{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. The BlueiceExtendedModel: Rate and shape parameters\n",
    "In this tutorial, we'll learn about the `BlueiceExtendedModel`, which is based on [blueice](https://github.com/JelleAalbers/blueice) likelihoods and dive into its rate and shape parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Initializing the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This statistical model has the same basic functionalities to `generate_data` and then `fit` the data. However, the likelihood is a bit more complex than in the previous example. Thus, the definition of the parameters and of the individual likelihood terms are conveniently defined in a YAML configuration file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from alea import BlueiceExtendedModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Just some plotting settings\n",
    "import matplotlib as mpl\n",
    "\n",
    "mpl.rcParams[\"figure.dpi\"] = 200\n",
    "mpl.rcParams[\"figure.figsize\"] = [4, 3]\n",
    "mpl.rcParams[\"font.family\"] = \"serif\"\n",
    "mpl.rcParams[\"font.size\"] = 9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial we'll use the example provided in `alea/alea/examples/configs/unbinned_wimp_statistical_model_simple.yaml` and we initialize the model from this file via the `.from_config()` method. You can have a look at the file but **it's not necessary to understand every single entry now**. But here are some comments to help understand the file. You'll find the entries `parameter_definition` and `likelihood_config`:\n",
    "\n",
    "**parameter_definition**:  \n",
    "Dictionary of dictionaries containing all parameters defined in the model. The key of each entry is used as `name` of the parameter (for example `wimp_mass`, `livetime`, `wimp_rate_multiplier`, etc.). The entries of each dictionary correspond to respective attributes of the `Parameter` class (for example `nominal_value`, `fittable`, `description`, etc.). To find out the meaning of each of these attributes you can either read about the class in the [documentation](https://alea.readthedocs.io/en/latest/basics/parameters.html), directly look at the `Parameter` class docstring, or execute the following code snippet in the notebook:\n",
    "\n",
    "```python\n",
    "from alea import Parameter\n",
    "Parameter?\n",
    "```\n",
    "\n",
    "**likelihood_config**:  \n",
    "This defines the likelihood created by the class. The most important entry is the `likelihood_terms`, which is a list containing the individual terms that are added to the likelihood. In our case there, there is only one term called `science_run`. The entry `sources` is a list of all the included signal and background sources -- in our case `er` background and `wimp` signal defined in this term of the likelihood. Each source contains information to the location of the template and related parameters.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing/loading models on one core: 100%|██████████| 5/5 [00:00<00:00, 646.85it/s]\n"
     ]
    }
   ],
   "source": [
    "config_path = \"unbinned_wimp_statistical_model_simple.yaml\"\n",
    "model = BlueiceExtendedModel.from_config(config_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, let's start by looking at which parameters we have defined in the model. Printing the `parameters` object will show us a table with all parameters and their attributes/properties. Especially the last column with the description can be helpful to understand the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      nominal_value  fittable     ptype relative_uncertainty  uncertainty    blueice_anchors fit_limits parameter_interval_bounds  fit_guess                                               description\n",
      "wimp_mass                      50.0     False      None                 None          NaN               None       None                      None        NaN                                      WIMP mass in GeV/c^2\n",
      "livetime                        2.0     False  livetime                 None          NaN               None       None                      None        NaN                                         Livetime in years\n",
      "wimp_rate_multiplier            1.0      True      rate                 None          NaN               None  [0, None]                   [0, 50]        NaN                                                      None\n",
      "er_rate_multiplier              1.0      True      rate                 True          0.2               None  [0, None]                      None        1.0                                                      None\n",
      "er_band_shift                   0.0      True     shape                 None          NaN  [-2, -1, 0, 1, 2]    [-2, 2]                      None        NaN  ER band shape parameter (shifts the ER band up and down)\n"
     ]
    }
   ],
   "source": [
    "print(model.parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we already saw in the configuration file, the model consists of a **WIMP signal component** (`wimp_mass`. `wimp_rate_multiplier`) and an **electronic recoil (ER) background** component (`er_rate_multiplier`, `er_band_shift`). In addition there is a parameter called `livetime`, which correspond to the exposure of the data acquisition period."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Generate Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before discussing rate and shape parameters, let's have a look at some data generated from the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = model.generate_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['science_run', 'ancillary', 'generate_values'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is a dictionary containing data from the `science_run` and the `ancillary` measurements. It also contains the `generate_values`, which are tha values of the parameters used to generate the data. We'll discuss the `ancillary` measurements later and for now focus on the `science_run` data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       (68.44320406, 3155.93004636, 0), (55.00676274, 3462.91569182, 0),\n",
       "       (17.76962908, 2602.89832165, 0), (33.03122814, 2028.82638288, 0),\n",
       "       (60.67813307, 4579.73600473, 0), (74.56522825, 3297.72698752, 0),\n",
       "       (96.13090003, 4924.74929966, 0), (49.15274966, 3332.78534465, 0),\n",
       "       (55.95124853, 3292.34324256, 0), (22.80345093, 2253.88313136, 0),\n",
       "       (35.90775266, 2615.75056535, 0), (23.75075588, 2339.49381065, 0),\n",
       "       (29.6360634 , 3059.10578319, 0), (30.047926  , 1657.56734752, 0),\n",
       "       (42.86083471, 2532.67483856, 0), (75.46709544, 4078.07748096, 0),\n",
       "       (82.22212648, 3805.86426963, 0), (20.65173078, 1691.93132694, 0),\n",
       "       (45.5937925 , 2620.24949834, 0), (95.64652263, 4073.12526828, 0),\n",
       "       (93.96055465, 5057.54015506, 0), (45.76430875, 2867.05041601, 0),\n",
       "       (80.39205202, 3652.6697906 , 0), (95.75150144, 4714.52332147, 0),\n",
       "       (96.38310989, 3772.32264321, 0), (34.20251163, 2708.39390472, 0),\n",
       "       (56.44356423, 3218.49946806, 0), (76.26045978, 4368.17329309, 0),\n",
       "       (90.65298167, 4940.38360564, 0), (42.05280463, 3763.46295303, 0),\n",
       "       (42.37399449, 3785.36554735, 0), (52.71575968, 4387.89206591, 0),\n",
       "       (65.69511666, 4873.0765053 , 0), (70.234827  , 4120.14182016, 0),\n",
       "       (92.88912372, 4876.58170337, 0), ( 4.025399  ,  566.11264405, 0),\n",
       "       (37.14365666, 2872.92035461, 0), (75.69292851, 4355.80433649, 0),\n",
       "       (25.51461303, 2513.31547872, 0), (62.52632232, 4771.90286321, 0),\n",
       "       (32.42711825, 2237.81808532, 0), (48.90552393, 3777.75452122, 0),\n",
       "       ( 9.15698942, 2309.60187959, 0), (25.83894059, 1850.49755543, 0),\n",
       "       (76.36571446, 4415.29319308, 0), (91.39568953, 5303.83150729, 0),\n",
       "       (17.86559898, 2698.99883584, 0), (18.35801221, 1964.00930464, 0),\n",
       "       (19.92824225, 1976.03741814, 0), (16.61942732, 3920.82655288, 0),\n",
       "       (13.54456386, 1400.16980021, 0), (98.25770111, 4830.37651145, 0),\n",
       "       (66.20768799, 3240.53277354, 0), (94.65898271, 3665.38438429, 0),\n",
       "       (39.71077396, 4386.65315835, 0), (28.32322495, 3047.52610652, 0),\n",
       "       (87.26135634, 4738.22891098, 0), (99.96445521, 4948.95715011, 0),\n",
       "       (96.30083445, 4015.98290423, 0), (21.62178828, 1923.48658187, 0),\n",
       "       (61.65518162, 2792.05287265, 0), (42.35578337, 3094.44826272, 0),\n",
       "       (14.0774194 , 1579.17094461, 0), (81.29938045, 4428.53610514, 0),\n",
       "       (18.65411202, 1625.88948154, 0), (63.76745533, 2320.97725261, 0),\n",
       "       (52.4164088 , 3790.53138428, 0), (94.9166354 , 4910.20968293, 0),\n",
       "       (12.93948443, 1944.16557375, 0), (30.12727207, 2752.4106284 , 0),\n",
       "       (60.01405614, 3449.38067592, 0), (91.16016063, 3870.97936764, 0),\n",
       "       (43.29416308, 2624.10870392, 0), (28.37552046, 3354.17682865, 0),\n",
       "       (73.63398366, 4678.67682201, 0), (76.21466032, 4218.19854926, 0),\n",
       "       (42.79405807, 2533.85919697, 0), (83.32854204, 5379.40839497, 0),\n",
       "       (67.60993504, 3782.19768293, 0), (26.97945349, 3415.30129998, 0),\n",
       "       (24.92358013, 1913.30958068, 0), (44.46913505, 3029.564816  , 0),\n",
       "       (50.35054685, 3293.76248448, 0), (70.17883443, 2813.3465762 , 0),\n",
       "       (31.59857826, 3593.9112905 , 0), (38.03923643, 3285.22559774, 0),\n",
       "       (25.56172342, 2061.65895621, 0), (46.18446571, 3839.49329893, 0),\n",
       "       (99.42406487, 5114.89490339, 0), (74.21091672, 4143.46284441, 0),\n",
       "       (51.14994833, 3958.83933253, 0), (11.03211897, 1605.57136971, 0),\n",
       "       (88.51549465, 5398.96975774, 0), (48.73571745, 4196.71098211, 0),\n",
       "       (91.95169585, 3490.95445037, 0), (44.0582672 , 2947.81669686, 0),\n",
       "       (15.47613473, 2042.10447424, 0), (66.77380602, 2445.12154441, 0),\n",
       "       (33.83196538, 3014.80962753, 0), (57.87705397, 4190.53957453, 0),\n",
       "       (71.97777099, 3254.31265132, 0), (92.61786434, 4882.83533928, 0),\n",
       "       (93.36240996, 4891.06153804, 0), (92.42348716, 3931.95814582, 0),\n",
       "       (99.42845118, 4303.80511514, 0), (47.53735267, 3027.98015662, 0),\n",
       "       (26.68268748, 2870.1603711 , 0), (51.14171645, 2096.45842192, 0),\n",
       "       (39.69918869, 3547.69459528, 0), (52.84481319, 3763.70021584, 0),\n",
       "       ( 9.69704276, 1623.77758512, 0), (20.69758285, 2683.98092971, 0),\n",
       "       (15.30051199, 2357.60532322, 0), (25.74989559, 2297.9141931 , 0),\n",
       "       (75.07742219, 3409.02968489, 0), (99.83849592, 3712.55544336, 0),\n",
       "       (21.89106668, 1268.7124031 , 1), (22.25391349, 1741.85631721, 1),\n",
       "       (25.39297309, 1461.17584857, 1), (40.5943938 ,  970.07142816, 1),\n",
       "       (12.2914983 ,  399.62894221, 1), (32.13263347, 1914.80990635, 1),\n",
       "       (19.55609784,  755.73889724, 1), (14.64466764, 1268.97865745, 1),\n",
       "       ( 5.75005414,  253.37373347, 1), ( 9.15530106,  719.98306115, 1),\n",
       "       (11.37849705,  554.13822876, 1), (34.70918416, 1754.97794449, 1),\n",
       "       ( 4.70830379,  577.77809631, 1), (13.57051102,  803.33999888, 1),\n",
       "       (35.9823066 , 3080.62400746, 1), ( 9.79152803,  774.20510151, 1),\n",
       "       (14.91601385,  968.47859131, 1), (54.51938694, 2984.62453438, 1),\n",
       "       (11.44859844,  658.47714632, 1), (43.15755448, 3550.28679552, 1),\n",
       "       (20.87919452, 1554.15442442, 1), (34.7684597 , 2160.44569079, 1),\n",
       "       ( 7.4289749 , 1090.33002927, 1), (37.81165423, 1892.6155401 , 1)],\n",
       "      dtype=[('cs1', '<f8'), ('cs2', '<f8'), ('source', '<i8')])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"science_run\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the data has entries `cs1`, `cs2`, and `source`. S1 and S2 are the prompt scintillation and delayed signal from drifted electrons, respectively. The \"c\" in front denotes that those are corrected for detector effects. The key `source` refers to the origin of each event. It is the index of the source name list, which we can look up with the `get_source_name_list` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['er', 'wimp']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source_name_list = model.get_source_name_list(likelihood_name=\"science_run\")\n",
    "source_name_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the toy data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x129231fd0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vdBfLCuKiU1EE9QAAT7m5kqcfRuesgonldTs0d8Eq08mi2Tw+Xt25sLNfVs/pX5hv+p6ZBGRmEzKLCvJ6dfgevmyKLnpghVZu3N3rPQyTQXw7k1fr6q0ru9htq5lNDS2+nghqd+XXdDomdhbLcmswwS3k1AMAPOXmSp5+GJ2zytdu6+wyzevN5vHxMq/Yzn5ZPaemYqDpe2YSkKUyt2OPRfUhszKK3fdhQJF5ZyS8uzWlY55o8mh3fg9Q7a6fkM6iYOksluXFvBQnMVIPAPCcW6OJfhidSzZya3bnIFvHJ1EgO2/mEVkfwbezX2bPSeXuhd07Eal0+JJdR2af+fBlU1QbbtTZ9/5NXT1G9KMr8do9x3aq89gJUL2eX5LNu1DJ3jvXatRLBPUA4Ctef8kGTarHy81UHyvdg4nldTtM65+7defAq4momeoZkA0uieSyX/vU23HXQW24UeffvyJ2jKOLSD19de/9SKXDl+g6SjZvo3JIf9Pjnso5t2rr0NIilQ/oZytA9cP8kmwG1qkulpULQoZhlgGGIAqHw6qqqpIkbd68WZWVlR63CEAq7Ob0ZrsNQelUpHu8Ivvoj9G5uQtWafH67b22zxpX4UqwYfX5Q0sLtbO5dyqJW+1Khdl1kBeSKof0V1Nru2mqzLQx5fr9V49P+j6Jrier6yjZOXXinNtpa7LfZa+vvb4sW/EaI/UA4BNeT+L0w8hdKtI9Xn4anfP6zkGqE1Hr6ps0d8EqX3X6zK6DLiNxisq7W3vnr6c6amx1HSVL43HinCdrq53fZT/ML/FSkAYw7CKoBwCf8PpL1utORaq8Pl6psAogvM7rtfr8SFt7l9EM726Nbe9eC31R7VbPgqNUVn9NxokOX7I0HqfOeaK22vld9sP8Eq8EbQDDLoJ6APAJr79k/RIk2x1B8/p42ZUsgPD6zoHdiah5IfWa4Hmgo0vXPvV2bLsXwVGyOvBmxo/IXtvsjMRn+5zb+V32+i5Rd91/5yPzIwztaW1Pq5No5+9H0AYw7CKoBwCf8PpLNltBciq3uVMZQfP6eHWXaB+DGECYjSbX1e81DRbNAn03981OHfju+uWHdONZR2atPV7ffZHs/S77oZ1S4pr7qXYS7f798MsAhtMI6gEEWi7lRXr9JZuNIDnV29ypBMBeHq+eI4urN+9We2ckuu25j0ENIHqOJkdz6e2I7psbv5/dr4O6+iaFd7f26mhE5YWkO84/OuvXiNt3X3oe59kTRtr6Xfb6LpGUvOZ+Kp1Eu38/gnKXL1UE9QACKxfzIr38knU6SK4NN+pLj65MaZQ61QDYi+NlZzXP7vuYKwGE3ZQcKbJv2fr9tOooRK+DaFWa1z/YqZa2zrjXdhnSbS+s0+HDSwP7N6Inq+McmeuwzRdVnhKx01G0Wm3Z7nv1/Pvhp7t8TiKoBxBYbqY15NIdgUScCpKTBb5WQXoQAmC7q3k6We3ED8w6fbMnjNANz7zTa99mTxiRcofODjsdheg1fNr8ZabX0s7mNl3wwArXOv/Z/tth9XdwUe02z0fhzfQ8HtE1BhKJrracrFNo9++H13dFs4WgHoDvWX0pupXWkIt3BLItWeBrFaQHIQC2m4LS0NKm0+YvU3V5/8CMmiZj1uk7fHhp0kC/u0x+P5N15Lv/rWho6V1n3+w12eTG344gpXeZHY9++SEV5ueZLsLW04GOLv3w+XV68qrjTR9P5e+HH1KPnEZQD8DXEn0pujWqG8SJjl5LFPgmCtKDMIJmt9rKzuY27WxuS2uyX5DuCpnl3tvp0KWzn4kCWDtpUT1fk21u/O0Iwt2tKLPj0d5p6LjRQzSopJ82NbRoSP9CGYb09uY9poH+qo0Nqg03ml4rQfj7kU0E9QB8LdGXolujukEaCfOLRMvY//ryqQm/ZP0+gmZ23YVCkWsvPy+kfnl5vVYxtRvI5cJdITsdunT3M1EAa3V3qDA/pLbO3on/bgS9bvztsPo7OHvCCN8tFGZ1PPa0tvUafbda8daQ9KVHV6p8QKHpfvn970c25XndAABIJNGXYnRUZta4CtVUlGrWuIqsBD/V5f0ttvtvJMwv5s2sUVFB/FdMUUFe0oA+CLpfd9Xl/ZUXkgxD2t/epZYDnWrsEdBH2QnkEnVirdSGGzV3wSqdNn+Z5i5Ypdpw79VS3WT1+zK0tCj2+5nKfnbfv6bWdhXm976u5s08wvJvxaFlJabXYjqd/1SPtRt/O8z+Dv74vIm64Zl3tHj9dtXVN2vx+u264IEVnl8bg0v6mW43Ox7zZtYoZPE+0Ttg6eyX335fnMRIfYCNHz8+7uf2dvMvEiDI7KzOmO1RmSDkebshlXQJJ26D+zkNJXrdmZV5tKimaBq49NzHuvq9pq+16hD4cWTf6vele4fO7gj2s29tiVvcSorUmZ82ply797XFXVdWfytqKgbq3kuPyTglI9VjXRtuVFNru0KKvyay8bfDTgqU1ymDteFGvb25dwDdLz9kmfM+dXS5Vm5sSPi+qeyXH39fnERQD3jEzwGLn/ghoO7reZpSel+GmXS4gvLlaxWcJgrkor/7dfV742qq19U3K89iaNJqZNeP8z3s/L7YyQOvDTf2CuilSA72wOJ++v1X49M1Ev2tcKLzn8qxNsvvD0k6bky5/nf2kVm/hv2QMtjzO66ptd00R/6Y6iGWx+PGs460NU/C7n758ffFSQT1AbZ27dq4n8PhsKqqqjxqDVIRlIDFD/wSUPflPE3J/S9DNz7PiY61VXA6dXR5bOJf92s22WTOLqN37fdEnVg/BG9mkv2+2Oms372kznIRKbP9y/bfilSOtdn1a0gaWNzPlb9dXk+eNfuOs0ql2b3PukpRz3Pa0BKZfN6T3f3y6++LUwjqAQ/k+miB0/p6QO0Hbn8ZZvvznOpYWwWnN55lPhprp8Z95ZD+qqkYaCsw9Tp4S5edADzRhFur/cvm34pUjnWyKj12O5PpdjydusOZ6PMTPWbVqTGT7FrtubBYz05xKvsV1N8XuwjqAQ/k+mgBck8qE9yckO0vX6c61qmODtupcV9TMdB2G4JU+aSnZAG41TWQF5Jj6XepBM2pBMpWbR/Sv9B2ZzKTjqdTc1qsPl9Swralk5ZmR6b75Yd0zmwiqAc8kOujBfBONuZq1IYbtXrznl7brSa4OSHZl2+m+5lJx9rss+0G4clq3DsR5PRc/Cmo6X1m10BeSLrzwkmO7EeqQXMqAaVZ2/vlh/TPj/ba7kxm2vHM9K5F4gpFRsK2WV3nx40p18Di3mlpqchkv/ySzpktBPWAB3J9tADuqw036ofPr9WqjbtjI2FOBXM/fH6d2k3qfH+yYqDt9z04ObRZ+9o61L8wXzUVAy2D8URfvmbB2Mvvbf84baXUVoBvd6Jmz+BdSjxCmYxVoBpNuXEiyHGi8okfJvJnOwBLJ2i2G1D2bPvgkkK9vXlPr/ULoqKdye7Hfcue1oTPzbZ0Or7Rx6y+45yYJJzptZnL6ZwE9YAHcn20AO5KNPnSibkaa7eZ13HesMtecGHVvk0NrUlHRs3abRaMdRmRIGRTwz5bQbadOwFmwfvRlYMzHj3N9u9+pul9fprIn80ALNtpkN3bPnfBKtPKL1HV5QNsr4jr1h3dxB1fI2mp4Wxc5366Nv2IoB7wSC6PFsBdySZfZhykWM1wsylR+9LpdCTLS7fznsmCDqtRXKsOTirHONu/+5mm96VautHtEX2nPtPNNEg7q+zamUTt5h3dZB3fZHebs3GdZ6vIhB/uTDmBoB4AAi5ZkJtpkDJ+5CCt3Li79/YR9r70krUv1U5Hsrx0u++ZKOiwbLNFByfRMbYTMDgZVGSa3md3BNuLUVOrz/zxeRO1qHZrSsfPzTRIq2t2aGlRbFEuq+M+oChfowaXuH5HN1nH14u7zdm4u5JLo/8E9QAQcImCXCeClBvPGq/z7n8tLq++X35IN551ZMbtizyeWqfDLBjL9D17v968zeNHlmlNeI/tQNBOwOB0UJFp6oPdEWwvSvNafWb3RarsHj+nUkTsdMjsrLJrVWHq+LFDPV1ILNP5BXbZOY7ZuLuSSyWmCeoB+Eau3AJ1m1nA4OTqlRMqy/TM1Sc6WkYuKlmnw+qaiAZjdfVNcauy2nlPO581uKRQ/fJDcR2ZaP15SbaPhZ2AIRtBRc/a3qn8XtkdwfaiNK/VZ/ZcpMru8cs0MLXbIUvWgagNN+rtzb1Tu7JZYcpP7B7HbNxdyaUS0wT1AHzBydHKvtY5cGPypVNl5Orq92pfW6f6F+appmJQwnYmuybig9bMR1t7TlIszM/TcaOHaE9rW6/3tXss7AQM2Qwq0vm9sns9eVGa107qVZQbQVkqHbJEv0N3L6kznUh7TPWQnP7bFWX3OGbjb10ulZgmqAfgiEwDaadGK3MpPzKZdI/5s29t0W0vrFNTa4cGlRToe587UuceMyqrbbUKaGrDjZaLJKXyRZ/pbXKzz2rrjEyMPX7sIWkHDnYChmwGFen+Xtk5pnZGTZ3uYFuVBO05Ui+5E5Q51SGzep/d+9pSblMQpXIcnU77yaUS0wT1ADLmRCDt1Jej3/IjUw1q7D7f9jHfulp65XZp90ZpyGgtPfQKXfPSwVrZO5vbdM2Tb0tS1gP7npLtg5u3xa0+q+VApxav3552x9BOwJDNoCKbx9BOSonTHWw7i21J7gVlTnXIcmm0OB2JVuDN9srIuVRimqAeQMacCKSd+lLzU35kKkFNqotH2TrmW1dLj5whdeyP/LxjvY7/52IdFbpZ7xpj41572wvrXQ/qk+2Dm4FOsrSOdDuGdgKGnnMEIulJBbp7SV3GQUy2j2GylJJsdLDNPvPw4aWeBGVOdchyabQ4HWb7X5ifp7c27Y7Na8nmXddcKTFNUA8gY04E0k59qflpxMsqqLn4odc/TumoMV0htefzzYIgW8f8ldsPBvQfKw61a17Bs/py+7Vx25ssVrrMpmT74GagY6eiTrodQzsBw4TKMs2beUS366DN9kJa3fW80zN7wkjPgkU3O9heBWVOjfLmymhxuulWZvvf1NrWq5RuUKvSuIWgHkDGUg2kk1U0yeRLzU8jXnZTOtJZPMrWMd+90fT9qkP1vbYNsiinl03J9iGbq1Imuv5e/3CnWg50WrbLyc/sLtOR7cQ13Le5Hiz6qYOdTU51KII+WpxpulXP/T9t/jLT5wWxKo1bCOoBZMxuIB1JMVmnVRsbLFNMMv1S89OIl92UjnQWj7J1zIeMlnas7/XaTUZFr23f+9y4hG3IBjv7kI1a2Mkq6pjdObG6np2c/5DpyLZVp2BR7TZPgkU/dbCRfU6nW/WVTqGT8rxuAIDgiwbSs8ZVqKaiVLPGVfQKWKKBzcpuAX1U9A+/k+15+LIpeuma6Xr4sime3cKeN7NGRQWJ/8xGOh79LR+3CoLsHHPNuF5tocK41+03+unujnNVmJ+nwvw8DS0t0vyLJrmeTy/Z3AeHJQo8UmlX9HpevH676uqbtXj9dl3wwArVhnvXGrfzmZIsrwO7QYyf5pNI7p7faBWl0+Yv09wFq0zPA7LL6evP7O8nncLEGKkH4IhkI6rppJgEXVxKxwc71dJmntIxb+YRaS0elXQUe+Rk/fvsZ7TpmZtVqXptMip0d8e5ej//cD3tkxKfbqcc2A080rmeM5r/oMxHtv04sunG+TW7E/Lye9t154XedFb7KqevPz/ddQ0KgnoArrCTYpKLi0ZFg5pn39oSKx3Z3bY9rZKUtS+vIyadrP1Dn9VPur33jzJ872ycJ7fOvd3AI1l7UhmVtPuZmQYxfTXdxayD1WVI1z71tg4fXhr4vyFBkY3rL+jzDNxGUA/AFYnyy4sK8jR7woicXjRqUe1W0+1rtzXpggdWaOFVJ2Tty8vJL0a7+eFmQbEk00DZzZHW2RNG6uX3tsctVmS2YFKyfUxlVDKVYMeplXv70simVQeryxCVUlzUV68/PyGoB+AKs8AmJGnq6HLdeNaRvls0ymmJ7lQEaT+tztOXHl2pX19+nGWQvqxuh0IKqa2zd6Ds1khrbbhRNzzzTlxAnxeSfnzexFi7715Sp9c/3JX0Wkw1UHcr2OlLI5vR87Vld6vlc3Ixrc/P+tL150cE9QBckSyw8dskv1QlS9cYnKRkpFf7mWrai9V52tncFrvjYBakRxaQiZ8iHQ2U6+r3mr6n0yOtVp2HRbXbdPjwUsu1AqK6n6NUA3WCHWclWtuhuyH9CxM+nmkbci1dEMFGUA/ANYkCGz9O8rPLXkpKKOF7eLGf6dSVTpRGZbdEZ3ebGlq0z2QCcffHnZKo45hsIrdkngPvdaCebmDpl4A03XbYOV+S9Nam3aoNNzq+b5nWZPfL8UduoaQlAF8IcvkyOyUL97S2Wb4+lf10snSf3VKL3SUr05msRGdP1eUD1L/QenzJyc5OopKRyToi/fJDvrsWUymr6cTrnFQbbtRFD6zQnF/8La122O04tncajpbLjUrndyfKD8cfuYmgHoAveFGz3Cl2UoesAsqhpUUpje45GQykk/IUPU9DS83TGiJpKDUqzI//einIC/XaFu3M1FSUmr5XXkiOBtKJOo7JOiKhJHdavJBuYJlJQOqEg2tW7E57zYpUOo7ZSG3LJF3Q6+OP3EVQD8A3/LJoVKrsLBpkFVD++vKptvfT6WAg3cWOJlSW6deXH5fwzorRI1wLhaTbz59o2mkzOzZ5IenOCyc5eg0k6jgmuwPR1nnwOPtloaN0A0uv5684sWaFnYXdorKR2pbJQmFeH3/kLnLqAeQMr+qn26mE4kQFlGys2JhuXelE+zN3waqPJ8Ye1N5paFHtNtMcdK+rw0TPccWgIu1r61Rja3uv9kuR45xpLrWT0p2Hku7rnPr9SpY609DSljQPPnrNXPzQ62o5YD0nI1spfJn87gR5/hD8LWQYRu+/XAikcDisqqoqSdLmzZtVWVnpcYsA95hVwygqyMso2Hr2rS269qm3e9U0N3vPSMCT3aB07oJVWrx+e6/ts8ZVpD1hMxvtPm3+MtOgpaaiVC9dMz2j93aa2XWTF1LcOY+aNa5CkuH4OUhXutd8Oq9z8vfL6jruzu57W73XgKJ8HT92aFbrpKf7u5ONv1UIlmzFa4zUA8gJTte5rw039groE72nG5VQ7IwOWo2mWm3PRruDNBJpVeayZ2AfPc7XPvW26ft4kTqR7h2OdF7n5O+X2XXck933tvqd+N2Xj896gJzu7w6LNCFbCOoBBEKyW/9Op6bcvaTOdLQ27j23rpZeuV3avVEaMlqacb00cnJan2dHsmDAKjXkx+dN1A3PvONayogTy8W7VfLP6rqpHFKimopBvY6z3zosmQSWqbzOyd+vntfxlj2tpik0dt47qAGyH8qhIvcQ1APwBbMgTooE13X1exXe3RoLss2CUqeDrUR5v9XlAyIB/SNnSB37Ixt3rJc+XCpd8WLWA3urYMBqNPW2F9a5ulpvpoGWm3nrVtdNTcUg02PjRIclm7LVGXL696v7dWyVQmP3vQmQgQiCegApydZk1J5B3PK6nTJkmE5YlHoHpZkGWz33y2oF2FiZxVe+ejCgj+rYLy27Q7rkCVuf6ZRo25fX7TR9vKm1w3R7dOKnE+fT7H3SDbScTqVKJNXrxs8jw9nsDGWzM+P3jhIQFAT1AGzLVtBgFsS1dSZfLbL77flMgi2z/SrMz1O//FBcpyKuzOLujeZv1rAh6ec5yWzSXU+DSgq0s7n34leDSwodOZ9OXxdulvxL57rx68hwNjtD2ezM+LmjBAQJQT0A27IVNNhdHbKnnrfn0w22rDoV08aUa2BxP/NAY8joSMpNT+VjUv78TCSr+V1UkKfvfe7IuJz66HZJCc+n3VF8p6+LrOStJ5j/4GWQ7uSdr2x3hrJ5nPzaUQKChKA+wMaPHx/3c3t7u0ctQV+RraDBKohLxMnb81b79e7WRo0aXPJxsNVj5HDG9ZEc+u4pOAXF0vTvONImu6zaXpifp8/UDIu1+/Dhpfrh8+u0dmujFJJGlw9Q7RbzRZNSrcfup/r5pjya/5CM03c4/DaJF4C7WFEWgG2ZrKKYiNnqkNH0l+7yQpE2dF8J1AlW+9VyoFN19c1avH67LnhgRfzKoSMnR4LCT35OGjYu8n8PgkSrtn+mZlivVXnXhPeopa1TLQc6tXZbk1rbzRftqS4fkNLqtU5fF4lWfk3LK7dbz3/wkNMrBFutWkxuurf8sgIxch8j9QG2du3auJ+7L2YAZEO2JrRZ5dRKciXPNu262SMnuz4pNiqatlFX32xaU332hBGau2BVLK2jqbU94f51f22q9dizcV04mo7hk/kPPTl9h4PcdP/x0wrEyH0E9QBsy/ZkObMgzo08WyfrZkdls8661Sqo0drqsyeM6FWXPmT1Zt0MKMqPLdqTairH0ZVlWrutSZJ01Mgy/e/sI/0TtPhk/kNPVse4oaVNteHGtI4fuen+4mYlJ4CgHkBK0gka3FpIKNP2OFU3O9ujc1aroEZrq89dsKrX4xbraMU5fuzQWPvMRt/zQtLsCSPiXmPWwXh7857UdihLoue5aMdnNT+0RIXGwQpAbaFCXbNllg4sWOXZ9Wh1h2hnc5sueGAFo7k5wM1KTgA59QCyKhr0LV6/3To/3cHPSpa7aqc9meYmO50r3VOyQMHq8USj9T33b0JlmX583kTldXtRlyHd8Mw7cccq2/uaru7nedHOQ3Xe/pu0pOtYNQ08XEu6jtV5+2/Sop2HZvV6tOPoyjLlm3wT++EYInPZmocEmGGkHkBWuXX72e7ouJ32ZJpmlO3RuWSpMVaPH9etROfgkkKFQtLufW2W+7eodmtcrr7U+1j5dSSy53l+1xirK9uu1dDWQu1si6/Z70U6hJ31Bbw+huny2505L7GwFtxEUA8gq7Id9EUDiNc/2GWr82C3PZnkJme7tGCyQMHq8VTz3O0cK7+WUbRqe6LVdd2UbH0BKbPceq8wMTQek5fhJtJvAJf01bJm2bz93D3FoqXNvDxjz2DNjdvh2S4tmKzko1MlIe0cK7+WUbRq+6AS87EstzshdhZci+bWB+lvhV/TsbwUHSB46ZrpvcrMAk5ipB5wQV8evcrG7efY6PyHvUfne+oZrLlxO9yN0blkdxKcqIJi51j5dSTSqu1Wq+u63Qmxu+Ba0Cql+DUdC+gLCOoBF/TlsmZOB312cpG761mtxa0gNBdKC9o9Vn7c10RtP3x4qeedELNOR0jmVYqCFBD7NR0L6AsI6gEXpDN6FeTJZmZtdyros5OL3N2i2m0695hRcduSBaFBPvZO8ypgd+IcWLXdD50Qs05HU2u7Vm5s6PXcIAXETAwFvENQD7gg1dErJ9J1vApMs51qZCcXOf75BztOdo5JX06V8ou+cg56di7M7kIFLSD2azoW0BcwURZwQaqTCTOdbOZmbfiesj1RzmoCZJ5FEfZox8nuMWGin/f66jlwaoKz15gYimzpqwUn7GKkHsgCsxHhVEavMp1s5mUOf7Ynys2bWaPldTvV1hm/f3mhkPLzpPbOg1nJ3TtOdo9JECb65Xp6UBDOQbb4ITUI8KO+cgcvEwT1gMMS/eF5+LIpsYDs2qfetgzIMp1s5mVQlO2JchMqyzSpqkwrN+6O297RZWjamHIZhrR2a6MUksaPOHhc7R4Tu+0PUnpTum31ah+ZbOk/ud6RhP/15YITdpF+Azgs0R8euykgsyeM7JVOkkpurZdLk7tRt3xPa7vp9m2NrVoT3qOWtk61HOjUyo0NseNr95jYab8X6U3R284XP/R6Sqkp6bbVyxQuv9a+76u8vBaAqL58B88ugnrAYYn+8NjJFa4NN+qGZ95RV7fadnkh6cfnTbQ9MpbtoChRXqMbecFWAfq+tk7L42v3mNhpv9s533GLbB2wt8hWVLpt9TKvPVdyy3NFX53jAH/xcrAqKEi/ARyWKHXAKvDqvt3sC7TLMC/NaCWbFSjspH+YVfWYu2BV3K376L6mczvfqmxe/8ICSW29nr+poSWlY5Isr9ntESM7ZTytvtjSbavXo2LklvuH19cCIFEu1Q6CesBhif7w3L2kLmmusFNfoNkKilLNazTrBCyv2ylDRmxSa6oTnqwC9Ggnoafo8XXqmDid850sXzlZGU+zL7bacKN++Pw6fbDD/LpJ1lby2hHFtQA/oFxqcgT1gMMS/eGxM9Lg9y9QqwCzrr6p12j8hMoy005Az8o1UuoTnswCdLdGcpz8HDt3PqyuiQFF+Tp+7NBeX2y14Uadf/8K0+Nst62MiiGKawF+wR28xEKGYZitSo0ACofDqqqqkiRt3rxZlZWVHrcIZiKjstYjDVYL0Pglp3juglVavH57r+15IcXNA4i2+dqn3jYNSM3UVJTqpWumZ9S+ZMfXKU59jtXxnDWuIvblleo1YfWekjS0tEi/vnxqCtVvGBUD1wLgpGzFawT1OYSgPnf4+QvULMDsGdBHzRpXIcmwDDDNnp9oFCYXy+qdNn+ZaaenZwcnlWvC6j3N3hcA4K5sxWuk3wA+5OdbjGbpRXX1e03TcjY1tOjOCyf1unVfmJ8Xl1MvJb+dn6sLj9hNt0rlmrB6T7P3DZJc7NQBgFMI6gGkrGeAGc2l76m6fIDlHANJKd2NyNWFR7KRr2y16m6//FBg86BT6dQR/KeOYwYEH+k3OYT0G3jFjXkAdtNUgigb6Va14Ubdumid3t0aWUNg/Igy3XjWkYEN1OzMPZD8PyfFjzhmgLtIvwHgW+mUGkt1ZNDvVYEykY10qwmVZfr9V4939D29ZLfUa67e0ckmjhmQGwjqATgilcDULJXi5fe2684LJ1kusEVZvb7NbqeOhZJSxzEDcgNBPZCiVEaYyVM1Z7Vq7rVPva3Dh5darvLKwiN9l91OXS7f0ckWjhmQGwjqgRSkOlkvF6q1ZKNjYjUy2GUo7pa/2Wdb3Q2gA5Xb7HbquKOTOo4ZkBuYKJtDmCibfXYn66X6XL/K1gS6RIsjRSe+pvLZTPRDd35e58FpTnVm+9IxA7zGRFnAhmyP1qaSe5oLeapWE+huXbROA4sLUjrO3c/N4JJChSSZjShEb/mnMnmPiX7ozs/rPDjJybuBfeWYAbmMoB45w410l1RyT3MhT9WqY7JyQ0MsIO9+nCWZdqrMRtIL8kLqNAx1v1fY/Za/nzpQZp1FyXxfAbfQmQXQHUE9coYbX3Cp5J7mQp6qVcek5wj7gY4u/fD5dVoT3mPaqTI7Nx1dho4bXa5BJf1Mb/n7pQNl1llcXrczbkXcoM6XQLDlwt1AAM4hqEfOcOMLLpUKLEGr1mI2Gm3WMbFKm1m7tdGyU2V1bva0tunJq8xrqZt9dr/8kJpa23Xa/GVxo+PZ7ECZdUh6rtQqMUIK9+XC3UAAziGoR85w6wsuldzToOSpJkpd6tkxaWpt08qNu3u/Scj8vSOvMz83DS1tvQL0qJ6dosElhXp78x6t3NjQq412OlDpzrew6pBY7Svglly4GwjAOQT1yBl8waUvWepS946JVaWZ8SPKYgF3d9EAu+e5kaSdzW3a2dxmmb7SvVM0d8GqXiPk3duYqAOVyXwLqw6J+XPdHSGljGffFrS7gQCyi6AeOYMvuPSlkrpkdZwlmQb70XPQ/TUNLZFgvrtk6SuZpFdlMt/CrLNYmJ8Xl1Mvud+BzJV1EJCZoNwNBJB9BPXIKX7/gvPTyGr3tjS0tJk+x2rk2eo4J+pUdX/NafOX9QrqpcQBeibpVZl0CBJ1YrKR7mMXlU8AAN0R1AMu8dPIqlkKTU/pjDzb7VSlE6Bnkl6V6XwLq/3KRrqPXVQ+AQB0l+d1AwA/qw03au6CVTpt/jLNXbBKteHGtN8r0ciq28zaIklDS4tUU1GqWeMqstrZmDezRkUF8X9+kgXo0RHzWeMqUm5jOp8Xs3W19PjF0r2fjvx/6+qkL3HjXFeX97fYTuUTLzj5twIA0sFIPWDB6dFWP42sWrWlfEA/vXTN9Kx/frrzH9JNr0p7vsXW1dIjZ0gd+yM/71gvfbhUuuJFaeRky5dZTax18lwzMdw//HQXDkDfRVAPWHA6Z9lPNaX90Ba35z+k9Xmv3H4woI/q2C8tu0O65AnTl9SGGxXebd5pcvL4MjHcP5jfAMAPCOoBC06PrKc7spqNCZd+H+WN7nNd/V7ta+tU/8IC1VSUuj+xePdG8+0NGyxfcveSOnWZrM6VF5Ljx9fvE8Mz5aeJ5Yn46S4cgL6LoB6w4PRodjojq9m6re/nUV7zSbxt2tSwz/2UhiGjIyk3PZWPsXyJVYBXOaTEF8c3KIKU0uKHO18AwERZwEJGkystREdWX7pmemzBpESyOeEy1ba4xWoSr+TBxOIZ10sFxfFtUKF+uPcsy4mQVhNYayoGOd68XOanieXJZONvBQCkiqAesJBJtRWnBOm2vlPVP6z2+eDjLu77yMnSFS+qqfqzqjMq9dfOY3X+gZv0qw8H64IHVpjuIwGeM4J07fvhbwUAkH6DwEol3zbd3FwncpYzyQtO57a+F3nITqZKWO3zwcddTmkYOVnfzr9eiw9sj9tsNRHSz6lNQRK0lJZcn98AwP8I6hFIqQSRXubmZvrZqU5o9Wpfnaz+YbbPUU6OeKfS+Ul11JgAL3N+n8wNAH5D+g0CKZV8Wy9zczP97FRv63u1r06mSnTf5+ryEg0tLVJ1eX9HUxqinZ/F67errr5Zi9dvt0ynkVjoyQuktABAahipRyClEkR6mZvrxGenMurr1b5mo1JQNke6U72zwKixN7jjAQD2EdQH2Pjx4+N+bm9v96gl7ksliPQyNzfdz04nL7423KiGljaLdmR3X4MW9KaTTkOePADAzwjqEUipBJFeBpzpfLZZXvyyuh2aXDVEe1rbTIN889ru9j7PCakEvX5YUCidzhajxgAAPwsZhmGy9iGCKBwOq6qqSpK0efNmVVZWetyi7IoEh/ZGTlN5rvXr0wtEU/3suQtWafH67ZaPS5FAvXt+sdVrhpYW6deXT3W8KlC6zDofPffFDX5pBwCg78lWvEZQn0P6WlDvFrcDwNPmL0tY0jFq1riK2Mix1WtqKkr10jXTYz93D+IHlxRq9ebdau88+Ccg24GtVeej+764JdOOHgAA6chWvEb6DZCEk+Ua7UhWpz2qe/63nXSSRCk6UanuV6oj/Z5M5N26Wnrldmn3RmnI6MgqsSMnk04DAMgplLQEknA7EDVbkdRM94DdziqmZp0TM3b3K9WykJE2u1wacutq6ZEzpLo/SzvWR/7/yBmR7W7aulp6/GLp3k9H/u/25wMAch5BPfqU2nCj5i5YpdPmL9PcBasSBqBRbgeiPetzHze6XIX5iQN2OzW9rTonPdndr3Rq4tvpfDjqlduljv3x2zr2S8vuyM7nmfFLxwIAkNNIv0Gfke5qq15Uz+mZGmIn/ztZOomdtJ5U9iudOxiul4bcvdF8e8OG7HyemUQdi0uecK8dAICcRlCPPiPd3Hg/1Ch3Iv/brHNSmJ+nSVWDPy6Vmdp+WXUShvQv1NwFqyzz7F3NZR8yOjI63lP5GHc+X/JHxwIAkPMI6tFnZJIbnwuTKp3unFh1Et7adLCijt27IVkz43rpw6XxI+UFxdL077jXBj90LAAAOY+gHn2GlyvL+kWmnZOe1W5+fN5ELardFuskNLW2aeXG3XGvyWaloKRGTpaueDGS6tKwIRJIT/9OZLtb/NCxCCA/LFIGAEFCUI8+w8uVZXOBnTkJp81fZvrarJasTGbkZG9z1/3QsQiYdOe/AEBfRlCPPsMPufF2+XGU0s6cBO6GWPC6YxEwbq8NAQC5gKAeOcsqMPZ7UODXUUo7cxK4GwIneLJIGQAEHHXqkZPSWRjJL9Kp/+4Gu/X6j64s04CifA0oyte0MeWed0YQPK4vUgYAOYCgHjnJr4GxHX4dpUy2cFS0I7Vy4261HOhUy4FOvb15jwctdRArwXrC9UXKACAHkH6DnJQsMPZjznqUq3npW1dHFkfavTFSenHG9ZYTOJPNSci5POjoSrDRqjU71keq2FzxIpNcsyxI818AwC8I6pGTEgXGteFGnXf/a3G11JfV7dAzV5/oi6DBtbz0NILWRHMS/HqHIW2sBOupIMx/AQA/If0GOSnR7fsfPr82FtBHtXca+uHz69xsoqXoKOWscRWqqSjVrHEV2clLTxS0piHn8qBZCRYAECCM1MNXnEqLSXT7fu3WJtPXrN3mn0m0roxSOhy05lzlG1aCBQAECEE9fMPpUo6WgXEo05YGQ9IOksNBa87lQbMSLAAgQAjq4RtuTbQcP6JMKzc29Np+1MiABp8mbHWQshC05lQeNCvBAgAChKAevuHWRMsbzzpS59+/Qm2dBzsQhfl5+t/ZR6b0Pn6uoGOrgxTQoNXV485KsACAgCCoh2+4VcpxQmWZnr46szQRv676GmW7gxSwoNXvxx0AAK8Q1MM33JxomWmaiF9qsluNWrta695FfjnuAAD4DUE9fMPpiZbZTNPwoiZ7z/2ZPWGkbnjmHdNR65yrRPOxnKuFDwCAQwjq4St2RtDtBOtmaRrL63ZqUlWZ9rS2x4LiRbVb0wr6rUbCh/QvtLmnqTHbn5ff266u+HL7caPWOVWJ5mO5egcCAIBMEdQjMGrDjfrh8+u0amODorGsVU61WZpGW2eXVm7cHXvd4vXbY4+lmps9b2aNltftjJtsK0lvbdqt2nCj48Gz2f70DOijoqPWOVWJ5mO5egcCAIBMsaIsAiE6Ur2yW0AfFR2d7s4qTSMRs/exMqGyTJOqegfu2VqZNpX9yeVRa9dW2wUAIGAYqYer0s1zNxup7q5nTrVVmkYyqeRm72ltN92+amOD46P1VvuTF4ofse8Lo9a5eAcCAIBMMVIP10RH2xev3x5Lf7nggRWqDTcmfW2ykeqeo9PzZtaoqCD1yzuVUe7q8v6m2w3J9oi/XWb7U1SQpzsvnJR41Hrraunxi6V7Px35/9bVjrbLrtpwo+YuWKXT5i/T3AWrbJ1zAABgHyP1cE0m5QgHl/SzfMxsdLpnJZ0h/Qv11qbdau+0SES3eJ9E5s2s0ZL123ulA0nOV2NJVBno3GNGmb9o62rpkTMOrhi7Y31kBdkrXnR1gSlqywMAkH0E9XBNZuUIQ6ZbB5f002+unGYaHPZM04ik/hwMimdPGKFFtdvSrg4zobJMU0eXa+XGhl6PZSOvPeW0k1duPxjQR3Xsj6wg6+KCU9SWBwAg+wjqkVXPvrVFt72wTk2tHZLpmLa9AHhPa5vp9uGDimwH4mZBseUot003nnVk3Ci05KO89t0bzbc3bHC1GdSWBwAg+8ipR9Y8+9YWXfPk29rZ3Ka2zi61maS+2A2ArfLXva704utqLENGm28vH+NqM/x67gAAyCWM1CNrbnvBvLRjYX6eRg/tn1LKS6r1ybO5mmxPvq3GMuP6SA599xScgmJp+ndcbQa15QEAyD6CemRNJOXG3EvXTE/pvRJNFO3JiYmZbnYKsmbk5Mik2GV3RFJuysdEAnoHJsmmcnxSOXduy4nzDACApJBhGNblQFzS1dWlcDgct626utqj1gRXOBxWVVWVJGnz5s2qrKz0tD1Tbv2rdjb3zoUfWlqkN/93VtY+d+6CVXGrxUbNGldha0S9Z6dAiows+yatxmO5cnxyZT8AAMGSrXgtazn1zzzzjE4++WSVlZVp1KhRuvTSS/XWW2+ZPnfz5s0aPXq0xowZozFjxmjs2LHZahZc9L3PHWmxfZzpdru1zJM9L9OJmYmqtcD8+BzR+S91Pv55z+vhp4LzDADIJVkJ6n/0ox/pwgsv1IoVK7R3715t27ZNv//973XcccfpuuuuU0dHfFrGoYceqqVLl2rJkiUqKiqSD24ewAHnHjNK8y+apKGlRSrMz9PQ0iLNv2iSacUZuwtT2XlephMzqdaSWM/jc1ToQy0svEWT9r0eqYVf9+dIfXyfB/acZwBALnE8qF+zZo1uuukmGYah6upqnXXWWTr77LM1btw4dXV16Wc/+5lmzpypvXv3xl5TVFSk6dOna8aMGcrPz3e6SfDQuceM0pv/O0t1t52pN/93lmUJSbujpnaeZ7X6qt2JmVRrSazn8ZlX8IyKQ+3xT4rWw/cxzjMAIJc4HtTfd999sf9/+OGH+uMf/6hnn31W7777rt555x1ddNFFevXVV3XqqaeqoaH3oj3om+yOmtp5XqZlJjPtFKTKbtqRX/Q8PtWh3vMXJLleDz9Vbp9nAACyyfHqN8uXL9e8efN01VVX9XrsqKOO0hNPPKE5c+boyiuv1GmnnaYlS5aorIxJaX1ddXl/1dU3m2wfkNbzMikz6Wa1Ficq9bit5/Fpba+S9oV7P9Hlevip8nNVHgAAUuV4UB8OhzVnzpyEz7nkkktUVVWl2bNn68wzz9Rf//pXDRjALe++zG4tc7dqnrtVez5ROpEva99/LO74bB0UyaH3uB5+Ony7xgAAAClyPKhvb29XSUlJ0ueddNJJeuGFF3TGGWdo9uzZ+stf/qKioiKnm4OAsDtq6pfRVafqm3s5WdOxGu1ZrIcPAADscTyoHz58uD744ANNnTo16XNPPPFE/fGPf9RZZ52lOXPm6E9/+pPTzUGA2B019Xp01cmUGbvpRE5zPO1n5GTpkiccbiUAALDL8YmyxxxzjH7xi1/Yfv4pp5yip59+WsuWLdP555+vrq6u5C8CPORkfXOvJmtSox0AgNzieFB/5plnasWKFZozZ47WrFlj6zVnnHGGnnzySf3lL3/R/v37k78AfZrX1WKcTJnJtFJPuqjRDgBAbnE8qL/ooovUv39/Pf/88zrmmGN0ww032HrdnDlz9Nvf/lZ5eVlb5BY5wO4iVdnkdH3zaDrRS9dM18OXTXFlfgA12gEAyC2O59QPGTJEv//977Vx40ZJ0uTJ9ifLXXTRRerXr5+WL1/udLOQI/xQLSZZBR7HJqBmkVtVhAAAgDtChmEYXjcCzgiHw6qqqpIkbd68WZWVlR63yHmnzV9mOrG0pqJUL10zPaX3yiT4rg036tZF6/Tu1sgdgvEjBunGs8ZLUtwEVCkSLPux7nxk/6nRDgCAm7IVrzk+Ug9kk1PVYpyo/vL25j2x16/cuFsXPLBCR1eWeX4nwS6vqwgBAADneJ7A3traqscee0w//elPSbtBUk5Vi8m0+ovV69duazJ9PhNQAQBANrkyUr9161adfPLJMgxDM2fO1EMPPSRJ+uijj/SZz3xGH3zwQey5c+fO1S9/+Us3mgUfsZsK49TiU5lWf7F6vRUmoAIAgGxyJah/7rnntGHDBh155JE67rjjYtuvu+46/etf/1JeXp4mTpyozZs36+GHH9aZZ56pc845x42mwUHp5qinmgrjRNpIpmk8Vq8/amRZXFqOlPxOQhAm1gIAAH9zZaLsmWeeqaamJr388ssqKiqSJDU0NOjQQw9VZ2ennnjiCV100UVqaWnRrFmzNHToUFaXTYOXE2V7BuaS/Qmicxes0uL123ttnzWuIms53+m2NxqA19U3K7x7n7q6/fZEXy/J9p2ETI4bAAAInmzFa67k1K9Zs0bXXHNNLKCXpOeff14dHR2aMmWKLrroIknSgAEDNG/ePP3jH/9wo1lwUCY56l4shJTOok/da+RvaogE9Hkhqbq85ODr8z7UhOVf1cPN39BLFffr4c/mJ3xPz1Z23bpaevxi6d5PR/6/dXV2Pw8AAGSVK+k3u3bt0tixY+O2vfDCCwqFQrr00kvjth9++OHatWuXG82CgzIJzJ2qaJOqVNN4zALwLkOqqRgUeZ+tq6VHzpA6Pl4Vecd66cOl0hUvSiPN12vwZGXXNNoJAAD8zZWR+kMOOUSNjQdX/Dxw4IBefPFFSdL5558f99zW1lYNHDjQjWbBQZmsUOpURZtsSxqAv3L7wUA5qmO/tOwOy/f0ZGXXNNoJAAD8zZWgfsyYMXE58r/+9a/V1NSkadOm9cojeuuttzRy5Eg3mgUHZRKYp5MK44WkAfjujeYvbNhg+Z6edGjSaCcAAPA3V9JvvvCFL+hb3/qWdu7cqQEDBujRRx9VKBTSlVdeGfe8uro6/fSnP9WnP/1pN5oFB2VaajIICyHNm1mjV9/faV3ZZsjoSCpLT+VjLN/TqRKdKUmjnQAAwN9cqX6zf/9+HX/88VqzZk1s27Rp0/Taa68pLy8ySnnKKadoxYoV6ujo0K9+9Stdfvnl2W5WzvGy+k06gljKMdJmiwC8Z666JBUU+y9XPSjtBAAgB2UrXnMlqJciufIPPfSQ1q9fr5qaGl111VUqKSmJPT5v3jw1NTUpFArpF7/4hfr3N091gDWvgvp0gvOcLeW4dXUkN71hQ2Tke/p3/BkoB6WdAADkmMAH9cg+L4L6dINzL2rTAwAAeC3Qdeol6YEHHtDPf/5z3XPPPdq8eXPcY62trfrP//xP/f3vf3erOXBIunXWPSnlCAAAkKNcmSi7fPlyfe1rX1MoFJIkTZw4MdZDkSTDMPTb3/5Wjz/+uO68807NmzfPjWbBAekG51a16Yf0L3SkXQAAAH2JKyP1zzzzjKRI3nw4HNb06dPjHu/fv7/q6ur0+c9/Xt/+9rf10ksvudEsOCDdOuvzZtaoML/35ffWpt2qDTeavAIAAABWXAnq//a3v+kLX/iCfvazn2nEiBGmzzn88MP12GOP6XOf+5x+/vOfu9EsOCDdOusTKss0qap3zn17p5E0dQcAAADxXEm/+eCDD/SDH/zA1nO//OUv96pfD//KpM76ntZ20+25mldvVSUoiKU9AQCAv7gS1Le0tFiO0PdUVVWlpqamLLcITrK7cFTP4HVwiXn+fENLm2rDjTkV2PasElRX36xX39+pH583UTc8806v7YEv7QkAAFzlSvrNIYccog0b7C1B/+GHH6q8vDzLLYLbokHt4vXbVVffrMXrt2v15t2mefU7m9t0wQMrciq33qpK0G0vrEurehAAAEB3rgT1U6dO1R133KG2traEzztw4IB+8pOfaOrUqW40Cy4yC2rbOw1NqhqsoaW9R+xzLbC1qhLU1Nph8fzcTEECAADZ4UpQf8UVV2jlypU68cQT9cc//lHNzfGlDJubm/Xcc8/pxBNP1KpVqzR37lw3mgUXWQW1e1rbVD7APA0nlwJbqypBg0rMM+CSVQ8CAADozpWg/pxzztH555+vf/zjHzr33HNVVlamiooKjR49WhUVFSorK9P555+vt956SxdddJHmzJnjRrPgokSlL9MtixkkVlWCvve5I9OqHgQAANCdKxNlJemxxx7TIYccooceekiGYWjHjh1xj+fl5enqq6/W/Pnz3WoSXDRvZo1efX9nXApO9+C152P98kNqam3XafOX5URFmERVgg4fXppW9SAAAICokGEYhpsf+N577+mpp57SO++8o8bGRpWVlenoo4/WhRdeqE9+8pNuNiXnhMPh2Eq9mzdvVmVlpcctihepfmMevHZ/bHBJod7evEdtnQeD/GMKNurRsS9rUOsWachoacb10sjJHu0JAABAerIVr7ke1CN7/B7U2zV3wSotXr899vNRoQ+1sPAWFYe61bUvKJaueJHAHgAABEq24jVXcuqBVPScVDuv4Jn4gF6SOvZLy+5wsVUAAAD+5VpOfV+1a9cu/fKXv9Rf/vIXNTU1qaOjQwcOHNCJJ56o7373uzriCCZE9lRd3l919QcrJFWHtps/scHe2gcAAAC5jqA+y2pra3XjjTfq8ccf1+c//3lJkdsun/3sZ3X88cfrvffe09ChQz1upb/0nFS7yRiuTyrc+4nlY1xumfd6rsob9AnEAADAGaTfZNngwYN15ZVXxgJ6SaqsrNRXvvIV7dq1S0uXLvWwdf4UrRQza1yFaipK9UbVl9WVXxT/pIJiafp3vGmgR8xW5c21lXcBAEB6+lxQ39XVpXvvvVeDBg1SKBTSxo0bbb3uwIEDuv322zV58mQNHDhQgwcP1vHHH68HHnhAXV1dlq+bNGmSHnzwwV7b9+zZI0mqrq5OZzdy3oTKMj182RS9dM103fiVS5V35V+kT35OGjYu8v8+OEnWbFXeXFt5FwAApKdPpd+sXbtWX/7yl/X666+n9LqdO3fq1FNPVW1trb7yla/onnvuUVtbm37xi1/o6quv1lNPPaVFixapuLg46Xt1dnbqxRdf1M9//nP913/9l6ZNm5bu7vQtIydLlzzhdSs8ZbUqby6tvAsAANLTZ0bqb775Zh1zzDHKz8/XDTfckNJrL7zwQtXW1mrevHn65S9/qZNOOkmnnnqqnn76aZ199tl6+eWXdfXVVyd9n0suuUSDBw/WxRdfrOuvv14/+clP0t0d9EF9YeVdAACQnj4T1N91112aP3++li9fntIiV08//bReeeUVFRcX6/vf/37cY6FQSD/60Y8kSQsWLNA//vGPhO/1xBNPqKmpSUuXLtVjjz2mqVOnavt2i8ouOa423Ki5C1bptPnLNHfBKvLCbZg3s0ZFBfG/st1X5QUAAH1Xn0m/WbdunUaNGpXy6x5++GFJ0qmnnqrBgwf3enzcuHEaN26c1q9fr0ceeUTHHntswvcLhUKaMmWKHn30UU2ZMkXf/e53Y58RROlUY4lO+Izmh9fVN+vV93dq4VUnUMklgegEYqtVeQEAQN/VZ4L6dAL6trY2LVmyRJI0depUy+dNnTpV69ev16JFi3TvvffGPXbgwAHl5+eroCD+UE+aNEn5+fl64403Um6XX6QbnCea8PnwZVOy2uagi04gBgAA6K7PBPXpWL9+vdrbIyuZjh492vJ50cf+/e9/q7GxUWVlBwPar371q6qpqdF3v/vduNds2rRJnZ2dGjZsmO32hMMmtdq72bZtm+33ckK6wTkTPgEAAJxFUJ/Apk2bYv9OFHx3fywcDscF9ZJ0zz336IwzztAxxxwjSaqvr9fcuXOVn5+v6667znZ7qqqqbD/XDekG5z1XjD24nQmfAAAA6SCoT2Dv3r2xfycqV9n9saamprjH/vu//1vDhg3TlVdeqc7OTrW3t6u5uVlTpkzRsmXLdOKJJzrfcJekG5z3XDFWYsInAABAJgjqHWAYRuzfoVAo7rGjjjrKsdKVmzdvTvj4tm3bdNxxxznyWXakG5z3iQmfW1dLr9wu7d4oDRktzbi+zy2WBYdxTQEAEiCoT2DgwIGxf+/fv9/yeQcOHDB9jdMqKyuz9t7pyCQ4z+kJn1tXS4+cIXV8fM3sWC99uLRProILh3BNAQCSIKhPoLq6OvbvHTt2WD6v+2N+C7yzLaeD83S9cvvB4CuqY7+07I4+vyou0sQ1BQBIos8sPpWOcePGqV+/fpKkjRs3Wj4v+thhhx3Wa5Is+qDdG823N2xwtRnIIVxTAIAkCOoTKCws1MyZMyVJb775puXzVq1aJUmaPXu2K+2Czw0Zbb69fIyrzUAO4ZoCACRBUJ/E3LlzJUlLlixRY2Njr8ffe+89rV+/XqFQSFdccYXbzYMfzbheKuhRLamgWJr+HW/ag+DjmgIAJEFQn8T555+vGTNmaP/+/brlllviHjMMI7ao1GWXXaZjjz3WiybCb0ZOjkxg/OTnpGHjIv9nQiMywTUFAEiiz0yU3b59u7Zv3y5J2rJlS2x7XV2dmpsjtdbHjBmjAQN611h/6qmndOqpp2r+/PlqbW3Vf/zHf6itrU333nuvnn32WZ166qm6//773dkRBMPIyUxghLO4pgAACfSZoP6+++7rNdIuSaeffnrs30uXLtWMGTN6PWfo0KFatWqV7rrrLj3xxBP6zW9+o/z8fI0bN0733XefvvrVryovj5seAAAA8EbI6L5yEgItHA6rqqpKUmShqr5WXhMAAMDvshWvMbwMAAAABBxBPQAAABBwfSanPheNHz8+7uf29naPWgIAAAAvMVIPAAAABBwj9QG2du3auJ+7T7wAAABA38FIPQAAABBwBPUAAABAwBHUAwAAAAFHUA8AAAAEHEE9AAAAEHAE9QAAAEDAEdQDAAAAAUdQDwAAAAQcQT0AAAAQcKwoC6Rj62rpldul3RulIaOlGddLIyd73SoAANBHEdQDqdq6WnrkDKljf+TnHeulD5dKV7xIYA8AADxB+g2QqlduPxjQR3Xsl5bd4U17AABAn8dIfYCNHz8+7uf29naPWtLH7N5ovr1hg6vNAAAAiGKkHkjVkNHm28vHuNoMAACAKEbqA2zt2rVxP4fDYVVVVXnUGp/KxoTWGddHcui7p+AUFEvTv5PZ+wIAAKSJoB65K1sTWkdOjrzHsjsiKTflYyIBPZNkAQCARwjqkbsSTWi95InM3nvk5MzfAwAAwCEE9chdfWFCK/XyAQCACOqRy4aMjqTc9JQrE1qplw8AAD5G9RvkrhnXRyawdpdLE1qplw8AAD7GSD2yqjbcqLuX1GlTwz5Vl/fXvJk1mlBZ5s6H5/qE1r6QXgQAAGwhqEfW1IYbdcEDK3Sgo0uSVFffrFff36mFV53gbmCfqxNacz29CAAA2Eb6DbLm7iV1sYA+6kBHl+5e8r5HLcoxuZ5eBAAAbGOkHlmzqWGfxfaW7H1oX6oGk+vpRQAAwDaCemRNdXl/1dU3m2wfkJ0P7IvVYHI5vQgAANhG+g2yZt7MGhUVxF9iRQV5mjfziOx8INVgAABAH8VIPbJmQmWZFl51gu5e8r42NbSounyA5s08InuTZKkGAwAA+iiCemTVhMoyPXzZFHc+jGowAACgjyL9BrmDajAAAKCPYqQ+wMaPHx/3c3t7u0ct8QmqwQAAgD6KoB65hWowAACgDyKoD7C1a9fG/RwOh1VVVeVRawAAAOAVcuoBAACAgCOoBwAAAAKO9Btg6+rIwlW7N0bKYs64nsm1AAAgUAjq0bdtXS09csbBlWh3rJc+XBqpokNgDwAAAoL0G/Rtr9x+MKCP6tgfKYsJAAAQEAT16Nt2bzTf3rDB1WYAAABkgqAefduQ0ebby8e42gwAAIBMENSjb5txvVRQHL+toDiyEi0AAEBAENSjbxs5OTIp9pOfk4aNi/yfSbIAACBgqH4DjJwsXfKE160AAABIGyP1AAAAQMAR1AMAAAABR1APAAAABBxBPQAAABBwBPUAAABAwBHUAwAAAAFHScsAGz9+fNzP7e3tHrUEAAAAXmKkHgAAAAg4RuoDbO3atXE/h8NhVVVVedQaAAAAeIWgHu7Yulp65XZp90ZpyGhpxvWRlVwBAACQMYJ6ZN/W1dIjZ0gd+yM/71gvfbhUuuJFAnsAAAAHENQjK2rDjbp7SZ02NezTHe0/0qRoQB/VsV9adod0yRPeNBAAACCHENTDcbXhRl3wwAod6OiSJJUUbjafkt2wwd2GAQAA5Ciq38Bxdy+piwX0krTJGG7+xPIxLrUIAAAgtxHUw3GbGvbF/Xx3x3nab/SLf1JBsTT9Oy62KoGtq6XHL5bu/XTk/1tXe90iAACAlBDUw3HV5f3jfn7XGKsL2m7W6v4nSMPGSZ/8nH8myUYn8db9OTKBt+7PkZ8J7AEAQICQUw/HzZtZo1ff3xmXgvN+/uEquPR3UmWZhy0z8crtB6vyRDGJFwAABAxBPRw3obJMC686QXcveV+bGlpUXT5A82YeoQl+C+ilSN18M0ziBQAAAUJQj6yYUFmmhy+b4nUzkhsyOpJ20xOTeAEAQICQU4++bcb1kUm73flpEi8AAIANBPXo20ZOjkza/eTn/DeJFwAAwCbSb4CRk5kUCwAAAo2RegAAACDgCOoBAACAgCOoBwAAAAKOoB4AAAAIOIJ6AAAAIOAI6gEAAICAI6gHAAAAAo469QE2fvz4uJ/b29s9agkAAAC8xEg9AAAAEHCM1AfY2rVr434Oh8OqqqryqDUAAADwCiP1AAAAQMAR1AMAAAABR1APAAAABBxBPQAAABBwBPUAAABAwBHUAwAAAAFHUA8AAAAEHEE9AAAAEHAE9QAAAEDAEdQDAAAAAUdQDwAAAAQcQT0AAAAQcAT1AAAAQMAVeN0AAAAAJxmGoZaWFjU1NWn//v3q7Oz0uknIQfn5+SouLtagQYM0YMAAhUIhT9tDUA8AAHJGV1eXNm3apNbWVq+bghzX0dGhAwcOqLGxUSUlJaqurlZenndJMAT1AAAgJxiG0SugD4VCys/P97BVyFWdnZ0yDEOS1Nraqk2bNumwww7zbMSeoB4AAOSElpaWWECfn5+vQw89VKWlpZ6OniJ3dXV1qbm5WR999JE6OzvV2tqqlpYWlZaWetIernIAAJATmpqaYv8+9NBDNWjQIAJ6ZE1eXp4GDRqkQw89NLZt79693rXHs08GAABw0P79+yVFUm68Gi1F31NaWhpLufFyLgdBPQAAyAnRKjf5+fmM0MM1eXl5sXkbXlZa4ooHAAAAAo6gHgAAAAg4gnoAAAAg4AjqAQAAgICjTj1gZutq6ZXbpd0bpSGjpRnXSyMne90qAAAAUwT1QE9bV0uPnCF1REqjacd66cOl0hUvEtgDAABfIqgPsPHjx8f93N7e7lFLcswrtx8M6KM69kvL7pAuecKbNgEAACRATj3Q0+6N5tsbNrjaDAAAALsYqQ+wtWvXxv0cDodVVVXlUWtyyJDRkZSbnsrHuN4UAAAAOxipB3qacb1UUBy/raBYmv4db9oDAACQBEE90NPIyZFJsZ/8nDRsXOT/TJIFAPQxhmFo4cKFOvPMMzVs2DAVFhZq+PDhOv300/X//X//nzo7O2PPffTRRxUKheL+k6Tnn39en/3sZzV06NDY9hkzZni0R7mNoB4wM3JyZFLs19+I/J+AHgDQhxw4cEAXXnihLrzwQjU1Nenee+/Vq6++qrvuuku7du3SZZddptNPP1379u2TJJ1zzjmqra3VI488EnuPn/3sZ/q///s/fe1rX9OLL76on/3sZ7FgH84jpx4AAABx/uu//ktPP/20Tj75ZC1dulT5+fmSpGnTpumiiy7S1KlTtWTJEn3729/WAw88oMGDB2vw4MHauXNn7D1+//vfa/ny5SoqKpIkTZkyRX/+85/V1tbmyT7lOkbqAQAAHFQbbtTcBat02vxlmrtglWrDjV43KSXvvfeefvnLX0qSbrvttlhAH1VQUKDrrrtOkvTII4+ovr7e9H2uu+66WEAf9fjjj+t3v/tdFloNgnoAAACH1IYbdcEDK7R4/XbV1Tdr8frtuuCBFYEK7J966ikZhqHi4mJNmzbN9Dmf+tSnJEXWyFm+fLnpc0466aRe24YOHapDDz3UucYihqAeAADAIXcvqdOBjq64bQc6unT3kvc9alHq1qxZI0nav3+/+vfvr4KCgl7/HXfccbHnb9q0yfR9hg0b5kp7EUFOPQAAgEM2Neyz2N7ickvS19gYuatQUVGhxYsXJ31+RUWF6faeaTvILoJ6AAAAh1SX91ddfbPJ9gEetCY9ZWVlkiIj9UcddZTHrYFdpN8AAAA4ZN7MGhUVxIdXRQV5mjfzCI9alLqjjz5aUmTE/qOPPrJ83sqVK/Xwww9r27ZtbjUNCRDUAwAAOGRCZZkWXnWCZo2rUE1FqWaNq9DCq07QhMoyr5tm24UXXqi8vEiI+Pzzz1s+7+qrr9a3vvUtDRgQnLsQuYygHgAAwEETKsv08GVT9NI10/XwZVMCFdBLkco2V111laRISctdu3b1es4jjzyit956S9/85jc1aNAgt5sIE+TUAwAAIM78+fO1a9cu/f73v9e0adP03e9+V0cffbR27typP/zhD3rwwQd1+umn6wc/+IEkqaWlRRs2bNCGDRti7/Huu+9KkoYMGaJRo0Z5sh99CUE9AAAA4hQWFup3v/udLr30Uj300EP6n//5HzU0NKi0tFRHH320HnzwQV1++eWxNJ1Vq1bplFNOiXuPCRMmSJIuu+wyPfroo27vQp9DUA8AAABTc+bM0Zw5c5I+b8aMGTIMw4UWwQo59QAAAEDAEdQDAAAAAUdQDwAAAAQcQT0AAAAQcAT1AAAAQMAR1AMAAAABR1APAAAABBxBPQAAABBwBPUAAABAwBHUAwAAAAFHUA8AAAAEHEE9AAAAEHAE9QAAAEDAEdQDAAAAAUdQDwAAAAQcQT0AAAAQcAT1AAAAQMAR1AMAACCrDMPQeeedp6FDh2rZsmVeNycnFXjdAKRv/PjxcT+3t7d71BIAAABrDQ0NevbZZyVJCxcu1PTp0z1uUe5hpB4AAABZdcghh+iGG27QCSecoKuuusrr5uQkRuoDbO3atXE/h8NhVVVVedQaAAAAaz/60Y+8bkJOY6QeAAAACDiCegAAAEiSLr/8coVCoV7/vfLKK3HPO/nkkxUKhbRu3bqkr++5bcaMGQlfM2PGDHV0dOj//u//VFNTo5KSEo0dO1bXX3+99u7dKykyj/DWW2/VEUccoeLiYo0ePVo33HCDDhw4EPfeGzduNN2XxYsXa/bs2Ro2bJiKioo0evRofetb39KOHTscP6ZuIagHAACAJOm2227TG2+8ofz8fEnSfffdp9raWk2dOjX2nG3btmnFihWSIpNee76+trZW1dXVuuKKK1RbW6tbbrlFtbW1uvrqqy0/s/vjhmHooosuUmtrq377299q4cKFGjVqlO644w6dddZZ6uzs1GWXXaaSkhI98cQTevzxx1VUVKTbb79dV155Zdx7jxo1SrW1taqtrY1te/DBB3XZZZdpzpw5+vOf/6w//elPOuaYY3TPPfdo0qRJqqury/xAeoCcegAAACdtXS29cru0e6M0ZLQ043pp5GSvW2XLqFGjNGrUKH3mM5/R0qVL9e677/YKxp9++ml1dXVJigT1N910U9zr9+7dq02bNulLX/qSjjrqqNhjw4cPT/iZ0cdfe+013Xbbbbr++utjz5k+fbpGjRql5cuX69JLL9V5552nz3/+85KkKVOm6Oijj9bhhx+uxx57TLfccos+8YlPSJL69esX1wZJ+uMf/6g1a9bEniNJp512ms455xz94Q9/0EUXXaQ333xTBQXBCpMZqQcAAHDK1tXSI2dIdX+WdqyP/P+RMyLbA+SCCy6QJD377LOxAD5q4cKFOvHEEyVJtbW1vUa2n3rqKY0cOTL2nFTl5eX16kiUlpZq2rRpkqTly5froosuinv8E5/4RCxIf/XVVxO+/3/8x3/EBfRR3//+9yVJa9asiZXfDBKCegAAAKe8crvUsT9+W8d+adkd3rQnTeeee65CoVBcqo0k1dfX69VXX9Vtt92m0aNHS+qdgrNw4UKdd955CoVCaX32EUccoUGDBvXaHh3JP+aYY0zf+9BDD5UUSQ9K5KSTTjLdPmnSJJWXl0uKjOYHDUE9AACAU3ZvNN/esMHVZmRqxIgROuGEEyRF0m2innnmGQ0bNkwnn3yyzj//fEnxQf3777+vd955JzbSn45DDjnEdHs0HSbZ4z0ny/YUDf7NRDsqPScABwFBPQAAgFOGjDbfXj7G1WY4IRq0P/PMM7FtTz31lM4991zl5eXFAvfVq1frgw8+iD1eUVGhk08+Oe3PzctLHJ4mezyZRLny/fv3lyQ1NTVl9BleIKgHAABwyozrpYLi+G0FxdL073jTngxEg/pNmzZp1apV2rFjh5YvXx7bPm3aNFVWVko6OFrfPej3q46ODsvH9u3bJ0mm6T9+598jDgAAEDQjJ0tXvCh98nPSsHGR/1/xYmCq33RXXV0dK2X59NNP65lnntHgwYNjdeZDoZDOO+88SZGg/oMPPtDbb7+tCy+80Ksm21JfX2/52MaNGyVJ48ePd6k1ziGoBwAAcNLIydIlT0hffyPy/wAG9FHRUfmnn35aCxcu1DnnnBOXvhJNwXnzzTf105/+VMOGDdP06dM9aatdVtVxVq9erYaGBknSnDlz3GySIwjqAQAAYCoa1P/rX//SkiVLYj9HnXjiibGJp7/85S91zjnnxBau8qvHH39cH374Ya/tt9xyiyTp6KOP1jnnnONyqzJHUA8AAABThx9+uCZOnChJKisr06xZs+Iez8vL07nnnispshKsWdWbLVu26N1339X27dslSS0tLXr33Xdj9e23b99u+viGDRviXr9nzx5J0p49e+Kev2HDBr377rtqaWkxfb+errnmGs2aNUsPPvig/vGPf+ivf/2rzjvvPP3hD3/QyJEj9eSTTwZu4SlJkoGcsXnzZkOSIcnYvHmz180BAMBVdXV1xrp164y6ujqvm5JTbrnlFkOS8Z//+Z+mjy9ZssSQZJSXlxvt7e29Hr/sssti8Un3/w477DDDMAzj5ptvNn18+vTpCV9/8803G4ZhGNOnT0/4eFR0+9KlS40333zTOPfcc43hw4cb/fr1M6qrq41vfvObRn19fVrHKJVrL1vxWsgwDMOd7gOyLRwOq6qqSpK0efPm2Ix0AAD6gvfff18dHR0qKCjQEUcc4XVz4DPRBauWLl0am+zrlFSuvWzFa6TfAAAAAAEXwIQh5JStqyNLau/eGFmwY8b1ga4SAAAA4AWCenhn62rpkTOkjv2Rn3eslz5cGth6vgAAwF/a29v1z3/+M27bhg0bNHToUFVWVmrw4MHeNCwLSL+Bd165/WBAH9WxX1p2hzftAQAAOWXLli2aMGGCJkyYENt2xRVXaMKECXruuee8a1gWMFIP7+zeaL69YYOrzQAAALlp9OjR6is1YRiph3eGjDbfXj7G1WYAAAAEHUE9vDPjeqmgOH5bQbE0/TvetAcAACCgCOrhnZGTI5NiP/k5adi4yP+ZJAsAAJAycurhrZGTpUue8LoVAAAAgcZIPQAAyAn5+fmSpM7OTnV1dXncGvQVXV1d6uzslHTwGvQCQT0AAMgJxcWReVqGYai5udnj1qCvaG5ujlXYKSkp8awdBPUAACAnDBo0KPbvjz76SE1NTYzYI2u6urrU1NSkjz76KLZt4MCBnrWHnHoAAJATBgwYoJKSErW2tqqzs1NbtmxRKBTyNCUCuauzszOuBn5JSYkGDBjgWXsI6gEAQE4IhUKqrq7Wpk2b1NraKimSitPR0eFxy5DrSkpKVF1drVAo5FkbCOoBAEDOyMvL02GHHaaWlhbt3bs3NmoPOC0/P18lJSUaOHCgBgwY4GlALxHUAwCAHBMKhVRaWqrS0lKvmwK4homyAAAAQMAR1AMAAAABR1APAAAABBxBPQAAABBwBPUAAABAwBHUAwAAAAFHUA8AAAAEHHXqc0j3FfO2bdvmYUsAAABgpnuM5uRqxwT1OWTHjh2xfx933HEetgQAAADJ7NixQ6NHj3bkvUi/AQAAAAIuZBiG4XUj4Iz9+/ertrZWkjRs2DAVFCS+EbNt27bYiP7KlSs1YsSIrLcR2cP5zC2cz9zC+cwtnM/c4vb57OjoiGVXTJgwQcXFxY68L+k3OaS4uFhTp05N67UjRoxQZWWlwy2CVzifuYXzmVs4n7mF85lb3DqfTqXcdEf6DQAAABBwBPUAAABAwBHUAwAAAAFHUA8AAAAEHEE9AAAAEHAE9QAAAEDAEdQDAAAAAcfiUwAAAEDAMVIPAAAABBxBPQAAABBwBPUAAABAwBHUAwAAAAFHUA8AAAAEHEE9AAAAEHAE9QAAAEDAEdQDAAAAAUdQDwAAAAQcQX0fdeDAAd1+++2aPHmyBg4cqMGDB+v444/XAw88oK6uLq+bh4+1t7fr6aef1n/+53/qU5/6lAYMGKDi4mJVV1fr/PPP15/+9Kek79HY2Kjvfe97GjdunPr376+hQ4fq1FNP1e9+9zsX9gB2XHDBBQqFQgqFQtq4cWPC53I+/emDDz7Qtddeq6OOOkplZWUaMGCAxo4dqzPPPFO33Xab6uvrTV/H+fSfv/3tb7r00ks1evRoFRcXq6SkREcccYSuvPJKrVmzJuFrOZ/e6Orq0r333qtBgwbZ+jvaXabnbOvWrZo3b54+8YlPqLi4WBUVFTrrrLP0l7/8Jc29yYCBPmfHjh3GhAkTDEnGV77yFePVV181lixZYpx77rmGJOPUU081WltbvW5mn7d582Zj1KhRhiSjurrauOeee4xly5YZb7zxhvHTn/7UOOSQQwxJxjnnnGPs37/f9D3ef/99o6qqysjLyzP+53/+x3j99deNRYsWGdOnTzckGV/4wheMzs5Ol/cM3T355JOGpNh/GzZssHwu59Of7rvvPqO4uNiYOXOm8cQTTxirVq0ylixZYlx77bVGfn6+Icn485//3Ot1nE//ufnmmw1JRlFRkXHTTTcZr7zyivHXv/7V+Na3vmXk5eUZ+fn5xv3332/6Ws6nN959913j+OOPt/13tLtMz9nrr79uDBkyxCgpKTHuuOMO4+9//7uxcOFCY+LEiYYk43/+538c2kt7COr7oBkzZhiSjHnz5sVt7+rqMs4++2xDknH55Zd70zjE1NbWGpKMUaNGGbt27er1+Jo1a4yCggJDkvH1r3+91+P79+83PvnJTxqSjPnz58c9duDAAWPKlCmGJOP73/9+tnYBSezYscMYPny4UVpamvTLiPPpT7/+9a8NScZ//dd/mT7+ox/9yDSo53z6z9KlS2O/h0888USvx//v//7PkGQUFBQY69ati3uM8+mNm266ySgsLDROOukk44YbbkgpqM/0nG3fvt0YOnSoIcl49tln4x7bs2ePUVVVZUgyHn300TT3LnUE9X3MwoULDUlGcXGxsXv37l6Pr1u3zpBkhEIh480333S/gYiJBvV33nmn5XO++MUvxkaV9u7dG/fYT3/6U0OSMXLkSKOjo6PXa1944QVDklFSUmJs2bLF8fYjuYsvvtgoKyszbrvttqRfRpxP/9m6dasxaNAg47DDDjMOHDhg+pydO3ca999/v7Fp06a47ZxP/7niiisMScbw4cNNH29ubjZCoZBpoMf59MagQYOMe++91+jq6op1sO0G9Zmes2984xuGJGPatGmm73/fffcZkoyKigpj3759Ke9bOsip72MefvhhSdKpp56qwYMH93p83LhxGjdunAzD0COPPOJy69Dd0KFDde211+rss8+2fM7RRx8tKTJH4p///GfcY7/61a8kSeecc47y8/N7vfa0007TwIED1draqscee8zBlsOO5557Tr/73e905513auTIkUmfz/n0n/vuu09NTU269NJLVVhYaPqcQw45RFdddZWqqqritnM+/WfLli2SpNGjR5s+PmDAAA0dOlSS9NFHH8U9xvn0xrp16/S1r31NoVAo5ddmcs7a2tr0m9/8RpJ0/vnnm75/dHt9fb2ef/75lNuXDoL6PqStrU1LliyRJE2dOtXyedHHFi1a5Eq7YO7QQw/VT3/6U33iE5+wfE73P0SlpaWxf2/YsEHr16+XZH2u8/PzNXnyZEmca7ft3r1bV199tT772c/qyiuvTPp8zqc/Pf7445KkT3/60ym9jvPpT4cddpik3gF7VHt7uxoaGiRJhx9+eGw759M7o0aNSut1mZ6z1157TY2NjQlfP3z4cFVXV5u+PlsI6vuQ9evXq729XZL1SET3x/7973/HLlr40/vvvy9JGjFiRNyXzDvvvBP7t51z3f35yL558+apublZDz30kK3ncz79Z+fOnfrwww8lRY774sWLdfbZZ2vEiBHq37+/qqurdckll2jFihW9Xsv59KcvfvGLCoVC2rRpk1577bVejz/55JPq7OxUeXm5vvjFL8a2cz6DJ9Nz5tdzTlDfh2zatCn272HDhlk+r/tj4XA4q21C+jo6OvT0009Lkq699tq4UftUz/Xu3bvV0tKSpZaiu0WLFuk3v/mNfvSjH8VGBpPhfPrP2rVrY/++9dZbNXv2bI0fP14LFy7U0qVL9ZWvfEV//OMfdeKJJ+qHP/xh3Gs5n/500kkn6Z577lFRUZEuvvhiPffcc2pqatKuXbu0YMECffOb31RNTY1efPFFDR8+PPY6zmfwZHrOUn395s2b025rKgpc+RT4wt69e2P/Li4utnxe98eampqy2iak71e/+pXq6+t13HHH6Vvf+lbcY+me6wEDBjjfUMQ0Njbqq1/9qk466SR9/etft/06zqf/RNMwJOmpp57Sb3/7W33hC1+IbZs2bZqmTJmiM888UzfddJPGjx+v8847TxLn08++/vWv6/TTT9f111+vc889N7Y9Ly9Pl19+uW688cZeI7Ocz+DJ9Jyl+nq3YilG6tGLYRixf6cz+QTZV1dXp+uuu07Dhw/X7373O/Xr1y+t9+Fcu+vaa6/Vrl279Ktf/Sorx5vz6Z7uo3Y1NTVxAX3UGWecoZNPPlmSdMstt6T8GZxPd3V1demuu+7SpEmT9Oqrr2r+/PlatmyZFi9erFtuuUVPPvmkDj/8cN10003q7OxM+f05n8GT6TmLvt6t881IfR8ycODA2L/3799v+bwDBw6Yvgb+UF9fr9mzZys/P18vvfSSxowZ0+s5nGv/eemll/SrX/1Kd9xxh2pqalJ6LefTf0pKSmL//sxnPmP5vFNOOUWvvvqq3nnnHdXX16uiooLz6VPXXHONfv7zn2vw4MFas2aNRowYEXts5syZmjNnjo499lj98Ic/VFtbm3784x9L4vcziDI9Zz1fb3XXJfp6t843I/V9SHQWtiTt2LHD8nndH6usrMxqm5Cajz76SKeeeqp27dqll156KVbSsqdUz/WQIUO4FZxFe/fu1Ze//GVNnTpV3/72t1N+PefTf8rLy2P/rqiosHxe9+oc0Txczqf/bN68Wb/4xS8kSd/61rfiAvqoiRMn6pJLLpEk3XXXXWpubpbE+QyiTM9Zqq/vWdI2Wwjq+5Bx48bF0jQ2btxo+bzoY4cddpjKyspcaBnsCIfDmj59urZv366XX345YVnSiRMnxv5t51x3fz6c949//EObNm3SP/7xDxUVFamgoCDuv+5lLQ8//PBe2zmf/nPUUUfF/p0oFaP77fsozqf//P3vf1dXV5ekxMe7+9og69at6/V8zmcwZHrO/HrOCer7kMLCQs2cOVOS9Oabb1o+b9WqVZKk2bNnu9IuJLdx40Z95jOf0d69e7Vs2TJNmjSp1+PRUSNJGjNmjD71qU9Jsj7XnZ2dWr16tSTOdbZNnTpVtbW1WrNmjd5+++1e//3gBz+IPfeFF17otZ3z6T/Dhg3TkUceKSm+EkZP0QWNQqFQbIIl59N/7OZOd39eQUEkg5nzGTyZnrMTTzwxNuhp9frt27fH/ja4ds5dWbcWvrFw4UJDklFcXGzs2bOn1+Pr1683JBmhUMh48803PWgheqqrqzOqqqqMqqoqo66uzvQ5koxf//rXcduiS2CPGjXK6Ozs7PWaP//5z7FrIRwOZ6PpsMnO8uacT/+5/fbbY8vMm50TwzCMk046yZBkfPrTn47bzvn0l3Xr1sV+B2+66SbL533xi180JBlFRUVGc3NzbDvn03t2/o52l+k5+8Y3vmH6ux11//33G5KMiooKY9++fSnvTzoI6vugGTNmGJKMa665Jm57V1eXce655xqSjMsvv9yj1qG7tWvXGiNGjDDGjh1rbNy40fJ5ZkH9/v37jZqaGkOScffdd8c91tbWZkydOtWQZHz/+9/PRtORAjtfRpxP/9m3b58xduxYQ5Lx85//vNfj0aAgLy/PWLp0adxjnE//OeWUUwxJxuDBg42tW7f2enzNmjVGv379DEnGVVddFfcY59N7qQb1mZ6z7du3G0OHDjUkGX/4wx/iHmtsbDQOO+ww0+/mbCKo74N27NhhTJgwIfaH6W9/+5vx8ssvG+eff74hyTj11FON1tZWr5vZ5/3rX/8yhg0bZkgyCgsLjQEDBlj+Z/WH4/333zeqqqqM/Px843vf+57x+uuvGy+88EKsY/eFL3zBcoQR2dXc3GzU1tYatbW1xq233hr7MvrLX/4S294T59N/3nvvPWPUqFFGfn6+8e1vf9v429/+ZrzxxhvGrbfeavTv398oLCw0HnnkEdPXcj79Zdu2bcbEiRMNScawYcOM+fPnG8uXLzeWLFli3HrrrUZpaakhyTjttNOMlpaWXq/nfLqvvr4+6d/R7ndUesr0nL3++uvGkCFDjP79+xs/+clPjJUrVxrPPPOMcfTRRxuSjBtuuCEbu22JoL6P2r9/v/HjH//YOProo40BAwYYgwYNMqZNm2bcd999/NHxiWeffTb2B8rOf1ajAXv27DG++93vGp/61KeM4uJio7y83DjllFOMJ554wt0dQpylS5cmPadmOJ/+s2fPHuPGG280JkyYYAwYMMAoLi42jjjiCOOqq64y3nvvvaSv5Xz6R1tbm/HQQw8Zp59+unHooYcahYWFRlFRkVFVVWWcc845xlNPPWV0dXVZvp7z6a6bb7456d/RnnfJesr0nG3ZssX45je/aYwdO9YoKioyhg0bZsyePdt48cUXHdjD1IQMw2RqPgAAAIDAoPoNAAAAEHAE9QAAAEDAEdQDAAAAAUdQDwAAAAQcQT0AAAAQcAT1AAAAQMAR1AMAAAABR1APAAAABBxBPQAAABBwBPUAAABAwBHUAwAAAAFHUA8AAAAEHEE9AAAAEHAE9QAAAEDAEdQDAAAAAUdQDwAAAAQcQT0AIGfs3btX3/jGN5SXl6fRo0d73RwAcE2B1w0AAMAJf/rTn/S1r31N4XDY66YAgOsYqQcABNru3bt10UUXac6cOTr99NN19tlne90kAHAdQT0AINDWrFmj1atX6+WXX9bDDz+swYMHe90kAHAd6TcAgECbOHGi3nnnHZWUlHjdFADwDCP1AICseu655/T//t//06GHHqp+/fpp8ODBOvbYYzVv3jwtW7Ys7rmtra267777dMIJJ2jEiBEqLCzUiBEjdMYZZ+jee+/Vli1ber1/eXk5AT2APo+gHgCQFQcOHNCFF16oc889V/X19brnnnv0xhtv6Le//a0+9alP6ec//7lmzJih++67T5K0b98+nXDCCfrGN76hqVOn6re//a1WrFihu+++W42NjfrGN76hk046yeO9AgB/Iv0GAJAV8+bN08KFCzV58mQtX75cxcXFscfOOussDR8+XHfddZfa2tokSQ8//LDefvttXXrppbr77rtjz50yZYrmzJmjY489Vi0tLa7vBwAEASP1AADHrV+/Xg8++KAk6cYbb4wL6KOuu+66uJ/XrVsnSSotLe313OLiYs2bN08zZ87MQmsBIPgYqQcAOO6pp56SYRiSZBmIjxw5Ui+//LLGjh0rSaqpqZEkPfroo5o0aZK+9KUvxXUGvvKVr+grX/lKllsOAMHESD0AwHHvvPOOJGno0KEaNGiQ5fNOOeUUHXbYYZKkq666Sscdd5za2tr0ta99TRUVFfr85z+vBQsWaNeuXa60GwCCiqAeAOC4xsZGSUqpKk3//v312muv6cEHH9S0adPU1NSkJ598UpdffrkOPfRQ/cd//Ie2bduWrSYDQKAR1AMAHFdWViYpUqIyFQUFBfryl7+sN954Q//+97/1s5/9TFOmTFFHR4cee+wxnXzyyUyWBQATBPUAAMcdffTRkqSdO3eqqanJ8nktLS3at2+f6WPV1dW65pprtGrVKj3xxBPKy8vTBx98oGeeeSYrbQaAICOoBwA47sILL1ReXuQrZvHixabP+de//qXS0tLY5Nd58+ZpxowZps+9+OKLNXHiREkiBQcATBDUAwAc96lPfUpXXXWVJOnWW2/V/v37ez3n5ptvVigU0te+9jVJkTz81157Te+9916v57a0tGjz5s2SpOOOOy6LLQeAYKKkJQAgK+bPn6+dO3fqySef1PTp03Xddddp7NixCofDeuihh/T888/rzjvv1AknnCBJCoVC6ujo0KxZs/Tf//3fOuaYY1RSUqIPPvhAd911l3bt2qUrr7zSdDR/w4YNsVz7PXv2SJLa29v17rvvxp5z1FFHZX2fAcArISNaSBgAgCx47rnn9PDDD2vVqlVqaGjQoEGDNG3aNH3zm9/UmWeeGXtec3OznnzySS1atEj//Oc/tXXrVu3du1eHHHKIjj32WF1xxRU6//zzTT9jxowZWrZsWcJ28HUHIJcR1AMAAAABR049AAAAEHAE9QAAAEDAEdQDAAAAAUdQDwAAAAQcQT0AAAAQcAT1AAAAQMAR1AMAAAABR1APAAAABBxBPQAAABBwBPUAAABAwBHUAwAAAAFHUA8AAAAEHEE9AAAAEHAE9QAAAEDAEdQDAAAAAUdQDwAAAAQcQT0AAAAQcAT1AAAAQMAR1AMAAAABR1APAAAABBxBPQAAABBwBPUAAABAwBHUAwAAAAH3/wPkOVY0trxC3QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, source_name in enumerate(source_name_list):\n",
    "    mask = data[\"science_run\"][\"source\"] == i\n",
    "    plt.scatter(\n",
    "        data[\"science_run\"][\"cs1\"][mask], data[\"science_run\"][\"cs2\"][mask], label=source_name, s=2\n",
    "    )\n",
    "plt.xlabel(\"cs1\")\n",
    "plt.ylabel(\"cs2\")\n",
    "plt.semilogy()\n",
    "plt.legend(loc=\"lower right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most WIMP events have lower values of `cs1` and `cs2` compared to the ER events. We can use this to discriminate WIMP signal events from ER background events."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 Rate parameters and shape parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Blueice distinguished between *rate parameters*, which only scale the expectation value of the respective component, and *shape parameters*, which also change the shape of the component. The only shape parameter used here is the `er_band_shift`, which shifts the ER band in the two dimensional parameter space. To illustrate this, let's look at some toy data:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rate parameters simply linearly scale the expectation values of each component of the model. Their nominal values are 1.0, which gives the following expectation values for the defined livetime:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'er': 400.0, 'wimp': 20.0}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_expectation_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can verify that this is roughly what we observed in the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source er has 400 events.\n",
      "Source wimp has 24 events.\n"
     ]
    }
   ],
   "source": [
    "for i, source_name in enumerate(source_name_list):\n",
    "    mask = data[\"science_run\"][\"source\"] == i\n",
    "    print(f\"Source {source_name} has {np.sum(mask)} events.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we choose a `wimp_rate_multiplier` of 10 we should thus see a 10-fold increase of the expected number of WIMP events:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'er': 400.0, 'wimp': 200.0}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_expectation_values(wimp_rate_multiplier=10.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source er has 393 events.\n",
      "Source wimp has 198 events.\n"
     ]
    }
   ],
   "source": [
    "wimp_rich_data = model.generate_data(wimp_rate_multiplier=10.0)\n",
    "for i, source_name in enumerate(source_name_list):\n",
    "    mask = wimp_rich_data[\"science_run\"][\"source\"] == i\n",
    "    print(f\"Source {source_name} has {np.sum(mask)} events.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's see what the shape parameters do. We generate a toy dataset with the nominal and the two extreme values of the `er_band_shift` parameter and see what this does to the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12a01c090>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for er_band_shift, c in zip([2, 0, -2], [\"crimson\", \"k\", \"dodgerblue\"]):\n",
    "    data = model.generate_data(er_band_shift=er_band_shift)\n",
    "    mask = data[\"science_run\"][\"source\"] == source_name_list.index(\"er\")\n",
    "    plt.scatter(\n",
    "        data[\"science_run\"][\"cs1\"][mask],\n",
    "        data[\"science_run\"][\"cs2\"][mask],\n",
    "        label=f\"er_band_shift={er_band_shift}\",\n",
    "        c=c,\n",
    "        s=2,\n",
    "    )\n",
    "plt.xlabel(\"cs1\")\n",
    "plt.ylabel(\"cs2\")\n",
    "plt.semilogy()\n",
    "plt.legend(loc=\"lower right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the parameter `er_band_shift` moves the ER band up and down in log(cs2). Note that you can choose any value between the highest and lowest `blueice_anchor` for this parameter, blueice will interpolate the er tamplate for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-2, -1, 0, 1, 2]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.parameters.er_band_shift.blueice_anchors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12a0d5e10>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "er_band_shift = 1.42\n",
    "data = model.generate_data(er_band_shift=er_band_shift)\n",
    "mask = data[\"science_run\"][\"source\"] == source_name_list.index(\"er\")\n",
    "plt.scatter(\n",
    "    data[\"science_run\"][\"cs1\"][mask],\n",
    "    data[\"science_run\"][\"cs2\"][mask],\n",
    "    label=f\"er_band_shift={er_band_shift}\",\n",
    "    c=\"mediumseagreen\",\n",
    "    s=2,\n",
    ")\n",
    "plt.xlabel(\"cs1\")\n",
    "plt.ylabel(\"cs2\")\n",
    "plt.semilogy()\n",
    "plt.legend(loc=\"lower right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "binf_env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}