example.ipynb

{
 "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",
    "Before starting the creation of template histograms, we remove any histograms that might still exist from previous runs.\n",
    "This is not required (they will be overridden if needed) but helps with the demonstration of which histograms are created at which step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "!rm -f histograms/*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the `templates` module to create all histograms needed to build the workspace defined in the configuration file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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.contrib.histogram_creator - reading input ntuples ['inputs/data.root:pseudodata']\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",
      "DEBUG - cabinetry.contrib.histogram_creator - reading input ntuples ['inputs/prediction.root:signal']\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.contrib.histogram_creator - reading input ntuples ['inputs/prediction.root:background']\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Background.npz\n",
      "DEBUG - cabinetry.route -       variation Modeling Up\n",
      "DEBUG - cabinetry.contrib.histogram_creator - reading input ntuples ['inputs/prediction.root:background_varied']\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Background_Modeling_Up.npz\n",
      "DEBUG - cabinetry.route -       variation WeightBasedModeling Up\n",
      "DEBUG - cabinetry.contrib.histogram_creator - reading input ntuples ['inputs/prediction.root:background']\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Background_WeightBasedModeling_Up.npz\n",
      "DEBUG - cabinetry.route -       variation WeightBasedModeling Down\n",
      "DEBUG - cabinetry.contrib.histogram_creator - reading input ntuples ['inputs/prediction.root:background']\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": 11,
   "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": 12,
   "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": 13,
   "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": 14,
   "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.contrib.histogram_reader - reading input histogram inputs/histograms.root:SR/Data/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",
      "DEBUG - cabinetry.contrib.histogram_reader - reading input histogram inputs/histograms.root:SR/Signal/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.contrib.histogram_reader - reading input histogram inputs/histograms.root:SR/Background/Nominal\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Background.npz\n",
      "DEBUG - cabinetry.route -       variation Modeling Up\n",
      "DEBUG - cabinetry.contrib.histogram_reader - reading input histogram inputs/histograms.root:SR/Background/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.contrib.histogram_reader - reading input histogram inputs/histograms.root:SR/Background/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.contrib.histogram_reader - reading input histogram inputs/histograms.root:SR/Background/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": 15,
   "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": 16,
   "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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",
      "text/plain": [
       "<Figure size 431.579x362.5 with 2 Axes>"
      ]
     },
     "execution_count": 16,
     "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": 17,
   "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": 18,
   "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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",
      "text/plain": [
       "<Figure size 600x175 with 1 Axes>"
      ]
     },
     "execution_count": 18,
     "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": 19,
   "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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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "execution_count": 19,
     "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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG - cabinetry.model_utils - total stdev is [[69.04078930836704, 58.34298734714868, 38.21175867029217, 45.29010378006829]]\n",
      "DEBUG - cabinetry.model_utils - total stdev per channel is [136.78737117029473]\n",
      "INFO - cabinetry.visualize.utils - saving figure as figures/Signal_region_prefit.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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kSaHj/Pz84OnpKUnjDwoKwujRo1GnTh3cvHkTK1asQHZ2NsaPH482bdrIrvEziIkcoR5EZaFZs2bo0KEDGjVqhLt371pc28l+yurIGMOYBIoqlr0bzc6dO3H9+nUkJiZCoVCgRYsWmD17tsklIuzd+KOjo7F+/XqL4wBg7dq1qFOnjiSNf8yYMThy5Aju3LkDtVqNpk2bokmTJmjfvr3sGj+DmMhR6sGjElQW/vvf/yI6Ohrx8fEW13ayL4YxJ2KtWM7YaGxp/B06dDC55EZhHj16hAsXLrDxM4gZOWs9XnzxRYtjiEg+yiqM8dOUduSsjcZejZ9BzLHqwSBmez2IiKTkFCfwp6enY/bs2ejevTv8/f2hUCiwbt06s3EKhcLiny5duhjH3bhxw+K4rVu3SrLPztxo2PhZDwPWQ69gPYiIpOQUR8aSk5Mxb9481KxZE0888QSOHj1a6LgNGzaY3Xb69Gl89tln6Nq1q9l9Q4YMQc+ePU1ue+qpp0q9v87eaNj4WQ+A9TAoST2IiGzhFGEsKCgIiYmJqF69Ok6fPm3xo6bDhw83u+3o0aNQKBQYMmSI2X0tWrQo9DGlIedGw8YvYj1ErAcRUek4RRhTq9WoXr16iR+n0Wjw7bffon379ggJCSl0TEZGBtzc3ODu7l7a3cTt27exa9cuWTYaNn4R6yFiPYiISs8pzhmzVUxMDB4+fIhhw4YVev/cuXPh4+MDDw8PREREYP/+/aXa3vr161GxYkX07t0bGo0GGo2m0HHO1mjY+EWsh4j1ICK50Wg0SEtLM/lTFmQdxjZt2gS1Wo0BAwaY3K5UKtG1a1csWrQIu3fvxqeffoqkpCT06NED+/bts3l7CxcuxMSJExEYGAg/Pz98+OGHZmMcudHExcWha9eu8PPzg0KhwK5du7B27VoolUocPHiQjd8BGj+DmMgR6kFE8vLhhx/Cz8/P+Cc0NLRMtut01xkznDO2du1ajB492uK4tLQ0VKtWDT169MCOHTuKfN6UlBSEh4ejUqVKxi+bLi7DdUiuXr2KqlWrGm9Xq9UmjcfRG03btm1x/fp1zJgxA5UqVUL79u2xePFifPbZZ9i7dy+ef/55AMDy5cvh5eVl9vtn49djEBPJuR5Eji4sLAyPP/449u7dW967Ui4UCgVmz56NOXPmFPsxBd/VSktLQ2hoKL+b0lbffvstsrOzLb5FWZC/vz/GjBmDy5cvIz4+3qZtVq1aFb6+vsY/zhTEsrKycPLkSbz00kuYOHEihg0bhvPnz8PPzw8nT540+dTp8uXLzS4twsavxyAmknM9iEpi3bp1ZpdRCgwMRMeOHfH999+X9+5RPmq12qSP2zOA5ecUJ/DbYtOmTfDz80OvXr2K/RjD4ciUlBSLJ/zbwp6NZsOGDUhMTMTYsWNL1Wju3bsHAKhUqZJJ4+/bty8bP4MYANbDQBAEq78nkpZi6p7y3gUAgLC4d6mfY968eahduzYEQcDdu3exbt069OzZE3v27ClRryL5keWRscTERBw5cgT/+c9/rDaBgq5duwYACAgIkGxfitto5syZA4VCgY8++girVq3C7Nmz0aBBA0yePBnZ2dnGcQqFAhMnTsS6detQp04dvPLKK6hRowaCg4ORkJCAsWPHolq1alCr1WjcuDHWrFlTZKOZM2cOatWqBQB46623oFQqMXz4cPTp0wfnz5+HQqHAjRs3AOgPe//111/46aefjP/CGzlyJBs/g5iRK9SDyBY9evTA8OHDMWLECEybNg3Hjx+Hm5sbtmzZUt67ZrOMjIzy3gVZkGUY27p1K3Q6ncW3KA1HgfJLSEjAmjVr0LRpUwQFBUmyHyVpNDk5OQCA1atXo3r16li4cCF69uyJzz//HOPHjzcZe+jQIbz++usIDw/H/Pnz0bJlS9y9exdt2rTBwYMHMXHiRHz22WeoV68eXnrpJYwdO9Zqo+nfvz8+/fRTAEC7du3Qr18/vPfee4U2/qVLlyIkJAQNGzbEjBkz0K9fP0yaNImNn0EMgOvUg0gKlSpVgqenJypUEN+k+uSTT9C2bVtUqVIFnp6eaNmyJaKjowt9/MaNG9G6dWt4eXmhcuXKaNeuXZFXBVi/fj0qVKiAt956y3jb/fv3MWLECPj6+qJSpUoYNWqU8R/i+U9JGT16NHx8fHD16lX07NkTFStWNPbZjIwMTJ06FaGhoVCr1XjsscfwySefIP9p6YZvv7H0DTr5z+0yHKC4cuUKRo8ejUqVKsHPzw9jxoxBZmamyWM1Gg3eeOMNBAQEoGLFiujTp4/NpxuVF6d5m3LZsmV4+PAhbt++DQDYs2eP8Zc9adIk+Pn5Gcdu2rQJNWrUQIcOHQp9rrfffhtXr15F586dUaNGDdy4cQNfffUVMjIy8Nlnn0myvyVtNOfOnQMAPPHEE8ZzCCIjI+Hr64vly5dj2rRpaNq0KQDg8uXLmDx5MqZNm2ZsNC+//DK0Wi3+/PNPY6MaN24c2rVrh+3bt+O3336z2GiaNm2KihUr4o033oBarcasWbMsNv6+ffti5syZcHNzg5ubG6ZMmcLGzyAGwLXqQWSL1NRUJCcnQxAEJCUl4YsvvkB6errJxcc/++wz9OnTB8OGDUNOTg62bt2KgQMHmnyICtBfmmnOnDlo27Yt5s2bB3d3d/z22284fPhwod84AwArV67Eq6++iv/+979YsGABAECn06F37974/fff8dprr6Fhw4b47rvvMGrUqEKfIy8vD926dcMzzzyDTz75BF5eXhAEAX369MGRI0fw0ksvoVmzZvjxxx/x1ltvISEhwfiPfVsMGjQItWvXxocffogzZ87g66+/RmBgID766CPjmJdffhkbN27E0KFD0bZtWxw+fNjkd+UMnCaMffLJJ7h586bx5x07dhg/JTl8+HBjGLt8+TL++OMPvPnmm1AqCz/w17VrV3z55ZeIiorCgwcPUKlSJbRr1w4zZ85EixYtSr2vtjQarVYLAHjjjTdM7p80aRKWL1+OmJgYPPbYYwCA2rVrmwQxQRDw7bffYtCgQRAEAcnJydBqtdi9ezeqVauG7OxsPHr0yOI+CIKAw4cPAwAaN25cZOPPzMyETqdj42cQM2I9iIr23HPPmfysVquxZs0ak+9Ojo2Nhaenp/HniRMnokWLFliyZIkxYFy5cgXz5s1Dv379EB0dbdLrLF0g4fPPP8eUKVMwb948zJw503j7rl27cPLkSSxduhSTJ08GALz22msm+5SfRqPBwIEDTS7d9N133+Hw4cNYsGABZsyYAUB/MGHgwIH47LPPMHHiRNStW7dYv6OCmjdvjtWrVxt/vn//PlavXm0MY+fPn8fGjRsxYcIEREVFGbc9bNgwXLhwwaZtlgenCWOGc5aK8thjj1n8y2gwZMiQQr8eSQq2NponnngC+/fvR/369U3G1K1bF0qlElevXsWmTZsAABERESaN5t69e3j48CFWrlyJlStXFrqtpKQkAMCdO3dMbvf19cXhw4fx559/AoDVBgboG396ejqCg4PZ+B2g8TOI6TlKPYisiYqKMv59u3v3LjZu3IiXX34ZFStWRP/+/QHAJIg9ePAAWq0Wzz77rMl5Zbt27YJOp8N7771ndtChsHn98ccf45133sHHH39s8vYkAPzwww9wc3PDuHHjjLcplUpERkYa/5Fe0GuvvWbyc0xMDFQqFV5//XWT26dOnYro6Gh8//33mDhxosXfizWvvvqqyc/PPvssdu7cibS0NPj6+iImJgYAzLY9ZcoUbN682aZtlgenCWPOICUlBTt27LCp0XzxxReFjjNMrLi4OOO/LPJfywzQH2YG9EcIhw8fjuPHjyMhIcHka6AMb3EWPB9u8uTJqFy5Mrp06VLkW7SGxu/j42PytnBh2Pj1GMREcq8HUVFat26NVq1aGX8eMmQImjdvjokTJ6JXr15wd3fH3r17sWDBApw7d87kelf55+zVq1ehVCoRHh5e5DZ/+ukn7Nu3D++8845ZEAOAmzdvIigoCF5eXia316tXr9Dnq1ChgtnVBm7evIkaNWqgYsWKJrc3atTIeL+tatasafJz5cqVAeiDqq+vL27evAmlUml25M3wTpKzYBiTUOvWraFSqfDWW2/Z3Gji4uJQu3Zt488XL16ETqeDh4cHRowYgenTp5s9n+Gkxby8PDx8+BAeHh6YPn16oY3mwIEDAPSN5rfffsPDhw/Rp08f419wS3777Tf8/fff6Nixo8WTSQ3Y+PUYxERyrweRLZRKJTp27IjPPvsMcXFxSElJQZ8+fdCuXTssX74cQUFBcHNzw9q1a20+ytO4cWM8fPgQGzZswCuvvGLSX2yhVqstngJUFEtrj+E0ncJYWoPsfb36qKgoREVFWd03Kcny05Tl5e2330ZcXBzefPNNi2OKajSG97wBfaOZMmUKAP3hXkuNRqVSoX///oiOjsbPP/9s1mjyf3r0ueeeQ+fOnaHRaKDVajFs2LBiNZqTJ08aG7+3tzcePnxY6Dg2fj0GMZHc60FUGnl5eQD0f/e//fZbeHh44Mcff8TYsWPRo0cPs/PMAP3pKzqdDhcvXizy+atWrYqDBw/Czc0NnTt3Nn4IzqBWrVpITEw0+4TilStXiv0aatWqhdu3b5udm2z4NhvDpZMM/+gv2D9Kc+SsVq1a0Ol0uHr1qsntly9ftvk5Af15ZxcvXsSpU6dK9TzFxTAmoaFDh5a60Vy/fh19+vTB559/jg4dOuDIkSPo27evxZMpAX2jeeqpp+Dj44PVq1dj+fLlWLlyJRYuXIhBgwaZHK4taaMx/IV+6qmnjI2/ZcuWuHDhAhYsWICtW7cazytg49djEBPJvR5EpZGbm4v9+/fD3d0djRo1gkqlgkKhMDkac+PGDezatcvkcX379oVSqcS8efOMp6kYFHbEKCQkBAcPHkRWVha6dOmC+/fvG+/r1q0bcnNzsWrVKuNtOp3O5MBAUXr27AmtVotly5aZ3P7pp59CoVCgR48eAPTnKFetWhXHjh0zGbd8+fJib6sgw3N//vnnJrcvXbrU5ucsD3ybUkJSNJpt27Zh5syZeOedd6BQKDB69GisWLHC4vMaGs29e/cQExODTZs2YceOHVi+fDmqVKmCxo0bGz91Ykvjv3TpEgDgySefNN7+3nvv4ebNm/j444/x6NEjtG/fHpUrV2bjB4NYfnKvB1FJff/998ajRUlJSdi8eTPi4uLw7rvvwtfXF88//zyWLFmC7t27Y+jQoUhKSkJUVBTq1atn8snAevXqYcaMGZg/fz6effZZ9O/fH2q1GqdOnUKNGjVMPumY/zH79+9Hhw4d0K1bNxw+fBi+vr7o27cvWrdujalTp+LKlSto2LAhdu/ejZSUFACW31rMr3fv3ujYsSNmzJiBGzduGD+Q9t1332HKlCkm53O9/PLLWLhwIV5++WW0atUKx44dQ2xsrM2/02bNmmHIkCFYvnw5UlNT0bZtWxw6dKhER/YcAcNYGShJozFMyIiIiEIbTf5/9RTWaJ566imzf50YHmet0YSFhZk8t6Hxv/nmm2b/KqtWrZrJF8+y8esxiInkXg8iW7z33nvG//fw8EDDhg2xYsUKvPLKKwCATp06YfXq1Vi4cCGmTJmC2rVr46OPPsKNGzfMLtNg+GqlL774AjNmzICXlxeaNm2KESNGWNx+kyZN8P333+O5555D79698cMPP8DT0xP79u3D5MmTsX79eiiVSvTr1w+zZ8/G008/DQ8PjyJfl1KpxO7du/Hee+9h27ZtWLt2LcLCwrBo0SJMnTrV7Hdw7949REdHY/v27ejRowe+//57BAYGluRXaWLNmjUICAjApk2bsGvXLnTq1An79u0zfsWhM1AI9j4LzgWkpaXBz8+v0G91L26jmTNnDubOnYulS5ciKyuLjZ+N34j10HOkehRVByJnt2vXLvTr1w8///wznn766fLenXJjrb9LieeM2VFJGo3hJM7k5ORybzRs/I7V+FkPx6sHkZxkZWWZ/KzVavHFF1/A19dXkguhU9H4NqWdlLTRGC66OmjQoHJvNGz8jtX4WQ/HqweRnEyaNAlZWVl46qmnoNFosGPHDpw4cQIffPCByUVoyX4YxuzAlkbz1FNPYfny5Q7RaNj4Havxsx7OVQ8iZ9OpUycsXrwYe/fuRXZ2NurVq4cvvvjC5qvmU8nxnDEJ5H9PWalUyrrRsPHrsR4i1oOI5KqszhnjkTEJtWzZEunp6XjqqaewatUq2TUaNn491kPEehCRHJX1Ffh5ZEwChuS8aNEiKJVKWTYaNn491kPEehCR3PHTlE4oJydHlo2GjV+P9RCxHkRE0mEYk9DQoUNl12jY+PVYDxHrQUQkLYYxCfn7+1u8zxkbDRu/HushYj2IiKTHMFYGnLHRsPHrsR4i1oOIyD4YxuzMGRsNG78e6yFiPURnz561ej8RUUkxjNmRszYaNn7WIz/WQ2SoBxGRlBjG7MSZGw0bP+thwHqI8teDiIqvQ4cO6NChQ3nvhkNjGLMDZ280bPysB8B65FewHlS+zp49i7lz52Lv3r0o6lKZsbGxWLBgAbZt21bkBTwTEhKwcOFCrFmzBhqNRpJ9nTNnDhQKBZKTkwu9//HHH3fooHLixAnMmTMHDx8+LJftb968GUuXLi2XbZclhjEJRUREoFGjRnjppZecutGw8bMerIeoJPUg+3OE+eFKTpw4gblz55YqjO3fvx/79++36bHlFcaioqIQHh6OiIiIMtkevw5JQj///DP27Nkj20bDxq/HeohYDypLjjA/XEVGRga8vb0leS53d3dJnqcsRUZGIjIy0ngFfnvjkTEJbd++XbaNho1fj/UQsR5Ulhxhftjb0aNHoVAosH37drz//vsICQmBh4cHOnfujCtXrpiN/+2339CzZ09UrlwZ3t7eaNq0KT777DOTMX///TcGDBgAf39/eHh4oFWrVmYfQlm3bh0UCgV++uknTJgwAYGBgQgJCcGcOXPw1ltvAQBq164NhUIBhUKBGzduAADWrl2LTp06ITAwEGq1GuHh4VixYoXZfhY8Z6y4r7NDhw7Yt28fbt68adx2WFgY0tPT4e3tjcmTJ5ttKz4+HiqVCh9++GGxf++OgEfGJHTv3j2MGzdOdo2GjV+P9RCxHlSWHGF+lKWFCxdCqVRi2rRpSE1Nxccff4xhw4bht99+M445cOAAevXqhaCgIEyePBnVq1fHpUuXsHfvXmNI+euvv/D0008jODgY7777Lry9vbF9+3b07dsX3377Lfr162ey3QkTJiAgIADvvfceMjIy0KNHD8TGxmLLli349NNPUbVqVQBAQEAAAGDFihVo3Lgx+vTpgwoVKmDPnj2YMGECdDodIiMjS/06Z8yYgdTUVMTHx+PTTz8FAPj4+MDHxwf9+vXDtm3bsGTJEpMab9myBYIgYNiwYaWoQNljGJPQ4MGDZddo2Pj1WA8R60FlyVHmh7W/51LLzs7GuXPnjG/vVa5cGZMnT8b//vc/PP7449BqtXjllVcQFBSEc+fOoVKlSsbH5v9Aw+TJk1GzZk2cOnXK+PomTJiAZ555Bu+8845ZGPP398ehQ4dMfm8tWrTAli1b0LdvX4SFhZmM/+mnn+Dp6Wn8eeLEiejevTuWLFlSrDBW1Ovs0qULgoOD8eDBAwwfPtzksSNHjsSmTZtw4MABdO/e3Xj7xo0b0a5dO9SsWbPI7TsSvk0poRo1ali8zxkbDRu/HushYj2orDnK/ChLY8aMMTnP6tlnnwUAXLt2DYD+7+n169cxZcoUkyAGwPg7SklJweHDhzFo0CA8evQIycnJSE5Oxv3799GtWzfExcUhISHB5LHjxo2z+nsrKH8QS01NRXJyMtq3b49r164hNTW11K/Tmueeew41atTApk2bjLf973//w4ULF8yCmzPgkbEy4IyNho1fj/UQsR5UHhxlfthTwddW8KhO5cqVAQAPHjwAAFy9ehWA/rIYlly5cgWCIGDWrFmYNWtWoWOSkpJM5mbt2rVLtN+//PILZs+ejZMnTyIzM9PkvtTU1CJPfC/qdVqjVCoxbNgwrFixApmZmfDy8sKmTZvg4eGBgQMHluh1OAKGMTtzxkbDxq/HeohYD1FsbKzV/SRpOcr8sJWHhwcAICsrq9D7MzMzjWMMLL2Goq6plp9OpwMATJs2Dd26dSt0TL169Ux+zn+kqyhXr15F586d0bBhQyxZsgShoaFwd3dHTEwMPv30U+P2rSnt6xw5ciQWLVqEXbt2YciQIdi8eTN69epVJp9+lBrDmB05a6Nh42c98mM9RIZ6zJw50+pzknQcfX4UpVatWgCAy5cvIzQ01OS+zMxM3Lp1C127di3Rc9atWxeA/m255557rtAxderUAQC4ublZHFMcln7/e/bsgUajwe7du02OcB05csTmbZVk+4D+yGDz5s2xadMmhISE4J9//sEXX3wh6fbLCs8ZsxNnbjRs/KyHAeshyl8PKn+OMD+Ko3PnznB3d8eKFSvMjhatXLkSeXl56NGjR4mes0WLFqhduzaWLl1qdjFWw1GlwMBAdOjQAV999RUSExPNnuPevXvF2pbhWmMFt2P4fec/ipWamoq1a9cW92UUe/vWzj8bMWIE9u/fj6VLl6JKlSol/l06Ch4Zk1BERARUKhVee+01BAYGOm2jYeNnPQDWI7+C9aDy5Qjzo7gCAwPx3nvvYebMmWjXrh369OkDLy8vnDhxAlu2bEHXrl3Ru3fvEj2nUqnEihUr0Lt3bzRr1gxjxoxBUFAQ/v77b/z111/48ccfAeivIv/MM8+gSZMmGDduHOrUqYO7d+/i5MmTiI+Px/nz54vcluG7WGfMmIHBgwfDzc0NvXv3RteuXeHu7o7evXvjlVdeQXp6OlatWoXAwMBCw5+tWrZsiW3btuHNN99EREQEfHx8TH5fQ4cOxdtvv42dO3fitddeg5ubmyTbjYqKQlRUVJFfoSUZgUotNTVVACCkpqYKeXl5wtatW4X58+cLly9ftvo4nU4n7NmzR5gzZ45w5syZIrfz008/CXPmzBF++umnIseeOXNGmDNnjrBnzx5Bp9NZHXv58mVh/vz5wtatW4W8vDyrY+Pj44UPP/xQWL16tZCdnW11bHJysrB48WJh2bJlwqNHj6yOffTokbBs2TJh8eLFQnJystWx2dnZwurVq4UPP/xQiI+PtzqW9RCxHiJ71YPIko0bNwpt2rQRvL29BbVaLTRs2FCYO3euybw9cuSIAED45ptvTB57/fp1AYCwdu1ak9t//vlnoUuXLkLFihUFb29voWnTpsIXX3xhMubq1avCyJEjherVqwtubm5CcHCw0KtXLyE6Oto4Zu3atQIA4dSpU4Xu+/z584Xg4GBBqVQKAITr168LgiAIu3fvFpo2bSp4eHgIYWFhwkcffSSsWbPGZIwgCEL79u2F9u3b2/Q609PThaFDhwqVKlUSAAi1atUy27+ePXsKAIQTJ04Uuv+lkb+/25NCEEpwRiAVyvB1CSkpKdi/f79T/4ufR2BYD9ZDVJJ6EFH56NevH/78889Cv6WgtAz9PTU1Fb6+vpI/vwHPGZPQrl27ZNto2PhFrIce60FE5S0xMRH79u3DiBEjyntXSsUpwlh6ejpmz56N7t27w9/fHwqFAuvWrTMbN3r0aOP3V+X/07BhQ7OxOp0OH3/8MWrXrg0PDw80bdoUW7ZsKdV+Xr16VZaNho1fxHrosR5EVJ6uX7+OjRs3YsiQIXBzc8Mrr7xS3rtUKk5xAn9ycjLmzZuHmjVr4oknnsDRo0ctjlWr1fj6669NbivsmiMzZszAwoULMW7cOEREROC7777D0KFDoVAoMHjwYJv2s3///rJrNGz8ItZDj/UgovL2008/YcyYMahZsybWr1+P6tWrl/culYpTnDOm0Wjw4MEDVK9eHadPn0ZERATWrl2L0aNHm4wbPXo0oqOjkZ6ebvX5EhISULt2bYwfPx7Lli0DoG8E7du3x/Xr13Hjxo0SLcDFeU/ZGRsNG7+I9dBjPYjIlfCcsXzUanWJUq9Wq0VaWprF+7/77jvk5uZiwoQJxtsUCgVee+01xMfH4+TJk6Xa34KcsdGw8YtYDz3Wg4jIPpwijJVEZmYmfH194efnB39/f0RGRpodKTt79iy8vb3RqFEjk9tbt25tvF8qztho2PhFrIce6yEq+OXKRESl5RTnjBVXUFAQ3n77bbRo0QI6nQ4//PADli9fjvPnz+Po0aOoUEH/chMTE1GtWjWzxTkoKAgAcPv2bZu2X/BonLu7Ow4ePOh0jYaNX4/10GM9RIZ6vPvuu1a3T0TOSaPRQKPRGH+29i6blGQVxj788EOTnwcPHowGDRpgxowZiI6ONp6Yn5WVVWhTK+oLXYtS8HvHhgwZgscee8zpGg0bP+thwHqI8tdDo9GwHg5Uj/KaHyQ/H374IebOnVvm25Xd25QFvfHGG1AqlTh48KDxNk9PT5Pka5CdnW283xa3bt1CamoqHj58iG3btqFu3bpc2NhojFgPPdZDxHqInK0eJE/Tp09Hamqq8c+tW7fKZLuyD2Oenp6oUqUKUlJSjLcFBQXhzp07KPhBUsP3adWoUcOmbfn6+qJixYo4fvw4Ll26hP79+3NhY6MBwHoYsB4i1kPkbPUg+VKr1fD19TX5UxZkH8YePXqE5ORkBAQEGG9r1qwZMjMzcenSJZOxv/32m/F+W3BhE7HRiFgPPdZDxHqI7FmPlStXYurUqZg4caLVfWAQo/ImmzCWnZ2NR48emd0+f/58CIKA7t27G2974YUX4ObmhuXLlxtvEwQBX375JYKDg9G2bVub9uHHH3+U9cLGRqPHeuixHiLWQ+Ro9SgKgxg5Aqc5gX/ZsmV4+PCh8ZOOe/bsQXx8PABg0qRJePDgAZo3b44hQ4YYv/7oxx9/RExMDLp3744XXnjB+FwhISGYMmUKFi1ahNzcXERERGDXrl04fvw4Nm3aZPP1hc6ePYtBgwbJemFjo2E9ANYjP9ZD5Gj1GDBgAKZOnWpxbEnrUZxwV5h169ZhzJgxxp/VajX8/f3RpEkTPP/88xgzZgwqVqxY4uc9ceIE9u/fjylTpqBSpUo27Rs5Bqe4Aj8AhIWF4ebNm4Xed/36dVSqVAmTJk3Cr7/+itu3b0Or1aJevXoYNmwYpk2bBjc3N5PH6HQ6fPTRR/jqq6+QmJiI+vXrY/r06Rg2bFiJ981whd5jx47h2WeftTrWmRc2NhrWA2A98mM9RI5YD4VCYaxJeno6vL29jWNtqcfYsWOt/QosMoSxefPmoXbt2sjNzcWdO3dw9OhRHDhwADVr1sTu3bvRtGnTEj3vJ598grfeegvXr19HWFiYTftG1pXVFfid5sjYjRs3ihyzYcOGYj+fUqnE9OnTMX369FLslaknnnjC6v3OvrCx0bAerIeI9RA5aj3yXyPq2LFj6Nq1K1Qqlc31KK0ePXqgVatWxp+nT5+Ow4cPo1evXujTpw8uXbpk86f5ybnJ5pwxRyeHhc0SNhoR66HHeohYD1FZ1mPHjh0IDw83junZsyfCwsKwadMmm+thD506dcKsWbNw8+ZNbNy4EQBw4cIFjB49GnXq1IGHhweqV6+OsWPH4v79+8bHzZkzB2+99RYAoHbt2lAoFFAoFMYDF2vXrkWnTp0QGBgItVqN8PBwrFixwi6vgUrPaY6MOTM5LGyWsNGIWA891kPEeojKOogNGDDA7PJFCQkJGD58OEaNGoXFixdLVo/SGjFiBP773/9i//79GDduHA4cOIBr165hzJgxqF69Ov766y+sXLkSf/31F3799VcoFAr0798fsbGx2LJlCz799FNUrVoVAIxXDlixYgUaN26MPn36oEKFCtizZw8mTJgAnU6HyMhIu70Wso3TnDPmyAzvKTdo0AAqlQqRkZHGv+xyWNgsYaMRsR56rIeI9RCVZT20Wi3CwsKMH/AqTHBwMG7evGlxP6QOYoZzxk6dOmXyNmV+lSpVQp06dXDmzBlkZWWZvV25detWDBkyxOTcZGvnjBX2HN27d0dcXByuXr1aqtfjCqKiohAVFQWtVovY2Fi7nzPGtykldOrUKVy8eJFBrAA2GhHrIWI99FgPkRT1OH78uNUgZtjO8ePHC72vLI+I5efj42O8PFP+EJWdnY3k5GS0adMGAHDmzJliPV/+50hNTUVycjLat2+Pa9euITU1VcI9l6fIyEhcvHgRp06dKpPtMYzZiVwWtsKw0YhYDz3WQ8R6iMqjHoZvUilKYePKK4gB+r83hstbpKSkYPLkyahWrRo8PT0REBCA2rVrA0Cxg9Qvv/yC5557Dt7e3qhUqRICAgLw3//+t0TPQWWH54zZgZwWtoLYaESshx7rIWI9ROVVj6CgIKuvwdK48gxi8fHxSE1NRb169QAAgwYNwokTJ/DWW2+hWbNm8PHxgU6nQ/fu3aHT6Yp8vqtXr6Jz585o2LAhlixZgtDQULi7uyMmJgaffvppsZ6DyhbDmMTktrDlx0YjYj30WA8R6yEqz3o8++yzCAkJQUJCgtkJ/ACgUCgQEhJick3I8gxigHhZpm7duuHBgwc4dOgQ5s6di/fee884Ji4uzuxxlmqwZ88eaDQa7N69GzVr1jTefuTIEYn3nKTCtykl9Msvv8huYTNgoxGxHnqsh4j1EJV3PVQqFRYuXGgxiAHA0qVLjftV3kHs8OHDmD9/PmrXro1hw4YZ96vg/i9dutTssYaL2D58+NDk9sKeIzU1FWvXrpVwz0lKPDImoWPHjqFHjx6yWtgANpr8WA891kPEeogcpR4PHz7EqFGjsH//fpNzw0JCQrB06VL0798fQMnrYetX5Rl8//33+Pvvv5GXl4e7d+/i8OHDOHDgAGrVqoXdu3fDw8MDHh4eaNeuHT7++GPk5uYiODgY+/fvx/Xr182er2XLlgCAGTNmYPDgwXBzc0Pv3r3RtWtXuLu7o3fv3njllVeQnp6OVatWITAwsNjn1FHZYhiTULt27WS5sLHR6LEeeqyHiPUQOVo9Fi9eDDc3N/j5+QEAYmJijFfgB2yrx4svvmhxTHEY3nZ0d3c3fjfl0qVLzb6bcvPmzZg0aRKioqIgCAK6du2K77//HjVq1DB5voiICMyfPx9ffvklfvjhB+h0Oly/fh2PPfYYoqOjMXPmTEybNg3Vq1fHa6+9hoCAAJu/0onsi9cZk0Bxv7vKmRc2NhrWA2A98mM9RI5aj4yMjEK/m9LWesycOdPiOJKnsvpuSp4zVkbksLBZwkYjYj1ErIce6yFy9noQ2QvDmIQiIiIQHh6OqKgok9u5sOmx0YhYDxHrocd6iKSqh7e3NwRBgCAI8Pb2tls9SH6ioqIQHh6OiIiIMtke36aUgLXDmHJa2ApioxGxHiLWQ4/1EMm5HiRvfJtSBriw6bHRiFgPEeuhx3qInK0eRFJhGLMTLmx6bDQi1kPEeuixHiJnqweRlBjG7IALmx4bjYj1ELEeeqyHyNnqQSQ1hjGJcWHTY6MRsR4i1kOP9RA5Wz2I7IEXfZXQ+fPncfjwYS5sbDRGrIeI9dBjPUTOVg8ie+GRMQnFxMRwYWOjMWI9RKyHHushcsZ6ENkLw5iEmjdvzoWNjQYA65Ef66HHeoictR5E9sIwJqFu3bpxYWOjYT3yYT305FCP7OxsKBQKKBQKZGRkmIx1lXoQ2QvDmIRat25d6BX4ATYaA0dtNKwH68F6iFgPvYL1INfBK/A7oaKu0MuFTY+NRsR6iFgPPUevR2Ffus16kNzxCvwywUaj5+iNpjCsh4j10GM9RKwHkXR4aQs74sKmx0YjYj1ErIeeM9bj9u3b+Pbbb1kPIonwyJidsNHoOWOjYT1ErIce62Fqy5YtrAeRhBjG7ICNRs8ZGw3rIWI99FgPcwEBAS5fDyIpMYxJjI1GzxkbDeshYj30WA/R7du3jf//4osvunQ9iKTGc8YkdOXKFcTExLDROGGjYT1ErIce6yFKSEjAli1bjD+7cj2I7IFHxiS0Y8cONhonbTSshx7rocd6iAz1CAgIsLp9QP71ILIXXmdMAobrkKxZswYjR45ko3HCRmNrPRStHwNy88zGqgQFQjVKKAUFbqm1yFVanmYKAQjRqKAWFIh31yJbZWVKCkCNHBW8tQrcVmuRUcTYwFwVKuUpcMddh7QKOstjAfjnKlE1V4lkNx1S3KyP9c1TonqOEg8rCEhy0wKWywFvrQI1NCpkqATcdrc+1kOrQEiOChqFgHi1FoKVsW46BUI1KugUAm6pddAqLP8upK7HoAatsG3bNpeaH3379jWuK4brjOXnCutVUXUg+Smr64zxbUoJLVy4EIsWLUJkZCQiIyPN7mcQEzlao7G5Hrl5wOv9TMb6aBUYddcTakGB9YGZyHWz3PjddcDwJC8E5iqxITAT2WrLIUglAAOSPVA/qwK2BWQhw1NrcaxCAHo+UKNVuht2+2cjzcc8MObXLtUdnVLVOOynQaxfjtWxzdMroE+KB0775CKmssZquKqfpcKL9zwR55mH6KrZVscGa5QYkeSFJDcdNgZmQrBy3L5KrgKjkrygUQhYXy0LWiuh1B71yNoZ63LzIy/P8t8hV1mvGMZcR1RUFKKioqDVWl5npcQwJqFTp05ZTM4MYiJHbDRS1aNg479fgsafUIIgFleCIHa2BEHsWAmDmLUjVwWDmLYEQSynBEEsvQRBTKp6CILgcvPDUhhzpfWKXIfhoIrhyJi98W1KCRR1GJNBTOSojcaSouqh8KkCVKsHAFBBh1BVBpQKAbe03sgVLO+DAgJCVBlQK7SI13ojW7D27yIBNVSZ8Fbk4bbWCxmCm9WxgaosVFLk4I7OC2k6dytjAX9lNqoqs5Gs80CKzsPqWF9lDqorM/FQcEeS1hPWDnN5K3JRQ5WJDKECbmu9rI71UOQhRJUBjaBCvNYbgpWxbgotQlUZ0AkK3NJ6Q2vltFd71qPygyt48803XWp+FPZ1SFyvSO74NqVMMIiJHLnRFKZY9RB0QN+Z8FHkYpRXHNQKLdZn1keulWDjDi2Ge11BoCoLGzLqI1vnbXGsCjoM8LyO+hXSsC2rDjLyLP8LTQEBPT1uoZVbMnZn10RablWLYwGgnXsiOnkk4nB2EGJzgqyObe6WjD4e/+B0blXEZIfCWriqXyEVL3peQ1yeL6KzasPa54SClRkY4R2HJK0nNmbWgwDLtauizMYorzhoBBXWZ9aH1kootXc99i6dyfnB9YpIMk7xacr09HTMnj0b3bt3h7+/PxQKBdatW2cyRqfTYd26dejTpw9CQ0Ph7e2Nxx9/HAsWLEB2drbZcyoUikL/LFy4ULL95sImknOjKdj475eg8SeUIIjFlSCInS1BEDtWwiBm7chVwSBm7chVwSCWU4Igll6CIGaPenh4eHB+cL0ikoxTHBlLTk7GvHnzULNmTTzxxBM4evSo2ZjMzEyMGTMGbdq0wauvvorAwECcPHkSs2fPxqFDh3D48GGzRatLly4YOXKkyW1SfbSZC5tIzo0GEBjE/uUqQWxbVh1UqGB56ZT7/ACATZs2wdfX1+XXKyKpOEUYCwoKQmJiIqpXr47Tp08jIiLCbIy7uzt++eUXtG3b1njbuHHjEBYWZgxkzz33nMljGjRogOHDh0u+vwxiImdrNCWth6dCyyAG1wpicXl+qGZhrNznhwHXKyJpOcXblGq1GtWrV7c6xt3d3SSIGfTrp7/swKVLlwp9XFZWVqFvY9qKQUzkbI3GlnooAAYxFwtilsh9fiQlJRn/PywsDJ6enhbHyn29IpKaU4Sx0rhz5w4AoGpV80a1bt06eHt7w9PTE+Hh4di8eXOptnX79m0GsX85W6OxtR5ZgopBjEFM9vNj/fr1aNasmfHn//znPwgLC8OOHTvMxsp9vSKyB9mHsY8//hi+vr7o0aOHye1t27bF+++/j127dmHFihVQqVQYNmwYVqxYYfO21q9fj4oVK6J3797QaDTQaDSFjmMQEzlCoylNPawFFQYxEYOYyNnmx/r16zF69GikpaWZPceAAQNMApkrrFdz587F3r17UdRVoWJjY7FgwQLjNzVYk5CQgIULF2LNmjUW+4bB/fv3sWTJEkRFRSE9Pd3q2PT0dERFRWHJkiW4f/++1bEajQZr1qzBwoULkZCQYHWsVqvFtm3bsGDBAsTGxlodKwgC9u7di7lz5+Ls2bNWxwL6esydOxfHjh0rcqw96qHRaPD3339jzpw5iIqKQnJycpH7IQWnu86Y4ZyxtWvXYvTo0VbHfvDBB5gxYwaWL1+O1157zerYnJwctGzZEvHx8bh9+7bVQ/AFWboo3OzZszFnzhyT2xjERI7QaEpbD4V3ZeDV/zMbyyAmkmMQq7ZpHO7cuSP7+ZGUlIQGDRogNTW10PsVCgVCQkJw/fp1ZGVlcb36l6OuV5awHqI333wTn376qdnt9r7OmGyPjG3btg0zZ87ESy+9VGQQA/TnnE2cOBEPHz7EH3/8YdM2r169itTUVOOf6dOnm9zPiSSS88LGICaSYxAzcIX5MWfOHItBDND/Dm7duoX9+/dzvfqXs61XrIcoISEB/v7+WLZsGZKSkpCamopbt25Z3RepOMWnKUvqwIEDGDlyJJ5//nl8+eWXxX5caGgoACAlJcWm7VatWtVicuZEEsl5YWMQE8k5iAFwifmRlZVldV8Ntm/fjiZNmnC9crL1ivUQGepRo0aNYn+qWEqyOzL222+/oV+/fmjVqhW2b99u9XpABV27dg0AEBAQIOk+cSKJ5LywMYiJ5BzEFBCg0WhcYn4MGjTI6v4aeHp6cr1ysvWK9RDZcnkXqcnqyNilS5fw/PPPIywsDHv37rV43te9e/fMAtejR4+wdOlSVK1aFS1btpRsn+Q8kfJ/V90PP/zgFBOJQUxkaxD7Ji0Iui/66u+YtB1wMw1Ecg9iPT1u4WhuriwbTcH54enpiZCQECQkJFg8QbpSpUqYM2eOw69XgPPXQ079A3C+etiT04SxZcuW4eHDh7h9+zYAYM+ePYiPjwcATJo0CUqlEt26dcODBw/w1ltvYd++fSaPr1u3Lp566ikAQFRUFHbt2oXevXujZs2aSExMxJo1a/DPP/9gw4YNcHe3/uXKxSX3ifTzzz8b//+ZZ56xOtYRJhKDmKg0R8R0yLE41hWCWCu3ZJxUq2XXaCzNj88++wwDBgyAQqEoNJAtXboUgYGBFp/XUdYrudSjMM7YP5ytHvbmNGHsk08+wc2bN40/79ixw/iRasNV9A0n2r377rtmjx81apQxjD399NM4ceIEvv76a9y/fx/e3t5o3bo11qxZg06dOkmyv64wkYrz0WPAMSaSveoBwKWCmCu/NZm/Hm5ulvcXcL5GY21+9O/fH9HR0Xj99ddNLnlQqVIlLF26FKNGjbL4vI60XsmlHgU5a/9wpnqUBacJYzdu3ChyTHGv0tGlSxd06dKllHtkmatMpHbt2uGDDz6wOtYRJpI96+GhyGMQg2sFsbO5VS1+HRLgfI2mOPOjf//+eO6554yX8Hn55Zfx/vvv84iYk61XrIdeSeph6wf6SsppwpizcKWJVNS5dY4wkexdDyUEBjEXC2LWOFujKcn8yP/JylmzZjGIOeF6xXqUvB6l/Wae4mIYk1BKSgp27NjhMhMpIyPD4lhHmUj2XtiyhQouHcSCFBkY4X2LQQzO2WhKMj82bdpk/NkZ1ytrnLEezt4/rHGkekh1DnlRGMYk1Lp1a6hUKrz11lucSA4ykey9sE2f/5HFsXIPYgAwxPsqkrR+DGIuMj+KwvVK5IjrFetR/HqMGzcOJ0+etFoLKcnuOmPl6e2330ZcXBzefPNNi2M4kURyXthcIYgBwD0eEXOZ+TFs2DCrr43rlcjZ1ivWQ2SoR+fOnREXF2fzN/KUFMOYhIYOHcqJ5CATiUHMvm9NGmzNqsMgxvnB9Sof1kMk53pIjWFMQq48kc6fP+9UE4lBTFTSk/WHeF81/pzLICbLRiP39Yr10GM9ROUZxACGsTIh94kE6L+nz1kmEoOYyJZPTd7TFv7NFvnJPYjl5OTIttHIfb1iPfRYD1F5BzGAYczu5D6RDJo3b+4UE4lBTGTr5Su2ZtWxug9yD2Lt3BORk5Mjy0Yj9/WK9dBjPUSOEMQAhjG7kvtEOn/+vPH/u3fv7vATiUFMVJrriLnyW5OGeri7u8uu0ch9vWI99FgPkaMEMYCXtrAbV5hI+b//09Enkr3qAcClgpj+ZP3cQse6ShA7nB1U5LWHnK3RlPQrwLZt24bU1FSnWq/kWg9n7R/OVI+ywCNjduAqE6mo5wQcYyLZsx5qhdbFgljhXCmIFVUPZ2s0JQ1iAHDv3j2nW6/kWA9n7h/OVI+ywCNjEnOlidShQwerYx1lItmzHhWgYxBjEDNyxkZTkvlhMGTIEKdbr+RYD2fuH85Sj7L6OiQeGZNQy5YtUb9+fRw6dMglJpJOpzN5Hq1Wa/zZUSaSvRe2bEHlekEsX90rJpzFCI/LDGJwzkZTkvmxbds24881atSwONZR1yu51cPZ+4ej12PJkiWoX78+Pv74Y6uvSyoMYxJ65ZVX8NZbb2HVqlWyn0g7d+5EeHi48f6ePXsiLCwMO3bscIiJVFYLm7VQI8sgFncCWB9p/PHRjg+x6rNPsPr8fQYxJ2o0tsyPe/fuWRxj4KjrlRzr4cz9wxnqoVar8dZbb+H333+3+tqkwjAmoZycHJeYSDt37sSAAQOQkJBgMi4hIQEDBgzA7Nmzy30ilffCJtsgtmchkH7f5OaMR6nI3r1If38hGMREjtJobJkfQ4YMsTgOcNz1Sq71cNb+4Wz18Pf3t/r6pMIwJqGhQ4fKfiLpdDpMnjwZgiAUuh+CIODAgQPo16+fQ0wkBjGp3prUAkdWWd03HP1aPy4fBjGRIzaakswPvjXpWPVwxv4hp3pITSEU1lWpRNLS0uDn5wd4+gFW/rJ5KPKghIBsoQJ0VpooAKgVWlSADtmCqsgvbHZXaOEGHTSCCnlFjHWDFu4KHXIEpdXrRQFABeigVmiRCyVyhH/HanMBTYbVx+lfgDegKvxtKyUEeCjyoIMC2YL1z5AoIMBToYUAIEtQAVZ/b/qxin/HWgsqgET1UFYAxq8x2V/ZBTEAuPUn8M0Mq/sHABj4PhDaBIC8g1i1TePw/fffy7bRsPGLWA+RK9bD0N9TU1Ph6+tr9TWXBj9NKaWxXwFqL7Obna3RAOaN3xiC/v4JiFls9bEAgM6vAg3bm91csPGjBJ/SQwnOScqUUeMHHOBTkxkpVvev4Dg5BzEAyM3NlUWjKQwbv4j1ELEe9sUjYxIwHhmL3GoWxpyx0Vht/DYcITHg5RJEThXEAPjG/4G07XOt7icAYOD78KnZUPb1iIt6HV41g5DkprV6sNZbq0ANjQoZKgG33a2P9dAqEJKjgkYhIF6thWBlrJtOgVCNCjqFgFtqHbQKy8u4SlAgVKOEUlDgllqLXKXlsQoBCNGooBYUiHfXIltlpT0IQI0cFby1CtxWa5FRxNjAXBUq5Slwx12HtAo6y2MB+OcqUTVXiWQ3HVLcrI/1zVOieo4SDysIdq/HoAatTD5VasAgJpJbEOORMRlw1kZjtfEHhwM+VcxO4jZRsap+XP6HMYgZOVsQq6LMxogGFbCqoh8yHqVa3tmKVeEdUt8l6rGiZhDuTe1jtZnXz1LhxXueiPPMQ3TVbKtjgzVKjEjyQpKbDhsDMyFYOdugSq4Co5K8oFEIWF8tC1orIchHq8Cou55QCwqsD8xErpvlse46YHiSFwJzldgQmIlsteUQpBKAAckeqJ9VAdsCspDhqbU4ViEAPR+o0SrdDbv9s5Hmk2f5xQFol+qOTqlqHPbTINYvx+rY5ukV0CfFA6d9chFTWWP3emTtjDUbyyAmklsQK0s8gd9OnLnRWG38ShXQcZzV50GHl/Xj/sUgJnLGIDbKKw65CjdoO463ur8eHcdgtM81l6hHkpv1I1cFG7+2BI0/pwRBLL0EQex+CYJYQgmCWFwJgtjZEgSxYyUMYuVRDwYxEYNY6ZTJkbFr165Bo9GgUaNGZbG58rN5KqBQQtWsO4a3beC0jabIxt+oMZ7wfBH7Yn5AVnq+IyUVq+qDWP22xpsYxETOGsQM9ciuFw70Vuk/VZn/yGjFqvDoOAYvPVHFZepRkiMwDGIMYgxiImcJYlFRUYiKikJenvW/t1KR9MjY559/jsGDB5vcNmbMGNSvXx+PP/44WrVqhaSkJCk36ViGLob76M8xSs5B7N/Gr6rfBtmjVoh39JsNvLSKQcwCZw9ixnrUbwuMihIH9psN75eXu1QQs1oPBjEADGL5MYiJnCWIAUBkZCTOnj2LyZMnW3wuKUkaxr7++mtUq1bN+POPP/6I9evXY/z48fjiiy9w7do1zJ1bjJOAnZSb3BtNgcYvKPMdWA1pzLcmLZBNEDNQitvxDm3gMm9NMogxiAEMYvnJNYgBJfvmCSlI+jblzZs3Td6K3L59O2rXro0VK/RHUO7cuYMNGzZIuUmH8qLXNQSqBHk2Gn7JNADWo6BhnlehVqhYDwYxAPIPYjk5OQxi/3KFIJaUlITBgweXyUEkScNYwatk7N+/Hy+88ILx57CwMNy5c0fKTTqUAFUWNmQ0kV+jcYDGzyAmcoR6GOjr0dC168EgBkD+QaxKrgK3bt1iEIPrBLERI0agYsWKFsdKSdK3KRs0aICdO3cC0L9Fefv2bfTo0cN4f3x8PCpVqiTlJh3K1oy68ms0DtD4GcREjlAPb0Wu8f83ZdZ17XowiAFwjSA2KskLSqWSQcyFgpi1ekhN0iNj06ZNw9ChQ1G5cmVkZGSgUaNG6Natm/H+w4cPo1mzZlJu0qEkyq3RlKDxBykyMML7FoMYHKMe9gzGQz2vYuW/P6cILlwPBjEArhPENAoBoaGhDGIMYnYhaRgbPHgwqlSpgpiYGFSqVAkTJkxAhQr6TaSkpMDf3x/Dhw+XcpNOwSkbTQkaPwAM8b6KJK0fg5gD1MPeRyiVuZYbs4Hc6+GtVTCIwbWC2PpqWehYwXLLZBATMYiVnOTXGevSpQu6dOlidru/vz927Ngh9eYcnjM2mpIGMQC4p/XENg2DWHnXoyzeKl6XWdfqa3OFekCjYhBzsSBmrR4MYiIGMdtIes6YSqXC5s2bLd6/bds2q4WRG2dtNCV5a9Jga1YdBjEXCGLrM+u79luT/9YjQyUwiDGIAWAQy49BzHaShrGivnNcq9VaLaTT2zwVWBcJnNvn1I2muI1/iPdV48+5DGLlXg9+eEKvLOpx213LIMYgxiCWj9yCWFRUFMLDwxEREWHxOaQk+duUloqVlpaGH3/8EVWrWl9IndrQxYDay+kbTXEbf0KWp9XtA2z8+TGI6cmhHiX5kmkGMQYxBjGRMwQxQH8F/sjISDx48AD+/v4Wn0sqpT4yNnfuXKhUKqhUKigUCgwfPtz4c/4/lStXxoYNG8y+Lklu5NBoitv4t2bVsboPbPwiBjE92deDQcyIQUyPQUzkLEHMQKvVYteuXRbvl1Kpj4y1bt0aEyZMgCAIWL58Obp06WL2F0ihUMDb2xstW7ZE//79S7tJh6WUe6Mp0PhzkWtxLBu/SM5BTP8VYDdZDzCI5ccgpscgJnLGIBYdHY2rV69aHCOlUoexHj16GC/smpGRgVdffRVPPvlkqXfMGfX1vIn6FXLk2WgKbfyFhzEGMZGcgxgADPa8hkCVlvVgEDOScxDLy8tjEPuXKwSxuLg49O/fH++//77FsVKR9AT+tWvX2iWIpaenY/bs2ejevTv8/f2hUCiwbt26QsdeunQJ3bt3h4+PD/z9/TFixIhCv+hTp9Ph448/Ru3ateHh4YGmTZtiy5YtpdrPunJtNA7Q+BnERI5QD4MA1oNBLB85BzEfrf7rkBjEXCeIDRo0CPXq1bM4VkqSn8Cv1Wrx448/4tq1a3jw4IHZJywVCgVmzZpVoudMTk7GvHnzULNmTTzxxBM4evRooePi4+PRrl07+Pn54YMPPkB6ejo++eQT/Pnnn/j999/h7u5uHDtjxgwsXLgQ48aNQ0REBL777jsMHToUCoXC5vPavs2qjWsqmTUaB2j8DGIiR6iHG7TGY6JbMuoisYIL14NBzEjuQWzUXU8c0ekYxFwoiDVo0ABpaWkWx0tJ0jB2+vRp/Oc//0F8fLzFy1zYEsaCgoKQmJiI6tWr4/Tp0xY/avrBBx8gIyMDf/zxB2rWrAlAf05bly5dsG7dOowfPx6A/i/I4sWLERkZiWXLlgEAXn75ZbRv3x5vvfUWBg4caNP10K7l+cJSb3TKRlOCxu+vyMYor1sMYnCMetgzGL/oeQ0b/v05UXDhejCIGblCEFMLCoSGhjKIuVAQK0uSvk05YcIEZGVlYdeuXUhJSYFOpzP7o9UW/VUqBanValSvXr3Icd9++y169eplDGIA8Nxzz6FBgwbYvn278bbvvvsOubm5mDBhgvE2hUKB1157DfHx8Th58mSJ99Eap2w0xWn8bh7Am7tRZdp2jK7EIAbIP4gN97qCYE8tgqdtAd7crf87UAi518NDq2AQ+5erBLH1gZkm764UxCAmYhArOUnD2IULF/DOO++gd+/eqFSpkpRPXaSEhAQkJSWhVatWZve1bt0aZ8+eNf589uxZeHt7o1GjRmbjDPfbJCcT0OT7k5frlI3GkRo/gxjrkZ+j1CMkR8UgBtcKYtbqwSAmcvYgVqtWLaSlpZn8KQuSvk0ZEhJS5FX47SUxMRGA/i3NgoKCgpCSkgKNRgO1Wo3ExERUq1bN7C+f4bG3b9+2bSdWjTX9uc1g9Oz0jNM1GjZ+PdZDj/UQGeqxSSEwiDGIAWAQy8/Zg1iDBg0wZ84czJ071+Jj7EXSMPbOO+/gk08+wfjx4+Hr6yvlUxcpKysLAAotvoeHh3GMWq02/tfaOJuMWwO4e/37g4Du3necstGw8bMeBqyHKH894tVaCAxiDGIMYkZyCGIAMH36dLz55pvGcWlpaQgNDbX4PFKRNIw9evQIPj4+qFevHgYPHozQ0FCz4ikUCrzxxhtSbhYA4Omp/2oejUZjdl92drbJGE9Pz2KNKzF3L0Dtla/RpDplo2HjZz0A1iO/gvWwFigYxEQMYnoMYiJHDmKA/oBO/v0vq3f7JA1j06ZNM/6/4VOKBdkrjBneYjS8XZlfYmIi/P39jb/goKAgHDlyBIIgmPxlNDy2Ro0aNu+HHBoNGz/rwXqISlQPBjEjBjE9BjGRowexggRBwI8//mjxfilJGsauX78u5dOVSHBwMAICAnD69Gmz+37//Xc0a9bM+HOzZs3w9ddf49KlSwgPDzfe/ttvvxnvt43MGw0bvxHrocd6iBjERAxiegxiImcMYvv27bP9A30lJOmnKWvVqlWsP/byn//8B3v37sWtW7eMtx06dAixsbEYOHCg8bYXXngBbm5uWL58ufE2QRDw5ZdfIjg4GG3btrVp+1094uXbaNj4jVgPPdZDxCAmknMQ0+l0DGL/coUg9scff6Bnz54Wx0lJ8ivwA/oiHTt2DElJSfjPf/6DkJAQaLVapKamws/Pz6YLqi5btgwPHz40ftJxz549iI+PBwBMmjQJfn5++O9//4tvvvkGHTt2xOTJk5Geno5FixahSZMmGDNmjPG5QkJCMGXKFCxatAi5ubmIiIjArl27cPz4cWzatMmm/QOAFm73sTu7gfwaDRu/Eeuhx3qIGMREcg5i7jr9t7wwiLlOEOvTpw/q1q1rcayUFIKEZ6cJgoCpU6di2bJlyMvLg0KhwIEDB9CpUyekpqYiNDQU8+bNw5QpU0r83GFhYbh582ah912/fh1hYWEAgL/++gtvvvkmfv75Z7i7u+P555/H4sWLUa1aNZPH6HQ6fPTRR/jqq6+QmJiI+vXrY/r06Rg2bFiJ9y0tLQ1+fn5oOiUKF5TWP3XhdI2Gjd+I9dBjPQAcngK83o9BLB+5B7HhSV44uSEaBw4cYBBzkSDWvHlzY39PTU2161UiJA1jH3/8MaZPn4533nkHnTt3RpcuXXDw4EF06tQJADB69GhcvXoVx48fl2qTDsFQLERuBdReFsc5TaP5Fxu/iPXQYz3+dXgKqrzWn0HsX64QxAJzlfjj5xPYv3+/xbEMYiI5BDEAZRbGJD1nbNWqVRg5ciQ++OCDQk+Cb9q0KWJjY6XcpNNwqkYDNv78WA891kPkpuMRMQNXCWIbAjON16IsDIOYSC5BrCxJGsZu3bpl9eR3b2/vMvtqgXKxeSqwLhI4t8/kZmdrNGz8ItZDj/UQVVFmI1SjYhCDawUxa/VgEBPJJYhFRUUhPDwcERERFh8rJUlP4A8MDDT5JGNBf/zxh8mXeMvO0MVmb1M6Y6Nh49djPfRYD5GhHjsYxBjE/sUgJpJLEAOAyMhIREZGiqch2ZmkR8b69++PL7/8EteuXTPeZvgLsX//fqxbt87kEhNy56yNho2f9TBgPUT563FLrWMQYxBjEMtHTkGsPEgaxubOnYugoCA0a9YMI0eOhEKhwEcffYRnnnkGPXr0QNOmTfHf//5Xyk06LGduNGz8rAfAeuRXsB5aBYMYgxiDmAGDWOlJGsb8/Pzw66+/4u2330ZCQgI8PDzw008/4eHDh5g9ezaOHz8OLy/LnzaUC2dvNGz8rAfrISpRPRjEjBjE9BjERM4YxH755Rer90tF8ou+enp6YubMmZg5c6bUT+0UZN1o2PiNWA891kPEICZiENNjEBM5YxA7duwYjh07ZnWMVCQ9MrZ8+XLcu3dPyqd0Km3d78i30bDxG7EeeqyHiEFMJOcgJggCg9i/XCGIHTlypMhaSEXSMDZx4kQEBwejS5cuWL16NVJSUqR8eofXTn1Hno2Gjd+I9dBjPUQMYiI5BzGVACQmJjKIwXWCWMeOHfH0009bHSsVScPY33//jZkzZyIxMRHjxo1DUFAQevbsiQ0bNsj7+mL/OqapLr9Gw8ZvxHrosR4iBjGR3IPYgGQPpKenM4i5UBArq6NigMRhrEGDBnjvvffwv//9D3/++SfefvttXLt2DaNGjUK1atXQt29fbN26VcpNOpQTOdWt3u90jYaN34j10GM9RAxiIlcIYvWzKqBGjRoMYgxidiHpd1Nacv78eWzduhXLly9HRkYG8vKsLxjOxnhRuMrBgEIJNOsJNHveZIzTNRo2fiPWQ4/1EKkOvYFXBw5lEIPrBLFtAVlo+MMl7N69u9CxDGIiOQSxqKgoREVFQavVIjY21u7fTSn5pykLunDhArZv347o6Gg8evQInp6e9t5k+SnkCvyA8zUaNn4R66HHeoh8FLmoqlEyiMG1glicpxYNLYxlEBPJIYgBTn4FfoOLFy9i9uzZaNSoEZo3b47FixcjPDwcGzduxN27d+2xSYfljI2GjV+P9dBjPUSGeigZxFwuiFnCICaSSxArD5IeGZs/fz62b9+OixcvQqVSoXPnznj33XfRt2/fMkmWjsZZGw0bP+thwHqI8tfjllqLXAYxBjEGMSMGsdKR9JwxNzc3tG/fHi+++CL69+9vtRhyYjyMGbnV+DalMzcaNn7WA2A98jOrx+7PgbzCw40CAkJUGVArtIjXeiNbsPZvXgE1VJnwVuThttYLGVb2ARAQqMpCJUUO7ui8kKZztzIW8Fdmo6oyG8k6D6RY+fsDAL7KHFRXZuKh4I4krSdgpR7eilzUUGUiQ6iA21ovq2M9FHkIUWVAI6gQr/W2Wmc3hRahqgzoBAVuab2t1lkFHUJVGVAqBNzSeiNXsFxnKetRIzseCQkJ+pEMYkZyDmKG/u5U54wlJCQgMDAQGo0GZ86cQVJSEp5++mlUrWp9EZcbp280bPysB+thVGg9er1d6NiC9cguQT0ySlCPtBLUI7aE9bAWrgrWw9qZLgXrIZTkuz9LUI/cEsyP0tZDu2kcAAax/OQcxAD9BxDLgqTnjAUGBuLzzz9HUFAQnnnmGfTv3x8XLlwAACQnJ6Nq1apYs2aNlJt0OLJoNBaw8YtYDxHrocd6iORcDwYxkdyD2NmzZxETE2N1jFQkDWNr167FlClT0L17d6xevRr53wGtWrUqOnXqJOvrjDV1u8+FDWw0+bEeItZDj/UQOVs9ADCI/csVgtju3buLfE6pSBrGFi9ejBdeeAGbN29G7969ze5v2bIl/vrrLyk36VB6etziwsZGY8R6iFgPPdZD5Iz10Gg0DGJwnSDWsmVLdOvWzepYqUgaxq5cuYIePXpYvN/f3x/379+XcpMO5UxuFS5sbDQAWI/8WA891kPkrPXIzc1lEHOhIFZUPaQk6Qn8lSpVQnJyssX7L168iOrVrX9lkDPbv+ZTQKEq9Ar8ABc2AzYaEeshYj30WA+Ro9XjpFrNIOYiQezGjRto3LgxtFrLlzaRkqRhrGfPnli5ciUmTJhgdt9ff/2FVatWYezYsVJu0rEMXVLoFfgBLmwGbDQi1kPEeuixHiJHrIebm7VLjzCIGTh7EDPUY+LEic55Bf4FCxZAq9Xi8ccfx8yZM6FQKLB+/XoMHz4crVq1QmBgIN577z0pN+kUuLDpsdGIWA8R66HHeoicsR4MYnpyCWJlTdIwVqNGDfzxxx/o3r07tm3bBkEQsGHDBuzZswdDhgzBr7/+6nLXHOPCpsdGI2I9RKyHHushcsZ6MIjpMYjZTvIvCg8MDMTXX3+Nr7/+Gvfu3YNOp0NAQACUSrt8DaZD48Kmx0YjYj1ErIce6yFyxnowiOkxiJWO5GEsv4CAAHs+vUPjwqbHRiNiPUSshx7rIXLGejCI6TGIlZ7rHa4qA1zY9NhoRKyHiPXQYz1EzlgPBjE9uQexK1euWH0uqTCMSYwLmx4bjYj1ELEeeqyHyBnrwSCmJ/cgFhsbix07dlh9PqnY9W1KV1OnQhpe9EzkwsZGY8R6iFgPPdZD5Iz1YBDTc4Ugtn37dtStW9fqc0qFR8Yk9B/P61zY2GiMWA8R66HHeoicsR45OTkMYnCdIFa/fn307dvX6vNKRSHk/zZvsonhonAV/QPwCGr91fcLuQI/wIUtPzYaPdZDxHqIWA89R6rHH1+8iZiYGAYxFwhi165dw6+//gqtVovY2FikpqbC19fX6nZKg2FMAoYwpozcDJ3a8l94LmwiNho91kPEeohYDz1Hq8cHSz5BTp1Aq2N985SonqPEwwoCkty0sFIOeGsVqKFRIUMl4La79bEeWgVCclTQKATEq7UQrIx10ykQqlFBpxBwS62DVmG5zasEBUI1SigFBW6ptchVWh6rEIAQjQpqQYF4dy2yVVbigwDUyFHBW6vAbbUWGUWMDcxVoVKeAnfcdUiroLM8FoB/rhJVc5VIdtMhxc362FLXQ6sD/rxh9zDGc8YkpOPCxkbDehixHnqsh8jZ65FTJxB4vZ/Fsc3TK6BPigdO++QiprLGauOvn6XCi/c8EeeZh+iq2VbHBmuUGJHkhSQ3HTYGZkKwcoJRlVwFRiV5QaMQsL5aFrRWQpCPVoFRdz2hFhRYH5iJXDfLY911wPAkLwTmKrEhMBPZasshSCUAA5I9UD+rArYFZCHD0/L3OyoEoOcDNVqlu2G3fzbSfPIsvzgA7VLd0SlVjcN+GsT65VgdK0k9sjRA5DKr25ECzxkrA1zYRGw0eqyHiPUQsR56TlmPAo3f2pGrgo1fW4IgllOCIJZegiB2vwRBLKEEQSyuBEHsbAmC2LESBjGp6mEvDGN2xoVNxEajx3qIWA8R66HnlPVgEAPAIFYasgpjo0ePhkKhsPgnISEBANChQ4dC7+/evbuk+8OFTcRGo8d6iFgPEeuh55T1YBADwCBWWrI6Z+yVV17Bc889Z3KbIAh49dVXERYWZvKpj5CQEHz44YcmY2vUqCHZvnBhE7HR6LEeItZDxHroOWU9GMQAMIhJQVZh7KmnnsJTTz1lctvPP/+MzMxMDBs2zOR2Pz8/DB8+3C77wYVNxEajx3qIWA8R66HnlPVgEAMg/yAWpFEi0eqWpSGrtykLs3nzZigUCgwdOtTsvry8PKSnp0u6PS5sIjYaPdZDxHqIWA89p6wHgxgA+QexYI0Sg+95Wd22VGQdxnJzc7F9+3a0bdsWYWFhJvfFxsbC29sbFStWRPXq1TFr1izk5uaWanvV8pIxqMKfiM9UYMODGsjJs/yXmAubiI1Gj/UQsR4i1kPPYerBIAZAxkEsNw/I0iDoYS4G/aNCgi7L6valIqu3KQv68ccfcf/+fbO3KOvWrYuOHTuiSZMmyMjIQHR0NBYsWIDY2Fhs27bN5u3d/WoiPs1/Q5vBQFvzI3Jc2ERsNHqsh4j1ELEeeo5SD988JYMYZBzEAGDf78Duk0gETPu5ncn6CvxDhw5FdHQ0EhMTrX69AwCMHz8eq1atwsmTJ9GmTZsSbcdwBf5Br8/ATm04cg0Lm8oNqGC6cHFhE7HR6LEeItZDxHroOVI9MnYuRP3xAxjE5BrEANRI1+HFRDXuuemwLSATuZocYNpKu1+BX7ZvU6anp+O7775Dt27digxiADB16lQAwMGDB23e5k5tOHLVFQG1l/4Pg5hFbDR6rIeI9RCxHnqOVo+HFQTXC2KaXGDsYmDsYiizc2UdxII1Sox84IvUim7YXlOLXG814OludV+kItu3KXft2lXopygtCQ0NBQCkpKTYvM1cLmxsNKyHkZzrAQACFNiXXRP7smtaHWdwLKfoWhicza1aZI0N4vL8sOCR9S9VNkjQeWPho2bFGntf54El6U2KNTZdcENURnixxuZAhTWZjxVrrBZKbMuqW6yxZVIPt/8r0VfqOH0QK6DffTXqa+UbxIpbD3uQ7ZGxTZs2wcfHB3369CnW+GvXrgEAAgICJN8XZ2s0bPwi1kPEeugpINszO6goLhzEAKCujI+IlWcQA2Qaxu7du4eDBw+iX79+8PIy/VhqWloaNBqNyW2CIGDBggUAgG7dukm6L87YaNj49VgPEeuhZ6gHUX5yDmLKfE/zbVUGMXuR5duU27ZtQ15eXqFvUZ45cwZDhgzBkCFDUK9ePWRlZWHnzp345ZdfMH78eLRo0UKy/XDWRsPGz3rkx3ro5a9Hcd8KI/mTcxBTCUDf+2p8++/PVz11gIU5zSBWOrL8NOVTTz2Fa9eu4fbt21CpTP/iXL9+He+88w5OnTqFO3fuQKlUolGjRhg3bhzGjx8PhaLk34lg+DQlIrfqT9yHczcaNv7i14OIXMjhKcDr/Yw/yj2IDUj2QK1UHT7+4N+vDlzxOqA2XztlHcSyNEDkMrt/mlKWR8ZOnjxp8b7atWtj+/btdt0+g5hIzkHMHVqr2yUi+XKFIFY/qwK2VE21vLOQeRADUDlXgQdW91IasjxnrDwxiInkHsSGe12xeD8RyZerBLFtAVn/vjVZOLkHsSq5Cgwto69DkuWRsfKi3PwGBFUeTrdqizONRzKIyTyIBarK5msyiMhxuFIQi/PUAprCx8o9iHnvPwfh0Hls0hX9KVMpMIxJ6LXxLwPu3jwi5iJBbENGfYvjiEh+vLUK1wpiFsg9iFXJVWBU46ehebwt1vk+BF7/wuo+S4FhTEI5ggpbGMRcJohZqwcRyU8NjYpBzBWCWL56ZOSUzWccec6YhDZn1mUQYxAjIpnKUAmuF8Tyv013OR497ru5TBCzVg+p8ciYhDIYxBjEiEi2brtrrV6FX3ZB7I84YNNh8eelO/Czry8uDeyE6zWtf00Vg1jJ8MhYGWAQEzGIEZHTcrUgFrUbeJhucnNaWhqur96lv98CBrGSYxizMwYxEYMYEcmR7IKYTgdsPlz4gw22HDF9C/NfDGK2YRizIwYxEYMYEcmR7IIYAMQmAA/SzW/PL+WRflw+DGK2YxizEwYxEYMYEcmRLIMYYPbWpEX5xjGIlQ7DmB0wiInkHMRUKJuLARKR45FtEAMAPx/L9+VXST9OzkHM29pGJcRPU0pp81QoFAq0aN0S6tatGcRkHsQGeF7HtizrnygiIvmRcxBTCECPgDr42dcXaWlpll+Yf0WgQbB8g9ihs1AcPgc36y9JMgxjEvIethCjKyfwiBhcI4jVr2BloSIiWZJ7EOv5QI1WmW64NLAT0lbvsvzihnREu0ce8gxiAHw6tMCoRk8DWTmIev8jq69NCnybUkJDva4yiMF1gti2rDoWxxGR/HhoFfIPYv9e0PX603WByD7GtyKN/CsCkX3Qrl5j+QaxfPXYHJBp9bVJhUfGJOSu0GJ9ZjiDmIsEMWv1ICL5CclRuUQQM15Zv2V9ILwmELlM//Mb/YHGteR9RKxAPR7k8euQnM7mzLoMYgxiRCRTGoXgOkHMQJnvxTYIcakgZq0eUuORMQk9YBBjECMi2YpXayG4UhAr4OlUN3TKZhCzBx4ZKwMMYiIGMSJyVtYChdyDGAC0T2MQsxceGbMzBjERgxgRyZErBDEA+MlXg1+KWAYZxGzDI2N2xCAmYhAjIjmSexB7OlVcf3/xy7U6lkHMdgxjdsIgJmIQIyI5knsQa5fqjvZpaqtjDBjESodvU0pp81RAoYSqWXcMb9uAQQzyDmIKCFZ/T0QkX64QxDqlqnHA95HVcYBMg9ihs8Dhc4CubEIaw5iUhi6Gu1rNI2L/knsQ6+lxC/uya1ocQ0Ty5CpB7LCfBr94uOhbk52bw71jcwz6R4WN8z62vHGJ8G1KCbnxrUkjVwhirdySLY4hInlypSDmyp+aNNQjILdsYhKPjEnoRa9rCFQJDGIuEsR286gYkUtx0zGIGbhCEAvMVWI9vw7J+QSosrAhowmDmIsEsaLqQUTyEqpRuV4QU7sBa6aajHWVILYhMBOJOsv1kBLfppTQ1oy6DGIMYkQkUzpXC2KFcKUgZq0eUuORMQklMogxiBGRbN1S66BlEGMQswMeGSsDDGIiBjEiclZaBYMYg5h9MIzZGYOYiEGMiOSIQUzEIGYbhjE7YhATMYgRkRwxiIkYxGynEAShfL8DQAbS0tLg5+cHVA4GFEqgWU+omvVgEPsXgxgRycLej4E8MbyooEOoKgNKhYBbWm/kCpbXQQUEhKgyoFZoEa/1RrZg7ZRtATVUmfBW5OG21gsZVtZMQECgKguVFDm4o/NCms7d6kvwV2ajqjIbyToPpFhZXwHAV5mD6spMPBTckaT1BKz0D29FLmqoMpEhVMBtrZfVsR6KPISoMqARVIjXelvtS24KLUJVGdAJCtzSelvtS5LWI+0ukHoXEAQgT4PU1FT4+vpafL7S4gn8Uhq6GFB78YhYPgxiRCQbvd42/m/B9Sq3BOtVdgnWq4wSrFdpJegfsSXsH9bCVcH+Ye1Nt4L9QyhB/9CWoH9IVQ+lJh26qKEW75cK36aUGIOYSO5BrJ17otX7iUienHW9Yv8oeT36et60eL+UGMYkpOREMnKUiWTvehCRa3Hm9Yr9o+T1qFshzeIYKTGMSaiv501OJDjWRLJ3PYjIdTj7esX+UfJ6fJtV2+I4KckqjB09ehQKhaLQP7/++qvJ2BMnTuCZZ56Bl5cXqlevjtdffx3p6eml2n5dTiSHm0jlWQ8ikhc5r1fsH3oF63Etz34n7ecnyxP4X3/9dURERJjcVq9ePeP/nzt3Dp07d0ajRo2wZMkSxMfH45NPPkFcXBy+//57m7f7bVZtXFNxIjnSRGIQIyKpyHW9Yv/QK7we/KJwmz377LMYMGCAxfv/+9//onLlyjh69Kjxo6phYWEYN24c9u/fj65du9q03Wt5vrD0d40TScQgRkTOSI7rFfuHXknqYQ+yepsyv0ePHiEvz/zCd2lpaThw4ACGDx9ucs2QkSNHwsfHB9u3b5d8XziRRI4wkRjEiMgWcluv2D/0yjuIATINY2PGjIGvry88PDzQsWNHnD592njfn3/+iby8PLRq1crkMe7u7mjWrBnOnj1r+4ZzMgFNvj95uZxI+TjCRGIQIyKpOeN6xf6hZ1aPbC/TPp7DtylLzN3dHf/5z3/Qs2dPVK1aFRcvXsQnn3yCZ599FidOnEDz5s2RmKi/HEFQkPlf1KCgIBw/ftz2HVg11vTnNoPRs9MznEhgECMieXLG9Yr9Q6/Qevy+Gfh1q8XH2Iuswljbtm3Rtm1b4899+vTBgAED0LRpU0yfPh0//PADsrKyAABqtdrs8R4eHsb7bTJuDeDu9e8PArp73+FEAoMYEcmTM65X7B96FuvReiDQsq84MCfT/ECLHcgqjBWmXr16eOGFF7Bjxw5otVp4enoCADQajdnY7Oxs4/02cfcC1F75JlIqJxKDGBHJkDOuV+wfelbrUcFN/8eobL6+W5bnjBUUGhqKnJwcZGRkGN+eNLxdmV9iYiJq1KhRqm1xIonkHsSauyVbvZ+I5MlZ1yv2j5LXo6tHvMX7peQSYezatWvw8PCAj48PHn/8cVSoUMHkpH4AyMnJwblz59CsWbNSbIkTycBRJpK960FErsWZ1yv2j5LXo4XbfYtjpCSrMHbv3j2z286fP4/du3eja9euUCqV8PPzw3PPPYeNGzfi0aNHxnEbNmxAeno6Bg4caPP2u3rEcyLBsSaSvetBRK7D2dcr9o+S1yMmO9TiOCnJ6pyxF198EZ6enmjbti0CAwNx8eJFrFy5El5eXli4cKFx3Pvvv4+2bduiffv2GD9+POLj47F48WJ07doV3bt3t3n7LdzuY3d2A04kB5pI9l7YiMh1OPt6xf5R8npcyPWyOFZKsjoy1rdvXyQnJ2PJkiWYMGECtm3bhv79++P06dNo1KiRcVyLFi1w8OBBeHp64o033sDKlSvx0ksvITo6ulTbj8kO5URysIlUXvUgIvmR63rF/qFX0npISSEIQtl8VEDG0tLS4OfnB0RuBdSWUzQnkp6jTCQGMSIqiWBlhuzWK/YPPYv10GQCUYORmppq8q09UpPVkTFHxomkxyBGRM5KbusV+4deeR4RM5DVOWPlbvNUQKEEmvUEmj1vvJkTSc9RJhKDGBFJyRnXK/YPPYv1OLcPOBcDCDqLj5US36aUgLW3KTmR9BjEiEiOnHG9Yv/QK1Y9+Dal8+NE0mMQIyI5csb1iv1DzxHemjTZHx4ZK73insBPRETywCAmknUQ45ExIiIix8MgJpJ1EAPQ1v2O1fulwjBGRERUTAxiIrkHsXbuiWinZhgjIiJyGAxiIlcIYp08EnFMU93qOKnw0hZERETFwCCm5ypB7HB2EE7kWH5eKfHIGBERUTEwiLlWECuqHlJiGCMiIioGBjEGMXvh25RSsnAFfiIicn4MYi4UxHgFfufD64wREbkuBjGRLIJYfrzOGBERkWNjEBPJLoiVIYYxIiIiGzCIiRjESodhjIiIqIQYxEQMYqXHMEZERFQCDGIiuQexpm73rd4vFYYxIiKiYmIQE8k9iDV3S0ZPj1tWx0iFYYyIiKgYGMRErhDE+nj8gzO5VayOkwqvM0ZERFQMDGJ6rhLETudWxf7ssgljPDJGRERUDAxirhXEYrJDASv1kBIv+ioB40VfKwfzCvxERDLV3C2ZQcxFgti+Xy8A577XX4H/QYLdL/rKMCYBXoGfiIhIhngFfiIiIiL5YxgjIiIiKkcMY0RERETliGGMiIiIqBwxjBERERGVI4YxIiIionLEMEZERERUjhjGiIiIiMoRv5tSSpun8gr8REREzu7cPuBcjP4K/GWAV+CXAK/AT0REJEO8Aj8RERGR/DGMEREREZUjhjEiIiKiciSrMHbq1ClMnDgRjRs3hre3N2rWrIlBgwYhNjbWZNzo0aOhUCjM/jRs2LCc9pyIiIhclaw+TfnRRx/hl19+wcCBA9G0aVPcuXMHy5YtQ4sWLfDrr7/i8ccfN45Vq9X4+uuvTR7v5+dX1rtMRERELk5WYezNN9/E5s2b4e7ubrztxRdfRJMmTbBw4UJs3LjReHuFChUwfPjw8thNIiIiIiNZvU3Ztm1bkyAGAPXr10fjxo1x6dIls/FarRZpaWlltXtEREREZmQVxgojCALu3r2LqlWrmtyemZkJX19f+Pn5wd/fH5GRkUhPTy/dxnIy9dckMfzJyy3d8xEREVHZycs17eM5mWWyWVm9TVmYTZs2ISEhAfPmzTPeFhQUhLfffhstWrSATqfDDz/8gOXLl+P8+fM4evQoKlSw8deyaqzpz20GA22HlmLviYiIqMz8/g3w69Yy36ysr8D/999/48knn0Tjxo1x/PhxqFQqi2M/+OADzJgxA1u2bMHgwYNLtB3jFfjHrQHc812BX+UGVHCzdfeJiIioLOXlAtp872rlZAKrxvIK/La6c+cOnn/+efj5+SE6OtpqEAOAN954A0qlEgcPHrR9o+5e+q9DMvxhECMiInIeFdxM+7h72XzFoSzfpkxNTUWPHj3w8OFDHD9+HDVq1CjyMZ6enqhSpQpSUlLKYA+JiIiI9GQXxrKzs9G7d2/Exsbi4MGDCA8PL9bjHj16hOTkZAQEBNh5D4mIiIhEsgpjWq0WL774Ik6ePInvvvsOTz31lNmY7Oxs5ObmomLFiia3z58/H4IgoHv37mW1u0RERETyCmNTp07F7t270bt3b6SkpJhc5BUAhg8fjjt37qB58+YYMmSI8euPfvzxR8TExKB79+544YUXymPXiYiIyEXJ6tOUHTp0wE8//WTxfkEQ8PDhQ0yaNAm//vorbt++Da1Wi3r16mHYsGGYNm0a3NxKftK98dOUkVv1J/wRERGR89NkAlGD7f5pSlkdGTt69GiRYypVqoQNGzbYf2eIiIiIikG2l7YgIiIicgayOjJW7jZPBRRKoFlPoNnz5b03REREZItz+4BzMYCgK5PNyeqcsfLCc8aIiIhkqIzOGePblERERETliGGMiIiIqBwxjBERERGVI4YxIiIionLEMEZERERUjhjGiIiIiMoRwxgRERFROWIYIyIiIipHvAK/lHgFfiIiIufHK/A7H16Bn4iISIZ4BX4iIiIi+WMYIyIiIipHDGNERERE5YhhjIiIiKgcMYwRERERlSOGMSIiIqJyxDBGREREVI4YxoiIiIjKEa/ALyVegZ+IiMj58Qr8zodX4CciIpIhXoGfiIiISP4YxoiIiIjKEcMYERERUTliGCMiIiIqRwxjREREROWIYYyIiIioHDGMEREREZUjhjEiIiKicsQr8EuJV+AnIiJyfrwCv/PhFfiJiIhkiFfgJyIiIpI/hjEiIiKicsQwRkRERFSOXDaMaTQavPPOO6hRowY8PT3x5JNP4sCBAzY/FwAgL1fCPXQSebnAic187a6Gr52v3dXwtbvua0e+Pm8nLhvGRo8ejSVLlmDYsGH47LPPoFKp0LNnT/z8888lfi5jkXQu+BdVmwv8ulX/X1fD187X7mr42vnaXY2OYcxufv/9d2zduhUffvghFi1ahPHjx+Pw4cOoVasW3n777bLZiXP77P+YstiGLcpqv8ri91VSfO2O9xi+dvvia3e8x/C1OxyXDGPR0dFQqVQYP3688TYPDw+89NJLOHnyJG7dumX/nTgXY//HlMU2bFFW+1UWv6+S4mt3vMfwtdsXX7vjPYav3eG45EVfz549iwYNGphdM6R169YAgHPnziE0NLTYz2e8VFt6iukdSjeggpuFB+n01y8piZI+piy2kZNp+l9H2S9bHsPXXvzxfO187fbaRlk9hq+9+ONd6bXn5ZqecvRvX7f3JVld8qKvjz/+OKpVq4ZDhw6Z3H7x4kU0btwYX375JV555ZViP9+1a9dQt25dqXeTiIiIHMDVq1dRp04duz2/Sx4Zy8rKglqtNrvdw8PDeH9JhIWF4erVq3Bzc4NCoTDerlarC90OEREROR6NRmNysr4gCMjNzUVYWJhdt+uSYczT07PQT0ZkZ2cb7y8JpVJp18RMRERE8uWSJ/AHBQUhMTHR7HbDbTVq1CjrXSIiIiIX5ZJhrFmzZoiNjUVaWprJ7b/99pvxfiIiIqKy4JJhbMCAAdBqtVi5cqXxNo1Gg7Vr1+LJJ58s0ScpiYiIiErDJc8Ze/LJJzFw4EBMnz4dSUlJqFevHtavX48bN25g9erV5b17RERE5EJc8tIWgP5k/VmzZmHjxo148OABmjZtivnz56Nbt27lvWtERETkQlw2jBERERE5Apc8Z4yIiIjIUTCMEREREZUjhjEiIiKicsQwRkRERFSOGMaIiIiIyhHDGBEREVE5YhgjIiIiKkcMY0RERETliGGMiIiIqBy55HdTSk2n0+HGjRtwc3ODQqEw3q5Wq6FWq8txz4iIiKi4NBoNNBqN8WdBEJCbm4uwsDAolfY7fsUwJoEbN26gbt265b0bREREZAdXr15FnTp17Pb8DGMScHNzAwD8/vvvCAoKMt6e/8jY+fPnERMTg+bNm6Nbt25o3bo1Tp06VejzXblyBTt27EDdunXRt29fqFQqAEBERITZY27fvo2tW7ciICAAgwYNMjkSV3B8SkoKNm/eDHd3dwwdOhQ+Pj5m2zY8Jj09HZs3b0ZOTg6GDh0Kf3//Qvc1LS0NoaGhWLRoEdLT0zF48GDUqFHD4u9Kq9WicePGGDt2LPr374969epZHCsIAn788UecPXsWW7ZswYULFyyOBYBffvkFx44dQ7t27fD0008X+vsyKFgPhUJhcbylehhe+61bt+Dr62t13wys7VN5Pqak4/na+dr52k3lX6969uyJJ554wuo2Cq5X1varsPXK0muxtF4VNt5a/yjstc+aNQu+vr4W+4dBeno6mjdvjsmTJ1vtH4D+SNT27dtx7949bNiwAWfPnrU4VqvVYteuXbh69aqxf1j6HRdWj4K/q/wM9WjTpg1atmxpvD0xMRGtW7c29nl7YRiTgGFiBAUFISQkxOz+s2fP4vDhw3j66afx/PPPQ6FQQKVSFTqhY2NjERMTgyZNmmDAgAEmE6ngYxISErBr1y7UrFkTw4YNM5tI+cffv38fO3bsgJ+fH0aNGmVxIqlUKiiVSuzcuRNKpRKvvPIKqlSpYvG1Gw7npqenY9y4cQgODrY4VqvVIjo6Gnl5eRg5ciQaNGhgcawgCNi3bx8uXbqEQYMG4bvvvrO6+B87dgy///47evTogXbt2pm9/vwKq4el8dbqYeDr61vsxmRpn8r7MbZsA+Br52u3zzac7bUXXK+aN29udRuFrVeW9svSelXYY4qzXhnGP3r0yGr/yC8lJcX42l999dUig9jOnTuhUCiK1T+io6Px6NEjjBs3Dtu3b7dYE0P/SEhIMOkfhf2OLdXD0nhr9TAo7PcuJZ7Ab2dnz57F7t270bJlS5OJFBkZaTY2NjYW27dvR/369QudSPkfk5CQgA0bNiAwMNDiRDKMv3//PtavXw+1Wm01iAHAyy+/jPXr10Oj0WDUqFFFTqTt27cDAAYPHlysIBYXF4fXXnutWEHsjz/+QJ8+fdC8efNCf18Gx44dw5EjR9CxY0eTiVTYYyzVo7DxRdXDFtZeR3k+xpZtlBRfu+M9hq+99Apbr6xtw9J6VdhjrK1XBR9TkvVq2LBhRfYPg/v372Pz5s0AUKwjYob+8eabbxbZPzZt2oSkpCSMGDECwcHBFmuSv38MGjTIpH8UfIy1ehQ2vqh6lBWFIAhCuW1dJuLj442Hr/MfGSvuRAJK1viLE8QMShLE8k+k4gSxTZs24Z9//sHcuXORmppa5L9oCptIBRU1kQoqyUSSuh5paWnw8/Oz+trliq+dr52v3bnWKwNb+odOp8Nbb71lte629I/8QcyS8u4flvq71Pg2pQQMf5nz/6V2pIlkryCWlJSEkSNHmr32/Mp7IhnYox5qtRqzZ892yU/M8rXztbuagq/d2dYrwPb+MXjwYKSnp1scL9cgBhTe3+2BR8YkUPBfTI42kewVxJxhIgH2XdisvX4ikidnXa/YP/RKUo/jx4+jXbt2dj8azCNjEoqIiEBubi4aNWqE1157jRPJASYSFzY91kNkr3qQa+D8EMl5vYqKisLixYuRmppq9fmkwjAmoa+//hqHDx/mRHKAiQRwYTNgPUQMYlQanB8iua9Xbdu2RXJyMho1aoQXX3zR6lgpMIxJKCYmxurHjw04kfS4sIlYD5Gz1YNcB+eHniutV88++6zVsVLhpS0k1Lx5c04kB5tIrAfrATCIkTQ4P7he2QvDmIQKuzJyfpxIeo4ykVgPPdZDxCBG1nB+cL2yF4YxCXEiOc9EYj30WA8RgxgVhfOD65W9MIxJKCIiAuHh4YiKijK5nRNJz1EmEuuhx3qISloPooI4P/Tksl5FRUUhPDwcERERVh8rFV5nTALWrkrNiaTHxi9iPUTOWo+xY8daHEOuh/NDT47rVVl960SxPk1py8KjUCiwevXqEj9OTjiR9Nj4RayHyJnrQWTA+aEn9/XK3ooVxg4fPlzinSzPF+UIOJH0HGUisR56rIeoNPUgAjg/DOS+XpWFYoWxGzdu2Hk35IUTSc9RJhLrocd6iOxVD3IdnB96cl+vrly5YvW5pMIT+CXGiaTnKBOJ9dBjPUQMYlRanB96rrBe7dixw+rzSaVUV+D/9ddfceTIESQlJWHChAmoX78+MjMz8ffff6NBgwZWCy5HV65cQUxMDCeSA00kLmysR34MYlRanB96rrJe1a1b1+pzSsWmMJaTk4PBgwfju+++gyAIUCgU6N27N+rXrw+lUomuXbvijTfewIwZM6TeX4fWtWtXeHh44J133uFEcpCJxIWN9TBgEKPS4vzQc4X1aurUqTh79iy8vb2tPq9UbHqbctasWdi7dy9WrFiBy5cvI//VMTw8PDBw4EB89913ku2ks1i0aBFu3ryJSZMmWRzDiSTiwqbHeoicsR7btm3DggULEBsba3EcoK/H3r17MXfuXJw9e9bqWEBfj7lz5+LYsWNFjj179izmzp2LvXv3oqirFcXGxmLBggXYtm0btFqt1bEJCQlYuHAh1qxZU+T11e7fv48lS5YgKioK6enpVsemp6cjKioKS5Yswf37962O1Wg0WLNmDRYuXIiEhASrY+1dD84P11mvhg8fjps3b+LUqVNWn1sqNoWxLVu24LXXXsP48ePh7+9vdn+jRo1w7dq1Uu+cs+nbty8nkoNMJC5srIcB6yFiPfRYDxHrISpJPaRmUxhLSkpCkyZNLN6vUqmQmZlp8045K04k55lIrIeI9dBjPUSsh4j10GM97MumMBYaGoq///7b4v2//PIL6tWrZ/NOyQ0nksgRJhLrIWI99FgPEeshYj30WA/7symMDR06FF999RVOnjxpvM3wQletWoXt27dj5MiR0uyhk+NEEjnCRGI9RKyHHushYj1ErIce61E2bPpuypycHPTu3RuHDx9Go0aN8Ndff6FJkyZISUlBfHw8evbsie+++67cXlRZM3x3VYMGDaBSqRAZGYnIyEhOpHwcYSKxHiLWQ4/1ELEeItZDz5XrERUVhaioKGi1WsTGxtr9uyltOjLm7u6OH374AWvXrkWdOnXQsGFDaDQaNG3aFOvWrcOePXtsCmLp6emYPXs2unfvDn9/fygUCqxbt65Yjz106BDGjh2LBg0awMvLC3Xq1MHLL7+MxMREs7EdOnSAQqEw+9O9e/cS73N+p06dwsWLFxnECuDCpsd6iFgPEeuhx3qIWA9RedUjMjISFy9eLLNPU9p80VeFQoHhw4dj+PDhku1McnIy5s2bh5o1a+KJJ57A0aNHi/3Yd955BykpKRg4cCDq16+Pa9euYdmyZdi7dy/OnTuH6tWrm4wPCQnBhx9+aHJbjRo1pHgZnEj5cGHTYz1ErIeI9dBjPUSsh8gR6nH79m2r25VKqa7AL7WgoCAkJiaievXqOH36NCIiIor92CVLluCZZ56BUike7OvevTvat2+PZcuWYcGCBSbj/fz8JA2SBpxIIkeYSKyHiPXQYz1ErIeI9dBjPUQJCQnYunWr1W1LpVhhrFOnTiV+YoVCgUOHDpXoMWq12uwIVnEVVqx27drB398fly5dKvQxeXl5yM7Oluxrm27fvo1du3ZxIsFxJhIXNj3WQ4/1ELEeItZDj/UQGeoREBBgdftSKdY5YzqdDoIgmPz5559/cPToUZw9exapqalITU3FuXPncPToUdy6davIq0CXhfT0dKSnp6Nq1apm98XGxsLb2xsVK1ZE9erVMWvWLOTm5pZqe1u3buVEgmNNJNaD9TBgPUSsh4j10GM9RPnrMWjQIKv7IJViHRkreO7Wzz//jD59+mDVqlUYNWoUKlTQP01eXh7Wrl2Ld955p9gn3tvT0qVLkZOTgxdffNHk9rp166Jjx45o0qQJMjIyEB0dbfz6jG3bttm8PR8fH/Tu3RsajQYajQZqtdpsUnEiibiw6bEeItZDxHrosR4i1kNkr3pcu3YN//d//4eAgABjPy8LNl3aok2bNnj66aexePHiQu+fOnUqfv75Z/z2228275jhnLG1a9di9OjRJX78sWPH0LlzZ/Tv379YAWv8+PFYtWoVTp48iTZt2pRoW4ZLWxQ0e/ZszJkzx/gzJ5KIC5se6yFiPUSshx7rIWI9RPasx6hRowo9xcohL21x4cIF1KlTx+L9tWvXxp9//mnzTpXW33//jX79+uHxxx/H119/XazHTJ06FQBw8OBBm7d769Yt41u2qampmD59uvE+TiQRFzY91kPEeohYDz3WQ8R6iOxdj4EDByIpKcnYx2/dumV1f6RiUxirUaMGtm3bhry8PLP78vLysG3bNskuE1FSt27dQteuXeHn54eYmBhUrFixWI8LDQ0FAKSkpNi8bV9fX5M/hsnFiSTiwqbHeohYDxHrocd6iFgPUVnUY/To0QgICDDp5WXBpktbvP3223j11VfRpk0bvPrqq8bvoYyLi8OXX36Jc+fOYfny5ZLuaHHcv38fXbt2hUajwaFDhxAUFFTsx167dg0AJP/kBCeSiAubHushYj1ErIce6yFiPUSOUA97simMjR8/HiqVCjNmzMD48eONvxRBEBAQEIAvv/wS48aNk3RH80tMTERqairq1q0LNzc3AEBGRgZ69uyJhIQEHDlyBPXr1y/0sWlpaWYn1guCYLwOWbdu3STbT04kkSNMJNZDxHrosR4i1kPEeuixHmXH5ou+vvTSSxg1ahROnz6NmzdvAgBq1aqFVq1aGT9daYtly5bh4cOHxqve7tmzB/Hx8QCASZMmwc/PD9OnT8f69etx/fp1hIWFAQCGDRuG33//HWPHjsWlS5dMri3m4+ODvn37AgDOnDmDIUOGYMiQIahXrx6ysrKwc+dO/PLLLxg/fjxatGhh877nx4kkcoSJxHqIWA891kNUnHpkZGQYbzd8eIv14PwA5F+PsmDTpyntKSwszBjuCjKEr9GjR5uFMWuPq1WrFm7cuGF8jnfeeQenTp3CnTt3oFQq0ahRI4wbN87kKF9JFPyi8NGjR6NChQqcSHCMicSFTcR66LEeouLWI38Y++CDDzB+/HjWg/NDtvUwfFF4Tk4Orl69avdPU9ocxrRaLTZu3Ih9+/aZHBnr1asXhg0bZtMXhTsrQxhLTU1Fbm4uJ9K/uLDpsR4i1kPkbPW4e/eu8RtSbt68iZo1a1ocy3qIOD9EzliPr776CjNmzHDMS1ukpqbi6aefxtixY7F//37k5uYiNzcXBw4cwJgxY/DMM88gLS1N6n11eCkpKZxI/3KUicR66LEeeqyHqKT12LRpk/Fn1qP868H5oWfveri7u1vdV6nYFMZmzJiBP/74A1988QXu3buHM2fO4MyZM0hKSsKyZctw+vRpzJgxQ+p9dXibN2/mRIJjTSTWg/UwYD1EttajKKyHiPND5Mz1GDp0qNX9lYpNYWznzp2YMGECJkyYYPw0IwC4ubnhtddew2uvvYZvv/1Wsp10Fu7u7pxIDjaRWA/WA2A98rO1HsOGDbP62lgPEeeHSM71kJJNYez+/ft47LHHLN7fsGHDUl081VkNHTqUE8mJJhLrIWI99FgPEeshYj30WA/7sSmM1atXD7t377Z4/+7du1G3bl2bd8pZcSI5z0RiPUSshx7rIWI9RKyHHuthXzaFsQkTJmD//v3o2bMn9u/fjxs3buDGjRv48ccf8fzzz+PAgQOYOHGi1Pvq8CIiIhAeHo6oqCiT2zmRRI4wkVgPEeuhx3qILNVDq9Uaxxw7dgxarZb1yIfzQySHekRFRSE8PBwRERFW918qNl/aYs6cOVi4cCFyc3NNbndzc8P06dMxe/ZsSXbQGeS/tEXBj75yIom4sOmxHiLWQ+TI9dixYwdef/11JCQkGMcGBwfjhRdeQGhoKOvB+WEkt3pY6+9SKtVFX5OTk3Hw4EGT64w999xzqFq1qmQ76AwsFYsTScSFTY/1ELEeIkeux44dOzBgwABYahUrV660+vV3rIeI80PkLPVwijBGeoUVixNJxIVNj/UQsR4iR66HVqtFWFiY8SvpClIoFAgJCcH169cL3W/WQ8T5IXKmepRVGLP9SyQB5ObmIiEhAQ8ePCj0X01Sfc+js+FEEpX3RAJYj/xYDz3WQ2StHsePH7cYxAD97/HWrVs4fvw4OnToYHIf6yHi/BA5Yz3Kgk1h7OHDh5g2bRo2bdqEnJwcs/sFQYBCoTA54dNVcCKJHGUisR56rIce6yEqqh6JiYlWXq3lcayHiPND5Iz12Lx5s9XXJBWbwtjo0aOxZ88eDB48GE8++ST8/Pyk3i+n1LJlS6Snp+Opp57CqlWrOJEcYCJxYdNjPfRYD1Fx6hEUFGTx8ZbGsR4izg+Rs9VjyZIlWLRoUZkdVLLpnDEvLy+88sor+PTTT+2xT07H8J7yokWLoFQqOZEcYCJxYROxHnqsh6i49dBqtahVq5bJpyjzK3jOGOsh4vwQOXM9+vfvj9q1azvmF4VXqVIF9erVk3pfnF5OTo5LTaSMjAwoFAooFApkZGQAcLyJ5Er1KAzrocd6iEpSj7y8PLzwwgsAYPa6DD8vXbqUQawAzg+Rs9fD39/f6uuTik1hbPz48di6dSt0Op3U++PUhg4dyonkYBOJ9WA9WA+RLfUIDQ3FypUrUaNGDZP7Q0JCEB0djf79+7Me+XB+iORcD6nZdM7YrFmzoNFo0KpVK4wYMQIhISGF/uL69+9f6h10JtYStNwnUlxcHPbu3es0E0nu9XC2hY31EDlqPV588UXj+cExMTHo2rUrj4gVwPkhknM97MGmMJaQkIDDhw/j3LlzOHfuXKFjXPXTlIWR+0QC9BeGbNy4sVNMJLnXw9kWNtZD5Mj1yL/f7dq1YxArgPNDJOd62ItNYWzs2LE4c+YMpk+fzk9TFkHuE8mgbt26TjGR5F4PZ1vYWA8R6yFiPfRYD5Ej1MOebApjP//8M9555x3MnTtX6v1xahEREVCpVIiMjERkZKTsJ1JcXJzx//v16+fwE0nu9XC2hY31ELEeItZDj/UQlUc9oqKiEBUVVWbv8Nl0An/16tXL7BMGlqSnp2P27Nno3r07/P39oVAosG7dumI//uHDhxg/fjwCAgLg7e2Njh074syZM6Xap1OnTuHixYsuEcRiY2OxY8cO48+ONpEKcoV6OPLCVhDrIXK2egDAzp07WQ84Rj04P0RS1iMyMhIXL17EqVOnrLxq6dgUxqZOnYqvv/66zL4moDDJycmYN28eLl26hCeeeKJEj9XpdHj++eexefNmTJw4ER9//DGSkpLQoUMHk6M9tnKViVS7dm2TbRX2LwgubCJXXtjyYz1EzlYPg6tXr7IeDlAPzg+RPetRFmx6mzI7Oxtubm6oV68eBg0ahNDQULNfqEKhwBtvvCHJThYmKCgIiYmJqF69Ok6fPo2IiIhiPzY6OhonTpzAN998gwEDBgCA8S/y7NmzS/X1B64ykR48eIAVK1YYb+/ZsydCQkLw2WefGT9F6ygTyRXq4UwLG+uh54z1MOjfvz/r4QD14PzQs2c9tm/fbvF+KdkUxqZNm2b8/2XLlhU6xt5hTK1Wo3r16jY9Njo6GtWqVTO59EZAQAAGDRqEjRs3QqPRWC28JRqNBtHR0bKfSA8ePMCnn35q9uXwCQkJGDBgAKKjo/Hkk086xETiwqbnKAsb66HnjPXYuXOn8ef69etbHMt6iDg/RM5aj3v37lkcIyWbwtj169el3o8ydfbsWbRo0QJKpem7tK1bt8bKlSsRGxuLJk2alPh5P/jgA2RmZqJr1664evUqHj16hIYNGyI7OxsXL140jtNqtTh06BDy8vIwaNAgCIJgdr5aWFgY/P39kZSUhM2bN+PSpUto3749BEFAXFwc6tevD61Wi/Pnz5s87syZM/jnn3/w3HPPITg42Ox5g4ODUa1aNTx48ADff/89fvrpJzRq1AhBQUH4+++/0ahRI+PvKH/YunnzJs6dO4eGDRvizTffNAtigPgF8a+++ioGDhyIqlWrIjw83OT3+eeffyI3N9f4mIcPH+LXX3+Fn58funTpgtjYWJPnrFKlCmrVqoWsrCz88ccf2Lt3L3JyctCrVy/8888/xsl06dIlZGVlGR+Xk5ODU6dOISMjA927d8fdu3dx9+5d4/1+fn6oW7cucnNzce7cORw6dAj//PMPunTpgvT0dGi1WqhUKsTFxeHRo0cmr/Hvv//GlStXjPXI/zv29vbGY489ZqzFmTNncOrUKURERMDHxwfZ2dnw8PDA9evX8eDBA5PXmpycjJMnTxrrcfbsWeN9arUajRs3BgBcuHABV69exYEDB1CzZk3UqVMHWVlZ8PHxQXx8PJKSkkyeNy8vD4cPH4afnx/Cw8Px119/Ge9TKpVo1qwZAODixYu4c+cO9u7dC3d3d/Tq1Qt5eXkAgDt37uD27dsmz+vm5oZjx44hIyMDrVq1ws2bN3Hz5k3j/c2aNYNSqURsbCxSUlLw/fffIyUlBc8//7xxcU1OTsY///xj8ryenp64cOECLl++jCZNmiA9Pd3kd/z444/D3d0d165dw4MHD3D8+HHj/DB8R+LDhw9x7do1k+f18PBAcnIyjhw5gpCQEPj4+Jg8b8OGDeHl5YV//vkHycnJ+Pvvv43z44knnoBCoUB6errZ39EKFSrAw8MD27dvh4eHB+rUqWMyL+vVqwdfX18kJiYiMTERSUlJ2LdvH/z9/dG2bVuo1WqzNcKgVq1aWL9+PR4+fIiuXbuabNuwRty7dw+3bt1CZmamcX4MHjwYVapUKXSNAIAGDRpg+/btuHTpEjp37mwyP/KvEdevXzeuV//88w9iYmLQo0cPAOZrhOF3ePjwYRw6dAgREREm86NatWoIDg7Go0ePjKeDGOZHmzZtjI2/4BoB6MPflStXsHHjRoSGhprMj/xrxKVLlwDo1yvD/DA0/oJrBADUrl0bmZmZWL58OSpUqGAyP/KvEX/++ScA/d8rw/yYPn06fHx8zNYIAAgNDYWnpyeWL1+OO3fuoFevXsb5UXCNAPTrlWF+TJkyBcHBwYWuEUFBQQgMDMT//d//4dSpU8b16syZM2ZrRF5eHgRBMM6PkSNHonnz5oWuEVWrVkXNmjWxf/9+fPvtt8b16syZM2ZrRHZ2NgAY50fHjh3x/PPP4+7du2ZrRKVKlVCnTh3873//w/Lly43rleHvZP41wnDak2F+1KtXD8OGDcOjR49M1iwA8PHxQYMGDXDv3j18+OGHxvXKMD/yrxEPHz4EAOP88PDwwKRJk6BSqcz6o4eHB+rWrYu0tDQsWbIEKSkpeOaZZ1AmBBk4deqUAEBYu3ZtscZ7e3sLY8eONbt93759AgDhhx9+KNH2U1NTBQBmf5o0aSIIgiDExcUVev/ly5cFQRCENm3amN23YcMGQafTCa+++qrZfV27drW63e+++04QBEHo3bu32X2LFy8WBEEQPvroI7P7mjdvbnxN7u7uZvcvWrRIOHjwYKHbtPYnODjY+LzBwcFm97/++uvCo0ePhHfffdfsvpdeekkQBEH4/fffze5zd3c3Pm/z5s3N7h86dKgQHx8vLF682Oy+3r17C4IgCImJiYXuc2pqqiAIgtC1a1ez+3r27CmcOXNG2LBhg9l9bdq0Me5TYc8bFxcnCIIgDBs2zOy+9u3bC3v27BG+//57s/vq1q1rfN7KlSub3X/ixAlBEAThjTfeKHSfVq9eLZw8edLsvooVKxqf97HHHrP4d+mDDz4o9O/L4sWLhQsXLhT6WrOzswVBEIRnn33W7L5Vq1YJgiAIq1atMruvUaNGwvz584U///yz0Oe9deuWIAiCMGDAALP7PvjgA0EQBOG7774zuy8sLEyYM2eO8NNPPwkVK1Y0u/+PP/4QBEEQJkyYYHbfG2+8IQiCIJw4ccLsvsqVKwvz588Xtm7dKtStW9fsfsN6Mnv2bLP7hg0bZnWNWLx4sbBs2TIhIiLC7L4NGzYIgiAIy5YtM7uvqDVi6dKlwocffih06dKl0G0KgiBs37690JpbWyOWLVsmzJkzR+jbt6/Zfe+++64gCIJw5MgRs/uKWiNWrlwpzJkzp9CaG9aI//3vf2b3FbVGfPnll8KHH34oDBo0yOw+wxqRlJRU6O/Q2hphqNvQoUPN7ivNGjFr1ixh69atwsiRI83uy79GVK1a1ex+a2vEhAkThJ9++kkYP3682X3514jw8HCz+3ft2mVxjRgwYIBw+fJlYdq0aYW+VsMa0b59e7P7li9fbnGNaN++vZCcnFxoHwOsrxEzZ860uEaEh4cLM2bMsFpve7Hpi8IdjeGcsbVr12L06NFFjlepVHjllVewfPlyk9sPHz6Mzp07Y+fOnejbt2+xt2/4ovD/+7//Q61atYy3BwYGmhwZy/8vzC5duqBfv34AgMuXLxu/29GgVq1aOHnyJI4dO4ZmzZqhYcOGxvsqVqxodmQs/xGYUaNGwc3NDVevXkVqaqrJ8wYHB+P27dvYtm0bqlevjmeffdZ4aNnT09PsyFj+f2FOnDgRe/bswdChQ4v8ncydOxe9evUCoD+CUvDIWP5/YU6ePBlBQUFISEgwOXoF6P/VW6VKFaxatQq3bt1Cr169UKlSJQD6t8INh9wN/+rN/y/MV199FY0bN8bdu3fNvujYz88PYWFh2Lp1K3755Rd06dLFpHZPPPGEyZExId+/MF988UV06tQJKSkpuHHjhsnzGv7Ve+zYMWzatAkRERFo0aKF8f7w8HCzI2OGf2E+/fTTGDFiBB49eoQrV66YPK/hX72xsbH4/PPPERISgs6dOxsP9Tdo0MDsyJjhX5g1a9bE66+/Dq1Wi7///tvkeQ3/6r1//z4++ugjKJVK9OrVC15eXgCAOnXqoFKlSiZHxgz/wlSpVHjzzTdRsWJF/O9//zP7O9CsWTPk5uZi8eLFSExMxPPPP4/A/2/vzuOiKvc/gH9g2FExlDVQCMQUVNSQRDFTE0XxGgGKmkuWS6jp7dpyvRSWaaVe8yYqLrmQpoZlPwst9SIplhtgGXJRFJVNIRYB2Xl+f0xzzgyzMAwzMHP4vl8vXjXnPGd5+PA85/HMWeztAQC9evVCz549Zc6MSdpHYWEhXn/9dXh6eip8qLSPjw9MTU2xe/duXL58Gc899xzXPpydneHo6Ch3Ziw1NRVpaWmYNm0aRo0ahfT0dLnXuUnOjCUmJuK7775Dv379uPZhb28PFxcXuTNjd+/exZkzZxAYGIiwsDBkZmbKXfArOTOWmpqKPXv2wNbWFhMnToSZmRmeeOIJuLu7y50Zk7QPDw8PzJkzB3l5eXJ9hOTM2J07dxAXF8edMe7evbvCPgLgz8CYmZlh7ty5qKmpUdhHODg4oLi4GLt27eL6q969eyvsIwBw7aO4uBgvvfQSbG1t8eeff8qsV/rM2OHDh7n+asiQIQr7CInMzExcv34dAQEB8PX1lTurI31m7OTJk1x/NXbsWJiYmMj1ERIPHz7EL7/8gt69e2PcuHEoLi6WWa/0mbFz587JnDG2srKS6yMkHj9+jPPnz8PMzAxTpkzBo0ePZNYrfWbs119/lTljbG9vr7CPAMTt4+rVqyguLkZwcDCakz4zdu3aNSQlJXFnjJ9++mmFfYREdnY2MjIyMHz4cPTs2VNmXvMzY+np6dwZ48DAQHh4eMj1ERKlpaX45Zdf4O7uDi8vL7mvJqXPjN2+fZs7Yzxx4kR4enrK9RES9fX1SElJgampKQYPHsz1VxLSZ8by8/NlvlHp37+/wj5CUteLFy+ioKAAgwYNQo8ePVBVVYXg4GCdvyi8Uw7GunTpgmnTpmH37t0y0xMTEzFp0iScPHkSQUFBam9fMhhTFZZQvuM/e/Ysnn/+eZXbAICkpCSMHj1a4Ty65oLXGa65oDwoD2mUhxjlwdPnPNQ5vmuDRo+2MHSSOzGbk0xr/kLcthJSQwoMDISLi4vSZY2MjODq6orAwECF86lj43XGjk0RyoNHefAoDzHKg6cPeehKpxyM+fr6IjU1Ve5riosXL8LKykrlH3trCa0hiUQibN68GQDk1iH5/NlnnylsfPrQkISWR3OG1rFRHjzKg0d5iFEePH3IQ5fUHowZ6ku/CwoKkJmZKXMNQlhYGB48eCDzBPni4mJ8/fXXCAkJ0eixFooItSGFhoZyjweR5uLigoSEBJlHhkjoQ0MSah4ShtaxUR48yoNHeYhRHjx9yEPX1H60RY8ePRAUFITJkydjwoQJsLOz0+V+qWXLli0oKyvjLho8fvw4cnNzAQBLly6FjY0N3n33Xezbtw937tyBm5sbAPFg7Nlnn8W8efOQkZGBnj17YuvWrWhsbNTa+zaF3pD8/f2xcOFC7veVmJiI8ePH0xkx6tgoD1Ae0igPMcqDZ2h5tAe1L+D//PPPkZiYiOTkZNTV1XHhTJo0CUOHDtX1firk5uYm81wjaZLB19y5c+UGY4D4Lo+VK1fi2LFjqK6uhp+fHzZs2IBnnnmm1fshucBPcrfI4sWLYW9vL/iGZGNjg9dffx2AuBFYW1vLldWHhkQdG4/y4FEeYpQHj/LgdfY8pF8UnpWVpX93U1ZXV+P06dM4ceIEEhMTce/ePTg6OmLChAmYPHkyXnjhBXTt2lVX+6uXpO+2sLa27jQNaerUqdwfvKLBGHVsPCF1bFVVVdyylZWVMDExoTz+YigHGgnKg0d58CgPscbGRuzfvx+vvPKKzgdjrX4Cv6WlJUJCQhASEgIAuH79On744QecOHEC06dPh5GREUaOHIng4GBMmjRJ5vlYQtfZGpLk6eyK6EtD6kx5dFTHdvjwYcoD+pMHtQ8xyoPXljweP34s848v6X90d4Y8srOzlZbRpjbfTenj44O3334bZ8+eRVFREeLj4+Hq6or169fD29sbn3zyiTb20yAcO3ZM7xoSdWyUh67yAEADsb/oQx7UPniUB4/yENM0D0U3pOmCRu+mVMbGxgYRERGIiIgAAFy+fFmbq9d72dnZmD17NjUkPWpI1LHpbiAGAEVFRZg/fz7lQe0DAOUhQXnwDD0PR0dHpWW1SauDseb8/Px0uXq9ExoaSg1JzxoS5aGbPCQiIyMpDz3Ig9qHGOXBozzE2ppH89dZ6UqnfOirrkRGRqJ///6IjY2VmyfEhmRtbQ3GGBhjqKmp0cuGpIwQ85Cmy47tq6++4j7funVL6TMIKQ+evh5olKE8eJQH7+bNm50mj1OnTqF///7tdlJJEO+m7GgtvbtKXxoSdWxilAevtXn8/e9/x5dffinzr0UXFxds3rxZ5toKyoNH7YNHeYgZUh7Sd09HR0fD29u7U+VB76YUiI5uSBLUsYlRHjxNBmJbt26VO22fl5eHsLAw7o0WlAeP2geP8hAzxDwkPDw8OnUeukSDMR3Sl4ZEHZsY5cFrbR7x8fE4cOCAwvmSk+vLly9HXV0d5fEXah88ykPMEPO4efMm9/8vvvhip81D13R6AX9npi8NiTo2McqDp0kely5dQnl5udJyjDHcv38fa9asgZmZGeVB7YNDeYgZah7S73DurHm0B40HY4WFhdi9ezdSU1NRXl6OpqYmmflGRkY4c+ZMm3fQEOlTQ6KOjfKQpmkegwYNUlpOWmZmJtasWUN5UPsAQHlIGHIe7u7uMttS9A5ioefRHjQajP32228YPXo0qqur0bdvX/z+++/o378/ysrKkJeXBw8PD7i6ump7Xw2CvjUk6tgoD4m25CH9VYUqISEhlAe1DwCUh4Qh51FaWopt27Zx04ODg+Vu2BF6Hj/++KPS+dqk0d2UwcHBuH79Os6fPw8rKyvY29vj9OnTGDNmDL7++mssXrwYiYmJGDZsmC72We9Ivyi8uroaAwYMwJo1azq8IVHHpl8dmyHn0djYCDc3N+Tl5Sn9l6KjoyNyc3OV1o/y4FH74FEeYvqWR2lpKTZt2iTX3iXLJiQkwN/fX7B5bNmyBZ9++imqqqpQUlKin3dTpqSkYOHChejVqxeMjcWrkHxNGR4ejpkzZ2LlypXa20sD8eGHH2L+/Pk0ENODhqRvHZuh5yESibB582YAUFhfIyMjxMbG0kCM2gfl8RdDzsPDwwNHjhxR+A8vybSlS5di3759gs3Dzc0N8+fPx7Fjx5SW0yaNBmNNTU1wcHAAAHTv3h0ikQglJSXc/AEDBuDq1ava2UMDkpaWphcNiTo2/erYhJJHaGgoEhIS4OzsLDPd0dERCQkJSt/hRnnwqH3wKA8xfczD3t4eubm5Kvc5Pz8fZWVlgs9D3etl20qjwZi7uzvu3LkjXoGxMdzd3XH69Glu/oULF9C9e3et7KAhCQ4O1ouGRB2bfnVsQsojNDQUv//+O/d5586dyM3NpYEYtQ/K4y9CyOPhw4cq1y3h4+PTqfPQJo0GY+PHj8fXX3/NfV68eDF27dqFcePGYezYsdi3bx9mzJihtZ00FC2NoKljE9OXhkR5iLU2j//7v//jPkdGRtJXk9Q+KI+/CCUPJycnleuX6NWrl9J5Qs9D2zS6m3LVqlWIjIxEfX09TE1NsXz5clRVVeHo0aMQiUSIjo7GP//5T23vq0Gjjk1MXxoS5SGmSR7Z2dlKy0hQHjxqHzzKQ0zf8wgMDISLi4vSG3aMjIzg4uKCwMBAhesVeh66QO+m1ALpuylFIhGioqIQFRXFzaeOTUxfGhLlIaZpHpMnT+Z+x5WVlbC2tpYpS3nwqH3wKA8xQ8njm2++QVhYGLfPEtJ3Uyq6PEEoecTGxiI2NhaNjY3IysrSz7spx4wZo/KBrklJSRgzZoxGO1RbW4u3334bzs7OsLS0hL+/P06dOtXicm5ubjAyMlL406dPH5myysp9/PHHGu2zxOXLl5GRkUEDMQU6e8cmzdDzaN6epFEePGofPMpDzJDykNywI7lZT8LFxUXwAzEAiIqKQkZGBi5fvqx0eW3S6GvKs2fP4tVXX1U6/+HDh0hOTtZoh+bOnYuEhAQsX74cffr0wd69exEcHIykpCSMHDlS6XKfffYZKisrZabdvXsX//rXvzB+/Hi58i+88AJmz54tM03bpyapYxOjjo0nhDyqqqoUlqU8eNQ+eJSHmCHm4e/vj4ULF2L16tUAgMTERIVP4AeEn4euafw6JFVh37p1C127dm31Oi9duoRDhw5h/fr1+Mc//gEAmD17Nnx8fPDWW2/hwoULSpedOnWq3LQ1a9YAAGbOnCk3z8vLC7NmzWr1PqpLHxoSdWw8ykNMG3lYW1vLXUdCefCoffAoDzFDzkP6zNioUaNoIKYjag/G9u3bh3379nGf16xZg507d8qVKysrw2+//Ybg4OBW70xCQgJEIhEWLFjATbOwsMD8+fPxz3/+E/fv32/Va5YOHjwId3d3BAQEKJxfXV0NIyMjWFhYtHpfVdGnhkQdG+UhQXnwKA8e5SFGefCk85g6dSpef/11pWWFnkdKSorK+dqi9jVjjx8/RlFREYqKigAAFRUV3GfJT3FxMczNzbFo0SLs2rWr1TuTlpYGLy8vuYvkJK9VSk9Pb9W6bty4ofQRG3v37oW1tTUsLS3Rv39/HDx4sNX7q4i+NSTq2CgPgPKQRnnwKA8xyoNHefB+/vln/PzzzyrLaIvaZ8YWL16MxYsXAxA/9HXz5s2YMmWKVnemoKBA4fNNJNPy8/PVXteBAwcAKP6KMiAgABEREXB3d0d+fj5iY2Mxc+ZMlJeXc3XUxKlTp3DlyhWMGjUKvr6+qK2tVfqHTA1JjDo2HuXBozzEKA8e5cFrzzwaGhoUlhVqHrW1taitrUVKSgp+/vlnPPPMM0rXqU169WgLDw8P9O3bF4mJiTLTb9++DQ8PD2zatAnLly9vcT1NTU3o1asX7O3tkZqa2mL5uro6DB06FLm5ucjPz4elpWWr9lvyaIvm3n//fcTExMhNp45NrDN2bMpQHjzKQ4zy4FEevPbOo6qqivt9Sx5lI+Q8YmJiuBsWpOn60RYaX8AvUVFRgfLycu5F4dJUPZ1XEUtLS9TW1spNr6mp4earIzk5GXl5eVixYoVa5c3MzLBkyRIsWrQIV69eVXnXpioJCQl44YUXuM+KOhfq2MQ6a8emCOXBozzEKA8e5cGjPMR0mcfIkSPxzjvvYNSoURgxYgQePXrUqmvVNaXxYGzbtm3497//jdu3byst09jY2Kp1Ojk5IS8vT256QUEBAMi9oFiZAwcOwNjYGJGRkWpvW/LLln7heWu98MILKkfO1JDEqGPjUR48ykOM8uBRHjx9ySMhIUHQeaSkpGDixIkt5qFtGj30dfv27YiKioKnpyfWrFkDxhiWL1+Od955B46Ojhg0aBB2797d6vX6+voiKysLjx49kpl+8eJFbn5LamtrcfToUYwePVrtwRsAblBpZ2en/g63gr40JOrYxCgPMcqDR3nwKA8xyoN/lE1xcbHgB2Lq5qELGg3GPv/8cwQFBeHEiRPcYygmTZqEjz76CBkZGaioqMCff/7Z6vWGhYWhsbERO3bs4KbV1tZiz5498Pf3585e3bt3D5mZmQrXkZiYiLKyMoUX7gPg7gaVVlFRgc8++ww9e/bE0KFDW73fLaGOTUxfGhLlIUZ58CgPHuUhRnnwKA/d0+hryuzsbO6VP6ampgDEF8EDgI2NDV599VVs3boVb775ZqvW6+/vj/DwcLz77rt4+PAhPD09sW/fPuTk5MicaZs9ezaSk5MVvsD0wIEDMDc3x0svvaRwG7GxsTh27BhCQkLQq1cvFBQU4IsvvsC9e/cQHx8PMzOzVu1zS6ghielLQ6I8xCgPHuXBozzEKA8e5dE+NBqM2djYcLe7duvWDVZWVrh//z43v2vXrigsLNRoh/bv34/o6GjEx8ejtLQUAwcOxPfff6/WL+nRo0f44YcfMGnSJIV3NwLAiBEjcOHCBezatQt//vknrK2tMWzYMHzxxRcav09Tws/PT+ZF4dSQxPSlIVEeYpQHj/LgUR5ilAevM+ch/aLw9qDRoy3Gjh0LNzc37mzVuHHjUFJSguPHj6OpqQmTJ0+GsbEx0tLStL7D+kjyaAvpW1+pIYlRx8ajPHiUhxjlwaM8eJSHmD7koej4rgsanRmbNWsWtm/fzj3UdPXq1Rg3bhz3KAtTU1McPXpUqztqSKghielDQwIoDwnKg0d58CgPMcqDR3nwrl27pnK+tmjtoa+3b9/G8ePHIRKJMH78eJW/OKGRHjlnZ2dTQ4L+NCTq2MQoDx7lwaM8xCgPHuXBS0tLw5EjR/Dxxx/r55kxRZ566im88cYb2lqdQbp27Rr++9//UkPSo4ZEHRvlIY3y4FEeYpQHj/LgSfJoaZ3aorXBGBE/VmPEiBHUkPSoIVHHRnlIUB48ykOM8uBRHjzpPAIDA1WW1Ra1vqY0NjZW+cehTHvdhdDRJF9THj58GOHh4dSQ9KghUcdGeQC6y4N0LqtXr6b20cn6q4qKCv25gP+9996T+wP59ttv8ccffyAoKAh9+/YFAGRmZuKnn36Cj48Ppk6dqvWd1XfR0dGIiYnhHm3RHDUkMTrw8ygPnqHlQTofah+dp7/KycmBt7d3u51UUmswFhMTI/N5x44dePjwIa5fv84NxCRu3LiBMWPGtOpVREJx+fJlpSNnakhidODnUR48Q8uDdE7UPjpXf7VkyRLumy9d0+h1SOvXr8eSJUvkBmIA0K9fPyxZsgSffvppm3dOKKghidGBn0d58AwxD0Kao/YhJvT+Slc0Gozl5uZyr0FSxNTUFLm5uRrvlJBQQxLTl4ZEeYhRHjxN8iBEGrUPMaH3V7qk0WDMx8cHW7duRV5enty83NxcbN26FQMGDGjzzhk6akhi+tKQKA8xyoOnaR6ESFD7EBN6f6VrGj3aYtOmTQgKCoKXlxdefPFFeHp6AgBu3ryJY8eOgTGGL7/8Uqs7amioIYnpS0OiPMQoD15b8iAEoPYhIfT+qj1o/AT+69evIzo6Gj/99BOqq6sBAJaWlggKCsLq1as71ZkxyQV+Xl5eEIlECA8Ph6mpKTUkPWlI1LGJUR48XeVBOg9qH2JC7a8kLwqvqanBnTt3dP5oiza/DqmpqQlFRUUAADs7Oxgba/TNp0GTfh1SYWEhNSR0fEOSoI5NjPLg0UCMtBW1D7HO0F/t378fH330kX48Z0wVY2NjODg4aGNfDN6tW7eQmJhIDUmPGhJ1bJSHNBqIkbai9iHWWforDw8PlevUls53GkuHvvnmG2pIetaQKA/KQ4IGYqStqH2Idab+qr0eYE+DMS3y8PCghqRnDYnyoDwAGogR7aD2Qf2VrtBgTIumTp1KDcmAGhLlwaM8xGggRlSh9kH9la60+QJ+InsBv7IL/Kgh8fShIekqD0JI52Vo/RUdP3jK8lDn+K4NdGZMi/z8/NC/f3/ExsbKTKeGxKOBGCFEiAytv6LjB09RHrGxsejfvz/8/PxUbkdbDHYwVltbi7fffhvOzs6wtLSEv78/Tp061eJyMTExMDIykvuxsLBo074AwPnz55GRkYGoqChuntAb0rFjx7BhwwZMmDBBrxqSMtrMo7a2FjExMZ3yXYVUd6p7Z6Oq7obQX0lr7fFj7969OHjwICIiIjrFQAwAoqKikJGRgfPnzwPQ/TtpDfZrysjISCQkJGD58uXo06cP9u7di8uXLyMpKQkjR45UulxMTAxWr16Nbdu2yfyxikQiREZGarQvubm5cHV1xf379+Hi4sJN15eGpMt/0aSkpODjjz9u8RSu0AZiQPudvtZHVHeqO9VdzFD6KwlNjh/37t3D6tWrVeYupIGYNGXHd20zyDNjly5dwqFDh7Bu3TqsX78eCxYswH//+1/07t0bb731llrrCAsLw6xZs7gfTQdiyrTUkKS/ylS3IcXGxraqIW3evLnVA7Evv/yyVQ0pODhYaTmJ5g2p+de40pQ1JEXLtNSQpJdRp2OTlNflV5Oq6t6Ry2iyjdaiuuvfMlT3tmnpwC+9DXUHYrGxsa0eiL322mutGoidOXOmVf+Qnz59utJygOKBmLLfr6qBmKJlWhqISS+j7pP1gY69WF8hZoBWrlzJRCIRKy8vl5m+du1aBoDdu3dP6bLvv/8+A8AePnzIysvLWVNTU5v35/79+wwAu3//PmOMsdzcXLZu3Tq2e/duVlNTo3CZfv36McYYKy4uZhs3bmRbtmxhFRUVKrfTt29ftmXLFrZx40ZWXFyssmxNTQ1zcnJi69atY7m5uSrLNjQ0sEOHDrEPP/yQeXh4qCzb1NTEjh8/zmJiYlhqaiorLy9nAOSykEhOTmYxMTEsOTmZmyape3OpqaksJiaGHT9+XC6X5sv873//Yx9++CE7dOgQa2hoULg+yTLq5CEp35o88vLyVNZd1T61Rnss09ryLeWujW201zJUd/VR3fm6q+qvmm9Dnf5Kok+fPmr1V4zxxw9HR8cW+6uKigru+OHl5aWybE1NDdu9ezd3/FCVu/Tx43//+x83XdHvt/nxo7nmyyg6fihbRp08JOVbk8fly5dlju+60uYn8HeEtLQ0eHl5yZ0uHTZsGAAgPT0drq6uKtfx1FNPobKyEtbW1pg6dSo2btyo8ZsE2F/f9BYUFKCwsBBHjx5Fjx49MH78eNTW1ir8rrmxsRE5OTk4ePAgzMzM8OKLL6KpqQmPHj1SuI3KykqUlpaivLwcM2bMgKmpqdKytbW1OHLkCOrr6zF16lR07dpVadnGxkYcO3YM2dnZCA0NRXx8vNKyjDH8+OOPSEtLQ3BwMDw8PLiyipZJSUnBzz//jFGjRsHX15cr09jYKFf+2rVrSExMxODBgxEYGIiKigq5/ZQsc+vWLXzzzTfw8PDA+PHjUVVVpbRumZmZOHToEOzs7BASEqI0DwCoq6tDXFyc2nl88cUXSuuujKK668MyrS2vKndtbaO9lqG6U93VIV33lvor6W2kpqaq1V8BQH5+PkpLS9G1a9cW+6uSkhLu+NGlS5cW+6uDBw+irq4OM2bMQFxcXIvHj6KiIkyfPl3m+NF8mebHD0dHR6X9vKrjh/T6JNOUHT+aa2xsxLlz59TKAwBqamqwf/9+pXlI/84LCwuxZ88ebv91ySCvGfPx8YGDgwPOnDkjMz0jIwPe3t7Yvn07Fi5cqHDZzZs349atWxg+fDjMzc1x7tw5xMbGwt3dHVeuXNHoOojbt2+32ysTCCGEENK+srOz8dRTT+ls/QZ5Zqy6ulrhd+iSOyKrq6uVLvvGG2/IfH7ppZcwbNgwzJw5E1u3bsU777zT6v1xc3NDdnY2TE1NZb6jNjc3V/ldPyGEEEL0R/OzkYwx1NfXw83NTafbNcjBmKWlpcJTtzU1Ndz81pgxYwbefPNNnD59WqPBmLGxsU5HzIQQQggRLoO8m9LJyQkFBQVy0yXTnJ2dW71OV1dXlJSUtHnfCCGEEEJawyAHY76+vsjKypK7oO/ixYvc/NZgjCEnJwd2dnba2kVCCCGEELUY5GAsLCwMjY2N2LFjBzettrYWe/bsgb+/P3cn5b1795CZmSmzbFFRkdz6tm3bhqKiIkyYMEG3O04IIYQQ0oxB3k0JABEREfj222+xYsUKeHp6Yt++fbh06RLOnDnDPRhu9OjRSE5Olrkl1crKCtOmTcOAAQNgYWGB8+fP49ChQxg0aBBSUlJgZWXVUVUihBBCSCdkkBfwA8D+/fsRHR2N+Ph4lJaWYuDAgfj+++9bfFXCzJkzceHCBRw9ehQ1NTXcU/tXrVpFAzFCCCGEtDuDPTNGCCGEECIEBnnNGCGEEEKIUNBgjBBCCCGkA9FgjBBCCCGkA9FgjBBCCCGkA9FgTMrly5exZMkSeHt7w9raGr169UJERASysrLkyt64cQMTJkxAly5dYGtri5dfflnhM8yamprw6aefwt3dHRYWFhg4cCC++uqr9qhOq6hb97lz58LIyEju5+mnn5Zbp6HU/Y8//kB4eDieeuopWFlZoWfPnhg1ahSOHz8uV1ZouatbdyHmrshHH30EIyMj+Pj4yM27cOECRo4cCSsrKzg6OmLZsmWorKyUK1dbW4u3334bzs7OsLS0hL+/P06dOtUeu98myuo+evRohdkrei6jIdT97NmzCutjZGSEX3/9Vaas0DJXt+5Cy1xaamoqpkyZAltbW1hZWcHHxwf/+c9/ZMp0RO4G+2gLXfjkk0+QkpKC8PBwDBw4EIWFhdiyZQuGDBmCX3/9leukcnNzMWrUKNjY2GDt2rWorKzEhg0b8Pvvv+PSpUswMzPj1rlq1Sp8/PHHeO211+Dn54fvvvsOM2bMgJGREaZPn95RVZWjbt0B8QvQd+3aJbO8jY2N3DoNpe53795FRUUF5syZA2dnZzx+/BhHjx7FlClTEBcXhwULFgAQZu7q1h0QXu7N5ebmYu3atbC2tpabl56ejrFjx6Jfv37497//jdzcXGzYsAE3b97EiRMnZMrOnTsXCQkJWL58Ofr06YO9e/ciODgYSUlJGDlyZHtVp1VU1R0AXFxcsG7dOplpil47Z0h1X7ZsGfz8/GSmeXp6cv8v5MxbqjsgzMx/+uknhISEYPDgwYiOjkaXLl2QnZ2N3NxcrkyH5c4IJyUlhdXW1spMy8rKYubm5mzmzJnctMWLFzNLS0t29+5dbtqpU6cYABYXF8dNy83NZaampiwqKoqb1tTUxAIDA5mLiwtraGjQYW1aR926z5kzh1lbW7e4PkOquyINDQ1s0KBBrG/fvtw0IeauiKK6d4bcp02bxsaMGcOee+455u3tLTNv4sSJzMnJiZWXl3PTdu7cyQCwH3/8kZt28eJFBoCtX7+em1ZdXc08PDzY8OHDdV8JDamqu6JpihhK3ZOSkhgA9vXXX6ssJ8TM1a270DJnjLHy8nLm4ODAXnzxRdbY2Ki0XEflToMxNQwZMoQNGTKE+2xvb8/Cw8Plynl5ebGxY8dyn2NjYxkA9scff8iUO3jwIAPAzp07p7ud1pLmdZcclBsaGmT+WJsTQt0nT57MHBwcuM+dKffmdRd67snJyUwkErHffvtN7kBUXl7OTExM2MqVK2WWqa2tZV26dGHz58/npq1cuZKJRCK539HatWsZAHbv3j3dVkQDqurOGH9grq+vZxUVFUrXYyh1lx6QPHr0iNXX18uVEWrm6tSdMeFlzhhj27ZtYwBYRkYGY4yxyspKuUFZR+ZO14y1gDGGBw8eoGfPngCAvLw8PHz4EM8884xc2WHDhiEtLY37nJaWBmtra/Tr10+unGS+Pmted4nHjx+jW7dusLGxga2tLaKiouS+TzfEuldVVaG4uBjZ2dnYtGkTTpw4gbFjxwIQfu6q6i4h1NwbGxuxdOlSvPrqqxgwYIDc/N9//x0NDQ1y2ZuZmcHX11cuey8vL3Tr1k2mrKT+6enp2q9AG7RUd4msrCxYW1uja9eucHR0RHR0NOrr62XKGFrd582bh27dusHCwgLPP/88rly5ws0TcuaA6rpLCC3z06dPo1u3bsjLy0Pfvn3RpUsXdOvWDYsXL0ZNTQ2Ajs2drhlrwYEDB5CXl4cPPvgAAFBQUAAAcHJykivr5OSEkpIS1NbWwtzcHAUFBXBwcICRkZFcOQDIz8/X8d63TfO6A+J9f+uttzBkyBA0NTXh5MmT2Lp1K65du4azZ8/CxET8J2WIdX/zzTcRFxcHADA2NkZoaCi2bNkCQPi5q6o7IOzct2/fjrt37+L06dMK57eU/blz52TKKisH6F/9W6o7AHh4eOD555/HgAEDUFVVhYSEBKxZswZZWVk4fPgwV85Q6m5mZoaXXnoJwcHB6NmzJzIyMrBhwwYEBgbiwoULGDx4sGAzV6fugPAyB4CbN2+ioaEBf/vb3zB//nysW7cOZ8+exeeff46ysjJ89dVXHZo7DcZUyMzMRFRUFIYPH445c+YAAKqrqwGIL2ZuzsLCgitjbm7O/VdVOX2lqO4A5C7onD59Ory8vLBq1SokJCRwF2gbYt2XL1+OsLAw5Ofn48iRI2hsbERdXR0A4eeuqu6AcHP/888/8d577yE6Ohp2dnYKy7SUvXSdDKn+6tQdAHbv3i3z+eWXX8aCBQuwc+dOrFixAs8++ywAw6l7QEAAAgICuM9TpkxBWFgYBg4ciHfffRcnT54UbObq1B0QXuYAUFlZicePH2PRokXc3ZOhoaGoq6tDXFwcPvjggw7Nnb6mVKKwsBCTJk2CjY0NEhISIBKJAACWlpYAxLe0Nic51SkpY2lpqVY5faOs7sqsWLECxsbGMv+6NsS6P/300xg3bhxmz56N77//HpWVlQgJCQFjTPC5q6q7MkLI/V//+hdsbW2xdOlSpWVayl66ToZUf3Xqrsybb74JAAadvTRPT0/87W9/Q1JSEhobGwWbuSLN666MoWcu2ZfIyEiZ6TNmzAAA/PLLLx2aOw3GFCgvL8fEiRNRVlaGkydPytzOKzkFKTmdKa2goAC2trbcaNnJyQmFhYVyBzTJsopuE+5oququjKWlJXr06IGSkhJumiHWvbmwsDBcvnwZWVlZgs+9Oem6K2Poud+8eRM7duzAsmXLkJ+fj5ycHOTk5KCmpgb19fXIyclBSUlJi9k37x+UlQP0p/7q1l0ZV1dXAJDL3hDqroyrqyvq6upQVVUlyMxVka67qjKA4WYu2RcHBweZ6fb29gCA0tLSDs2dBmPN1NTUICQkBFlZWfj+++/Rv39/mflPPvkk7OzsFF7weOnSJfj6+nKffX198fjxY9y4cUOm3MWLF7n5+qSluitTUVGB4uJima86DK3uikhOM5eXlws6d0Wk666Moeeel5eHpqYmLFu2DO7u7tzPxYsXkZWVBXd3d3zwwQfw8fGBiYmJXPZ1dXVIT0+Xyz4rKwuPHj2SKatv9Ve37srcvn0bAOSyN4S6K3P79m1YWFigS5cugsxcFem6qyoDGG7mQ4cOBSD+25cmubbLzs6uY3Nv1b2XAtfQ0MCmTJnCTExM2A8//KC03KJFi5ilpaXMraunT59mANi2bdu4affv31f6zKUnn3xSr565pE7dq6ur2aNHj+Smr1y5kgFg33zzDTfNkOr+4MEDuWl1dXVsyJAhzNLSkru1W4i5q1N3oeZeVFTEvv32W7kfb29v1qtXL/btt9+y3377jTHG2IQJE5iTk5PM72HXrl0MADtx4gQ37ddff5V79lBNTQ3z9PRk/v7+7Ve5Fqhb9/LyclZTUyOzbFNTE5s2bRoDwK5evcpNN5S6P3z4UG5aeno6MzU1ZVOmTOGmCS1zxtSruxAzZ4yx1NRUBoDNmDFDZnpkZCQzMTFheXl5jLGOy50GY1LeeOMNBoCFhISw+Ph4uR+Je/fusR49ejAPDw/2n//8h61du5Y98cQTbMCAAXJ/xJID1oIFC9jOnTvZpEmTGAB24MCB9q6eSurU/c6dO6x79+5s8eLFbPPmzWzz5s0sODiYAWATJkyQe2aLodR96tSpbMyYMSwmJobt3LmTffjhh+zpp59mANjGjRu5ckLMXZ26CzV3ZRQ9a+vq1avM3NycDR48mG3bto2tWrWKWVhYsPHjx8stHx4ezj2rKC4ujgUEBDATExOWnJzcXlXQWPO6JyUlMUdHR7ZixQoWGxvLNmzYwEaMGMHl25wh1P35559nwcHBbM2aNWzHjh1s+fLlzMrKitnY2HDPoGJMmJmrU3chZi7xyiuvMAAsIiKCxcbGsvDwcAaAvfvuu1yZjsqdBmNSnnvuOQZA6Y+069evs/HjxzMrKyvWvXt3NnPmTFZYWCi3zsbGRrZ27VrWu3dvZmZmxry9vdmXX37ZXlVSmzp1Ly0tZbNmzWKenp7MysqKmZubM29vb7Z27VpWV1cnt05DqftXX33Fxo0bxxwcHJiJiQl74okn2Lhx49h3330nV1ZouatTd6Hmroyyp4+fO3eOBQQEMAsLC2ZnZ8eioqIUnjGsrq5m//jHP5ijoyMzNzdnfn5+7OTJk+2x623WvO63b99m4eHhzM3NjVlYWDArKys2dOhQtn37dtbU1CS3vCHUffPmzWzYsGHM1taWmZiYMCcnJzZr1ix28+ZNubJCy1ydugsxc4m6ujoWExPDevfuzUxNTZmnpyfbtGmTXLmOyN2IMRW3SxFCCCGEEJ2iC/gJIYQQQjoQDcYIIYQQQjoQDcYIIYQQQjoQDcYIIYQQQjoQDcYIIYQQQjoQDcYIIYQQQjoQDcYIIYQQQjoQDcYIIYQQQjoQDcYIIYQQQjoQDcYIIYK1d+9eGBkZIScnp6N3hRBClKLBGCGENJOYmIiYmBidbiM/Px8xMTFIT0/X6XYIIfqP3k1JCBGsxsZG1NfXw9zcHEZGRmovt2TJEsTGxkKX3eOVK1fg5+eHPXv2YO7cuTrbDiFE/5l09A4QQoiuiEQiiESijt4NQghRib6mJIQIlqJrxk6cOIHAwEBYW1uja9eumDRpEv744w9u/ty5cxEbGwsAMDIy4n7UNXr0aPj4+ODq1asICAiApaUl3N3dsX37dq7M2bNn4efnBwCYN28et429e/e2rcKEEINEZ8YIIZ1GfHw85syZg6CgIHzyySd4/Pgxtm3bhpEjRyItLQ1ubm5YuHAh8vPzcerUKcTHx2u0ndLSUgQHByMiIgKRkZE4cuQIFi9eDDMzM7zyyivo168fPvjgA7z33ntYsGABAgMDAQABAQHarC4hxEDQNWOEEMHau3cv5s2bhzt37qBnz55wdXVFeHg4duzYwZV58OAB+vbti4iICG56W64ZGz16NJKTk7Fx40b8/e9/BwDU1dXB398f+fn5yM3NhampKV0zRgjh0NeUhJBO4dSpUygrK0NkZCSKi4u5H5FIBH9/fyQlJWltWyYmJli4cCH32czMDAsXLsTDhw9x9epVrW2HECIM9DUlIaRTuHnzJgBgzJgxCud369ZNa9tydnaGtbW1zDQvLy8AQE5ODp599lmtbYsQYvhoMEYI6RSampoAiK8bc3R0lJtvYkLdISGkY1DvQwjpFDw8PAAA9vb2GDdunMqyrbl7UpH8/HxUVVXJnB3LysoCALi5uWllG4QQ4aBrxgghnUJQUBC6deuGtWvXor6+Xm5+UVER9/+SQVRZWZlG22poaEBcXBz3ua6uDnFxcbCzs8PQoUO1sg1CiHDQmTFCSKfQrVs3bNu2DS+//DKGDBmC6dOnw87ODvfu3cMPP/yAESNGYMuWLQDADZiWLVuGoKAgiEQiTJ8+Xe1tOTs745NPPkFOTg68vLxw+PBhpKenY8eOHTA1NQUgPlPXvXt3bN++HV27doW1tTX8/f3h7u6u/coTQvQanRkjhHQaM2bMwJkzZ/Dkk09i/fr1eOONN3Do0CH4+vpi3rx5XLnQ0FAsXboUJ0+exMsvv4zIyMhWbeeJJ55AYmIirly5gpUrV+L+/fvYsmULXnvtNa6Mqakp9u3bB5FIhEWLFiEyMhLJyclaqyshxHDQc8YIIYK1e/duvPrqq7h//z5cXFzaZZujR49GcXExrl+/3i7bI4QYPjozRggRrIKCAhgZGcHW1rajd4UQQpSia8YIIYLz4MEDJCQkYPv27Rg+fDisrKzavM6SkhLU1dUpnS8SiWBnZ9fm7RBCOh8ajBFCBOfGjRtYuXIlhg0bhp07d2plnaGhoSqv6erdu7fMC8kJIURddM0YIYSo4erVqygtLVU639LSEiNGjGjHPSKECAUNxgghhBBCOhBdwE8IIYQQ0oFoMEYIIYQQ0oFoMEYIIYQQ0oFoMEYIIYQQ0oFoMEYIIYQQ0oFoMEYIIYQQ0oFoMEYIIYQQ0oH+H/66ltWCk4JjAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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",
    "The colors for each sample are customizable upon figure creation via a dictionary which accepts [named colors supported by `matplotlib`](https://matplotlib.org/stable/gallery/color/named_colors.html).\n",
    "The example below showcases this functionality.\n",
    "\n",
    "To demonstrate additional figure customization, following the creation of the figure by `cabinetry`, 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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - cabinetry.visualize.utils - saving figure as figures/Signal_region_prefit.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colors = {\"Signal\": \"gold\", \"Background\": \"maroon\"}\n",
    "figures = cabinetry.visualize.data_mc(\n",
    "    model_pred, data, config=cabinetry_config, colors=colors, close_figure=True\n",
    ")\n",
    "\n",
    "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": 22,
   "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": 23,
   "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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG - cabinetry.model_utils - total stdev is [[13.168169023239283, 7.199509273985892, 7.061005745552113, 7.731278636665921]]\n",
      "DEBUG - cabinetry.model_utils - total stdev per channel is [20.71940086773956]\n",
      "INFO - cabinetry.visualize.utils - saving figure as figures/Signal_region_postfit.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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": 25,
   "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": 26,
   "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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",
      "text/plain": [
       "<Figure size 800x565 with 2 Axes>"
      ]
     },
     "execution_count": 26,
     "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": 27,
   "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": 27,
     "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": 28,
   "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": 29,
   "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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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 29,
     "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": 30,
   "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": 31,
   "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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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 31,
     "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": 32,
   "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": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}