{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cabinetry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by configuring the output from `cabinetry`. It uses the `logging` module to send messages at different verbosity levels. This customization is optional, and you can also use the `logging` module directly for further customization. The `set_logging` function just sets up a verbose default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cabinetry.set_logging()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The configuration file\n",
    "\n",
    "The configuration file is the central place to configure `cabinetry`.\n",
    "Let's have a look at the example configuration file used in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - cabinetry.configuration - opening config file config_ntuples.yml\n",
      "INFO - cabinetry.configuration - the config contains:\n",
      "INFO - cabinetry.configuration -   3 Sample(s)\n",
      "INFO - cabinetry.configuration -   1 Regions(s)\n",
      "INFO - cabinetry.configuration -   1 NormFactor(s)\n",
      "INFO - cabinetry.configuration -   3 Systematic(s)\n"
     ]
    }
   ],
   "source": [
    "cabinetry_config = cabinetry.configuration.load(\"config_ntuples.yml\")\n",
    "cabinetry.configuration.print_overview(cabinetry_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The configuration file is split into four different blocks of settings. There are general settings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Measurement': 'minimal_example',\n",
       " 'POI': 'Signal_norm',\n",
       " 'HistogramFolder': 'histograms/',\n",
       " 'InputPath': 'inputs/{SamplePath}'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cabinetry_config[\"General\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The list of phase space regions (channels), in this case we are considering just a single one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Name': 'Signal_region',\n",
       "  'Variable': 'jet_pt',\n",
       "  'Filter': 'lep_charge > 0',\n",
       "  'Binning': [200, 300, 400, 500, 600]}]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cabinetry_config[\"Regions\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A list of samples, including data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Name': 'Data',\n",
       "  'Tree': 'pseudodata',\n",
       "  'SamplePath': 'data.root',\n",
       "  'Data': True},\n",
       " {'Name': 'Signal',\n",
       "  'Tree': 'signal',\n",
       "  'SamplePath': 'prediction.root',\n",
       "  'Weight': 'weight',\n",
       "  'DisableStaterror': True},\n",
       " {'Name': 'Background',\n",
       "  'Tree': 'background',\n",
       "  'SamplePath': 'prediction.root',\n",
       "  'Weight': 'weight'}]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cabinetry_config[\"Samples\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A list of normalization factors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Name': 'Signal_norm', 'Samples': 'Signal', 'Nominal': 1, 'Bounds': [0, 10]}]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cabinetry_config[\"NormFactors\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally a list of systematic uncertainties. In this case there are three systematic uncertainties:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Name': 'Luminosity',\n",
       "  'Up': {'Normalization': 0.05},\n",
       "  'Down': {'Normalization': -0.05},\n",
       "  'Type': 'Normalization'},\n",
       " {'Name': 'Modeling',\n",
       "  'Up': {'SamplePath': 'prediction.root', 'Tree': 'background_varied'},\n",
       "  'Down': {'Symmetrize': True},\n",
       "  'Samples': 'Background',\n",
       "  'Type': 'NormPlusShape'},\n",
       " {'Name': 'WeightBasedModeling',\n",
       "  'Up': {'Weight': 'weight_up'},\n",
       "  'Down': {'Weight': '0.7*weight'},\n",
       "  'Samples': 'Background',\n",
       "  'Type': 'NormPlusShape'}]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cabinetry_config[\"Systematics\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regions, samples, normalization factors and systematics all can be identified by their names."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating template histograms from ntuples\n",
    "\n",
    "We use the `templates` module to create all histograms needed to build the workspace defined in the configuration file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG - cabinetry.route -   in region Signal_region\n",
      "DEBUG - cabinetry.route -     reading sample Data\n",
      "DEBUG - cabinetry.route -       variation Nominal\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Data.npz\n",
      "DEBUG - cabinetry.route -     reading sample Signal\n",
      "DEBUG - cabinetry.route -       variation Nominal\n",
      "WARNING - cabinetry.histo - Signal_region_Signal has empty bins: [0]\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Signal.npz\n",
      "DEBUG - cabinetry.route -     reading sample Background\n",
      "DEBUG - cabinetry.route -       variation Nominal\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Background.npz\n",
      "DEBUG - cabinetry.route -       variation Modeling Up\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Background_Modeling_Up.npz\n",
      "DEBUG - cabinetry.route -       variation WeightBasedModeling Up\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Background_WeightBasedModeling_Up.npz\n",
      "DEBUG - cabinetry.route -       variation WeightBasedModeling Down\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Background_WeightBasedModeling_Down.npz\n"
     ]
    }
   ],
   "source": [
    "cabinetry.templates.build(cabinetry_config, method=\"uproot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histograms are saved to the folder specified under `HistogramFolder` in the `General` settings in the configuration file.\n",
    "In this case, this folder is `histograms/`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Signal_region_Background.npz\r\n",
      "Signal_region_Background_Modeling_Up.npz\r\n",
      "Signal_region_Background_WeightBasedModeling_Down.npz\r\n",
      "Signal_region_Background_WeightBasedModeling_Up.npz\r\n",
      "Signal_region_Data.npz\r\n",
      "Signal_region_Signal.npz\r\n"
     ]
    }
   ],
   "source": [
    "!ls histograms/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be useful to apply additional post-processing after building template histograms.\n",
    "Such processing can for example replace ill-defined statistical uncertainties in empty bins by zero.\n",
    "It is also performed via the `templates` module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG - cabinetry.route -   in region Signal_region\n",
      "DEBUG - cabinetry.route -     reading sample Data\n",
      "DEBUG - cabinetry.route -       variation Nominal\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Data_modified.npz\n",
      "DEBUG - cabinetry.route -     reading sample Signal\n",
      "DEBUG - cabinetry.route -       variation Nominal\n",
      "WARNING - cabinetry.histo - Signal_region_Signal has empty bins: [0]\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Signal_modified.npz\n",
      "DEBUG - cabinetry.route -     reading sample Background\n",
      "DEBUG - cabinetry.route -       variation Nominal\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Background_modified.npz\n",
      "DEBUG - cabinetry.route -       variation Modeling Up\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Background_Modeling_Up_modified.npz\n",
      "DEBUG - cabinetry.route -       variation WeightBasedModeling Up\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Background_WeightBasedModeling_Up_modified.npz\n",
      "DEBUG - cabinetry.route -       variation WeightBasedModeling Down\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Background_WeightBasedModeling_Down_modified.npz\n"
     ]
    }
   ],
   "source": [
    "cabinetry.templates.postprocess(cabinetry_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "New histograms have now appeard in the `histograms/` folder.\n",
    "These \"modified\" histograms include the changes applied by the postprocessor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Signal_region_Background.npz\r\n",
      "Signal_region_Background_Modeling_Up.npz\r\n",
      "Signal_region_Background_Modeling_Up_modified.npz\r\n",
      "Signal_region_Background_WeightBasedModeling_Down.npz\r\n",
      "Signal_region_Background_WeightBasedModeling_Down_modified.npz\r\n",
      "Signal_region_Background_WeightBasedModeling_Up.npz\r\n",
      "Signal_region_Background_WeightBasedModeling_Up_modified.npz\r\n",
      "Signal_region_Background_modified.npz\r\n",
      "Signal_region_Data.npz\r\n",
      "Signal_region_Data_modified.npz\r\n",
      "Signal_region_Signal.npz\r\n",
      "Signal_region_Signal_modified.npz\r\n"
     ]
    }
   ],
   "source": [
    "!ls histograms/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optional: reading existing template histograms\n",
    "\n",
    "Besides providing ntuples that first need to be turned into histograms, it is also possible to provide existing histograms to `cabinetry`.\n",
    "The configuration options for this are slightly different, since less information is needed to read an existing histogram.\n",
    "\n",
    "The following loads a `cabinetry` configuration using histogram inputs, collects all provided histograms (storing them in the format used internally by `cabinetry` for further processing) and applies post-processing.\n",
    "The resulting histograms are equivalent to those created when reading the provided ntuples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - cabinetry.configuration - opening config file config_histograms.yml\n",
      "DEBUG - cabinetry.route -   in region Signal_region\n",
      "DEBUG - cabinetry.route -     reading sample Data\n",
      "DEBUG - cabinetry.route -       variation Nominal\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Data.npz\n",
      "DEBUG - cabinetry.route -     reading sample Signal\n",
      "DEBUG - cabinetry.route -       variation Nominal\n",
      "WARNING - cabinetry.histo - Signal_region_Signal has empty bins: [0]\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Signal.npz\n",
      "DEBUG - cabinetry.route -     reading sample Background\n",
      "DEBUG - cabinetry.route -       variation Nominal\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Background.npz\n",
      "DEBUG - cabinetry.route -       variation Modeling Up\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Background_Modeling_Up.npz\n",
      "DEBUG - cabinetry.route -       variation WeightBasedModeling Up\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Background_WeightBasedModeling_Up.npz\n",
      "DEBUG - cabinetry.route -       variation WeightBasedModeling Down\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Background_WeightBasedModeling_Down.npz\n",
      "DEBUG - cabinetry.route -   in region Signal_region\n",
      "DEBUG - cabinetry.route -     reading sample Data\n",
      "DEBUG - cabinetry.route -       variation Nominal\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Data_modified.npz\n",
      "DEBUG - cabinetry.route -     reading sample Signal\n",
      "DEBUG - cabinetry.route -       variation Nominal\n",
      "WARNING - cabinetry.histo - Signal_region_Signal has empty bins: [0]\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Signal_modified.npz\n",
      "DEBUG - cabinetry.route -     reading sample Background\n",
      "DEBUG - cabinetry.route -       variation Nominal\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Background_modified.npz\n",
      "DEBUG - cabinetry.route -       variation Modeling Up\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Background_Modeling_Up_modified.npz\n",
      "DEBUG - cabinetry.route -       variation WeightBasedModeling Up\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Background_WeightBasedModeling_Up_modified.npz\n",
      "DEBUG - cabinetry.route -       variation WeightBasedModeling Down\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Background_WeightBasedModeling_Down_modified.npz\n"
     ]
    }
   ],
   "source": [
    "cabinetry_config_histograms = cabinetry.configuration.load(\"config_histograms.yml\")\n",
    "cabinetry.templates.collect(cabinetry_config_histograms, method=\"uproot\")\n",
    "cabinetry.templates.postprocess(cabinetry_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Workspace building\n",
    "\n",
    "Next, we build a `pyhf` workspace and serialize it to a file.\n",
    "The `workspace` module takes care of this task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - cabinetry.workspace - building workspace\n",
      "DEBUG - cabinetry.workspace - adding NormFactor Signal_norm to sample Signal in region Signal_region\n",
      "DEBUG - cabinetry.workspace - adding OverallSys Luminosity to sample Signal in region Signal_region\n",
      "DEBUG - cabinetry.workspace - adding OverallSys Luminosity to sample Background in region Signal_region\n",
      "DEBUG - cabinetry.workspace - adding OverallSys and HistoSys Modeling to sample Background in region Signal_region\n",
      "DEBUG - cabinetry.workspace - normalization impact of systematic Modeling on sample Background in region Signal_region is 0.800\n",
      "DEBUG - cabinetry.workspace - adding OverallSys and HistoSys WeightBasedModeling to sample Background in region Signal_region\n",
      "INFO - pyhf.workspace - Validating spec against schema: workspace.json\n",
      "DEBUG - cabinetry.workspace - saving workspace to workspaces/example_workspace.json\n"
     ]
    }
   ],
   "source": [
    "workspace_path = \"workspaces/example_workspace.json\"\n",
    "ws = cabinetry.workspace.build(cabinetry_config)\n",
    "cabinetry.workspace.save(ws, workspace_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model structure\n",
    "\n",
    "It can be helpful to visualize the modifier structure of the statistical model we have built to catch potential issues.\n",
    "The `visualize.modifier_grid` function creates a figure showcasing the information about which modifiers (indicated by color) act on which region and sample when a given parameter (on the horizontal axis) is varied.\n",
    "To split this visualization from one table per region to one table per sample, use `split_by_sample=True`.\n",
    "\n",
    "We need the fit model (containing the probability density function) for the visualization, which we get from the workspace object.\n",
    "We will also extract data from it (observed bin yields and including auxiliary data for auxiliary measurements, see the [HistFactory documentation](https://cds.cern.ch/record/1456844)), which we will use in the next step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - pyhf.workspace - Validating spec against schema: workspace.json\n",
      "INFO - pyhf.pdf - Validating spec against schema: model.json\n",
      "INFO - pyhf.pdf - adding modifier Modeling (1 new nuisance parameters)\n",
      "INFO - pyhf.pdf - adding modifier WeightBasedModeling (1 new nuisance parameters)\n",
      "INFO - pyhf.pdf - adding modifier Signal_norm (1 new nuisance parameters)\n",
      "INFO - pyhf.pdf - adding modifier Luminosity (1 new nuisance parameters)\n",
      "INFO - pyhf.pdf - adding modifier staterror_Signal_region (4 new nuisance parameters)\n",
      "INFO - cabinetry.visualize.utils - saving figure as figures/modifier_grid.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 310.737x261 with 2 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ws = cabinetry.workspace.load(workspace_path)\n",
    "model, data = cabinetry.model_utils.model_and_data(ws)\n",
    "cabinetry.visualize.modifier_grid(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting\n",
    "\n",
    "With the workspace built, we can perform a maximum likelihood fit.\n",
    "The results for the fitted parameters are reported.\n",
    "The `cabinetry.model_utils.model_and_data` function has an `asimov` keyword argument, which we can set to `True` to instead study the expected performance with an Asimov dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - cabinetry.fit - performing maximum likelihood fit\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 18.54                      │              Nfcn = 330              │\n",
      "│ EDM = 1.04e-06 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 18.535252 at best-fit point\n",
      "INFO - cabinetry.fit - fit results (with symmetric uncertainties):\n",
      "INFO - cabinetry.fit - Modeling                   = -0.2753 +/- 0.5683\n",
      "INFO - cabinetry.fit - WeightBasedModeling        = -0.5277 +/- 0.6477\n",
      "INFO - cabinetry.fit - Signal_norm                =  1.5938 +/- 0.9757\n",
      "INFO - cabinetry.fit - Luminosity                 = -0.0794 +/- 0.9910\n",
      "INFO - cabinetry.fit - staterror_Signal_region[0] =  1.0012 +/- 0.0411\n",
      "INFO - cabinetry.fit - staterror_Signal_region[1] =  0.9883 +/- 0.0384\n",
      "INFO - cabinetry.fit - staterror_Signal_region[2] =  1.0216 +/- 0.0469\n",
      "INFO - cabinetry.fit - staterror_Signal_region[3] =  0.9829 +/- 0.0610\n"
     ]
    }
   ],
   "source": [
    "fit_results = cabinetry.fit.fit(model, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also visualize the fit results.\n",
    "Below are the pulls:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - cabinetry.visualize.utils - saving figure as figures/pulls.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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9yVhE3BwRV0XEIxExH/h3YDRZbxvAbmRDszdFxFMRcXdEXAIgaTfgZOBfI+IvEbEoIs4Dbs/XQ5Ys3hAR5+av8QKyJGgjEXE/8DjwbkkiS8Yu62PXM4ArIuIHeZ3fIUu+zsi3/zOwH/DhiHggIm4j+9sUek9+XP8tIv6ev/aT89c7va/4NuG6PI7HgG8Bz5Ml6pAlYAF8NCIejojryeYhvixNTU00Njb2PGbPnv1yqzQzM9us2bNn9/ruaWraqM+n30rtGbsF2BY4lKxHamFELJd0K1lPzEiyL+7HyeaXjQZuVO/bbIwA7t9E/fsC1xStu5usp6fQE0VzlZ4BJvTzNbwHmA/UAQcA35e0MiK+ACBpR+Br+euYQNbrN5osKQH4FVkP1OOSbiDr5fl9flLDK/P9Fxa95nrghfz5lD5e451kPWR9uYwsKXqK7NhfB3yyaJ8pwMVF6+4gS/y6tzdHxNNFbRaaCkwGWotiH0nWE9lfPT/xIyIkLeOlv82+wIMRsa5g/7tLqLtPzc3NNDQ09Cy7V6x6tLX1f99H1ozklIX7br1g+tA2r41Fp2926iwAe12wF2MOKqmbb0B0tXfx2GmPATD5osnU1HsSv1l/nXnmmcycObNnuaWlpeyErKRkLCIek7SErKdjPFmvGBHxtKRm4DX5tpvJkjGAtwJLi6pq5+UpPoMv6H8vX3PeawMwX9JewFclnZ0nCVeSzeH6NPBkHuudZEkkEdEsaV+y3qY3Aj8AZkmaRvaaO8mGPosnWZTwtdHL1WRDwWcDP4uIDdo695AbA9wHfKCPbctLqOfl/G3K0tDQ0CsZs+qx7bb933e0RO2owb0RdsMRDdRNqNto8n6huh3rkk2grx1Vy5Srp2x5RzPbyEBOiSnnS3IuWa/RdLKesm63AccCh+X7PEyWyOwWEY8VPZo3UfcCsl63QsXL/bGerIeqPzrJktIR+fJRwAURcV1EPET2GrYvLBARayPi9xFxOtlxOJKsV+z+vN0JfbzmZXnx+WTztgodwSZExAqy+XPT6HuIsrvOo4rWHUX2N+je3iRp5820OY/sDNPn+oh9oC49sgB4paTC/73l/H3NKoJqxcRZEzezA0w8Y6LPZDSrcuUmY0cDB5L3jOVuJZsLNgKYmw8jngd8V9JJkvaSdJCkT0k6aRN1fx84Lj+Dcm9JHyNL8Eq99MUTZJfb2EPS9vkE9m6vkLSTpImSjiXrAZsbEd0z7x4FPiRpSj7Z/mpgbXfh/EzIUyQdIGlPshMU1pJd92thvv9PJZ0gaZKkw5SdUfrWvIoLgLdIOiN/jZ9k00OU3WYA20fEI5vYPgeYkZ9RubekmWTXADsv334TsBC4UtJUSa8Fvl5Ux9Vk87t+K+m1eezTJV2Qn8U5EH5O9n/u4vz4vpmX5rUNmeu8mQ2k8ceMZ9KcSdRNqOu1vm7HOiadOynpdcbMbGgodc4YZMnYKLILkhae1Xcr2ZmICyLimXzdl8iGuM4E9gReJOuB+UZfFUfEHcouLnsW2bytPwHfZeM5UltyHtlw48N5rJMKtt2U/9tJNtfsOrKzMLudQjb/ah7QTDbR/byC7S+Snbn5HbJesL8Db4+I7jlhJ5OdhfhtYFeyBOf/gD/kr/H/JH2U7AzKr+TxfI3sWPUpItZSkBD2sf1aSZ8mS2y+R3apkZMj4pZ8e1d+xuWlZHO0ngBOJzszs7uONZJeRzbp/jdkf8ulwJ+B8k8R6R1ni6S3Az8E/kZ27L5ClqSt20xRs4o2/pjxjJs2jrb72+h4voO67esY8+oxyXvEutZ18eipjwKw9w/2pmak54yZpaAhdOH5Pkm6BNgvIl6bOhYbeJI+QHa3gcY86SylbAOwatWqVZ4z1ss8smmLVmj+mlF88BHPjyrUubaTB47OLvcx9fapgz6nrlJctd98powu6eOpStxHwWU6q15LSwuNjY2QfZ+V1IlRTs/YViXpDOBGsivQHwucBJyaNCgbMJI+THa27VKyMzi/RXZ5EX/SmQ2ymroa9jxvz57nZpbGkEvGyE4A+BzZMNnjwOkR8ZO0IdkA2olsaHInsmHiX9F7mNjMBom2EeNePy51GGZVb8glYxFxYuoYbOuJiHPJLtVhZmZmDMFkzMzMBkd0Bm33Z5dAHAonFJhVKydjZmZVqmt9F49+LDub0hP4zdJxMmZmVqWEGLnnyJ7nZpaGkzEzsypVM6qG/X+1f+owzKqez2U2MzMzS8jJmJmZmVlCTsbMzKpU9+2QHj31UbrWdaUOx6xqec6YmVmVigha72rteW5maTgZMzOrUjV1Nezx1T16nptZGk7GzMyqlLYR2x23XeowzKqefwqZmZmZJeSeMTOzKhWdwZpH1gAwer/Rvh2SWSJOxszMqlTX+i4WfHgB4NshmaXkZMzMrEoJMWLnET3PzSwNJ2NmZlWqZlQNB/zhgNRhmFU9T+A3MzMzS8jJmJmZmVlCTsbMzKpUV3sXi2YuYtHMRXS1+3ZIZql4zpiZWZWKrmDVrat6nptZGk7GzMyqVM02Nez2xd16nptZGk7GzMyqlOrE9idsnzoMs6rnn0JmZmZmCblnzMysSkVXsG7xOgBGThqJanzhV7MU3DM2jEkKSccPYnu3SDp/sNozs/6LzqD13lZW3LCC1ntbic6gq72L+SfOZ/6J8302pVlC7hkbJJKuAMZFxPGD2OzOwMpBbO8EoKN7QdITwPkRcf4gxmBmRVbevJIlc5bQ8VzP25O6CXXs8uld2GacvwbMUvO7cBiLiGWD3N6KwWzPzLZs5c0rWTxr8UbrO57r4MkvPsmkOZMYf8z4BJGZWTcnY4lJmkHWezSuYN3xwDURoXz5bOB44ALgbGA74KfAp4DPAjPJhpy/FxFfL6gngHdGxLWS9gAWA+/Kyx0OPAp8PCLuLCjzLuArwGTgGeD7EfHtgu2nAv8BNAGrgL9ExLvzbbcAf4uIz+TPdwe+K+m7efExeZ0fiYhfF73eq4GdIqK1xENo1qfVq3svr1kTdK7tTBNMItEZLDl3yWb3WTJnCWMPG4tqq3u+WO2o2tQhWBVzMlY59gKOBd6SP/81sCewEJgGvAa4TNJNEXHXZur5OnAGWSL2deAXkiZHxAZJBwO/JEv4/iev8weSXoiIKyQdQpYQfgj4K1lS+NpNtHMC8ABwMXAJQESslvTfwMl5/N1OBn5dbiLW0tLSa7m+vp76+vpyqrJhZMyY4jXryP5LWqGO5zp4cNqDqcNI7qD7DkodglWY9vZ22tvbe5aLv4tK4Qn8laOGrEfp4Yj4PTAX2Bf4TEQsiIjLgQXA67dQz3kR8ceIWAicRdZ7NTnfNhP4c0R8NSIWRsQVwIXArHz7bsBq4A8R8WRE3B8RF/TVSD5k2Qm0RsSygiHTnwBvlrQzgKQJwHHAZaUdjpc0NTXR2NjY85g9e3a5VZmZmfXL7Nmze333NDU1lV2Xe8YqxxNFPUfPAp0R0VW0bsIW6in8CfxM/u8E4BFgCvDbov3vAD4jqRa4EXgSeFzSDcANZMOpa/r7IiLibkkPAScB3wQ+mNd5W3/rKNbc3ExDQ0PPsnvFDKCtrffyI2tGcsrCfdMEk0jbvDYWnb6oX/secOMBHqozK8GZZ57JzJkze5ZbWlrKTsicjKXXBRRP1qjrY7+OouXYxLot9XYWlum+GV2/ekgjolXSQcB04E1kc8vOlnRoRLzYnzpyPwFOI0vGTgYuj4iyb4zX0NDQKxkzA9h2297Lo6WqSzYajmigbkJdr7Moi9WOrWWnf9uJurF1qK66542ZlWIgp8R4mDK95cBYSYVfHQcmimU+cFTRuqOAhRHRCRARGyLipoj4HPAqYA/gmE3Utx7o69vvKmB3SacD+wNXDkDsZlZEtWLirImb2QF2+/Ju7PjBHZ2ImSXknrHB1SjpwKJ1DwNrgG9IuoDsLMcZgxxXt28D90j6EtkE/iOBTwKnAkh6G9lJA7eRXb/sOLKEfsEm6nsCeF0+ab89Ip4HiIiVkn4DzAH+NyI2f7qXmZVt/DHjYQ4bX2dsxzomnjHRl7UwGwKcjA2u6cD9ResuJZs3NQf4KPBnsrMZLx7MwAAiYp6kE8mGH79ENqfsy/lEfoAXyc6SPBsYSXZG5vsi4qFNVPll4MfAIqCe3sOxlwLv52VM3Dez/hl/zHjGTRtH2/1tdDzfQd32dYx59RgQtD+dnQ02YqcRvh2SWSJ6GVN1zMom6UPAd4FdImJ9mXU0AKtWrVrlOWO9zAMOTh3EkDN/zSg++MiU1GEMKZ1rO3ng6OxyH1Nvn1p1c+r666r95jNl9NrUYQxB9wG+JEi3lpYWGhsbARojoqTrXLhnzAaVpNFkt2n6AvDjchMxMxsYNSM9ddgsNSdjNtg+B3yRbN6ZLwhmllDtqFoOvOPA1GGYVT3/JLJBFRFnR0RdRLwhItq2XMLMzGx4czJmZmZmlpCHKc3MqlTX+i6av9UMQNPnm6gZ4d/nZin4nWdmVqWiM3jh2hd44doXiE6fWW+WinvGzMyqlLYRO5+6c89zM0vDPWNWyUYAtLe3p45jSGlvX8/ZZ4MPS2/r24Onf/w0Xeu7UocyZNTU1bDjh3YkNsRLd6q1Hl3ru3j6x0+zvt0Hp1B7O/lnjK9MVKjgu2hEqWWdjFklqwcnY8Xa29dzzjlOxoqtb4dlFy8j1vuLtVCsDx+XTeg+Nuv9XuqlvZ38M8bJWKGC76KS7x7uZMyq0kUXXTSsy5Wr3OYqpVy5lv9y+bAsFxFseHFDz/PBaLPSypWrUt4Tg/1eqpTP0MH+7HUyZlWpUt7YTsYGtly5KiVxKLVc17ouHnrHQz3PB6PNSitXrkp5TzgZG9hy5fIEfqt4ra2ttLSUdBswOjs7Sy5TKeVaWlbn/5bcHJ2dw7dcW2vW89O5urPk9qIr6GwbfuU61720b+fqTjpHDd1YU5Tr/r/S1hq0jC6tvUp4T5Rbrnv/7LPGn73dWltbS26nm28UbhVL0h7A4tRxmJmZFZgUEU+UUsDJmFUsSQJ2Acr/OWJmZjZwxgJPR4nJlZMxMzMzs4Q8gd/MzMwsISdjZmZmZgk5GTMzMzNLyMmYmZmZWUJOxszMzMwScjJmZmZmlpCTMTMzM7OEnIyZmZmZJeRkzMzMzCwhJ2M2LEj6naSnJK2T9Iykn0naJXVcKUnaQ9KlkhZLWitpkaRzJI1IHVtqkr4o6a+S1kh6MXU8KUk6TdIT+XvnLkmHpY4pNUmvk/R7SU9LCknHp45pKJB0pqR7JLVKek7StZL2TR1XapI+IelBSS35405Jx5ZSh5MxGy7mAicC+wLvAvYCfp00ovT2I3uPfwz4J+A/gI8D30gZ1BAxAvgV8MPUgaQk6T3Ad4BzgIOAB4A/SZqQNLD0tiU7FqelDmSImQZcBBwBvBGoA/5X0rZJo0pvCfAF4GDgEOBm4LeS/qm/FfjelDYsSfoX4FqgPiI6EoczZEiaBXwiIvZMHctQIGkGcH5EjEscShKS7gLuiYhP5ss1QDPw/Yj4ZtLghghJAbwzIq5NHctQI2kH4DlgWkTcljqeoUTSCmBWRFzan/3dM2bDjqTtgA8Af3UitpFGYEXqICy9fLj6YOCm7nUR0ZUvH5kqLqsojfm//kzJSaqV9F6y3tU7+1vOyZgNG5K+JWk18AKwG/COxCENKZImA58Cfpw6FhsStgdqgWeL1j8L7DT44VglyXtRzwfuiIh/JA4nOUmvlNQGtAM/IutNfbi/5Z2M2ZAl6Zv55NnNPfYrKDIHeDXwJqAT+KkkJQl+KyrjuCBpV+AG4FcRcUmayLeuco6LmZXtIuAA4L2pAxkiFgAHAoeTzUW9UtL+/S3sOWM2ZOXzEV6xhd0ej4j1fZSdSDb35TUR0e+u4kpQ6nHJzyq9Bfg/YEY+FDXslPP/pZrnjOXDlGuAdxfOh5J0JTAuItyzjOeM9UXShWQjD6+LiMWp4xmKJN0ELIqIj/Vn/222cjxmZYuI5cDyMot39/rWD1A4Q0YpxyXvEZsL3AecPFwTMXjZ/1+qTkSsl3Qf8Aayk126h57eAFyYMDQbovKRhu8D7wSmOxHbrBpK+P5xMmYVT9LhwKHA7cBKsstafBVYRAkTKIebPBG7BXgSOAPYoXvUNiKWpYssPUm7AduRzS2slXRgvumxiGhLFtjg+w7ZcMq9wN3AZ8gmHl+eMqjUJI0BJhesmpT/H1kREU+liWpIuAh4P1mvWKuk7rmFqyJibbqw0pI0G7geeAoYS3aMpgNv7ncdHqa0SifplcD3gKlkXyTPkM2P+lpELE0ZW0r5EFyfX6oRMezm0pVC0hXASX1sen1E3DK40aQl6ZPALLJJ+38DTo+Iu5IGlZik6WQ9ysWujIgZgxrMEJIP2fbl5Ii4YjBjGUokXUrWo7wzsAp4EPhWRNzY7zqcjJmZmZml47MpzczMzBJyMmZmZmaWkJMxMzMzs4ScjJmZmZkl5GTMzMzMLCEnY2ZmZmYJORkzMzMzS8jJmJmZmVlCTsbMzMzMEnIyZmY2xEgaL+ksSTunjsXMtj4nY2ZmiUgaK+noPjZ9DzgM+OEgh7RZko5NHYPZcORkzMwsAUkCvgTcVbT+rcDYiHgr8KKkD6SIbxOWSjoldRBmw41vFG5mloCkDwMvRsTvUsdSCkkXAl+NiGdTx2I2XDgZMzMbZJJGAH8BjoqIDanjKYWk1wHvjYhTU8diNlx4mNLMbPC9AVhYaYlY7nbgHZLqUgdiNlw4GTMzG3xvA+YVr1RmpqTFktZIulZS40A3/nLaiYgu4EngtUMhHrPhwMmYmdngOxxY2Mf6OcAngJPIkp2DgbO3Qvsvt52FZK9hI5Iul/S1guUjJYWkP27FeMwqmpMxM7PBtzPQUrhC0uHATOA9EXFbRNwHXAIcN5AND1A7LcCOfdRdS9brV3hSwinAL4A3SNplK8VjVtGcjJmZDb7tgdaidWcAf46IwuHLZ/N9B9JAtNMC7NDH+tcAHcA9AJLGAO8BzgfmAjO2UjxmFc3JmJnZ4GsD6rsXJNUDbwWuKdpvJLBqoBodwHZGsnEyCfAvwO/jpdP0TwSWRcTdwNXAR/Lrqw10PGYVzcmYmdngexYonKB+EDAK+Laktu4HcC59zy0r1xbbkfQ2SQskPSrp3zZRT2P+Goq9g42HKK/On19LNjw7rZR4zKrBNqkDMDOrQo+RJSbd9gFWAwcW7fdH4I7iwpK+CXx+C21MiYhHitZtth1J2wDfAV5P1jN1n6RrIuKFov13Am4pimkKsAvw53x5X7JhyxkAEdEm6bdkCVp32ZJet9lw5WTMzGzw/RF4NXBlvtwAPB8Rj3XvIGl3YG/g//VR/tvAFVto4/E+1m2pncOAhyJiab7teuBNZBPwC72Kjed//QtwY0Ssy5dPAe6JiEcL9rka+JWkT0bEqn7EY1YVnIyZmQ2+35NdxqHb80CjJBXMt/oicF1EPFxcOCKWA8vLaHez7Uh6N7C0YP+lwK6FFUjaDXi8j96ydwAX5/tsA3wY+GbRPv8LrAHeB/xoS/GU8frMKpKTMTOzQRYRT0u6T9J++VDizWST1r8g6b+BDwBvJ+upGkgD0c6HKLoGmKQJwCFkvWOQXd5iR+Afkg4oKn8bWa/ZjwYoHrOK5wn8ZmZpnEPeO5bfdHsG2YVPHwKOAI6OiOaBbLAf7TxN756wXfN1AEjaFpgQEbcWVf124O6IeD5fPiX/90bg70WPdwKHSHrVYL1us6HONwo3M0tE0pFAV0TclToW6BlenA9MJ5/AD7yme0hS0keAX0dE8QVrfwfcHhHnDm7EZsODhynNzBKJiDsljUodR7eI2CDps2QXaK0Bzi2aG/aLiFjbR9Hb2XiSv5n1k3vGzMzMzBLynDEzMzOzhJyMmZmZmSXkZMzMzMwsISdjZmZmZgk5GTMzMzNLyMmYmZmZWUJOxszMzMwScjJmZmZmlpCTMTMzM7OEnIyZmZmZJeRkzMzMzCyh/w+7zetabqQjTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 600x175 with 1 Axes>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cabinetry.visualize.pulls(fit_results, exclude=[\"Signal_norm\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We excluded the `\"Signal_norm\"` parameter, which does not have an associated constraint term in our fit model. The result for it was reported above in the fit output:\n",
    "```\n",
    "INFO - cabinetry.fit - Signal_norm                =  1.5938 +/- 0.9757\n",
    "```\n",
    "We can also look at the correlation between parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - cabinetry.visualize.utils - saving figure as figures/correlation_matrix.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cabinetry.visualize.correlation_matrix(fit_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These visualizations were also saved as `.pdf` figures in the `figures/` folder."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing templates\n",
    "\n",
    "What did we fit?\n",
    "The `visualize` module also contains functionality to plot data/MC distributions: `visualize.data_mc`.\n",
    "We first need to create a model prediction, which is achieved with `model_utils.prediction`.\n",
    "By default this creates the pre-fit model, but the optional `fit_results` argument allows to create the model corresponding to a given best-fit configuration.\n",
    "\n",
    "The `config` keyword argument of `visualize.data_mc` is optional, but required for correct horizontal axis labels, since the observable and bin edges are not part of the `pyhf` workspace.\n",
    "Since this argument is optional, you can use `cabinetry.visualize.data_mc` with any workspace: it does not matter whether it was created with `cabinetry` or otherwise, since you do not need a configuration file.\n",
    "\n",
    "`visualize.data_mc` returns a list of dictionaries, we can extract a figure from there to further customize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG - cabinetry.model_utils - total stdev is [[69, 58.3, 38.2, 45.3]]\n",
      "DEBUG - cabinetry.model_utils - total stdev per channel is [137]\n",
      "INFO - cabinetry.visualize.utils - saving figure as figures/Signal_region_prefit.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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rVjBgwADKy8u54oorOPfcc+vuh2DSpEkMHTqU8vJyrrvuukZjSTZWXoNLJC3pPO655556587r3ma9bNkyunXrxoABA0Ju9erV/Pa3vwVgz549tGvXjrZt27JhwwZuvPFG5s+fD8D5559P9+7dKSwsJCcnp9HlR76yprXxq089r7jPKaecQlpaGjt37iQvL48XX3wRCBwN1XHggQcyceJEXn/9dU455RQWLFjAsccey8SJEwHo2rUrXbt2DY2/du1aRo8ezZQpU/jTn/4EQG1tLf/4xz/47LPPyMzM5Be/+AVTpkxh2bJl9eK56qqryM3NBeCxxx7jnHPOYciQIQDcfPPNdOnSJebT3QUFBRQXFwMwZcoULrjgAjZs2EB1dTXvv/8+r7zyCunp6Rx11FGMHRvbdctEklLvgysvL+eMM86gS5cuMXce4W+7fv/998nKymLMmDGkpdX/v0GfPn148MEHue666/jiiy/Ytm0bVVVV5Ofnc//997Njxw4+++wzunfvzvjx4+nYsWOD5e/YsYNFixZx0kknNRq/252neru9YgfPPPMMW7dupaqqirvvvpvRo0eHLmkcffTR5OXl0blzZ+6///7Qabzvv/+e/v37NznP5557jl27dvH73/8+NGzjxo3U1NTU+094YzezhA9bu3YtBQUFoc8dO3aka9eu/PDDDzF9tx49eoT+zszMBAL91tq1a+nSpQtZWVkhH76cWFmyZAnTpk0D6NziiRshpRJcTU0NTzzxBOedd14D11Tncc8997BmzRo++ugjrr/+eoqKiupt5DomTJjAf//3f3PYYYdRWFjIxo0befbZZ6POv46tW7fy3XffMX78eIYMGUKbNm349ttvY55efWp7xT7atm3LqaeeStu2bVm+fDmTJk1i3LhxfP/991RUVPD73/8+dHqvT58+rFq1qsl5FRcXc8IJJ3DiiSeyc+dOgNAbvesuaUD9/5DXYcxPN5r36tWL1atXhz7v3LmTTZs2kZ+fH0pOlZWVIR/rW8Z79uzJli1bQrEBfPfddzFNG87YsWOZO3cuBB7oETcpleByc3Nb3HlMmjSJo48+msMOO4whQ4Zw2223NTp9x44dmThxIueeey7Z2dmMHDmSFStWRJ1/nV+5ciV9+/alsLCQzMxMrrnmGg4//HBycnJ4+umnre5c1WtyUxoiIjz77LNs2bKFgQMHsn37dnJzc8nIyOC9995jwYIFoXEnT57MK6+8wpNPPklNTQ2bNm3io48+qje/u+++mwEDBjB27Fh27doVSqAzZsygsrKSL7/8kkceeaTZmCZOnMhDDz3ERx99xO7du7n66qsZMWIEhYWF5OXlkZ+fz/z586mtreXBBx9sNumGU1BQwLBhw5g+fTrV1dUsX76cJUsSW7oRCymV4FpTB9enTx+mTZvGF198wT/+8Q8yMzMZM2ZM6H9NddOfccYZzJw5k6+++ort27ezatUqbrrppibnP2/ePGbOnBnyJSUloQvOADfccEPoYvM333xjbeeqXpObUp+xY8fSsWNHOnXqxDXXXMPDDz/MoEGDuPfee/nzn/9MdnY2N9xwAxMmTAhN07dvX55//nnuuOMOcnNzOfjgg/n444/rzdcYw9y5c+nduze/+c1vQqdAKyoq6NGjB2eddRYTJ04kPT29ydiOPfZYbrzxRk477TR69uzJqlWrePzxx0O+pKSE2267ja5du7Jy5UpGjRoV8/desGABK1asIDc3l+uvv56zzz67BWstQThRLe6V1tL3weXn50txcbG1T7BQr15Rwrniiivk7LPPdjuMuMEPz6JMdmvp++C6dOkijz76aJPe7c5NvXoltfniiy/k448/lr1798qKFSuka9eu8vTTT7sdVtxogosjwdnSOalXnwivpA7vvfee9O/fXzp06CCFhYVy0003yd69e90OK240wbWi7bvvvlJUVCRTp061tnNSr16Tm5KqLF68WIqLiwX4j2iCa1nT98Gp97NXFL+gR3CtaPn5+dZ2TurVa3JTlACa4FrRbHofnHr1TnlF8Rua4FrRIssERNzvnNSr1+SmKPXRBOdAgnO7c1KvPl6vKH5EE1ycCc6Gzkm9+ni9ojjFrFmz5Nxzz3U7DBHRBBdXgrOlc1KvPhE+1XFz/QLyn//8p96w6dOny+TJk1s8r3gZPXq0lJSUJGTeif5OmuBa0bQOTr3ffarj9vq1IcHt3btXamtrPZngtA4ujqZ1cOr97FMdG9ZvtAT32muvSX5+vtx+++2Sl5cnPXr0kAcffDA0bmVlpVx22WXSt29f6dSpkxx++OFSWVkpIiLvvPOOHHbYYdK5c2c58MAD5bXXXgtNN3r0aLn66qtl1KhRkpGRIZMmTZI2bdpIenq6ZGVlyR/+8AcREbn44ould+/ekp2dLUOGDJE33nij0ThLS0sFkHnz5kmfPn2ka9euMnPmTBERWbp0qbRr107S0tIkKytLDjzwQHnyySdlyJAh9b73HXfcIePGjWvtetQjuJY2rYNT71ef6tiyfmNJcG3btpXrrrtOqqur5bnnnpMOHTrI5s2bRUTkggsukNGjR8uaNWukpqZG3nrrLamqqpI1a9ZIbm6uPPfcc1JbWysvvfSS5Obmyo8//igigQTXp08f+eyzz2TPnj1SXV3d6BHco48+KuXl5bJnzx65/fbbZZ999pFdu3Y1iLMuwZ133nlSWVkpH330kbRv314+//zzBuOKiFRVVUmXLl1CXkTk4IMPlqeeeqq161ETXEub1sGp96NXJOHrP1ZiSXAZGRmyZ8+ekM/Ly5N33nlHamtrJSMjQz766KMG873lllvkzDPPrDfs+OOPl3nz5olIIMFdd9119XwspyhzcnJCy2sswX3//fehcQ899FBZuHBhg3Hr+P3vfy9XX321iIh89tlnkpOTI1VVVc0uvymcSnD6PjjL3/elXr2+Dy46iV7/sdK2bVv27NlTb9iePXto165d6HPXrl1JS0sLfc7MzGTHjh2Ul5dTVVVF//79G8x39erVLFq0iJycnFBbvnw569atC43Tp0+fqPHdfvvtDBw4kM6dO5OTk0NFRQXl5eVNjt+jR48GcTbFlClTWLBgASLCo48+yoQJE5p9N10ySKkEF4nbnZN69fF6JUCi13+s9O3bt8F2KS0tpaCgIOq03bp1IyMjo9G3aPfp04ezzjqLrVu3htrOnTu58sorQ+MYY+pNE/n5zTffZPbs2Tz55JNs2bKFrVu30rlz58CpvBYSOW+AkSNH0r59e958800WLFjAWWed1eL5Ok3KJjgbOif16pN1ZJFquHXkfMYZZzBz5kzWrFnD3r17eeWVV1iyZAmnn3561GnbtGnDOeecw2WXXcbatWupra3lnXfeYffu3Zx55pksWbKEF198kdraWqqqqli2bBlr1qxpcn777LMP3377bejz9u3bSUtLIy8vj5qaGm644Qa2bdsW83eLnHdZWRl79+6tN/zss8/mwgsvpF27dhxxxBGtmrejOHGe0ytt3333leLiYikpKbH6mop69XpNrvW4uX4rKyvl8ssvl4KCAunUqZMccsgh8uyzz4Z83V2U4RQUFMjLL78cmv6SSy6RXr16SadOneTII48M3UX57rvvylFHHSVdunSRbt26yYknniirV68Wkcavt7399tuy3377SU5Ojlx00UVSU1Mjv/vd7yQ7O1t69Oght956a71lN3YNLvxaYfgyysvL5fDDD5ecnBw55JBDQuOsXr1ajDHy5z//ucXrTkTLBOJqQ4cOdb3zUa9ek5viVyorK6Vjx47y9ddfxzUf9C7Kljetg1PvZ68obnPHHXfI0UcfHfd8PJ3ggAuBD4DdwLyw4YWAADvC2nVhPh14ENgGrAcua8lytQ5OvV+9orhNQUGB9O3bV/7973/HPS+vJ7hTgVOA+5pIcGlNTHcz8CbQBRgYTHInxLpcrYNT70evKH7D0wkutHCY2cIEtxY4PuzzjcDjsS5P3wen3m9eUfyI3xPcD8Aa4CGgW9B1Cbp9wsY/Hfg01uXp++DU+80rih/xa4LrCAwD0oB9gKeAF4OuTzDBZYSNfxxQFuvy+vbtK0OHDpWhQ4fK4MGD5dRTT7W681KvXr2dXnGeOXPmhPrnlvTrzTWrElwjvkcwqWWHHcF1D/OnteYIzu2dQ7169d71SuLx5RFcI36fYFLrHPy8FjguzN/Q0mtwbu8c6tWrd9f369dPRowY0arpleTg6QQXPAWZEbwr8tHg32nACGAAgUeIdQWeAF4Lm+4W4PXg0dz+wLqW3kXp9s6lXr16d/2IESNk9OjRLZ5eSR5eT3Azgkdm4W0GMBEoBXYGk9cjQI+w6cLr4DagdXDq1atvoR89enSDBBfL9LFQUFAgGRkZ0rFjR+ncubMcdthhct9990ltbW3UaUtLSxs8HitV8XSCc6tpHZx69ant58+fL+np6QJIQUGBzJ8/P+bpYyH82Y5bt26VZ599VgoLC2Xq1KlRpy3VBBdCE1wrmtbBqVefun7+/PmSmZlZ78xRhw4dpKioqNXzjyQ8wdWxYsUKMcbIp59+Kv/85z/l4IMPluzsbOndu7dMnz49NF6fPn0EkKysLMnKypK3335bvvnmGzn66KMlNzdXunbtKpMmTZItW7bEFIuX0QTXilb3NoHFixeLiF07n3r16hPrCwoKIi+LCNDg6f6xzr8xGktwIoHkde+998prr70mn3zyidTW1srHH38s3bt3l6effjq0vMgjuP/85z/y0ksvSVVVlfz4449y5JFHyiWXXBJzPF5D3yYQRws/grNt51OvXn1ivTGm0QRnjGnx/JuiqQQ3YsQImTlzZoPhl1xyifzxj38MLTPaKcqnn35aDj744BbF5EX0CC6OBGfjzqdevfrE+qaO4AoKClo0/+ZoKsH17t1b7r33Xnn33XdlzJgx0q1bN+nUqZOkp6fLmWeeGVpuZIJbv369nHHGGdKrVy/Jzs6WrKws6d27d4vj8hqa4FqZ4Gzd+dSrV59YP3/+fOnQoUO95JaZmSnz58+Pef7RaCzBvffee2KMkU8++UT69esnf/nLX2TXrl0iEjiCq3vJaFlZWYMEd84550hRUZFs2rRJRAJHcE2dUvUTmuBa0bQOTr361PZFRUXSvn37endRxjp9LIQnuIqKClmyZIn069dPzjrrLBERycvLk3nz5olI4OaTvLy8UILbuXOntGnTRr766qvQ/MaPHy/nnXee1NTUyJo1a2TUqFGa4DTBNd60Dk69evXJqoPr1KmTjBw5Uu6++26pqakREZFFixZJ3759pWPHjnLSSSfJH/7wh1CCExG57rrrpFu3btK5c2d555135LPPPpMhQ4ZIVlaWHHTQQXL77bdrgtME13jTOjj16tVHJrhYp1eShya4VrTIMgER+3Y+9erVe8crzqJlAnE0fR+cevXqnfJK4tAjuDgTnNs7h3r16r3rlcSiCS6OBOf2zqFevXrveiXxaIJrZYJze+dQr169d72SHDTBtaJpHZx69epb65XkoQmuFU3r4NSrV99aryQPTXCtaAUFBQ3KBETs2HnUq08V39o6NLe9kni0TCCOpu+DU6/efR+e4GyMryVeSQx6BOdAgnN751CvPhV9XYKzNb5YvZI4NMHFmeDc3jnUq09VP3r0aBkxYoS18cXilcSiCS6OBOf2zqFefSr7ESNGSL9+/ayNL5pXEo8muFYmOLd3DvXqU93369dPRowYYW18mtzcRxNcK5rWwalX774fMWJEg9fV2BRfU15JHk4lOBOYV2rQu3dvWb58OYWFhQ1cWVkZixYtYvz48erj9D+7/CzYU1vPd64xDKpMY2VmDRVpDX9z6lvnbz5sHFdeeaVV2z+anzp1KgDLli2zMr6mfGPDlcRgjPlQRIbFO580J4LxCmlpadx0002MHTuWsWPHhobbsPP4yu+phVNGhXxhVVvGl2ewqFsVFRm1DaZX33pftbrKvu3vU/+nP/2pgVOcZcmSJSxZsgSgsxPzS6kjuGHDhskHH3xQb5gtO4+fvPnNqFCCC++cy6J03upb7i/5uIb8/Hyrtn80P2bMGCBwBGdjfC3xSmLQIzgHcHvn8K3/v53w4w90blNNr7QtzK3pQsXe9g2mVx+/f+7HT3j55Zft2v4p4hX7cSXBGWMuBKYCg4GFIjI1OHwkcCMwFKgFlgEXi8i6oJ8BXAPsDpvdgSLybUtjcHvn8LXfW0vhkWMZ36GURbt+RkVtdoPpC9tuV++Af+PFH+zb/jH4qqoqq+PT5OYP2ri03LXATODBiOFdgLlAIVAAbAceihjnCRHpGNY0uVnm001tqHMui9J5q4/PZ2RkNPBub/9YktvGjRutjU+Tm39wJcGJyP+KyDPApojhS0VkkYhsE5FK4G7gcCeX7fbOkQo+r02VFZ1/Knobtn9z/q9//Ssffvgh3377LWPGjOGxxx6zKj5Nbv7CrSO4WDkKWBkxbKwxZrMxZqUx5vyWzKy6utrqnccvfuPeDCs7f797W7Z/c8ntiiuuoKamBoDVq1czbdq0UJJzOz5Nbv7D2gRnjDkQ+DMQfm/uk8BAIA8oBv5sjJkY6zy///57Hn30UU4//XSGDRvG3LlzQ87tncdPfre0beDd7vz97m3a/k3566+/nj179tQbXllZyTXXXGNFfNH8bbfdRllZWQOn3hl/2mmnceCBBzJs2DCAbo2O2EKsvIvSGLMvsBS4RETerBsuIp+Hjfa2MeZ/gNOBhbHMt0+fPnzyyScNhtuw8/jZu935+927vX1j9RUVFQ0cwHfffWdFfOrd9XfccUfIG2PKG4zYCqw7gjPGFACvADeKyKNRRhfAxDrv9u0b3mpty8b1q3e78/e799LdiH379m3gATp37mxFfOrt8/HiSoIzxqQZYzKAtkBbY0xGcFg+8Cpwt4jc38h0vzHGdDEBhgMXA8+2Ng63N140P3LkSI455hhr4/NVclt4Mzx5tb3xNeF//PFHa7d/pJ81axaZmZn1xmnXrh3Tp0+3Ij71dnkncOsI7lpgF3AlcGbw72uB84B+wAxjzI66FjZdEfANgfKBR4BbReTh1gTg9saLxW/cuJG8vDxr4/NNcvOw7969u5XbvzE/efJk5s6dS3p6OgA5OTnMnj2bP/7xj1bEp94e7xSuXIMTkRnAjCb09c1MF/MNJc3h9saL1efl5Xmyzknr4JLnCzz2+5g8eTJ/+9vf2LhxI//617+si0+9Hd4prLsGl2hs2Hixei8mt7IyrYPTG078e2ZCfXK8U6RUgisvL+eMM86gS5cuVm9cr3uv1sF5/cjTlu3v1zMT6hPvr7/+ev7973+DQ28TSKkEV1NTwxNPPMF5553XwNmwcf3ivVgHl25q6d5ml7Xx+SW5efnMhPrE++nTp7Nw4UKAxmtKWoiVdXCJIjc31+qN61fvducfi69ts4sf93Zgt6Xx+SW5qVffUh8PKXUE58U6OC/VOXk1uY3vUBpIbh488vT670O9+kQlN0ixBBeJ2xsvluTm5aeuu935t8R7Mbl5rQ5OvfqWeCdI2QTn9saLxXv5bjMbOv9U8F6qgwtHjzzVJzq5QYomOLc3Xqzeq3ebef1uRC95L/4+vH5mQn3ivVOkVIKrqKhg4sSJXH/99VZv3Drvxc6rrEzr4PSGE/+emVCfWP/3v/+dM844A7RMoOV06NCBIUOGeOLZd2+99Rbvvvsur7/+OoWFhTz22GNWxdec91wd3BfLYN1XsOYzNs+9gIc+XGtXfDF6W7a/X89MqE+837JlC0888QQ4VCaQUglu8+bNVm/c8OQ2bdo0du/eDQReDFlcXMxVV11lRXzRvKdu2PhiGbx8D9QG3lO2raKC8hcfCAy3Ib4YvU3bP5rX5Ka+pb61GBFxbGa20zYzW/bmFjQYnm5qyWtTxca9GY12zkn3678Odbj1v0Aa9BjgfnzRfFp7OO2nR4q63fk360vOhe0bG0xDdh4UP+B+fFH86A/vYN68eVZ3TurVt9QbYz4UkWENRm4hKVXovbdTDzjj5nrD6t0qHu1W8mT5v/ym8S9QW+uN+JvwtiUHoPHkBrC93Ir4tA5OfSr7eEmpIzjTYz9h8l9Cn93unJr0MRxVWB2/h/zmuRewrbE3TWfnUfj7v7oeXzS/96lrqBjYi4q0hvtx5xrDoMo0VmbWqHfA33zYOK688sqQd7vz97PXI7g4saFzatIfcRbm5buRmuqfhqWlwxFn2RGfj/xDR0yGFx+Amt0/ybR0uh013or4ovm5A3tRcfphDX1VW8aXZ7CoWxUVGbXqHfBVq6tC3ubk4AfvFCl1kwlVO+Hlu+le+roVnVOT/oChnDh2XOCaGwSO3I77AwwcY0d8PvLlA04IrNu27QIyO49uvzqX3w3tZUV80XxjRx7hnXNZlM5bfcu9252/n/2SJUuYOHEiuF0mYIy5zBhzcPDvkcaY74wxpcaYhv+dtIWMLApPmMKUwV2s6Jya8yv3/S303B96HxA4LanJLXF+4BjoOQB6H0Dh7//qmeTWqLc8OXjd25wc/OAHDx7MkCFDwIIygUuB0uDfNwN/AWYCf40zpoTRHn3ChvqmveefwGJ5cvC637p1q9XJwU/eKeJJcJ1FpMIYkw0cBPxNRB4AGt7Hbgld2uy2t3NS73py8/L74Nzu/FPBr1y50orOPxV9a4nnJpPvjTGjgEHAGyJSa4zpBDT8dVjClr3pbLCxc1Lvuvfy++Bs6PxTwVcPGmRl5+93Hw/xJLg/AU8B1cBpwWEnA+/FG1SiqMZDT9gI4vnTZh7xd3n0fXCda4wVnX8q+Cm1OQ28252/3328tDrBicjzQK+IwYuAJ+OKKIm43Tn5/rSZh7wXk1th2+30qkyzovNPRe925+937wTx3EW5OXKYiOwB1sYVUZKwoXOK5rsHT5vZGp969/3KzBorO3+/e7c7f797p4jnJpN2kQOMMe2gkfOAtuCVOrig/9Gjp83UJ89rHZwmNz95p+vgWnyK0hjzJiBAhjHmjQjdG3jbicASQrAOzpbOya+nzdTrDSd+9TYnBz/4ujq4xx9/3JE6uNZcg/s7YIBDgQfChguwAXjVgbgSgtbBqff1DT2WJweve62DS56/4oorGvjW0OJTlCLysIjMAw4J/l3XHhGRF4PX4ZrFGHOhMeYDY8xuY8y8CHeMMeZLY0ylMeY1Y0xBmEs3xjxojNlmjFlvjLmsJbFrHZx6397QY3ly8IPXOjjvXZOL5y7KL40xxwMHAx0j3J+jTL6WwFNPfgV0qBtojOkG/C9wHrAEuBF4AhgZHGUGsB9QAPQAXjPGfC4iL8QSs9bBqW/Kax2ceq2Ds9PHQ6sTnDHmbmAC8BpQGaaivn9HRP43OI9hBK7b1XEqsFJEFgX9DKDcGLO/iHwJTAGmisgWYIsxpgSYCsSU4DxXBzfhJrvj85OfMMuTyU3r4JLntQ4u+T5e4in0ngQcJCLfOxUMgaeifFz3QUR2GmNWAYOMMRuAnuE++PcprV2Y252TevXxeq2D0xtO/OqdIJ4EVw5sdSiOOjoCkW/6rACy+ek0aEUjLjYqK+CxwGW79tTy82EHsugXE6ztvNSrj+bnZtZEfZ+ZjcnB697tzt+Pfu7cucydO7dulG4NJmoF8SS4O4DHjDE3E7h7MoSIfNvKee4AOkUM6wRsD7q6z1URLjYyO8Pkv1jTOalXH6/XOjhNbn7x06ZNY9q0aZSVlfGzn/2svMGErSCeBHdf8N+TI4YLrS/2XkngOhsAxpgsoD+B63JbjDHrCLy54OXgKAcFp4kZmzon9eod95YnB697W5OD37xTtPpJJiLSpokWNbkZY9KMMRkEEmFbY0yGMSYNeBo4wBhzWtD/GfgkeIMJwCPAtcaYLsaY/YFiYF6sMWsdnHpfe8uTg9e91sElzztFPI/qAsAY08cYMzL6mPW4FtgFXAmcGfz7WhHZSODNBLOALcAIoChsuunAKmA18DpwW6wlAqB1cOp97C1PDn7wWgfnvRtO4ikT6AssJFAHJ0BHY8zpwAkicl5z04rIDAI1bY25V4D9m3C7gXOCrcVoHZx6X3oLOv9U8FoH563kBvEdwc0BniNwF2Pd00teBo6LN6hE4bk6OPXqo3itg0uez8nJaeDd7vz97uMlnptMhgMnicheY4wAiEiFMcaRp0AnhODbBOg3HPoPd71zUq8+Xq91cHrDiZ/8kiVLWLJkCTj0NoF4juA2APuGDzDG/AL4Lq6IEklGFhx3oSY39b7x+j44TW5+8mPHjq2rhXPkbQLxJLjbgX8aY34HpBljJhJ4buStTgSWSGzpnNSrj9drHZwmNz96p4jnYcsPGmM2Af8FfA+cDVwnIs84FFtCsKlzUq/ecW95cvC6t6HzTwXvFK0+gjPGtBWRZ0XkRBEZJCK/tj25aR2cel97y5OD173WwaVWHdx6Y8y9xpjDHYsmwWgdnHrfesuTgx+81sF5727KeBLc8QSeD7nQGFNqjLnZGDPYobgSwpa96XZ2TurVa3Kz3g/SOjhPJTeI71Fd/yciV4hIXwLvZOsCvGqM+cSp4JymuqoqUCaw6r3QMNc7J/Xq4/BaB5c8r3VwifdLlixh2rRpYEGZQDhfAl8QKBEodGiezhNWJgDud07q1cfrB2kdnN5w4iNvTZmAMSbHGHOuMeZfQCkwhkCJQHcnAks0NnRO6tXH67UOTpObH71TxPMkk7XA28BjwGkistWRiJKALZ2TevXx+opt+8LcH+r5zm2q6ZW2hbk1XajY277B9Opb55dnBd7F7HbnnwreKeJJcP2BwQSe9v9bYKwxZhjQSURedSK4RGBT56Refdx++OlN+opoyVF9i3zNh3dY0fmngneKeK7BnQbcC3wNHBUctguYGW9QiULr4NSrV99aX1VVZUXnnwreKeJJcJcCx4rILcDe4LAvgQFxR5UgtA5OvXr1rfU//vijFZ1/KvrWEk+CyybwiC4IvA8OoB1QHVdECWTTrlrKXni4XpkA2LHzqFev3m7fvXt3Kzt/P/lPP/3UmjKBNwi8kTuci4HX4phnQqlJz65XJgD27Dzq1au322dkZDTwNiUHP3inywTiucnkImCJMaYYyDbGfAVsB052IrBkYNPOo169ert9QYS3LTn4zTtBPG8TWGeMORQ4FCggcLryPRHZ2/yUdmDbzqNevXrveLc7f797p4jnCA4REeC9YPMMbu8c6tWr9653u/NPBe8UTj2qyzO4vXOoV6/eu96Gzj8VvFOkVILTOjj16tW31msdXGrVwXmOzOoK7nvmX5R9/UUD5/bOo169eru91sEl3nfp0oWbbroJHCoTMIHLaKlBeo9+Uj35fxoMt2HnUa9evd3+rifmsntAfgPfucYwqDKNlZk1VKQ17E/Vt8IvfudDERnWYOQWEtdNJl6jmrYNhtmy86hXr95uv3tAPpwyqr4Pe+tARZS3EqhvgV/8ToNxW0NKnaKMxKadR7169R7zlr/Sx+veCaxMcMaYHRGt1hjzt6ArNMZIhL+upctwfedQr169d73lycHr3imsPEUpIh3r/jbGdATWA5H3juaISE1r5u/6zqFevXrvesuTgx98WYOhrcPKI7gITgN+BN50Ymau7xzq1av3rreg808F7xReSHBTgEek4e2eq40xa4wxDxljusUyI62DU69efWt95xpjReefCt4prC4TMMYUAN8C+4pIaXBYR2B/4COgK3APkC0iv4o2v845OVKVkfvT3ZSDj4cDTwDc33nUq1dvt+/1/hzWThjpeufvV//zF1ay7KMPqDbA6g2rRaSwwYgtxMprcGGcBSyvS24AIrID+CD4cYMx5kJgnTEmW0S2NzezqoxctA5OvXr1rfFzM2ui3upuVfK49YmAv2SSnfFF+hMGUX3K/oGB59xR3mDEVmD7EdzXwC0i8mAz4+xD4CaUHBFp9h1Cpsd+wuS/1Btmy86jXn1rvJJEvrm52To465LHrU+Qvtdw8Vm/szO+5vw5dzhS6G3tNThjzCggn4i7J40xI4wxA4wxbYwxXYG7gGXRkltjuN05qVcfr1fcw9rkECR9r6H7njbWxpeMUgFrj+CMMXOATBE5K2L4ROAmoDuwDXgZuEJE1kedZ9gRnA2dk5+9oviOsCM4tzv/WHzt7Cf4sd1edl81wcr4mk1uDh3BWXsNTkT+q4nhC4GF8czb7c7f715R/IzbnX+s/q52e9ndpuEBjC3xaR1cAnC7808Fryh+xYbOP1bv1eSWanVwjpG2ezud/nUnD3+6xdrk4AevKH5E6+AS7w9Zvpr7XnwGHHpdjrWnKBNB1w5t2XbMpfxocXLwg1cUPzKoMs3q5OALf0QBO47tDW990uKbBhsjpRLclr3pbLA8OfjRK4ofWOm1Ojif+HhIqVOU+j44TW6K0loae1mn251/k/6dL2DVOvhqDZuvmsND3/7brvhi9PGSUkdwkbjd+fvdK4qfcbvzbza5PfwS1ASGbauogAUvQLu9cNhA9+OL0TtBSh3BheN25+93ryh+xu3Ov1n/v29CdcSbxKprAsNtiC8G7xQpmeDc7vz97hXFz7jd+UdNDpuaKNUJDnc7vli8U6RWgqvaScdX7uSQ75Zamxz84BXFr9jQ+UfznTo3cYd912wr4mvOd3+vlE4PvAIOlQmkVIJrn5HB+accw//1/bW1ycEPXlH8iFfq4Nr/9khoH3F7Rfs0uo0dbUV8zfkpvQ5g27nHAmiZQEvp0ma3651/KnhF8SNeqYMr7z0gcEPJQy8GbjTpmk23saP5Xb8hVsSXzGtyKZXgtA5Or8kpSmvxVB3cYQPhjU8C3ivvg9M6uPjQOjhNborSWjxVBxckfa83Tqsm6m7KlEpwkbjd+fvdK4qfcbvzjyW5pfr74FLqFGU4bnf+fveK4mfc7vxj8bV72gTeB2dpfFoH5zRVO+Hlu+le+rrVycHrXlH8jNudf6z+Ry++D+6jVXR84GXQMoFWkJFF4QlTmDK4i7XJwQ9eUfyK1ckhwnsuuQGF+/+c8391CjhUJpBSCa49ta53/qngFcWPeKUOzg/eKVLqGpzWwWkdnKK0Fq/UwYX8f59hd3wt8K0lpRKc1sHpNTlFaS2eqoPzkY+HlDpFqXVwmtwUpbV4sQ7O6z5eUirBReJ25+93ryh+xu3O3+/eCYxIw/+V+BWT00voOxj6Dafw5wOtTg5e94riO957CmqqAejcpppBaVtYWdOFir3tG4yqvpV+yw+B9uO334jIfg0mbCEpdQ2OjCw47kLXO3+/e0XxJcNPB+r//iui7B/qW+fLbpusZQKtwe3OPxW8ovgVG/avVPBOkVIJTuvgvPXjVBSbsGX/SgXvFNYmOGPMMmNMlTFmR7B9FeYmGWNWG2N2GmOeMcbkxjJPrYPz1o9TUWzClv0rFX1rsTbBBblQRDoG2wAAY8wgYA5wFrAPUAncG8vMtuxNt3Lj+d0rih+wdf/yu48HL95kMhlYIiJvABhjrgO+MMZki0izF4C0Ds5bP05FsQkb9y+/+3ixtkzAGLMMGAQY4CvgGhFZZox5FnhbRG4NG3cHMFpEPmx2np26C5k/PaS660GjOWfkz6zduF73iuJn3N6/fOc/eQE+fSkgN3yzWkQKG0zUQmw+gvtv4HOgGigClhhjDgY60vBJ0xVA9B42szNM/gtg4cb1mVcUP+P2/uVLf+AJgQbwl3HlDSZqBdZegxORFSKyXUR2i8jDwFvAicAOoFPE6J2AmO9Pt3Lj+sgrip9xe/9KBe8U1ia4RhACpytXAgfVDTTG9APSga9jmYkNG8/vXlH8ig37Vyp4p7AywRljcowxvzLGZBhj0owxk4GjgBeAx4CxxpgjjTFZwA3A/0a7wQS0Ds5rP05FsQlb9q9U8E5h6zW4dsBMYH+gFvgSOEVEvgYwxvyeQKLrCrwC/C6WmWodnLd+nIpiE7bsX6noW4uVCU5ENgKHNuMXAAtaOl99H5y3fpyKYhO27l9+9/Fg5SnKRKF1cN76cSqKTdi4f/ndx4uVR3AJo2onvHw39BsO/Ye7vvH87hXFz7i9f/nSr3oPvn0PoHODCVpBaiW44OtywNKN6yOvKH7G7f3Lt75/4OCDT19y5HU51j7JJBGYHvtJXaG3oihKa7A2OfjIl902+UMRGdZAtpCUuganKIoSDzZ0/qngnUITnKIoSgzY0vmngncKTXCKoigxYEvnn4q+tWiCUxRFiQFbO3+/+3hIrbsoI8oEFEVRYsXGzt93XssE4iCsTEBRFCUerEsOfvAOlwnoKUpFUZQWYmVy8JF3Ck1wiqIoLcDtzj8VvFNoglMURYkRGzr/VPBOoQlOURQlBmzp/FPBO4UmOEVRlBiwpfNPRd9aUusuSi0TUBSlldja+fvKf/2Fo2UC+rBlRVEUxS7+Mk4ftqwoiqIoTaEJTlEURfElmuAURVEUX6IJTlEURfElmuAURVEUX6JlAoqiKIod6NsE4kDfJqAoimIv+jYBRVEURYmOJjhFURTFl1iX4Iwx6caYB4wxq40x240xHxljfh10hcYYMcbsCGvXuR2zoiiKYh82XoNLA74HRgPfAScCTxpjBoeNkyMiNW4EpyiKongD647gRGSniMwQkTIR2Ssi/wRKgaFux6YoiqJ4B+sftmyM2QdYDRwMVBFIdmsBAV4G/iQi5THNq1N3ITPs7tPBx8OBJzgcsaIoitJiPnkBPn0p8PeGb1aLSGG8s7Q6wRlj2gFLgVUi8l/GmI7A/sBHQFfgHiBbRH4V0/z0bQKKoij249DbBGy8BgeAMaYN8ChQDVwIICI7gA+Co2wwxlwIrDPGZIvIdnciVRRFUWzEygRnjDHAA8A+wIkisqeJUesOP627lqgoiqK4i5UJDrgPGAgcKyK76gYaY0YAW4H/AF2Au4BlIuJI1buiKIriH6w78jHGFAD/ReCmkvVh9W6TgX7AC8B24DNgNzDRrVgVRVEUe7HuCE5EVgOmmVEWJisWRVEUxbtYdwSnKIqiKE5g3RFcQtHX5SiKotiLvi4nDvR1OYqiKPair8tRFEVRlOhoglMURVF8iSY4RVEUxZdoglMURVF8iSY4RVEUxZek1l2UWiagKIpiL1omEAdaJqAoimIvWiagKIqiKNHRBKcoiqL4Ek1wiqIoii/RBKcoiqL4Ek1wiqIoii9JrbsotUxAURTFXrRMIA60TEBRFMVetExAURRFUaKjCU5RFEXxJZrgFEVRFF+iCU5RFEXxJZrgFEVRFF+SWndRapmAoiiKvWiZQBxomYCiKIq9aJmAoiiKokRHE5yiKIriSzyZ4IwxucaYp40xO40xq40xk2KasNKRo97k8skLbkfQcrwYM3gzbi/GDN6MW2NOJt2cmIknExxwD1AN7ANMBu4zxgyKOtWubdHnvOo9u8b59CW74nEqZieXp+s6Octycl66rpMzjlf3RciLZaRoeC7BGWOygNOA60Rkh4gsBxYDZzmygG9jWPnJHCcWvBizk8vTdZ28ZXkxbo05ectLdtxRMCKStIU5gTHmEOAtEckMG3Y5MFpExkaZtgqoDRu0ESiPGK0zEO1cZjLH6UbDGN2Mx6mYkx1TKq9rp5bl5Lx0XSdnHC/ti9346citrYhkRJlPVLxYJtARiDzXWAFkR5vQiRWmKIqieAPPnaIEdgCdIoZ1Ara7EIuiKIpiKV5McF8DacaY/cKGHQSsdCkeRVEUxUI8dw0OwBjzOCDAecDBwPPAKBHRJKcoiqIA3jyCA7gA6AD8CCwEztfkpiiKooTjySM4RVEURYmGV4/gFEVRFKVZNMEpiqIovkQTnKIoiuJLNMEpiqIovkQTnKIoiuJLNMEpiqIovkQTnKIoiuJLvPiw5VaTnp4uaWk/feVu3bqRl5dHdXU1mzdvJjc3l127dtG5c+d604X79u3bU1FRUW+cSA/UG6cxHz5OUx5g48aNdO7cuUkPUF5eTk1NTZO+urqatWvX0qtXryb95s2bSUtLo1u3hu8ZbOn62bhxI3l50V/nFLke3R4nlrhTOWYn5+XFdb1u3Tp69uzZYLjX+49k9w9NfX8RYevWrQBUVlbuduTh+CKSMi0zM1MiKS0tldmzZ0tpaamIiBQXFzfrI8dpzIeP05SvG6c5LyIyePDgZn1paakMHz68WT979mwpKipq1peWljb47o3FH8v6GTp0aKPLiqSx5bk5Tixxp3LMTs7LL+vaD/1HsvuHWL4/sFMc6PNdTzrJbJEJrrGVu3jx4qgrv26c5n5cixcvjvrjKykpifrjy8/Pj/rjLCkpadaXlpbW+15Nfb/IcVq7fmJNcI3F5OY4scSdyjE7OS8/rGu/9B/J7h9i+f6a4OJMcNF+PLb4wYMHWx1fYz7WBGcbXozbizGLeDPu8Jht3v/CvRf7DxFNcK1qffv2dWTlJ9PPmTPH6vgao7GYvYAX4/ZizCLejLsuZrf3L7/3H6WlpQKUiSa4lrV9991XioqKZOrUqVZvXPXqW+uVxOL29vW7LykpkeHDhwvwH9EE17IW6wVX9eq96JXE4vb2TSUPfCCa4FrWYr3gql6917ySeGze/n7zmuBa0bx6wVW9ek1u7mPr9vej1wTXihZLHYt69V7yiru4vf396jXBtaLtu+++UlxcHFMdhnr1XvCKe9iw/f3mFy9eLMXFxcm5yQQ4J5bmRCDJaF6sY1GvvjmvuIMt29+vPilHcMBrMbRXnQgkGa0uwbm98dSr19OW3sXt7ZsKXk9RtjLB2bDx1KtPlFcSi9vbN1W8KwkO6AqcBfwp+LkX0NuJQJLRCgoKZPjw4TE9u1G9eq95JbG4vX1TwU+dOlWKioqSX+gNjAbKgReA7WHDljgRSDKa1sGpV69evf0+6UdwwP8BxwT/3hL8NwPY4EQgyWhaB6devXr19ns3EtyWsL83B/9tA2xyIpBkNK2DU69evXr7vRsJ7i3gV1I/wR0PLHMikGS0WN7npF69evXq3fVuJLiRwWtwDwO7gDnAWuBQJwJJRgsv9LZ546pXr159KvqkFno3GBnygSuAe4ArW3IHJXAh8AGwG5jXzHhTgA+BbcAaYDaQFuaXAVXAjmD7KtYYtA5OvXr16u33nquDA04FTgHui5LgzgeOBNoHE+qHwJVhfhlwXmti0Do49erVq7ffJ+tJJo8Cj0RrLVogzGwuwTUy/mXhpQjxJDh9H5x69erV2++TleCmh7W/BU8bPgbcBMwHKoC7WrTAlie4Z4Bbwj4vAzYGrwe+BYyJdV5aB6devXr19ns3bjJ5ETgyYtgRwIstWmALEhyBhzmvAbqFDRsBZAPpwet124H+scyvV69eMnTo0FCbM2eOKxtPvXr16tXX97NmzQr1zUCZJDnBVQDtIoa1A7a1aIExJrjg9boNwOAo470AXBTLsrUOTr169ert924cwS0DbgM6BD93CN7h+EaLFhhDggNOCJ6GHB7D/JYCF8eybK2DU69evXr7vRsJrhB4G6gOHllVBz8Xxjh9GoFHe90cvHklg7Db/8PG+yWwCTiqEZcD/KpuWmAysBP4eSwx6Pvg1Kt3348ePVpGjBhhbXzq3fdJT3ChCaBP8DpY3xZONwOQiDYD6Eugnq1vcLzXgBp+qnPbASwNujzg/eB1t63Au8BxscZQV+hdUlJi9cZVr97PfsSIEdKvXz9r41Pvnl+8eLE7bxOQQILpApwNXBX8t4sTQSSraR2cevXu+379+smIESOsjU+9+96NU5SHAZuDpyUXErhFfzNwmBOBJKNpHZx69e77ESNGyOjRo62NT7373o0EtwIoihh2BvC+E4Eko2kdnHr17vvRo0c3SHA2xafefe9GgtsCtIkY1paw1+jY3vR9cOrVu+8jE5xt8al337uR4N4DJkUMK3IqkGS08LcJJHLjqFevvmkfnuBsjE+9e961twkAo4LX3N4FngiestwMjHIikGQ0rYNTr95dP3/+fElPTxdA8vPzpaioyKr41NvhXSkTCN5FeSaBV+acCeQ6EUSymtbBqVfvnp8/f75kZmbWKxXq0KGDzJ8/34r41NvjXauD83LT98GpV++eLygoiKyDFUAKCgqsiE+9Pd6Na3B9gQeAfwNfhzcnAklG0zo49erd88aYRhOcMcaK+NTb490qE3gYOBk4Jrw5EUgyWkFBgQwfPlxKSkoSunHUq1ffkPz8/GaP4NyOT737furUqe48yYTA2wTaOLFQt5rX6uD0bjP1fvJFRUXSoUOHesktMzNT5s+fb0V86u3xbhzBzQeOdmKhbjWv1cHVJThb41OvvqU+/C7KgoICTW7qG/VuJLhc4EvgOeDB8OZEIMloXnsfnD51Xb0fvZ6ZUB/Nu5HgngVWArcDN4Y3JwJJRoss9LZ149ahT11X70evZybUN+XdLPTeDmQ7sdBG5n0h8AGwm+gvQ70UWA9sCx5Bpse6HK/VwelT19X70euZCfXRvBtHcG8R48tNWxwEnAqcAtzXXIIj8LLTDcCgYNH5MuCWWJfjtTo4feq6ej96PTOhPpp3I8HdGKx7uwo4J7w5EUhwGTOjJLgFwE1hn48B1sc6fy/Vwd15550NLsbbFJ969a31emZCfTTvRoJ7rYn2qhOBSGwJ7mPgjLDP3YK3G3eNZf5eeR/cnXfe2eCRRpmZmXLnnXdaEZ969erVJ9InPcElo8WQ4FYBJ4R9bhdMAIWxzD8jI6Ne0rj66qvlgw8+qDfskksuERGRnj17hoYNGTJESktLZfjw4fXG/eGHH2Tx4sX1hs2aNUskEFyonXzyyVJaWioDBw6sN1xEZM6cOfWGlZSUNFkQ27Zt29DfPXv2FBGR6dOn1xtn8eLFDb7T9OnTpbS0VLKzs+t9JxGpu6Abau+++26D7zRnzpy6H1297yQicvLJJ9cbXlpa2uA7LV68WN599916w4qLi0VEZMiQIaFh2dnZUlpa2uA7ffDBBw1imj59eoPtVFfnGPmdfvjhBykpKWnwnSK308CBA6W0tLTBdxIRmTVrVoPv9MMPP9QbNnz4cCktLa33neq20yWXXNLgO0Vup2OPPVZKS0sb/PZEpK74tdnf3qmnntrkdvrlL38Z9bc3derUJrfTAQccEPW3d/HFFze5nbp37x71t3fNNdfEtJ2a+u3Nnj07pu3U2G+ve/fuMnv27Ji2U2O/vQMOOEBmz54d03Zq7Dv98pe/lNmzZ8e0nRr7TkVFRTJ79uyYtlNj3+mSSy6R2bNnx7Sdmur3gkdeUbdTU/3er3/96/BhZY7kFCdm4lQjtiO4CWGfuwZXRsxHcI1h0/9cRGJ7pJHN8atXr159PJ4UPYJbAMwK+/xLWngNzomVn2gf60NpbY1fvXr16uPxvkpwQBqQAdwMPBr8O62R8U4gUCLwCyAHeJVW3EWZ6I0Tr2/stSJ1jzSyIT716tWrT6RPWoID9nViQVGWMaORI5YZBN5gsAPoGzbuZQRKBbYBD+HTOrg777xT0tLSQkdumtzUq1efKj6ZCe5LAuUBfwWOA9o5sWA3Wt2TTEpKSqzeuHVe6+DUq1efSn7x4sXJf5sA0A+4CFgKbCTw2K7/Ano7EUSympfq4EpLS+s9s8/G+Pzk9fFR6tXb4127Bgd0IPBOuHuBUuAT4BZggBMBJbJ5pQ6uzkcmONvi85PXx0ep95v3cv9hzU0mBB6b9SfgdCcCSmTT98Gpb8rr46PU+817uf+wJsF5qRUUFNR7m0BrV756/3l9fJR6v3kvnnZ37W0CfmheqYNTn1xfVFQk7du3b/SOVRviU6++Nd7Lp931CM6BBGfrxlWfPF9UVCQdOnRotObQhvjUq2+t9/Jpd01wcSY4mzeu+uT5pp77mZ+fb0V86tW31nv5tLsrCQ5oDwwGjg4+JuuXwC+dCCQZzWvvg1OfeN/Ucz8BK+JTr7413uun3ZOe4IAjgHXAZqAm+O8e4FsnAklG23fffaWoqEimTp1q9cZVnzzf1HM/8/PzrYhPvfqWei+fdi8pKal7a0tybzIB3gcuDf69Jfjvn4HLnQgkGc1rdXDqE+8be+5nhw4dGjwazdb41auP9H447e7GEVwF0EbqJ7j2wA9OBJKM5rU6OPXJ8fPnzw+dzsnPz9fkpt7T3g+n3d1IcN8BOcG/PyfwRP99gAonAklG88r74NQn33v5grx69eHeD6fd3UhwfwUmBf++nMAT/dcADzgRSDKa1sGpb8rrg63V+8X74bS762UCwZtOfg0YJwJJRqt7m0Ddk0xs3bjqk+/1wdbq/eS9etrdtSeZAHc1MfyvMU6fCzwN7ARW1x0NNjLeUgLvgKtr1cCnYb4M2BXmX4r1O2gdnPqmvJcfTKtefWPey6fd3ThFua2J4ZtinH4h8ATQMXj0VwEMimG6ZcCfwz6XAce25stqHZx69epTxXv5tHvSEhxwTrBVhv1d12YCX8Uwj6zgkdjPw4Y9CtwSZbpCoBYoDBsWV4KzYeOpV69efaK9l0+7JzPBvRZsNWF/vwa8GjwqGxnDPA4BKiOGXQ4siTLdn4FlEcPKgje4bAReAg6K9cv26tVL8vPzZfDgwTJ06FCZM2eOKxtPvXr16hPtvXbafdasWaH+GSiTZCS40Igws9ULgSOB9RHDiiOTVyPTfQNMjRh2OIGXrmYCVwHrCZYvRGtaB6devfpU8fo+uFbeRQkYoE1di2H8xo7g/l9zR3DB63Q7gI5R5v0lMDaWuLUOTr169anivfg+uDrcuMmkF4G7IDcFr4uFWgzT1l2D2y9s2CPNXYMDSoBHYpj3F8C4WL6D1sGpV69evf3ejQS3hMBdkAcTuAPyoGDCK45x+seD1+yygqcZm7yLMngKsoKINxUAfYPTtgcygD8Fr8V1jSUGfR+cevXq1dvv3Uhwm4Cs4N9bg//mAl/GOH0u8AyBOrjv+OmpKEcCOyLGnUigVs5EDB8EfBKcxybgX8CwWL9DeKG3zRtXvXr16lPRu1no/SOQHvy7DMgD0oHtTgSSjKZ1cOrVq1dvv3frFOVvg3/PIVAm8BzwmhOBJKNpHZx69erV2+/dSHA5QG7w7w7AdcCtQE8nAklG0/fBqVevXr39PukJzg9N6+DU+9Uricfm7e83n5QEB9wQS3MikGS0goKCem8TSNTGUa8+mV5JDrZufz/5pN5kAjwU1hYGa9neAhYAy4OfFzoRSDKa1sGp95tX3MXt7e9X78Y1uMeB0yKGnerlBGfrxlWvPlavuIcN29+v3o0EVwG0jRiWBlQ4EUgymr4PTr3fvOIOtmx/v3o3EtyHwMURwy4C/u1EIMlodYXeJSUlVm9c9er1tKW9uL19/ewXL14sRUVFrhR6HxIs8F4DrAj+WwYMcSKQZDStg1Pvd68kFre3b6p4V8oEgHbBR2udARwFtHMiiGQ1rYNT72evJBa3t28qea2Da0XTOjj1fvVK4rF5+/vNa4JrRdP3wan3o1eSg63b349eE1wrWvjbBOJZ+epj94riZ9zev/zmXXubQKJb8HU6TwdfhbO67nU6jYw3A9hD4G3fda1fLMvQOrjkekXxM27vX372vjuCCz4p5QmgI3AETbwQNZjg5rdmGX379nVk5SfTz5o1y+r4GvNz5sxpdBrb8WLcXoxZxJtxh8ds8/4X7r3Yf4iIAGXilwRH4C3f1cDPw4Y9CtzSyLitTnCZmZlRV24sL0ONpY5u6tSpUTduSUlJs760tFRiebxYLM/WjOW0bN048a6fxmJujMi43R4nlrhTOWYn5+Xlde23/qMu7uZ8+DjJ+P7ATnEgt7TBDn4O1IjI12HDPibwBu/GGGuM2WyMWWmMOb8lCyorK2PRokWMHz+ewsLCBn7BggXN+rKyMkpKSpr1ixYtoqqqqlk/fvx43nvvvRbH15hfsmRJ1Onrxmlu/kuWLHFk/cRKeNw2jBMLqRyz0/NK1rKcjNmP/Qckt3+I5fs7hZHAUZGrGGOOBBaJSI+wYcXAZBEZEzHuL4CtwAZgBPAP4DIRWRjDcqqA2rBBG4HyiNE6Ezg92hzJHKcbDWN0Mx6nYk52TKm8rp1alpPz0nWdnHG8tC92A/KCf7cVkYwo84lKWrwzcIgdQKeIYZ2A7ZEjisjnYR/fNsb8D3A6gWt4zeLEClMURVG8gS2nKL8G0owx+4UNOwhYGcO0ApiERKUoiqJ4FisSnIjsBP4XuMEYk2WMORz4DYEbTephjPmNMaaLCTAcuBh4NrkRK4qiKLZjRYILcgHQAfiRwOnG80VkpTHmSGPMjrDxioBvCJy+fAS4VUQeTnq0iqIoitVYcZOJoiiKojiNTUdwiqIoiuIYmuAURVEUX6IJTlEURfElnk9wxph0Y8wDxpjVxpjtxpiPjDG/DvPHGGO+NMZUGmNeM8YUREz7oDFmmzFmvTHmMhviNsYUGmPEGLMjrF1nSdzzjTHrgsv+2hhzXpizdV03GrPN6zki/v2MMVXGmPlhwyYFfzs7jTHPGGNyw1yuMebpoFttjJnkdszGmDHGmL0R63qKLTEbY5YF462L7aswZ+W6bipmD6zrImPMF8HlrzKBB30kpv9w4nlfbjYCz7GcARQSSNgnE7jDspBAZXwFMB7IAG4D3g2b9mbgTaALMBBYD5xgQdyFBOr70pqY1s24BwHpwb/3Dy57qOXruqmYrV3PEXG8FIxjftj32Q4cReDh5AuAx8PGj+nB5UmOeQywppnxXY0ZWAac18Rvx8p13UzM1q5r4DgCb4sZGez38oMtIf1H0n7wyWzAJ8BpwDTg7bDhWcAuYP/g57XA8WH+xvAfr4txR+t4rYgbGACsAyZ4ZV1HxGz9eiZQFvMkYQ8ZB24CFoSN05/Aw8qzacGDy5Mcc5OdriUxL6PxZGHtum4mZmvXNfA2cG4jwxPSf3j+FGUkxph9CDy8eSWB/319XOckUFC+ChhkjOkC9Az3NP+A54QSEXcdq40xa4wxDxljugXHcz1uY8y9xphK4EsCyeJ5LF/XTcRch63ruRNwAxB5OiZyXa8i2GnR8geXO0ozMQN0N8ZsMMaUGmPuNMZkBYe7GnMYNxtjyo0xbxljxgSHWbuugzQWM1i4ro0xbYFhQJ4x5pvgPne3MaYDCeo/fJXgjDHtgMeAh0XkSwKH4JEP9awg8L+vjmGfI11SaSTucuBQoIDAqbTsoAcL4haRC4LLO5LAE2h2Y/m6biJmq9czgf+lPiAiayKGR1vX25pwyaCpmL8EDibQUf2SwPr+S9C5HTPAfwP9CJwumwssMcb0x+513VTMtq7rfYB2BJ4dfGQwxkOAa0lQ/+GbBGeMaUPgULsauDA4uLmHOO8I+xzpkkZjcYvIDhH5QERqRGRDcPjxxphsLIlbRGpFZDnQGzgfD6zryJhtXs/GmIOBY4E7G9HR1nVMDy53muZiFpH1IvK5iOwVkVLgCgKn48HFmMPiWyEi20VktwSejPQWcGKU2FyNu6mYLV7Xu4L//k1E1olIOYHEG8t6hlbsi75IcMYYAzxA4H8Ip4nInqBaSeChzXXjZRE4h75SRLYQOFV1UNisYn3AsyM0E3ckdY+baWND3BGkEVynWLyuI6iLORKb1vMYAtcIvzPGrAcuB04zxvybhuu6H5BO4KHl8Ty4PJExRyL81P+4GXNT1D3E3dZ13RhNPXjeinUd3KfW8NN+Rtjfiek/knFhMQkXLu8H3gU6RgzPI3AoexqBO3Nupf6dObcArxO4M2f/4EpM2l1yzcQ9gsDNEG2ArgTueHrN7biB7gRuIOgItAV+BewExtm6rqPEbOV6Di47E+gR1m4Hngqu50EETjMdSeBi/Hzq39n3OIE75bKAw0nSXXJRYj6awKlgA/QBXgMecjvm4LJzgr+LDAL/+Zkc/I383OJ13VzMNq/rG4D3g/tlFwJ3Rt6YqP4j4V8oCSusgMD/AqoIHMrWtclBfyyBc9K7CNx1VBg2bTrwYPAHvIHAi1NdjxuYCJQGf7DrCDxUuofbcQd/hK8TeOHsNuBToDjMW7eum4vZ1vXcxPeYQfCOxODnScB3wdifBXLDXC7wTNB9B0xyO2YCN538AFQC3wN3Adk2xBz8jbxP4JTXVgL/6TzO5nXdXMyWr+t2wL3BmNcHY8sIOsf7D33YsqIoiuJLfHENTlEURVEi0QSnKIqi+BJNcIqiKIov0QSnKIqi+BJNcIqiKIov0QSnKIqi+BJNcIriMsaYlREPylUUxQE0wSmKy4jIIBFZ1tw45qeXs6bFu7zgizLPiz6mongbTXCKoiiKL9EEpyguY4wpM8Yca4xpY4y50hizyhizyRjzpDEmNzjaG8F/txpjdhhjDmtmflOD7we72xhTYYz50hhzTNDNIvBcxbuD87k7sd9OUdxDE5yi2MNFwCnAaKAXsAW4J+iOCv6bIyIdReSdKPMaQeCFkd2A6cD/GmNyReQaAg+4vTA4nwubm4mieBlNcIpiD78HrhGRNSKym8DDik9v5XW3H4G/isgeEXkC+Ao4yblQFcV+4r5grSiKYxQATxtj9oYNqyXwvsCW8oPUf5L6agJHhYqSMugRnKLYw/fAr0UkJ6xliMgP1H9JZCzkB1+oW0dfYG3wb32FiJISaIJTFHu4H5hljCkAMMbkGWN+E3Qbgb1Avxjn1R242BjTzhgzHhgIPB90G1owH0XxLJrgFMUe/gdYDLxkjNlO4CWWIwBEpBKYBbxljNlqjBkZZV4rgP2A8uB0p4vIprDlnG6M2WKMuSsB30NRrEBfeKooLmOM+Q44U0TeiDpybPObCpwnIkc4MT9F8Sp6BKcoLmKMyQPygDKXQ1EU36EJTlFcwhhzKPAf4G8i8l0Lp70/WKgd2e5PTLSK4j30FKWiKIriS/QITlEURfElmuAURVEUX6IJTlEURfElmuAURVEUX6IJTlEURfElmuAURVEUX/L/Af+F58HFbeSuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_pred = cabinetry.model_utils.prediction(model)\n",
    "figures = cabinetry.visualize.data_mc(model_pred, data, config=cabinetry_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This figure is also again saved in the `figures/` folder, like all figures in general.\n",
    "\n",
    "To demonstrate figure customization, let's use $\\LaTeX$ for the horizontal axis label. We can save the modified figure as well by using `.savefig()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratio_panel = figures[0][\"figure\"].get_axes()[1]\n",
    "ratio_panel.set_xlabel(\"jet $p_T$\")\n",
    "figures[0][\"figure\"]  # show figure again"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yield tables can also be created from a model prediction, and compared to data.\n",
    "Optional keyword arguments control whether yields per bin are shown (`per_bin=True`, default) and whether bins summed per region are shown (`per_channel=True`, disabled by default).\n",
    "The yield table is also saved to disk by default, in a format customizable via the `table_format` argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - cabinetry.tabulate - yields per bin for pre-fit model prediction:\n",
      "╒════════════╤═════════════════╤════════════════╤════════════════╤═══════════════╕\n",
      "│ sample     │ Signal_region   │                │                │               │\n",
      "│            │ bin 1           │ bin 2          │ bin 3          │ bin 4         │\n",
      "╞════════════╪═════════════════╪════════════════╪════════════════╪═══════════════╡\n",
      "│ Background │ 112.74 ± 69.04  │ 128.62 ± 58.33 │ 88.11 ± 38.06  │ 55.25 ± 45.20 │\n",
      "├────────────┼─────────────────┼────────────────┼────────────────┼───────────────┤\n",
      "│ Signal     │ 0.00 ± 0.00     │ 1.59 ± 0.08    │ 23.62 ± 1.18   │ 24.55 ± 1.23  │\n",
      "├────────────┼─────────────────┼────────────────┼────────────────┼───────────────┤\n",
      "│ total      │ 112.74 ± 69.04  │ 130.21 ± 58.34 │ 111.72 ± 38.21 │ 79.79 ± 45.29 │\n",
      "├────────────┼─────────────────┼────────────────┼────────────────┼───────────────┤\n",
      "│ data       │ 112.00          │ 112.00         │ 124.00         │ 66.00         │\n",
      "╘════════════╧═════════════════╧════════════════╧════════════════╧═══════════════╛\n",
      "INFO - cabinetry.tabulate - saving table as tables/yields_per_bin_pre-fit.txt\n"
     ]
    }
   ],
   "source": [
    "_ = cabinetry.tabulate.yields(model_pred, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also have a look at the table we saved to disk.\n",
    "Other supported formats include `\"html\"` and `\"latex\"`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sample      Signal_region    Signal_region    Signal_region    Signal_region\r\n",
      "            bin 1            bin 2            bin 3            bin 4\r\n",
      "----------  ---------------  ---------------  ---------------  ---------------\r\n",
      "Background  112.74 ± 69.04   128.62 ± 58.33   88.11 ± 38.06    55.25 ± 45.20\r\n",
      "Signal      0.00 ± 0.00      1.59 ± 0.08      23.62 ± 1.18     24.55 ± 1.23\r\n",
      "total       112.74 ± 69.04   130.21 ± 58.34   111.72 ± 38.21   79.79 ± 45.29\r\n",
      "data        112.00           112.00           124.00           66.00\r\n"
     ]
    }
   ],
   "source": [
    "!cat tables/yields_per_bin_pre-fit.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also take a look at the post-fit model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG - cabinetry.model_utils - total stdev is [[13.2, 7.2, 7.06, 7.73]]\n",
      "DEBUG - cabinetry.model_utils - total stdev per channel is [20.7]\n",
      "INFO - cabinetry.visualize.utils - saving figure as figures/Signal_region_postfit.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_pred_postfit = cabinetry.model_utils.prediction(model, fit_results=fit_results)\n",
    "_ = cabinetry.visualize.data_mc(model_pred_postfit, data, config=cabinetry_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Beyond simple maximum likelihood fitting \n",
    "\n",
    "`cabinetry` provides a range of useful utilities for statistical inference besides simple maximum likelihood fitting.\n",
    "To start, let's look at ranking nuisance parameters by their impact on the parameter of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 18.54                      │              Nfcn = 330              │\n",
      "│ EDM = 1.04e-06 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 18.535252 at best-fit point\n",
      "INFO - cabinetry.fit - calculating impact of Modeling on Signal_norm\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 38.93                      │              Nfcn = 274              │\n",
      "│ EDM = 2.61e-05 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │       SOME Parameters at limit       │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 38.933945 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 0.000024, difference to nominal is -1.593742\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 21.62                      │              Nfcn = 265              │\n",
      "│ EDM = 8.01e-09 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 21.622209 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 3.039836, difference to nominal is 1.446071\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 21.51                      │              Nfcn = 217              │\n",
      "│ EDM = 7.95e-06 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 21.505866 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 0.440453, difference to nominal is -1.153312\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.54                      │              Nfcn = 245              │\n",
      "│ EDM = 2.71e-06 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 19.542408 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 2.468905, difference to nominal is 0.875140\n",
      "INFO - cabinetry.fit - calculating impact of WeightBasedModeling on Signal_norm\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 21.81                      │              Nfcn = 253              │\n",
      "│ EDM = 3.34e-05 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │       SOME Parameters at limit       │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 21.805618 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 0.046114, difference to nominal is -1.547652\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 20.84                      │              Nfcn = 224              │\n",
      "│ EDM = 2.04e-05 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 20.838788 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 2.781734, difference to nominal is 1.187969\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.66                      │              Nfcn = 201              │\n",
      "│ EDM = 2.82e-05 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG - cabinetry.fit - -2 log(L) = 19.662341 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 0.644792, difference to nominal is -0.948973\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.51                      │              Nfcn = 222              │\n",
      "│ EDM = 6.61e-06 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 19.507292 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 2.407076, difference to nominal is 0.813311\n",
      "INFO - cabinetry.fit - calculating impact of Luminosity on Signal_norm\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.54                      │              Nfcn = 251              │\n",
      "│ EDM = 9.08e-05 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 19.540404 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 1.434503, difference to nominal is -0.159262\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.55                      │              Nfcn = 251              │\n",
      "│ EDM = 5.17e-06 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 19.546177 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 1.767688, difference to nominal is 0.173923\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.52                      │              Nfcn = 251              │\n",
      "│ EDM = 9.19e-05 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 19.522365 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 1.435786, difference to nominal is -0.157979\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.53                      │              Nfcn = 251              │\n",
      "│ EDM = 4.72e-06 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 19.528062 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 1.766161, difference to nominal is 0.172396\n",
      "INFO - cabinetry.fit - calculating impact of staterror_Signal_region[0] on Signal_norm\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.59                      │              Nfcn = 270              │\n",
      "│ EDM = 6.15e-05 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 19.586174 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 1.461954, difference to nominal is -0.131811\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.59                      │              Nfcn = 270              │\n",
      "│ EDM = 4.14e-06 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 19.592124 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 1.719186, difference to nominal is 0.125421\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.53                      │              Nfcn = 270              │\n",
      "│ EDM = 5.76e-05 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG - cabinetry.fit - -2 log(L) = 19.532562 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 1.465410, difference to nominal is -0.128355\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.54                      │              Nfcn = 270              │\n",
      "│ EDM = 2.95e-06 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 19.538020 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 1.715952, difference to nominal is 0.122187\n",
      "INFO - cabinetry.fit - calculating impact of staterror_Signal_region[1] on Signal_norm\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.6                       │              Nfcn = 286              │\n",
      "│ EDM = 3.77e-06 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 19.599005 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 1.747681, difference to nominal is 0.153916\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.6                       │              Nfcn = 269              │\n",
      "│ EDM = 0.0001 (Goal: 0.0002)      │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 19.604940 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 1.426444, difference to nominal is -0.167321\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.53                      │              Nfcn = 286              │\n",
      "│ EDM = 3.65e-06 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 19.532056 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 1.742768, difference to nominal is 0.149002\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.54                      │              Nfcn = 269              │\n",
      "│ EDM = 9.53e-05 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 19.537490 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 1.432287, difference to nominal is -0.161478\n",
      "INFO - cabinetry.fit - calculating impact of staterror_Signal_region[2] on Signal_norm\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.58                      │              Nfcn = 270              │\n",
      "│ EDM = 2.15e-05 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 19.578176 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 1.401209, difference to nominal is -0.192556\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.57                      │              Nfcn = 271              │\n",
      "│ EDM = 0.00011 (Goal: 0.0002)     │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 19.568416 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 1.808117, difference to nominal is 0.214352\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.54                      │              Nfcn = 270              │\n",
      "│ EDM = 2.3e-05 (Goal: 0.0002)     │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG - cabinetry.fit - -2 log(L) = 19.539799 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 1.404519, difference to nominal is -0.189246\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.53                      │              Nfcn = 270              │\n",
      "│ EDM = 0.000111 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 19.530558 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 1.804686, difference to nominal is 0.210921\n",
      "INFO - cabinetry.fit - calculating impact of staterror_Signal_region[3] on Signal_norm\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.52                      │              Nfcn = 271              │\n",
      "│ EDM = 8.59e-05 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 19.518509 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 1.726688, difference to nominal is 0.132923\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.53                      │              Nfcn = 269              │\n",
      "│ EDM = 4.79e-05 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 19.530219 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 1.491218, difference to nominal is -0.102547\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.53                      │              Nfcn = 271              │\n",
      "│ EDM = 8.25e-05 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 19.529643 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 1.727432, difference to nominal is 0.133667\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.54                      │              Nfcn = 269              │\n",
      "│ EDM = 4.78e-05 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 19.541530 at best-fit point\n",
      "DEBUG - cabinetry.fit - POI is 1.490819, difference to nominal is -0.102947\n"
     ]
    }
   ],
   "source": [
    "ranking_results = cabinetry.fit.ranking(model, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous cell ran a lot of maximum likelihood fits to calculate all the input needed to rank nuisance parameters. We will visualize them next."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - cabinetry.visualize.utils - saving figure as figures/ranking.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x565 with 2 Axes>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cabinetry.visualize.ranking(ranking_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results are contained in the `ranking_results` object. It is a simple named tuple, we can have a look at its content."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RankingResults(bestfit=array([-0.27529071, -0.52768031, -0.07941828,  1.00123651,  0.9883001 ,\n",
       "        1.02156575,  0.98291109]), uncertainty=array([0.5683095 , 0.64774531, 0.99100884, 0.04114037, 0.03842981,\n",
       "       0.04690882, 0.06096864]), labels=['Modeling', 'WeightBasedModeling', 'Luminosity', 'staterror_Signal_region[0]', 'staterror_Signal_region[1]', 'staterror_Signal_region[2]', 'staterror_Signal_region[3]'], prefit_up=array([-1.59374151, -1.54765161, -0.15926218, -0.13181116,  0.1539161 ,\n",
       "       -0.19255584,  0.13292265]), prefit_down=array([ 1.44607054,  1.18796859,  0.17392294,  0.12542056, -0.16732123,\n",
       "        0.21435221, -0.10254726]), postfit_up=array([-1.15331231, -0.94897302, -0.15797906, -0.12835492,  0.14900244,\n",
       "       -0.18924588,  0.13366721]), postfit_down=array([ 0.87514006,  0.81331124,  0.1723961 ,  0.12218725, -0.16147842,\n",
       "        0.2109206 , -0.10294653]))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranking_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also perform likelihood scans for parameters.\n",
    "The example below performs a scan for the `Modeling` nuisance parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 18.54                      │              Nfcn = 330              │\n",
      "│ EDM = 1.04e-06 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 18.535252 at best-fit point\n",
      "INFO - cabinetry.fit - performing likelihood scan for WeightBasedModeling in range (-1.823, 0.768) with 11 steps\n",
      "DEBUG - cabinetry.fit - performing fit with WeightBasedModeling = -1.823\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 22.38                      │              Nfcn = 225              │\n",
      "│ EDM = 5.23e-05 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 22.383546 at best-fit point\n",
      "DEBUG - cabinetry.fit - performing fit with WeightBasedModeling = -1.564\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 21.01                      │              Nfcn = 224              │\n",
      "│ EDM = 2.34e-05 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 21.008036 at best-fit point\n",
      "DEBUG - cabinetry.fit - performing fit with WeightBasedModeling = -1.305\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.93                      │              Nfcn = 224              │\n",
      "│ EDM = 9.15e-06 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 19.932185 at best-fit point\n",
      "DEBUG - cabinetry.fit - performing fit with WeightBasedModeling = -1.046\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.16                      │              Nfcn = 221              │\n",
      "│ EDM = 5.01e-06 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 19.158841 at best-fit point\n",
      "DEBUG - cabinetry.fit - performing fit with WeightBasedModeling = -0.787\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 18.69                      │              Nfcn = 219              │\n",
      "│ EDM = 2.98e-06 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 18.692966 at best-fit point\n",
      "DEBUG - cabinetry.fit - performing fit with WeightBasedModeling = -0.528\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 18.54                      │              Nfcn = 199              │\n",
      "│ EDM = 4.69e-05 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 18.535298 at best-fit point\n",
      "DEBUG - cabinetry.fit - performing fit with WeightBasedModeling = -0.269\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 18.7                       │              Nfcn = 201              │\n",
      "│ EDM = 5.44e-06 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG - cabinetry.fit - -2 log(L) = 18.696625 at best-fit point\n",
      "DEBUG - cabinetry.fit - performing fit with WeightBasedModeling = -0.009\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 19.22                      │              Nfcn = 200              │\n",
      "│ EDM = 7.7e-07 (Goal: 0.0002)     │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 19.219275 at best-fit point\n",
      "DEBUG - cabinetry.fit - performing fit with WeightBasedModeling = 0.250\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 20.27                      │              Nfcn = 201              │\n",
      "│ EDM = 0.0001 (Goal: 0.0002)      │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 20.270438 at best-fit point\n",
      "DEBUG - cabinetry.fit - performing fit with WeightBasedModeling = 0.509\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 22.13                      │              Nfcn = 291              │\n",
      "│ EDM = 5.14e-05 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │       SOME Parameters at limit       │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 22.125564 at best-fit point\n",
      "DEBUG - cabinetry.fit - performing fit with WeightBasedModeling = 0.768\n",
      "INFO - cabinetry.fit - Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 26.26                      │              Nfcn = 256              │\n",
      "│ EDM = 3.92e-06 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │       SOME Parameters at limit       │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 26.261757 at best-fit point\n"
     ]
    }
   ],
   "source": [
    "scan_results = cabinetry.fit.scan(model, data, \"WeightBasedModeling\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting figure looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - cabinetry.visualize.utils - saving figure as figures/scan_WeightBasedModeling.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cabinetry.visualize.scan(scan_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With `cabinetry.fit.limit`, we can evaluate observed and expected 95% confidence level upper parameter limits.\n",
    "The implementation uses Brent bracketing to efficiently find the `CLs=0.05` crossing points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - cabinetry.fit - calculating 95% confidence level upper limit for Signal_norm\n",
      "INFO - cabinetry.fit - determining observed upper limit\n",
      "DEBUG - cabinetry.fit - Signal_norm = 0.1000, observed CLs = 0.9184\n",
      "DEBUG - cabinetry.fit - Signal_norm = 10.0000, observed CLs = 0.0000\n",
      "DEBUG - cabinetry.fit - Signal_norm = 9.4610, observed CLs = 0.0000\n",
      "DEBUG - cabinetry.fit - Signal_norm = 4.7805, observed CLs = 0.0001\n",
      "DEBUG - cabinetry.fit - Signal_norm = 2.4403, observed CLs = 0.1975\n",
      "DEBUG - cabinetry.fit - Signal_norm = 4.1886, observed CLs = 0.0012\n",
      "DEBUG - cabinetry.fit - Signal_norm = 3.3144, observed CLs = 0.0287\n",
      "DEBUG - cabinetry.fit - Signal_norm = 2.8773, observed CLs = 0.0862\n",
      "DEBUG - cabinetry.fit - Signal_norm = 3.1526, observed CLs = 0.0446\n",
      "DEBUG - cabinetry.fit - Signal_norm = 3.1049, observed CLs = 0.0504\n",
      "DEBUG - cabinetry.fit - Signal_norm = 3.1099, observed CLs = 0.0498\n",
      "INFO - cabinetry.fit - successfully converged after 11 steps\n",
      "INFO - cabinetry.fit - observed upper limit: 3.1099\n",
      "INFO - cabinetry.fit - determining expected -2 sigma upper limit\n",
      "DEBUG - cabinetry.fit - Signal_norm = 0.1000, expected -2 sigma CLs = 0.7631 (cached)\n",
      "DEBUG - cabinetry.fit - Signal_norm = 2.4403, expected -2 sigma CLs = 0.0002 (cached)\n",
      "DEBUG - cabinetry.fit - Signal_norm = 2.2875, expected -2 sigma CLs = 0.0004\n",
      "DEBUG - cabinetry.fit - Signal_norm = 1.1938, expected -2 sigma CLs = 0.0285\n",
      "DEBUG - cabinetry.fit - Signal_norm = 0.6469, expected -2 sigma CLs = 0.1538\n",
      "DEBUG - cabinetry.fit - Signal_norm = 1.0998, expected -2 sigma CLs = 0.0383\n",
      "DEBUG - cabinetry.fit - Signal_norm = 0.9993, expected -2 sigma CLs = 0.0524\n",
      "DEBUG - cabinetry.fit - Signal_norm = 1.0162, expected -2 sigma CLs = 0.0497\n",
      "DEBUG - cabinetry.fit - Signal_norm = 1.0112, expected -2 sigma CLs = 0.0505\n",
      "INFO - cabinetry.fit - successfully converged after 9 steps\n",
      "INFO - cabinetry.fit - expected -2 sigma upper limit: 1.0162\n",
      "INFO - cabinetry.fit - determining expected -1 sigma upper limit\n",
      "DEBUG - cabinetry.fit - Signal_norm = 1.1938, expected -1 sigma CLs = 0.0839 (cached)\n",
      "DEBUG - cabinetry.fit - Signal_norm = 2.2875, expected -1 sigma CLs = 0.0034 (cached)\n",
      "DEBUG - cabinetry.fit - Signal_norm = 1.6542, expected -1 sigma CLs = 0.0261\n",
      "DEBUG - cabinetry.fit - Signal_norm = 1.4637, expected -1 sigma CLs = 0.0434\n",
      "DEBUG - cabinetry.fit - Signal_norm = 1.4025, expected -1 sigma CLs = 0.0506\n",
      "DEBUG - cabinetry.fit - Signal_norm = 1.4079, expected -1 sigma CLs = 0.0500\n",
      "INFO - cabinetry.fit - successfully converged after 6 steps\n",
      "INFO - cabinetry.fit - expected -1 sigma upper limit: 1.4079\n",
      "INFO - cabinetry.fit - determining expected upper limit\n",
      "DEBUG - cabinetry.fit - Signal_norm = 1.6542, expected CLs = 0.1009 (cached)\n",
      "DEBUG - cabinetry.fit - Signal_norm = 2.2875, expected CLs = 0.0231 (cached)\n",
      "DEBUG - cabinetry.fit - Signal_norm = 2.0688, expected CLs = 0.0408\n",
      "DEBUG - cabinetry.fit - Signal_norm = 1.9727, expected CLs = 0.0513\n",
      "DEBUG - cabinetry.fit - Signal_norm = 1.9849, expected CLs = 0.0499\n",
      "DEBUG - cabinetry.fit - Signal_norm = 1.9799, expected CLs = 0.0505\n",
      "INFO - cabinetry.fit - successfully converged after 6 steps\n",
      "INFO - cabinetry.fit - expected upper limit: 1.9849\n",
      "INFO - cabinetry.fit - determining expected +1 sigma upper limit\n",
      "DEBUG - cabinetry.fit - Signal_norm = 2.4403, expected +1 sigma CLs = 0.0902 (cached)\n",
      "DEBUG - cabinetry.fit - Signal_norm = 2.8773, expected +1 sigma CLs = 0.0332 (cached)\n",
      "DEBUG - cabinetry.fit - Signal_norm = 2.7485, expected +1 sigma CLs = 0.0457\n",
      "DEBUG - cabinetry.fit - Signal_norm = 2.7087, expected +1 sigma CLs = 0.0503\n",
      "DEBUG - cabinetry.fit - Signal_norm = 2.7137, expected +1 sigma CLs = 0.0497\n",
      "INFO - cabinetry.fit - successfully converged after 5 steps\n",
      "INFO - cabinetry.fit - expected +1 sigma upper limit: 2.7087\n",
      "INFO - cabinetry.fit - determining expected +2 sigma upper limit\n",
      "DEBUG - cabinetry.fit - Signal_norm = 3.3144, expected +2 sigma CLs = 0.0809 (cached)\n",
      "DEBUG - cabinetry.fit - Signal_norm = 4.1886, expected +2 sigma CLs = 0.0071 (cached)\n",
      "DEBUG - cabinetry.fit - Signal_norm = 3.6805, expected +2 sigma CLs = 0.0334\n",
      "DEBUG - cabinetry.fit - Signal_norm = 3.5525, expected +2 sigma CLs = 0.0465\n",
      "DEBUG - cabinetry.fit - Signal_norm = 3.5215, expected +2 sigma CLs = 0.0502\n",
      "DEBUG - cabinetry.fit - Signal_norm = 3.5265, expected +2 sigma CLs = 0.0496\n",
      "INFO - cabinetry.fit - successfully converged after 6 steps\n",
      "INFO - cabinetry.fit - expected +2 sigma upper limit: 3.5215\n",
      "INFO - cabinetry.fit - total of 43 steps to calculate all limits\n",
      "INFO - cabinetry.fit - summary of 95% confidence level upper limits:\n",
      "INFO - cabinetry.fit - observed         : 3.1099\n",
      "INFO - cabinetry.fit - expected -2 sigma: 1.0162\n",
      "INFO - cabinetry.fit - expected -1 sigma: 1.4079\n",
      "INFO - cabinetry.fit - expected         : 1.9849\n",
      "INFO - cabinetry.fit - expected +1 sigma: 2.7087\n",
      "INFO - cabinetry.fit - expected +2 sigma: 3.5215\n"
     ]
    }
   ],
   "source": [
    "limit_results = cabinetry.fit.limit(model, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, the results are visualized:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - cabinetry.visualize.utils - saving figure as figures/limit.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cabinetry.visualize.limit(limit_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The observed limits are above the expected limits.\n",
    "We can calculate the discovery significance with `cabinetry.fit.significance`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - cabinetry.fit - calculating discovery significance for Signal_norm\n",
      "INFO - cabinetry.fit - observed p-value: 4.889%\n",
      "INFO - cabinetry.fit - observed significance: 1.656\n",
      "INFO - cabinetry.fit - expected p-value: 14.915%\n",
      "INFO - cabinetry.fit - expected significance: 1.040\n"
     ]
    }
   ],
   "source": [
    "significance_results = cabinetry.fit.significance(model, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we observe a 1.7 sigma excess (and expected 1.0 sigma)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "stats",
   "language": "python",
   "name": "stats"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}