2a/9tp+zJkzlQ0b1tCjR0MGD76Znj0viOs+iIiUiCl3lOz6ugzb5Sx777038+YFB1rvvPMOw4YNY9q0aTz44IPUrVuXzz4L7h75xRdfUKlSJQCqVq3KggUL2Lp1K3vvvTeTJ0+mQYN8467Y4hrc7r4TaG1macDrwKHFWHYkMBKCvsrjUV/9+o156aUF2c/PPff33m9Gjpya7zLXXJO3oVlGRmcyMjrnu2zuabujbt2GzJ6d/0tw4IHNefrpGYUu/+c/D8/x/B//eGGP6hERKa82bdpEjRo1gOCWnwcccED2tEMOOSTHvD169OCtt96iT58+vPDCC/Tr14/p06ezpxLSqtzdNwBTgA5AmpllfWFoCCxLRA0iIiK7Y+vWrbRu3ZpDDz2UQYMGccMNNwBw/vnnc9ddd9GhQweuv/56vvrqqxzLZd2ve9u2bcyfP5/27duXSD1xC24zSw+PtDGzvYFuwGKCAM+62fRA4I141SAiIrKnsk6Vf/7550ycOJEBAwbg7rRu3Zpvv/2WIUOGsG7dOo466igWL16cvVyrVq34/vvveeGFF+jRo0eJ1RPPU+X1gNHhde4KwEvuPt7MFgFjzew24BPgqTjWICIiUmI6dOjAmjVrWL16NXXq1CE1NZXevXvTu3dvKlSowIQJE3LcovOPf/wjV199NVOnTmXt2rUlUkPcjrjdfb67t3H3Vu5+uLvfEo7/1t3buXtTd/+Tu/8arxqiaPv2Xxk2rC89ezZl4MD2BfZwNnPmRHr3PoSePZsyatSd2eOXLfuOgQPb07NnU4YN68uOHduLtd6ieOedsTz11O2FzrNx4zouvrgbvXodzMUXd2PTpvX5zjd+/Gh69TqYXr0OZvz40dnjFy+eQ9++LenZsyn33HM5WfeNL+p6RUTi4fPPP2fnzp3UqlWLGTNmsH598Ddo+/btLFq0KMc1bwhOp9900020bNmyxGoo9z2nlTZvvPEU1arVYNy4r+nf/yoefvjveebZuXMnd911CQ899DYvv7yId955gW+/XQTAww//nf79r2LcuK+pVq0Gb7zxVJHXG+vNN0fx+OPD8502c+bbdOzYvdDlR426k3btuvL661/Rrl3XHF8usmzcuI4nnriZUaNmMXr0RzzxxM3ZQXzHHRdx/fVP8PrrX7FkyVfMnDmxyOsVESlJWde4W7duTd++fRk9ejQVK1bkm2++4fjjj6dly5a0adOGjIwMzjjjjBzLNmzYkMsvv7xE6yn3fZVfdll3DjvsSD7/fC4HHtiCW255hipV9klaTdOmvcHgwcMB6Nq1D3fffSnunuMnXgsXfsT++zfN7j3txBPPYtq0N2jS5DA+/vh/3HbbGABOPXUgI0cOp0+fi4q03qJwd778ch6HHtp2l/uR1br+1FMHMnhwZy6//K4c83zwwTu0a9eN6tVrAtCuXTdmzpxIRkZnfv55Ey1bHg1Ajx4DmDp1HMccc3KR1isiZVgRfr5V0nbu3Jnv+AEDBjBgwIB8p+V3e8/OnTvTuXPnPa6n3B9x//DDF/TpczGvvLKYqlX35eWX/1ni2xg06Nh8+yWfNeu/eeZdtWoZdevuD0BKSgqpqdXZuHFtgfMA1KnTkFWrlrFx41qqVUsjJSUlx/iirrcovvjiEw4++IhdBv66dSuzu4CtVWs/1q1bmWee1atz7kfdug1ZvXpZWGvDPOOLul4RkbKsXB9xA9Stuz+tWx8DQI8e5zB27EM5fs9dEmJ7MSvNNmxYy8UXBzcv2bhxHZmZ25k2bRwAt9zyLE2btmTmzIl5bhO6K2ZW7CP7ZK5XRKQ0K/fBnV9/5CVt0KBj+eWXzXnGX3HFvbRvf0KOcXXqNGDlyiXUrduQzMxMtmzZSPXqtfKdJ8uqVUupU6cB1avXYvPmDWRmZpKSkpI9vqjrTUurld0N65tvjmL58u/zdN7y4YeTuPvu4A5ql156EuvWreSwwzLydMVas2Zd1qxZQe3a9VizZgU1atTJs//p6Q2YM2dq9vOVK5dy5JGdw1qX5hifnt6gyOsVESnLyv2p8p9++jG7y9KJE8fQunWnEt/Gk09OZ8yYeXkeuUMb4Ljj/pjduvrdd1/hqKP+kOfLRPPmR7FkyVcsW/YdO3ZsZ9KksRx33B8xMzIyuvDuu68AQYvt448/vcjr3ZUtWzayc2cmaWlB4D/yyDuMGTMvT2gDHH/879uLrSNWhw4nMWvWJDZtWs+mTeuZNWsSHTqcRO3a9ahadV8+++xD3J0JE57JXr4o6xWRsiXrVyVl0e7sW7kP7gMOOISXX36UPn0OY9Om9fTpc1FS6zn99AvYuHEtPXs25fnn7+fSS4NW06tXL+fyy4Mf8KekpDBkyCNcdtlJ9OlzGCeccCYHHdQCgMsuu4vnn7+fnj2bsnHjWk4//YJC11scH344mXbt8n7ZyM/AgUOZNWsyvXodzEcf/ZfzzhsKwKJFs7n11kEAVK9ekwsuuIEBA45iwICjGDToxuyGakOH/pNbbx1Ez55NadDgII455uRC1ysiZVOVKlVYu3ZtmQxvd2ft2rVUqVKlWMtZFF6MjIwMnz179h6vZ+LEOaSnH5n9fPny77nyylNz9FcuBQuCdFB2a+/SbPXqOXTvfuSuZxSREjVi8pcFTruqW7Nir2/Hjh0sXbqUbdu27UlZpVaVKlVo2LBh9s1JspjZHHfPyG+Zcn+NW4ouv1PiIiLxVKlSJZo0aZLsMkqVcn2qPPfdwUREREq7ch3cpdmKFT9w0UVdOeusVgwe3DlHK+tYgwd3pnfvQ7J/G75u3SoA5s59j7PPbkv79in897+v5FjmwQev4cwzW9Cnz2E5uhMVEZHST6fKS6kHHriaU04ZwKmnDuTjj//HI48M49Zbn8133ttue57mzXNeCtlvv0YMHz6KZ5+9N8f4Tz+dyaefzuCFF+YDMGhQJ+bMmbbH9wwXEZHEKNfBvXXrzwwdeiarVi1l586dDBp0Ayee2JfFi+cwYsRf+eWXLaSl1Wb48FHUrl2PwYM706zZEcydO43MzExuvPFpDj+8XVxq++67RVx11f0AZGR04eqrexZr+fr1GwNQoULOkypmxvbt28KbjziZmTuoVatuSZQsIiIJUK6De+bMiaSn1+fBB98Cgt8pZ31EhAcAABXuSURBVGbu4J57LuO++96gRo10Jk16kUcfvY6bbnoagG3bfmHMmHnMnfset9xyfrGukRenI5aDDz6CKVNeo1+/K5gy5XV+/nkzGzaszf4Ndaybb/4/KlasyB/+cAYXXHB9ob/PbtWqAxkZXejevR7uzplnXkqTJocVOL+IiJQu5Tq4mzZtyQMP/I2HHvo7xx57Km3aHMvXXy/gm28WcMkl3YCgc/msvrEBTjqpHwBt2x7Hzz9vYvPmDVSrllak7RWn69Mrr7yXu+++lDffHEXbtsdRp04DKlasmGe+2257njp1GvDzz5u55pozeOutZzn11Pw7vQdYsuRrvvtuMRMmBNfML7mkG598Mp02bY4tcm0iIpI85Tq4DzigGc89N5cZMybwr39dz1FHdaVLl14ceGAL/v3vD/JdZk+6SC3OEXd6en3uuec1AH75ZQv/+9+r+X5ByOrStGrVanTv3p+FCz8qNLinTHmdli2PZp99UgHo2PFk5s//QMEtIhIR5Tq4V69ezr771qRHj3OoVi2NceOe5LzzhrJ+/Wrmz/+AVq06kJm5gx9++DK7Z7JJk14kI6ML8+a9T2pqdVJTqxd5e8U54t6wYQ377luTChUq8O9/38Ef/3h+nnmCPsc3kJZWm8zMHUyfPn6XPZvtt18jxo17gszMYYAzd+40+vW7ssh1iYhIcpXr4P7668948MEhVKhQgZSUSgwd+i8qVdqLu+56hXvvvTy7b+5+/a7MDu7KlavQv38bMjN3cOONT8etttmzp/Loo8MwM9q0OY6///3R7Gn9+7dmzJh57NjxK5deehKZmTv47bedtGt3Ar16XQjAwoUfM2RILzZtWs/06W8ycuRNvPTSQrp27cPHH/+Ps85qiZnRoUN3jjvutLjth4iIlKxy3eVpcQ0e3Jkrr7w3z0+vpPRRl6ciyVHSXZ6WV4V1eaoOWERERCKkXJ8qL66RI6cmuwQRESnndMQtIiISIQpuERGRCFFwi4iIREi5usadlrYXq1fPSXYZkgBpaXsluwQRkbgoV8F99NEtk12CiIjIHtGpchERkQhRcIuIiESIgltERCRCFNwiIiIRouAWERGJEAW3iIhIhCi4RUREIkTBLSIiEiEKbhERkQhRcIuIiESIgltERCRCFNwiIiIRouAWERGJEAW3iIhIhCi4RUREIiRuwW1m+5vZFDNbZGYLzeyKcPxwM1tmZvPCR4941SAiIlLWpMRx3ZnA39x9rplVA+aY2eRw2gh3vzeO2xYRESmT4hbc7r4CWBEObzazxUCDeG1PRESkPIjnEXc2M2sMtAFmAccAl5rZAGA2wVH5+nyWGQwMBmjUqFEiyhQRkTgaMfnLAqdd1a1ZAiuJtrg3TjOzVOBV4Ep33wT8CzgIaE1wRH5ffsu5+0h3z3D3jPT09HiXKSIiEglxDW4zq0QQ2s+7+2sA7r7S3Xe6+2/AE0C7eNYgIiJSlsSzVbkBTwGL3f3+mPH1YmbrBSyIVw0iIiJlTTyvcR8DnAt8ZmbzwnHXAv3MrDXgwPfAn+NYg4iISJkSz1bl7wOWz6QJ8dqmiIhIWaee00RERCJEwS0iIhIhCm4REZEIUXCLiIhESEJ6ThMRkWhRL2ell464RUREIkTBLSIiEiEKbhERkQhRcIuIiESIgltERCRCFNwiIiIRouAWERGJEAW3iIhIhCi4RUREIkQ9p4mISLEU1quaxJ+OuEVERCJEwS0iIhIhCm4REZEIUXCLiIhEiIJbREQkQhTcIiIiEaLgFhERiRAFt4iISIQouEVERCJEwS0iIhIhCm4REZEIUXCLiIhEiIJbREQkQhTcIiIiEaLgFhERiRAFt4iISIQouEVERCJEwS0iIhIhCm4REZEISUl2ASIiIiMmf1ngtKu6NUtgJaWfjrhFREQiRMEtIiISIQpuERGRCFFwi4iIRIiCW0REJEIU3CIiIhESt+A2s/3NbIqZLTKzhWZ2RTi+pplNNrOvwn9rxKsGERGRsiaeR9yZwN/cvTlwNHCJmTUHhgLvuvvBwLvhcxERESmCuAW3u69w97nh8GZgMdAAOB0YHc42GugZrxpERETKmoT0nGZmjYE2wCygrruvCCf9BNQtYJnBwGCARo0axb9IKdiUOwqe1mVY4uoQEZH4N04zs1TgVeBKd98UO83dHfD8lnP3ke6e4e4Z6enp8S5TREQkEuIa3GZWiSC0n3f318LRK82sXji9HrAqnjWIiIiUJfFsVW7AU8Bid78/ZtJ/gIHh8EDgjXjVICIiUtbE8xr3McC5wGdmNi8cdy1wJ/CSmV0A/ACcGccaREREypS4Bbe7vw9YAZO7xmu7IiIiZZl6ThMREYkQBbeIiEiEKLhFREQiRMEtIiISIQnpOU3KMPWqJiKSUDriFhERiRAFt4iISIQouEVERCJEwS0iIhIhCm4REZEIUXCLiIhEiIJbREQkQhTcIiIiEaLgFhERiRAFt4iISIQouEVERCJEwS0iIhIhCm4REZEIUXCLiIhEiIJbREQkQhTcIiIiEaLgFhERiRAFt4iISIQouEVERCIkJdkFSCkx5Y5kVyAiIkVQrCNuM6tqZhXjVYyIiIgUrtDgNrMKZtbfzN4ys1XA58AKM1tkZveYWdPElCkiIiKw6yPuKcBBwDBgP3ff393rAJ2AD4G7zOycONcoIiIioV1d4z7B3XfkHunu64BXgVfNrFJcKhMREZE8Cj3izgptM3s297SscfkFu4iIiMRHURuntYh9EjZQO7LkyxEREZHC7Kpx2jAz2wy0MrNN4WMzsAp4IyEVioiISLZdnSq/w92rAfe4+77ho5q713L3YQmqUUREREK7OuJuDFBQSFugYcmXJSIiIvnZVavye8ysAsFp8TnAaqAK0BToAnQFbgKWxrNIERERCRQa3O7+JzNrDpwNnA/sB2wFFgMTgNvdfVvcqxQRERGgCK3K3X0RcBvwJkFgfwd8DLyi0BYREUmsot5kZDSwCXgofN4feAY4Mx5FiYiISP6KGtyHu3vzmOdTzGxRPAoSERGRghW1A5a5ZnZ01hMzaw/Mjk9JIiIiUpCiHnEfCcw0sx/D542AL8zsM8DdvVVcqhMREZEcihrc3Yu7YjN7GjgVWOXuh4fjhgMXEvysDOBad59Q3HWLiIiUV0UKbnf/YTfWPQp4hKARW6wR7n7vbqxPRESk3CvqNe5ic/f3gHXxWr+IiEh5VNRT5SXpUjMbQNC47W/uvj6/mcxsMDAYoFGjRgksrwybckeyKxARkT0UtyPuAvwLOAhoDawA7itoRncf6e4Z7p6Rnp6eqPpERERKtYQGt7uvdPed7v4b8ATQLpHbFxERibqEBreZ1Yt52gtYkMjti4iIRF3crnGb2QtAZ6C2mS0luItYZzNrDTjwPfDneG1fRESkLIpbcLt7v3xGPxWv7YmIiJQHiW6cJiIiIntAwS0iIhIhCm4REZEIUXCLiIhEiIJbREQkQpLR5anEk7o1FZEyZsTkLwucdlW3ZgmspHTQEbeIiEiEKLhFREQiRMEtIiISIQpuERGRCFFwi4iIRIiCW0REJEIU3CIiIhGi4BYREYkQBbeIiEiEqOc0KV0K6/mty7DE1SEiUkrpiFtERCRCFNwiIiIRouAWERGJEAW3iIhIhCi4RUREIkTBLSIiEiEKbhERkQhRcIuIiESIgltERCRCFNwiIiIRouAWERGJEAW3iIhIhCi4RUREIkTBLSIiEiEKbhERkQhRcIuIiESIgltERCRCFNwiIiIRouAWERGJkJRkFyAiIskzYvKXyS5BiklH3CIiIhGi4BYREYkQBbeIiEiEKLhFREQiRMEtIiISIXELbjN72sxWmdmCmHE1zWyymX0V/lsjXtsXEREpi+J5xD0K6J5r3FDgXXc/GHg3fC4iIiJFFLfgdvf3gHW5Rp8OjA6HRwM947V9ERGRsijR17jruvuKcPgnoG6Cty8iIhJpSes5zd3dzLyg6WY2GBgM0KhRo4TVJSVoyh0FT+syLHF1iIiUIYk+4l5pZvUAwn9XFTSju4909wx3z0hPT09YgSIiIqVZooP7P8DAcHgg8EaCty8iIhJp8fw52AvAB8AhZrbUzC4A7gS6mdlXwAnhcxERESmiuF3jdvd+BUzqGq9tioiIlHXqOU1ERCRCFNwiIiIRouAWERGJEAW3iIhIhCi4RUREIiRpPafJHiisR7KyTD2xiYjoiFtERCRKFNwiIiIRouAWERGJEAW3iIhIhCi4RUREIkTBLSIiEiEKbhERkQhRcIuIiESIgltERCRC1HNaIqjHr7zKa+9vIiJ7SEfcIiIiEaLgFhERiRAFt4iISIQouEVERCJEwS0iIhIhCm4REZEIUXCLiIhEiIJbREQkQhTcIiIiEaKe00oz9S4mIlKoEZO/LHDaVd2aJbCSxNERt4iISIQouEVERCJEwS0iIhIhCm4REZEIUXCLiIhEiIJbREQkQhTcIiIiEaLgFhERiRAFt4iISISo57RkU+9oIiJSDDriFhERiRAFt4iISIQouEVERCJEwS0iIhIhCm4REZEISUqrcjP7HtgM7AQy3T0jGXWIiIhETTJ/DtbF3dckcfsiIiKRo1PlIiIiEZKs4HZgkpnNMbPBSapBREQkcpJ1qryTuy8zszrAZDP73N3fi50hDPTBAI0aNUpGjVJWFNY7XZdhiatDRKQEJOWI292Xhf+uAl4H2uUzz0h3z3D3jPT09ESXKCIiUiolPLjNrKqZVcsaBk4EFiS6DhERkShKxqnyusDrZpa1/THuPjEJdYiIiEROwoPb3b8Fjkj0dkVERMoC/RxMREQkQhTcIiIiEaLgFhERiRAFt4iISIQouEVERCIkmTcZESk5hfWOJiLl0ojJX+Y7/qpuzRJcScnSEbeIiEiEKLhFREQiRMEtIiISIQpuERGRCFFwi4iIRIiCW0REJEIU3CIiIhGi4BYREYkQBbeIiEiEqOe04iisd64uwxJXh4iUSwX1BAaF9wZW2HKS0+6+xomkI24REZEIUXCLiIhEiIJbREQkQhTcIiIiEaLgFhERiRAFt4iISIQouEVERCJEwS0iIhIhCm4REZEIKZ89p6kHNMlS2GehMPqcSCmj3tGKLuqvlY64RUREIkTBLSIiEiEKbhERkQhRcIuIiESIgltERCRCFNwiIiIRouAWERGJEAW3iIhIhCi4RUREIqR89pxWmN3tSWt3lxORcqewnruu6tYsgZVIcZSW901H3CIiIhGi4BYREYkQBbeIiEiEKLhFREQiRMEtIiISIUkJbjPrbmZfmNnXZjY0GTWIiIhEUcKD28wqAo8CJwPNgX5m1jzRdYiIiERRMo642wFfu/u37r4dGAucnoQ6REREIicZwd0AWBLzfGk4TkRERHah1PacZmaDgcHh0y1m9kURFqsNrIlfVaWa9j2hrk3s5gpWXt/3Mrvff931LGV234ug1O57Ed634jqgoAnJCO5lwP4xzxuG43Jw95HAyOKs2Mxmu3vGnpUXTdp37Xt5Ul73G7Tv5XXfYyXjVPnHwMFm1sTM9gLOAv6ThDpEREQiJ+FH3O6eaWaXAu8AFYGn3X1housQERGJoqRc43b3CcCEOKy6WKfWyxjte/lUXve9vO43aN/LPXP3ZNcgIiIiRaQuT0VERCIkksFtZq3N7EMzm2dms82sXTjezOyhsCvV+WbWNmaZgWb2VfgYmLzq95yZXWZmn5vZQjO7O2b8sHDfvzCzk2LGl6kuZs3sb2bmZlY7fF7m33czuyd8z+eb2etmlhYzrVy871nK6n5lMbP9zWyKmS0K/49fEY6vaWaTw8/yZDOrEY4v8PMfRWZW0cw+MbPx4fMmZjYr3L8Xw0bNmFnl8PnX4fTGyaw7odw9cg9gEnByONwDmBoz/DZgwNHArHB8TeDb8N8a4XCNZO/Hbu57F+C/QOXweZ3w3+bAp0BloAnwDUHjv4rh8IHAXuE8zZO9H3uw//sTNGz8Aahdjt73E4GUcPgu4K7y9L7HvA5lcr9y7WM9oG04XA34Mnyf7waGhuOHxnwG8v38R/VB8JPoMcD48PlLwFnh8GPAReHwxcBj4fBZwIvJrj1Rj0gecQMO7BsOVweWh8OnA8944EMgzczqAScBk919nbuvByYD3RNddAm5CLjT3X8FcPdV4fjTgbHu/qu7fwd8TdC9bFnrYnYEcA3BZyBLmX/f3X2Su2eGTz8k6P8Ays/7nqWs7lc2d1/h7nPD4c3AYoLeJU8HRoezjQZ6hsMFff4jx8waAqcAT4bPDfgD8Eo4S+79zno9XgG6hvOXeVEN7iuBe8xsCXAvMCwcX1B3qmWpm9VmwLHhqaFpZnZUOL7M77uZnQ4sc/dPc00q8/uey/kER1hQ/va9rO5XvsLTv22AWUBdd18RTvoJqBsOl6XX5AGCL+a/hc9rARtivrTG7lv2fofTN4bzl3mlucvT/wL75TPpOqArcJW7v2pmZwJPAScksr542sW+pxCc+j0aOAp4ycwOTGB5cbWLfb+W4JRxmVTYvrv7G+E81wGZwPOJrE0Sz8xSgVeBK919U+zBpLu7mZWpnwSZ2anAKnefY2adk11PaVZqg9vdCwxiM3sGuCJ8+jLhaRUK7k51GdA51/ipJVRqidvFvl8EvObBhZ2PzOw3gv57C+tKdpddzJYWBe27mbUkuIb7afgHrCEwN2yYWObfdwAzOw84Fegavv9QRt73YihSl8lRZ2aVCEL7eXd/LRy90szqufuK8FR41mWysvKaHAP80cx6AFUILoc+SHDqPyU8qo7dt6z9XmpmKQSXTdcmvuwkSPZF9t15EFzz6RwOdwXmhMOnkLORxkfh+JrAdwQNlGqEwzWTvR+7ue9/AW4Jh5sRnCoyoAU5Gyl9S9CQJyUcbsLvjXlaJHs/SuB1+J7fG6eVh/e9O7AISM81vry972Vyv3LtowHPAA/kGn8PORun3R0O5/v5j/KD4At3VuO0l8nZOO3icPgScjZOeynZdSfs9Ul2Abv5pnYC5oT/aWcBR4bjDXiUoNXpZ0BGzDLnEzTc+Rr4v2Tvwx7s+17Ac8ACYC7wh5hp14X7/gVhq/twfA+ClqnfEJx2Tfp+lMDrEBvc5eF9/5rgS9q88PFYeXzfy/J+xexfJ4LGl/Nj3u8eBNdv3wW+IvhlSc1w/gI//1F95AruA4GPwv8DL/P7L2qqhM+/DqcfmOy6E/VQz2kiIiIREtVW5SIiIuWSgltERCRCFNwiIiIRouAWERGJEAW3iIhIhCi4RSQPM0szs4uTXYeI5KXgFpH8pBHcfUlEShkFt4jk507gIAvueX9PsosRkd+pAxYRySO8K9V4dz88yaWISC464hYREYkQBbeIiEiEKLhFJD+bgWrJLkJE8lJwi0ge7r4WmGFmC9Q4TaR0UeM0ERGRCNERt4iISIQouEVERCJEwS0iIhIhCm4REZEIUXCLiIhEiIJbREQkQhTcIiIiEaLgFhERiZD/B5Lz9DZpY8FYAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 50\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e4)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2693.988\n", + "test 0 : tsm = 209.790, tbsm = -395.010\n", + "Reaching the end of test data. Stop tests at 185. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 6.97 sigmas\n", + "Partial test after 1 epochs (took 36.61 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 40\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e4)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2558.585\n", + "test 0 : tsm = 261.937, tbsm = -172.302\n", + "Reaching the end of test data. Stop tests at 195. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 4.94 sigmas\n", + "Partial test after 1 epochs (took 37.19 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 30\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e4)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2436.280\n", + "test 0 : tsm = 163.723, tbsm = -5.391\n", + "Reaching the end of test data. Stop tests at 205. \n", + "===> delta1 = 0.005, delta2 = 0.000\n", + "p = 0.005 +/- 0.005\n", + "Separation = 2.99 sigmas\n", + "Partial test after 1 epochs (took 38.35 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 20\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e4)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2325.162\n", + "test 0 : tsm = 119.495, tbsm = 196.631\n", + "Reaching the end of test data. Stop tests at 214. \n", + "===> delta1 = 0.023, delta2 = 0.016\n", + "p = 0.136 +/- 0.028\n", + "Separation = 1.16 sigmas\n", + "Partial test after 1 epochs (took 38.88 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 10\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e4)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2273.448\n", + "test 0 : tsm = 230.866, tbsm = 149.098\n", + "Reaching the end of test data. Stop tests at 219. \n", + "===> delta1 = 0.031, delta2 = 0.027\n", + "p = 0.315 +/- 0.042\n", + "Separation = 0.46 sigmas\n", + "Partial test after 1 epochs (took 39.24 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 5\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e4)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/tutorial_particle_physics/TestEstimator.ipynb b/examples/tutorial_particle_physics/TestEstimator.ipynb new file mode 100644 index 000000000..a5dbfd08b --- /dev/null +++ b/examples/tutorial_particle_physics/TestEstimator.ipynb @@ -0,0 +1,1785 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting tabulate\n", + " Downloading tabulate-0.8.7-py3-none-any.whl (24 kB)\n", + "Installing collected packages: tabulate\n", + "Successfully installed tabulate-0.8.7\n", + "\u001b[33mWARNING: You are using pip version 20.1.1; however, version 20.2.3 is available.\n", + "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n" + ] + } + ], + "source": [ + "! pip install tabulate" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=========== Random Seed: 1 ===========\n" + ] + } + ], + "source": [ + "from OurTrainingTools import *\n", + "\n", + "def test_model(madminermodel, test_input_sm, test_input_bsm, epochs, e, n_meas, pm, verbose_t=True, verbose_period_t=1e5, title=''):\n", + " \n", + " def compute_t(madminermodel, nev, counter, test_input):\n", + " n_gen = 0\n", + " while n_gen == 0:\n", + " n_gen = np.random.poisson(nev)\n", + " \n", + " if (counter + n_gen) >= len(test_input):\n", + " return 0., -1\n", + " \n", + " points = test_input[int(counter): int(counter+n_gen)]\n", + " #print('counter = %d, counter+n_gen = %d'%(counter, counter+n_gen))\n", + " #ratio = (madminermodel.evaluate_log_likelihood_ratio(points.cpu().numpy(), \n", + " # np.array([0.]))[0][0])\n", + " log_ratio = (madminermodel.evaluate_log_likelihood_ratio(points.numpy(), \n", + " np.array([0.]))[0][0])\n", + " log_ratio = torch.tensor(log_ratio)\n", + " #ratio = 1./ratio\n", + " log_ratio = log_ratio\n", + " out = 2 * (NBSM - NSM - (log_ratio+torch.log(torch.tensor(NBSM/NSM))).sum(0))\n", + " return out, int(counter+n_gen)\n", + " \n", + " test_start = time.time()\n", + " if verbose_t:\n", + " print(\"NSM = %.3f --- NBSM = %.3f\"%(NSM, NBSM))\n", + " tsm = torch.empty(n_meas)\n", + " tbsm = torch.empty(n_meas)\n", + " \n", + " tsmcount = torch.zeros(n_meas+1)\n", + " tbsmcount = torch.zeros(n_meas+1)\n", + " \n", + " for i in range(n_meas):\n", + " tsm[i], tsmcount[i+1] = compute_t(madminermodel, NSM, tsmcount[i], \n", + " test_input_sm)\n", + " tbsm[i], tbsmcount[i+1] = compute_t(madminermodel, NBSM, tbsmcount[i], \n", + " test_input_bsm)\n", + " \n", + " if (tsmcount[i+1] < 0) or (tbsmcount[i+1] < 0):\n", + " print('Reaching the end of test data. Stop tests at %d. '%i)\n", + " tsm, tbsm = tsm[: i], tbsm[: i]\n", + " n_meas = i\n", + " break\n", + " \n", + " if i % (verbose_period_t) == 0:\n", + " print('test %s: tsm = %.3f, tbsm = %.3f'%(\n", + " str(i).ljust(4), tsm[i], tbsm[i]))\n", + " \n", + " test_duration = time.time() - test_start\n", + " \n", + " mu_sm = tsm.mean().item()\n", + " mu_bsm = tbsm.mean().item()\n", + " sigma_sm = tsm.std().item()\n", + " sigma_bsm = tbsm.std().item()\n", + " med_sm = tsm.median().item()\n", + " \n", + " sep = (mu_sm - mu_bsm)/sigma_bsm\n", + " p = 1.*len([i for i in tbsm if i > med_sm])/len(tsm) \n", + " #print(len([i for i in tbsm if i>med_sm]))\n", + " delta1 = (p * (1 - p)/n_meas)**0.5\n", + " delta2 = (sigma_sm/sigma_bsm) * np.exp(-((mu_bsm - mu_sm)**2)/(\n", + " 2 * sigma_bsm**2))/(2*(n_meas**0.5))\n", + " print('===> delta1 = %.3f, delta2 = %.3f'%(delta1, delta2))\n", + " deltap = (delta1**2 + delta2**2)**0.5\n", + " \n", + " if verbose_t:\n", + " print('p = %.3f +/- %.3f' %(p, deltap))\n", + " print('Separation = %.2f sigmas'%(sep))\n", + " training_properties = '/toydata/madminer-carl-'+title\n", + " plot_histogram(tsm, tbsm, int(NSM), int(NBSM), p, deltap, sep, epochs, e, \n", + " training_properties, results_path)\n", + " print('Partial test after %d epochs (took %.2f seconds)\\n'\n", + " %(e, test_duration))\n", + " \n", + " \n", + " return tsm, tbsm, NSM, NBSM" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_histogram(tsm, tbsm, nsm, nbsm, p, deltap, sep, epochs, \n", + " e, training_properties, results_folder):\n", + " mint = torch.min(torch.cat((tsm, tbsm))).item()\n", + " maxt = torch.max(torch.cat((tsm, tbsm))).item()\n", + " \n", + " # for some reason the code complains if i don't detach the variables \n", + " # from their grad-on versions\n", + " tsm, tbsm = tsm.detach(), tbsm.detach()\n", + " \n", + " bins = np.linspace(mint, maxt, 60)\n", + " plt.figure(figsize=(8, 6))\n", + " ax = plt.subplot()\n", + " plt.hist(tsm, bins, alpha=0.5, label='SM')\n", + " plt.hist(tbsm, bins, alpha=0.5, label='BSM')\n", + " plt.legend(loc='upper right')\n", + " \n", + " sn = 'nsm = %s \\nnbsm = %s'%(str(nsm), str(nbsm))\n", + " sp = 'p '+'= '+ ('%.3f +/- %.3f'%(p, deltap))\n", + " ssep = 'sep ' + '= ' + ('%.3f'%(sep))\n", + " \n", + " plt.text(x=0.05, y=0.85, transform=ax.transAxes, \n", + " s=sn+'\\n'+sp+'\\n'+ssep, bbox=dict(facecolor='blue', alpha=0.2))\n", + " plt.xlabel('t')\n", + " plt.ylabel('p(t)')\n", + " if epochs == e:\n", + " plt.title('Final test\\n' + training_properties)\n", + " filename = results_folder + training_properties \\\n", + " + ' histogram.pdf'\n", + " plt.savefig(filename)\n", + " return \n", + " plt.title(training_properties)\n", + " plt.show()\n", + " \n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from madminer import ParameterizedRatioEstimator\n", + "from madminer.ml.morphing_aware import MorphingAwareRatioEstimator\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# In Sample Test" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_50_in.h5', 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e2)\n", + "results_path = os.getcwd()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2841.340\n", + "test 0 : tsm = 372.309, tbsm = -467.241\n", + "Reaching the end of test data. Stop tests at 176. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 12.22 sigmas\n", + "Partial test after 1 epochs (took 35.50 seconds)\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "(tensor([372.3088, 536.2662, 427.9580, 395.0504, 382.5649, 323.1160, 467.0016,\n", + " 439.1082, 480.2043, 362.3309, 409.7758, 399.1116, 393.8975, 372.7985,\n", + " 456.2903, 561.3904, 382.0223, 520.1276, 438.5421, 411.9276, 526.2781,\n", + " 347.1064, 417.7982, 369.4116, 543.7470, 513.4364, 478.0151, 596.1177,\n", + " 464.7519, 485.3652, 630.7734, 396.8385, 473.9955, 489.5366, 636.2640,\n", + " 508.2391, 476.0079, 494.3373, 460.8384, 447.6069, 423.5204, 377.7553,\n", + " 309.0608, 487.5273, 570.1453, 490.5325, 531.3895, 473.8409, 538.1675,\n", + " 409.8674, 309.6638, 474.1954, 482.0854, 447.6241, 429.2579, 373.3405,\n", + " 376.9269, 497.1060, 502.5411, 437.8635, 507.7893, 567.9786, 514.7694,\n", + " 486.7926, 492.5654, 413.1345, 395.5750, 385.2564, 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', verbose_t=True, verbose_period_t=1e5, title='gphi50_in-sample')" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 6502.484\n", + "test 0 : tsm = 4019.440, tbsm = -11030.900\n", + "Reaching the end of test data. Stop tests at 76. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 49.91 sigmas\n", + "Partial test after 1 epochs (took 23.96 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphi_toydata_test_indataf = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_200_in.h5', 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e2)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl')\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi200_in-sample')\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 21984.864\n", + "test 0 : tsm = 30013.719, tbsm = -94763.453\n", + "Reaching the end of test data. Stop tests at 22. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 114.76 sigmas\n", + "Partial test after 1 epochs (took 18.93 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_500_in.h5', 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e2)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl')\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi500_in-sample')\n", + "f.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# In Sample Test - 10 Epochs" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2841.340 --- NBSM = 2225.504\n", + "test 0 : tsm = 1668.024, tbsm = 532.493\n", + "Reaching the end of test data. Stop tests at 175. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 21.08 sigmas\n", + "Partial test after 1 epochs (took 35.73 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 50\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_50_in.h5', 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "\n", + "NBSM = NBSMList[0]\n", + "\n", + "temp = NSM\n", + "NSM = NBSM\n", + "NBSM = temp\n", + "\n", + "n_epochs = int(1e2)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl10-1')\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_10_in-sample'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2841.340 --- NBSM = 2225.504\n", + "test 0 : tsm = 2028.608, tbsm = 955.530\n", + "Reaching the end of test data. Stop tests at 175. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 18.62 sigmas\n", + "Partial test after 1 epochs (took 36.18 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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T50c7BBGRsFDnNBERkQApUi1uEREJlsOHD7Nu3ToOHDgQ7VDConTp0lSvXp0SJUocfWWfEncBsmLFQgYPvp29e3dRrFhxbrnlIdq16wzAt99+zrBhfXDuD8qUiWXAgJGcemrdtG0///x9HnywI6NHf0fDhons2LGVBx/syNKl33H55Tfz4IPDo1UtEZE8W7duHeXKlaNWrVqkn1k1+JxzbN26lXXr1lG7du1cb6fEXYCULn0Cjz02mho16pGcvIEbbzybFi0uoVy5OAYPvp1nn51E7dpn8N57L/PGG08wYMBIAPbu3c348cNo3Lh52r5KlSrN7bc/zqpVi1m9enGUaiQicnwOHDhQKJM2gJlRqVIlkpOTj2m7Ip24N2xYw113tSchoRWLFs0mPr4azz47idKlyzB+/Au8//6rFC8eQ+3aDXnyyfH8+98D2LDhF9av/5nff/+Ne+8dyo8/zmH27E+oUqUaQ4d+RExM7k93ZFSzZv20+/Hxp1CxYhW2b0+mXLk4wNi715vDY8+encTHn5K27quv9qdbtwcZPXpIWlmZMmVJSGjF2rWr8hyPiEhBUBiTdqq81K3Id05bu/Yn/va3O3n33SWUKxfH//73PgAjRw7m7be/Z/z4RfzrX6+mrb9u3WpeffV/PPfcf+nf/0YSE9vwzjs/UqpUGb766uNM+x89egjXX5+Q6TZkyF05xrV48bccPnyI6tXrANC//+v07t2BDh2qM2XKW3Tr1heA5csX8Pvva2nV6rL8ekpERCTEoEGDaNSoEU2bNiUhIYG5c+fSunVratSoQeh8H0lJScTGxoY9niLd4gY45ZTanH56AgANGpzNhg1rAKhXrykPP3wDrVsn0bp1Utr6LVu2JyamBHXrNuGPP47QsqU3KVbduk3Stg3VtWsfunbtc0wxbdmykUceuYnHHhtFsWLed6uxY4cybNgUGjduzujRQxg69F4eemgEzz13b9opcxGRwm7o9JX5ur97Lq6f4/JvvvmGyZMns2DBAkqVKsWWLVs4dMgbDyQuLo6vv/6aVq1asWPHDjZu3JivsWWnyCfuEiVKpd0vXrw4Bw96sxA+//zHfP/9F3zxxUe8+eYgxo//EYCSJb31ixUrRkxMibTTHGbFOHIkJdP+R48ewtSpmWf3O/PMC+jT54VM5Xv27KJ378u4445BNGlyLgDbtyezcuUPadew27XrTK9el7Jv325Wr17MP/7RGoCtW3/n3nuv5Lnn/kvDhol5fUpERMS3ceNGKleuTKlS3md/5cqV05Z16dKF8ePH06pVKz744AOuueYalixZEvaYinzizsoff/zBpk1rSUxsQ0JCK6ZNG8/+/dlN5ZuzY2lxHz58iD59ruayy7py0UUd08rLlavAnj07+fXXldSsWZ85c6ZTq9YZxMaW5/PPt6St16NHa+6++xklbRGRfNKuXTsGDhxI/fr1ueiii+jcuTMXXnghAG3btuW2227jyJEjjB8/nhEjRvD444+HPSYl7iz88ccR+ve/kT17duKco0uXu/wOYuE1ffq7LFjwBTt3bmXy5JEAPProSE4/PYGHH36NBx64lmLFilGuXAUeeeTNo+7viitqsXfvLg4fPsSsWRMZPnwap53WMMy1EBEpPGJjY5k/fz5ffvklM2bMoHPnzgwePBjwztK2atWK8ePHs3//fiI1GZaFXlgvqBITE11+TDIydep8DXlaRCQnz+fSS/VaiwTdsmXLOOOMM9IeR/oad0YTJkxg1KhR7N69m2eeeYZ9+/Zx9dVXM2DAAHr16kVsbCx79hzbGdqMdQQws/nOuSxPn6rFHW3z/pP9ssS/Ry4OEZEwyynpHmsCjZQVK1ZQrFgx6tWrB8DChQupWbMmixd742Ocf/759OvXj+uuuy5iMSlxi4iIZGPPnj306tWLHTt2EBMTQ926dRkxYgQdO3r9kMyM+++/P6IxKXGLiEhgRLplfvbZZzN79uxM5TNnzsxy/WM9TZ4XRX4Aluz06NGapUuP/7p6fjhwYB+9e1/Gtdc2oFOnRrz4Yt+0ZWPGPMff/taQLl2acvvtbdm40Zt7fcWKhfz97y3o1KkRXbo0Zdq0d9K2GTDgZq68snbaYDArViyMeJ1ERCRv1OIOiJtuup/ExDYcPnyI229vy9dff8J557WnQYMz6dhxHqVLn8CECa/wwgsP8OST7+Q47jnAXXcNSfeTMxERCYYinbhzGqscYMqUt3jiie6kpKTwyCNv0rhxM+bPn8Wzz/b292C89toXLFs2nxEjHiU2No7Vq3/koos6UbduE8aNG8bBg/t59tmJaUOX5kXp0ieQmNgGgBIlStKgwVls3rwOIK0coHHjc5kyZQxwtHHPRUQkqIp04gZvrPJBg8bx8MOv0bdvJ/73v/fp0OFGwDtFPXbsQhYs+IKBA2/h3XcXM2bMMzzwwEskJJzHvn17KFmyNAArV/7AhAnLOPHEilx11WkkJXVn9OhvGTduGO+88yL33fd8uuPOmzeD5567B/ZtS1deumRJ3nzgoWzj3b17B19++RFduvTOtGzSpDdo2bJ9pvKM454DvPzyQ7z++kDOOactvXoNThsRTkRECrYin7izG6sc4JJLvO79Z511AXv37mL37h385S/nMXTovbRvfwNt2lxD1arVAWjY8BwqVz4ZgOrV69C8eTvAG8N83rwZmY6bmNiGsWMX5vxzsAxSUlJ46KHr6Nz5LqpXPy3dsilTxrBs2TxGjJiVrjyrcc979nySSpVO4vDhQwwa1INRo57ittseyXUcIiISPUU+cWc3Vjlknm7NzLj55r60anUZX301hVtvPY/hwz8FSNdiNSuW9ji7Mczz0uIeNKgHp55aj+uvvztd+dy5n/Hmm4MYMWJWujiyGvccSPuCUbJkKa644u+MGfNMlscTEZGCp8gn7pxMm/YOiYltWLjwK2JjyxMbW55161ZTt24T6tZtwtKl37FmzXJiY4/9uvGxtrhffvlh9uzZSf/+r6crX778e/7v//7Biy9OpWLFKmnl2Y17Dl4rvHLlk3HOMWvWROrUaXzM8YuIFBXFixenSZMmOOcoXrw4w4cPp2XLluzbt4/bbruNRYsW4ZwjLi6OqVOnEhsbi5lxww03MGaM1+8oJSWFk08+mebNmzN58uTjikeJOwelSpXm+uvPJCXlcNrY4GPHPs+8eTMoVqwYp53WiJYt27No0TfhCcBP6pu2b+PNNwdR66STufGaWnBCRTp16klSUndeeKEP+/fvoW/fvwFQtWoNhg7971HGPb+B7duTcc5x+ukJ9Ov3ajYBiIgUMDOezN/9tel31FXKlCnDwoXez2Y//fRT+vXrx6xZsxg2bBhVq1blxx+92SNXrFhBiRIlAChbtiyLFy9m//79lClThunTp1OtWrV8CblIJ+5TTqnFu+8uTnt8001/jn4zYsTMLLd54IEXM5UlJrYmMbF1lttmXJYXVStUZN6rIZOKhAyF+vLLn2W5TYcON6Z1ssvo1Vf/d1zxiIgUVbt27aJChQqAN+VnzZo105adfvrp6dbt0KEDH3/8MR07dmTcuHFcd911fPnll8cdgwZgERERycH+/ftJSEigQYMGdO/enf79+wNwyy238NRTT9GiRQsefvhhfvrpp3Tbpc7XfeDAARYtWkTz5s3zJR4lbhERkRyknipfvnw5U6dOpWvXrjjnSEhI4Oeff6ZPnz5s27aNc845h2XLlqVt17RpU9asWcO4cePo0KFDvsVTpE+Vi4iIHIsWLVqwZcsWkpOTqVKlCrGxsVxzzTVcc801FCtWjClTpqSbovPKK6/k/vvvZ+bMmWzdujVfYlCLu4A5dPgw/V57haT+fek2+HE2bNmS5XqzZ0/lmmtOJympLiNHDk4rX7/+F7p1a05SUl369evM4cOHvP0eOki/fp1JSqpLt27N0/1e/Vh9+ul43nhjUI7r7Ny5jTvuuJirr67HHXdczK5d27Ncb/LkUVx9dT2uvroekyePSitftmw+nTs3ISmpLkOG3EXqvPG53a+ISDgsX76cI0eOUKlSJb7++mu2b/c+gw4dOsTSpUvTXfMG73T6o48+SpMmTfIthrAlbjMrbWbfmtkPZrbEzB7zy2ub2VwzW2Vm75hZyXDFEESTvv6ScieUZeLjg7m+bTte/PC9TOscOXKEp566kxde+IT33lvKp5+O4+eflwLw4osPcv319zBx4irKlavApElvePud9AblylVg4sRVXH/9Pbz44oM5xvHRRyP5978HZLls9uxPaNny0hy3HzlyMM2ateXDD3+iWbO26b5cpNq5cxuvvfYYI0fOZdSob3nttcfSEvGTT97Oww+/xocf/sTatT8xe/bUXO9XRCQ/pV7jTkhIoHPnzowaNYrixYuzevVqLrzwQpo0acKZZ55JYmIi1157bbptq1evzl133ZWv8YTzVPlB4K/OuT1mVgL4ysw+Ae4FhjrnxpvZq8CtwCthjCNbGzasoVevSznjjLNZvnwBp53WiIEDR1O69AnRCAeAWYu+p8flVwHQ9qxEnh7/Ns65dIPBLFnyLaeeWjdt9LR27bowa9Ykatc+g++++x9PPDEWgMsv78aIEQPo2PF2Zs2aRI8eA7z9tu3I00/3zLTf3HDOsXLlQho0OCvnesyalNa7/vLLu9GjR2vuuuupdOt8882nNGt2MeXLVwSgWbOLmT17KomJrdm7d1faoDEdOnRl5syJnHde+1ztV0QKsVz8fCu/HTlyJMvyrl270rVr1yyXZTW9Z+vWrWnduvVxxxO2FrfzpEZewr854K/ABL98FJAUrhhy49dfV9Cx4x1MmLCMsmVP5L33Xs73Y3Tvfn7aFJqht7lzM/+Ua/OOHVSt4CWymOLFiS1Thp17078BNm9eT9Wqp6Y9rlKlOps3r2fnzq2UKxdHTExMuvKM28TExBAbW56dO4/9esuKFd9Tr95fjprwt23blDZCW6VKJ7Ft26ZM6yQnp69H1arVSU5e78daPVN5bvcrIlKYhbVzmpkVB+YDdYGXgNXADudc6hig64D8+UV6HlWteioJCecB3m+fx49/Id3vufPD66/n8Lu9YxirPNx27NjKHXe0BbzT2Ckph5g1ayIAAwe+Rd26TZg9e2qWE5nkxMyOuWUfzf2KiBRkYU3czrkjQIKZxQEfAg1yu62Z9QB6ANSoUSM8AZL1eOT5rXv389m3b3em8t69n6F58fRlVeLi2LR9G1UrVCTlyBH27N9P+bKx6depUo1Nm9amPd68eR1VqlSjfPlK7N69g5SUFGJiYtLKQ7epWrU6KSkp7Nmzk/LlK6Xbb1xcJW8YVrxr3Bs2rOEf/xiQbp05c6bx9NPvA9Cz5yVs27aJM85IzDQUa8WKVdOGVt2yZSMVKlQho/j4asyfPzPt8aZN6zj77NZ+rOvSlcfHV8v1fkVECrOI9Cp3zu0AZgAtgDgzS/3CUB1Yn802I5xzic65xPj4+LDF9vvvv6UNWTp16lgSElrl+zFef/1Lxo5dmOnWvPlFmda9oGkCk7+ZDcDnC+ZxzukNMn2ZaNjwHNau/Yn163/h8OFDTJs2ngsuuBIzIzGxDZ9/7l2JmDx5FBde6F0vv+CCK9N6bX/++QTOOeevx/wlZc+enRw5kkJcnJfwhw//lLFjF2ZK2gAXXvjn8ULjCNWixSXMnTuNXbu2s2vXdubOnUaLFpdQufLJlC17Ij/+OAfnHFOmjE7bPjf7FZHCJfVXJYVRXuoWzl7l8X5LGzMrA1wMLMNL4KmzXnQDJoUrhtyoWfN03nvvJTp2PINdu7bTsePt0QyHq867gJ1795DUvy9vfzaNnld7T1Xyju3c9eJQwLtG3afPcHr1uoSOHc/goos6UadOIwB69XqKt99+jqSkuuzcuZWrrrrV2+9Vt7Jz51aSkury9tvP0bPnsffGnjNnOs2aZf6ykZVu3foyd+50rr66Ht9++xk339wXgKVL5/H4490BKF++Irfe2p+uXc+ha9dz6N79kbSOan37vszjj3cnKaku1arV4bzz2ue4XxEpnEqXLs3WrVsLZfJ2zrF161ZKly59TNtZuJ4MM2uK1/msON4XhHedcwPN7DRgPFAR+B640Tl3MKd9JbpLgIkAABhlSURBVCYmunnz5h13TFOnzic+/uy0xxs2rOHuuy9PN155xOXlGnfIWOWR5CXS7ummCC2okpPnc+mlZx99RRGJmKHTV2a77J6L62dZfvjwYdatW8eBAwfCFVZUlS5dmurVq6dNTpLKzOY75xKz2iZs17idc4uAM7Mo/xloFq7jSvhkdUpcRCScSpQoQe3ataMdRoFSpEdOyzg7mIiISEFXpBN3QbbgpxXcMGgAze/ozmfz018mGDbsATp1akTHjmekGw40o/HjX+TaaxvQqVMjhg17APCuU99449l07tyEG288m++++3OKz+yGGRURkYJDk4wUUCdVqMSAbrfy1vSp6cp/WL2KH374mnHjFgHQvXsr5s+flWnO73nzZvDFF5MYN+4HSpYsxbZtmwGIi6vM0KEfER9/CqtWLaZXr0v45BOvY3/qMKONGzend+8OzJ49Na1TmIiIFAxFOnHv37+Xvn07sXnzOo4cOUL37v1p164zy5bNZ+jQe9m3bw9xcZUZMGAklSufTI8eralf/y8sWDCLlJQUHnnkTRo3Ds/l+lMqVwagmKU/KWIGhw4d8CcPcaSkHKZSpaqZtp8w4RW6detLyZKlAKhY0fu9c4MGf3Y7qFOnEQcP7ufQoYPs2rUt22FGRUSk4CjSiXv27KnEx5/CsGEfA97vlFNSDjNkSC+efXYSFSrEM23aO7z00kM8+uibABw4sI+xYxeyYMEXDBx4yzFdI89yIJZ92+h9bSean9EoV/toelpdEhPbcOmlJ+Oco1OnntSufUam9X77bSULF37Jyy8/RKlSpend+xkaNTon3Tqff/4+DRqcRcmSpXIcZlRERAqOIp2469ZtwvPP38cLLzzI+edfzplnns+qVYtZvXoxd955MeANLp86NjbAJZdcB8BZZ13A3r272L17B+XKxeXqeFkOfXqMPwdbu3kTv/yyjClTvJHF7rzzYr7//kvOPPP8dOulpKSwc+c2Ro6cw5Il39GvXycmTfo5bdCV1auX8OKLD/LSS9OO6fgiIhJdRTpx16xZnzFjFvD111N45ZWHOeectrRpczWnndaI//znmyy3OZ4hUvOjxT1j4QKaNDmXE07whkFt2bI9ixZ9kylxV61anb/+9RrMjMaNm2FWjB07tlChQjybNq2jT5+reeyx0VSvXgcgx2FGRUSk4CjSiTs5eQMnnliRDh1upFy5OCZOfJ2bb+7L9u3JLFr0DU2btiAl5TC//roybWSyadPeITGxDQsXfkVsbHliY8vn+nj50eI+qWIlJi6YRUpKP8CxYMEsrrvu7kzrXXhhEvPmzSAxsQ2//rqSlJRDxMVVZvfuHdx992X07Dk4bXIVIN0wo40bN2fKlNF06tTrmGITEZHwK9KJe9WqHxk2rA/FihUjJqYEffu+QokSJXnqqQk888xdaWNzX3fd3WmJu1Sp0lx//ZmkpBzmkUfeDFtsS9b8Qp9Xh7Nr316+/HEhIyZP5N1Hn6DtWYl8t/UPunRpgpnRosWlXHDBFYA3stm11/6Thg0TueqqWxg48BY6dWpMiRIlGTBgFGbGO+8MZ+3aVbz++kBef30gAMOHT6NixSr07fsyAwbczMGD+2nZsr06pomIFEBhG/I0P4VryNNj1aNHa+6++xkaNsxyFLq8CdCQp0GiIU9FCp68DHlaVOU05KkGYBEREQmQIn2q/FiNGDEz2iGIiEgRpxa3iIhIgChxi4iIBIgSt4iISIAUqWvccXElSU6eH+0w0tux9Ni3KWh1KIDi4kpGOwQRkbAoUon73HObRDuEzErlYcjRNvqZk4hIUaVT5SIiIgGixC0iIhIgStwiIiIBosQtIiISIErcIiIiAaLELSIiEiBK3CIiIgFSpH7HXWjMeDL7ZW36RS4OERGJOLW4RUREAkSJW0REJECUuEVERAJEiVtERCRAlLhFREQCRIlbREQkQJS4RUREAkSJW0REJECUuEVERAJEiVtERCRAlLhFREQCRIlbREQkQJS4RUREAkSJW0REJECUuEVERAIkbInbzE41sxlmttTMlphZb798gJmtN7OF/q1DuGIQEREpbGLCuO8U4D7n3AIzKwfMN7Pp/rKhzrlnwnhsERGRQilsids5txHY6N/fbWbLgGrhOp6IiEhREJFr3GZWCzgTmOsX9TSzRWb2pplVyGabHmY2z8zmJScnRyJMERGRAi/sidvMYoH3gbudc7uAV4A6QAJei/zZrLZzzo1wziU65xLj4+PDHaaIiEgghDVxm1kJvKT9tnPuAwDn3Cbn3BHn3B/Aa0CzcMYgIiJSmISzV7kBbwDLnHPPhZSfHLLa1cDicMUgIiJS2ISzV/l5wE3Aj2a20C/7F3CdmSUADlgD/COMMYiIiBQq4exV/hVgWSyaEq5jioiIFHYaOU1ERCRAlLhFREQCRIlbREQkQJS4RUREAkSJW0REJECUuEVERAJEiVtERCRAlLhFREQCRIlbREQkQJS4RUREAkSJW0REJECUuEVERAJEiVtERCRAlLhFREQCJJzzcYuISBEzdPrKaIdQ6KnFLSIiEiBK3CIiIgGixC0iIhIgStwiIiIBosQtIiISIErcIiIiAaLELSIiEiBK3CIiIgGiAVhERCSwchrw5Z6L60cwkshRi1tERCRAlLhFREQCRIlbREQkQJS4RUREAkSJW0REJECUuEVERAJEiVtERCRAlLhFREQCRIlbREQkQJS4RUREAkSJW0REJECUuEVERAJEiVtERCRAlLhFREQCRIlbREQkQMKWuM3sVDObYWZLzWyJmfX2yyua2XQz+8n/WyFcMYiIiBQ24WxxpwD3OecaAucCd5pZQ6Av8Llzrh7wuf9YREREciFsids5t9E5t8C/vxtYBlQDrgJG+auNApLCFYOIiEhhE5Fr3GZWCzgTmAtUdc5t9Bf9DlSNRAwiIiKFQUy4D2BmscD7wN3OuV1mlrbMOefMzGWzXQ+gB0CNGjXCHWZ4zXiyYByrTb/IxSEihdbQ6SujHUKRFtYWt5mVwEvabzvnPvCLN5nZyf7yk4HNWW3rnBvhnEt0ziXGx8eHM0wREZHACGevcgPeAJY5554LWfRfoJt/vxswKVwxiIiIFDbhPFV+HnAT8KOZLfTL/gUMBt41s1uBX4FOYYxBRESkUAlb4nbOfQVYNovbhuu4IiIihZlGThMREQkQJW4REZEAUeIWEREJECVuERGRAAn7ACwiIiJHk9OgLvdcXD+CkRR8anGLiIgEiBK3iIhIgChxi4iIBIgSt4iISIAocYuIiASIEreIiEiAKHGLiIgEiBK3iIhIgChxi4iIBIgSt4iISIAocYuIiASIEreIiEiAKHGLiIgEiBK3iIhIgBxT4jazsmZWPFzBiIiISM5yTNxmVszMrjezj81sM7Ac2GhmS81siJnVjUyYIiIiAkdvcc8A6gD9gJOcc6c656oArYA5wFNmdmOYYxQRERFfzFGWX+ScO5yx0Dm3DXgfeN/MSoQlMhEREckkxxZ3atI2s7cyLkstyyqxi4iISHjktnNao9AHfge1s/M/HBEREcnJ0Tqn9TOz3UBTM9vl33YDm4FJEYlQRERE0hztVPmTzrlywBDn3In+rZxzrpJzrl+EYhQRERHf0VrctQCyS9LmqZ7/YYmIiEhWjtarfIiZFcM7LT4fSAZKA3WBNkBb4FFgXTiDFBEREU+Oids59zczawjcANwCnATsB5YBU4BBzrkDYY9SREREgFz0KnfOLQWeAD7CS9i/AN8BE5S0RUREIutop8pTjQJ2AS/4j68HRgOdwhGUiIiIZC23ibuxc65hyOMZZrY0HAGJiIhI9nI7AMsCMzs39YGZNQfmhSckERERyU5uW9xnA7PN7Df/cQ1ghZn9CDjnXNOwRCciIiLp5DZxXxrWKERERCRXcpW4nXO/hjsQERERObrcXuMWERGRAkCJW0REJECUuEVERAIkbInbzN40s81mtjikbICZrTezhf6tQ7iOLyIiUhiFs8U9kqx7ow91ziX4tylhPL6IiEihE7bE7Zz7AtgWrv2LiIgURbn9HXd+6mlmXfFGXrvPObc9q5XMrAfQA6BGjRoRDE9ERAqSodNXRjuEAiXSndNeAeoACcBG4NnsVnTOjXDOJTrnEuPj4yMVn4iISIEW0cTtnNvknDvinPsDeA1oFsnji4iIBF1EE7eZnRzy8GpgcXbrioiISGZhu8ZtZuOA1kBlM1sHPAq0NrMEwAFrgH+E6/giIiKFUdgSt3PuuiyK3wjX8URERIoCjZwmIiISIErcIiIiAaLELSIiEiBK3CIiIgGixC0iIhIgStwiIiIBosQtIiISIErcIiIiAaLELSIiEiBK3CIiIgGixC0iIhIgStwiIiIBosQtIiISIErcIiIiARK2aT2lAJrxZPbL2vSLXBwiIpJnanGLiIgEiBK3iIhIgChxi4iIBIgSt4iISIAocYuIiASIEreIiEiAKHGLiIgEiBK3iIhIgGgAFhERyWTo9JXRDkGyoRa3iIhIgChxi4iIBIgSt4iISIAocYuIiASIEreIiEiAKHGLiIgEiBK3iIhIgChxi4iIBIgSt4iISIAocYuIiASIEreIiEiAKHGLiIgEiBK3iIhIgChxi4iIBIgSt4iISICELXGb2ZtmttnMFoeUVTSz6Wb2k/+3QriOLyIiUhiFs8U9Erg0Q1lf4HPnXD3gc/+xiIiI5FLYErdz7gtgW4biq4BR/v1RQFK4ji8iIlIYRfoad1Xn3Eb//u9A1QgfX0REJNCi1jnNOecAl91yM+thZvPMbF5ycnIEIxMRESm4Ip24N5nZyQD+383ZreicG+GcS3TOJcbHx0csQBERkYIs0on7v0A3/343YFKEjy8iIhJo4fw52DjgG+B0M1tnZrcCg4GLzewn4CL/sYiIiORSTLh27Jy7LptFbcN1TBERkcJOI6eJiIgEiBK3iIhIgChxi4iIBIgSt4iISICErXOaiIhINA2dvjLL8nsurh/hSPKXWtwiIiIBosQtIiISIErcIiIiAaLELSIiEiBK3CIiIgGixC0iIhIgStwiIiIBosQtIiISIErcIiIiAaKR00REirDsRheTgkstbhERkQBR4hYREQkQJW4REZEAUeIWEREJECVuERGRAFHiFhERCRAlbhERkQBR4hYREQkQJW4REZEA0chpx2LGk9kva9MvcnGIiEiRpRa3iIhIgChxi4iIBIgSt4iISIAocYuIiASIEreIiEiAKHGLiIgEiBK3iIhIgChxi4iIBIgSt4iISIBo5DTxaFQ4EZFAUItbREQkQJS4RUREAkSJW0REJECUuEVERAJEiVtERCRAotKr3MzWALuBI0CKcy4xGnGIiIgETTR/DtbGObcliscXEREJHJ0qFxERCZBotbgdMM3MHPBv59yIjCuYWQ+gB0CNGjXy9+jhGGwkp32KiETR0Okrox1CgZLT83HPxfUjGEneRKvF3co5dxbQHrjTzC7IuIJzboRzLtE5lxgfHx/5CEVERAqgqCRu59x6/+9m4EOgWTTiEBERCZqIJ24zK2tm5VLvA+2AxZGOQ0REJIiicY27KvChmaUef6xzbmoU4hAREQmciCdu59zPwF8ifVwREZHCQD8HExERCRAlbhERkQBR4hYREQkQJW4REZEAieZY5QWTRkDLLBwjzYmISJ6oxS0iIhIgStwiIiIBosQtIiISIErcIiIiAaLELSIiEiBK3CIiIgGixC0iIhIgStwiIiIBosQtIiISIErcIiIiAaLELSIiEiBK3CIiIgGixC0iIhIgStwiIiIBosQtIiISIErcIiIiAaLELSIiEiBK3CIiIgESE+0AREREgmDo9JXZLrvn4voRi0MtbhERkQBR4hYREQkQJW4REZEAUeIWEREJECVuERGRAFHiFhERCRAlbhERkQBR4hYREQkQJW4REZEA0chpcnxmPJn9sjb9IheHSCGR0+hcOYnkyF2FWV6f/0hSi1tERCRAlLhFREQCRIlbREQkQJS4RUREAkSJW0REJECikrjN7FIzW2Fmq8ysbzRiEBERCaKIJ24zKw68BLQHGgLXmVnDSMchIiISRNFocTcDVjnnfnbOHQLGA1dFIQ4REZHAiUbirgasDXm8zi8TERGRoyiwI6eZWQ+gh/9wj5mtiMBhKwNbInCcaIpgHf8VmcNkptexcFAdj8G9+bGT8CgSr+O9+V/HmtktiEbiXg+cGvK4ul+WjnNuBDAiUkEBmNk851xiJI8Zaapj4aA6Fg6qY+EQ6TpG41T5d0A9M6ttZiWBLsB/oxCHiIhI4ES8xe2cSzGznsCnQHHgTefckkjHISIiEkRRucbtnJsCTInGsY8ioqfmo0R1LBxUx8JBdSwcIntZ1zkXyeOJiIjIcdCQpyIiIgFS5BK3md1jZkvMbLGZjTOz0n5Hubn+EKzv+J3mMLNS/uNV/vJa0Y0+a2b2ppltNrPFIWUVzWy6mf3k/63gl5uZveDXaZGZnRWyTTd//Z/MrFs06pKdbOo4xMyW+/X40MziQpb18+u4wswuCSkvsMPtZlXHkGX3mZkzs8r+48C9jtnVz8x6+a/jEjN7OqS8ULyGZpZgZnPMbKGZzTOzZn554F5DADM71cxmmNlS/zXr7ZcXms+cHOpYMD5znHNF5oY30MsvQBn/8bvAzf7fLn7Zq8Dt/v07gFf9+12Ad6Jdh2zqdQFwFrA4pOxpoK9/vy/wlH+/A/AJYMC5wFy/vCLws/+3gn+/QrTrdpQ6tgNi/PtPhdSxIfADUAqoDazG6whZ3L9/GlDSX6dhtOuWUx398lPxOnP+ClQO6uuYzWvYBvgMKOU/rlLYXkNgGtA+5HWbGdTX0I/vZOAs/345YKX/ehWaz5wc6lggPnOKXIsbr0NeGTOLAU4ANgJ/BSb4y0cBSf79q/zH+MvbmplFMNZccc59AWzLUBwae8Y6jXaeOUCcmZ0MXAJMd85tc85tB6YDl4Y/+tzJqo7OuWnOuRT/4Ry8MQHAq+N459xB59wvwCq8oXYL9HC72byOAEOBB4DQDimBex2zqd/twGDn3EF/nc1+eWF6DR1won+/PLDBvx+41xDAObfRObfAv78bWIbXKCo0nznZ1bGgfOYUqcTtnFsPPAP8hpewdwLzgR0hL0boEKxpw7P6y3cClSIZ83Go6pzb6N//Hajq389uyNmgD0V7C963eihEdTSzq4D1zrkfMiwqLHWsD5xv3qWoWWZ2jl9eWOoHcDcwxMzW4n3+9PPLA19H8y4fngnMpZB+5mSoY6iofeYUqcTtX3O5Cu9UxilAWQrIN7xwct65nEL78wEzewhIAd6Odiz5ycxOwBs39pFoxxJGMXinSs8F+gDvFsSzWsfpduAe59ypwD3AG1GOJ1+YWSzwPnC3c25X6LLC8pmTXR2j/ZlTpBI3cBHwi3Mu2Tl3GPgAOA/v1E3qb9pDh2BNG57VX14e2BrZkPNsk386Cv9v6inI7IaczdVQtAWNmd0MXA7c4H9YQOGpYx28L5k/mNkavHgXmNlJFJ46rgM+8E+jfgv8gTe2dWGpH0A3vM8agPfwTp9CgOtoZiXwEtrbzrnUuhWqz5xs6lgwPnMicaG/oNyA5sASvGvbhncdphfeP1No57Q7/Pt3kr5z2rvRrkMOdatF+g4xQ0jfUeRp//5lpO8o8q1fXhGv414F//YLUDHa9TpKHS8FlgLxGdZrRPqOIj/jdRKJ8e/X5s+OIo2iXa+c6phh2Rr+7JwWyNcxi9fwn8BA/359vNOKVpheQ7zro639+22B+QF/DQ0YDTyfobzQfObkUMcC8ZkT9ScoCi/IY8ByYDHwlv9EnwZ8i9eh4D3+7OFa2n+8yl9+WrTjz6ZO4/Cu2R/Ga8Hcinct/nPgJ7xeuxVD3pAv4fV0/BFIDNnPLX5dVwF/j3a9clHHVf4H/UL/9mrI+g/5dVyB36PXL++A10N0NfBQtOt1tDpmWL6GPxN34F7HbF7DksAY//9xAfDXwvYaAq3w+tL8gHed9OygvoZ+bK3wToMvCvnf61CYPnNyqGOB+MzRyGkiIiIBUtSucYuIiASaEreIiEiAKHGLiIgEiBK3iIhIgChxi4iIBIgSt4hkYmZxZnZHtOMQkcyUuEUkK3F4s+OJSAGjxC0iWRkM1PHnkB4S7WBE5E8agEVEMvFnRJrsnGsc5VBEJAO1uEVERAJEiVtERCRAlLhFJCu7gXLRDkJEMlPiFpFMnHNbga/NbLE6p4kULOqcJiIiEiBqcYuIiASIEreIiEiAKHGLiIgEiBK3iIhIgChxi4iIBIgSt4iISIAocYuIiASIEreIiEiA/D+kr9iriYmMbgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 50\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_50_in.h5', 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "\n", + "NBSM = NBSMList[0]\n", + "\n", + "temp = NSM\n", + "NSM = NBSM\n", + "NBSM = temp\n", + "\n", + "n_epochs = int(1e2)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl10-2')\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_10_in-sample'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2841.340 --- NBSM = 2225.504\n", + "test 0 : tsm = 1592.248, tbsm = 472.456\n", + "Reaching the end of test data. Stop tests at 176. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 17.62 sigmas\n", + "Partial test after 1 epochs (took 35.94 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 50\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_50_in.h5', 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "\n", + "NBSM = NBSMList[0]\n", + "\n", + "temp = NSM\n", + "NSM = NBSM\n", + "NBSM = temp\n", + "\n", + "n_epochs = int(1e2)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl10-3')\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_10_in-sample'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Out Sample Test Data\n", + "Old Format" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2841.340\n", + "test 0 : tsm = 296.204, tbsm = -524.268\n", + "Reaching the end of test data. Stop tests at 175. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 11.66 sigmas\n", + "Partial test after 1 epochs (took 40.61 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 50\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e2)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl')\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_out-sample'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2268.608\n", + "test 0 : tsm = 181.149, tbsm = 126.704\n", + "Reaching the end of test data. Stop tests at 220. \n", + "===> delta1 = 0.032, delta2 = 0.029\n", + "p = 0.323 +/- 0.043\n", + "Separation = 0.42 sigmas\n", + "Partial test after 1 epochs (took 36.21 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = '45e-1'\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%s.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e2)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl')\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%s_out-sample'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2268.608\n", + "test 0 : tsm = 220.502, tbsm = 209.234\n", + "Reaching the end of test data. Stop tests at 220. \n", + "===> delta1 = 0.032, delta2 = 0.030\n", + "p = 0.332 +/- 0.043\n", + "Separation = 0.41 sigmas\n", + "Partial test after 1 epochs (took 40.48 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = '45e-1'\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%s.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e2)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl')\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%s_out-sample'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Models traied on 24.Sep.2020" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2841.340\n", + "test 0 : tsm = 210.031, tbsm = -600.851\n", + "Reaching the end of test data. Stop tests at 176. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 13.85 sigmas\n", + "Partial test after 1 epochs (took 38.77 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 50\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e2)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl10')\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_10_out-sample'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2841.340\n", + "test 0 : tsm = 288.174, tbsm = -621.788\n", + "Reaching the end of test data. Stop tests at 175. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 13.36 sigmas\n", + "Partial test after 1 epochs (took 37.83 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 50\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e2)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl10')\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_10_out-sample-star'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2841.340\n", + "test 0 : tsm = 350.299, tbsm = -586.113\n", + "Reaching the end of test data. Stop tests at 176. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 13.04 sigmas\n", + "Partial test after 1 epochs (took 41.87 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 50\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e2)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl50-1')\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_50-1_out-sample'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2841.340\n", + "test 0 : tsm = 463.541, tbsm = -325.429\n", + "Reaching the end of test data. Stop tests at 175. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 12.40 sigmas\n", + "Partial test after 1 epochs (took 40.79 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 50\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e2)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl50-2')\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_50-2_out-sample'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2841.340\n", + "test 0 : tsm = 245.246, tbsm = -481.885\n", + "Reaching the end of test data. Stop tests at 175. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 12.45 sigmas\n", + "Partial test after 1 epochs (took 37.49 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 50\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e2)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl50-3')\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_50-3_out-sample'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2841.340\n", + "test 0 : tsm = 250.435, tbsm = -240.987\n", + "Reaching the end of test data. Stop tests at 175. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 13.99 sigmas\n", + "Partial test after 1 epochs (took 71.87 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 50\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e2)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl10-bigbatch')\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_10-bigbatch_out-sample'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2841.340\n", + "test 0 : tsm = 770.850, tbsm = 164.990\n", + "Reaching the end of test data. Stop tests at 175. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 13.61 sigmas\n", + "Partial test after 1 epochs (took 45.83 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 50\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e2)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl50-bigbatch')\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_50-bigbatch_out-sample'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2841.340\n", + "test 0 : tsm = 317.294, tbsm = -419.716\n", + "Reaching the end of test data. Stop tests at 176. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 12.91 sigmas\n", + "Partial test after 1 epochs (took 37.76 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 50\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e2)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl1000-bigbatch')\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_1000-bigbatch_out-sample'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Out Sample Test Data\n", + "New 2D format" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2841.340\n", + "test 0 : tsm = 338.832, tbsm = -431.554\n", + "Reaching the end of test data. Stop tests at 176. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 10.13 sigmas\n", + "Partial test after 1 epochs (took 29.76 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 50\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e2)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl')\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new_out-sample'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2693.988\n", + "test 0 : tsm = 280.535, tbsm = -460.285\n", + "Reaching the end of test data. Stop tests at 185. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 6.98 sigmas\n", + "Partial test after 1 epochs (took 70.00 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 40\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e2)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl')\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new_out-sample'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2558.585\n", + "test 0 : tsm = 230.843, tbsm = -65.782\n", + "Reaching the end of test data. Stop tests at 195. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 5.32 sigmas\n", + "Partial test after 1 epochs (took 82.50 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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TktKeV1/9mrJlj+KTT/6T5fgTJjxE797JWR4PPTQo13otWfI5+/fvo06d4zOUp6bu5513XqR16/Oy7DNlynO0bt0lfdnMuO66Tlx++en8+99j8/VzERGRwMiRI2natCmnnHIKycnJzJ8/n3bt2lG3bl0i5/vo3r07CQkJhV6fEt3jBqhVqwEnnJAMwIknns6aNSsBaNToFIYNu4x27brTrl339O1bt+5CfHxpGjZsxsGDB9IDtGHDZun7RurTZzB9+gzOV502blzLnXdewd13j6dUqYx/W91//7U0b342p53WNkP5ggWzmDLlOZ599pP0smef/YTq1WuzefN6rruuI/Xrn0jz5mfnqy4iIsXJqJnfF+jxbuzYONf1n332GdOmTePLL7+kbNmybNy4kX37gvFAkpKS+PTTT2nTpg1bt25l7dq1BVq3nJT44C5dumz687i4OH77bQ8A//znf/jqqznMmTOV558fyaRJXwNQpkywfalSpYiPL51+mcOsFAcOpGY5/oQJDzF9+stZyk877WwGDx6TpXznzu3ccMP5XHvtSJo1a5Vh3dixd7Nlywb+/venM5T/8MNi7rmnP2PGvEtSUpX08urVawNQuXJ12rXrwTfffK7gFhHJh7Vr11K1alXKlg3+769a9fd7hi655BImTZpEmzZt+Pe//82FF17IN98U+uy1Cu7sHDx4kHXrfiElpT3JyW2YMWMSe/bsPKxj5afHvX//PgYP7sH55/fh3HN7Zlg3efKzzJv3Hk888UGGXvivv/6PwYMvZMSIFzN8Rr5nzy4OHjxI+fKJ7Nmzi/nzZ9C//52H1QYRkZKqU6dOjBgxgsaNG3PuuefSq1cvzjnnHAA6dOjA1VdfzYEDB5g0aRJjx47lnnvuKfQ6KbizcfDgAe6443J27tyGc45LLhlEYmJSoZ935szX+PLLOWzbtolp08YBcNdd4zjhhGTuu++vHHNMPfr1OxOA9u0v5Oqr7+SZZ0awbdsmHnjgWoD0r31t2rSOwYN7AHDgQCqdO/fO9nNxERHJWUJCAgsXLuTjjz9m1qxZ9OrVi/vvvx8IrtK2adOGSZMmsWfPHopqMiyL/GC9uEpJSXEFMcnI9OkLNeRpCbFhw0LOO0+vtYjvli1bxkknnZS+XNSfcWf2xhtvMH78eHbs2MHDDz/M7t276dGjB8OHD2fgwIEkJCSwc2f+rtBmbiOAmS10zqVkt7163CIiJVhOQZjfQItV3333HaVKlaJRo0YALFq0iHr16rFkyRIA2rZty9ChQ7n00kuLrE4KbhERkRzs3LmTgQMHsnXrVuLj42nYsCFjx46lZ8/gPiQz45ZbbinSOim4RUTEG0V9JeD0009n7ty5Wco/+uijbLfP72Xyw1HiB2DJyYAB7Vi6tHiM7b13725uuOF8LrroRC6+uCn/+teQ9HVTp47j3HOrpQ/sMnnys+nrWrSISy+/8cYL0ss///wDLrusOb17J3PVVW345ZflRdoeERE5fOpxe+KKK24hJaU9+/fv45prOvDpp+9y1lnBKGkdO/bittsey7JP2bJHMXHioizl999/DY88MoUGDU7i9def4Lnn7mX48HGF3QQRESkAJTq4cxurHOCdd17k3nv7k5qayp13Ps/JJ7dg4cLZPPLIDeERjGeemcOyZQsZO/YuEhKSWLHia84992IaNmzGK6+M5rff9vDII5OzDF2aH+XKHU1KSnsASpcuw4knNj/CMcmNXbu2A7Bz5zaqVat1BMcSEZGiVKKDG4KxykeOfIVhw55hyJCL+fDDN+na9XIguEQ9ceIivvxyDiNG9OO115bw0ksPc+utj5OcfBa7d++kTJlyAHz//X95441lVKhQmW7djqN79/5MmPA5r7wymldf/Rc33/zPDOddsGAWjz56Y5b6lCt3NM8/n/XzlDQ7dmzl44+ncsklN6SXffjhm3z11Rzq1m3MTTeN4phjjgVg3769XHFFCnFx8Vx55ZD0oVvvuONZbrihK2XLHkX58hV44YV5R/ZDFBGRIlPigzunscoBOncObu9v3vxsdu3azo4dWzn11LMYNeomunS5jPbtL6RGjToANGlyBlWr1gSgTp3jadmyExCMYb5gwaws501JaZ/tZezcpKamcvvtl9Kr1yDq1DkOgLZt/0TnzpdSpkxZ3nzzaYYP78tTT30IwNSpP1O9em1WrfqRa675Aw0bNqNOneOZOHEUo0e/w8knt2TChIcYNeom7rjj2dxOLSIixUSJD+6cxiqHrNOtmRlXXjmENm3O55NP3uGqq87iscfeA34fwzzYrlT6ck5jmB9Oj3vkyAEce2wjevf+W3pZ5Njk3bv3Z8yYW9OX08Yqr1PnOE4/vR3ffvsV5ctX4Pvv/8vJJ7cEoFOnXgwcqBHVRER8UeKDOzczZrxKSkp7Fi36hISEiiQkVGTVqhU0bNiMhg2bsXTpF6xc+S0JCfkfDjW/Pe4nnhjGzp3bsvSMN25cm97TnzPnbRo0CEbf2b59C+XKHU2ZMmXZunUj//3vp/TpcyuJiZXYuXMbP//8PfXqNWbevJnUr39SlvOJiEggLi6OZs2a4ZwjLi6Oxx57jNatW7N7926uvvpqFi9ejHOOpKQkpk+fTkJCAmbGZZddxksvvQQEV0xr1qxJy5YtmTZt2hHVR8Gdi7Jly9G792mkpu7nzjufB2DixH+yYMEsSpUqxXHHNaV16y4sXvxZodZj3bpVPP/8SOrXP5HLL28OwMUXX0/37v2ZNGkMc+a8TVxcPBUqVE6/O/ynn5bxj3/8hVKlSnHw4EH69h3Cccc1AWDYsGe49daLKFWqFImJldLbJiJS7M26r2CP137oITc56qijWLQo6Gi99957DB06lNmzZzN69Ghq1KjB118Hs0d+9913lC5dGoDy5cuzZMkS9uzZw1FHHcXMmTOpXbt2gVRZY5VLTNJY5SJ5U9yHPM0yjncUgjty/PHXX3+dl19+mcmTJzNo0CDq1avHzTffnO0+gwYNonnz5vTs2ZM+ffrQtGlTPv744yw97vyOVa4BWERERHKxZ88ekpOTOfHEE+nfvz933HEHAP369eOBBx7gzDPPZNiwYfzwww8Z9kubr3vv3r0sXryYli1bFkh9FNwiIiK5SLtU/u233zJ9+nT69OmDc47k5GR+/PFHBg8ezObNmznjjDNYtmxZ+n6nnHIKK1eu5JVXXqFr164FVh99xi0iIpJHZ555Jhs3bmTDhg1Ur16dhIQELrzwQi688EJKlSrFO++8k+Gy9wUXXMAtt9zCRx99xKZNmwqkDupxFzP79v3G0KG96N69IX37tszwvfJIc+dO58ILT6B794aMG3d/evnq1T/Rt29LundvyNChvdi/f1++jpsX7703ieeeG5nrNtu2bebaazvSo0cjrr22I9u3b8l2u2nTxtOjRyN69GjEtGnj08uXLVtIr17N6N69IQ89NIi0ezHyelwRkcLw7bffcuDAAapUqcKnn37Kli3B/0H79u1j6dKl1KtXL8P2/fr146677qJZs2YFVgcFdzEzZcpzJCZWYvLk5fTufSP/+tdtWbY5cOAADzxwHWPGvMvrry/lvfde4ccflwLwr3/dRu/eNzJ58nISEysxZcpzeT5upKlTx/H008OzXTd37ru0bp37d7/HjbufFi068NZbP9CiRYcMf1yk2bZtM888czfjxs1n/PjPeeaZu9OD+L77rmHYsGd4660f+OWXH5g7d3qejysiUpDSPuNOTk6mV69ejB8/nri4OFasWME555xDs2bNOO2000hJSeGiiy7KsG+dOnUYNGhQgdanRF8qX7NmJQMHnsdJJ53Ot99+yXHHNWXEiAmUK3d01Oo0e/YUBgwYDkCHDj158MHrcc5lGAzmm28+59hjG6aPntap0yXMnh1MGvLFFx9y770TAfjjH/syduxweva8Jk/HzQvnHN9/v4gTT2x+yHaMHftRej0GDGjHoEEPZNjms8/eo0WLjlSsWBmAFi06MnfudFJS2rFr13aaNWsFQNeuffjoo8mcdVaXPB1XRGJYHu4CL2gHDhzItrxPnz706dMn23XZTe/Zrl072rVrd8T1KfE97p9//o6ePa/ljTeWUb58BV5//YkCP0f//m3Tp9eMfMyf/36WbdevX02NGsFY4/Hx8SQkVGTbtk05bgNQvXod1q9fzbZtm0hMTCI+Pj5DeV6PmxffffcVjRqdesjA37x5XfrAMFWqHMPmzeuybLNhQ8Z21KhRhw0bVod1rZOlPK/HFRGJZSW6xw1Qo8axJCefBUDXrpczadIYrrjilgI9x7PPflygxyssW7du4tprOwDBZezU1H3Mnj0ZgBEjXqRhw2bMnTud1q275Ou4Zpbvnn00jysiUpyV+ODObjzygta/f1t2796RpfyGGx6mZctzM5RVr16bdet+oUaNOqSmprJz5zYqVqyS7TZp1q9fRfXqtalYsQo7dmwlNTWV+Pj49PK8HjcpqUr6MKxTp45jzZqV/OUvwzNsM2/eDB588E0Arr++M5s3r+Okk1KyDMVauXKN9OFYN25cS6VK1bO0v1q12ixc+FH68rp1qzj99HZhXVdlKK9WrXaejysiEstK/KXyX3/9X/qQpdOnTyQ5uU2Bn+PZZz9m4sRFWR6ZQxvg7LMvSL+7+oMP3uCMM/6Q5Y+JJk3O4JdffmD16p/Yv38fM2ZM4uyzL8DMSElpzwcfvAEEd2yfc063PB/3UHbu3MaBA6npE5s89th7TJy4KNuZxc455/fzRdYj0plndmb+/Bls376F7du3MH/+DM48szNVq9akfPkKfP31PJxzvPPOhPT983JcEYktPozwebgOp20lPrjr1TuB119/nJ49T2L79i307HlNVOvTrdtVbNu2ie7dG/Lyy49y/fXBXdMbNqxh0KDgC/zx8fEMHvwYAwd2pmfPkzj33Is5/vimAAwc+AAvv/wo3bs3ZNu2TXTrdlWux82PefNm0qJF1j82stO37xDmz59Jjx6N+Pzz97nyyiEALF26gHvu6Q9AxYqVueqqO+jT5wz69DmD/v3vTL9RbciQJ7jnnv50796Q2rWP56yzuuR6XBGJTeXKlWPTpk0xGd7OOTZt2kS5cuXytV+JHqt8zZqV/O1vf+S115Yc8bFLgiBI+6ff7V2caaxykbwp7mOV79+/n1WrVrF3795oV6VQlCtXjjp16qRPTpImt7HKS/xn3JJ32V0SFxEpTKVLl6ZBgwbRrkaxUqIvldeqVV+9bRER8Yp63MXU1KnjGD16cPpd4Wnzb2c2cOB5bNy4lgMHUklObstttz1OXFwco0cPZs6cqZQuXYY6dY7nrrteIDExiSVLPucf/xgABJ+vDBgwnPbtexRp20RE5PApuIuxjh17cdttj+W6zX33vUZCQgWcc9x6a0/ef/91One+hJYtO3LddfcRHx/PmDG38cIL9zFo0AM0bHgyEyYsID4+no0b13LppafStu2f0gdtERGR4q1E/2+9Z88uhgy5mPXrV3HgwAH697+DTp16sWzZQkaNuondu3eSlFSV4cPHUbVqTQYMaEfjxqfy5ZezSU1N5c47n+fkk1tEtQ0JCRUAOHAgldTUfelf8WrVqlP6Ns2atUr/iljkcK6//bZXA5iIiHimRAf33LnTqVatFqNH/wcIvqecmrqfhx4ayCOPTKFSpWrMmPEqjz9+O3fd9TwAe/fuZuLERXz55RxGjOiXr8/I8zMQC8CHH77JV1/NoW7dxtx00yiOOebYLNtAMBDKN998TuvWXejQoWeW9W+//TwdO/ZKX16yZD4jRvRj7dqfGTHiRfW2RUQ8UqL/x27YsBn//OfNjBlzG23b/pHTTmvL8uVLWLFiCddd1xEIBpdPGxsboHPnSwFo3vxsdu3azo4dW0lMTMrT+fIz9Gnbtn+ic+dLKVOmLG+++TTDh/flqac+zHbbxx57j99+28uwYZfxxRcf0qpVx/R1zz03kri4eLp0uSy97OSTW/Laa9/w00/LuOuuvrRu3YWyZfP3PUIREYmOEh3c9eo15qWXvuTTT9/hySeHccYZHWjfvgfHHdeUF174LNt9jmSI1Pz0uNNGJwPo3r0/Y8bcmuuxy5YtxznndHMDSXAAABQPSURBVGP27CnpwT116jg++WQaTz75Qbb1bNDgJI4+OoEVK5bQpEm2XxcUEZFipkQH94YNa6hQoTJdu15OYmISkyc/y5VXDmHLlg0sXvwZp5xyJqmp+/n55+/TRyabMeNVUlLas2jRJyQkVCQhoWKez5efHnfaeNwAc+a8TYMGJ2XZZvfunezevYOqVWuSmprKp5/+h+TktkDwMcCECQ8yduzsDJ9rr179EzVqHEt8fDxr1/7MypXfUqtW/TzXS0REoqtEB/fy5V8zevRgSpUqRXx8aYYMeZLSpcvwwANv8PDDg9LH5r700r+lB3fZsuXo3fs0UlP3c+edzxda3SZNGsOcOW8TFxdPhQqVGT58XPq63r2TmThxEXv27OKmmy5g377fOHjwICkp7bnoor8C8OCD17N//2/pl/xPPrkVf//7Uyxa9Anjx99PfHxpzEoxZMgTJCVVLbR2iIhIwSrRQ57m14AB7fjb3x7WZWUPaMhTkbwp7kOellS5DXlaokdOExER8U2JvlSeX2PHfhTtKoiISAlXaD1uMzvWzGaZ2VIz+8bMbgjLK5vZTDP7Ify3UmHVQUREJNYU5qXyVOBm51wToBVwnZk1AYYAHzjnGgEfhMsiIiKSB4UW3M65tc65L8PnO4BlQG2gGzA+3Gw80L2w6iAiIhJriuQzbjOrD5wGzAdqOOfWhqt+BWoURR0AkpLKsGHDwqI6nURRUlKZaFdBRKRQFHpwm1kC8CbwN+fc9sgRvJxzzsyy/T6amQ0ABgDUrVu3QOrSqlWzAjmOiIhItBTq18HMrDRBaL/snPt3WLzOzGqG62sC67Pb1zk31jmX4pxLqVatWmFWU0RExBuFeVe5Ac8By5xzj0asehvoGz7vC0wprDqIiIjEmsK8VH4WcAXwtZktCsv+DtwPvGZmVwE/AxcXYh1ERERiSqEFt3PuEyCnqbM6FNZ5RUREYpmGPBUREfGIgltERMQjCm4RERGPKLhFREQ8ouAWERHxiIJbRETEIwpuERERjyi4RUREPKLgFhER8YiCW0RExCMKbhEREY8ouEVERDyi4BYREfGIgltERMQjCm4RERGPKLhFREQ8ouAWERHxiIJbRETEIwpuERERjyi4RUREPKLgFhER8YiCW0RExCPx0a6AiIgUrlEzvy/QfW7s2PhIqiNHSD1uERERjyi4RUREPKLgFhER8YiCW0RExCMKbhEREY8ouEVERDyi4BYREfGIgltERMQjCm4RERGPKLhFREQ8ouAWERHxiIJbRETEIwpuERERjyi4RUREPKLgFhER8YiCW0RExCMKbhEREY8ouEVERDyi4BYREfGIgltERMQjCm4RERGPKLhFREQ8ouAWERHxiIJbRETEIwpuERERjyi4RUREPBIf7QpICTXrvuzL2w8t2nqIiHhGPW4RERGPKLhFREQ8ouAWERHxiIJbRETEIwpuERERjyi4RUREPKLgFhER8YiCW0RExCMKbhEREY8UWnCb2fNmtt7MlkSUDTez1Wa2KHx0Lazzi4iIxKLC7HGPA87LpnyUcy45fLxTiOcXERGJOYUW3M65OcDmwjq+iIhISRSNSUauN7M+wALgZufcluw2MrMBwACAunXrFmH1REQkN6Nmfp/juhs7Ni7CmpRMRX1z2pPA8UAysBZ4JKcNnXNjnXMpzrmUatWqFVX9REREirUiDW7n3Drn3AHn3EHgGaBFUZ5fRETEd0Ua3GZWM2KxB7Akp21FREQkq0L7jNvMXgHaAVXNbBVwF9DOzJIBB6wE/lJY5xcREYlFhRbczrlLsyl+rrDOJyIiUhJo5DQRERGPKLhFREQ8ouAWERHxiIJbRETEIwpuERERjyi4RUREPKLgFhER8YiCW0RExCPRmB1MSopZ90W7BiIiMUc9bhEREY8ouEVERDyi4BYREfGIgltERMQjCm4RERGPKLhFREQ8ouAWERHxiIJbRETEIwpuERERjyi4RUREPKLgFhER8YiCW0RExCOaZEQOLbfJQtoPLR71yE1R1lEkNGrm9zmuu7Fj4yI9n8QW9bhFREQ8ouAWERHxiIJbRETEIwpuERERjyi4RUREPJKv4Daz8mYWV1iVERERkdzlGtxmVsrMepvZf8xsPfAtsNbMlprZQ2bWsGiqKSIiInDoHvcs4HhgKHCMc+5Y51x1oA0wD3jAzC4v5DqKiIhI6FADsJzrnNufudA5txl4E3jTzEoXSs1EREQki1x73GmhbWYvZl6XVpZdsIuIiEjhyOvNaU0jF8Ib1E4v+OqIiIhIbg51c9pQM9sBnGJm28PHDmA9MKVIaigiIiLpDnWp/D7nXCLwkHOuQvhIdM5Vcc5p5gYREZEiluvNaWZW3zm3MqeQNjMDajvnVhVK7aToHO7MWwWtuNRDRApcUc+YFqsOdVf5Q2ZWiuCy+EJgA1AOaAi0BzoAdwEKbhERkSKQa3A75/7PzJoAlwH9gGOAPcAy4B1gpHNub6HXUkRERIA83FXunFsK3AtMJQjsn4AvgDcU2iIiIkXrUJfK04wHtgNjwuXewATg4sKolIiIiGQvr8F9snOuScTyLDNbWhgVEhERkZzldQCWL82sVdqCmbUEFhROlURERCQnee1xnw7MNbP/hct1ge/M7GvAOedOKZTaiYiISAZ5De7zCrUWIiIikid5Cm7n3M+FXRERERE5tLx+xi0iIiLFgIJbRETEIwpuERERj+T15jQRf+U2cUl7TXIn/shtko7iwoc6+k49bhEREY8ouEVERDyi4BYREfGIgltERMQjCm4RERGPKLhFREQ8ouAWERHxiIJbRETEIwpuERERjxRacJvZ82a23syWRJRVNrOZZvZD+G+lwjq/iIhILCrMHvc4ss7jPQT4wDnXCPggXBYREZE8KrTgds7NATZnKu4GjA+fjwe6F9b5RUREYlFRf8Zdwzm3Nnz+K1CjiM8vIiLitajNDuacc2bmclpvZgOAAQB169YtsnpJPuU281Ys04xjUkg0u5YcSlH3uNeZWU2A8N/1OW3onBvrnEtxzqVUq1atyCooIiJSnBV1cL8N9A2f9wWmFPH5RUREvFaYXwd7BfgMOMHMVpnZVcD9QEcz+wE4N1wWERGRPCq0z7idc5fmsKpDYZ1TREQk1mnkNBEREY8ouEVERDyi4BYREfGIgltERMQjCm4RERGPKLhFREQ8ouAWERHxiIJbRETEI1GbZESkWNBkISLiGfW4RUREPKLgFhER8YiCW0RExCMKbhEREY8ouEVERDyi4BYREfGIgltERMQjCm4RERGPKLhFREQ8ouAWERHxiIJbRETEIwpuERERjyi4RUREPKLZwWKNZrsSKfZGzfw+2lUQj6nHLSIi4hEFt4iIiEcU3CIiIh5RcIuIiHhEwS0iIuIRBbeIiIhHFNwiIiIeUXCLiIh4RMEtIiLiEQW3iIiIRxTcIiIiHlFwi4iIeESTjPgot4lECmM/EREpNtTjFhER8YiCW0RExCMKbhEREY8ouEVERDyi4BYREfGIgltERMQjCm4RERGPKLhFREQ8ouAWERHxiIJbRETEIwpuERERjyi4RUREPKLgFhER8YhmBxMRkagbNfP7w9rvxo6NC7gmxZ963CIiIh5RcIuIiHhEwS0iIuIRBbeIiIhHFNwiIiIeUXCLiIh4RMEtIiLiEQW3iIiIRxTcIiIiHonKyGlmthLYARwAUp1zKdGoh4iIiG+iOeRpe+fcxiieX0RExDu6VC4iIuKRaPW4HTDDzBzwtHNubOYNzGwAMACgbt26RVw9EZFAbpNflMQJLoqbkvj6RKvH3cY51xzoAlxnZmdn3sA5N9Y5l+KcS6lWrVrR11BERKQYikpwO+dWh/+uB94CWkSjHiIiIr4p8uA2s/Jmlpj2HOgELCnqeoiIiPgoGp9x1wDeMrO08090zk2PQj1ERES8U+TB7Zz7ETi1qM8rIiISC/R1MBEREY8ouEVERDyi4BYREfGIgltERMQjCm4RERGPKLhFREQ8ouAWERHxiIJbRETEI9Gcj1ukeJt1X9Hul5P2Qwv2eJKt3GaZKsh9RI6UetwiIiIeUXCLiIh4RMEtIiLiEQW3iIiIRxTcIiIiHlFwi4iIeETBLSIi4hEFt4iIiEcU3CIiIh5RcIuIiHhEwS0iIuIRBbeIiIhHNMlIQcltYonDnSSioCerED8d7vtAk5NkoUlBBHJ/H9zYsXER1uTwqMctIiLiEQW3iIiIRxTcIiIiHlFwi4iIeETBLSIi4hEFt4iIiEcU3CIiIh5RcIuIiHhEwS0iIuIRBbeIiIhHFNwiIiIeUXCLiIh4RMEtIiLiEc0OlllhzPIlEg2H+172/HdAM4BJrFOPW0RExCMKbhEREY8ouEVERDyi4BYREfGIgltERMQjCm4RERGPKLhFREQ8ouAWERHxiIJbRETEIwpuERERjyi4RUREPKLgFhER8UjJnGQkt0kUitN+IoWlgN+Tnz13S47r5tUdkOO6Gzs2PqzzaSIRKSyH+9463Pfy4VCPW0RExCMKbhEREY8ouEVERDyi4BYREfGIgltERMQjCm4RERGPKLhFREQ8ouAWERHxiIJbRETEI1EJbjM7z8y+M7PlZjYkGnUQERHxUZEHt5nFAY8DXYAmwKVm1qSo6yEiIuKjaPS4WwDLnXM/Ouf2AZOAblGoh4iIiHeiEdy1gV8illeFZSIiInII5pwr2hOa9QTOc871D5evAFo6567PtN0AIG1aoROA7w7jdFWBjUdQXd+UtPZCyWuz2hvb1N7Yl9c213POVctuRTSm9VwNHBuxXCcsy8A5NxYYeyQnMrMFzrmUIzmGT0pae6HktVntjW1qb+wriDZH41L5F0AjM2tgZmWAS4C3o1APERER7xR5j9s5l2pm1wPvAXHA8865b4q6HiIiIj6KxqVynHPvAO8UwamO6FK7h0pae6HktVntjW1qb+w74jYX+c1pIiIicvg05KmIiIhHYia4zeweM1tsZovMbIaZ1QrLzczGhMOrLjaz5hH79DWzH8JH3+jVPv/M7CEz+zZs01tmlhSxbmjY3u/MrHNEubdDzZrZ/5nZN2Z20MxSMq2LufZmFkttiWRmz5vZejNbElFW2cxmhr+XM82sUlie4++yL8zsWDObZWZLw/fzDWF5TLbZzMqZ2edm9t+wvXeH5Q3MbH7YrlfDG5Uxs7Lh8vJwff1o1v9wmVmcmX1lZtPC5YJtr3MuJh5AhYjng4CnwuddgXcBA1oB88PyysCP4b+VwueVot2OfLS3ExAfPn8AeCB83gT4L1AWaACsILgJMC58fhxQJtymSbTbkY/2nkTwff6PgJSI8phsb6a2x0xbsmnb2UBzYElE2YPAkPD5kIj3dra/yz49gJpA8/B5IvB9+B6OyTaH9U4In5cG5ofteA24JCx/CrgmfH5txP/dlwCvRrsNh9num4CJwLRwuUDbGzM9bufc9ojF8kDah/fdgAkuMA9IMrOaQGdgpnNus3NuCzATOK9IK30EnHMznHOp4eI8gu/DQ9DeSc6535xzPwHLCYaZ9XqoWefcMudcdoPwxGR7M4mltmTgnJsDbM5U3A0YHz4fD3SPKM/ud9kbzrm1zrkvw+c7gGUEI0fGZJvDeu8MF0uHDwf8AXgjLM/c3rSfwxtABzOzIqpugTCzOsD5wLPhslHA7Y2Z4AYws5Fm9gtwGXBnWJzTEKuxNPRqP4K/yqFktDdSSWhvLLUlL2o459aGz38FaoTPY+rnEF4WPY2gFxqzbQ4vGy8C1hN0kFYAWyM6HpFtSm9vuH4bUKVoa3zE/gncChwMl6tQwO31KrjN7H0zW5LNoxuAc+5259yxwMvA9bkfrfg7VHvDbW4HUgna7LW8tFdKFhdcQ4y5r76YWQLwJvC3TFcLY67NzrkDzrlkgquCLYATo1ylQmNmfwTWO+cWFuZ5ovI97sPlnDs3j5u+TPA98bvIeYjV1UC7TOUfHXElC9Ch2mtmVwJ/BDqEv+yQ+5CyhxxqNpry8fpG8ra9+ZCnYYJjyDozq+mcWxteFl4flsfEz8HMShOE9svOuX+HxTHdZgDn3FYzmwWcSXDJPz7sZUa2Ka29q8wsHqgIbIpKhQ/PWcAFZtYVKAdUAEZTwO31qsedGzNrFLHYDfg2fP420Ce8O7MVsC28JPUe0MnMKoV3cHYKy7xgZucRXI65wDm3O2LV28Al4d2KDYBGwOfE7lCzJaG9sdSWvHgbSPuWR19gSkR5dr/L3gg/v3wOWOacezRiVUy22cyqWfiNFzM7CuhI8Ln+LKBnuFnm9qb9HHoCH0Z0Soo959xQ51wd51x9gt/TD51zl1HQ7Y323XcF9SD4C3YJsBiYCtR2v9/V+DjB5ypfk/GO5H4ENzMtB/4c7Tbks73LCT4bWRQ+nopYd3vY3u+ALhHlXQnuYl0B3B7tNuSzvT0IPhv6DVgHvBfL7c2m/THTlkztegVYC+wPX9+rCD7j+wD4AXgfqBxum+Pvsi8PoA3BZfDFEb+7XWO1zcApwFdhe5cAd4blxxH8gb0ceB0oG5aXC5eXh+uPi3YbjqDt7fj9rvICba9GThMREfFIzFwqFxERKQkU3CIiIh5RcIuIiHhEwS0iIuIRBbeIiIhHFNwikoWZJZnZtdGuh4hkpeAWkewkEcxcJCLFjIJbRLJzP3C8BfPbPxTtyojI7zQAi4hkEc5cNc05d3KUqyIimajHLSIi4hEFt4iIiEcU3CKSnR1AYrQrISJZKbhFJAvn3CbgUzNbopvTRIoX3ZwmIiLiEfW4RUREPKLgFhER8YiCW0RExCMKbhEREY8ouEVERDyi4BYREfGIgltERMQjCm4RERGP/D82PuGfuBTNEgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 30\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e2)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl')\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new_out-sample'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2436.280\n", + "test 0 : tsm = 253.924, tbsm = -10.344\n", + "Reaching the end of test data. Stop tests at 205. \n", + "===> delta1 = 0.000, delta2 = 0.000\n", + "p = 0.000 +/- 0.000\n", + "Separation = 3.07 sigmas\n", + "Partial test after 1 epochs (took 32.12 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 20\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e2)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl')\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new_out-sample'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2325.162\n", + "test 0 : tsm = 259.370, tbsm = 97.816\n", + "Reaching the end of test data. Stop tests at 215. \n", + "===> delta1 = 0.021, delta2 = 0.014\n", + "p = 0.102 +/- 0.025\n", + "Separation = 1.20 sigmas\n", + "Partial test after 1 epochs (took 32.83 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 10\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e2)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl')\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new_out-sample'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NSM = 2225.504 --- NBSM = 2273.448\n", + "test 0 : tsm = 176.557, tbsm = 129.605\n", + "Reaching the end of test data. Stop tests at 219. \n", + "===> delta1 = 0.029, delta2 = 0.031\n", + "p = 0.256 +/- 0.042\n", + "Separation = 0.53 sigmas\n", + "Partial test after 1 epochs (took 33.03 seconds)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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vXwzAwQe34aab+tGlS0+6dOmZvfxRR/UgObkyTZu2ZufOHdkB2rRp6+x1czrvvCGcd96QEtW0Zs0KbrnlXG67bQyVKuX+2+quu/5Ou3bHcPjhR+eaPmPGVF5//SmefPKT7GlPPvkJtWvX4+efV3HppcfTqFEL2rU7pkS1iIjsSUZOWVim27vy+GZFzp82bRpvvfUWs2bNokqVKqxZs4Zt24L+QFJTU/n000/p3Lkz69atY8WKFWVaW2EqfHBXrlwl+3FSUhK//bYFgH/+823+85+P+OijNxk9+g4mTPgagL32CpavVKkSycmVs09zmFVix47MfNsfO/ZeJk16Lt/0ww8/hiFDRuWbvmnTBi6//GT+/vc7aN26Y655GRm38csvq7nhhidyTf/uuzncfvsARo16h9TUmtnTa9euB0CNGrXp0uV05s37QsEtIlICK1asoFatWlSpEvzur1Xr93uG+vTpw4QJE+jcuTOvvvoqvXr1Yt68eTGvqcIHd0F27tzJypVLSU/vStu2nZk8eQJbthQ23HDRSnLEvX37NoYMOZ2TTz6P447rnWvea689yeefv8ujj76X6yj8p5+WMGRIL4YPfzbXNfItWzazc+dO9t23Glu2bGb69MkMGHBLqdogIlJRnXDCCQwfPpxmzZpx3HHHcfbZZ3PssccC0K1bNy6++GJ27NjBhAkTyMjI4Pbbb495TQruAuzcuYObb+7Ppk3rcXf69LmMatVSY77fKVNeZNasj1i/fi1vvfUMAMOGPUPz5m0ZMeJv/OEPDbnwwk4AdO3ai4svvoV//Ws469ev5e67/w6Q/bWvtWtXMmTI6QDs2JFJ9+59C7wuLiIihUtJSWHmzJl8/PHHTJ06lbPPPpu77roLCM7Sdu7cmQkTJrBlyxbiNRiW5bywvqdKT0/3shhkZNKkmerytIJYvXomJ56o91ok6ubPn88hhxyS/Tze17jzevnllxkzZgwbN27kvvvu49dff+X000/n1ltvZfDgwaSkpLBpU8nO0OZtI4CZzXT39IKW1xG3SEU0dUShs0ZmnlHovJL+koOif9GWZnsi8fTtt99SqVIlDj74YABmz55Nw4YNmTt3LgBHH300Q4cO5ZxzzolbTQpuERGRQmzatInBgwezbt06kpOTadq0KRkZGfTuHdyHZGZcc801ca1JwS0iIpER77M07du357PPPss3/YMPPihw+ZKeJi8NBXchBg7swhVX3EfLlgVeYoirrVt/5brrzmTZskUkJSVx9NGnMnhwcHPEuHEP8PrrT5KUlMz++6dxyy2jOeCAhsyYMZUHHrgyexuLFy/gzjsn0KVLT4YPv4j582fg7jRo0Ixbb32GffaJfacBIiKy+yp8z2lRce651/DKKwt47rn/8NVXn/Lpp+8A0KLF4Tz77AwmTJhDt269GTXqWgDS07vy/POzef752Tz22PtUrboPHTueAMBVV41k/PivmDBhDn/4QwNefPHhhLVLRERKpkIfcRfVVznAxInP8o9/DCAzM5NbbhnNoYceycyZH3L//ZeHWzD+9a+PmD9/JhkZw0hJSWXRoq857rizaNq0NePHP8hvv23h/vtfy9d1aUlUrboP6eldAahceS9atGiX3Sd51nSAQw/tyMSJ4/Kt/957L3PUUT2oWnUfAFJS9gOCkWmCDmfKbz/AIiLlTYU/4i6sr3IITlE///xsrr/+UYYPvxCAcePu49prH+H552fz5JMfU6VKEPILF37FDTc8zksvzWfixGdZsmQhY8d+Qc+eA3jhhYfy7XfGjKkF9mF+4YVH5Vs2p40b1/Hxx29yxBHd8s3L21d5lsmTJ9C9e+47Hm+77QK6d/8DixcvoE+fwbt+oUREZI9QoY+4ofC+yoHssGvX7hg2b97Axo3rOOywPzJy5FX06NGPrl17UadOfQBatjyCWrUOAKB+/SZ06BCclm7atDUzZkzNt9+sU9klkZmZyY03nsPZZ19G/foH5Zo3ceI45s+fQUbGh7mmr1mzgu+//5pOnbrnmj5s2NPs2LGDe+8dzOTJL/DnP19QolpERCQxKnxwF9ZXOeQfbs3M+Mtfrqdz55P55JOJXHTRH3n44XeB3/swD5arlP28sD7M8948lqVq1X0YPTr/HYwAd9wxkAMPPJi+fa/INX369H8zevQdZGR8mKsOCHpj69r19AKHG01KSuKEE/owduw9Cm4RkYio8MFdlMmTXyA9vSuzZ39CSkp1UlKqs2zZIpo2bU3Tpq355psvWbx4ASkpJe8OtaRH3I8+ehObNq3n5pufzDV9wYL/cOedf+WhhyZRo0btfOu9++54Bg36vbMNd2fZskUceGBT3J2PPnqDRo1alLh+EZGKIikpidatW+PuJCUl8fDDD3PUUUfx66+/cvHFFzNnzhzcndTUVCZNmkRKSgpmRr9+/Rg3LrjvKDMzkwMOOIAOHTrw1ltv7VY9Cu4iVKlSlb59Dyczczu33DIagOef/yczZkylUqVKHHRQK446qgdz5kyLaR0rVy5j9Og7aNSoBf37twPgrLMG0bPnAEaNGsKWLZu4/vozAahTpwEjR74BBDffrVy5lHbtjs3elrszbNj5bN68AXenWbPDuP76x2Jav4hImSmi179S6Tp0l4vsvffezJ4dHGi9++67DB06lA8//JAHH3yQOnXq8PXXweiR3377LZUrB2c39913X+bOncuWLVvYe++9mTJlCvXq1SuTkit0cNet24gXX5yb/fzcc3/v/SYj44MC17n22vw3mqWndyE9vUuB6+adVxp16tRnxoyC+5R/9NF/F7pe3bqNeOedH3NNq1SpEqNHf7pb9YiIVFQbNmxg//33B4IhPxs2bJg9r3nz5rmWPemkk3j77bfp3bs348eP55xzzuHjjz/e7Roq/F3lIiIiRdmyZQtt27alRYsWDBgwgJtvvhmACy+8kLvvvptOnTpx00038d133+VaL2u87q1btzJnzhw6dOhQJvUouEVERIqQdap8wYIFTJo0ifPOOw93p23btvzwww8MGTKEn3/+mSOOOIL58+dnr9emTRsWL17M+PHjOemkk8qsngp9qlxERKQkOnXqxJo1a1i9ejW1a9cmJSWFXr160atXLypVqsTEiRNzDdH55z//mWuuuYYPPviAtWvXlkkNOuLew2zb9htDh55Nz55NOf/8Drm+V57lp5+W8te/duXMM1ty1lmtGD/+wex5TzxxKz161Mvu0OWTTyZmz/vuuzlccEEnzjqrFWef3ZrffttaqhozM7fTr1+7XS739NMj6NmzKb16NWfatHcLXObHH//L+ed3oGfPpgwdejbbt2/LNf+9914hPd345ptgPPbPP59C//7tOfvs1vTv354vv3y/VG0QESmNBQsWsGPHDmrWrMmnn37KL7/8AsC2bdv45ptvcl3zhuB0+rBhw2jdunWZ1aAj7j3M668/RbVq+/Paa9/z7rsTeOih6xgx4oVcyyQnJ3PllffTokU7Nm/eyLnntqdDh+M56KCWAPTte2WuG+0g+CrCzTf3Z/jwZ2nW7DDWrVtb4He7czr11Ea8+ebifNNnz/6Eww77Y5Hr/vDDN0yePIEXX5zH6tXL+fvfj+PVVxeSlJSUa7mHHrqOvn2vpHv3Ptx55994/fWn6N37EgA2b97IhAkPcuihv18XSk2txciRb5KWVpfvv5/L4MHd892AJyJSlrKucUPwzZwxY8aQlJTEokWLuOSSS3B3du7cycknn8wZZ+Qez75+/fpcdtllZVpPhQ7u5csXM3jwiRxySHsWLJjFQQe1Yvjwsdl9eifChx++zsCBtwLQrVtv7rlnEO6eqzOYWrUOyO6lbd99q9Go0SGsWvVjdnAX5PPPJ3PwwW1o1uwwAFJTa5a6xs8+m1Rg16p523HCCX3Ya68q1KvXmAMPbMq8eV/Qpk2n7GXcnS+/fJ9//ON5AE455XwyMm7NDu7HH7+Z88+/jrFj781ep0WLw7MfN2nSit9+28K2bb/l63hGRMqpYnx9q6zt2LGjwOnnnXce5513XoHzChres0uXLnTp0mW366nwp8r/979v6d3777z88nz23Xc/Xnrp0TLfx4ABRxfYL/n06fm/yrVq1Y/UqXMgEBxZp6RUZ/36wq+LLF++mG+//U+uo9IXX3yYPn3acNttF7JhQ3AaZ8mShYAxaFB3+vVrx5gx95S6PTNmTN3lV9xytgOgdu36rFqV+8h4/fq1VKuWSnJycr5lFiyYxU8/LaVz55ML3cd7771CixbtFNoiUqFU6CNugDp1DqRt2+C070kn9WfChFH5TjPvrief3P3v7RXk1183ce21Z3D11f/MHvGrd+9LGDDgZsyMxx67mZEjr2bYsNHs2JHJV199wtixX1K16j5cckk3DjmkPUcemXuwkrvvvpSvvgq+57169XL69g1OD3XrdiYXXXQjq1b9SPXqNWJ6VmLnzp088MBV3HrrM4Uus2jRPB566DoeeWRyzOoQEdkTVfjgLqg/8rI2YMDR/PrrxnzTL7/8Pjp0OC7XtNq167Fy5VLq1KlPZmYmmzatp3r1/Ke1MzO3c+21Z3Diif340596ZU+vWbNO9uPTT7+YK644JdxufQ4//BhSU2sB8Mc/nsSCBbPyBfd11z2S/fjUUxvl65b1s88m0bFjMGDJiy8+wmuv/QuABx+cSFpa3XztyLJq1TJq187da1D16jXZuHEdmZmZJCcnZy/z668bWbRoLn/9axcA1q79iauu+jMPPPAGLVums3LlMoYMOZ3bbhu7W8OliohEUcyC28xGA6cAq9z90HDavcCpwDZgEXCBu6+LVQ3F8dNPS5gzZxpt2nRi0qTnadu2c5nvoyRH3Mcc82feemsMbdp04r33XuaII/6U748Jd2f48Ito3PgQ+ve/Kte8NWtWZF//njr1/2jS5FAAOnXqztix97B1668kJ+/FrFkf0rdv/kFOdmXatElccsntAJx11qWcddalhbbjppv60q/fVaxevZylS7+jVasjcy1jZqSnd+W9916me/c+vPXWGI499jRSUqrz3ntrspcbOLALV1xxHy1bprNx4zquuOJkBg26K/tMiYiUb3nv8ylP3AvuFbMosbzG/QxwYp5pU4BD3b0NsBCI/10GeTRs2JyXXnqE3r0PYcOGX7JvjEqU0067iPXr19KzZ1Oee+4BBg26CwhOW192WfAF/q+++pSJE5/lyy/fz/e1rwcfvJazz25Nnz5tmDFjKldfPRKA/fbbn379ruK8846gb9+2NG/ersjrxwXZsWMHS5d+X6xBSZo0acVxx53FmWe2ZPDgE7n22key7yi/7LKTWL16OQCDB9/Nc889QM+eTVm/fi2nnXZRkdt94YWHWbr0e558cnh223/+eVWJ2iEi0VG1alXWrl1bqoDb07k7a9eupWrVqiVaz2L5YphZI+CtrCPuPPNOB3q7e79dbSc9Pd1nzJix2/VMmjSTtLT22c+XL1/MFVeckqu/cinc7NmfMHHiOG644fFEl7JLq1fP5MQT2+96wYqqiIEaRmaeUei8K49vVuJdjZyysEy3JxXL9u3bWbZsGVu3lq7fiT1d1apVqV+/fvbgJFnMbKa7pxe0TiKvcV8IvFDYTDMbCAwEaNCgQbxqkiK0bds5JpcSZBeKGg0pAV+NEYmnypUr07hx40SXsUdJyNfBzOxGIBN4rrBl3D3D3dPdPT0tLS0mdeQdHUxERGRPF/cjbjP7C8FNa928PF60KCPbtv3GsGHnMX/+TKpXr8mIES9Qt26jfMudemoj9tmnGklJSSQlJfPss8ElhQcfHMJHH71J5cp7Ub9+E4YNe5pq1VJZvnwxZ555CA0bBsPPHXpox0ic+hYRkUBcg9vMTgSuBY5191/jue+oKU7Xp1meeGJq9te8snTocDyXXjqC5ORkRo26jqefHsFll90NQL16TfJ9zUtERKIhZqfKzWw8MA1obmbLzOwi4GGgGjDFzGabWUIP9bZs2czll5/MOeccxllnHcrkyUEwzp8/k4EDj6V///YMGtSdNWtWAMHXku6773L69m3LWWcdyty5X8Sstg8/fJ1TTjkfCLo+/eKL90p0V2XHjidk90jWunVHVq1aFpM6RRbtnI4AABXLSURBVEQkvmJ2xO3u5xQw+alY7a80PvtsEmlpdXnwwbcB2LRpPZmZ27n33sHcf//r7L9/GpMnv8Ajj9zIsGGjAdi69Veef342s2Z9xPDhF5boGnlJOmIprOvTvEfWZsall56AmdGr11/p1Wtgvu2/8cZojj/+7Ozny5f/l759DyclZT8uueQfHH740cVug4iIJFaF7jmtadPW/POfVzNq1HUcffQpHH740Xz//VwWLZrLpZceDwTfXc7q0ASge/fg75F27Y5h8+YNbNy4jmrVUou1v1h0ffrkk59Qu3Y9fv55FZdeejyNGrWgXbtjsuc/9dQdJCUl06NH8K27WrUO4K23lpCaWpP582dyzTU9eeGFedldpoqIyJ6tQgd3w4bNGDduFp9+OpHHHruJI47oRteup3PQQa14+ulpBa6zO12kxqLr06xuRGvUqE2XLqczb94X2cH95pvP8Mknb/HYY+9l17nXXlWyB+U45JD21KvXhCVLFtKyZYFfFxQRkT1MhQ7u1auXs99+NTjppP5Uq5bKa689yV/+cj2//LI6uxvUzMzt/O9/C2nSpBUAkye/QHp6V2bP/oSUlOqkpFQv9v7KuuvTLVs2s3PnTvbdtxpbtmxm+vTJDBhwCxBcBhg79h4yMj7MNSDIL7+sZr/9apCUlMSyZT+wdOl31Kt3ULHrEhGRxKrQwf3991/z4INDqFSpEsnJlbn++seoXHkv7r77Ze677zI2bVrPjh2ZnHPOFdnBXaVKVfr2PZzMzO3ccsvomNV22mkXccst59KzZ1P2268Gd945AQj+2Lj99gGMGjWRtWtXMmTI6QDs2JFJ9+59OeqooJfZe+4ZxPbtv2Wf8s/62tesWR/xxBO3kJxcGbNKDB36ONWr14hZO0REpGxV6ODu1Kk7nTp1zze9efO2/OtfHxW4To8e/bn66n/GujSqVKnK3Xe/lG96WlpdRo0K+iWvX/8gxo//qsD1X3vt+wKnd+t2Bt26Fd6lpYiI7NkS0nOaiIiIlE6FPuIuqYyMDxJdgoiIVHA64hYREYkQBbeIiEiEKLhFREQipEJd405N3YvVq2cmugyJg9TUvRJdgohITFSo4O7YsXWiSxAREdktOlUuIiISIQpuERGRCFFwi4iIRIiCW0REJEIU3CIiIhGi4BYREYmQCvV1MJESmTqi8Hldh8avjqLEoMaOSzKKmHtfqbZZGiOnLCx03pXHN9tjtikSbzriFhERiRAFt4iISIQouEVERCJEwS0iIhIhCm4REZEIUXCLiIhEiIJbREQkQhTcIiIiEaLgFhERiRAFt4iISIQouEVERCJEwS0iIhIhCm4REZEIUXCLiIhEiIJbREQkQhTcIiIiEaLgFhERiRAFt4iISIQouEVERCIkZsFtZqPNbJWZzc0xrYaZTTGz78J/94/V/kVERMqjWB5xPwOcmGfa9cB77n4w8F74XERERIopZsHt7h8BP+eZfBowJnw8BugZq/2LiIiUR/G+xl3H3VeEj38C6sR5/yIiIpGWnKgdu7ubmRc238wGAgMBGjRoELe6JKKmjih8Xteh8aujKFGoUUT2ePE+4l5pZgcAhP+uKmxBd89w93R3T09LS4tbgSIiInuyeAf3G8D54ePzgdfjvH8REZFIi+XXwcYD04DmZrbMzC4C7gKON7PvgOPC5yIiIlJMMbvG7e7nFDKrW6z2KSIiUt6p5zQREZEIUXCLiIhEiIJbREQkQhTcIiIiEaLgFhERiRAFt4iISIQouEVERCJEwS0iIhIhCm4REZEIUXCLiIhEiIJbREQkQhTcIiIiEaLgFhERiRAFt4iISIQouEVERCJEwS0iIhIhCm4REZEIUXCLiIhEiIJbREQkQpITXYCIAFNHJLqChJj21DWFz2wwMH6FiESIjrhFREQiRMEtIiISIQpuERGRCFFwi4iIRIiCW0REJEIU3CIiIhGi4BYREYkQBbeIiEiEKLhFREQiRMEtIiISIQpuERGRCFFwi4iIRIiCW0REJEIU3CIiIhGi4BYREYkQBbeIiEiEKLhFREQiRMEtIiISIQpuERGRCElIcJvZlWY2z8zmmtl4M6uaiDpERESiJu7BbWb1gMuAdHc/FEgC+sS7DhERkShK1KnyZGBvM0sG9gGWJ6gOERGRSIl7cLv7j8B9wBJgBbDe3SfHuw4REZEoSo73Ds1sf+A0oDGwDnjJzPq7+7g8yw0EBgI0aNAg3mWKRMK0H9YWOq8TI+JXyNSi9nVGXPc3MrNs9zdyysJC5115fLM9ZptScSTiVPlxwH/dfbW7bwdeBY7Ku5C7Z7h7urunp6Wlxb1IERGRPVEignsJ0NHM9jEzA7oB8xNQh4iISOQk4hr3dOBlYBbwdVhDRrzrEBERiaK4X+MGcPdhwLBE7FtERCTK1HOaiIhIhCi4RUREIkTBLSIiEiEKbhERkQhRcIuIiESIgltERCRCFNwiIiIRouAWERGJEAW3iIhIhCi4RUREIkTBLSIiEiEKbhERkQhRcIuIiESIgltERCRCFNwiIiIRouAWERGJkBIFt5nta2ZJsSpGREREilZkcJtZJTPra2Zvm9kqYAGwwsy+MbN7zaxpfMoUERER2PUR91SgCTAU+IO7H+jutYHOwOfA3WbWP8Y1ioiISCh5F/OPc/fteSe6+8/AK8ArZlY5JpWJxMPUEXv8Nqf9sDau6xVl5JSFBU6/cle/ScpYkW1rEL86RBKhyCPurNA2s2fzzsuaVlCwi4iISGwU9+a0VjmfhDeotS/7ckRERKQou7o5baiZbQTamNmG8GcjsAp4PS4VioiISLZdnSof4e7VgHvdfb/wp5q713T3oXGqUUREREK7OuJuBFBYSFugftmXJSIiIgXZ1b2g95pZJYLT4jOB1UBVoCnQFegGDAOWxbJIERERCRQZ3O5+ppm1BPoBFwJ/ALYA84GJwB3uvjXmVYqIiAhQjLvK3f0b4B/AmwSB/V/gS+BlhbaIiEh8FbfbhDHABmBU+LwvMBY4KxZFiYiISMGKG9yHunvLHM+nmtk3sShIREREClfcDlhmmVnHrCdm1gGYEZuSREREpDDFPeJuD3xmZkvC5w2Ab83sa8DdvU1MqhMREZFcihvcJ8a0ChERESmWYgW3u/8v1oWIiIjIrhX3GreIiIjsARTcIiIiEaLgFhERiRAFt4iISIQouEVERCIkIcFtZqlm9rKZLTCz+WbWKRF1iIiIRE1xv8dd1h4EJrl7bzPbC9gnQXWIiIhEStyD28yqA8cAfwFw923AtnjXISIiEkWJOOJuDKwGnjazw4CZwOXuvjnnQmY2EBgI0KBBg7gXKXueaU9dU+i8TgfVjGMlRddSmHjXGAsdl2QUPKMctG3klIWJLiEhCmv3lcc3i3MlUlyJuMadDLQDHnP3w4HNwPV5F3L3DHdPd/f0tLS0eNcoIiKyR0pEcC8Dlrn79PD5ywRBLiIiIrsQ9+B295+ApWbWPJzUDdDY3iIiIsWQqLvKBwPPhXeU/wBckKA6REREIiUhwe3us4H0ROxbREQkytRzmoiISIQouEVERCJEwS0iIhIhCm4REZEIUXCLiIhEiIJbREQkQhTcIiIiEaLgFhERiRAFt4iISIQouEVERCJEwS0iIhIhCm4REZEIUXCLiIhEiIJbREQkQhTcIiIiEaLgFhERiRAFt4iISIQouEVERCIkOdEFiMTatKeuKdV6nQ6qWcaVFG7aD2vjtq9YKKr+jmTEsZKidVxS8lo+bzAwBpWIlJ6OuEVERCJEwS0iIhIhCm4REZEIUXCLiIhEiIJbREQkQhTcIiIiEaLgFhERiRAFt4iISIQouEVERCJEwS0iIhIhCm4REZEIUXCLiIhEiIJbREQkQhTcIiIiEaLgFhERiRAFt4iISIQouEVERCJEwS0iIhIhCm4REZEIUXCLiIhESMKC28ySzOw/ZvZWomoQERGJmkQecV8OzE/g/kVERCInIcFtZvWBk4EnE7F/ERGRqEpO0H7/CVwLVCtsATMbCAwEaNCgQZzKkhKbOqLweV2Hxq8OkUSIwed/5JSFpVrvyuOblfk2Zc8U9yNuMzsFWOXuM4tazt0z3D3d3dPT0tLiVJ2IiMieLRGnyv8I/NnMFgMTgD+Z2bgE1CEiIhI5cQ9udx/q7vXdvRHQB3jf3fvHuw4REZEo0ve4RUREIiRRN6cB4O4fAB8ksgYREZEo0RG3iIhIhCi4RUREIkTBLSIiEiEKbhERkQhRcIuIiESIgltERCRCFNwiIiIRouAWERGJEAW3iIhIhCi4RUREIkTBLSIiEiEKbhERkQhRcIuIiESIgltERCRCFNwiIiIRouAWERGJEAW3iIhIhCi4RUREIiQ50QVIBEwdUfbrdR1aum2K7ELHJRlx29e0H9YWOu/zzIWl2mbp67+vVNv8vMHAAqePnFJ4/Vce36z4ZUmZ0xG3iIhIhCi4RUREIkTBLSIiEiEKbhERkQhRcIuIiESIgltERCRCFNwiIiIRouAWERGJEAW3iIhIhCi4RUREIkTBLSIiEiEKbhERkQhRcIuIiESIgltERCRCFNwiIiIRouAWERGJEAW3iIhIhCi4RUREIkTBLSIiEiEKbhERkQiJe3Cb2YFmNtXMvjGzeWZ2ebxrEBERiarkBOwzE7ja3WeZWTVgpplNcfdvElCLiIhIpMT9iNvdV7j7rPDxRmA+UC/edYiIiERRIo64s5lZI+BwYHoB8wYCAwEaNGgQ17oqpKkjSrXatB/WFjqv00E1C1/vqWtKvE5p6yitst5mLGqUPVfHJRlx3d/IKQsLndcxjnUUpagarzy+WRwribaE3ZxmZinAK8AV7r4h73x3z3D3dHdPT0tLi3+BIiIie6CEBLeZVSYI7efc/dVE1CAiIhJFibir3ICngPnu/kC89y8iIhJliTji/iNwLvAnM5sd/pyUgDpEREQiJ+43p7n7J4DFe78iIiLlgXpOExERiRAFt4iISIQouEVERCJEwS0iIhIhCm4REZEIUXCLiIhEiIJbREQkQhTcIiIiEaLgFhERiRAFt4iISIQouEVERCJEwS0iIhIhCm4REZEIUXCLiIhEiIJbREQkQhTcIiIiEaLgFhERiRAFt4iISISYuye6hl1KT0/3GTNmlN0Gp44ou21l6Tq00FkjpywsdN6Vya8Uvl7mGQVO77gko9B1Pm8wsFT7mvbD2kLniUj5VdTvjMIU9buktL8Li9zf8c1KtV5h+yvt9orMjiLaXRpmNtPd0wuapyNuERGRCFFwi4iIRIiCW0REJEIU3CIiIhGi4BYREYkQBbeIiEiEKLhFREQiRMEtIiISIQpuERGRCFFwi4iIRIiCW0REJEIU3CIiIhGi4BYREYkQBbeIiEiEKLhFREQiRMEtIiISIQpuERGRCFFwi4iIRIiCW0REJEISEtxmdqKZfWtm35vZ9YmoQUREJIriHtxmlgQ8AvQAWgLnmFnLeNchIiISRYk44j4S+N7df3D3bcAE4LQE1CEiIhI5iQjuesDSHM+XhdNERERkF8zd47tDs97Aie4+IHx+LtDB3QflWW4gMDB82hz4Nq6FxlYtYE2ii0gAtbtiqajthorbdrW77DR097SCZiSX8Y6K40fgwBzP64fTcnH3DCAjXkXFk5nNcPf0RNcRb2p3xVJR2w0Vt+1qd3wk4lT5l8DBZtbYzPYC+gBvJKAOERGRyIn7Ebe7Z5rZIOBdIAkY7e7z4l2HiIhIFCXiVDnuPhGYmIh97yHK5SWAYlC7K5aK2m6ouG1Xu+Mg7jeniYiISOmpy1MREZEIUXDHmJktNrOvzWy2mc0Ip9Uwsylm9l347/6JrrMsmNloM1tlZnNzTCuwrRYYFXZ7O8fM2iWu8t1TSLtvNbMfw/d9tpmdlGPe0LDd35pZ98RUvfvM7EAzm2pm35jZPDO7PJxert/zItpdrt9zM6tqZl+Y2Vdhu28Lpzc2s+lh+14IbzrGzKqEz78P5zdKZP2lVUS7nzGz/+Z4v9uG02P/OXd3/cTwB1gM1Moz7R7g+vDx9cDdia6zjNp6DNAOmLurtgInAe8ABnQEpie6/jJu963ANQUs2xL4CqgCNAYWAUmJbkMp230A0C58XA1YGLavXL/nRbS7XL/n4fuWEj6uDEwP38cXgT7h9MeBS8LHfwceDx/3AV5IdBvKuN3PAL0LWD7mn3MdcSfGacCY8PEYoGcCaykz7v4R8HOeyYW19TRgrAc+B1LN7ID4VFq2Cml3YU4DJrj7b+7+X+B7gm6AI8fdV7j7rPDxRmA+QS+I5fo9L6LdhSkX73n4vm0Kn1YOfxz4E/ByOD3v+531OXgZ6GZmFqdyy0wR7S5MzD/nCu7Yc2Cymc0Me4MDqOPuK8LHPwF1ElNaXBTW1orQ9e2g8FTZ6ByXQ8plu8PToIcTHI1UmPc8T7uhnL/nZpZkZrOBVcAUgrMH69w9M1wkZ9uy2x3OXw/UjG/FZSNvu9096/2+I3y/R5pZlXBazN9vBXfsdXb3dgSjoV1qZsfknOnBuZUKcWt/RWor8BjQBGgLrADuT2w5sWNmKcArwBXuviHnvPL8nhfQ7nL/nrv7DndvS9Dj5ZFAiwSXFBd5221mhwJDCdp/BFADuC5e9Si4Y8zdfwz/XQX8H8GHfWXWqZPw31WJqzDmCmtrsbq+jSp3Xxn+Z98J/IvfT42Wq3abWWWC8HrO3V8NJ5f797ygdleU9xzA3dcBU4FOBKeCs/oEydm27HaH86sDa+NcapnK0e4Tw0sm7u6/AU8Tx/dbwR1DZravmVXLegycAMwl6OL1/HCx84HXE1NhXBTW1jeA88I7MDsC63OcXo28PNe0Tid43yFod5/wjtvGwMHAF/GuryyE1yufAua7+wM5ZpXr97ywdpf399zM0swsNXy8N3A8wfX9qUDvcLG873fW56A38H54BiZSCmn3ghx/nBrBdf2c73dsP+exuhNPPw5wEMHdpF8B84Abw+k1gfeA74B/AzUSXWsZtXc8wSnC7QTXdS4qrK0Ed1w+QnCN7GsgPdH1l3G7nw3bNSf8j3xAjuVvDNv9LdAj0fXvRrs7E5wGnwPMDn9OKu/veRHtLtfvOdAG+E/YvrnALeH0gwj+EPkeeAmoEk6vGj7/Ppx/UKLbUMbtfj98v+cC4/j9zvOYf87Vc5qIiEiE6FS5iIhIhCi4RUREIkTBLSIiEiEKbhERkQhRcIuIiESIgltE8jGzVDP7e6LrEJH8FNwiUpBUgtGdRGQPo+AWkYLcBTQJxxm+N9HFiMjv1AGLiOQTjnr1lrsfmuBSRCQPHXGLiIhEiIJbREQkQhTcIlKQjUC1RBchIvkpuEUkH3dfC3xqZnN1c5rInkU3p4mIiESIjrhFREQiRMEtIiISIQpuERGRCFFwi4iIRIiCW0REJEIU3CIiIhGi4BYREYkQBbeIiEiE/D9HaWKDL9uz1gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gphival = 5\n", + "f = h5py.File(os.getcwd()+'/toydata/gphi_toydata_test_%d_out.h5'%(gphival), 'r')\n", + "Data = np.array(f['Data'])\n", + "Labels = np.array(f['Labels'])\n", + "NSM = np.array(f['NSM'])\n", + "NBSMList = np.array(f['NBSMList'])\n", + "\n", + "NBSM = NBSMList[0]\n", + "\n", + "n_epochs = int(1e2)\n", + "results_path = os.getcwd()\n", + "\n", + "Idx_test = torch.randperm(len(Data))\n", + "Data_test = torch.Tensor(Data[Idx_test])\n", + "Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",\n", + ")\n", + "\n", + "estimator.load(os.getcwd()+'/models/carl')\n", + "\n", + "test_model(estimator, Data_test[Label_test==0, :], Data_test[Label_test==1, :], 1, 1, 4000, 'plus', \n", + " verbose_t=True, verbose_period_t=1e5, title='gphi%d_new_out-sample'%(gphival))\n", + "\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/tutorial_particle_physics/Untitled.ipynb b/examples/tutorial_particle_physics/Untitled.ipynb new file mode 100644 index 000000000..3ec290be3 --- /dev/null +++ b/examples/tutorial_particle_physics/Untitled.ipynb @@ -0,0 +1,593 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "import h5py, os\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "f = h5py.File(os.getcwd()+'/data/lhe_data.h5', 'r+')\n", + "#f = h5py.File(os.getcwd()+'/data/lhe_data_shuffled.h5', 'r+')" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bm = f['benchmarks']\n", + "bm.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mp = f['morphing']\n", + "mp.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ob = f['observables']\n", + "ob.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pm = f['parameters']\n", + "pm.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ss = f['sample_summary']\n", + "ss.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "samples = f['samples']\n", + "samples.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3., 3., 3., ..., 9., 9., 9.])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.array(f['samples/sampling_benchmarks'])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "f.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Samples" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(6000000, 9)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "observations = np.array(samples['observations'])\n", + "observations.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.00000000e+10, 1.00000000e+10, 1.00000000e+10, ...,\n", + " 1.00000000e+10, 1.00000000e+10, 1.00000000e+10],\n", + " [1.00000000e+10, 1.00000000e+10, 1.00000000e+10, ...,\n", + " 1.00000000e+10, 1.00000000e+10, 1.00000000e+10],\n", + " [1.00000000e+10, 1.00000000e+10, 1.00000000e+10, ...,\n", + " 1.00000000e+10, 1.00000000e+10, 1.00000000e+10],\n", + " ...,\n", + " [1.00000000e+10, 1.00000000e+10, 1.00000000e+10, ...,\n", + " 1.00000000e+10, 1.00000000e+10, 2.08103695e-12],\n", + " [1.00000000e+10, 1.00000000e+10, 1.00000000e+10, ...,\n", + " 1.00000000e+10, 1.00000000e+10, 2.08103695e-12],\n", + " [1.00000000e+10, 1.00000000e+10, 1.00000000e+10, ...,\n", + " 1.00000000e+10, 1.00000000e+10, 2.08103695e-12]])" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weights = np.array(samples['weights'])\n", + "weights" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0msampling_benchmarks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sampling_benchmarks'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'num of sm events: '\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msampling_benchmarks\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;31m#print('num of 50 events: '+str(sum(sampling_benchmarks == 4)))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m#print('num of neg_50 events: '+str(sum(sampling_benchmarks == 5)))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m#print('num of 200 events: '+str(sum(sampling_benchmarks == 6)))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "sampling_benchmarks = np.array(samples['sampling_benchmarks'])\n", + "print('num of sm events: '+str(sum(sampling_benchmarks == 3)))\n", + "#print('num of 50 events: '+str(sum(sampling_benchmarks == 4)))\n", + "#print('num of neg_50 events: '+str(sum(sampling_benchmarks == 5)))\n", + "#print('num of 200 events: '+str(sum(sampling_benchmarks == 6)))\n", + "#print('num of neg_200 events: '+str(sum(sampling_benchmarks == 7)))\n", + "#print('num of 500 events: '+str(sum(sampling_benchmarks == 8)))\n", + "#print('num of neg_500 events: '+str(sum(sampling_benchmarks == 9)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Embedding toy data" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "toydataFile = h5py.File(os.getcwd()+'/toydata/gphi_toydata.h5', 'r')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### data" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "del f['samples/observations']\n", + "f.create_dataset('samples/observations', data=np.array(toydataFile['Data']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### sampling_benchmarks" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "N = 500000\n", + "sampling_benchmarks = np.concatenate([np.concatenate([np.ones(N)*3, np.ones(N)*i]) for i in range(4, 10)])\n", + "del f['samples/sampling_benchmarks']\n", + "f.create_dataset('samples/sampling_benchmarks', data=sampling_benchmarks)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### weights" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "Weights = np.array(toydataFile['Weights'])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1.0429338e-12, 1.0429338e-12, 1.0429338e-12, ..., 2.8697630e-12,\n", + " 6.3399472e-12, 4.3035228e-10], dtype=float32)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Weights" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "weights = np.ones([N*12, 10])*(1e10)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "#weights[N*0:N*1, 3] = Weights[N*0]\n", + "#weights[N*1:N*2, 4] = Weights[N*1]\n", + "#weights[N*:N*1, 3] = Weights[N*0]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sm data, pos=3\n", + "bsm data, pos=4\n", + "sm data, pos=3\n", + "bsm data, pos=5\n", + "sm data, pos=3\n", + "bsm data, pos=6\n", + "sm data, pos=3\n", + "bsm data, pos=7\n", + "sm data, pos=3\n", + "bsm data, pos=8\n", + "sm data, pos=3\n", + "bsm data, pos=9\n" + ] + } + ], + "source": [ + "for i in range(12):\n", + " if i%2==0:\n", + " print('sm data, pos=%d'%(3))\n", + " weights[N*i:N*(i+1), 3] = Weights[N*i]\n", + " else:\n", + " print('bsm data, pos=%d'%(3+(i+1)/2))\n", + " weights[N*i:N*(i+1), int(3+(i+1)/2)] = Weights[N*i]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "del f['samples/weights']\n", + "f.create_dataset('samples/weights', data=weights)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### observable numbers" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f['observables'].keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[b's' b'theta' b'thetaZ' b'thetaW' b'Sin(phiZ)' b'Sin(phiW)' b'Cos(phiZ)'\n", + " b'Cos(phiW)' b'Pt']\n", + "[b's' b'theta' b'thetaZ' b'thetaW' b'Sin(phiZ)' b'Sin(phiW)' b'Cos(phiZ)'\n", + " b'Cos(phiW)' b'Pt']\n" + ] + } + ], + "source": [ + "print(np.array(f['observables/definitions']))\n", + "print(np.array(f['observables/names']))" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "del f['observables/definitions']\n", + "del f['observables/names']\n", + "\n", + "observables = ['s', 'theta', 'thetaZ', 'thetaW', 'Sin(phiZ)', 'Sin(phiW)', 'Cos(phiZ)', 'Cos(phiW)', 'Pt']\n", + "f.create_dataset('observables/definitions', data=(np.array(observables, dtype='S')))\n", + "f.create_dataset('observables/names', data=(np.array(observables, dtype='S')))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/tutorial_particle_physics/data/setup.h5 b/examples/tutorial_particle_physics/data/setup.h5 new file mode 100644 index 000000000..b85f9d3d5 Binary files /dev/null and b/examples/tutorial_particle_physics/data/setup.h5 differ diff --git a/examples/tutorial_particle_physics/lhe_data_small.h5 b/examples/tutorial_particle_physics/lhe_data_small.h5 new file mode 100644 index 000000000..61d75a7cb Binary files /dev/null and b/examples/tutorial_particle_physics/lhe_data_small.h5 differ diff --git a/examples/tutorial_particle_physics/models/ChPgw_355.pth b/examples/tutorial_particle_physics/models/ChPgw_355.pth new file mode 100644 index 000000000..72516dd4c Binary files /dev/null and b/examples/tutorial_particle_physics/models/ChPgw_355.pth differ diff --git a/examples/tutorial_particle_physics/models/carl10-bigbatch-gw_settings.json b/examples/tutorial_particle_physics/models/carl10-bigbatch-gw_settings.json new file mode 100644 index 000000000..5f8cc37d1 --- /dev/null +++ b/examples/tutorial_particle_physics/models/carl10-bigbatch-gw_settings.json @@ -0,0 +1 @@ +{"n_observables": 9, "n_parameters": 1, "features": null, "n_hidden": [60, 60], "activation": "tanh", "dropout_prob": 0.0, "estimator_type": "parameterized_ratio", "components": [[0], [1], [2]], "morphing_matrix": [[0.025707846975058346, -0.016944346398729104, 0.9912364994236706], [0.03799880312433507, -0.022988794064928323, -0.015010009059406743], [0.001945868673346318, 0.0016481722234193553, -0.0035940408967656736]]} \ No newline at end of file diff --git a/examples/tutorial_particle_physics/models/carl10-bigbatch-gw_state_dict.pt b/examples/tutorial_particle_physics/models/carl10-bigbatch-gw_state_dict.pt new file mode 100644 index 000000000..5e9adf52b Binary files /dev/null and b/examples/tutorial_particle_physics/models/carl10-bigbatch-gw_state_dict.pt differ diff --git a/examples/tutorial_particle_physics/models/carl10-bigbatch-gw_x_means.npy b/examples/tutorial_particle_physics/models/carl10-bigbatch-gw_x_means.npy new file mode 100644 index 000000000..d90edc80c Binary files /dev/null and b/examples/tutorial_particle_physics/models/carl10-bigbatch-gw_x_means.npy differ diff --git a/examples/tutorial_particle_physics/models/carl10-bigbatch-gw_x_stds.npy b/examples/tutorial_particle_physics/models/carl10-bigbatch-gw_x_stds.npy new file mode 100644 index 000000000..c057b1c29 Binary files /dev/null and b/examples/tutorial_particle_physics/models/carl10-bigbatch-gw_x_stds.npy differ diff --git a/examples/tutorial_particle_physics/models/carl10-bigbatch_settings.json b/examples/tutorial_particle_physics/models/carl10-bigbatch_settings.json new file mode 100644 index 000000000..6179ebeed --- /dev/null +++ b/examples/tutorial_particle_physics/models/carl10-bigbatch_settings.json @@ -0,0 +1 @@ +{"n_observables": 9, "n_parameters": 1, "features": null, "n_hidden": [60, 60], "activation": "tanh", "dropout_prob": 0.0, "estimator_type": "parameterized_ratio", "components": [[0], [1], [2]], "morphing_matrix": [[0.09913344511950417, 1.0677416245579467, -0.16687506967745092], [0.02892049642498369, 0.00036638053405734503, -0.029286876959041037], [0.0013488982891598808, -0.00360618238624564, 0.002257284097085759]]} \ No newline at end of file diff --git a/examples/tutorial_particle_physics/models/carl10-bigbatch_state_dict.pt b/examples/tutorial_particle_physics/models/carl10-bigbatch_state_dict.pt new file mode 100644 index 000000000..444dfd69b Binary files /dev/null and b/examples/tutorial_particle_physics/models/carl10-bigbatch_state_dict.pt differ diff --git a/examples/tutorial_particle_physics/models/carl10-bigbatch_x_means.npy b/examples/tutorial_particle_physics/models/carl10-bigbatch_x_means.npy new file mode 100644 index 000000000..840cb9f0f Binary files /dev/null and b/examples/tutorial_particle_physics/models/carl10-bigbatch_x_means.npy differ diff --git a/examples/tutorial_particle_physics/models/carl10-bigbatch_x_stds.npy b/examples/tutorial_particle_physics/models/carl10-bigbatch_x_stds.npy new file mode 100644 index 000000000..d2c2f4c92 Binary files /dev/null and b/examples/tutorial_particle_physics/models/carl10-bigbatch_x_stds.npy differ diff --git a/examples/tutorial_particle_physics/models/carl1000-bigbatch_settings.json b/examples/tutorial_particle_physics/models/carl1000-bigbatch_settings.json new file mode 100644 index 000000000..6179ebeed --- /dev/null +++ b/examples/tutorial_particle_physics/models/carl1000-bigbatch_settings.json @@ -0,0 +1 @@ +{"n_observables": 9, "n_parameters": 1, "features": null, "n_hidden": [60, 60], "activation": "tanh", "dropout_prob": 0.0, "estimator_type": "parameterized_ratio", "components": [[0], [1], [2]], "morphing_matrix": [[0.09913344511950417, 1.0677416245579467, -0.16687506967745092], [0.02892049642498369, 0.00036638053405734503, -0.029286876959041037], [0.0013488982891598808, -0.00360618238624564, 0.002257284097085759]]} \ No newline at end of file diff --git a/examples/tutorial_particle_physics/models/carl1000-bigbatch_state_dict.pt b/examples/tutorial_particle_physics/models/carl1000-bigbatch_state_dict.pt new file mode 100644 index 000000000..c50e76ed7 Binary files /dev/null and b/examples/tutorial_particle_physics/models/carl1000-bigbatch_state_dict.pt differ diff --git a/examples/tutorial_particle_physics/models/carl1000-bigbatch_x_means.npy b/examples/tutorial_particle_physics/models/carl1000-bigbatch_x_means.npy new file mode 100644 index 000000000..0e26e7d0f Binary files /dev/null and b/examples/tutorial_particle_physics/models/carl1000-bigbatch_x_means.npy differ diff --git a/examples/tutorial_particle_physics/models/carl1000-bigbatch_x_stds.npy b/examples/tutorial_particle_physics/models/carl1000-bigbatch_x_stds.npy new file mode 100644 index 000000000..e5d65a048 Binary files /dev/null and b/examples/tutorial_particle_physics/models/carl1000-bigbatch_x_stds.npy differ diff --git a/examples/tutorial_particle_physics/models/carl10_settings.json b/examples/tutorial_particle_physics/models/carl10_settings.json new file mode 100644 index 000000000..6179ebeed --- /dev/null +++ b/examples/tutorial_particle_physics/models/carl10_settings.json @@ -0,0 +1 @@ +{"n_observables": 9, "n_parameters": 1, "features": null, "n_hidden": [60, 60], "activation": "tanh", "dropout_prob": 0.0, "estimator_type": "parameterized_ratio", "components": [[0], [1], [2]], "morphing_matrix": [[0.09913344511950417, 1.0677416245579467, -0.16687506967745092], [0.02892049642498369, 0.00036638053405734503, -0.029286876959041037], [0.0013488982891598808, -0.00360618238624564, 0.002257284097085759]]} \ No newline at end of file diff --git a/examples/tutorial_particle_physics/models/carl10_state_dict.pt b/examples/tutorial_particle_physics/models/carl10_state_dict.pt new file mode 100644 index 000000000..d2c0b77c1 Binary files /dev/null and b/examples/tutorial_particle_physics/models/carl10_state_dict.pt differ diff --git a/examples/tutorial_particle_physics/models/carl10_x_means.npy b/examples/tutorial_particle_physics/models/carl10_x_means.npy new file mode 100644 index 000000000..840cb9f0f Binary files /dev/null and b/examples/tutorial_particle_physics/models/carl10_x_means.npy differ diff --git a/examples/tutorial_particle_physics/models/carl10_x_stds.npy b/examples/tutorial_particle_physics/models/carl10_x_stds.npy new file mode 100644 index 000000000..d2c2f4c92 Binary files /dev/null and b/examples/tutorial_particle_physics/models/carl10_x_stds.npy differ diff 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"n_hidden": [60, 60], "activation": "tanh", "dropout_prob": 0.0, "estimator_type": "parameterized_ratio", "components": [[0], [1], [2]], "morphing_matrix": [[0.09913344511950417, 1.0677416245579467, -0.16687506967745092], [0.02892049642498369, 0.00036638053405734503, -0.029286876959041037], [0.0013488982891598808, -0.00360618238624564, 0.002257284097085759]]} \ No newline at end of file diff --git a/examples/tutorial_particle_physics/models/carl50-2_state_dict.pt b/examples/tutorial_particle_physics/models/carl50-2_state_dict.pt new file mode 100644 index 000000000..7d5d074d8 Binary files /dev/null and b/examples/tutorial_particle_physics/models/carl50-2_state_dict.pt differ diff --git a/examples/tutorial_particle_physics/models/carl50-2_x_means.npy b/examples/tutorial_particle_physics/models/carl50-2_x_means.npy new file mode 100644 index 000000000..aa81614fa Binary files /dev/null and b/examples/tutorial_particle_physics/models/carl50-2_x_means.npy differ diff --git 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[1], [2]], "morphing_matrix": [[0.09913344511950417, 1.0677416245579467, -0.16687506967745092], [0.02892049642498369, 0.00036638053405734503, -0.029286876959041037], [0.0013488982891598808, -0.00360618238624564, 0.002257284097085759]]} \ No newline at end of file diff --git a/examples/tutorial_particle_physics/models/carl_state_dict.pt b/examples/tutorial_particle_physics/models/carl_state_dict.pt new file mode 100644 index 000000000..6fad7023c Binary files /dev/null and b/examples/tutorial_particle_physics/models/carl_state_dict.pt differ diff --git a/examples/tutorial_particle_physics/models/carl_x_means.npy b/examples/tutorial_particle_physics/models/carl_x_means.npy new file mode 100644 index 000000000..ff7c75b94 Binary files /dev/null and b/examples/tutorial_particle_physics/models/carl_x_means.npy differ diff --git a/examples/tutorial_particle_physics/models/carl_x_stds.npy b/examples/tutorial_particle_physics/models/carl_x_stds.npy new file mode 100644 index 000000000..dfb3cf268 Binary files /dev/null and b/examples/tutorial_particle_physics/models/carl_x_stds.npy differ diff --git a/examples/tutorial_particle_physics/models/models.tar.gz b/examples/tutorial_particle_physics/models/models.tar.gz new file mode 100644 index 000000000..790a03006 Binary files /dev/null and b/examples/tutorial_particle_physics/models/models.tar.gz differ diff --git a/examples/tutorial_particle_physics/test_quadratic.ipynb b/examples/tutorial_particle_physics/test_quadratic.ipynb new file mode 100644 index 000000000..bdd10d5d9 --- /dev/null +++ b/examples/tutorial_particle_physics/test_quadratic.ipynb @@ -0,0 +1,1237 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Testing the quadratic classifier" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: need to use the `quadratic-classifier` branch of MadMiner: https://github.com/diana-hep/madminer/tree/quadratic-classifier" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function, unicode_literals\n", + "\n", + "%matplotlib inline\n", + "\n", + "import os\n", + "import h5py\n", + "import logging\n", + "import numpy as np\n", + "import matplotlib\n", + "from matplotlib import pyplot as plt\n", + "import torch\n", + "import time\n", + "\n", + "from madminer.analysis import DataAnalyzer\n", + "from madminer.sampling import SampleAugmenter, combine_and_shuffle\n", + "from madminer import sampling\n", + "#from madminer.ml import ParameterizedRatioEstimator, MorphingAwareRatioEstimator, QuadraticMorphingAwareRatioEstimator \n", + "#from madminer.plotting import plot_distributions, plot_1d_morphing_basis\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# MadMiner output\n", + "logging.basicConfig(\n", + " format='%(asctime)-5.5s %(name)-20.20s %(levelname)-7.7s %(message)s',\n", + " datefmt='%H:%M',\n", + " level=logging.INFO\n", + ")\n", + "\n", + "# Output of all other modules (e.g. matplotlib)\n", + "for key in logging.Logger.manager.loggerDict:\n", + " if \"madminer\" not in key:\n", + " logging.getLogger(key).setLevel(logging.WARNING)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Shuffle dataset just in case" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that `toy_data_for_madminer.h5` is the file that was called `lhe_data_shuffled.h5` originally." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# combine_and_shuffle([\"data/toy_data_for_madminer.h5\"], \"data/toy_data_for_madminer.h5\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's have a look at the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "16:06 madminer.analysis.da INFO Loading data from data/toy_data_for_madminer.h5\n", + "16:06 madminer.analysis.da INFO Found 1 parameters\n", + "16:06 madminer.analysis.da INFO Did not find nuisance parameters\n", + "16:06 madminer.analysis.da INFO Found 10 benchmarks, of which 10 physical\n", + "16:06 madminer.analysis.da INFO Found 9 observables\n", + "16:06 madminer.analysis.da INFO Found 6000000 events\n", + "16:06 madminer.analysis.da INFO 3000000 signal events sampled from benchmark sm\n", + "16:06 madminer.analysis.da INFO 500000 signal events sampled from benchmark 50\n", + "16:06 madminer.analysis.da INFO 500000 signal events sampled from benchmark 200\n", + "16:06 madminer.analysis.da INFO 500000 signal events sampled from benchmark 500\n", + "16:06 madminer.analysis.da INFO 500000 signal events sampled from benchmark neg_50\n", + "16:06 madminer.analysis.da INFO 500000 signal events sampled from benchmark neg_200\n", + "16:06 madminer.analysis.da INFO 500000 signal events sampled from benchmark neg_500\n", + "16:06 madminer.analysis.da INFO Found morphing setup with 3 components\n", + "16:06 madminer.analysis.da INFO Did not find nuisance morphing setup\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "analyzer = DataAnalyzer(\"data/toy_data_for_madminer.h5\")\n", + "_ = plot_1d_morphing_basis(analyzer.morpher, xrange=(-25, 25))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# _ = plot_distributions(\"data/toy_data_for_madminer.h5\", uncertainties=\"none\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare training data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "16:06 madminer.analysis.da INFO Loading data from data/toy_data_for_madminer.h5\n", + "16:06 madminer.analysis.da INFO Found 1 parameters\n", + "16:06 madminer.analysis.da INFO Did not find nuisance parameters\n", + "16:06 madminer.analysis.da INFO Found 10 benchmarks, of which 10 physical\n", + "16:06 madminer.analysis.da INFO Found 9 observables\n", + "16:06 madminer.analysis.da INFO Found 6000000 events\n", + "16:06 madminer.analysis.da INFO 3000000 signal events sampled from benchmark sm\n", + "16:06 madminer.analysis.da INFO 500000 signal events sampled from benchmark 50\n", + "16:06 madminer.analysis.da INFO 500000 signal events sampled from benchmark 200\n", + "16:06 madminer.analysis.da INFO 500000 signal events sampled from benchmark 500\n", + "16:06 madminer.analysis.da INFO 500000 signal events sampled from benchmark neg_50\n", + "16:06 madminer.analysis.da INFO 500000 signal events sampled from benchmark neg_200\n", + "16:06 madminer.analysis.da INFO 500000 signal events sampled from benchmark neg_500\n", + "16:06 madminer.analysis.da INFO Found morphing setup with 3 components\n", + "16:06 madminer.analysis.da INFO Did not find nuisance morphing setup\n" + ] + } + ], + "source": [ + "sampler = SampleAugmenter('data/toy_data_for_madminer.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "16:06 madminer.sampling.sa INFO Extracting training sample for ratio-based methods. Numerator hypothesis: 10 benchmarks, starting with ['morphing_basis_vector_0', 'morphing_basis_vector_1', 'morphing_basis_vector_2'], denominator hypothesis: sm\n", + "16:06 madminer.sampling.sa INFO Starting sampling serially\n", + "16:06 madminer.sampling.sa INFO Sampling from parameter point 1 / 10\n", + "16:06 madminer.sampling.sa INFO Sampling from parameter point 2 / 10\n", + "16:06 madminer.sampling.sa INFO Sampling from parameter point 3 / 10\n", + "16:06 madminer.sampling.sa INFO Sampling from parameter point 4 / 10\n", + "16:06 madminer.sampling.sa INFO Sampling from parameter point 5 / 10\n", + "16:06 madminer.sampling.sa INFO Sampling from parameter point 6 / 10\n", + "16:06 madminer.sampling.sa INFO Sampling from parameter point 7 / 10\n", + "16:06 madminer.sampling.sa INFO Sampling from parameter point 8 / 10\n", + "16:06 madminer.sampling.sa INFO Sampling from parameter point 9 / 10\n", + "16:06 madminer.sampling.sa INFO Sampling from parameter point 10 / 10\n", + "16:06 madminer.sampling.sa INFO Effective number of samples: mean 555043.8, with individual thetas ranging from 369533.0 to 2220083.0\n", + "16:06 madminer.sampling.sa INFO Starting sampling serially\n", + "16:06 madminer.sampling.sa INFO Sampling from parameter point 1 / 10\n", + "16:06 madminer.sampling.sa INFO Sampling from parameter point 2 / 10\n", + "16:06 madminer.sampling.sa INFO Sampling from parameter point 3 / 10\n", + "16:06 madminer.sampling.sa INFO Sampling from parameter point 4 / 10\n", + "16:06 madminer.sampling.sa INFO Sampling from parameter point 5 / 10\n", + "16:06 madminer.sampling.sa INFO Sampling from parameter point 6 / 10\n", + "16:06 madminer.sampling.sa INFO Sampling from parameter point 7 / 10\n", + "16:06 madminer.sampling.sa INFO Sampling from parameter point 8 / 10\n", + "16:06 madminer.sampling.sa INFO Sampling from parameter point 9 / 10\n", + "16:06 madminer.sampling.sa INFO Sampling from parameter point 10 / 10\n", + "16:06 madminer.sampling.sa INFO Effective number of samples: mean 2220083.0, with individual thetas ranging from 2220083.0 to 2220083.0\n" + ] + } + ], + "source": [ + "x, theta, _, y, _, _, _ = sampler.sample_train_ratio(\n", + " theta0=sampling.benchmarks(list(sampler.benchmarks.keys())),\n", + " theta1=sampling.benchmark(\"sm\"),\n", + " test_split=0.01,\n", + " validation_split=0.25,\n", + " n_samples=1000000,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "16:25 madminer.sampling.sa INFO Extracting training sample for ratio-based methods. Numerator hypothesis: 10 benchmarks, starting with ['morphing_basis_vector_0', 'morphing_basis_vector_1', 'morphing_basis_vector_2'], denominator hypothesis: sm\n", + "16:25 madminer.sampling.sa INFO Starting sampling serially\n", + "16:25 madminer.sampling.sa INFO Sampling from parameter point 1 / 10\n", + "16:25 madminer.sampling.sa INFO Sampling from parameter point 2 / 10\n", + "16:25 madminer.sampling.sa INFO Sampling from parameter point 3 / 10\n", + "16:25 madminer.sampling.sa INFO Sampling from parameter point 4 / 10\n", + "16:25 madminer.sampling.sa INFO Sampling from parameter point 5 / 10\n", + "16:25 madminer.sampling.sa INFO Sampling from parameter point 6 / 10\n", + "16:25 madminer.sampling.sa INFO Sampling from parameter point 7 / 10\n", + "16:25 madminer.sampling.sa INFO Sampling from parameter point 8 / 10\n", + "16:25 madminer.sampling.sa INFO Sampling from parameter point 9 / 10\n", + "16:25 madminer.sampling.sa INFO Sampling from parameter point 10 / 10\n", + "16:25 madminer.sampling.sa INFO Effective number of samples: mean 187451.6, with individual thetas ranging from 124446.99999999999 to 749930.0\n", + "16:25 madminer.sampling.sa INFO Starting sampling serially\n", + "16:25 madminer.sampling.sa INFO Sampling from parameter point 1 / 10\n", + "16:25 madminer.sampling.sa INFO Sampling from parameter point 2 / 10\n", + "16:25 madminer.sampling.sa INFO Sampling from parameter point 3 / 10\n", + "16:25 madminer.sampling.sa INFO Sampling from parameter point 4 / 10\n", + "16:25 madminer.sampling.sa INFO Sampling from parameter point 5 / 10\n", + "16:25 madminer.sampling.sa INFO Sampling from parameter point 6 / 10\n", + "16:25 madminer.sampling.sa INFO Sampling from parameter point 7 / 10\n", + "16:25 madminer.sampling.sa INFO Sampling from parameter point 8 / 10\n", + "16:25 madminer.sampling.sa INFO Sampling from parameter point 9 / 10\n", + "16:25 madminer.sampling.sa INFO Sampling from parameter point 10 / 10\n", + "16:25 madminer.sampling.sa INFO Effective number of samples: mean 749930.0, with individual thetas ranging from 749930.0 to 749930.0\n" + ] + } + ], + "source": [ + "x_val, theta_val, _, y_val, _, _, _ = sampler.sample_train_ratio(\n", + " theta0=sampling.benchmarks(list(sampler.benchmarks.keys())),\n", + " theta1=sampling.benchmark(\"sm\"),\n", + " test_split=0.0,\n", + " validation_split=0.25,\n", + " n_samples=100000,\n", + " partition=\"validation\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "model_kwargs = {\"n_hidden\": (100, 100)}\n", + "train_kwargs = {\n", + " \"method\": \"carl\",\n", + " \"x\": x, \"y\": y, \"theta\": theta,\n", + " \"x_val\":x_val, \"y_val\": y_val, \"theta_val\": theta_val,\n", + " \"optimizer\": \"adam\", \"batch_size\": 200\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train morphing-aware estimator" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "16:25 madminer.ml.morphing INFO Setting up morphing-aware ratio estimator with 3 morphing components\n", + "16:25 madminer.ml.paramete INFO Starting training\n", + "16:25 madminer.ml.paramete INFO Method: carl\n", + "16:25 madminer.ml.paramete INFO Batch size: 200\n", + "16:25 madminer.ml.paramete INFO Optimizer: adam\n", + "16:25 madminer.ml.paramete INFO Epochs: 50\n", + "16:25 madminer.ml.paramete INFO Learning rate: 0.001 initially, decaying to 0.0001\n", + "16:25 madminer.ml.paramete INFO Validation split: 0.25\n", + "16:25 madminer.ml.paramete INFO Early stopping: True\n", + "16:25 madminer.ml.paramete INFO Scale inputs: True\n", + "16:25 madminer.ml.paramete INFO Scale parameters: False\n", + "16:25 madminer.ml.paramete INFO Shuffle labels False\n", + "16:25 madminer.ml.paramete INFO Samples: all\n", + "16:25 madminer.ml.paramete INFO Loading training data\n", + "16:25 madminer.ml.paramete INFO Found 1000000 samples with 1 parameters and 9 observables\n", + "16:25 madminer.ml.paramete INFO Found 100000 separate validation samples\n", + "16:25 madminer.ml.base INFO Setting up input rescaling\n", + "16:25 madminer.ml.base INFO Disabling parameter rescaling\n", + "16:25 madminer.ml.paramete INFO Creating model\n", + "16:25 madminer.ml.paramete INFO Training model\n", + "16:25 madminer.utils.ml.tr INFO Training on CPU with single precision\n", + "16:27 madminer.utils.ml.tr INFO Epoch 2: train loss 0.53136 (xe: 0.531)\n", + "16:27 madminer.utils.ml.tr INFO val. loss 0.53147 (xe: 0.531)\n", + "16:28 madminer.utils.ml.tr INFO Epoch 4: train loss 0.52491 (xe: 0.525)\n", + "16:28 madminer.utils.ml.tr INFO val. loss 0.52260 (xe: 0.523)\n", + "16:29 madminer.utils.ml.tr INFO Epoch 6: train loss 0.52278 (xe: 0.523)\n", + "16:29 madminer.utils.ml.tr INFO val. loss 0.52248 (xe: 0.522)\n", + "16:30 madminer.utils.ml.tr INFO Epoch 8: train loss 0.52116 (xe: 0.521)\n", + "16:30 madminer.utils.ml.tr INFO val. loss 0.52161 (xe: 0.522)\n", + "16:32 madminer.utils.ml.tr INFO Epoch 10: train loss 0.52026 (xe: 0.520)\n", + "16:32 madminer.utils.ml.tr INFO val. loss 0.52186 (xe: 0.522)\n", + "16:33 madminer.utils.ml.tr INFO Epoch 12: train loss 0.51871 (xe: 0.519)\n", + "16:33 madminer.utils.ml.tr INFO val. loss 0.51956 (xe: 0.520)\n", + "16:34 madminer.utils.ml.tr INFO Epoch 14: train loss 0.51981 (xe: 0.520)\n", + "16:34 madminer.utils.ml.tr INFO val. loss 0.52091 (xe: 0.521)\n", + "16:35 madminer.utils.ml.tr INFO Epoch 16: train loss 0.51766 (xe: 0.518)\n", + "16:35 madminer.utils.ml.tr INFO val. loss 0.53106 (xe: 0.531)\n", + "16:36 madminer.utils.ml.tr INFO Epoch 18: train loss 0.51735 (xe: 0.517)\n", + "16:36 madminer.utils.ml.tr INFO val. loss 0.52110 (xe: 0.521)\n", + "16:37 madminer.utils.ml.tr INFO Epoch 20: train loss 0.51692 (xe: 0.517)\n", + "16:37 madminer.utils.ml.tr INFO val. loss 0.51923 (xe: 0.519)\n", + "16:38 madminer.utils.ml.tr INFO Epoch 22: train loss 0.51637 (xe: 0.516)\n", + "16:38 madminer.utils.ml.tr INFO val. loss 0.51888 (xe: 0.519)\n", + "16:39 madminer.utils.ml.tr INFO Epoch 24: train loss 0.51576 (xe: 0.516)\n", + "16:39 madminer.utils.ml.tr INFO val. loss 0.51903 (xe: 0.519)\n", + "16:40 madminer.utils.ml.tr INFO Epoch 26: train loss 0.51598 (xe: 0.516)\n", + "16:40 madminer.utils.ml.tr INFO val. loss 0.52001 (xe: 0.520)\n", + "16:41 madminer.utils.ml.tr INFO Epoch 28: train loss 0.51503 (xe: 0.515)\n", + "16:41 madminer.utils.ml.tr INFO val. loss 0.51947 (xe: 0.519)\n", + "16:42 madminer.utils.ml.tr INFO Epoch 30: train loss 0.51495 (xe: 0.515)\n", + "16:42 madminer.utils.ml.tr INFO val. loss 0.51780 (xe: 0.518)\n", + "16:44 madminer.utils.ml.tr INFO Epoch 32: train loss 0.51467 (xe: 0.515)\n", + "16:44 madminer.utils.ml.tr INFO val. loss 0.51824 (xe: 0.518)\n", + "16:45 madminer.utils.ml.tr INFO Epoch 34: train loss 0.51430 (xe: 0.514)\n", + "16:45 madminer.utils.ml.tr INFO val. loss 0.51745 (xe: 0.517)\n", + "16:46 madminer.utils.ml.tr INFO Epoch 36: train loss 0.51398 (xe: 0.514)\n", + "16:46 madminer.utils.ml.tr INFO val. loss 0.51787 (xe: 0.518)\n", + "16:47 madminer.utils.ml.tr INFO Epoch 38: train loss 0.51389 (xe: 0.514)\n", + "16:47 madminer.utils.ml.tr INFO val. loss 0.51779 (xe: 0.518)\n", + "16:48 madminer.utils.ml.tr INFO Epoch 40: train loss 0.51367 (xe: 0.514)\n", + "16:48 madminer.utils.ml.tr INFO val. loss 0.51796 (xe: 0.518)\n", + "16:49 madminer.utils.ml.tr INFO Epoch 42: train loss 0.51360 (xe: 0.514)\n", + "16:49 madminer.utils.ml.tr INFO val. loss 0.51744 (xe: 0.517)\n", + "16:50 madminer.utils.ml.tr INFO Epoch 44: train loss 0.51334 (xe: 0.513)\n", + "16:50 madminer.utils.ml.tr INFO val. loss 0.51714 (xe: 0.517)\n", + "16:51 madminer.utils.ml.tr INFO Epoch 46: train loss 0.51314 (xe: 0.513)\n", + "16:51 madminer.utils.ml.tr INFO val. loss 0.51731 (xe: 0.517)\n", + "16:52 madminer.utils.ml.tr INFO Epoch 48: train loss 0.51307 (xe: 0.513)\n", + "16:52 madminer.utils.ml.tr INFO val. loss 0.51730 (xe: 0.517)\n", + "16:53 madminer.utils.ml.tr INFO Epoch 50: train loss 0.51283 (xe: 0.513)\n", + "16:53 madminer.utils.ml.tr INFO val. loss 0.51727 (xe: 0.517)\n", + "16:53 madminer.utils.ml.tr INFO Early stopping after epoch 39, with loss 0.51671 compared to final loss 0.51727\n", + "16:53 madminer.utils.ml.tr INFO Training time spend on:\n", + "16:53 madminer.utils.ml.tr INFO initialize model: 0.00h\n", + "16:53 madminer.utils.ml.tr INFO ALL: 0.46h\n", + "16:53 madminer.utils.ml.tr INFO check data: 0.00h\n", + "16:53 madminer.utils.ml.tr INFO make dataset: 0.00h\n", + "16:53 madminer.utils.ml.tr INFO make dataloader: 0.00h\n", + "16:53 madminer.utils.ml.tr INFO setup optimizer: 0.00h\n", + "16:53 madminer.utils.ml.tr INFO initialize training: 0.00h\n", + "16:53 madminer.utils.ml.tr INFO set lr: 0.00h\n", + "16:53 madminer.utils.ml.tr INFO load training batch: 0.04h\n", + "16:53 madminer.utils.ml.tr INFO fwd: move data: 0.01h\n", + "16:53 madminer.utils.ml.tr INFO fwd: check for nans: 0.02h\n", + "16:53 madminer.utils.ml.tr INFO fwd: model.forward: 0.17h\n", + "16:53 madminer.utils.ml.tr INFO fwd: calculate losses: 0.01h\n", + "16:53 madminer.utils.ml.tr INFO training forward pass: 0.18h\n", + "16:53 madminer.utils.ml.tr INFO training sum losses: 0.01h\n", + "16:53 madminer.utils.ml.tr INFO opt: zero grad: 0.01h\n", + "16:53 madminer.utils.ml.tr INFO opt: backward: 0.13h\n", + "16:53 madminer.utils.ml.tr INFO opt: clip grad norm: 0.00h\n", + "16:53 madminer.utils.ml.tr INFO opt: step: 0.08h\n", + "16:53 madminer.utils.ml.tr INFO optimizer step: 0.21h\n", + "16:53 madminer.utils.ml.tr INFO load validation batch: 0.01h\n", + "16:53 madminer.utils.ml.tr INFO validation forward pass: 0.02h\n", + "16:53 madminer.utils.ml.tr INFO validation sum losses: 0.00h\n", + "16:53 madminer.utils.ml.tr INFO early stopping: 0.00h\n", + "16:53 madminer.utils.ml.tr INFO report epoch: 0.00h\n" + ] + } + ], + "source": [ + "morphing_aware = MorphingAwareRatioEstimator(\n", + " **model_kwargs, morphing_setup_filename=\"data/toy_data_for_madminer.h5\", optimize_morphing_basis=False\n", + ")\n", + "_ = morphing_aware.train(**train_kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train squared estimator" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "16:53 madminer.ml.paramete INFO Starting training\n", + "16:53 madminer.ml.paramete INFO Method: carl\n", + "16:53 madminer.ml.paramete INFO Batch size: 200\n", + "16:53 madminer.ml.paramete INFO Optimizer: adam\n", + "16:53 madminer.ml.paramete INFO Epochs: 50\n", + "16:53 madminer.ml.paramete INFO Learning rate: 0.001 initially, decaying to 0.0001\n", + "16:53 madminer.ml.paramete INFO Validation split: 0.25\n", + "16:53 madminer.ml.paramete INFO Early stopping: True\n", + "16:53 madminer.ml.paramete INFO Scale inputs: True\n", + "16:53 madminer.ml.paramete INFO Scale parameters: False\n", + "16:53 madminer.ml.paramete INFO Shuffle labels False\n", + "16:53 madminer.ml.paramete INFO Samples: all\n", + "16:53 madminer.ml.paramete INFO Loading training data\n", + "16:53 madminer.ml.paramete INFO Found 1000000 samples with 1 parameters and 9 observables\n", + "16:53 madminer.ml.paramete INFO Found 100000 separate validation samples\n", + "16:53 madminer.ml.base INFO Setting up input rescaling\n", + "16:53 madminer.ml.base INFO Disabling parameter rescaling\n", + "16:53 madminer.ml.paramete INFO Creating model\n", + "16:53 madminer.ml.paramete INFO Training model\n", + "16:53 madminer.utils.ml.tr INFO Training on CPU with single precision\n", + "16:54 madminer.utils.ml.tr INFO Epoch 2: train loss 0.52139 (xe: 0.521)\n", + "16:54 madminer.utils.ml.tr INFO val. loss 0.52099 (xe: 0.521)\n", + "16:55 madminer.utils.ml.tr INFO Epoch 4: train loss 0.51826 (xe: 0.518)\n", + "16:55 madminer.utils.ml.tr INFO val. loss 0.51897 (xe: 0.519)\n", + "16:55 madminer.utils.ml.tr INFO Epoch 6: train loss 0.51713 (xe: 0.517)\n", + "16:55 madminer.utils.ml.tr INFO val. loss 0.51823 (xe: 0.518)\n", + "16:56 madminer.utils.ml.tr INFO Epoch 8: train loss 0.51624 (xe: 0.516)\n", + "16:56 madminer.utils.ml.tr INFO val. loss 0.51742 (xe: 0.517)\n", + "16:57 madminer.utils.ml.tr INFO Epoch 10: train loss 0.51557 (xe: 0.516)\n", + "16:57 madminer.utils.ml.tr INFO val. loss 0.51780 (xe: 0.518)\n", + "16:58 madminer.utils.ml.tr INFO Epoch 12: train loss 0.51508 (xe: 0.515)\n", + "16:58 madminer.utils.ml.tr INFO val. loss 0.51684 (xe: 0.517)\n", + "16:59 madminer.utils.ml.tr INFO Epoch 14: train loss 0.51468 (xe: 0.515)\n", + "16:59 madminer.utils.ml.tr INFO val. loss 0.51614 (xe: 0.516)\n", + "17:00 madminer.utils.ml.tr INFO Epoch 16: train loss 0.51427 (xe: 0.514)\n", + "17:00 madminer.utils.ml.tr INFO val. loss 0.51653 (xe: 0.517)\n", + "17:01 madminer.utils.ml.tr INFO Epoch 18: train loss 0.51394 (xe: 0.514)\n", + "17:01 madminer.utils.ml.tr INFO val. loss 0.51590 (xe: 0.516)\n", + "17:01 madminer.utils.ml.tr INFO Epoch 20: train loss 0.51364 (xe: 0.514)\n", + "17:01 madminer.utils.ml.tr INFO val. loss 0.51661 (xe: 0.517)\n", + "17:02 madminer.utils.ml.tr INFO Epoch 22: train loss 0.51340 (xe: 0.513)\n", + "17:02 madminer.utils.ml.tr INFO val. loss 0.51665 (xe: 0.517)\n", + "17:03 madminer.utils.ml.tr INFO Epoch 24: train loss 0.51325 (xe: 0.513)\n", + "17:03 madminer.utils.ml.tr INFO val. loss 0.51581 (xe: 0.516)\n", + "17:04 madminer.utils.ml.tr INFO Epoch 26: train loss 0.51307 (xe: 0.513)\n", + "17:04 madminer.utils.ml.tr INFO val. loss 0.51546 (xe: 0.515)\n", + "17:05 madminer.utils.ml.tr INFO Epoch 28: train loss 0.51282 (xe: 0.513)\n", + "17:05 madminer.utils.ml.tr INFO val. loss 0.51587 (xe: 0.516)\n", + "17:06 madminer.utils.ml.tr INFO Epoch 30: train loss 0.51277 (xe: 0.513)\n", + "17:06 madminer.utils.ml.tr INFO val. loss 0.51570 (xe: 0.516)\n", + "17:07 madminer.utils.ml.tr INFO Epoch 32: train loss 0.51257 (xe: 0.513)\n", + "17:07 madminer.utils.ml.tr INFO val. loss 0.51559 (xe: 0.516)\n", + "17:07 madminer.utils.ml.tr INFO Epoch 34: train loss 0.51243 (xe: 0.512)\n", + "17:07 madminer.utils.ml.tr INFO val. loss 0.51619 (xe: 0.516)\n", + "17:08 madminer.utils.ml.tr INFO Epoch 36: train loss 0.51232 (xe: 0.512)\n", + "17:08 madminer.utils.ml.tr INFO val. loss 0.51603 (xe: 0.516)\n", + "17:09 madminer.utils.ml.tr INFO Epoch 38: train loss 0.51224 (xe: 0.512)\n", + "17:09 madminer.utils.ml.tr INFO val. loss 0.51542 (xe: 0.515)\n", + "17:10 madminer.utils.ml.tr INFO Epoch 40: train loss 0.51212 (xe: 0.512)\n", + "17:10 madminer.utils.ml.tr INFO val. loss 0.51568 (xe: 0.516)\n", + "17:11 madminer.utils.ml.tr INFO Epoch 42: train loss 0.51201 (xe: 0.512)\n", + "17:11 madminer.utils.ml.tr INFO val. loss 0.51515 (xe: 0.515)\n", + "17:12 madminer.utils.ml.tr INFO Epoch 44: train loss 0.51190 (xe: 0.512)\n", + "17:12 madminer.utils.ml.tr INFO val. loss 0.51521 (xe: 0.515)\n", + "17:13 madminer.utils.ml.tr INFO Epoch 46: train loss 0.51186 (xe: 0.512)\n", + "17:13 madminer.utils.ml.tr INFO val. loss 0.51569 (xe: 0.516)\n", + "17:14 madminer.utils.ml.tr INFO Epoch 48: train loss 0.51178 (xe: 0.512)\n", + "17:14 madminer.utils.ml.tr INFO val. loss 0.51518 (xe: 0.515)\n", + "17:14 madminer.utils.ml.tr INFO Epoch 50: train loss 0.51172 (xe: 0.512)\n", + "17:14 madminer.utils.ml.tr INFO val. loss 0.51525 (xe: 0.515)\n", + "17:14 madminer.utils.ml.tr INFO Early stopping after epoch 42, with loss 0.51515 compared to final loss 0.51525\n", + "17:14 madminer.utils.ml.tr INFO Training time spend on:\n", + "17:14 madminer.utils.ml.tr INFO initialize model: 0.00h\n", + "17:14 madminer.utils.ml.tr INFO ALL: 0.35h\n", + "17:14 madminer.utils.ml.tr INFO check data: 0.00h\n", + "17:14 madminer.utils.ml.tr INFO make dataset: 0.00h\n", + "17:14 madminer.utils.ml.tr INFO make dataloader: 0.00h\n", + "17:14 madminer.utils.ml.tr INFO setup optimizer: 0.00h\n", + "17:14 madminer.utils.ml.tr INFO initialize training: 0.00h\n", + "17:14 madminer.utils.ml.tr INFO set lr: 0.00h\n", + "17:14 madminer.utils.ml.tr INFO load training batch: 0.04h\n", + "17:14 madminer.utils.ml.tr INFO fwd: move data: 0.01h\n", + "17:14 madminer.utils.ml.tr INFO fwd: check for nans: 0.02h\n", + "17:14 madminer.utils.ml.tr INFO fwd: model.forward: 0.10h\n", + "17:14 madminer.utils.ml.tr INFO fwd: calculate losses: 0.01h\n", + "17:14 madminer.utils.ml.tr INFO training forward pass: 0.13h\n", + "17:14 madminer.utils.ml.tr INFO training sum losses: 0.01h\n", + "17:14 madminer.utils.ml.tr INFO opt: zero grad: 0.00h\n", + "17:14 madminer.utils.ml.tr INFO opt: backward: 0.09h\n", + "17:14 madminer.utils.ml.tr INFO opt: clip grad norm: 0.00h\n", + "17:14 madminer.utils.ml.tr INFO opt: step: 0.06h\n", + "17:14 madminer.utils.ml.tr INFO optimizer step: 0.15h\n", + "17:14 madminer.utils.ml.tr INFO load validation batch: 0.01h\n", + "17:14 madminer.utils.ml.tr INFO validation forward pass: 0.02h\n", + "17:14 madminer.utils.ml.tr INFO validation sum losses: 0.00h\n", + "17:14 madminer.utils.ml.tr INFO early stopping: 0.00h\n", + "17:14 madminer.utils.ml.tr INFO report epoch: 0.00h\n" + ] + } + ], + "source": [ + "quadratic = QuadraticMorphingAwareRatioEstimator(**model_kwargs)\n", + "_ = quadratic.train(**train_kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train agnostic parameterized estimator" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "17:14 madminer.ml.paramete INFO Starting training\n", + "17:14 madminer.ml.paramete INFO Method: carl\n", + "17:14 madminer.ml.paramete INFO Batch size: 200\n", + "17:14 madminer.ml.paramete INFO Optimizer: adam\n", + "17:14 madminer.ml.paramete INFO Epochs: 50\n", + "17:14 madminer.ml.paramete INFO Learning rate: 0.001 initially, decaying to 0.0001\n", + "17:14 madminer.ml.paramete INFO Validation split: 0.25\n", + "17:14 madminer.ml.paramete INFO Early stopping: True\n", + "17:14 madminer.ml.paramete INFO Scale inputs: True\n", + "17:14 madminer.ml.paramete INFO Scale parameters: True\n", + "17:14 madminer.ml.paramete INFO Shuffle labels False\n", + "17:14 madminer.ml.paramete INFO Samples: all\n", + "17:14 madminer.ml.paramete INFO Loading training data\n", + "17:14 madminer.ml.paramete INFO Found 1000000 samples with 1 parameters and 9 observables\n", + "17:14 madminer.ml.paramete INFO Found 100000 separate validation samples\n", + "17:14 madminer.ml.base INFO Setting up input rescaling\n", + "17:14 madminer.ml.paramete INFO Rescaling parameters\n", + "17:14 madminer.ml.base INFO Setting up parameter rescaling\n", + "17:14 madminer.ml.paramete INFO Creating model\n", + "17:14 madminer.ml.paramete INFO Training model\n", + "17:14 madminer.utils.ml.tr INFO Training on CPU with single precision\n", + "17:15 madminer.utils.ml.tr INFO Epoch 2: train loss 0.51601 (xe: 0.516)\n", + "17:15 madminer.utils.ml.tr INFO val. loss 0.89877 (xe: 0.899)\n", + "17:16 madminer.utils.ml.tr INFO Epoch 4: train loss 0.51244 (xe: 0.512)\n", + "17:16 madminer.utils.ml.tr INFO val. loss 0.70609 (xe: 0.706)\n", + "17:16 madminer.utils.ml.tr INFO Epoch 6: train loss 0.51121 (xe: 0.511)\n", + "17:16 madminer.utils.ml.tr INFO val. loss 0.65394 (xe: 0.654)\n", + "17:17 madminer.utils.ml.tr INFO Epoch 8: train loss 0.51038 (xe: 0.510)\n", + "17:17 madminer.utils.ml.tr INFO val. loss 0.66274 (xe: 0.663)\n", + "17:17 madminer.utils.ml.tr INFO Epoch 10: train loss 0.50966 (xe: 0.510)\n", + "17:17 madminer.utils.ml.tr INFO val. loss 0.61921 (xe: 0.619)\n", + "17:18 madminer.utils.ml.tr INFO Epoch 12: train loss 0.50913 (xe: 0.509)\n", + "17:18 madminer.utils.ml.tr INFO val. loss 0.62511 (xe: 0.625)\n", + "17:19 madminer.utils.ml.tr INFO Epoch 14: train loss 0.50872 (xe: 0.509)\n", + "17:19 madminer.utils.ml.tr INFO val. loss 0.61156 (xe: 0.612)\n", + "17:20 madminer.utils.ml.tr INFO Epoch 16: train loss 0.50827 (xe: 0.508)\n", + "17:20 madminer.utils.ml.tr INFO val. loss 0.61724 (xe: 0.617)\n", + "17:20 madminer.utils.ml.tr INFO Epoch 18: train loss 0.50793 (xe: 0.508)\n", + "17:20 madminer.utils.ml.tr INFO val. loss 0.61857 (xe: 0.619)\n", + "17:21 madminer.utils.ml.tr INFO Epoch 20: train loss 0.50764 (xe: 0.508)\n", + "17:21 madminer.utils.ml.tr INFO val. loss 0.63050 (xe: 0.630)\n", + "17:21 madminer.utils.ml.tr INFO Epoch 22: train loss 0.50739 (xe: 0.507)\n", + "17:21 madminer.utils.ml.tr INFO val. loss 0.61979 (xe: 0.620)\n", + "17:22 madminer.utils.ml.tr INFO Epoch 24: train loss 0.50721 (xe: 0.507)\n", + "17:22 madminer.utils.ml.tr INFO val. loss 0.61728 (xe: 0.617)\n", + "17:23 madminer.utils.ml.tr INFO Epoch 26: train loss 0.50699 (xe: 0.507)\n", + "17:23 madminer.utils.ml.tr INFO val. loss 0.61431 (xe: 0.614)\n", + "17:23 madminer.utils.ml.tr INFO Epoch 28: train loss 0.50682 (xe: 0.507)\n", + "17:23 madminer.utils.ml.tr INFO val. loss 0.62633 (xe: 0.626)\n", + "17:24 madminer.utils.ml.tr INFO Epoch 30: train loss 0.50665 (xe: 0.507)\n", + "17:24 madminer.utils.ml.tr INFO val. loss 0.62473 (xe: 0.625)\n", + "17:25 madminer.utils.ml.tr INFO Epoch 32: train loss 0.50650 (xe: 0.507)\n", + "17:25 madminer.utils.ml.tr INFO val. loss 0.62306 (xe: 0.623)\n", + "17:25 madminer.utils.ml.tr INFO Epoch 34: train loss 0.50641 (xe: 0.506)\n", + "17:25 madminer.utils.ml.tr INFO val. loss 0.61798 (xe: 0.618)\n", + "17:26 madminer.utils.ml.tr INFO Epoch 36: train loss 0.50627 (xe: 0.506)\n", + "17:26 madminer.utils.ml.tr INFO val. loss 0.62298 (xe: 0.623)\n", + "17:26 madminer.utils.ml.tr INFO Epoch 38: train loss 0.50620 (xe: 0.506)\n", + "17:26 madminer.utils.ml.tr INFO val. loss 0.61933 (xe: 0.619)\n", + "17:27 madminer.utils.ml.tr INFO Epoch 40: train loss 0.50607 (xe: 0.506)\n", + "17:27 madminer.utils.ml.tr INFO val. loss 0.62716 (xe: 0.627)\n", + "17:28 madminer.utils.ml.tr INFO Epoch 42: train loss 0.50598 (xe: 0.506)\n", + "17:28 madminer.utils.ml.tr INFO val. loss 0.63235 (xe: 0.632)\n", + "17:28 madminer.utils.ml.tr INFO Epoch 44: train loss 0.50589 (xe: 0.506)\n", + "17:28 madminer.utils.ml.tr INFO val. loss 0.62864 (xe: 0.629)\n", + "17:29 madminer.utils.ml.tr INFO Epoch 46: train loss 0.50582 (xe: 0.506)\n", + "17:29 madminer.utils.ml.tr INFO val. loss 0.62357 (xe: 0.624)\n", + "17:29 madminer.utils.ml.tr INFO Epoch 48: train loss 0.50577 (xe: 0.506)\n", + "17:29 madminer.utils.ml.tr INFO val. loss 0.62300 (xe: 0.623)\n", + "17:30 madminer.utils.ml.tr INFO Epoch 50: train loss 0.50571 (xe: 0.506)\n", + "17:30 madminer.utils.ml.tr INFO val. loss 0.63021 (xe: 0.630)\n", + "17:30 madminer.utils.ml.tr INFO Early stopping after epoch 15, with loss 0.60835 compared to final loss 0.63021\n", + "17:30 madminer.utils.ml.tr INFO Training time spend on:\n", + "17:30 madminer.utils.ml.tr INFO initialize model: 0.00h\n", + "17:30 madminer.utils.ml.tr INFO ALL: 0.26h\n", + "17:30 madminer.utils.ml.tr INFO check data: 0.00h\n", + "17:30 madminer.utils.ml.tr INFO make dataset: 0.00h\n", + "17:30 madminer.utils.ml.tr INFO make dataloader: 0.00h\n", + "17:30 madminer.utils.ml.tr INFO setup optimizer: 0.00h\n", + "17:30 madminer.utils.ml.tr INFO initialize training: 0.00h\n", + "17:30 madminer.utils.ml.tr INFO set lr: 0.00h\n", + "17:30 madminer.utils.ml.tr INFO load training batch: 0.04h\n", + "17:30 madminer.utils.ml.tr INFO fwd: move data: 0.01h\n", + "17:30 madminer.utils.ml.tr INFO fwd: check for nans: 0.02h\n", + "17:30 madminer.utils.ml.tr INFO fwd: model.forward: 0.07h\n", + "17:30 madminer.utils.ml.tr INFO fwd: calculate losses: 0.01h\n", + "17:30 madminer.utils.ml.tr INFO training forward pass: 0.10h\n", + "17:30 madminer.utils.ml.tr INFO training sum losses: 0.00h\n", + "17:30 madminer.utils.ml.tr INFO opt: zero grad: 0.00h\n", + "17:30 madminer.utils.ml.tr INFO opt: backward: 0.05h\n", + "17:30 madminer.utils.ml.tr INFO opt: clip grad norm: 0.00h\n", + "17:30 madminer.utils.ml.tr INFO opt: step: 0.03h\n", + "17:30 madminer.utils.ml.tr INFO optimizer step: 0.09h\n", + "17:30 madminer.utils.ml.tr INFO load validation batch: 0.01h\n", + "17:30 madminer.utils.ml.tr INFO validation forward pass: 0.02h\n", + "17:30 madminer.utils.ml.tr INFO validation sum losses: 0.00h\n", + "17:30 madminer.utils.ml.tr INFO early stopping: 0.00h\n", + "17:30 madminer.utils.ml.tr INFO report epoch: 0.00h\n" + ] + } + ], + "source": [ + "agnostic = ParameterizedRatioEstimator(**model_kwargs)\n", + "_ = agnostic.train(**train_kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random baseline" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "17:30 madminer.ml.paramete INFO Starting training\n", + "17:30 madminer.ml.paramete INFO Method: carl\n", + "17:30 madminer.ml.paramete INFO Batch size: 200\n", + "17:30 madminer.ml.paramete INFO Optimizer: adam\n", + "17:30 madminer.ml.paramete INFO Epochs: 5\n", + "17:30 madminer.ml.paramete INFO Learning rate: 0.001 initially, decaying to 0.0001\n", + "17:30 madminer.ml.paramete INFO Validation split: 0.25\n", + "17:30 madminer.ml.paramete INFO Early stopping: True\n", + "17:30 madminer.ml.paramete INFO Scale inputs: True\n", + "17:30 madminer.ml.paramete INFO Scale parameters: True\n", + "17:30 madminer.ml.paramete INFO Shuffle labels True\n", + "17:30 madminer.ml.paramete INFO Samples: all\n", + "17:30 madminer.ml.paramete INFO Loading training data\n", + "17:30 madminer.ml.paramete INFO Found 1000000 samples with 1 parameters and 9 observables\n", + "17:30 madminer.ml.paramete INFO Found 100000 separate validation samples\n", + "17:30 madminer.ml.base INFO Setting up input rescaling\n", + "17:30 madminer.ml.paramete INFO Rescaling parameters\n", + "17:30 madminer.ml.base INFO Setting up parameter rescaling\n", + "17:30 madminer.ml.paramete INFO Shuffling labels\n", + "17:30 madminer.ml.paramete INFO Creating model\n", + "17:30 madminer.ml.paramete INFO Training model\n", + "17:30 madminer.utils.ml.tr INFO Training on CPU with single precision\n", + "17:30 madminer.utils.ml.tr INFO Epoch 1: train loss 0.69346 (xe: 0.693)\n", + "17:30 madminer.utils.ml.tr INFO val. loss 0.69289 (xe: 0.693)\n", + "17:31 madminer.utils.ml.tr INFO Epoch 2: train loss 0.69320 (xe: 0.693)\n", + "17:31 madminer.utils.ml.tr INFO val. loss 0.69473 (xe: 0.695)\n", + "17:31 madminer.utils.ml.tr INFO Epoch 3: train loss 0.69318 (xe: 0.693)\n", + "17:31 madminer.utils.ml.tr INFO val. loss 0.69324 (xe: 0.693)\n", + "17:31 madminer.utils.ml.tr INFO Epoch 4: train loss 0.69317 (xe: 0.693)\n", + "17:31 madminer.utils.ml.tr INFO val. loss 0.69311 (xe: 0.693)\n", + "17:32 madminer.utils.ml.tr INFO Epoch 5: train loss 0.69315 (xe: 0.693)\n", + "17:32 madminer.utils.ml.tr INFO val. loss 0.69314 (xe: 0.693)\n", + "17:32 madminer.utils.ml.tr INFO Early stopping after epoch 1, with loss 0.69289 compared to final loss 0.69314\n", + "17:32 madminer.utils.ml.tr INFO Training time spend on:\n", + "17:32 madminer.utils.ml.tr INFO initialize model: 0.00h\n", + "17:32 madminer.utils.ml.tr INFO ALL: 0.02h\n", + "17:32 madminer.utils.ml.tr INFO check data: 0.00h\n", + "17:32 madminer.utils.ml.tr INFO make dataset: 0.00h\n", + "17:32 madminer.utils.ml.tr INFO make dataloader: 0.00h\n", + "17:32 madminer.utils.ml.tr INFO setup optimizer: 0.00h\n", + "17:32 madminer.utils.ml.tr INFO initialize training: 0.00h\n", + "17:32 madminer.utils.ml.tr INFO set lr: 0.00h\n", + "17:32 madminer.utils.ml.tr INFO load training batch: 0.00h\n", + "17:32 madminer.utils.ml.tr INFO fwd: move data: 0.00h\n", + "17:32 madminer.utils.ml.tr INFO fwd: check for nans: 0.00h\n", + "17:32 madminer.utils.ml.tr INFO fwd: model.forward: 0.01h\n", + "17:32 madminer.utils.ml.tr INFO fwd: calculate losses: 0.00h\n", + "17:32 madminer.utils.ml.tr INFO training forward pass: 0.01h\n", + "17:32 madminer.utils.ml.tr INFO training sum losses: 0.00h\n", + "17:32 madminer.utils.ml.tr INFO opt: zero grad: 0.00h\n", + "17:32 madminer.utils.ml.tr INFO opt: backward: 0.00h\n", + "17:32 madminer.utils.ml.tr INFO opt: clip grad norm: 0.00h\n", + "17:32 madminer.utils.ml.tr INFO opt: step: 0.00h\n", + "17:32 madminer.utils.ml.tr INFO optimizer step: 0.01h\n", + "17:32 madminer.utils.ml.tr INFO load validation batch: 0.00h\n", + "17:32 madminer.utils.ml.tr INFO validation forward pass: 0.00h\n", + "17:32 madminer.utils.ml.tr INFO validation sum losses: 0.00h\n", + "17:32 madminer.utils.ml.tr INFO early stopping: 0.00h\n", + "17:32 madminer.utils.ml.tr INFO report epoch: 0.00h\n" + ] + } + ], + "source": [ + "random = ParameterizedRatioEstimator(**model_kwargs)\n", + "_ = random.train(shuffle_labels=True, **train_kwargs, n_epochs=5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluation functions based on TestEstimatorLight.ipynb" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_t_original(model, theta, nev, counter, test_input, NSM, NBSM):\n", + " \"\"\" Original test statistic, but fixing the likelihood ratio evaluation to be at the correct parameter point \"\"\"\n", + " \n", + " # computing test statistics t (or lambda) in equation (2) of paper\n", + " \n", + " # generate number of points for testing under Poisson distribution\n", + " n_gen = 0\n", + " while n_gen == 0:\n", + " n_gen = np.random.poisson(nev)\n", + "\n", + " # stop if there are no more points to test\n", + " if (counter + n_gen) >= len(test_input):\n", + " return 0., -1\n", + "\n", + " points = test_input[int(counter): int(counter+n_gen)]\n", + "\n", + " # compute test statistics\n", + " log_ratio = model.evaluate_log_likelihood_ratio(points.numpy(), np.array([[theta]]))[0][0]\n", + " log_ratio = torch.tensor(log_ratio)\n", + " out = 2 * (NBSM - NSM - (log_ratio + torch.log(torch.tensor(NBSM/NSM))).sum(0))\n", + "\n", + " #return test statistics and the starting point for the next batch\n", + " return out, int(counter + n_gen)\n", + "\n", + "\n", + "def compute_t_modified(model, theta, nev, counter, test_input):\n", + " \"\"\" Kinematics-only test statistic \"\"\"\n", + " \n", + " # generate number of points for testing under Poisson distribution\n", + " n_gen = 0\n", + " while n_gen == 0:\n", + " n_gen = np.random.poisson(nev)\n", + " n_gen = max(n_gen, 1000)\n", + "\n", + " # stop if there are no more points to test\n", + " if (counter + n_gen) >= len(test_input):\n", + " return 0., -1\n", + "\n", + " points = test_input[int(counter): int(counter+n_gen)]\n", + "\n", + " # compute test statistics\n", + " log_ratio = model.evaluate_log_likelihood_ratio(points.numpy(), np.array([[theta]]))[0][0]\n", + " t = -2.0 * nev * np.mean(log_ratio) # Rescale to number of expected events to remove xsec influence\n", + " \n", + " return t, int(counter + n_gen)\n", + "\n", + "\n", + "def hypo_test(\n", + " model, theta, test_input_sm, test_input_bsm, epochs, e, n_meas, pm, NSM, NBSM, verbose_t=True, verbose_period_t=1e5, title='', include_xsec=False,\n", + "):\n", + " test_start = time.time()\n", + " if verbose_t:\n", + " print(\"NSM = %.3f --- NBSM = %.3f\"%(NSM, NBSM))\n", + " tsm = torch.empty(n_meas)\n", + " tbsm = torch.empty(n_meas)\n", + "\n", + " # empty array to store values\n", + " tsmcount = torch.zeros(n_meas+1)\n", + " tbsmcount = torch.zeros(n_meas+1)\n", + "\n", + " for i in range(n_meas):\n", + " if include_xsec:\n", + " tsm[i], tsmcount[i+1] = compute_t_original(model, theta, NSM, tsmcount[i], test_input_sm, NSM=NSM, NBSM=NBSM)\n", + " tbsm[i], tbsmcount[i+1] = compute_t_original(model, theta, NBSM, tbsmcount[i], test_input_bsm, NSM=NSM, NBSM=NBSM)\n", + " else:\n", + " tsm[i], tsmcount[i+1] = compute_t_modified(model, theta, NSM, tbsmcount[i], test_input_sm)\n", + " tbsm[i], tbsmcount[i+1] = compute_t_modified(model, theta, NSM, tbsmcount[i], test_input_bsm)\n", + "\n", + " if (tsmcount[i+1] < 0) or (tbsmcount[i+1] < 0):\n", + " if verbose_t: \n", + " print('Reaching the end of test data. Stop tests at %d. '%i)\n", + " tsm, tbsm = tsm[: i], tbsm[: i]\n", + " n_meas = i\n", + " break\n", + "\n", + " if verbose_t and i % (verbose_period_t) == 0:\n", + " print('test %s: tsm = %.3f, tbsm = %.3f'%(str(i).ljust(4), tsm[i], tbsm[i]))\n", + "\n", + " test_duration = time.time() - test_start\n", + "\n", + " # compute mean and variation of the test statistics in two hypotheses\n", + " mu_sm = tsm.mean().item()\n", + " mu_bsm = tbsm.mean().item()\n", + " sigma_sm = tsm.std().item()\n", + " sigma_bsm = tbsm.std().item()\n", + " med_sm = tsm.median().item()\n", + "\n", + " # compute separation and p-value\n", + " sep = (mu_sm - mu_bsm)/sigma_bsm\n", + " p = 1.*len([i for i in tbsm if i > med_sm])/len(tsm) \n", + " delta1 = (p * (1 - p)/n_meas)**0.5\n", + " delta2 = (sigma_sm/sigma_bsm) * np.exp(-((mu_bsm - mu_sm)**2)/(2 * sigma_bsm**2))/(2*(n_meas**0.5))\n", + " \n", + " deltap = (delta1**2 + delta2**2)**0.5\n", + "\n", + " if verbose_t:\n", + " print('===> delta1 = %.3f, delta2 = %.3f'%(delta1, delta2))\n", + " print('p = %.3f +/- %.3f' %(p, deltap))\n", + " print('Separation = %.2f sigmas'%(sep))\n", + " training_properties = '/toydata/madminer-carl-'+title\n", + " plot_histogram(tsm, tbsm, int(NSM), int(NBSM), p, deltap, sep, epochs, e, training_properties, results_path)\n", + " print('Partial test after %d epochs (took %.2f seconds)\\n'%(e, test_duration))\n", + "\n", + " # return tsm, tbsm, NSM, NBSM\n", + " return p, sep\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def test(estimator, gphival=50, gphi_fname='50', include_xsec=False, n_meas=4000): #Wilson coefficient value (*10^-2 TeV^-2)\n", + " #toy data file path\n", + " with h5py.File(os.getcwd() + '/toydata/gphi_toydata_test_%s_out.h5'%(gphi_fname), 'r') as f:\n", + " Data = np.array(f['Data'])\n", + " Labels = np.array(f['Labels'])\n", + " NSM = np.array(f['NSM'])\n", + " NBSMList = np.array(f['NBSMList'])\n", + " NBSM = NBSMList[0]\n", + " \n", + " print(NSM)\n", + " print(NBSM)\n", + "\n", + " #randomise\n", + " Idx_test = torch.randperm(len(Data))\n", + " Data_test = torch.Tensor(Data[Idx_test])\n", + " Label_test = torch.Tensor(Labels[Idx_test])\n", + "\n", + " #select data from each hypothesis\n", + " SM_Data = Data_test[Label_test==0, :]\n", + " BSM_Data = Data_test[Label_test==1, :]\n", + "\n", + " #for plotting/ printing\n", + " n_epochs = current_epoch = int(1e4)\n", + " results_path = os.getcwd()\n", + " charge = 'plus'\n", + " \n", + " # MadMiner parameter normalization\n", + " theta = 0.9399 / 50 * gphival\n", + "\n", + " p, sep = hypo_test(\n", + " estimator, theta, SM_Data, BSM_Data, n_epochs, current_epoch, n_meas, charge,\n", + " verbose_t=False, verbose_period_t=1e5, title='gphi%d_new'%(gphival), NSM=NSM, NBSM=NBSM, include_xsec=include_xsec\n", + " )\n", + " \n", + " return p, sep\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "20:21 madminer.ml.morphing INFO Setting up morphing-aware ratio estimator with 3 morphing components\n", + "20:21 madminer.ml.base INFO Loading model from /madminer/madminer/examples/tutorial_particle_physics/models/carl10-bigbatch\n", + "20:21 madminer.ml.base WARNING Parameter scaling information not found in /madminer/madminer/examples/tutorial_particle_physics/models/carl10-bigbatch\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2225.5037678738263\n", + "2284.0421363853184\n" + ] + }, + { + "ename": "TypeError", + "evalue": "can't multiply sequence by non-int of type 'float'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetcwd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'/models/carl10-bigbatch'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'6e-3'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude_xsec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mtest\u001b[0;34m(estimator, gphival, include_xsec, n_meas)\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0;31m# MadMiner parameter normalization\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m \u001b[0mtheta\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.9399\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m50\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mgphival\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 30\u001b[0m p, sep = hypo_test(\n", + "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'float'" + ] + } + ], + "source": [ + "from madminer import ParameterizedRatioEstimator\n", + "from madminer.ml.morphing_aware import MorphingAwareRatioEstimator\n", + "\n", + "\n", + "estimator = MorphingAwareRatioEstimator(\n", + " morphing_setup_filename=os.getcwd()+'/data/setup.h5',\n", + " n_hidden=(60,60),\n", + " activation=\"tanh\",)\n", + "\n", + "model = estimator.load(os.getcwd()+'/models/carl10-bigbatch')\n", + "\n", + "p, sep = test(model, '6e-3', include_xsec=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluate all models, with and without cross section effects" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "quadratic for g_phi = 5: p = 0.4605, sep = 0.1059121360585815\n", + "quadratic for g_phi = 10: p = 0.4275, sep = 0.17704975194358652\n", + "quadratic for g_phi = 20: p = 0.35975, sep = 0.37412015640255114\n", + "quadratic for g_phi = 30: p = 0.27225, sep = 0.5632854480605051\n", + "quadratic for g_phi = 40: p = 0.20875, sep = 0.7006092155142076\n", + "quadratic for g_phi = 45: p = 0.19075, sep = 0.7629671483119467\n", + "quadratic for g_phi = 50: p = 0.18025, sep = 0.8284891044786227\n", + "quadratic for g_phi = 200: p = 0.00125, sep = 2.3304304901495807\n", + "quadratic for g_phi = 500: p = 0.00025, sep = 3.6502129974428503\n", + "morphing-aware for g_phi = 5: p = 0.47325, sep = 0.06396665050004251\n", + "morphing-aware for g_phi = 10: p = 0.424, sep = 0.1762729085251339\n", + "morphing-aware for g_phi = 20: p = 0.375, sep = 0.31531835237258565\n", + "morphing-aware for g_phi = 30: p = 0.29275, sep = 0.5161449975456953\n", + "morphing-aware for g_phi = 40: p = 0.225, sep = 0.6639995819813942\n", + "morphing-aware for g_phi = 45: p = 0.2075, sep = 0.7533180441939995\n", + "morphing-aware for g_phi = 50: p = 0.17525, sep = 0.8256219026877551\n", + "morphing-aware for g_phi = 200: p = 0.004, sep = 2.3424621537011365\n", + "morphing-aware for g_phi = 500: p = 0.0, sep = 3.7581515952681497\n", + "agnostic for g_phi = 5: p = 0.473, sep = 0.09983864365708298\n", + "agnostic for g_phi = 10: p = 0.474, sep = 0.094015050007883\n", + "agnostic for g_phi = 20: p = 0.36075, sep = 0.3373266553474599\n", + "agnostic for g_phi = 30: p = 0.3045, sep = 0.5041788907641448\n", + "agnostic for g_phi = 40: p = 0.24425, sep = 0.6619105561496281\n", + "agnostic for g_phi = 45: p = 0.20425, sep = 0.7463597431578026\n", + "agnostic for g_phi = 50: p = 0.179, sep = 0.8110355417331399\n", + "agnostic for g_phi = 200: p = 0.00275, sep = 2.2436653525289754\n", + "agnostic for g_phi = 500: p = 0.0, sep = 3.796508577259097\n", + "random for g_phi = 5: p = 0.50975, sep = -0.008590825006389125\n", + "random for g_phi = 10: p = 0.51775, sep = -0.05140702143278684\n", + "random for g_phi = 20: p = 0.52175, sep = -0.07146779570484978\n", + "random for g_phi = 30: p = 0.527, sep = -0.09095610148746165\n", + "random for g_phi = 40: p = 0.5565, sep = -0.13833726440982844\n", + "random for g_phi = 45: p = 0.57225, sep = -0.16186177427662532\n", + "random for g_phi = 50: p = 0.57125, sep = -0.18179349116403026\n", + "random for g_phi = 200: p = 0.7465, sep = -0.6662711472938935\n", + "random for g_phi = 500: p = 0.88425, sep = -1.1736239331985094\n" + ] + } + ], + "source": [ + "models = {\"quadratic\": quadratic, \"morphing-aware\": morphing_aware, \"agnostic\": agnostic, \"random\": random}\n", + "gphis = [5, 10, 20, 30, 40, 45, 50, 200, 500]\n", + "\n", + "pvals = {key:[] for key in models.keys()}\n", + "seps = {key:[] for key in models.keys()}\n", + "\n", + "for key, model in models.items():\n", + " for gphi in gphis:\n", + " p, sep = test(model, gphi, include_xsec=False)\n", + " \n", + " print(f\"{key} for g_phi = {gphi}: p = {p}, sep = {sep}\")\n", + " pvals[key].append(p)\n", + " seps[key].append(sep)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "quadratic for g_phi = 5: p = 0.0091324200913242, sep = 2.156249067642847\n", + "quadratic for g_phi = 10: p = 0.0, sep = 4.017805352816178\n", + "quadratic for g_phi = 20: p = 0.0, sep = 7.181279288627266\n", + "quadratic for g_phi = 30: p = 0.0, sep = 10.537343623977943\n", + "quadratic for g_phi = 40: p = 0.0, sep = 12.589139805946788\n", + "quadratic for g_phi = 45: p = 0.0, sep = 14.550243155883475\n", + "quadratic for g_phi = 50: p = 0.0, sep = 15.285524261766955\n", + "quadratic for g_phi = 200: p = 0.0, sep = 49.82163349759285\n", + "quadratic for g_phi = 500: p = 0.0, sep = 137.80754731499692\n", + "morphing-aware for g_phi = 5: p = 0.3105022831050228, sep = 0.45577044133980626\n", + "morphing-aware for g_phi = 10: p = 0.018604651162790697, sep = 2.068196247950657\n", + "morphing-aware for g_phi = 20: p = 0.0, sep = 5.060923577504371\n", + "morphing-aware for g_phi = 30: p = 0.0, sep = 8.991340621136636\n", + "morphing-aware for g_phi = 40: p = 0.0, sep = 12.098425405656048\n", + "morphing-aware for g_phi = 45: p = 0.0, sep = 13.808479263894094\n", + "morphing-aware for g_phi = 50: p = 0.0, sep = 15.96187120827293\n", + "morphing-aware for g_phi = 200: p = 0.0, sep = 54.20402372349474\n", + "morphing-aware for g_phi = 500: p = 0.0, sep = 129.40888161075887\n", + "agnostic for g_phi = 5: p = 0.1461187214611872, sep = 0.9346884323004425\n", + "agnostic for g_phi = 10: p = 0.013953488372093023, sep = 2.540956374307756\n", + "agnostic for g_phi = 20: p = 0.0, sep = 6.13874807594249\n", + "agnostic for g_phi = 30: p = 0.0, sep = 9.602166451774954\n", + "agnostic for g_phi = 40: p = 0.0, sep = 12.893467377793911\n", + "agnostic for g_phi = 45: p = 0.0, sep = 14.689080612126105\n", + "agnostic for g_phi = 50: p = 0.0, sep = 14.314401925310495\n", + "agnostic for g_phi = 200: p = 0.0, sep = 45.56478234109493\n", + "agnostic for g_phi = 500: p = 0.0, sep = 124.73676976932347\n", + "random for g_phi = 5: p = 0.1963470319634703, sep = 0.9925700912145264\n", + "random for g_phi = 10: p = 0.018691588785046728, sep = 2.0658960369456376\n", + "random for g_phi = 20: p = 0.0, sep = 4.012032497296448\n", + "random for g_phi = 30: p = 0.0, sep = 6.59406602728482\n", + "random for g_phi = 40: p = 0.0, sep = 8.966595342893173\n", + "random for g_phi = 45: p = 0.0, sep = 10.806131641863107\n", + "random for g_phi = 50: p = 0.0, sep = 11.254725531303045\n", + "random for g_phi = 200: p = 0.0, sep = 50.40185209940169\n", + "random for g_phi = 500: p = 0.0, sep = 142.9221678922827\n" + ] + } + ], + "source": [ + "pvals_with_xsec = {key:[] for key in models.keys()}\n", + "seps_with_xsec = {key:[] for key in models.keys()}\n", + "\n", + "for key, model in models.items():\n", + " for gphi in gphis:\n", + " p, sep = test(model, gphi, include_xsec=True)\n", + " \n", + " print(f\"{key} for g_phi = {gphi}: p = {p}, sep = {sep}\")\n", + " pvals_with_xsec[key].append(p)\n", + " seps_with_xsec[key].append(sep)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot results" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "colors = [\"C1\", \"C0\", \"C3\", \"0.6\"]\n", + "linestyles = [\"-.\", \"--\", \"-\", \":\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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sn2SnT5/O5s2badmyJStXrsRkMjF58mTbLuKBgYFMmjSJVatWYbFYWLJkCZ06dSItLY3x48eTnp5Oz549+eGHH9i5c6ftU3i5X375hccff9xW8Xj+/PmEhIQwbNgw3njjDbp160ZERASjRo3ixRdfZObMmbRt25axY8dy5513kpmZicVi4ZVXXuHOO+8kISGB2267jYEDB7JlyxZWrFhBfHw8s2bNorCwkHbt2jF//nxdVbmKDO7ulAzuQ1zCafJxJg9n8nDhvHJhrFMM3k4l7Cppx6aC37aMcXEQXA0WRvdoi5ubOyfPpHAmMxc3FydcXZxwdXHBzdUVd5MzYigffin7teJwjAiqqIiC2Fhyo6PJKhvGMri7YwoPx2Q2Y4qIwBTeDWPZ/kOa1pDoBAfrROM1cSnkFRXj6qT/SGrTvR9t+d2527sFcP+NgeQXlTB5/i+/+/rd3VtxT4/WZJwv4qHPd1b62td/vLFK73vkyBGWLFnC3Llz6dmzJ1988QUxMTF8++23vPbaa7Ru3ZqIiAhWrFjB+vXrmThxom3ewc6dO4mJicFkMrFgwQJ++eUXDhw4gKurKz179mT48OH4+vpy+PBhvvzyS+bNm8eYMWNYtmxZpaJe5Xx9fdm1axcffPABc+bM4eOPP+avf/0rgwYN4rnnnuOHH35g7ty5F/0+OnXqRHR0NA4ODqxdu5YZM2awbNkyoqKi2LRpE4GBgTg4ONjmWMTExHDffffh4uLC8uXL8fT05OzZs/Tu3dtWHj4+Pp758+fzwQcfcPbsWV555RXWrl2Lm5sbb775Ju+88w4vvvhilf6cGzujhwdhr75CWGocFGRBYbb114JsVP5wpDCL0OwSyC4lrfNEzuLF2eP7STy+j+bT78fFpwUffzifj4qtk2ApAc5bj1/dHsDJ5M6/ioazydIR36BwfL088Ss6hX/eYe6+72EwGMhJPkxzB0HlGMjfv5+83bvJ372Hs//+N5SWggjO7dv/lvBEmHEKDNRzV7R6T/80BzoHeKIUxJ/JIaKNt73D0WpBUFAQYWFhAHTp0oWbb74ZESEsLIyEhAROnDjBsmXLABg0aBDp6em2lRp33HFHpW78W265xVZtdPTo0cTExDBy5EiCgoIwm80AdO/evdJKlYpGjx5te80333wDWBOR5cuXAzB06FDbfIkLZWVlMWnSJA4fPoyI2OZD9OvXj/fee4+goCCGDx/OmjVryMvLIyEhgZCQECwWCzNmzCA6OhqDwcDp06dJSUkBoG3btvTu3RuArVu3Ehsby0033QRY51/ceGPVkkitjHsz63GB8vShddnxm46gRttaz42/lcdTj3P2XBZpWec5m53PufOFODWbAgVZOJ1oSkmGIwdT8kk7mk1uYQm+4svdZcNQT/5nA2sym9OUbPwdz+Pv4k+HIbfy1F8GUZBm4PjBsxQcTaDohx84t2QJAEYvL2vCU570hHXF0IA2YdQaB53gYK2FAxCXrBOc2na5HheTk/GyX/dxc6pyj82FKhbvMhgMtrbBYLjk3JryT7TlG25eeP7CdsX3MBqNtvLtl4rFaDTadg2/1OT/f/3rX8ybNw+A77//npkzZzJw4ECWL19OQkKCbafgnj17smPHDoKDg7nllls4e/Ys8+bNo3v37gAsWrSItLQ0du7ciaOjI4GBgbZVPBW/P6UUt9xyC19++eVF49FqSMV/U54tMHm2uEgiZDW97ChXYCkhK99ia4++KYyQ44mknrOQkiMkFzhTlHQWY9qruAEzCl9mX8ueSItS2p5Po1f2MfqcTyDs5Elyy3aqVgYDxUHtcewWjmf3SHx798CpZUvdy6PVaTrBAVp5m/DzcCYzr8jeoWh1RFRUFIsWLWLmzJls2LABX19fPD09L/raNWvWkJGRgclkYsWKFXz66afX/f59+/Zl8eLFPPPMM/z4449kZmYC8PDDD/Pwww/bXpeVlWUr/b5gwQLbeScnJ1q3bs3ixYuZOXMmaWlpPPnkk7ZNRbOysmjWrBmOjo789NNPnDhx4qJx9O7dm4cffpgjR47Qvn178vLySExMpGPHjtf9PdZXdX03cRdHIy6Ov030vbVvb27te8GLlILzkyHjGK8dTSAxK5NU/76kZBeQujOdBOcQRv7fTErOneOBJ/5Ji7OphGYkEPLtSli+hGzA2NQb14hI1hqbkx3cGcfOnfFr6om/pzPBfu609NIbGGv2pRMcrJ+4tz13MwaD/jSiWc2ePZspU6bQrVs3XF1d+eyzzy752r59+3L//fdz5MgRxo8fT48ePS45HFVVs2bNYty4cXz99df079+fgIAAPC4yEfTpp59m0qRJvPPOOwwaNKjS1/r168e6detwdXWlX79+JCYm0q9fPwAmTJjAiBEj6NGjB2azmU6dOl00Dj8/PxYsWMC4ceMoLLQWw3zllVcadYJzNauo6iwR29BZ1za96Vrxa0NCwGLtbTR6efGvCU6knVGkpntwJqMtOWcVrdNTaZtxmvwtadxw3vpjxGIwcqRJK6J92hLfM4xJD43B4OfHV9tPMiS0OX4ezr+PQ9NqkK6Do9WqhlYHZ8GCBezYsYP333+/Wu9bWFiI0WjEwcGBLVu28NBDD1UqrlbXNKY6OOUa5XNGKcjLgIyjkHEMMo5RfCKO/PP+ZOf6k7tjByWx+xBrKRxKmwew3rEFB32DcY6M5KYBkdzaNYAmJsfLv4+mXQVdB+cKdp7I5PXv45hzTziBvm5XvkDTatDJkycZM2YMpaWlODk52ebdaJpdiYBbU+vRuhdg/SHiUXZQUow6uYOC01nkHUkmf8N/Gbx7J4NP7YTdS8j83J2lTYPoPfJmggf1xaVTCKK3RdBqiP6XVcbZwcCOE5kcTMrWCY5WZZMnT2by5MnVft8OHTqwe/fuar+vptUoowMS1BtTEJj6AveNR8WvpmjNx+Tt2EFJmpHw9GMYPthPwgfvUuxiIrlVB7xu6EWnIf1wN4djcNZDWVr10AlOmfbN3DEahNjkLIZ3C7B3OJqmafWfgxPS5U6cu9yJ87lTeO9ZBLsWYjlzhryspmzJCMNwIhWPRR9zetHHlBgdoFMozfrcgGuP7pgiI3URQu2aNagE52pWN5SUlJCbm2vbf8XF0Uh7P3fiknNqOEpN07RGyKs1DHgWop7C8ehPNNm1gKHxqykcFEZM5Er2rI6maNcuep87iXH+fNLnzbMWIewUQpPbR+B1z90YL7GSUdMupkElOFezumHDhg2kpKQwfvx427nQFp5sOZpekyFqmnYRInIjcB/QDwgA8oEDwHfA50qprMtcXqvq+jLxOs9ghA6DrUduKs45ydwc0JGbw5uT9/57ZPd9kWbdhnFk41Y++3AFfdKPEvz226S9/z5eo0fjc/99OAUG2vu70OqBBpXgXI3Q0FCCg4NRStmKVfUO9iGnwEJhcQnODnrDOE2rDSKyGkgCVgKvAqmAC9ARGAisFJF3lFLf2i/K3zSIZeJ1RcUqz0W5uAb3xrV1ELi60jzUl64TevNx6t1kHzjE+MQt9P56MZmLFuE+YAA+kybi2ru3LjaoXZJeJq7Vqoa2TLyqXnvtNWbMmGFr9+nTh82bN9sxoup1PcvERcRXKXX2el9T2/Rzpob99BpsfBPl4MqG4Cd4O6UHWUlpfBmQTMHSxZRkZODcoQM+kybiefvtGC7Y9VxrPC71rDHYI5i6Iisri7S0tN+dLylteEmfZl+vvfZapXZDSm6uV3niIiKPiMhF90qpa8mNVgsGPAfT1yNd7mTg4df4b95EFvQ/TZuHp9Ju/TpiRj/EeUspyS/M5MjAQaT+4x9YUlLtHbVWhzTqBGfDhg2/+0Fz978389TSvXaKSKsNI0eOpHv37nTp0sW2S/cnn3xCx44dGTBgANOnT+eRRx4BrMvAH3vsMfr06UNwcDBLly4FrHs0PfXUU3Tt2pWwsDC+/vprAJKTk4mKisJsNtO1a1c2bdrEs88+S35+PmazmQkTJgDg7u5ui+ett94iLCyM8PBwnn322dr8o6hrmgPbRWSxiAwVPfbQuIlAy+4w6kN4aAuG9v3psGM2/COclM0LWODRhRHhD7Lgnqcp7NSV9A8/4sjgwZx++mnyDxy0d/RaHdBo5+AA3HjjjTg6Vq6o6eHiQGxStp0ialzOvPYahXGHqvWezp070bzCUNDFfPrpp/j4+JCfn0/Pnj0ZPnw4L7/8Mrt27cLDw4NBgwYRHh5ue31ycjIxMTEcOnSIO+64g7vvvptvvvmGPXv2sHfvXs6ePUvPnj2Jioriiy++4NZbb+X555+npKSEvLw8+vXrx/vvv3/RSsSrV69mxYoVbNu2DVdXVzIyMqr1z6M+UUq9ICIzgSHAFOB9EVkMfKKUOmrf6DS7atYJ7v0cTu+EdS/TIvoZ1nu05ct+/+D9Pc587X8nox+4g0ezdpP77Qqyv11F04cexO+xx/QcnUasUffgNGvWDG/vyj3ioS08OZKaS2FxiZ2i0mrae++9R3h4OL179+bUqVMsXLiQ/v374+Pjg6OjI/fcc0+l148cORKDwUBoaCgpKSkAxMTEMG7cOIxGI/7+/vTv35/t27fTs2dP5s+fz+zZs9m/f/9F94+qaO3atUyZMgVXV1cAfHx8auabrieUdVLgmbKjGPAGlorIW3YNTKsbWnaHiStg0iqcm7ZhclQnop8eyFMDWxNn8KTFCzNov3ED7qNGkf7vD0l6+hlKi/Qmyo1Vo+7BUUpx8uRJnJ2dad68OQChAU0oLlUcTsmla8smdo6wYbtST0tN2LBhA2vXrmXLli24uroyYMAAQkJCiIuLu+Q1zhUqq5ZPyr/U5PyoqCiio6P57rvvuP/++3nqqaeYOHHiJe9dcRVfYycijwGTgLPAx8BTSimLiBiAw8DT9oxPq0OCoqwH4KoUDyc9y5+auiEOURS4uDKuyc3cH2UkatVSis6coc37/8TYRD/PG5tG3YMjIvz8888cOHDAdq5zgPUTd2yyHqZqiLKysvD29sbV1ZVDhw6xdetW8vLy2LhxI5mZmRQXF7Ns2bIr3icqKoqvv/6akpIS0tLSiI6OplevXpw4cYJmzZoxffp0pk2bxq5duwBwdHTEYrH87j5Dhgzh008/JS8vD6BRD1EBvsBopdStSqklSikLgFKqFLjdvqFpdVrkRKTbGAAsRUXc2c7IJ23780aPCeTs3M3OEXeRGHvEzkFqta3KPTgi4gYUKKUa1NjNsGHDKk34DGzqxv292xKk96NqkIYOHcqHH35It27dCAkJoXfv3rRs2ZIZM2Zwww030KJFC0JDQ20Vri9l1KhRbNmyhfDwcESEt956i+bNm/PZZ5/x9ttv4+joiLu7O//5z38AeOCBB+jWrRuRkZEsWrSoUjx79uyhR48eODk5MWzYsN+tuGroRKR8XO7dC9oAKKUylFKX7mLTGjcRCB9ra3rEL+Wpg4/wl9Y38VOPB1ka0pbbF/+drKkT8fnXP3Ht3t2OwWq16ZJ1cMq6hccCE4CeQCHgDKQB3wNzlVKHaynOq6LrU9RddbUOTm5uLu7u7hQXFzNq1CimTp3KqFGj7B1WvXGddXCOA+UPogvH65RSKrh6oqxe+jlTRxWdhx3zYdtHkHUSvNpwutk48uf/hOV0EufHTCTs2b/g7OJk70i1anItdXB+AtoBzwHNlVKtlVLNsJZS3wq8ISL31Ui0tchisbBv3z7b5FGw1sE5mpZ7yXkWWsMze/Zs29LuoKAgRo4cae+QGg2lVJBSKrjsCLrgqHPJjYiMEJG5WVl1ZvcIrSInN+jzCDy2G8YshCatafnrmwTdFIt0CcDtqwVED7uLrGMJ9o5Uq2GXG6IaXD4GXpFSKgNYBiwTEcffX1a/GAwGtm/fjtlsxt/fH4Cvtp/k+eUH2PT0QFr7uNo5Qq02zJkzx94haICItATaUuHZpJSKtl9Ev6e3aqgnjA4Qeof1SNqD8Zd5dDJ9xw+mIJruSuTYyNH4z5xJi3t0T21DdckEpzy5EZF2QKJSqlBEBgDdgP8opc5dLAGqb4xGI/fdd1+llTKdA6w71sYmZ+sER9NqiYi8CdwLxALlc/0UUKcSHK0eamGGkf+Ckn8w9NRW1u5M4e54V5wAACAASURBVNw7n+AycwaSn0TAxIftHaFWA6qyimoZUCIi7YFPgCDgixqNqpZVTG4AOjX3QATi9EoqTatNI4EQpdQwpdSIsuMOewelNSBGBwjsy+C77qLlp/M54eFP6nsfU1pQYO/ItBpQlQSnVClVDIwC3lVK/QUIqNmwaldOTg7R0dGkp6cD4OrkQFBTN13RWNNq1zGg3g97a/XDjSH+hDw1FafcAjIWfGbvcLQaUJUExyIi47AW4Ppv2bl6/xDKjY4mZ8MGwDoP59ixY1ScNNi5haeuhaNptSsP2CMiH4nIe+WHvYPSGq6gMRPxuGUwqR9+RNaJRHuHo1WzqtTBmQI8CLyqlDouIkHA5zUbVs07+9FcKCnBY8AA3NzcmDRpUqWKspNuDORceJGuNKtdUWBgIDt27MDX19feodR335YdmlZ7Jt2PZd0GtjzxNEOXNajZF41eVRKcE8BfKhT4OwnU+09VJnM4mf9ZSGlREQYnp98lMb2CGveeQI2FUgqlFAZDoy7qXScopT4TESegY9mp+IawkEGr21pFdmdL5w50PbibvaujCb8tyt4hadWkKk/1dYCpQtsErK2ZcGqPyWxGWSwUxsYCcObMGVatWkVubi5g/cG35Wg6B5N0rYuGJiEhgc6dO/OnP/2JyMhIpk2bRo8ePejSpQuzZs2yvS4wMJBZs2YRGRlJWFgYhw5Zdz5PT09nyJAhRERE8Mc//rFSvaR33nmHrl270rVrV959913b+3Xq1Ik//OEPdO3alQkTJrB27VpuuukmOnTowC+//FK7fwB1VNkqzcPAv4APgF9FRP+00WqWwciQx+8kw8WDM6++TGFRsb0j0qpJVRIcF6VUbnmj7Pf1fu20q9kMQN7uPYB1uXhxcTEFFWbTP/LFLj7bnGCP8BqNVatWER8fD0BpaSmrVq3i8GFrgezi4mJWrVrF0aNHASgqKmLVqlUcP34cgIKCAlatWsWJEycAbPs5VUV8fDwTJ05k9+7d/O1vf2PHjh3s27ePjRs3sm/fPtvrfH192bVrFw899JCtVs5f//pX+vbty+7du7njjjs4efIkADt37mT+/Pls27aNrVu3Mm/ePHbv3g3AkSNH+POf/8y+ffs4dOgQX3zxBTExMcyZM6fRbc1wGX8Dhiil+iulooBbgb/bOSatEWjSZwKqlxdtziby3zfn2jscrZpUJcE5LyKR5Q0R6Q7k11xItcPBzw/Hli3J32NNcPz8/Bg1apRtHoWIENrCk7jkHHuGqdWQtm3b0rt3bwAWL15MZGQkERERHDx4kNiyXj2A0aNHA9C9e3cSEhIAiI6O5r77rEW8hw8fjre3NwAxMTGMGjUKNzc33N3dGT16NJs2bQIgKCiIsLAwDAYDXbp04eabb0ZECAsLs91Xw1EpFV/eUEr9SgNY0KDVA0YHbnpgPGea+tB+xXxKcvRzvyGoyhycx4ElIpJU1g7AWoyr3jOZzeRdZi+Z0ABP5m9OwFJSiqNRz9GoCSNGjLD93mAwVGo7ODhUajs5OVVqu7i4VGq7ula9Y9HNzbqZ6vHjx5kzZw7bt2/H29ubyZMnV+rFK6+RVN7DV+5iE88vt7VHxVpLBoPB1jYYDJXu28jtEJFPgIVl7QnATjvGozUiYh5P76i3SVheytkPPsD/mWfsHZJ2na74U1sptR3oBDwE/AnorJRqEA8dk9lMcUoKluRkAH799Ve++OIL2w+czgGeFBWXciztvD3D1GpQdnY2bm5uNGnShJSUFFavXn3Fa6Kiomw7gq9evZrMzEzb+RUrVpCXl8f58+dZvnw5/fr1q9H4G5iHgIPAY8CfsVY0ftCuEWmNh4MTplGP0yT4POmf/Yfvvttq74i063TJHhwRGaSUWi8ioy/4UgcRQSn1TQ3HVuNMZfNw8vfswTEgAFdXV5o3b47FYsHBwYHQFtYtG+KSswlp7mHPULUaEh4eTkREBF26dCE4OJibbrrpitfMmjWLcePGERkZSf/+/WnTpg0AkZGRTJ48mV69egHwhz/8gYiICD0EVUVKqULgnbJD02pfxP349Z5DyikHzs15i+S+iwhoYrrydVqdJJfqVheRvyqlZonI/It8WSmlptZsaNeuR48easdlhp7KKYuF+J698L53DP7PPfe7rxeXlHIwyZrcuDgaayLURicuLo7OnTvbOwytml3s71VEdiqletgppKsmIsHA80ATpdTdV3p9VZ8zWj2z5QOOvv9vinYW8809/8eMl6bpWmh13KWeNZccolJKla+XfUkpNaXiAbxcU4HWJnF0xNS1K3llE43LlZRYS/44GA2Et/bSyY2m1XEi8qmIpIrIgQvODxWReBE5IiLPXu4eSqljSqlpNRupVuf1mELwhxvI829Fr9X/4bvdJ+0dkXaNqrrZ5oWWVncg9mKKMFMQG0dpYSEAW7du5auvvrJNGN11MpN3foy/7ARSTdOql4gYRMTzKi5ZAAy94B5GrDV1bgNCgXEiEioiYSLy3wuOZtUWvFa/OZoQj6a0n/08rXLT+GXOv8nK1/Um66NLJjgi0klE7gKaiMjoCsdkwKXWIqxhJrMZLBYKDlqXBgcEBNC5c2dbQrPv1DneW3+E1JxCe4bZoOhksWGprr9PEflCRDxFxA3rBON4EXmqijFEAxkXnO4FHCnrmSkCvgLuVErtV0rdfsGRWsUYHxCRHSKyIy0t7Sq+O61eUYomCW9BsA9jYtfgmpNp74i0a3C5HpwQ4HbACxhR4YgEptd8aLXDNtG4rCBb27ZtiYyMtJXuD23RBEBvvFlNXFxcSE9P10lOA6GUIj09HReXavnME6qUygZGAt8DbYD7r+N+LYFTFdqJZecuSkSaisiHQISI/H5SHqCUmquU6qGU6uHn53cdoWl1mgiEj6Xd4xMxllhIe/cflJbqZ1Z9c8lVVEqplcBKEblRKbWlFmOqVQ5Nm+LYurWt4B9YK+rm5+fj5uZGpwDr6qnYpGwGhuhe7OvVqlUrEhMT0Z9+Gw4XFxdatWpVHbdyFBFHrAnO+0opy3VO7rzYxZf8KaWUSkcvS9fKRU7ECfCZmE3GJ58yWzrx7qxxuDpVpXycVhdU5W9qt4g8DHShwtBUXV5FdbVMZjN5W7fadg5fvXo1FouFkSNH4uniSGsfE3G6B6daODo6EhQUZO8wtLrpIyAB2AtEi0hb4Ho2g0sEWldotwKSLvHaKhOREcCI9u3bX++ttLquMBff8GLONmnC0PWL+HvvCJ4f0dXeUWlVVJVJxguB5lj3hdmI9SHRoOpYm8zhFKelUZxkffZ17dqV8PBw29dDAzxJzKz3u1NoWl23SinVUik1TFnHME8C1/NBajvWul1BZbuUjwW+vd4glVKrlFIPNGnS5HpvpdV1JUUYd7xPy0FN6Zx5gmNffcPeU+fsHZVWRVVJcNorpWYC55VSnwHDgbCaDat2lc/DKV8u3rZt20q9DH+/18zyP/WxS2ya1ohUWrFZluR8VZULReRLYAsQIiKJIjJNKVUMPAL8D4gDFiulDlZzzFpD5uoDPafRxOlnHEPaMy32e2Z+9QuWklJ7R6ZVQVUSnPL1cedEpCvQBAissYjswCUkBDGZyN+z13YuOzubc+esmbqrk4Mu9KRpNaQ6VmwqpcYppQKUUo5KqVZKqU/Kzn+vlOqolGqnlHq1muIdISJzs7KuZ/RMqzdufBRxdKHlQHe887OI2LSS6F/1HML6oCoJzlwR8QZmYu3ejQXeqtGoapk4OGAKC6s00fi7777jl19+ASCvqJi/fL2H7/cn2ytETWvI6tWKTT1E1ci4+0GPqZiy/keToYMYe3wT/dx02ZD64IqTjJVSH5f9diMQXLPh/N7Vlk+/ViazmfRPP6W0oACDiwv9+vWz7U5tcjSyNi4FVycjw8ICaioETWuUGsuKTa0eu+kx2P4xft2LyI52JOXNtyh48XXa+bljMOje/brqij04IuIlIo+JyDsi8l75UZWb16fy6SazGYqLKThoHaJv1aoVPj4+5fESGuCpa+FoWs16UES8yhsi4i0in9ozoIvRQ1SNkEdziJyI4/Fv8J08ltx163hmxsd8uV1v41CXVWWI6nusc272AzsrHFWxgHpSPt1ktq6aKi/4V1payqlTp8jIsBZH7RzgyaHkHEp0sSdNqyndlFK2JSpKqUwgwo7xXJQeomqk+j4OCD5tEnFs05rH4/7L2/89yJmsAntHpl1CVRIcF6XUE0qp+Uqpz8qPqty8tsqnVwcHHx8c27aptPHmmjVrOHToEAChLTzJt5RwIv18bYWkaY2NoWy+HwAi4kPVanVpWs1r0grM4zHs/wL/Pz+EX0YSNx/exAsrDujK7HVUlergiMh0EQkQEZ/y4zres9rLp5e97rr3iHE1m8nfsxelFAaDgREjRtCjh3UH9i4tPAnx99CbrmlazfkbsFlEXhaRl4DNNLAFDVo9F/UkTFiK+2134nbTTUz6dQ3b9xzl+/1n7B2ZdhFVSXCKgLex1pgoH57acR3vedXl05VSD5Yt83z9Mq+77j1iTGYzJWfPYjl9GgA/Pz+cnJwA6NKiCf/7SxQRbbwvdwtN066RUuo/wF1ACpAGjFZKLbRvVL+n5+A0Yl5tILg/YjDg/9yzOBYW8MSpn0jN0cNUdVFVEpwnsBb7C1RKBZUd17OaqkbKp1eH3zbetA5TFRYWcuDAATIz9U6ymlZLfLAWFf0nkCYidW5fDz0Hp5FTCv73PM6nluA9YTy9DkYzzkcnOHVRVRKcg0BeNb5njZRPrw7OHTogrq62ejilpaVs3ryZ02U9Oh9sOMLQd6PtGaKmNVgiMgt4BigfinYEPrdfRJp2ESKQlw75Gfg98ghGLy/OvPoqaw+eYeuxdHtHp1VQlQl8JcAeEfkJsFU3Uko9dqULy8qnDwB8RSQRmKWU+kREysunG4FP60r59AsL/plMJu677z5bPRwno4FDZ3JIzy2kqbuzPUPVtIZoFNZVU7sAlFJJIuJh35A07SJG/htEMAJ+jz/OmVmz+OH9z/klqDv/ezwKk5PR3hFqVK0HZwXwKtYJf1e1TLw2y6dXF1OEmYL4eErzrZtrlic3YF0qDhCX3KD2GtW0uqKobP8pBSAibnaOR9MurnzrnqTdeA2/GefQzkw5sIqU1HP8fe2v9o1Ns7liglO2JHwxsPVql4nXtuqY/Gcr+HfAWpvw3LlzxMTEkJubWyHB0QX/NK0GLBaRjwAvEZkOrAXm2Tmm39GTjDUAMo7D3AHIjo9pPmMGhrRU/pq3i483HWN/ov63URdUpZLxCGAP8ENZ2ywidWLOzIWqY/KfKdxa8K+8Hk5xcTGHDx8mKysLHzcnApq46IrGmlaNRMQZQCk1B1iKdVfxEODFssnGdYqeZKwB4BMEIcNg6we4du2I57DbCP95FSGc5+ll+/SO43VAVYaoZmMtzncOQCm1B6hzKxuqi4O3N06BgbaVVE2bNmXSpEm0bGkt1XNP91aEtdQPNk2rRlsARGShUmqNUuoppdSTSqk19g5M0y4r6ikoOAfbP6bZU08hIrya8hPT+gbhoPeosruqTDIuVkpliVT6y2rQZRtNZjO50dEopRARKn7vTwwJsWNkmtYgOYnIJKCPiIy+8ItKqW/sEJOmXVnLSGg/GLa8j+MNf6Tp9D+g/vk+t/1xCiKtbD9DNPuoSg/OAREZDxhFpIOI/BPrhOMGy2Q2U5KRgeWUteByYmIi33//PcXFxQAUWErILyqxZ4ia1pA8CPQGvIARFxy32zEuTbuyqKety8Z3zKfptGk4tmhBymuvsXhrAn/4bAelev9Cu6lKgvMo0AXrEvEvgWzg8ZoMyt5MEWUF/yrUw8nPzycvL49TGXmEvvgDq/bWidqEmtYQBCilHgKeU0pNueCYau/gNO2y2twAQVGw+T0MRkWzp5+mMD4e75++Y92hVL7ecerK99BqRFVWUeUppZ5XSvUs2wrheaVUgy7b6Ny+PQY3N1uC06ZNG+666y48PT1p6WXCxdGoJxprWvUpL+z3oF2jqCK9ikr7nainITcFdi3E49YhuPbqRduVnzMwwJnXvo8jJbtB/8iss6qyiqqjiMwVkR9FZH35URvBXa3qevCI0YhLt7BKO4uXMxiETs09dIKjadUnvayQaJCIfHvhYe/gLqRXUWm/E9gX2twIP7+LlBTh//wMSrKzeS4thqLiUmbqHcftoipDVEuA3cALwFMVjjqnOh88JrOZwvhfKc2z7lJx8OBBli9fjlKK0BaexCVl63+wmlY9hmPtxTmLdUfxCw9Nq9tEYMBzEHY3lBThEhKC171jKFm+lOdDnfgxNoUDp/WH4tpW1VVU/67xSOoY14gI0ktKyN9/ALcbeuHs7IyHhwfFxcV0DvDk860nSczMp7WP65VvpmnaJSmlioCtItJHKZUmIm5KqfP2jkvTrkpwf+tRxu+xx8j+fjVRP37OstlzCGule/xqW1V6cFaJyJ9EJEBEfMqPGo/MzsoL/pXPw2nfvj2DBw/G0dGRPu18mXl7KK56vxFNq07tRSQWiAMQkXAR+cDOMWla1SkFh9fA4TU4eHvj9+ij5G/dSofDuwA4fS7fzgE2LlVJcCZhHZKquBfVjpoMqi4wNmmCU3Aw+bt3VzpfWlpKkK8b0/oG6Q03Na16vQvcCqQDKKX2AlF2jUjTrta6l2CztQC399h7ce7QgdQ33+KnfaeIeusnNh85a+cAG4+qrKIKusgRXBvB2ZvJbCZ/717bXJvo6GhWrlwJQHJWPrtPZtozPE1rcJRSF66p1QWntPpDBO79HCYstTYdHPB/fgaWxEQ6b1pFK28Tzy3fr+uo1ZJLJjgiMqjs19EXO2ovRPsxmcMpyczEcuIEAM2bN6dNmzYAvPpdHI9+uftyl2uadnVOiUgfQImIk4g8SdlwVV2il4lrl+XdFhycoLgQSktx690bj1tu4dzHH/NGVHNOpOfx7jq943htuFwPTvlsqQsrizaa6qIms7XgX/ly8Y4dO9K9e3cAQlt4kpiZT1a+xW7xaVoD8yDwMNASSATMZe06RS8T164o/Sj8IxzirFUOmj3zNJSU0GbpfMb2bM3Hm45z4LROkGvaJRMcpdSsst8+eGF1UeDJ2gnv6lT3Jyvn9u0xuLvbJhqDdQ5OQUEBoQGeABzS9XA0rVoopc4qpSYopfyVUs2UUvcppdLtHZemXTXvQHByh+g5oBROrVrhM3UK2atW8X8tCmnu6aJrqdWCqkwy/kZEbMvJRaQ5UCd3+a3uT1ZiMGDq1o38PXtt55YvX87GjRttCY7+R6ppmqZVYjBCv/+DlP0QvxoA3wcewMHfn9w5b7LuiX6M6dHazkE2fFVJcFYAS0XEKCKBwI/8Vlq9wTOZzRT++isludayHGFhYXTs2BE/D2d83Z2ITdIJjqZpmnaBsHusPTnRb4FSGFxdafbkkxQcPEjBt9bFKmtiUzh+Vpd8qilVWUU1D2uPzQpgFdYhqx9rOrC6whQRAaWlFBzYD1jn4QQFBSEi/HNcJI/d3MHOEWqapml1jtEB+j4BSbvhyDoAPG8fjikyktS/v0tmSgZPLN7Dc9/s01Xxa8jlVlE9UX4ALkBrYA/Qu+xco2AK7wZQaR5Obm4uubm53Niuqa5krGnVREReExGvCm1vEXnFnjFp2nUJHwdNWsPGN0EpRAT/GTMoycigaP48ZgzrzNZjGXy9Xe84XhMu14PjUeFwB5YDRyqcaxSMnp44tW9H/m5rglNaWsqSJUvYs2cP6bmFfL71hK5OqWnV4zal1LnyhlIqExhmx3g07fo4OMFNf4bEX+D4RgBMXbvQ5K7RZHz+OaN8i+kd7MOr38eRqnccr3aXW0X118sdtRmkvZnMZvL37EEphcFgoH///oSGhpJ+vogXVhxg2zG90EPTqoFRRGzlwUXEBOhy4Vr9FnE/uDeHjW/bTjX7y18wuLiQ+sYbvD66G0XFpby48qAdg2yYqjLJuNFzNZspycqi6HgCAMHBwfj4+BDs64aTg4E4vZJK06rD58A6EZkmIlOxzv37zM4xadr1cXSx9uIUZkG+tYPSoWlTfB9+mPPRm/A9sJ3nh3fmllB/PRenmlVlN/FGr7zgX/6ePTgHB1FSUsKZM2fw8PCgU3MPvVRc06qBUuotEdkP3AwI8LJS6n92Dut3RGQEMKJ9+/b2DkWrL3o9AL0fsm7lUMZnwnjOLV5M6utvcP+qbxEnJzsG2DA1qB6cmiqh7hQcjMHT0zbR2GKx8N1333H06FE6N/ckNilbZ96aVg2UUquVUk8qpf6vLiY3oCsZa9fA6GBNbvIzIdO69Y84OeE/4zmKTpwgY+FCABZuSeDl/8baMdCGpcoJjohMqslAqkNNPXh+K/hnTXBcXFy4/fbbCQ0NJbSFJ5l5FtJyCqv1PTWtsRCRmLJfc0Qku8KRIyK6e1RrGEpL4aP+8P1TtlPu/frhPmAAZz/4N8VpaSRm5vNJzHG2HNXzOqvD1fTg/LnGoqgHTGYzhYcPU5KbC0CLFi1wdnZmdGRL9s0eQjNPFztHqGn1k1Kqb9mvHkopzwqHh1LK097xaVq1MBjg1lfh5hcrnfZ/9hlKi4pIfefvPD64I22buvLcN/sosOgdx69XgxqiqkkmsxmUomDfPgDy8/OJjY3FUFKEp4ujnaPTtPpPRBZW5Zym1VudR0DzrpVOOQUG4jPxfrKWL4f4WF4fFUZCeh7vrj1spyAbjssmOCLyk4isF5GfgPblvxeR9bUUX51hCu8GIradxfPz84mJiSE5OZkFPx/nw41H7RyhptV7XSo2yvbA626nWDStZmQnw7LpkPLbsnDfhx7C6OtLyiuvcmOwD2N6tOLjTcdI0jXWrsuVVlFNLvtVgO+AKTUaTR1m9PDAuX172zwcLy8vxo4di4eHB3O27CI+JYcH+7ezc5SaVv+IyHPADMBUYc6NAEXAXLsFpmk1wcEZ4r+H0mK4Zz4ARnd3mj3xBMkzZpC9ahXPDxvGqIhWtPAy2TnY+u2yPThKqRNlRwJQWKF9onbCq1usBf/2okpLMRgMeHp6IiKEtvAkIf085wuL7R2iptU7SqnXlVIewNsXzL9pqpRqNBv7ao2Eqw/0mg4Hl0Par7bTTUbeiUu3bqTO+RvupUXc2K4pAJnni+wVab13NXNwEmoqiPrCZDZTmp1N0fHjAGRkZLB582ZCmrmiFBw6k2PnCDWt/lJKPVe2/1QvEYkqP+wdl6ZVuxsfAUcTbPqb7ZQYDDR/fgbFaWmkf/QRAN/sSqTvm+tJ0DuOX5MqJzhKqdE1GUh9YIr4reAfWDfdjIuLo7W7tQaOrmisaddORP4ARAP/A/5a9utse8akaTXCzRd6TIX9SyDjmO20KTycJnfeScaCBRSdOEGfdr4YRJixfL+utXYN9Cqqq+AUGIihSRNbgtOqVSumTJlC58CWtPQykauHqDTtevwZ6AmcUEoNBCKANPuGpGk1pM+jYHCATe9UOu33xBPg6EjKm2/RvIkLzw7rxOaj6SzZkWinQOsvneBcBTEYMIX/VvDPYDBgMBgQEWKeGagnGWva9SlQShUAiIizUuoQEGLnmDStZng0h+6TYO+XcO6k7bSjfzN8H3yQ3PXryY35mXE929AryIdXvoslNUfvOH41GlSCU1NbNVRkMpspPHKUkhzrfJsTJ06wdu3aGns/TWtEEkXEC1gBrBGRlUCSnWPStJpz058BgZh3K532mTwJxzZtSHn9daSkmDdGh6GAXScy7RJmfXXFBEdEeouIR4W2h4jcULNhXZva2CPGtazgX/7e3wr+ZWZm8suRM9zxfgzH9WQwTbsmSqlRSqlzSqnZwEzgE2CkfaPStBrUpBVETIB9i6Hwt0UqBicn/J99hqKjR8n88kuC/dzZ/OwghnYNsGOw9U9VenD+DeRWaJ8vO9couXTrBgaDbZgqJCSEe+65B3c3V/YlZnEwqeZ6jzStoStbRdUNyAESga5XuETT6rcBz8HDW8HZo9Jp94EDcbvpJtL++T7FGRl4lFXMXxubQla+xR6R1jtVSXBEVZi+rZQq5coFAhsso7s7zh062BIcEQGgQzMPHI1CbJJeSaVp10JEXgb2Af8E/lZ2zKnF9x8pIvNEZKWIDKmt99UaOY/m1p4cgNLf9p8SEfxnPEdpfj5p7/4DgBPp53lg4Q7eWH3IHpHWO1VJcI6JyGMi4lh2/Bk4dsWrGjCT2Uz+XmvBP4C9e/fy07o1tPNzJ1YvFde0azUGaKeU6q+UGlh2DKrKhSLyqYikisiBC84PFZF4ETkiIs9e7h5KqRVKqelYK7jfe63fhKZdtZJiWDgK1s6udNq5XTt8Jozn3JIlFMTG0rapG9P6BvHlLyfZekzvOH4lVUlwHgT6AKexdhnfADxQk0HVdSazmdKcHIqOWvefKl9NFRrgoWvhaNq1OwB4XeO1C4ChFU+IiBH4F3AbEAqME5FQEQkTkf9ecDSrcOkLZddpWu0wOoBfJ/Bq87sv+T78MEYvL8689hpKKZ64JYQ2Pq48981+veP4FVxxqEkplQqMrYVY6g2TORyAvD17cO7QgbCwMMLCwsjemUhBcSmFxSU4OxjtHKWm1TuvA7vLemEKy08qpe640oVKqWgRCbzgdC/giFLqGICIfAXcqZR6Hbj9wnuIdbz5DWC1UmrXxd5HRB6g7ANemza//2Gkadds6OsXPW309MTv8cc5M2sWOatX4zlsGK+NCuO+T7bx3rrDPD20Uy0HWn9UZRVVRxFZV971KyLdROSFmg+t7nIKDMTo5WWbh1NudGRLPpjQXSc3mnZtPgPexJpk/K3Cca1aAqcqtBPLzl3Ko8Bg4G4RefBiL1BKzVVK9VBK9fDz87uO0DTtIkqKYe9XkF95ObjX3XfhHNqZlP9v797joqrzx4+/3twEFK+I4hU0FRVkQFDygm6p0BJycQAAIABJREFUuVmmZalprmXW1ubeftVWW2m1td8tu2xb7a5WmmWpZVqW7qa7mmKaouL9Xpioq4J3Abn4+f0xOCIOMigzZxjez8djHsM5c+acN5x5nHnzOZ/P+/PKZM7n5dGrXTjje0fTvklYOTtS4NotqqnAk0AhgDFmEzW8RUdECImPJy9jo2Pdf/7zHxYvXgxAYfF5q0JTqjrLNsa8aYxZaoz59sLjGvYnTtaVW+++5NhdjTG/NMb84xqOq9TVyd4J8x6E76dcslr8/Wn61FMUHTpEztR3AfjjoE4MSbhSvq5cSXBCjTFryqyr8XMShCTYKNi7l+KSooLh4eFERERw29sreezTjRW8WynlxDoR+bOIXC8iiRce17C/LKBlqeUWVEHhQE8UFFU1VJPO0OFmWP0O5F/anzM0KYm6N99MznvvUXjgAADGGKat/JEZqzI9H2s14EqCky0ibSn5z0dEhgGH3BpVNRBiK5l4c5O94F98fDw2m43w2kE6kkqpq5MApAAvUTXDxNcC7UQkWkSCsLc8f3mtQXqioKiqwVIfg/wTsPbdy16KeOxREOHwy6841q3ck8NLC7ezL0eLzJblSoLzK+CfQIyIHAB+Czzk1qiqgZC4OHvBvw0X++EYY+jYJJS9R89q73alKqnU0PDSD1eHiX8CrAI6iEiWiIwzxhQBj2CflXw7MMcYs9V9v4FSVaB5IlzXD1a9BQWXJi2BkZE0Gn8/p//9b86u/h4R4YUhnQnw89MZx52oMMExxvxgjOkHNAZijDG9jDGZbo/My/nVrk2tDh0cHY2NMcyaNYvIc/spPm/YffhMBXtQSpUmIk1E5D0RWVSy3ElExrnyXmPMSGNMpDEm0BjTwhjzXsn6hcaY9saYtsaYF6soTr1Fpdwr9XHIzYH09y97qdG4cQQ2a8bhP/8ZU1REZL0Q/vDzGFbuyeHTdTrjeGmujKL6jYjUBXKB10VkvbdW+fT0hSfEFk/epk2Y4mJEhNjYWNq1iQZg2yG9+ClVSdOxt7Y0K1nehb3F2KvoLSrldq26Q3QqrHwTCvMueckvOJiIxx/n3M6dnPj0UwBGdWtFclQDXlq4nTPnanwXWQdXblHdZ4w5BQwAIoB7sQ/j9DqevvCE2mycP3OGc3vsBf/i4uJIim3PuF7RXBehw/eUqqRwY8wc4DxAyS0mvderaqbUx+HsEVg/47KXwm4aQGi3bhx9468UnziBn5/wf3d04R+ju1KnVo2dSekyLs1FVfJ8MzDNGLMR58MvaxxHR+NS9XDy8/N49MZourZuYFVYSlVXZ0WkERcHNKQAXtcUqreolEdE9YJWPSDtDSg6d8lLIkKTPz5F8enTHP3bWwC0bVyHlDaNAMgt0FYccC3BWSci32BPcP4tImGU/IdV0wW2aoV/gwaOBKegoICPPvqILVu3sf3QKe3wpVTl/B77KKe2IrISmIG9+J5X0VtUyiNEoN9EuPFZkMuLxwZ36ECDEcM5PmsW+bt2OdZPW/kj/V9bzql8nXHclQRnHPAEkGyMyQWCsN+mqvFExD7xZkmCExQURGpqKjvPBvPzv64g63heBXtQSl1QMj1CH+xz3z0IdC4pLKpUzdQqBWwj7XNVORE+YQJ+depw+KU/O/6hTmjVgEMn83j5XzrjuCsJzr3GmPXGmBMlyyeAoW6MqVoJsdko+PFHik/Y/zwxMTHEtrFXl9x6UOvhKFVJ3YB4IBH75JhjLI5HKWsVF8LKv8LWeZe9FNCgAY0nTCB39WpOl1TSt7Wsz709o/lo9U+szTzm6Wi9iisJzo0islBEIkUkFlgNaA/aEo5+OBvt1YuLi4upz1nq+BdrwT+lKkFEPsRe2K8XkFzySLI0KCe0D47yKL8A2PwZ7F3q9OUGI4ZTq107jvzlZc6fs/fV+X8D2tOiQQh/mLupRtdkc6UOzt3YJ8HbDCwEfmuMedTdgVUXIXGx4O9PbsltqlOnTvHNoq9JaQLbNcFRqjKSgJ7GmIeNMRNKHr+2OqiytA+O8igRuHchDH7T+csBATT541MUHjjAsWnTAAgNCuCloXFkHc8jY/8Jp++rCVypg9MO+A0wF8gE7hGRUDfHVW34hYZSq0N7Rz+c+vXrc9NNN1EnPJJteotKqcrYAjS1OgilvE6tkpsmJ7Pg/OVjfGqnpBDWvz/Z/5xC4f/+B0Bq+8ak/eFnjpFVNZErt6gWAM8YYx7E3gFwN/Y5XlSJUFsC+RsvFvxr3bo1Y3pdx4tDY3UklVKuCwe2ici/ReTLCw+rg1LKKxxYB3+1wfYvnL4c8YfHobiYI5NfvbguLBiAtN3ZFJ+ved9FriQ43Ywx/wEwdq8CQ9wbVvUSkmDjfG4u53bvBiA3N5fQ3MP0iK6PiJYMUspFk7BfW0pPtvnqld6gVI0RaYMGUbB8stNWnKAWLWg47j5OffUVuevXO9Z//0MOo9/7nmkrf/RgsN6h3ARHRHoBlFQxvoQxZreI1C3pdFzjlS34d+zYMZYvX86S9bvZlFVz738qVRnGmG+dPayOqyztZKws4ecPvf8fHN4CuxY53SR8/HgCmjTh8J9exBTbOxd3i27IDTERvPrNLvYfy/VkxJa7UgvOHSLynYg8KyKDRKSbiKSKyH0lox2+AkI8FKdXC2zRAv9GjRwzizdt2pS77rqLZ5ccZMaqfRZHp5R3E5G0kufTInKq1OO0iHhdRzbtZKwsE3envRXn25fBSfcHv9BQIh59lPxt2zjx+eeAvV7bn4bE4ifUuBnHy01wjDG/AwYBh4A7gRewVxptB/zTGJNqjNG+OFxe8C8gIID69evTMbKudjRWqgLGmAutxWHGmLqlHmHGmLpWx6eU1/APsLfiHMqAPUucblL3lkGEJCZy9PU3KD59GoBm9e0zjq/Ync3n6w94MmJLXbEPjjHmuDFmqjFmrDHmJmPMEGPMk8aYNE8FWF2E2OIp2LePouPHAcjOzqZr6DH2HDlNQZHObKFURURknJN1Xjmxr1KW6TIC6rUstxXHMU/V8eNkv/2OY/3o7q3p17EJwYGXT/vgq1zpZKxcEFqmH05OTg6hZw4QIoXsPXrGytCUqi6GicioCwsi8g4QYWE8SnmfgCDo9VvIWgM/Ou+iFtK5M/WH3cGxjz7i3A8/AODnJ7z7iyQGdYn0ZLSW0gSnigTHxkJAAHkZ9orGbdu2JXXQME4WBehtKqVcczswVkRGisgMoMAYc5/VQZWlnYyV5WyjISwSvn2l3E0a//a3+AUHc/jP/3dJv5vM7LPMXvsTp2vAZJw+leBYeeHxCwkhuEOHS/rhXNekLgt/3Ztb45t5PB6lqgsRaSgiDbEPWrgfeBw4BTxfst6raCdjZbnAYHtfnLrNoDDf6SYBjRoR/qtfcXbFCs4sW+ZYv2H/cf4wdzM5Zwo8FKx1XKlkHCoiz4jI1JLldiJyi/tDqzyrLzwhCQnkbd6MKSoC4Kd9mRz/cTNBAT6VRypV1dYB6SXPS4H62Ac4XFivlCqr23i4Y6o92SlHw1F3ExQdzeH/+z/OF/h+QlOWK9+804BzwPUly1nAn9wWUTUWYrNhShX8O3HiBHszf+LPC7fWqKF5SlWGMSbaGNOmzPOFRxur41PKq/1vMxzZ4fQlCQqiyVNPUrjvJ47PmOHhwKznSoLT1hjzMlAIYIzJA7Q8rxMXCv7lbtgAQEJCArS5nn8uz+R/p5w3IypV010oKnqF17WoqFLOFJ2DGbfBspfK3aRO797U6duX7Hf+TtHRox4MznquJDgFIhICGAARaYu9RUeVEdi8Gf6Nwx39cESETpH2Mh46s7hS5dKiokpdjYBaMPwjuNX5TOMXNHniD5wvLOTIa697KDDv4EqCMxH4F9BSRGYC/8HeCVCVISKE2myOkVQAhUf2MjD8uI6kUqocWlRUqWvQugeE1HdaE+eCoKgoGv1iDCfnzaNX0REW/y6VZvV9/3+GChMcY8xiSoZvAp8AScaYZe4Nq/oKsdko/OkninJyAPAz52kQ7M82bcFRqlxaVFSpa3B4G/wzFQ5vLXeTRr98CP/G4ZyZ/Beua1y7Rgx+cWUUVSrQGTiNfehmp5J1ygnHxJsb7a043bt350T99hw5pXf1lPIFWgdHeZ2wpnDsB/tM4+Xwr1ObiN/9nvyNm1gw+X1O5modHIDHSj2eARYAk9wYU7UW3LmzveBfycSbAG+MsPHZQz0sjEopVVWsLkeh1GVCG9qHjW+dB0d3lbtZvSG3kdc2hkYzp5B9zPfvKrhyi+rWUo/+QCxw2P2hVU9+wcEEd+zo6GgMsHTJYtLStKVdKaWUm1z/CASGwIpXy91E/Pw42f8WGp47DUePeDA4a1zNTbgs7EmOKkeIzUbeli2Ogn916tZj8Z5TfLnxoMWRKeW9qlNRUaW8Tu1wSLoPNn9qv11VDuMf4MGgrOVKH5y/icibJY+3gBXAxoreV5OFJtgweXnk79wJQM/rU5i/L4BVe7Mtjkwpr6ZFRZW6Fj0mgF8ArHjN6ki8gistOBdKqK8DVgF/MMaMdmtU1Zyjo/GG0vVwwthxSDslKnUFWlRUqWsR1hS6/gI2fgInfrI6GstV2FZljPnAE4H4koDISAIiIuz9cEaPori4mFS/HXyfG0JR8XkC/H1/eJ5SV6HaFxUtLCwkKyuL/HytXO5LgoODadGiBYGBgVaHUrGev4H0aZD2BtxyeUtObPO65ABN65U/h5WvKDfBEZHNlFxoyr4EGGNMF7dFVc2JiL0fTklHY39/f2pHtOKnAyfIzDnLdRFhFkeolFcqW1S0J/b6W9VGVlYWYWFhREVFIaKNT77AGENOTg5ZWVlER0dbHU7F6rWAhFGw9z9QVAABQZe8XCvAH4BAP9//fF6pBUc7912DEJuN0998Q1F2NgHh4XTrlsyn+zZx5lyx1aEp5ZWMMYtFZD2Qgv0fqd8YY6pVx7X8/HxNbnyMiNCoUSOOVqd5nPpNgsDQy5IbgEMn7Y2iJ/IKifBsVB5XboJjjNnnyUB8jaMfTkYGYf36EdO0Lp+OT8LPT29PKeVMqQKip0ueO4kIxpjlVsV0NTS58T3V7pyGNLA/FxVA8TmodfGuwcGTeUQCp2pAguPKKKoUEVkrImdEpEBEikXE9ysEXaPgzp0gMNBxm+rUqVPMmDGDvXv3WhyZUl6rWhQVramVjMeOHctnn31WqffMnz+fbdu2OZafffZZlixZUtWhKWcK8+CtJPj2ZasjsYwrzQlvASOB3dhn9L0f+Js7g/IFfrVqEdypI7klCU5YWBhFjdry5L+0Z7tSzlSXoqJayfhSxcXl33Yvm+A8//zz9OvXzxNhqcAQSBwDbW+wOhLLuHS/xBizB/A3xhQbY6YBP3NvWL4h1GYjf8tWTGEhIkKtJm3IOFzE0dPVamCIUlbRoqJX6cUXX6RDhw7069ePkSNHMnnyZPr27Ut6ejoA2dnZREVFAZCZmUnv3r1JTEwkMTGR7777DrB3rn3kkUfo1KkTgwYN4siRi5Vvo6KieP755+nVqxeffvopU6dOJTk5mfj4eO644w5yc3P57rvv+PLLL3nsscew2Wzs3bv3klagtWvX0qNHD+Lj4+nWrRunT59GVbHUR6Ftzf26dqWkYa6IBAEZIvIycAio7d6wfENIQgLHPphB/o6dhMTFEtOkNs1qFbB1/zH6doq0OjylvIqI/I2LIzf9ABvVvajotEEVb9P+Juj564vb2+62j4I5mwNzxly67b1fV7i7devWMWvWLDZs2EBRURGJiYl07dq13O0jIiJYvHgxwcHB7N69m5EjR5Kens68efPYuXMnmzdv5vDhw3Tq1In77rvP8b7g4GDHFDQ5OTmMHz8egKeffpr33nuPCRMmMHjwYG655RaGDRt2yTELCgoYPnw4s2fPJjk5mVOnThESElLx30pV3tlsWPWWffj4hb45AMbZIGnf4kqCcw/2i80jwO+AlsAd7gzKV1ws+LeBkLhYGvvnM7b5UXZk7tcER6nLpZf6uQj4xBiz0qpgqqsVK1YwdOhQQkNDARg8ePAVty8sLOSRRx4hIyMDf39/du2yT9a4fPlyRo4cib+/P82aNeOGGy691TF8+HDHz1u2bOHpp5/mxIkTnDlzhptuuumKx9y5cyeRkZEkJycDULdu3Ur/nspFpw9B2usQEAx9nyC+ZX2OAM0bhFodmdu5kuAkAguNMaeA59wcj08JbNqUgKZN7R2Nx9xDdMtIJp5uSuTJatYjXykP8Mmioi60uJS7fe1GlX9/CWejfgICAjh//jzAJYUIX3/9dZo0acLGjRs5f/48wcHBV9yPI7zaFxvyx44dy/z584mPj2f69OksW7bsivEZY6rfyKTqqmkcdLgZVr8DKQ8TUDKStwaUwXGpD85gYJeIfCgig0Sk5szUVQVKF/wLDAykh60jtqjGFkellPcQkc0issnJY7OIbLI6vuomNTWVefPmkZeXx+nTp1mwYAFg7zezbt06gEtGQ508eZLIyEj8/Pz48MMPHZ2GU1NTmTVrFsXFxRw6dIilS5eWe8zTp08TGRlJYWEhM2fOdKwPCwtz2rcmJiaGgwcPsnbtWsf7i0omJ1ZukPoY5J+EtVM5cCIPgOO5hRYH5X4VJjjGmHuB64BPgbuBvSLyrrsDuxreOHwzxBZP4cGDFJZ00Bt3fXNSGhU4/pNSSnELcKuTx4X1qhISExMZPnw4NpuNO+64g969ewPw6KOP8ve//50ePXqQnX2xfuLDDz/MBx98QEpKCrt27XK0zAwdOpR27doRFxfHQw89RJ8+fco95gsvvED37t3p378/MTExjvUjRozglVdeISEh4ZISGUFBQcyePZsJEyYQHx9P//79dXoLd2qeCNf1h1Vvc/i4PeE8nV9gcVDuJ8bFjkYiEggMBO4FehtjvLYZIikpyVwYLWC1vIwMMkeMpPmbf6XugAHs2rWLZcuWcfPgIbRo6utlllRNJiLrjDFJVsfhLs6uM9u3b6djx44WReTcpEmTqFOnDo8++qjVoVRr3nhuK2X/GnivP0uLxtP0s68J+OhT2iX5xiDF8q41rhT6Gygi04E9wDDgXUB7yLqoVqdOSGAgeRn2wSCB9RozZX8EK/blWRyZUt5Fi4oq5UYtu0F0HzhUc+76utKfZiwwC3jQGKMFXCrJLyiI4M6dHf1wWjeuT75fCNv/pzUflCrjLWAE9tvhScAY7LfH1TWYNGmS1SEob9HncfjvE1ZH4TGu9MEZYYyZb4w5JyI6AedVCLHZyN+yBVNQgJ+f0K1RIbnHvK5Aq1KW06KiSrlRVC8IK7kB4/tlcFyrZFzK826JwseF2GyYggLyd+wAICYoh8bF1WqSZKU84ZKioiLyO7SoqFJVKqFrDwBaNvL9OjiVTXBqwMj5qheSkADguE2Vlt+SzMDWVoaklDcqXVT0LFpUVKkqJ43b258R2DIXzp2xOCL3qWyC86BbovBxgU0iCGgWSe6GDQCM7tWO27pGWRuUUt4nETDGmFPGmOeMMb8vuWWllKoi+4/lAnDswA8wdzysetviiNzHlVFUwSLyexH5HPiDiPxORIIrep+6VKjN5hhJlVAvn45h2l9bqTIsLSoqIh1F5B8i8pmIPOTJY1c3mZmZxMY6H2L87LPPsmTJEg9HpFyVfdb+3XO6VgSM/Rp6/97iiNzHlRacGUBn4G/YRzl0BD50Z1C+KMRmo+jQIQoPHyZ9fQabtm6zOiSlvMq1FBUVkfdF5IiIbCmzfqCI7BSRPSJyxeEjxpjtxphfAndhH8VVo11tZeHnn3+efv36VXE0Vc8YowVXW18P/oFw+jB88YjP3a5yJcHpYIwZZ4xZWvJ4AGjv7sB8zcWJNzOYeSicr482tDgipbyPMaYQWIS9NMU64DYX3zodeyFSBxHxB94Gfg50AkaKSCcRiRORr8o8IkreMxhIA/5TJb+QBTIzM4mJieH+++8nNjaWUaNGsWTJEnr27Em7du1Ys2YNx44dY8iQIXTp0oWUlBQ2bbLXRpk0aRIPPPAAAwYMYMyYMUyfPp3bbruNgQMH0qFDB5577uJ0hMXFxYwfP57OnTszYMAA8vLstb3Gjh3rmAoiKiqKiRMnkpiYSFxcHDtKBlocPXqU/v37k5iYyIMPPkjr1q0vqa58wZo1a+jRowcJCQn06NGDnTt3AnDzzTc7Yk5ISOD55+3jX5555hneffddzpw5w4033ug47hdffOH423Ts2JGHH36YxMRE9u/fzzfffMP1119PYmIid955J2fO+NaXvEsOroeMmfDxXVBw1upoqowrTcAbRCTFGLMaQES6AzrDbyUFx8QgtWqRl5FBUfD1nBd/q0NSyquIyEDsdXB+BizDXlT0Llfea4xZLiJRZVZ3A/YYY34o2f8s4DZjzJ+xTwPhbD9fAl+KyNfAx05ifAB4AKBVq1YVxjX8n6suW3dLl0juuT6KvIJixk5bc9nrw7q24M6klhw7W8BDH6275LXZD15f4TEB9uzZw6effsqUKVNITk7m448/Ji0tjS+//JKXXnqJli1bkpCQwPz58/nvf//LmDFjyCgZBLFu3TrS0tIICQlh+vTprFmzhi1bthAaGkpycjKDBg0iPDyc3bt388knnzB16lTuuusu5s6dy+jRoy+LJTw8nPXr1/POO+8wefJk3n33XZ577jluuOEGnnzySf71r38xZcoUp79HTEwMy5cvJyAggCVLlvDUU08xd+5cUlNTWbFiBVFRUQQEBLBypf0rKS0tjdGjRxMcHMy8efOoW7cu2dnZpKSkOGZV37lzJ9OmTeOdd94hOzubP/3pTyxZsoTatWvzl7/8hddee41nn33Wpb+zz+jwc7h9Knw+HmbeBaPmQFD1H8DoSoLTHRgjIj+VLLcCtovIZuwdAru4LTofIqUK/rXq2ZH6xb4/D4hSlTSWqi0q2hzYX2o5C/v1zCkR6QvcDtQCFjrbxhgzBZgC9qkaqiBGt4iOjiYuLg6Azp07c+ONNyIixMXFkZmZyb59+5g7dy4AN9xwAzk5OVyYw2/w4MGEhIQ49tW/f38aNWoEwO23305aWhpDhgwhOjoaW0nLdNeuXcnMzHQay+233+7Y5vPPPwfsici8efMAGDhwIA0aNHD63pMnT/KLX/yC3bt3IyIUFtoniOzduzdvvvkm0dHRDBo0iMWLF5Obm0tmZiYdOnSgsLCQp556iuXLl+Pn58eBAwc4fNhee6x169akpKQAsHr1arZt20bPnj0BKCgo4PrrXUsiqyu/C7O4l52mKW6Y/dmHkhxXEpyBFW+iXBFis3H8ww9p1+cY54t1qgalSjPGjLjws4jcYoz56hp36aysRblJiTFmGfaWoypzpRaXkCD/K77esHaQyy02ZdWqVcvxs5+fn2PZz8+PoqIiAgIuv/RLyRffhck2y64vu1z6GP7+/o5bVOXF4u/v7+jXU94ciG+//TZTp04FYOHChTzzzDP87Gc/Y968eWRmZtK3b18AkpOTSU9Pp02bNvTv35/s7GymTp1K165dAZg5cyZHjx5l3bp1BAYGEhUV5ZjMs/TvZ4yhf//+fPLJJ07j8UW2Vg04ALQOd5K8lE5yPh4Od8+u1kmOK5WM913p4YkgfUWILR5TWMje/YYfAqOtDkcpb1YVRUWzsNfSuaAFcPBadyoit4rIlAstHtVRamoqM2fOBGDZsmWEh4dTt25dp9suXryYY8eOkZeXx/z58x2tHdeiV69ezJkzB4BvvvmG48ePA/CrX/2KjIwMMjIyaNasGSdPnqR58+YATJ8+3fH+oKAgWrZsyZw5c0hJSaF3795MnjzZMXP6yZMniYiIIDAwkKVLl7Jvn/OvqpSUFFauXMmePfZqBLm5uezateuaf79qLW4YDJ0C+1bak5xq3CensnVw1DW40NF4VJ3TjEyJsjYYpbxbVRQVXQu0E5HokgrJI4Avr3WnxpgFxpgH6tWrd80BWmXSpEmkp6fTpUsXnnjiCT744INyt+3Vqxf33HMPNpuNO+64g6Skax9gNnHiRL755hsSExNZtGgRkZGRhIWFXbbd448/zpNPPknPnj0pLi6+5LXevXvTpEkTQkND6d27N1lZWY4EZ9SoUaSnp5OUlMTMmTOJiYlxGkfjxo2ZPn06I0eOdHS4vtAR2ldl5tjr4Bw5lV/+Rl3uLJPk5Hoouqol5TUVVmdJSUkmPT3d6jCc2nNjP4707kWTe+6hbdu2VoejlNuIyDpjzFV9G4pIN2PM5T1wy9/+E6AvEA4cBiYaY94TkZuBNwB/4H1jzItXE48zzq4z27dvp2PHjlV1CMtNnz6d9PR03nrrrSrd77lz5/D39ycgIIBVq1bx0EMPOTo5eytfObff/uNjIt54AflgNjHdK+hCu2kO7P0v3PY2+HnvwJjyrjUeLaSl7K04PwYHc2T7Tk1wlCqlpIDow0AvwIhIGvB3Y8wV/tW0M8aMLGf9QsrpMHwNcd4K3HrddTrR+dX66aefuOuuuzh//jxBQUGOfjfKy3S5y/4AOP0/qFUXgqrPHFZ6i8rDQmw2Ej6YwbeZ+qdXqoxqUVTUF25RuWrs2LFV3noD0K5dOzZs2MDGjRtZu3YtycnJVX4MVYHK3L0pKoAPbrV3Pq5GtAXHw0JsNsQYmmXttToUpbxNB2NMfKnlpSKy0bJolPJFchXd2wKCIPVxqF9x7Sdvos0IHhYc04H9CQk0LjhsdShKeZsNIpJyYcFbi4r6wigqVXMF+NkTHP/Kfvt3uRNalZSR2jofCr2/1IkmOB4mgYEc7BKP1Nf5SpUqozvwnYhkikgmsAroIyKbRWSTtaFdVJNuUSnfE9fC/rlt1fAq69sc3QWf3QufjPD6JEdvUVmg5VdLyG3UxOowlPI2WlRUKW/XuD3c9g7Mf8ie5IycBYEhFb/PAtqCY4HGYbVo1aj69ERXyhO0qGj19tJLL10/5QI8AAAP8klEQVSy3KNHD4siUVfyw1H7ZKL/O3kNrS+2kTDkHfjhW/hkpNe25GiCY4FjHa4jt4W24ChVHWkfHOfKJjjfffedRZGoKzmZb58uI7eguIItK2C7214f54dlMOtur0xyNMGxwMFmkRwKrWN1GEqpq1Ad+uAMGTKErl270rlzZ8dM3e+99x7t27enb9++jB8/nkceeQSwDwX/9a9/TY8ePWjTpg2fffYZYJ+n6bHHHiM2Npa4uDhmz54NwKFDh0hNTcVmsxEbG8uKFSt44oknyMvLw2azMWrUKADq1Ll4jXv55ZeJi4sjPj6eJ554wpN/CuVOCaPgtrdg71KvTHK0D44FmnypfXCU8nX/e+klzm2v2rL/tTrG0PSppyrc7v3336dhw4bk5eWRnJzMoEGDeOGFF1i/fj1hYWHccMMNxMdfHJF/6NAh0tLS2LFjB4MHD2bYsGF8/vnnZGRksHHjRrKzs0lOTiY1NZWPP/6Ym266iT/+8Y8UFxeTm5tL7969eeutt5xWI160aBHz58/n+++/JzQ0lGPHjlXp30RZLGG0vabOlxNg1igY8TEEescgGk1wlFLKx7z55pvMmzcPgP379/Phhx/Sp08fGjZsCMCdd955yaSSQ4YMwc/Pj06dOnH4sL2ERVpaGiNHjsTf358mTZrQp08fR1G+++67j8LCQoYMGYKtZI698ixZsoR7772X0FB7v8MLMSgfkniP/fmbp+H4jxDhHVNaaIJjgeOd21Pspb3OlVJVw5WWFndYtmwZS5YsYdWqVYSGhtK3b186dOjA9u3by31PrVq1HD9fmJ+wvHkKU1NTWb58OV9//TX33HMPjz32GGPGjCl338YY5GqKyym3CAqw90y5UA+nyiTeAzGDILQkgT1fbPn8VdoHxwK5LZtzLry+1WEopa6Ct3cyPnnyJA0aNCA0NJQdO3awevVqcnNz+fbbbzl+/DhFRUXMnTu3wv2kpqYye/ZsiouLOXr0KMuXL6dbt27s27ePiIgIxo8fz7hx41i/fj0AgYGBFBYWXrafAQMG8P7775Oba5+RWm9RWatzM3vfsZYN3PBP9oXkJu11+PguKKxwGjm30hYcCzT/11Ltg6NUNWWMWQAsSEpK8sqJeQYOHMg//vEPunTpQocOHUhJSaF58+Y89dRTdO/enWbNmtGpUycq6iQ9dOhQVq1aRXx8PCLCyy+/TNOmTfnggw945ZVXCAwMpE6dOsyYMQOABx54gC5dupCYmMjMmTMviScjI4OkpCSCgoK4+eabLxtxpTzJA61pIQ3tDz9rUwwprxmyOktKSjLp6elWh1GuzT+/BYlsTuz7/7Q6FKXcRkTWGWOSrI7DXZxdZ7Zv307Hjt7R/6CsM2fOUKdOHYqKihg6dCj33XcfQ4cOtTqsasObz21lrJ8xl5CXnibkw9lEJXdx34GMsc97deaIfRZyN3Y8Lu9ao7eoLHCkYwdOah0cpZQHTZo0yTG0Ozo6miFDhlgdkrLAmXP224h5hddYB6ciIvZbVNNuhjn3QNE59x7PCb1FZYGjjRpRSzvdKaU8aPLkyVaHoGqawGC4/lfw1W9h9mgY/hEE1Kr4fVVEW3As0HjhUvxXb7U6DKXUVfD2TsZKucRT3VOS7oVb3oDd38Bsz7bkaIKjlFKVUB0qGStVLivuHiTdC7e8Drv/7dEkRxMcCxyL78TZqGZWh6GUUqqGCQ6016YJ9Pfw13/SfTDoNXuSM2eMR5IcTXAskN+4EUV1dS4qpZRSnhXTtC4ALRqEev7gyeNg0Kuw618w5xduT3K8PsERkSEiMlVEvhCRAVbHUxWaLVlBvU27Kt5QKaUsFhUVRXZ2ttVhKF+RfH9JkrMI1n3g1kO5NcERkfdF5IiIbCmzfqCI7BSRPSJyxalljTHzjTHjgbHAcDeG6zFN6wVzXePaVoehlPJxxhjOnz9vdRjKi+w6fBqArOO51gWRfD+M+cL+7EbubsGZDgwsvUJE/IG3gZ8DnYCRItJJROJE5Ksyj4hSb3265H3V3sHOHcluEWl1GEopH5SZmUnHjh15+OGHSUxMZNy4cSQlJdG5c2cmTpzo2C4qKoqJEyeSmJhIXFwcO3bYZz7PyclhwIABJCQk8OCDD14yJ9Vrr71GbGwssbGxvPHGG47jxcTEcP/99xMbG8uoUaNYsmQJPXv2pF27dqxZs8azfwB1RbkFRQCcKyyyNpA2fcHPD07sh68fhaKCKj+EWxMcY8xyoOzEI92APcaYH4wxBcAs4DZjzGZjzC1lHkfE7i/AImPM+vKOJSIPiEi6iKQfPXrUfb9UFTgeFsaxgECrw1BKudmCBQvYuXMnAOfPn2fBggXs3r0bgKKiIhYsWMDevXsBKCgoYMGCBfz4448A5Ofns2DBAvbt2wfgmMvJFTt37mTMmDFs2LCBV199lfT0dDZt2sS3337Lpk2bHNuFh4ezfv16HnroIUednOeee45evXqxYcMGBg8ezE8//QTAunXrmDZtGt9//z2rV69m6tSpbNiwAYA9e/bwm9/8hk2bNrFjxw4+/vhj0tLSmDx5sk7L4G28rQZb5grYPMc+C3kVs6IPTnNgf6nlrJJ15ZkA9AOGicgvy9vIGDPFGJNkjElq3Lhx1UTqJg3/vRz/NdusDkMpdRWqQx2c1q1bk5KSAsCcOXNITEwkISGBrVu3sm3bxWvP7bffDkDXrl3JzMwEYPny5YwePRqAQYMG0aBBAwDS0tIYOnQotWvXpk6dOtx+++2sWLECgOjoaOLi4vDz86Nz587ceOONiAhxcXGO/SrllO1umLABGnewL1fhLVUrKhk7Sx/LrThkjHkTeNN94SillOsqM9nmrbfe6vjZz8/vkuWAgIBLloOCgi5ZDg4OvmQ5NNT1US+1a9v7+P34449MnjyZtWvX0qBBA8aOHUt+/sUZnmvVsleV9ff3p6jo4i0LcfJf/pXmLbywH7D/nheW/fz8LtmvUk7VbmR/XvEaHFgHw6ZBQNA179aKFpwsoGWp5RbAQQvisExOYhxn2rawOgyllI87deoUtWvXpl69ehw+fJhFixZV+J7U1FTHbOCLFi3i+PHjjvXz588nNzeXs2fPMm/ePHr37u3W+FXVq1PL3q4RHOiFMzUF1YYdX8Fn90Jx4TXvzorfcC3QTkSigQPACOBuC+KwTGG9MHDzPGdKKRUfH09CQgKdO3emTZs29OzZs8L3TJw4kZEjR5KYmEifPn1o1aoVAImJiYwdO5Zu3boBcP/995OQkKC3oKqZ6yLqcABoXt99s3tfte4P2p8XPQ7/fgpufuWadidXana8ViLyCdAXCAcOAxONMe+JyM3AG4A/8L4x5sWqPG5SUpJJT0+/8kafPwiBIXDrG1V5aJcs6dmfvIYR3LpgpsePrZSniMg6Y0yS1XG4i7PrzPbt2+nYsaNFESl38pVze2rxYg5M+DXR8+cRHBNjdTjOrf8QolOhQWuXNi/vWuPWFhxjzMhy1i8EFlb18UTkVuDW6667ruKNj/1gbw6zQPP6Ifg1CbPk2EoppWquHYdOEwbsP3aWdlYHU57Ee6pkN15fybgyqsskePviOnOopdbBUUop5VnniopLnj00m7iFvLCXke87ExTEOeNTuaVSSinlVTTBsUD9/3xHXsOIijdUSlU7xhinw6xV9eXOvqrKfbQZQSmlqkhwcDA5OTn6hehDjDHk5OQQHOyFo46uRQ34jGoLjgWyu9k4LzpVg1LV0ZUGM7Ro0YKsrCy8fboYVTnBwcG0aOEbtcvCQuzfPaFB/hZH4n6a4FjgfFAQBt//cCnli65UyTgwMJDo6GgLolLKNW0b1yELaFY/xOpQ3M6nEpxKDRO3UETaGu2Do5RSSrmRT/XBqdQw8YgYCG/v/qCcaNkwlJjIupYcWymlVM217eApAH7MPmtxJO7nUy04lTL4b5Ydek98HMFBtby3yJJSSimfVFhsn627qKjqZu32Vm6dqsEqInIS2O3CpvWAk1f5enmvOVvvbF04kO1CjO5Q0e/tzv24+h49N57fT1Wfm9bGmMaVjKHaEJGjwAn083wl3v55vtZtXD0Hztb7wnm5mn254zvA+bXGGONzD2BKVWx3pdfLe83Z+nLWpXv738cd+9FzU7PPja899PNcNX8fd+zHlfdc6zaungNn633hvFzNvjx5nfGpPjilLKii7a70enmvOVvvajyeUlXxXM1+9Nxcma+fG1+jn+cr8/bP87VuU5lz4E3npipjqey+PHad8clbVNWBiKQbH55puTrTc6N8iX6evZOeF/fz1Rac6mCK1QGocum5Ub5EP8/eSc+Lm2kLjlJKKaV8jrbgKKWUUsrnaIKjlFJKKZ+jCY5SSimlfI4mOF5CRNqIyHsi8pnVsahLicgQEZkqIl+IyACr41Hqaul1xnvpdabqaYLjRiLyvogcEZEtZdYPFJGdIrJHRJ4AMMb8YIwZZ02kNU8lz818Y8x4YCww3IJwlSqXXme8l15nrKUJjntNBwaWXiEi/sDbwM+BTsBIEenk+dBqvOlU/tw8XfK6Ut5kOnqd8VbT0euMZTTBcSNjzHLgWJnV3YA9Jf9JFQCzgNs8HlwNV5lzI3Z/ARYZY9Z7OlalrkSvM95LrzPW0gTH85oD+0stZwHNRaSRiPwDSBCRJ60JrcZzem6ACUA/YJiI/NKKwJSqJL3OeC+9znhIgNUB1EDiZJ0xxuQA+qG2Vnnn5k3gTU8Ho9Q10OuM99LrjIdoC47nZQEtSy23AA5aFIu6lJ4b5Sv0s+y99Nx4iCY4nrcWaCci0SISBIwAvrQ4JmWn50b5Cv0sey89Nx6iCY4bicgnwCqgg4hkicg4Y0wR8Ajwb2A7MMcYs9XKOGsiPTfKV+hn2XvpubGWTraplFJKKZ+jLThKKaWU8jma4CillFLK52iCo5RSSimfowmOUkoppXyOJjhKKaWU8jma4CillFLK52iCo5RSSimfowmOUkoppXyOJjjKciLSUUSWi8gmEXlMRPZYHZNSyvfotaZm0QRHWUpEAoCZwG+MMV2ANsAWa6NSSvkavdbUPJrgKKvdDmw0xmwoWd4GbLzwoog8KyLRlkSmlPIleq2pYTTBUVbrAmSUWo4FMkQkRET+jn2m3b+JSF1LolNK+Qq91tQwmuAoq+UA7QFExAaMxv5f1cPAh8D3wBLsFyellLpaeq2pYTTBUVb7EEgSkbXAfUCmMeYHIAHIBvYDccBW60JUSvkAvdbUMJrgKKvlG2O6G2OSsV9g5pWs/wJ4D+gJLDXGHLcqQKWUT9BrTQ0TYHUAqsb7nYiMAAqBlcDvAYwxn4pIEvCqMeaIlQEqpXyCXmtqGDHGWB2DUkoppVSV0ltUSimllPI5muAopZRSyudogqOUUkopn6MJjlJKKaV8jiY4SimllPI5muAopZRSyudogqOUUkopn6MJjlJKKaV8zv8HDlNo76nEdoUAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "data_sets = [pvals_with_xsec, pvals]\n", + "panel_labels = [\"p-value (xsec + kinematics)\", \"p-value (kinematic effects only)\"]\n", + "\n", + "fig = plt.figure(figsize=(8,4))\n", + "\n", + "for panel, (data, label) in enumerate(zip(data_sets, panel_labels)):\n", + " ax = plt.subplot(1,2,panel+1)\n", + "\n", + " for c, ls, (key, datum) in zip(colors, linestyles, data.items()):\n", + " plt.plot(gphis, datum, label=key, ls=ls, c=c)\n", + " \n", + " plt.legend()\n", + "\n", + " plt.xscale(\"log\")\n", + " plt.yscale(\"log\")\n", + " plt.xlabel(f\"$g_\\phi$\")\n", + " plt.ylabel(label)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(\"quadratic_test_pvals.pdf\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "data_sets = [seps_with_xsec, seps]\n", + "panel_labels = [\"Significance (xsec + kinematics)\", \"Significance (kinematic effects only)\"]\n", + "\n", + "fig = plt.figure(figsize=(8,4))\n", + "\n", + "for panel, (data, label) in enumerate(zip(data_sets, panel_labels)):\n", + " ax = plt.subplot(1,2,panel+1)\n", + "\n", + " for c, ls, (key, datum) in zip(colors, linestyles, data.items()):\n", + " plt.plot(gphis, np.abs(datum), label=key, ls=ls, c=c)\n", + " \n", + " plt.legend()\n", + "\n", + " plt.xscale(\"log\")\n", + " # plt.yscale(\"log\")\n", + " plt.xlabel(f\"$g_\\phi$\")\n", + " plt.ylabel(label)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(\"quadratic_test_significance.pdf\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/tutorial_particle_physics/toydata/gphi_toydata.h5 b/examples/tutorial_particle_physics/toydata/gphi_toydata.h5 new file mode 100644 index 000000000..e69de29bb diff --git a/examples/tutorial_particle_physics/toydata/gphi_toydata_test.h5 b/examples/tutorial_particle_physics/toydata/gphi_toydata_test.h5 new file mode 100644 index 000000000..54900e511 Binary files /dev/null and b/examples/tutorial_particle_physics/toydata/gphi_toydata_test.h5 differ diff --git a/examples/tutorial_particle_physics/toydata/gphi_toydata_test_indata.h5 b/examples/tutorial_particle_physics/toydata/gphi_toydata_test_indata.h5 new file mode 100644 index 000000000..273543efc Binary files /dev/null and b/examples/tutorial_particle_physics/toydata/gphi_toydata_test_indata.h5 differ