From c860baecd8e0f5fc66c6122432d8ae13c966628d Mon Sep 17 00:00:00 2001
From: Johann Brehmer <mail@johannbrehmer.de>
Date: Fri, 12 Jul 2019 15:50:59 -0400
Subject: [PATCH 1/2] Tutorial updates

---
 .../2b_delphes_level_analysis.ipynb           | 307 ++++++++++++++----
 1 file changed, 248 insertions(+), 59 deletions(-)

diff --git a/examples/tutorial_particle_physics/2b_delphes_level_analysis.ipynb b/examples/tutorial_particle_physics/2b_delphes_level_analysis.ipynb
index 3f1663ec2..82a0d5124 100644
--- a/examples/tutorial_particle_physics/2b_delphes_level_analysis.ipynb
+++ b/examples/tutorial_particle_physics/2b_delphes_level_analysis.ipynb
@@ -84,7 +84,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mg_dir = '/Users/johannbrehmer/work/projects/madminer/MG5_aMC_v2_6_5'"
+    "mg_dir = '/Users/johannbrehmer/work/projects/madminer/MG5_aMC_v2_6_5/'"
    ]
   },
   {
@@ -110,19 +110,19 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "10:09 madminer.utils.inter DEBUG   HDF5 file does not contain is_reference field.\n",
-      "10:09 madminer.core        INFO    Found 2 parameters:\n",
-      "10:09 madminer.core        INFO       CWL2 (LHA: dim6 2, maximal power in squared ME: (2,), range: (-20.0, 20.0))\n",
-      "10:09 madminer.core        INFO       CPWL2 (LHA: dim6 5, maximal power in squared ME: (2,), range: (-20.0, 20.0))\n",
-      "10:09 madminer.core        INFO    Found 6 benchmarks:\n",
-      "10:09 madminer.core        INFO       sm: CWL2 = 0.00e+00, CPWL2 = 0.00e+00\n",
-      "10:09 madminer.core        INFO       w: CWL2 = 15.00, CPWL2 = 0.00e+00\n",
-      "10:09 madminer.core        INFO       neg_w: CWL2 = -1.50e+01, CPWL2 = 0.00e+00\n",
-      "10:09 madminer.core        INFO       ww: CWL2 = 0.00e+00, CPWL2 = 15.00\n",
-      "10:09 madminer.core        INFO       neg_ww: CWL2 = 0.00e+00, CPWL2 = -1.50e+01\n",
-      "10:09 madminer.core        INFO       morphing_basis_vector_5: CWL2 = 13.86, CPWL2 = 16.47\n",
-      "10:09 madminer.core        INFO    Found morphing setup with 6 components\n",
-      "10:09 madminer.core        INFO    Did not find systematics setup.\n"
+      "15:30 madminer.utils.inter DEBUG   HDF5 file does not contain is_reference field.\n",
+      "15:30 madminer.core        INFO    Found 2 parameters:\n",
+      "15:30 madminer.core        INFO       CWL2 (LHA: dim6 2, maximal power in squared ME: (2,), range: (-20.0, 20.0))\n",
+      "15:30 madminer.core        INFO       CPWL2 (LHA: dim6 5, maximal power in squared ME: (2,), range: (-20.0, 20.0))\n",
+      "15:30 madminer.core        INFO    Found 6 benchmarks:\n",
+      "15:30 madminer.core        INFO       sm: CWL2 = 0.00e+00, CPWL2 = 0.00e+00\n",
+      "15:30 madminer.core        INFO       w: CWL2 = 15.00, CPWL2 = 0.00e+00\n",
+      "15:30 madminer.core        INFO       neg_w: CWL2 = -1.50e+01, CPWL2 = 0.00e+00\n",
+      "15:30 madminer.core        INFO       ww: CWL2 = 0.00e+00, CPWL2 = 15.00\n",
+      "15:30 madminer.core        INFO       neg_ww: CWL2 = 0.00e+00, CPWL2 = -1.50e+01\n",
+      "15:30 madminer.core        INFO       morphing_basis_vector_5: CWL2 = 13.86, CPWL2 = 16.47\n",
+      "15:30 madminer.core        INFO    Found morphing setup with 6 components\n",
+      "15:30 madminer.core        INFO    Did not find systematics setup.\n"
      ]
     }
    ],
@@ -146,39 +146,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "10:09 madminer.utils.inter INFO    Generating MadGraph process folder from cards/proc_card_signal.dat at ./mg_processes/signal_pythia\n",
-      "10:09 madminer.core        INFO    Run 0\n",
-      "10:09 madminer.core        INFO      Sampling from benchmark: sm\n",
-      "10:09 madminer.core        INFO      Original run card:       cards/run_card_signal_large.dat\n",
-      "10:09 madminer.core        INFO      Original Pythia8 card:   cards/pythia8_card.dat\n",
-      "10:09 madminer.core        INFO      Copied run card:         /madminer/cards/run_card_0.dat\n",
-      "10:09 madminer.core        INFO      Copied Pythia8 card:     /madminer/cards/pythia8_card_0.dat\n",
-      "10:09 madminer.core        INFO      Param card:              /madminer/cards/param_card_0.dat\n",
-      "10:09 madminer.core        INFO      Reweight card:           /madminer/cards/reweight_card_0.dat\n",
-      "10:09 madminer.core        INFO      Log file:                run_0.log\n",
-      "10:09 madminer.core        INFO    Creating param and reweight cards in ./mg_processes/signal_pythia//madminer/cards/param_card_0.dat, ./mg_processes/signal_pythia//madminer/cards/reweight_card_0.dat\n",
-      "10:09 madminer.utils.inter INFO    Starting MadGraph and Pythia in ./mg_processes/signal_pythia\n"
+      "15:30 madminer.utils.inter INFO    Generating MadGraph process folder from cards/proc_card_signal.dat at ./mg_processes/signal_pythia\n",
+      "15:30 madminer.core        INFO    Run 0\n",
+      "15:30 madminer.core        INFO      Sampling from benchmark: sm\n",
+      "15:30 madminer.core        INFO      Original run card:       cards/run_card_signal_small.dat\n",
+      "15:30 madminer.core        INFO      Original Pythia8 card:   cards/pythia8_card.dat\n",
+      "15:30 madminer.core        INFO      Copied run card:         /madminer/cards/run_card_0.dat\n",
+      "15:30 madminer.core        INFO      Copied Pythia8 card:     /madminer/cards/pythia8_card_0.dat\n",
+      "15:30 madminer.core        INFO      Param card:              /madminer/cards/param_card_0.dat\n",
+      "15:30 madminer.core        INFO      Reweight card:           /madminer/cards/reweight_card_0.dat\n",
+      "15:30 madminer.core        INFO      Log file:                run_0.log\n",
+      "15:30 madminer.core        INFO    Creating param and reweight cards in ./mg_processes/signal_pythia//madminer/cards/param_card_0.dat, ./mg_processes/signal_pythia//madminer/cards/reweight_card_0.dat\n",
+      "15:30 madminer.utils.inter INFO    Starting MadGraph and Pythia in ./mg_processes/signal_pythia\n",
+      "15:38 madminer.core        INFO    Finished running MadGraph! Please check that events were succesfully generated in the following folders:\n",
+      "\n",
+      "./mg_processes/signal_pythia/Events/run_01\n",
+      "\n",
+      "\n"
      ]
     }
    ],
    "source": [
-    "miner.run_multiple(\n",
-    "    sample_benchmarks=['sm', 'w'],\n",
+    "miner.run(\n",
+    "    sample_benchmark='sm',\n",
     "    mg_directory=mg_dir,\n",
     "    mg_process_directory='./mg_processes/signal_pythia',\n",
     "    proc_card_file='cards/proc_card_signal.dat',\n",
     "    param_card_template_file='cards/param_card_template.dat',\n",
     "    pythia8_card_file='cards/pythia8_card.dat',\n",
-    "    run_card_files=['cards/run_card_signal_large.dat'],\n",
+    "    run_card_file='cards/run_card_signal_small.dat',\n",
     "    log_directory='logs/signal',\n",
-    "    initial_command=\"source activate python2\"\n",
+    "    initial_command=\"source activate python2\",\n",
     ")"
    ]
   },
@@ -209,9 +214,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "u\"\\nminer.run(\\n    is_background=True,\\n    sample_benchmark='sm',\\n    mg_directory=mg_dir,\\n    mg_process_directory='./mg_processes/background_pythia',\\n    proc_card_file='cards/proc_card_background.dat',\\n    pythia8_card_file='cards/pythia8_card.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": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
     "miner.run(\n",
@@ -251,9 +267,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "15:38 madminer.utils.inter DEBUG   HDF5 file does not contain is_reference field.\n"
+     ]
+    }
+   ],
    "source": [
     "delphes = DelphesReader('data/setup.h5')"
    ]
@@ -269,11 +293,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "15:38 madminer.delphes     DEBUG   Adding event sample mg_processes/signal_pythia/Events/run_01/tag_1_pythia8_events.hepmc.gz\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "u\"\\ndelphes.add_sample(\\n    lhe_filename='mg_processes/background_pythia/Events/run_01/unweighted_events.lhe.gz',\\n    hepmc_filename='mg_processes/background_pythia/Events/run_01/tag_1_pythia8_events.hepmc.gz',\\n    sampled_from_benchmark='sm',\\n    is_background=True,\\n    k_factor=1.0,\\n\""
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "delphes.add_sample(\n",
     "    lhe_filename='mg_processes/signal_pythia/Events/run_01/unweighted_events.lhe.gz',\n",
@@ -282,13 +324,6 @@
     "    is_background=False,\n",
     "    k_factor=1.1,\n",
     ")\n",
-    "delphes.add_sample(\n",
-    "    lhe_filename='mg_processes/signal_pythia/Events/run_02/unweighted_events.lhe.gz',\n",
-    "    hepmc_filename='mg_processes/signal_pythia/Events/run_02/tag_1_pythia8_events.hepmc.gz',\n",
-    "    sampled_from_benchmark='w',\n",
-    "    is_background=False,\n",
-    "    k_factor=1.1,\n",
-    ")\n",
     "\n",
     "\"\"\"\n",
     "delphes.add_sample(\n",
@@ -309,9 +344,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "15:38 madminer.delphes     INFO    Running Delphes on HepMC sample at mg_processes/signal_pythia/Events/run_01/tag_1_pythia8_events.hepmc.gz\n",
+      "15:38 madminer.utils.inter DEBUG   Unzipping mg_processes/signal_pythia/Events/run_01/tag_1_pythia8_events.hepmc.gz\n",
+      "15:38 madminer.utils.inter DEBUG   Deleting mg_processes/signal_pythia/Events/run_01/tag_1_pythia8_events.hepmc\n"
+     ]
+    }
+   ],
    "source": [
     "delphes.run_delphes(\n",
     "    delphes_directory=mg_dir + '/Delphes',\n",
@@ -342,9 +389,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "15:38 madminer.delphes     DEBUG   Adding optional observable pt_j1 = j[0].pt with default 0.0\n",
+      "15:38 madminer.delphes     DEBUG   Adding required observable delta_phi_jj = j[0].deltaphi(j[1]) * (-1. + 2.*float(j[0].eta > j[1].eta))\n",
+      "15:38 madminer.delphes     DEBUG   Adding required observable met = met.pt\n"
+     ]
+    }
+   ],
    "source": [
     "delphes.add_observable(\n",
     "    'pt_j1',\n",
@@ -373,9 +430,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "15:38 madminer.delphes     DEBUG   Adding cut (a[0] + a[1]).m > 122.\n",
+      "15:38 madminer.delphes     DEBUG   Adding cut (a[0] + a[1]).m < 128.\n",
+      "15:38 madminer.delphes     DEBUG   Adding cut pt_j1 > 20.\n"
+     ]
+    }
+   ],
    "source": [
     "delphes.add_cut('(a[0] + a[1]).m > 122.')\n",
     "delphes.add_cut('(a[0] + a[1]).m < 128.')\n",
@@ -398,11 +465,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "15:38 madminer.delphes     INFO    Analysing Delphes sample mg_processes/signal_pythia/Events/run_01/tag_1_pythia8_events_delphes.root\n",
+      "15:38 madminer.delphes     DEBUG   Extracting nuisance parameter definitions from LHE file\n",
+      "15:38 madminer.utils.inter DEBUG   Parsing nuisance parameter setup from LHE file at mg_processes/signal_pythia/Events/run_01/unweighted_events.lhe.gz\n",
+      "15:38 madminer.delphes     DEBUG   Found 0 nuisance parameters with matching benchmarks:\n",
+      "15:38 madminer.utils.inter DEBUG   Parsing Delphes file mg_processes/signal_pythia/Events/run_01/tag_1_pythia8_events_delphes.root\n",
+      "15:38 madminer.utils.inter DEBUG   Not extracting weights\n",
+      "15:38 madminer.utils.inter DEBUG   Found 0 events\n",
+      "15:38 madminer.utils.inter DEBUG     First 10 values for observable pt_j1:\n",
+      "[]\n",
+      "15:38 madminer.utils.inter DEBUG     First 10 values for observable delta_phi_jj:\n",
+      "[]\n",
+      "15:38 madminer.utils.inter DEBUG     First 10 values for observable met:\n",
+      "[]\n",
+      "15:38 madminer.utils.inter DEBUG     0 / 0 events pass required observable delta_phi_jj\n",
+      "15:38 madminer.utils.inter DEBUG     0 / 0 events pass required observable met\n",
+      "15:38 madminer.utils.inter DEBUG     0 / 0 events pass cut (a[0] + a[1]).m > 122.\n",
+      "15:38 madminer.utils.inter DEBUG     0 / 0 events pass cut (a[0] + a[1]).m < 128.\n",
+      "15:38 madminer.utils.inter DEBUG     0 / 0 events pass cut pt_j1 > 20.\n",
+      "15:38 madminer.utils.inter WARNING   No observations remainining!\n",
+      "15:38 madminer.delphes     DEBUG   No observations in this Delphes file, skipping it\n",
+      "15:38 madminer.delphes     INFO    Analysed number of events per sampling benchmark:\n"
+     ]
+    }
+   ],
    "source": [
     "delphes.analyse_delphes_samples()"
    ]
@@ -416,9 +511,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "15:38 madminer.delphes     WARNING No observations to save!\n"
+     ]
+    }
+   ],
    "source": [
     "delphes.save('data/delphes_data.h5')"
    ]
@@ -439,9 +542,69 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "15:38 madminer.analysis    INFO    Loading data from data/delphes_data.h5\n",
+      "15:38 madminer.analysis    INFO    Found 2 parameters\n",
+      "15:38 madminer.analysis    DEBUG      CWL2 (LHA: dim6 2, maximal power in squared ME: (2,), range: (-10.0, 10.0))\n",
+      "15:38 madminer.analysis    DEBUG      CPWL2 (LHA: dim6 5, maximal power in squared ME: (2,), range: (-10.0, 10.0))\n",
+      "15:38 madminer.analysis    INFO    Did not find nuisance parameters\n",
+      "15:38 madminer.analysis    INFO    Found 6 benchmarks, of which 6 physical\n",
+      "15:38 madminer.analysis    DEBUG      sm: CWL2 = 0.00e+00, CPWL2 = 0.00e+00\n",
+      "15:38 madminer.analysis    DEBUG      w: CWL2 = 10.00, CPWL2 = 0.00e+00\n",
+      "15:38 madminer.analysis    DEBUG      morphing_basis_vector_2: CWL2 = 7.51, CPWL2 = -9.40e+00\n",
+      "15:38 madminer.analysis    DEBUG      morphing_basis_vector_3: CWL2 = -8.88e+00, CPWL2 = -5.37e+00\n",
+      "15:38 madminer.analysis    DEBUG      morphing_basis_vector_4: CWL2 = -7.84e+00, CPWL2 = 7.55\n",
+      "15:38 madminer.analysis    DEBUG      morphing_basis_vector_5: CWL2 = -3.97e+00, CPWL2 = -9.54e+00\n",
+      "15:38 madminer.analysis    INFO    Found 3 observables\n",
+      "15:38 madminer.analysis    DEBUG      0 pt_j1\n",
+      "15:38 madminer.analysis    DEBUG      1 delta_phi_jj\n",
+      "15:38 madminer.analysis    DEBUG      2 met\n",
+      "15:38 madminer.analysis    INFO    Found 39520 events\n",
+      "15:38 madminer.analysis    DEBUG     Did not find sample summary information\n",
+      "15:38 madminer.analysis    INFO    Found morphing setup with 6 components\n",
+      "15:38 madminer.analysis    INFO    Did not find nuisance morphing setup\n",
+      "15:38 madminer.plotting    DEBUG   Observable indices: [0, 1, 2]\n",
+      "15:38 madminer.plotting    DEBUG   Calculated 2 theta matrices\n",
+      "15:38 madminer.analysis    DEBUG   Sampling benchmark closest to None: None\n",
+      "15:38 madminer.analysis    DEBUG   Events per benchmark: nan\n",
+      "15:38 madminer.analysis    DEBUG   Sampling factors: [nan  1.]\n",
+      "15:38 madminer.utils.inter DEBUG   sampling_benchmarks field not found, probably due to the data being generated by an older MadMiner version\n",
+      "15:38 madminer.plotting    DEBUG   Loaded raw data with shapes (39520, 3), (39520, 6)\n",
+      "15:38 madminer.analysis    DEBUG   Distances from [0. 0.]: [0.0, 10.0, 12.034807443056737, 10.378163066492027, 10.888133853397726, 10.329190051315983]\n",
+      "15:38 madminer.analysis    DEBUG   Sampling benchmark closest to [0. 0.]: 0\n",
+      "15:38 madminer.analysis    DEBUG   Sampling factors: [1. 1. 1. 1. 1. 1. 1.]\n",
+      "15:38 madminer.utils.inter DEBUG   sampling_benchmarks field not found, probably due to the data being generated by an older MadMiner version\n",
+      "15:38 madminer.analysis    DEBUG   Distances from [10.  0.]: [10.0, 0.0, 9.725333290461062, 19.628343517013978, 19.37529758386497, 16.912744940880295]\n",
+      "15:38 madminer.analysis    DEBUG   Sampling benchmark closest to [10.  0.]: 1\n",
+      "15:38 madminer.analysis    DEBUG   Sampling factors: [1. 1. 1. 1. 1. 1. 1.]\n",
+      "15:38 madminer.utils.inter DEBUG   sampling_benchmarks field not found, probably due to the data being generated by an older MadMiner version\n",
+      "15:38 madminer.plotting    DEBUG   Plotting panel 0: observable 0, label pt_j1\n",
+      "15:38 madminer.plotting    DEBUG   Ranges for observable pt_j1: min = [22.48263168334961, 22.48263168334961], max = [300.11622103320695, 1107.879769402489]\n",
+      "15:38 madminer.plotting    DEBUG   Plotting panel 1: observable 1, label delta_phi_jj\n",
+      "15:38 madminer.plotting    DEBUG   Ranges for observable delta_phi_jj: min = [-3.1413567105876368, -3.1413567105876368], max = [3.14137310186495, 3.14137310186495]\n",
+      "15:38 madminer.plotting    DEBUG   Plotting panel 2: observable 2, label met\n",
+      "15:38 madminer.plotting    DEBUG   Ranges for observable met: min = [0.0753742903470993, 0.0753742903470993], max = [56.18803100212516, 137.90979010505572]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "_ = plot_distributions(\n",
     "    filename='data/delphes_data.h5',\n",
@@ -472,9 +635,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "15:38 madminer.sampling    DEBUG   Combining and shuffling samples\n",
+      "15:38 madminer.sampling    INFO    Copying setup from data/delphes_data.h5 to data/delphes_data_shuffled.h5\n",
+      "15:38 madminer.sampling    INFO    Loading samples from file 1 / 1 at data/delphes_data.h5, multiplying weights with k factor 1.0\n",
+      "15:38 madminer.utils.inter DEBUG   sampling_benchmarks field not found, probably due to the data being generated by an older MadMiner version\n",
+      "15:38 madminer.sampling    DEBUG   Sampling benchmarks: None\n",
+      "15:38 madminer.sampling    DEBUG   Combined sampling benchmarks: None\n",
+      "15:38 madminer.sampling    DEBUG   Recalculated event numbers per benchmark: None, background: None\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "unsupported operand type(s) for +: 'NoneType' and 'NoneType'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-15-8dadcb0a1776>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m combine_and_shuffle(\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m[\u001b[0m\u001b[0;34m'data/delphes_data.h5'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0;34m'data/delphes_data_shuffled.h5'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m )\n",
+      "\u001b[0;32m/Users/johannbrehmer/work/projects/madminer/madminer/madminer/sampling.pyc\u001b[0m in \u001b[0;36mcombine_and_shuffle\u001b[0;34m(input_filenames, output_filename, k_factors, overwrite_existing_file, recalculate_header)\u001b[0m\n\u001b[1;32m   2005\u001b[0m         \u001b[0moverwrite_existing_samples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moverwrite_existing_file\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2006\u001b[0m     )\n\u001b[0;32m-> 2007\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0mall_n_events_background\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_n_events_signal_per_benchmark\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2008\u001b[0m         save_sample_summary_to_madminer_file(\n\u001b[1;32m   2009\u001b[0m             \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_filename\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'NoneType' and 'NoneType'"
+     ]
+    }
+   ],
    "source": [
     "combine_and_shuffle(\n",
     "    ['data/delphes_data.h5'],\n",

From a78ff146a6c79a7cd32b62c200b7c9d22c9be274 Mon Sep 17 00:00:00 2001
From: Johann Brehmer <mail@johannbrehmer.de>
Date: Fri, 12 Jul 2019 16:48:01 -0400
Subject: [PATCH 2/2] Strangest bug fix ever: just change the morphing basis a
 little. See #371

---
 .../tutorial_particle_physics/1_setup.ipynb   |  43 ++-
 .../2b_delphes_level_analysis.ipynb           | 253 +++++++++---------
 2 files changed, 148 insertions(+), 148 deletions(-)

diff --git a/examples/tutorial_particle_physics/1_setup.ipynb b/examples/tutorial_particle_physics/1_setup.ipynb
index cde211576..6eb634356 100755
--- a/examples/tutorial_particle_physics/1_setup.ipynb
+++ b/examples/tutorial_particle_physics/1_setup.ipynb
@@ -118,8 +118,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "09:27 madminer.core        INFO    Added parameter CWL2 (LHA: dim6 2, maximal power in squared ME: (2,), range: (-20.0, 20.0))\n",
-      "09:27 madminer.core        INFO    Added parameter CPWL2 (LHA: dim6 5, maximal power in squared ME: (2,), range: (-20.0, 20.0))\n"
+      "16:29 madminer.core        INFO    Added parameter CWL2 (LHA: dim6 2, maximal power in squared ME: (2,), range: (-20.0, 20.0))\n",
+      "16:29 madminer.core        INFO    Added parameter CPWL2 (LHA: dim6 5, maximal power in squared ME: (2,), range: (-20.0, 20.0))\n"
      ]
     }
    ],
@@ -167,20 +167,20 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "09:27 madminer.core        INFO    Added benchmark sm: CWL2 = 0.00e+00, CPWL2 = 0.00e+00)\n",
-      "09:27 madminer.core        INFO    Added benchmark w: CWL2 = 15.00, CPWL2 = 0.00e+00)\n",
-      "09:27 madminer.core        INFO    Added benchmark neg_w: CWL2 = -1.50e+01, CPWL2 = 0.00e+00)\n",
-      "09:27 madminer.core        INFO    Added benchmark ww: CWL2 = 0.00e+00, CPWL2 = 15.00)\n",
-      "09:27 madminer.core        INFO    Added benchmark neg_ww: CWL2 = 0.00e+00, CPWL2 = -1.50e+01)\n"
+      "16:29 madminer.core        INFO    Added benchmark sm: CWL2 = 0.00e+00, CPWL2 = 0.00e+00)\n",
+      "16:29 madminer.core        INFO    Added benchmark w: CWL2 = 15.20, CPWL2 = 0.10)\n",
+      "16:29 madminer.core        INFO    Added benchmark neg_w: CWL2 = -1.54e+01, CPWL2 = 0.20)\n",
+      "16:29 madminer.core        INFO    Added benchmark ww: CWL2 = 0.30, CPWL2 = 15.10)\n",
+      "16:29 madminer.core        INFO    Added benchmark neg_ww: CWL2 = 0.40, CPWL2 = -1.53e+01)\n"
      ]
     }
    ],
    "source": [
     "miner.add_benchmark({'CWL2':0., 'CPWL2':0.}, 'sm')\n",
-    "miner.add_benchmark({'CWL2':15., 'CPWL2':0.}, 'w')\n",
-    "miner.add_benchmark({'CWL2':-15., 'CPWL2':0.}, 'neg_w')\n",
-    "miner.add_benchmark({'CWL2':0., 'CPWL2':15.}, 'ww')\n",
-    "miner.add_benchmark({'CWL2':0., 'CPWL2':-15.}, 'neg_ww')"
+    "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')"
    ]
   },
   {
@@ -214,8 +214,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "09:27 madminer.core        INFO    Optimizing basis for morphing\n",
-      "09:27 madminer.core        INFO    Set up morphing with 2 parameters, 6 morphing components, 5 predefined basis points, and 1 new basis points\n"
+      "16:29 madminer.core        INFO    Optimizing basis for morphing\n",
+      "16:29 madminer.core        INFO    Set up morphing with 2 parameters, 6 morphing components, 5 predefined basis points, and 1 new basis points\n"
      ]
     }
    ],
@@ -237,7 +237,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 468x360 with 2 Axes>"
       ]
@@ -288,20 +288,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "09:27 madminer.core        INFO    Saving setup (including morphing) to data/setup.h5\n"
-     ]
-    },
-    {
-     "ename": "TypeError",
-     "evalue": "_save_systematics() missing 1 required positional argument: 'systematics'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-7-5c72b20183f1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mminer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'data/setup.h5'\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~/work/projects/madminer/madminer/madminer/core.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, filename)\u001b[0m\n\u001b[1;32m    537\u001b[0m                 \u001b[0mmorphing_matrix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmorpher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmorphing_matrix\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    538\u001b[0m                 \u001b[0msystematics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystematics\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 539\u001b[0;31m                 \u001b[0moverwrite_existing_files\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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    540\u001b[0m             )\n\u001b[1;32m    541\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/work/projects/madminer/madminer/madminer/utils/interfaces/madminer_hdf5.py\u001b[0m in \u001b[0;36msave_madminer_settings\u001b[0;34m(filename, parameters, benchmarks, benchmarks_is_nuisance, morphing_components, morphing_matrix, systematics, overwrite_existing_files)\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[0m_save_benchmarks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbenchmarks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbenchmarks_is_nuisance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameter_names\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[0m_save_morphing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmorphing_components\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmorphing_matrix\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[0m_save_systematics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msystematics\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     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: _save_systematics() missing 1 required positional argument: 'systematics'"
+      "16:29 madminer.core        INFO    Saving setup (including morphing) to data/setup.h5\n"
      ]
     }
    ],
diff --git a/examples/tutorial_particle_physics/2b_delphes_level_analysis.ipynb b/examples/tutorial_particle_physics/2b_delphes_level_analysis.ipynb
index 82a0d5124..b006da322 100644
--- a/examples/tutorial_particle_physics/2b_delphes_level_analysis.ipynb
+++ b/examples/tutorial_particle_physics/2b_delphes_level_analysis.ipynb
@@ -110,19 +110,19 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "15:30 madminer.utils.inter DEBUG   HDF5 file does not contain is_reference field.\n",
-      "15:30 madminer.core        INFO    Found 2 parameters:\n",
-      "15:30 madminer.core        INFO       CWL2 (LHA: dim6 2, maximal power in squared ME: (2,), range: (-20.0, 20.0))\n",
-      "15:30 madminer.core        INFO       CPWL2 (LHA: dim6 5, maximal power in squared ME: (2,), range: (-20.0, 20.0))\n",
-      "15:30 madminer.core        INFO    Found 6 benchmarks:\n",
-      "15:30 madminer.core        INFO       sm: CWL2 = 0.00e+00, CPWL2 = 0.00e+00\n",
-      "15:30 madminer.core        INFO       w: CWL2 = 15.00, CPWL2 = 0.00e+00\n",
-      "15:30 madminer.core        INFO       neg_w: CWL2 = -1.50e+01, CPWL2 = 0.00e+00\n",
-      "15:30 madminer.core        INFO       ww: CWL2 = 0.00e+00, CPWL2 = 15.00\n",
-      "15:30 madminer.core        INFO       neg_ww: CWL2 = 0.00e+00, CPWL2 = -1.50e+01\n",
-      "15:30 madminer.core        INFO       morphing_basis_vector_5: CWL2 = 13.86, CPWL2 = 16.47\n",
-      "15:30 madminer.core        INFO    Found morphing setup with 6 components\n",
-      "15:30 madminer.core        INFO    Did not find systematics setup.\n"
+      "16:30 madminer.utils.inter DEBUG   HDF5 file does not contain is_reference field.\n",
+      "16:30 madminer.core        INFO    Found 2 parameters:\n",
+      "16:30 madminer.core        INFO       CWL2 (LHA: dim6 2, maximal power in squared ME: (2,), range: (-20.0, 20.0))\n",
+      "16:30 madminer.core        INFO       CPWL2 (LHA: dim6 5, maximal power in squared ME: (2,), range: (-20.0, 20.0))\n",
+      "16:30 madminer.core        INFO    Found 6 benchmarks:\n",
+      "16:30 madminer.core        INFO       sm: CWL2 = 0.00e+00, CPWL2 = 0.00e+00\n",
+      "16:30 madminer.core        INFO       w: CWL2 = 15.20, CPWL2 = 0.10\n",
+      "16:30 madminer.core        INFO       neg_w: CWL2 = -1.54e+01, CPWL2 = 0.20\n",
+      "16:30 madminer.core        INFO       ww: CWL2 = 0.30, CPWL2 = 15.10\n",
+      "16:30 madminer.core        INFO       neg_ww: CWL2 = 0.40, CPWL2 = -1.53e+01\n",
+      "16:30 madminer.core        INFO       morphing_basis_vector_5: CWL2 = -1.17e+01, CPWL2 = -1.34e+01\n",
+      "16:30 madminer.core        INFO    Found morphing setup with 6 components\n",
+      "16:30 madminer.core        INFO    Did not find systematics setup.\n"
      ]
     }
    ],
@@ -153,19 +153,19 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "15:30 madminer.utils.inter INFO    Generating MadGraph process folder from cards/proc_card_signal.dat at ./mg_processes/signal_pythia\n",
-      "15:30 madminer.core        INFO    Run 0\n",
-      "15:30 madminer.core        INFO      Sampling from benchmark: sm\n",
-      "15:30 madminer.core        INFO      Original run card:       cards/run_card_signal_small.dat\n",
-      "15:30 madminer.core        INFO      Original Pythia8 card:   cards/pythia8_card.dat\n",
-      "15:30 madminer.core        INFO      Copied run card:         /madminer/cards/run_card_0.dat\n",
-      "15:30 madminer.core        INFO      Copied Pythia8 card:     /madminer/cards/pythia8_card_0.dat\n",
-      "15:30 madminer.core        INFO      Param card:              /madminer/cards/param_card_0.dat\n",
-      "15:30 madminer.core        INFO      Reweight card:           /madminer/cards/reweight_card_0.dat\n",
-      "15:30 madminer.core        INFO      Log file:                run_0.log\n",
-      "15:30 madminer.core        INFO    Creating param and reweight cards in ./mg_processes/signal_pythia//madminer/cards/param_card_0.dat, ./mg_processes/signal_pythia//madminer/cards/reweight_card_0.dat\n",
-      "15:30 madminer.utils.inter INFO    Starting MadGraph and Pythia in ./mg_processes/signal_pythia\n",
-      "15:38 madminer.core        INFO    Finished running MadGraph! Please check that events were succesfully generated in the following folders:\n",
+      "16:30 madminer.utils.inter INFO    Generating MadGraph process folder from cards/proc_card_signal.dat at ./mg_processes/signal_pythia\n",
+      "16:30 madminer.core        INFO    Run 0\n",
+      "16:30 madminer.core        INFO      Sampling from benchmark: sm\n",
+      "16:30 madminer.core        INFO      Original run card:       cards/run_card_signal_small.dat\n",
+      "16:30 madminer.core        INFO      Original Pythia8 card:   cards/pythia8_card.dat\n",
+      "16:30 madminer.core        INFO      Copied run card:         /madminer/cards/run_card_0.dat\n",
+      "16:30 madminer.core        INFO      Copied Pythia8 card:     /madminer/cards/pythia8_card_0.dat\n",
+      "16:30 madminer.core        INFO      Param card:              /madminer/cards/param_card_0.dat\n",
+      "16:30 madminer.core        INFO      Reweight card:           /madminer/cards/reweight_card_0.dat\n",
+      "16:30 madminer.core        INFO      Log file:                run_0.log\n",
+      "16:30 madminer.core        INFO    Creating param and reweight cards in ./mg_processes/signal_pythia//madminer/cards/param_card_0.dat, ./mg_processes/signal_pythia//madminer/cards/reweight_card_0.dat\n",
+      "16:30 madminer.utils.inter INFO    Starting MadGraph and Pythia in ./mg_processes/signal_pythia\n",
+      "16:35 madminer.core        INFO    Finished running MadGraph! Please check that events were succesfully generated in the following folders:\n",
       "\n",
       "./mg_processes/signal_pythia/Events/run_01\n",
       "\n",
@@ -274,7 +274,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "15:38 madminer.utils.inter DEBUG   HDF5 file does not contain is_reference field.\n"
+      "16:35 madminer.utils.inter DEBUG   HDF5 file does not contain is_reference field.\n"
      ]
     }
    ],
@@ -302,7 +302,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "15:38 madminer.delphes     DEBUG   Adding event sample mg_processes/signal_pythia/Events/run_01/tag_1_pythia8_events.hepmc.gz\n"
+      "16:35 madminer.delphes     DEBUG   Adding event sample mg_processes/signal_pythia/Events/run_01/tag_1_pythia8_events.hepmc.gz\n"
      ]
     },
     {
@@ -353,9 +353,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "15:38 madminer.delphes     INFO    Running Delphes on HepMC sample at mg_processes/signal_pythia/Events/run_01/tag_1_pythia8_events.hepmc.gz\n",
-      "15:38 madminer.utils.inter DEBUG   Unzipping mg_processes/signal_pythia/Events/run_01/tag_1_pythia8_events.hepmc.gz\n",
-      "15:38 madminer.utils.inter DEBUG   Deleting mg_processes/signal_pythia/Events/run_01/tag_1_pythia8_events.hepmc\n"
+      "16:35 madminer.delphes     INFO    Running Delphes on HepMC sample at mg_processes/signal_pythia/Events/run_01/tag_1_pythia8_events.hepmc.gz\n",
+      "16:35 madminer.utils.inter DEBUG   Unzipping mg_processes/signal_pythia/Events/run_01/tag_1_pythia8_events.hepmc.gz\n",
+      "16:36 madminer.utils.inter DEBUG   Deleting mg_processes/signal_pythia/Events/run_01/tag_1_pythia8_events.hepmc\n"
      ]
     }
    ],
@@ -396,9 +396,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "15:38 madminer.delphes     DEBUG   Adding optional observable pt_j1 = j[0].pt with default 0.0\n",
-      "15:38 madminer.delphes     DEBUG   Adding required observable delta_phi_jj = j[0].deltaphi(j[1]) * (-1. + 2.*float(j[0].eta > j[1].eta))\n",
-      "15:38 madminer.delphes     DEBUG   Adding required observable met = met.pt\n"
+      "16:36 madminer.delphes     DEBUG   Adding optional observable pt_j1 = j[0].pt with default 0.0\n",
+      "16:36 madminer.delphes     DEBUG   Adding required observable delta_phi_jj = j[0].deltaphi(j[1]) * (-1. + 2.*float(j[0].eta > j[1].eta))\n",
+      "16:36 madminer.delphes     DEBUG   Adding required observable met = met.pt\n"
      ]
     }
    ],
@@ -437,9 +437,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "15:38 madminer.delphes     DEBUG   Adding cut (a[0] + a[1]).m > 122.\n",
-      "15:38 madminer.delphes     DEBUG   Adding cut (a[0] + a[1]).m < 128.\n",
-      "15:38 madminer.delphes     DEBUG   Adding cut pt_j1 > 20.\n"
+      "16:36 madminer.delphes     DEBUG   Adding cut (a[0] + a[1]).m > 122.\n",
+      "16:36 madminer.delphes     DEBUG   Adding cut (a[0] + a[1]).m < 128.\n",
+      "16:36 madminer.delphes     DEBUG   Adding cut pt_j1 > 20.\n"
      ]
     }
    ],
@@ -474,27 +474,38 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "15:38 madminer.delphes     INFO    Analysing Delphes sample mg_processes/signal_pythia/Events/run_01/tag_1_pythia8_events_delphes.root\n",
-      "15:38 madminer.delphes     DEBUG   Extracting nuisance parameter definitions from LHE file\n",
-      "15:38 madminer.utils.inter DEBUG   Parsing nuisance parameter setup from LHE file at mg_processes/signal_pythia/Events/run_01/unweighted_events.lhe.gz\n",
-      "15:38 madminer.delphes     DEBUG   Found 0 nuisance parameters with matching benchmarks:\n",
-      "15:38 madminer.utils.inter DEBUG   Parsing Delphes file mg_processes/signal_pythia/Events/run_01/tag_1_pythia8_events_delphes.root\n",
-      "15:38 madminer.utils.inter DEBUG   Not extracting weights\n",
-      "15:38 madminer.utils.inter DEBUG   Found 0 events\n",
-      "15:38 madminer.utils.inter DEBUG     First 10 values for observable pt_j1:\n",
-      "[]\n",
-      "15:38 madminer.utils.inter DEBUG     First 10 values for observable delta_phi_jj:\n",
-      "[]\n",
-      "15:38 madminer.utils.inter DEBUG     First 10 values for observable met:\n",
-      "[]\n",
-      "15:38 madminer.utils.inter DEBUG     0 / 0 events pass required observable delta_phi_jj\n",
-      "15:38 madminer.utils.inter DEBUG     0 / 0 events pass required observable met\n",
-      "15:38 madminer.utils.inter DEBUG     0 / 0 events pass cut (a[0] + a[1]).m > 122.\n",
-      "15:38 madminer.utils.inter DEBUG     0 / 0 events pass cut (a[0] + a[1]).m < 128.\n",
-      "15:38 madminer.utils.inter DEBUG     0 / 0 events pass cut pt_j1 > 20.\n",
-      "15:38 madminer.utils.inter WARNING   No observations remainining!\n",
-      "15:38 madminer.delphes     DEBUG   No observations in this Delphes file, skipping it\n",
-      "15:38 madminer.delphes     INFO    Analysed number of events per sampling benchmark:\n"
+      "16:36 madminer.delphes     INFO    Analysing Delphes sample mg_processes/signal_pythia/Events/run_01/tag_1_pythia8_events_delphes.root\n",
+      "16:36 madminer.delphes     DEBUG   Extracting nuisance parameter definitions from LHE file\n",
+      "16:36 madminer.utils.inter DEBUG   Parsing nuisance parameter setup from LHE file at mg_processes/signal_pythia/Events/run_01/unweighted_events.lhe.gz\n",
+      "16:36 madminer.delphes     DEBUG   Found 0 nuisance parameters with matching benchmarks:\n",
+      "16:36 madminer.utils.inter DEBUG   Parsing Delphes file mg_processes/signal_pythia/Events/run_01/tag_1_pythia8_events_delphes.root\n",
+      "16:36 madminer.utils.inter DEBUG   Not extracting weights\n",
+      "16:36 madminer.utils.inter DEBUG   Found 10000 events\n",
+      "16:36 madminer.utils.inter DEBUG     First 10 values for observable pt_j1:\n",
+      "[ 84.62783051 181.8666687   97.3727417   99.76499939  92.42367554\n",
+      "  48.54855347  47.51708603 100.8453064  135.95497131 126.97653198]\n",
+      "16:36 madminer.utils.inter DEBUG     First 10 values for observable delta_phi_jj:\n",
+      "[-1.20398968 -2.7168507  -2.76430232 -2.48814756 -0.34777592 -0.31459451\n",
+      " -3.09778506 -2.67325608  0.61564934  2.68325505]\n",
+      "16:36 madminer.utils.inter DEBUG     First 10 values for observable met:\n",
+      "[11.09266376  9.19657421 15.0783062  11.84642029 12.23457527  6.50921965\n",
+      "  9.26392937 10.58230686  6.73142433 29.73778152]\n",
+      "16:36 madminer.utils.inter DEBUG     9269 / 10000 events pass required observable delta_phi_jj\n",
+      "16:36 madminer.utils.inter DEBUG     10000 / 10000 events pass required observable met\n",
+      "16:36 madminer.utils.inter DEBUG     5354 / 10000 events pass cut (a[0] + a[1]).m > 122.\n",
+      "16:36 madminer.utils.inter DEBUG     5415 / 10000 events pass cut (a[0] + a[1]).m < 128.\n",
+      "16:36 madminer.utils.inter DEBUG     9967 / 10000 events pass cut pt_j1 > 20.\n",
+      "16:36 madminer.utils.inter INFO      4288 / 10000 events pass everything\n",
+      "16:36 madminer.delphes     DEBUG   Did not extract weights from Delphes file\n",
+      "16:36 madminer.delphes     DEBUG   Found 4288 events\n",
+      "16:36 madminer.delphes     DEBUG   Extracting weights from LHE file\n",
+      "16:36 madminer.utils.inter DEBUG   Parsing LHE file mg_processes/signal_pythia/Events/run_01/unweighted_events.lhe.gz\n",
+      "16:36 madminer.utils.inter DEBUG   Parsing header and events as XML with cElementTree\n",
+      "16:36 madminer.utils.inter DEBUG   Found entry event_norm = average in LHE header. Interpreting this as weight_norm_is_average = True.\n",
+      "16:36 madminer.delphes     DEBUG   Found weights [u'sm', 'w', 'neg_w', 'ww', 'neg_ww', 'morphing_basis_vector_5'] in LHE file\n",
+      "16:36 madminer.delphes     DEBUG   Applying Delphes-based cuts to LHE weights\n",
+      "16:36 madminer.delphes     INFO    Analysed number of events per sampling benchmark:\n",
+      "16:36 madminer.delphes     INFO      4288 from sm\n"
      ]
     }
    ],
@@ -518,7 +529,21 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "15:38 madminer.delphes     WARNING No observations to save!\n"
+      "16:36 madminer.delphes     DEBUG   Loading HDF5 data from data/setup.h5 and saving file to data/delphes_data.h5\n",
+      "16:36 madminer.delphes     DEBUG   Weight names: [u'sm', 'w', 'neg_w', 'ww', 'neg_ww', 'morphing_basis_vector_5']\n",
+      "16:36 madminer.utils.inter DEBUG   HDF5 file does not contain is_reference field.\n",
+      "16:36 madminer.utils.inter DEBUG   Benchmark morphing_basis_vector_5 already in benchmark_names_phys\n",
+      "16:36 madminer.utils.inter DEBUG   Benchmark neg_w already in benchmark_names_phys\n",
+      "16:36 madminer.utils.inter DEBUG   Benchmark neg_ww already in benchmark_names_phys\n",
+      "16:36 madminer.utils.inter DEBUG   Benchmark sm already in benchmark_names_phys\n",
+      "16:36 madminer.utils.inter DEBUG   Benchmark w already in benchmark_names_phys\n",
+      "16:36 madminer.utils.inter DEBUG   Benchmark ww already in benchmark_names_phys\n",
+      "16:36 madminer.utils.inter DEBUG   Combined benchmark names: [u'sm', u'w', u'neg_w', u'ww', u'neg_ww', u'morphing_basis_vector_5']\n",
+      "16:36 madminer.utils.inter DEBUG   Combined is_nuisance: [0 0 0 0 0 0]\n",
+      "16:36 madminer.utils.inter DEBUG   Combined is_reference: [1 0 0 0 0 0]\n",
+      "16:36 madminer.utils.inter DEBUG   Weight names found in event file: [u'sm', 'w', 'neg_w', 'ww', 'neg_ww', 'morphing_basis_vector_5']\n",
+      "16:36 madminer.utils.inter DEBUG   Benchmarks found in MadMiner file: [u'sm', u'w', u'neg_w', u'ww', u'neg_ww', u'morphing_basis_vector_5']\n",
+      "16:36 madminer.utils.inter DEBUG   Sorted benchmarks: [u'sm', u'w', u'neg_w', u'ww', u'neg_ww', u'morphing_basis_vector_5']\n"
      ]
     }
    ],
@@ -549,52 +574,53 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "15:38 madminer.analysis    INFO    Loading data from data/delphes_data.h5\n",
-      "15:38 madminer.analysis    INFO    Found 2 parameters\n",
-      "15:38 madminer.analysis    DEBUG      CWL2 (LHA: dim6 2, maximal power in squared ME: (2,), range: (-10.0, 10.0))\n",
-      "15:38 madminer.analysis    DEBUG      CPWL2 (LHA: dim6 5, maximal power in squared ME: (2,), range: (-10.0, 10.0))\n",
-      "15:38 madminer.analysis    INFO    Did not find nuisance parameters\n",
-      "15:38 madminer.analysis    INFO    Found 6 benchmarks, of which 6 physical\n",
-      "15:38 madminer.analysis    DEBUG      sm: CWL2 = 0.00e+00, CPWL2 = 0.00e+00\n",
-      "15:38 madminer.analysis    DEBUG      w: CWL2 = 10.00, CPWL2 = 0.00e+00\n",
-      "15:38 madminer.analysis    DEBUG      morphing_basis_vector_2: CWL2 = 7.51, CPWL2 = -9.40e+00\n",
-      "15:38 madminer.analysis    DEBUG      morphing_basis_vector_3: CWL2 = -8.88e+00, CPWL2 = -5.37e+00\n",
-      "15:38 madminer.analysis    DEBUG      morphing_basis_vector_4: CWL2 = -7.84e+00, CPWL2 = 7.55\n",
-      "15:38 madminer.analysis    DEBUG      morphing_basis_vector_5: CWL2 = -3.97e+00, CPWL2 = -9.54e+00\n",
-      "15:38 madminer.analysis    INFO    Found 3 observables\n",
-      "15:38 madminer.analysis    DEBUG      0 pt_j1\n",
-      "15:38 madminer.analysis    DEBUG      1 delta_phi_jj\n",
-      "15:38 madminer.analysis    DEBUG      2 met\n",
-      "15:38 madminer.analysis    INFO    Found 39520 events\n",
-      "15:38 madminer.analysis    DEBUG     Did not find sample summary information\n",
-      "15:38 madminer.analysis    INFO    Found morphing setup with 6 components\n",
-      "15:38 madminer.analysis    INFO    Did not find nuisance morphing setup\n",
-      "15:38 madminer.plotting    DEBUG   Observable indices: [0, 1, 2]\n",
-      "15:38 madminer.plotting    DEBUG   Calculated 2 theta matrices\n",
-      "15:38 madminer.analysis    DEBUG   Sampling benchmark closest to None: None\n",
-      "15:38 madminer.analysis    DEBUG   Events per benchmark: nan\n",
-      "15:38 madminer.analysis    DEBUG   Sampling factors: [nan  1.]\n",
-      "15:38 madminer.utils.inter DEBUG   sampling_benchmarks field not found, probably due to the data being generated by an older MadMiner version\n",
-      "15:38 madminer.plotting    DEBUG   Loaded raw data with shapes (39520, 3), (39520, 6)\n",
-      "15:38 madminer.analysis    DEBUG   Distances from [0. 0.]: [0.0, 10.0, 12.034807443056737, 10.378163066492027, 10.888133853397726, 10.329190051315983]\n",
-      "15:38 madminer.analysis    DEBUG   Sampling benchmark closest to [0. 0.]: 0\n",
-      "15:38 madminer.analysis    DEBUG   Sampling factors: [1. 1. 1. 1. 1. 1. 1.]\n",
-      "15:38 madminer.utils.inter DEBUG   sampling_benchmarks field not found, probably due to the data being generated by an older MadMiner version\n",
-      "15:38 madminer.analysis    DEBUG   Distances from [10.  0.]: [10.0, 0.0, 9.725333290461062, 19.628343517013978, 19.37529758386497, 16.912744940880295]\n",
-      "15:38 madminer.analysis    DEBUG   Sampling benchmark closest to [10.  0.]: 1\n",
-      "15:38 madminer.analysis    DEBUG   Sampling factors: [1. 1. 1. 1. 1. 1. 1.]\n",
-      "15:38 madminer.utils.inter DEBUG   sampling_benchmarks field not found, probably due to the data being generated by an older MadMiner version\n",
-      "15:38 madminer.plotting    DEBUG   Plotting panel 0: observable 0, label pt_j1\n",
-      "15:38 madminer.plotting    DEBUG   Ranges for observable pt_j1: min = [22.48263168334961, 22.48263168334961], max = [300.11622103320695, 1107.879769402489]\n",
-      "15:38 madminer.plotting    DEBUG   Plotting panel 1: observable 1, label delta_phi_jj\n",
-      "15:38 madminer.plotting    DEBUG   Ranges for observable delta_phi_jj: min = [-3.1413567105876368, -3.1413567105876368], max = [3.14137310186495, 3.14137310186495]\n",
-      "15:38 madminer.plotting    DEBUG   Plotting panel 2: observable 2, label met\n",
-      "15:38 madminer.plotting    DEBUG   Ranges for observable met: min = [0.0753742903470993, 0.0753742903470993], max = [56.18803100212516, 137.90979010505572]\n"
+      "16:36 madminer.analysis    INFO    Loading data from data/delphes_data.h5\n",
+      "16:36 madminer.analysis    INFO    Found 2 parameters\n",
+      "16:36 madminer.analysis    DEBUG      CWL2 (LHA: dim6 2, maximal power in squared ME: (2,), range: (-20.0, 20.0))\n",
+      "16:36 madminer.analysis    DEBUG      CPWL2 (LHA: dim6 5, maximal power in squared ME: (2,), range: (-20.0, 20.0))\n",
+      "16:36 madminer.analysis    INFO    Did not find nuisance parameters\n",
+      "16:36 madminer.analysis    INFO    Found 6 benchmarks, of which 6 physical\n",
+      "16:36 madminer.analysis    DEBUG      sm: CWL2 = 0.00e+00, CPWL2 = 0.00e+00\n",
+      "16:36 madminer.analysis    DEBUG      w: CWL2 = 15.20, CPWL2 = 0.10\n",
+      "16:36 madminer.analysis    DEBUG      neg_w: CWL2 = -1.54e+01, CPWL2 = 0.20\n",
+      "16:36 madminer.analysis    DEBUG      ww: CWL2 = 0.30, CPWL2 = 15.10\n",
+      "16:36 madminer.analysis    DEBUG      neg_ww: CWL2 = 0.40, CPWL2 = -1.53e+01\n",
+      "16:36 madminer.analysis    DEBUG      morphing_basis_vector_5: CWL2 = -1.17e+01, CPWL2 = -1.34e+01\n",
+      "16:36 madminer.analysis    INFO    Found 3 observables\n",
+      "16:36 madminer.analysis    DEBUG      0 pt_j1\n",
+      "16:36 madminer.analysis    DEBUG      1 delta_phi_jj\n",
+      "16:36 madminer.analysis    DEBUG      2 met\n",
+      "16:36 madminer.analysis    INFO    Found 4288 events\n",
+      "16:36 madminer.analysis    INFO      4288 signal events sampled from benchmark sm\n",
+      "16:36 madminer.analysis    INFO    Found morphing setup with 6 components\n",
+      "16:36 madminer.analysis    INFO    Did not find nuisance morphing setup\n",
+      "16:36 madminer.plotting    DEBUG   Observable indices: [0, 1, 2]\n",
+      "16:36 madminer.plotting    DEBUG   Calculated 2 theta matrices\n",
+      "16:36 madminer.analysis    DEBUG   Sampling benchmark closest to None: None\n",
+      "16:36 madminer.analysis    DEBUG   Events per benchmark: [4288.    0.    0.    0.    0.    0.]\n",
+      "16:36 madminer.analysis    DEBUG   Sampling factors: [1. 0. 0. 0. 0. 0. 1.]\n",
+      "16:36 madminer.utils.inter DEBUG   Sampling IDs: [0 0 0 ... 0 0 0]\n",
+      "16:36 madminer.utils.inter DEBUG   k-factors: [1. 1. 1. ... 1. 1. 1.]\n",
+      "16:36 madminer.plotting    DEBUG   Loaded raw data with shapes (4288, 3), (4288, 6)\n",
+      "16:36 madminer.analysis    DEBUG   Distances from [0. 0.]: [0.0, 15.200328943809078, 15.401298646542765, 15.102979838429237, 15.305227865013968, 17.77189833875769]\n",
+      "16:36 madminer.analysis    DEBUG   n_events_generated_per_benchmark: [4288    0    0    0    0    0]\n",
+      "16:36 madminer.analysis    DEBUG   Sampling benchmark closest to [0. 0.]: 0\n",
+      "16:36 madminer.analysis    DEBUG   Sampling factors: [1. 1. 1. 1. 1. 1. 1.]\n",
+      "16:36 madminer.analysis    DEBUG   Distances from [10.  0.]: [10.0, 5.200961449578337, 25.400787389370432, 17.947144619688114, 18.062391868188442, 25.487445178149255]\n",
+      "16:36 madminer.analysis    DEBUG   n_events_generated_per_benchmark: [4288    0    0    0    0    0]\n",
+      "16:36 madminer.analysis    DEBUG   Sampling benchmark closest to [10.  0.]: 0\n",
+      "16:36 madminer.analysis    DEBUG   Sampling factors: [1. 1. 1. 1. 1. 1. 1.]\n",
+      "16:36 madminer.plotting    DEBUG   Plotting panel 0: observable 0, label pt_j1\n",
+      "16:36 madminer.plotting    DEBUG   Ranges for observable pt_j1: min = [23.0212345123291, 23.0212345123291], max = [300.41729499795395, 714.2537841796875]\n",
+      "16:36 madminer.plotting    DEBUG   Plotting panel 1: observable 1, label delta_phi_jj\n",
+      "16:36 madminer.plotting    DEBUG   Ranges for observable delta_phi_jj: min = [-3.1396645307540894, -3.1396645307540894], max = [3.1406261588083666, 3.1406261588083666]\n",
+      "16:36 madminer.plotting    DEBUG   Plotting panel 2: observable 2, label met\n",
+      "16:36 madminer.plotting    DEBUG   Ranges for observable met: min = [0.24806453287601474, 0.24806453287601474], max = [58.92380250200134, 212.003926152471]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x288 with 3 Axes>"
       ]
@@ -642,25 +668,12 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "15:38 madminer.sampling    DEBUG   Combining and shuffling samples\n",
-      "15:38 madminer.sampling    INFO    Copying setup from data/delphes_data.h5 to data/delphes_data_shuffled.h5\n",
-      "15:38 madminer.sampling    INFO    Loading samples from file 1 / 1 at data/delphes_data.h5, multiplying weights with k factor 1.0\n",
-      "15:38 madminer.utils.inter DEBUG   sampling_benchmarks field not found, probably due to the data being generated by an older MadMiner version\n",
-      "15:38 madminer.sampling    DEBUG   Sampling benchmarks: None\n",
-      "15:38 madminer.sampling    DEBUG   Combined sampling benchmarks: None\n",
-      "15:38 madminer.sampling    DEBUG   Recalculated event numbers per benchmark: None, background: None\n"
-     ]
-    },
-    {
-     "ename": "TypeError",
-     "evalue": "unsupported operand type(s) for +: 'NoneType' and 'NoneType'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-15-8dadcb0a1776>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m combine_and_shuffle(\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m[\u001b[0m\u001b[0;34m'data/delphes_data.h5'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0;34m'data/delphes_data_shuffled.h5'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m )\n",
-      "\u001b[0;32m/Users/johannbrehmer/work/projects/madminer/madminer/madminer/sampling.pyc\u001b[0m in \u001b[0;36mcombine_and_shuffle\u001b[0;34m(input_filenames, output_filename, k_factors, overwrite_existing_file, recalculate_header)\u001b[0m\n\u001b[1;32m   2005\u001b[0m         \u001b[0moverwrite_existing_samples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moverwrite_existing_file\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2006\u001b[0m     )\n\u001b[0;32m-> 2007\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0mall_n_events_background\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_n_events_signal_per_benchmark\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2008\u001b[0m         save_sample_summary_to_madminer_file(\n\u001b[1;32m   2009\u001b[0m             \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_filename\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'NoneType' and 'NoneType'"
+      "16:36 madminer.sampling    DEBUG   Combining and shuffling samples\n",
+      "16:36 madminer.sampling    INFO    Copying setup from data/delphes_data.h5 to data/delphes_data_shuffled.h5\n",
+      "16:36 madminer.sampling    INFO    Loading samples from file 1 / 1 at data/delphes_data.h5, multiplying weights with k factor 1.0\n",
+      "16:36 madminer.sampling    DEBUG   Sampling benchmarks: [0 0 0 ... 0 0 0]\n",
+      "16:36 madminer.sampling    DEBUG   Combined sampling benchmarks: [0 0 0 ... 0 0 0]\n",
+      "16:36 madminer.sampling    DEBUG   Recalculated event numbers per benchmark: [4288    0    0    0    0    0], background: 0\n"
      ]
     }
    ],