diff --git a/lstchain/data/LST1_backg_irf_gheffi_0.40_theffi_0.40.csv b/lstchain/data/LST1_backg_irf_gheffi_0.40_theffi_0.40.csv
new file mode 100644
index 0000000000..ac81aa50f3
--- /dev/null
+++ b/lstchain/data/LST1_backg_irf_gheffi_0.40_theffi_0.40.csv
@@ -0,0 +1,191 @@
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diff --git a/lstchain/data/LST1_backg_irf_gheffi_0.70_theffi_0.70.csv b/lstchain/data/LST1_backg_irf_gheffi_0.70_theffi_0.70.csv
new file mode 100644
index 0000000000..86ec14d9b2
--- /dev/null
+++ b/lstchain/data/LST1_backg_irf_gheffi_0.70_theffi_0.70.csv
@@ -0,0 +1,191 @@
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diff --git a/lstchain/data/LST1_backg_irf_gheffi_0.90_theffi_0.90.csv b/lstchain/data/LST1_backg_irf_gheffi_0.90_theffi_0.90.csv
new file mode 100644
index 0000000000..513c06dd1a
--- /dev/null
+++ b/lstchain/data/LST1_backg_irf_gheffi_0.90_theffi_0.90.csv
@@ -0,0 +1,191 @@
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diff --git a/lstchain/data/LST1_gamma_irf_gheffi_0.40_theffi_0.40.csv b/lstchain/data/LST1_gamma_irf_gheffi_0.40_theffi_0.40.csv
new file mode 100644
index 0000000000..f352e4494a
--- /dev/null
+++ b/lstchain/data/LST1_gamma_irf_gheffi_0.40_theffi_0.40.csv
@@ -0,0 +1,351 @@
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diff --git a/lstchain/data/LST1_gamma_irf_gheffi_0.70_theffi_0.70.csv b/lstchain/data/LST1_gamma_irf_gheffi_0.70_theffi_0.70.csv
new file mode 100644
index 0000000000..f028c50db4
--- /dev/null
+++ b/lstchain/data/LST1_gamma_irf_gheffi_0.70_theffi_0.70.csv
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diff --git a/lstchain/data/LST1_gamma_irf_gheffi_0.90_theffi_0.90.csv b/lstchain/data/LST1_gamma_irf_gheffi_0.90_theffi_0.90.csv
new file mode 100644
index 0000000000..b22d3c8cd3
--- /dev/null
+++ b/lstchain/data/LST1_gamma_irf_gheffi_0.90_theffi_0.90.csv
@@ -0,0 +1,351 @@
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diff --git a/notebooks/LST1_observation_simulator.ipynb b/notebooks/LST1_observation_simulator.ipynb
new file mode 100644
index 0000000000..10db5b9db7
--- /dev/null
+++ b/notebooks/LST1_observation_simulator.ipynb
@@ -0,0 +1,1159 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8d7fb8ab-7034-47be-a392-17612fd611b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import astropy.units as u\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pyirf.spectral import CRAB_MAGIC_JHEAP2015, PowerLaw, LogParabola\n",
+    "from pyirf.statistics import li_ma_significance\n",
+    "from scipy.stats import moyal\n",
+    "from gammapy.modeling.models import EBLAbsorptionNormSpectralModel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c30c3f8d",
+   "metadata": {},
+   "source": [
+    "## Set cut efficiency"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ef3576a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#\n",
+    "# Cut efficiency: the same for the gammaness cut and the theta cut\n",
+    "# Options: 0.4, 0.7, 0.9\n",
+    "#\n",
+    "# Recommended: \n",
+    "# 0.7: standard cuts (safer for spectral analysis)\n",
+    "# 0.4: tight cuts, better for detection of weak sources\n",
+    "#\n",
+    "cut_efficiency = 0.7"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5dd2e6fd",
+   "metadata": {},
+   "source": [
+    "## Load files which contain the instrument response function and show table contents\n",
+    "They characterize the average performance of LST1 within 1 degree off-axis (computed from diffuse gamma MC and\n",
+    "real data for the cosmic ray rates)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0844c0a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#\n",
+    "# Load files for the requested efficiency:\n",
+    "#\n",
+    "datadir = os.environ['LSTCHAIN'] + '/data/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c59f8666",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_filename = datadir + f'LST1_gamma_irf_gheffi_{cut_efficiency:.2f}_theffi_{cut_efficiency:.2f}.csv'\n",
+    "gamma_data = pd.read_csv(input_filename)\n",
+    "\n",
+    "input_filename = datadir + f'LST1_backg_irf_gheffi_{cut_efficiency:.2f}_theffi_{cut_efficiency:.2f}.csv'\n",
+    "background_data = pd.read_csv(input_filename)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ad3a988e",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ZD_deg</th>\n",
+       "      <th>Etrue_min_TeV</th>\n",
+       "      <th>Etrue_max_TeV</th>\n",
+       "      <th>Aeff_m2</th>\n",
+       "      <th>emig_mu_loc</th>\n",
+       "      <th>emig_mu_scale</th>\n",
+       "      <th>emig_model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6.00</td>\n",
+       "      <td>0.011220</td>\n",
+       "      <td>0.014125</td>\n",
+       "      <td>1.523558e+03</td>\n",
+       "      <td>2.173221</td>\n",
+       "      <td>0.254397</td>\n",
+       "      <td>moyal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6.00</td>\n",
+       "      <td>0.014125</td>\n",
+       "      <td>0.017783</td>\n",
+       "      <td>3.045447e+03</td>\n",
+       "      <td>1.768732</td>\n",
+       "      <td>0.234880</td>\n",
+       "      <td>moyal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6.00</td>\n",
+       "      <td>0.017783</td>\n",
+       "      <td>0.022387</td>\n",
+       "      <td>5.419198e+03</td>\n",
+       "      <td>1.459977</td>\n",
+       "      <td>0.216000</td>\n",
+       "      <td>moyal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6.00</td>\n",
+       "      <td>0.022387</td>\n",
+       "      <td>0.028184</td>\n",
+       "      <td>8.858634e+03</td>\n",
+       "      <td>1.212684</td>\n",
+       "      <td>0.190464</td>\n",
+       "      <td>moyal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6.00</td>\n",
+       "      <td>0.028184</td>\n",
+       "      <td>0.035481</td>\n",
+       "      <td>1.299283e+04</td>\n",
+       "      <td>1.050773</td>\n",
+       "      <td>0.184902</td>\n",
+       "      <td>moyal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>345</th>\n",
+       "      <td>66.44</td>\n",
+       "      <td>11.220185</td>\n",
+       "      <td>14.125375</td>\n",
+       "      <td>1.919827e+06</td>\n",
+       "      <td>0.936101</td>\n",
+       "      <td>0.148488</td>\n",
+       "      <td>gauss</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>346</th>\n",
+       "      <td>66.44</td>\n",
+       "      <td>14.125375</td>\n",
+       "      <td>17.782794</td>\n",
+       "      <td>1.964986e+06</td>\n",
+       "      <td>0.933643</td>\n",
+       "      <td>0.150116</td>\n",
+       "      <td>gauss</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>347</th>\n",
+       "      <td>66.44</td>\n",
+       "      <td>17.782794</td>\n",
+       "      <td>22.387211</td>\n",
+       "      <td>2.261285e+06</td>\n",
+       "      <td>0.938107</td>\n",
+       "      <td>0.146014</td>\n",
+       "      <td>gauss</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>348</th>\n",
+       "      <td>66.44</td>\n",
+       "      <td>22.387211</td>\n",
+       "      <td>28.183829</td>\n",
+       "      <td>2.116637e+06</td>\n",
+       "      <td>0.945268</td>\n",
+       "      <td>0.145841</td>\n",
+       "      <td>gauss</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>349</th>\n",
+       "      <td>66.44</td>\n",
+       "      <td>28.183829</td>\n",
+       "      <td>35.481339</td>\n",
+       "      <td>2.213826e+06</td>\n",
+       "      <td>0.943924</td>\n",
+       "      <td>0.152568</td>\n",
+       "      <td>gauss</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>350 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     ZD_deg  Etrue_min_TeV  Etrue_max_TeV       Aeff_m2  emig_mu_loc  \\\n",
+       "0      6.00       0.011220       0.014125  1.523558e+03     2.173221   \n",
+       "1      6.00       0.014125       0.017783  3.045447e+03     1.768732   \n",
+       "2      6.00       0.017783       0.022387  5.419198e+03     1.459977   \n",
+       "3      6.00       0.022387       0.028184  8.858634e+03     1.212684   \n",
+       "4      6.00       0.028184       0.035481  1.299283e+04     1.050773   \n",
+       "..      ...            ...            ...           ...          ...   \n",
+       "345   66.44      11.220185      14.125375  1.919827e+06     0.936101   \n",
+       "346   66.44      14.125375      17.782794  1.964986e+06     0.933643   \n",
+       "347   66.44      17.782794      22.387211  2.261285e+06     0.938107   \n",
+       "348   66.44      22.387211      28.183829  2.116637e+06     0.945268   \n",
+       "349   66.44      28.183829      35.481339  2.213826e+06     0.943924   \n",
+       "\n",
+       "     emig_mu_scale emig_model  \n",
+       "0         0.254397      moyal  \n",
+       "1         0.234880      moyal  \n",
+       "2         0.216000      moyal  \n",
+       "3         0.190464      moyal  \n",
+       "4         0.184902      moyal  \n",
+       "..             ...        ...  \n",
+       "345       0.148488      gauss  \n",
+       "346       0.150116      gauss  \n",
+       "347       0.146014      gauss  \n",
+       "348       0.145841      gauss  \n",
+       "349       0.152568      gauss  \n",
+       "\n",
+       "[350 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gamma_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1d2b1efe",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ZD_deg</th>\n",
+       "      <th>Ereco_min_TeV</th>\n",
+       "      <th>Ereco_max_TeV</th>\n",
+       "      <th>BckgRate_per_second</th>\n",
+       "      <th>Theta_cut_deg</th>\n",
+       "      <th>Gammaness_cut</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6.00</td>\n",
+       "      <td>0.012589</td>\n",
+       "      <td>0.019953</td>\n",
+       "      <td>0.158753</td>\n",
+       "      <td>0.320000</td>\n",
+       "      <td>0.112810</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6.00</td>\n",
+       "      <td>0.019953</td>\n",
+       "      <td>0.031623</td>\n",
+       "      <td>11.244392</td>\n",
+       "      <td>0.320000</td>\n",
+       "      <td>0.176081</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6.00</td>\n",
+       "      <td>0.031623</td>\n",
+       "      <td>0.050119</td>\n",
+       "      <td>17.278245</td>\n",
+       "      <td>0.320000</td>\n",
+       "      <td>0.189099</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6.00</td>\n",
+       "      <td>0.050119</td>\n",
+       "      <td>0.079433</td>\n",
+       "      <td>10.838075</td>\n",
+       "      <td>0.320000</td>\n",
+       "      <td>0.197615</td>\n",
+       "    </tr>\n",
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+       "      <th>4</th>\n",
+       "      <td>6.00</td>\n",
+       "      <td>0.079433</td>\n",
+       "      <td>0.125893</td>\n",
+       "      <td>2.151692</td>\n",
+       "      <td>0.267352</td>\n",
+       "      <td>0.316888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>185</th>\n",
+       "      <td>66.44</td>\n",
+       "      <td>7.943282</td>\n",
+       "      <td>12.589254</td>\n",
+       "      <td>0.002647</td>\n",
+       "      <td>0.133676</td>\n",
+       "      <td>0.387355</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186</th>\n",
+       "      <td>66.44</td>\n",
+       "      <td>12.589254</td>\n",
+       "      <td>19.952623</td>\n",
+       "      <td>0.001408</td>\n",
+       "      <td>0.134832</td>\n",
+       "      <td>0.398923</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>187</th>\n",
+       "      <td>66.44</td>\n",
+       "      <td>19.952623</td>\n",
+       "      <td>31.622777</td>\n",
+       "      <td>0.000598</td>\n",
+       "      <td>0.138468</td>\n",
+       "      <td>0.426601</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>188</th>\n",
+       "      <td>66.44</td>\n",
+       "      <td>31.622777</td>\n",
+       "      <td>50.118723</td>\n",
+       "      <td>0.000177</td>\n",
+       "      <td>0.133650</td>\n",
+       "      <td>0.474480</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>189</th>\n",
+       "      <td>66.44</td>\n",
+       "      <td>50.118723</td>\n",
+       "      <td>79.432823</td>\n",
+       "      <td>0.000094</td>\n",
+       "      <td>0.137851</td>\n",
+       "      <td>0.536455</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>190 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     ZD_deg  Ereco_min_TeV  Ereco_max_TeV  BckgRate_per_second  Theta_cut_deg  \\\n",
+       "0      6.00       0.012589       0.019953             0.158753       0.320000   \n",
+       "1      6.00       0.019953       0.031623            11.244392       0.320000   \n",
+       "2      6.00       0.031623       0.050119            17.278245       0.320000   \n",
+       "3      6.00       0.050119       0.079433            10.838075       0.320000   \n",
+       "4      6.00       0.079433       0.125893             2.151692       0.267352   \n",
+       "..      ...            ...            ...                  ...            ...   \n",
+       "185   66.44       7.943282      12.589254             0.002647       0.133676   \n",
+       "186   66.44      12.589254      19.952623             0.001408       0.134832   \n",
+       "187   66.44      19.952623      31.622777             0.000598       0.138468   \n",
+       "188   66.44      31.622777      50.118723             0.000177       0.133650   \n",
+       "189   66.44      50.118723      79.432823             0.000094       0.137851   \n",
+       "\n",
+       "     Gammaness_cut  \n",
+       "0         0.112810  \n",
+       "1         0.176081  \n",
+       "2         0.189099  \n",
+       "3         0.197615  \n",
+       "4         0.316888  \n",
+       "..             ...  \n",
+       "185       0.387355  \n",
+       "186       0.398923  \n",
+       "187       0.426601  \n",
+       "188       0.474480  \n",
+       "189       0.536455  \n",
+       "\n",
+       "[190 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "background_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "90114613",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Available zeniths: [ 6.     9.579 16.08  23.16  30.39  37.66  44.92  52.16  59.34  66.44 ] (degrees)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# CHECK that we have the same pointing zenith values in both tables:\n",
+    "assert np.alltrue(np.unique(gamma_data.ZD_deg) == np.unique(background_data.ZD_deg))\n",
+    "\n",
+    "# Available zeniths:\n",
+    "zenith = np.unique(gamma_data.ZD_deg)\n",
+    "print('Available zeniths:', zenith, '(degrees)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc762a1a",
+   "metadata": {},
+   "source": [
+    "## Select zenith bin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a5a9386a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Selected ZD =  16.08 degrees\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Choose the bins among those above. Just set the bin number (from 0)\n",
+    "# Make sure you choose values which make sense for the declination of your source\n",
+    "\n",
+    "zd_bin = 2\n",
+    "print('Selected ZD = ', zenith[zd_bin], 'degrees')\n",
+    "\n",
+    "# Cuts for tables:\n",
+    "zd_selection_gamma = abs(gamma_data.ZD_deg - np.unique(gamma_data.ZD_deg)[zd_bin])<0.01\n",
+    "zd_selection_backg = abs(background_data.ZD_deg - np.unique(background_data.ZD_deg)[zd_bin])<0.01"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d1c2dcf",
+   "metadata": {},
+   "source": [
+    "## ON to OFF exposure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0c58505b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ratio of ON to OFF exposure (Li & Ma's \"alpha\")\n",
+    "# For standard wobble offset (0.4 deg) reasonable values are alpha=0.333 (3 off regions) above 0.2 TeV, \n",
+    "# and alpha=1 below 0.2 TeV.  For testing sensitivity with the standard definition, set alpha=0.2\n",
+    "\n",
+    "alpha = 1 # 0.333  # 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aaf2cba7",
+   "metadata": {},
+   "source": [
+    "## Observation time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7d8d3a2e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "effective_obs_time = 34 * u.h "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d662546b",
+   "metadata": {},
+   "source": [
+    "## Source redshift"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ea9bca3c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "redshift = 0\n",
+    "\n",
+    "# We will apply the Dominguez EBL model to simulate the absorption\n",
+    "\n",
+    "\n",
+    "try:\n",
+    "    os.environ['GAMMAPY_DATA']\n",
+    "except:\n",
+    "    # SET HERE THE GAMMAPY_DATA ENV VARIABLE IN CASE IT IS NOT SET\n",
+    "    # YOU MUST SET THE PATH TO THE CORRESPONDING DIRECTORY IN THE CONDA ENVIRONMENT YOU ARE USING:\n",
+    "    os.environ['GAMMAPY_DATA'] = '/fefs/aswg/workspace/abelardo.moralejo/miniconda3/envs/lst-dev/lib/python3.11/site-packages/gammapy/gammapy-datasets/1.1'\n",
+    "\n",
+    "dominguez = EBLAbsorptionNormSpectralModel.read_builtin(\"dominguez\", redshift=redshift)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5766b4e",
+   "metadata": {},
+   "source": [
+    "## Source (intrinsic) spectrum "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e24f72ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set here the simulated intrinsic spectrum (must take as argument an astropy quantity with energy units)\n",
+    "\n",
+    "# Crab Nebula:\n",
+    "def intrinsic_dFdE(E):\n",
+    "    return CRAB_MAGIC_JHEAP2015(E)  # Crab Nebula, MAGIC log-parabola\n",
+    "\n",
+    "# def intrinsic_dFdE(E):\n",
+    "#     return PowerLaw(normalization=5e-8 / (u.TeV * u.cm**2 * u.s), \n",
+    "#                     index=-2.0, \n",
+    "#                     e_ref=0.1 * u.TeV)(E)\n",
+    "\n",
+    "# def intrinsic_dFdE(E):\n",
+    "#     return LogParabola(normalization=5e-8 / (u.TeV * u.cm**2 * u.s), \n",
+    "#                     a=-2.0, b =-0.2, \n",
+    "#                     e_ref=0.1 * u.TeV)(E)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "014476b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# After EBL absorption:\n",
+    "def dFdE(E):\n",
+    "    return intrinsic_dFdE(E) * dominguez.evaluate(E, redshift, 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8613146f",
+   "metadata": {},
+   "source": [
+    "## END of user settings\n",
+    "#"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "80627c34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Other settings (change only for tests)\n",
+    "\n",
+    "min_signal_to_backg_ratio = 0.01     # safeguard against systematics\n",
+    "min_signi_in_flux_point = 3          # minimum significance to display a flux point\n",
+    "integral_significance_threshold = 5  # \"Detection significance\"\n",
+    "\n",
+    "min_Aeff = 100 *u.m**2         # Minimum required Aeff in Etrue bins. \n",
+    "                               # Just to avoid Etrue bins with little MC stats, hence noisy!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "24e8394c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "erecobins = background_data[zd_selection_backg].Ereco_min_TeV.to_numpy()\n",
+    "erecobins = np.append(erecobins, background_data[zd_selection_backg].Ereco_max_TeV.to_numpy()[-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "056ea33b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "etruebins = gamma_data[zd_selection_gamma].Etrue_min_TeV.to_numpy()\n",
+    "etruebins = np.append(etruebins, gamma_data[zd_selection_gamma].Etrue_max_TeV.to_numpy()[-1])\n",
+    "\n",
+    "effective_area = gamma_data.Aeff_m2[zd_selection_gamma].to_numpy()*u.m**2\n",
+    "\n",
+    "# Model to characterize the energy migration matrix (gauss or moyal):\n",
+    "emig_model = gamma_data[zd_selection_gamma].emig_model.to_numpy()\n",
+    "\n",
+    "# Parameters to characterize the energy migration matrix:\n",
+    "loc = gamma_data[zd_selection_gamma].emig_mu_loc.to_numpy()\n",
+    "scale = gamma_data[zd_selection_gamma].emig_mu_scale.to_numpy()\n",
+    "\n",
+    "\n",
+    "# Now we extrapolate Aeff to higher Etrue by using the same value of the highest available energy:\n",
+    "# (it is better than having zeros!) We also assume the same E-migration\n",
+    "factor = etruebins[-1]/etruebins[-2] # step in energy in each bin\n",
+    "while etruebins[-1] < 80: # extend to 80 TeV at least\n",
+    "    etruebins = np.append(etruebins, etruebins[-1]*factor)\n",
+    "    effective_area = np.append(effective_area, effective_area[-1])\n",
+    "    emig_model = np.append(emig_model, emig_model[-1])\n",
+    "    loc = np.append(loc, loc[-1])\n",
+    "    scale = np.append(scale, scale[-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "1b79d5b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "etruebincenters = (etruebins[:-1]*etruebins[1:])**0.5\n",
+    "erecobincenters = (erecobins[:-1]*erecobins[1:])**0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "2038bb5b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exclude too low eff. areas (in general, unreliable due to low MC stats)\n",
+    "\n",
+    "effective_area[effective_area<min_Aeff] = 0\n",
+    "\n",
+    "# Exclude also Ereco bins which have Ereco values below the first Etrue value with valid Aeff:\n",
+    "ereco_mask = erecobincenters >= etruebincenters[np.where(effective_area>0)[0][0]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "48a34458",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def integrate (dfde1, dfde2, e1, e2):\n",
+    "    # We cannot let numpy deal with the units, sometimes rounding leads to wrong units in result!\n",
+    "    # like TeV^(1e-15) :-D\n",
+    "    # in power-law approximation:\n",
+    "    gamma = np.log(dfde2/dfde1) / np.log(e2/e1)\n",
+    "    e1tev = e1.to_value(u.TeV)\n",
+    "    e2tev = e2.to_value(u.TeV)\n",
+    "    \n",
+    "    integral = dfde1.to_value(1/(u.TeV * u.cm**2 * u.s)) / (gamma+1) * e1tev**(-gamma) * (e2tev**(gamma+1) - e1tev**(gamma+1))\n",
+    "    return integral # (1/u.s/u.cm**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "54b22527",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "integrated_flux = []\n",
+    "for etruemin, etruemax in zip(etruebins[:-1], etruebins[1:]):\n",
+    "    integrated_flux.append(integrate(dFdE(etruemin*u.TeV),\n",
+    "                                     dFdE(etruemax*u.TeV),\n",
+    "                                     etruemin*u.TeV, \n",
+    "                                     etruemax*u.TeV))\n",
+    "integrated_flux = np.array(integrated_flux)\n",
+    "\n",
+    "# Too strong EBL absorption produces NaNs in high-E bins, jus trplace by 0's:\n",
+    "integrated_flux[np.isnan(integrated_flux)] = 0\n",
+    "    \n",
+    "integrated_flux = np.array(integrated_flux) * 1/(u.s * u.cm**2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0d668ca",
+   "metadata": {},
+   "source": [
+    "## Effective area"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "4eff20a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8,4))\n",
+    "plt.plot(etruebincenters, effective_area)\n",
+    "plt.xscale('log')\n",
+    "plt.yscale('log')\n",
+    "plt.xlabel('Etrue (TeV)')\n",
+    "plt.ylabel('Aeff (m2)')\n",
+    "plt.grid()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb2fb8b7",
+   "metadata": {},
+   "source": [
+    "## Background rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "9c89f6e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8,4))\n",
+    "plt.plot(erecobincenters[ereco_mask], \n",
+    "         background_data[zd_selection_backg].BckgRate_per_second.to_numpy()[ereco_mask])\n",
+    "plt.xlabel('Ereco (TeV)')\n",
+    "plt.ylabel('Background rate within theta cut\\n (events/s) in Ereco bins')\n",
+    "plt.yscale('log')\n",
+    "plt.xscale('log')\n",
+    "plt.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "5164274a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total gamma rate after cuts: 0.897 events/s\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_gamma_rate = (integrated_flux*effective_area).to(1/u.s)\n",
+    "print(f'Total gamma rate after cuts: {total_gamma_rate.sum().to_value(1/u.s):.3f} events/s')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "6a415916",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Number of realizations (random numbers taken from gaussian) for E-migration simulation \n",
+    "num_realizations = 10000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "31415b87",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.1, 4167178.6876828154)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8,4))\n",
+    "\n",
+    "total_bg_counts = effective_obs_time.to_value(u.s) * background_data[zd_selection_backg].BckgRate_per_second.to_numpy()\n",
+    "total_signal_counts = np.zeros(zd_selection_backg.sum())\n",
+    "\n",
+    "for ietrue in range(len(etruebincenters)):\n",
+    "    etrue = etruebincenters[ietrue]\n",
+    "    \n",
+    "    gamma_rate = total_gamma_rate[ietrue]\n",
+    "\n",
+    "    \n",
+    "    if emig_model[ietrue] == 'gauss':\n",
+    "        counts, _ = np.histogram(etrue*np.random.normal(loc[ietrue], scale[ietrue], num_realizations),\n",
+    "                                 bins=erecobins)\n",
+    "                  \n",
+    "    elif emig_model[ietrue] == 'moyal':\n",
+    "        counts, _ = np.histogram(etrue*moyal.rvs(loc[ietrue], scale[ietrue], num_realizations),\n",
+    "                                 bins=erecobins)\n",
+    "    else:\n",
+    "        continue\n",
+    "    \n",
+    "\n",
+    "    total_signal_counts += gamma_rate * effective_obs_time * counts / num_realizations\n",
+    "    # print(gamma_rate * effective_obs_time * counts / num_realizations)\n",
+    "\n",
+    "    \n",
+    "# Now set to zero values below the \"reliable minimum energy\":\n",
+    "total_signal_counts[~ereco_mask] = 0\n",
+    "total_bg_counts[~ereco_mask] = 0\n",
+    "    \n",
+    "plt.errorbar(0.5*(erecobins[:-1]+erecobins[1:]), total_signal_counts, total_signal_counts**0.5,\n",
+    "             label='gammas')\n",
+    "plt.errorbar(0.5*(erecobins[:-1]+erecobins[1:]), total_bg_counts, total_bg_counts**0.5,\n",
+    "             label='Background')\n",
+    "\n",
+    "plt.xscale('log')\n",
+    "plt.yscale('log')\n",
+    "plt.legend()\n",
+    "plt.xlabel('Ereco (TeV)')\n",
+    "plt.ylabel('Number of events after cuts in Ereco bins')\n",
+    "plt.grid()\n",
+    "plt.ylim(0.1, 2*np.max(total_bg_counts))\n",
+    "# plt.xlim(10, 80)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "b22d2e36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "significance = li_ma_significance(total_signal_counts+total_bg_counts,\n",
+    "                                  total_bg_counts/alpha, alpha)\n",
+    "\n",
+    "# integrating from each Ereco to max Ereco\n",
+    "integral_signal_counts = np.cumsum(total_signal_counts[::-1])[::-1]\n",
+    "integral_bg_counts = np.cumsum(total_bg_counts[::-1])[::-1]\n",
+    "\n",
+    "integral_significance = li_ma_significance(integral_signal_counts+integral_bg_counts,\n",
+    "                                           integral_bg_counts/alpha, alpha)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d27e4dfc",
+   "metadata": {},
+   "source": [
+    "## Signal to background ratio and significance in Ereco bins"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "2a3d41c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "signal_to_background_ratio = total_signal_counts / total_bg_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "9b5e3aa8",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(10,4))\n",
+    "fig.add_subplot(1, 2, 1)\n",
+    "plt.plot(erecobincenters, signal_to_background_ratio)\n",
+    "plt.scatter(erecobincenters, signal_to_background_ratio)\n",
+    "plt.grid()\n",
+    "plt.xlabel('Ereco (TeV)')\n",
+    "plt.ylabel('Signal / background ratio')\n",
+    "plt.yscale('log')\n",
+    "plt.xscale('log')\n",
+    "\n",
+    "fig.add_subplot(1, 2, 2)\n",
+    "points_mask = ((signal_to_background_ratio >= min_signal_to_backg_ratio) &\n",
+    "               (significance >= min_signi_in_flux_point))\n",
+    "plt.scatter(erecobincenters[points_mask], significance[points_mask])\n",
+    "\n",
+    "plt.scatter(erecobincenters[~points_mask], significance[~points_mask], color='orange')\n",
+    "\n",
+    "plt.grid()\n",
+    "plt.xlabel('Ereco (TeV)')\n",
+    "plt.ylabel('Li & Ma significance')\n",
+    "plt.xscale('log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca520d68",
+   "metadata": {},
+   "source": [
+    "## Integral significance (for Ereco>xx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "0e1df16e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "*************************\n",
+      "Detection successful! :-D\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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81cpSqVTIycmBk5MTrKw47lYVLFq0CF988QVu3bpl0nbL+rnTNV/jtAQiC1cd/1MlsjTVYeWQjRs3wtvbGw0aNMCvv/6KOXPmYPDgwUZPbKlq+Pzzz9GpUyfUrVsXp06dwqeffop33nnH3GFVCJNbIgtmjm0/iah6yszMxPz585GZmQl3d3e8+eabGrt4kWX7/fff8fHHH+P+/fto1KgRZs2ahfDwcHOHVSFMbokslHrbz2fnHam3/eROYUT0tLCwMISFhZk7DDKTFStWaO38Vl1xYguRBTLXtp9ERETmxuSWyAKdSbuvMRXhWSKAjOwCnEm7b7qgiIiITIDJLZEFMue2n0RERObE5JbIApl7208iIiJzYXJLZIHU236WtuCXgOJVE0y17ScREZGpMLklskDqbT8BaCW4xtz2k4iqD0EQsHv3boNeMzIyEm3bttUqc3Nzk9obM2YMBg4caNB2CWjSpAliYmLMHUaVwOSWyEKZY9tPIqoasrKyMHHiRDRq1Ai2traQy+Xo06cPTp8+LdXJyMhA3759Ddru7NmzceTIEenx5cuXERUVhS+//FJqb+XKlYiNjTVou9WRIAhaX1988UWFr3f27FlMmDDBgBFWX1znlsiCBfu6I9BHzh3KiMxNpQRuJACP/gQc3IDGXQEra6M19/rrr0OhUGDDhg3w9vbGn3/+iSNHjuD+/b9XSJHL5QZv18HBAQ4ODtLj1NRUAEBoaCgEofhzx9bW1uDtVkW3bt2Cp6dnmXXWr1+P4OBg6bGzs3OF26tfv36Fz7U0HLklsnDqbT9D2zZAl6Z1mdgSmVryHiDGF9jwGvDd+OJ/Y3yLy43g4cOHOHnyJJYuXYqAgAA0btwY/v7+CA8PR//+/aV6z05LSEhIQNu2bSGTydCxY0fs3r0bgiAgKSkJAPDTTz9BEAQcOXIEHTt2hL29Pbp27YqrV69K13h6WkJkZCRCQkIAAFZWVlJy++y0BJVKhejoaLRv3x52dnZo1KiRxs5oc+bMQYsWLWBvbw9vb2/MmzcPCoVCq81NmzahSZMmcHZ2xtChQ5Gbm6vRxtKlS9GsWTPY2tpqtZGeno4hQ4agTp06qFu3LkJDQ3H9+nW9X/tbt25h0aJFaNGiBaZNm1Zu/dq1a0Mul0tf5W11HBkZKY3Ge3h4aLTx7LSEK1euoHv37pDJZPDx8cHhw4c13vPr169DEAR8++23ePnll2FnZ4dOnTrh2rVrOHv2LDp27AgHBwcEBwfjr7/+kq579uxZBAYGol69enB2dsYrr7yCxMRE/V4oIzNrcnv8+HGEhITAw8ND64dMoVBgzpw5aNOmDWrVqgUPDw+MGjUKd+7c0bhGYWEh3n33XdSrVw+1atXCgAEDcPv2bRM/EyKqapQqEadT7+H7pHScTr3HDSvIPJL3AN+OAnI0/+9CTkZxuRESXPXo6e7du1FYWKjTObm5uQgJCUGbNm2QmJiIhQsXYs6cOSXWnTt3LpYtW4Zz586hRo0aGDduXIn1Zs+ejfXr1wMongKRkZFRYr3w8HBER0fj/fffx8WLF7F161a4ublJxx0dHREbG4vk5GSsXLkSX331ldZOWqmpqdi9ezf27duHffv2IT4+Hp988olGG0uXLsW8efOQnJys0UZeXh4CAgLg4OCA48eP4+TJk1JSV1RUVO5rl5eXh02bNqF3795o0qQJ9u/fj5kzZ2LdunXlnvvOO++gXr166NSpE7744guoVKpS6+7YsQMrVqzAl19+id9//x27d+9GmzZtSqyrUqkwcOBA2Nvb45dffsGaNWswd+7cEutGRETgo48+QmJiImrUqIFhw4YhLCwMK1euxIkTJ5Camor58+dL9XNzczF69GicOHECP//8M5o3b45+/fpp/DJhdqIZ7d+/X5w7d6743XffiQDEXbt2SccePnwo9u7dW9y2bZt45coV8fTp02Lnzp3FDh06aFxj0qRJYoMGDcS4uDgxMTFRDAgIEF988UXxyZMnOseRnZ0tAhCzs7N1ql9UVCTu3r1bLCoq0rkNospgn9PPj/+7I760+LDYeM4+6eulxYfFH/93x9yhVRvsc6KYn58vJicni/n5+RW7gPKJKC57QRQjnEr5chbFZa2K6xnYjh07xDp16ogymUzs2rWrGB4eLv76668adZ7+f3f16tVi3bp1NZ7rV199JQIQL1y4IIqiKB47dkwEIB4+fFiq88MPP4gApPMiIiLEF198UTq+a9cu8dlUY/To0WJoaKgoiqKYk5Mj2trail9++aX44MEDUalUlvvcoqOjNXKBiIgI0d7eXszJyZHK3n//fbFz584abXz11VclXm/t2rViy5YtRZVKJZUVFhaKdnZ24sGDB0uN46effhLHjh0rOjg4iN7e3uL8+fPFlJSUcuNXW7hwoZiQkCBeuHBB/Ne//iXa29uLCxcuLLX+smXLxBYtWpT6M9m4cWNxxYoVoiiK4o8//ijWqFFDzMjIkI7HxcVpvOdpaWkiAPHrr7+W6vz3v/8VAYhHjhyRypYsWSK2bNmy1LiePHkiOjo6inv37tXlaZerrJ87XfM1s8657du3b6mT2Z2dnREXF6dR9tlnn8Hf3x83b95Eo0aNkJ2djbVr10q/MQHA5s2b4enpicOHD6NPnz5Gfw5EVLUcuJiByZsTtbYezswuwOTNibyZjkznRoL2iK0GEchJL67n9bJBm3799dfRv39/nDhxAqdPn8aBAwcQHR2Nr7/+GmPGjNGqf/XqVfj5+UEm+/sGVH9//xKv7efnJ33v7l78s5SVlYVGjRrpHefly5dRWFiIXr16lVpnx44diImJQUpKCh49eoQnT57AyclJo06TJk3g6OioEVdWVpZObZw/fx4pKSka5wNAQUGBNGe4JD179oSdnR2WL1+OSZMmlftcn/XRRx9J36uncixYsECj/GlvvvkmYmJi4O3tjeDgYPTr1w8hISGoUUM7lbt69So8PT015lXr8n6qR7OfHhF2c3OTXkug+L2eP38+jh49ij///BNKpRJ5eXm4efOmDs/aNKrVDWXZ2dkQBAG1a9cGUNwhFQoFgoKCpDoeHh7w9fVFQkJCqcltYWGhxp9qcnJyABRPhXh6Hk9p1HV0qUtkCOxzulGqRCz54RJqWpc8BUEAsOSHS+jZnHOPy8M+V/zcRVGESqUq88/FpcrN0Gnunyo3A6jI9ctRs2ZN9OrVC7169cJHH32Et99+GxERERg1atTfbf//c1OpVBAEQeN5KpVKrToAYG1tLX0visU/a0+ePIFKpZIeq48/+6/6HPXrqr65TH2eulzt559/xtChQxEZGYmgoCA4Oztj27ZtWL58uUYMNjY2Wu+ROmZ1G6W9j0qlEh06dMCmTZu0jtWvX7/U9/7777/Hxo0bMWPGDKxZswYjR47E0KFDK3yjnr+/P3JycpCRkaExLUOtQYMGuHz5MuLi4nDkyBFMmTIFn376KY4dOwYbGxsA0Oivz76fT78X5b2fz5Y9XX/06NG4e/culi9fjsaNG8PW1hbdunVDYWFhxX5OnqHuRwqFAtbWmjdd6vp5VG2S24KCAnzwwQcYPny49BtbZmYmatasiTp16mjUdXNzQ2ZmZqnXWrJkCaKiorTKDx06BHt7e51jenZkmcjY2OfKN/OF8mo8xsEDP5oiFIvwPPe5GjVqQC6X49GjRzrNvdQ6X3CEQ/nVkCc44sn/D7IYk7e3Nx49eiQN6ABAfn4+cnJy0LhxY2zZsgV//fWXlAyePHkSAPD48WPk5OQgLy8PQPGcSysrK+kYAOm6hYWFUCqVUhv5+fkAoNGmQqHAkydPkJOTAzc3N9jZ2WH//v0YNWqU1rzNo0ePwtPTE++8845UlpKSAlEUpWs+2yZQnDOoVCqNNn744QeNxF6tVatW2LZtG2QymdaI8LOxP61Hjx7o0aMHHj58iB07dmDr1q0ICwtDz549MWTIEPTv31+vnOL06dOQyWSwsrIqtU2geMS4Z8+eGDVqFPz9/fHzzz/jxRdfhEqlQkFBAXJycuDp6YmbN28iJSUFrq6uAID4+HgAf7/njx49AvD3+wugxPe4oKBA4/U+efIkPv30U3Tv3h0AcPv2bdy9e1dqu7KKioqQn5+P48eP48mTJxrH1PGVp1oktwqFAkOHDoVKpcLnn39ebn1RFKW7MksSHh6OmTNnSo/VHSEoKKjEjl1SPHFxcQgMDJR+WyIyJvY53ez/XwbCvvut3HrRr/uhXxtOTSgL+1zxf+q3bt2Cg4ODxp/rddaqN0QnDyAnA4LWRBlAhAA4ecC+VW+DLgt27949DBkyBGPGjIGfnx8cHR1x7tw5fPbZZwgNDdX4f87Ozg5OTk4YN24cFi1ahPfffx9z5szBzZs3pf9vHRwc4OTkJCVqjo6O0jVq1aqlUcfW1hbW1tbScfXd/0+3aWNjgxo1asDJyQlOTk4ICwtDZGQkatasiVdffRV3797FpUuXMH78eLRu3Rq3b9/G/v370alTJ+zfvx8//PADBEGQrvlsmwCkJPHZNpycnNCtWzf89ddfUhvjx4/Hf/7zH4wePRqRkZFo2LAhbt68iV27dmH27Nlo2LBhma+3k5MTZs6ciZkzZ+LKlSvYsGEDoqKi8OOPP2LHjh0lnrN3715kZmaiS5cusLOzw7Fjx7Bo0SK8/fbbpS7pFRsbC6VSic6dO8Pe3h67d++GnZ0dfHx84OTkBCsrKylBDw0NRdOmTfHuu+9i6dKlyM3NxZIlSwAA9vb2cHJykpZsq1WrlvTalfQey2Qyjde7WbNm+O677/Dyyy8jJycHc+bMgZ2dXam/HOiroKAAdnZ26NGjh9bPna7Jc5VPbhUKBQYPHoy0tDQcPXpU44WTy+UoKirCgwcPNEZvs7Ky0LVr11KvaWtrW+I6ezY2Nnp9iOtbn6iy2OfK5upcC4XK8qcbuDrX4uuoo+e5zymVSgiCACsrK2kUSy9WVkDw0uJVESAAGgmuULxbYPAnEGoY9vV1cnJC586dsXLlSqSmpkKhUMDT0xNvv/02PvzwQ43non5utWvXxt69ezF58mS0b98ebdq0wfz58zF8+HDY29trvAbPfv90mXpgqaTj0jP//w0L1GXz589HjRo1sHjxYkybNg3u7u6YNGkSrKys8I9//APvvfcepk2bhsLCQvTv3x/z5s1DZGSkdP6zbZZUNn/+fNjY2CAyMhJ37tzRaEO9SsKcOXPwxhtvIDc3Fw0aNECvXr1Qu3Ztvd57Hx8fLF26FEuWLEFKSkqp59ra2uKLL77A7NmzoVKp4O3tjQULFmDq1KmlnuPi4oJPPvkEs2fPhlKpRJs2bbB3716NZPjp/rp7927885//ROfOneHt7Y1PP/0UISEher2fJb2H69atw4QJE9ChQwc0atQIixcvxuzZszXe08pQ96OSPnt0/SwSRPUECzMTBAG7du3SWPtOndj+/vvvOHbsmNZvM9nZ2ahfvz42b96MwYMHAyhebqRhw4bYv3+/zjeU5eTkwNnZGdnZ2TqP3O7fvx/9+vV7bj/0ybTY53SjVInovvQoMrMLShgnK04v5M4ynJzzKufcloN9rngEKS0tDV5eXhUbuVVL3gMcmKN5c5lTAyD4E8BnQOUDNZItW7Zg7NixyM7OLnf91cpSTyFQj0CS4Z06dQrdu3dHSkoKmjZtau5wSlXWz52u+ZpZR24fPXqElJQU6XFaWhqSkpLg4uICDw8PvPHGG0hMTMS+ffugVCqlebQuLi6oWbMmnJ2dMX78eMyaNQt169aFi4sLZs+ejTZt2kirJxDR88PaSkBEiA8mb04sYZysWESIDxNbMi2fAcAL/U26Q1lFbNy4Ed7e3mjQoAF+/fVXzJkzB4MHDzZ6YkvGsWvXLjg4OKB58+ZISUnB9OnT0a1btyqd2BqKWZPbc+fOISAgQHqsngernveyZ0/x4tbqJTLUjh07hp49ewIAVqxYgRo1amDw4MHIz89Hr169EBsbq3WHHRE9H4J93bF6ZHtE7U1GRnaBVC53liEixIfLgJF5WFkbfLkvQ8vMzMT8+fORmZkJd3d3vPnmmxq7eFH1kpubi7CwMNy6dQv16tVD7969sWzZMnOHZRJVZlqCOXFaAlV17HP6U6pEnEm7j6zcArg6yuDv5cIRWz2wzxlwWgKVi9MSSK3aT0sgIjIWaysBXZrWNXcYRERkYvz1iIiIqAz8AyeR6Rji543JLRERUQnU0zF0XTieiCpP/fNWmelQnJZARERUAmtra9SuXRtZWVkAihe4L2uDIKo4lUqFoqIiFBQUcM7tc0oUReTl5SErKwu1a9eu1MIATG6JiIhKIZfLAUBKcMk4RFFEfn4+7Ozs+AvEc6527drSz11FMbklIiIqhSAIcHd3h6urKxQKhbnDsVgKhQLHjx9Hjx49ntvVOah4KoIhlnJlcktERFQOa2trrp9uRNbW1njy5AlkMhmTW6o0TmwhIiIiIotRqeS2oKCg/EpERERERCaid3KrUqmwcOFCNGjQAA4ODvjjjz8AAPPmzcPatWsNHiARERERka70Tm4//vhjxMbGIjo6GjVr1pTK27Rpg6+//tqgwRERERER6UPv5Hbjxo1Ys2YNRowYoTG53s/PD1euXDFocERERERE+tA7uU1PT0ezZs20ylUqFZdJISIiIiKz0ju5bd26NU6cOKFVvn37drRr184gQRERERERVYTe69xGRETgrbfeQnp6OlQqFXbu3ImrV69i48aN2LdvnzFiJCIiIiLSid4jtyEhIdi2bRv2798PQRAwf/58XL58GXv37kVgYKAxYiQiIiIi0kmFdijr06cP+vTpY+hYiIiIiIgqRe+R27Nnz+KXX37RKv/ll19w7tw5gwRFRERERFQReie3U6dOxa1bt7TK09PTMXXqVIMERURERERUEXont8nJyWjfvr1Webt27ZCcnGyQoIiIiIiIKkLv5NbW1hZ//vmnVnlGRgZq1KjQFF4iIiIiIoPQO7kNDAxEeHg4srOzpbKHDx/iww8/5GoJRERERGRWeg+1Llu2DD169EDjxo2lTRuSkpLg5uaGTZs2GTxAIiIiIiJd6Z3cNmjQAL/99hu2bNmCX3/9FXZ2dhg7diyGDRsGGxsbY8RIRERERKSTCk2SrVWrFiZMmGDoWIiIiIiIKqVCye21a9fw008/ISsrCyqVSuPY/PnzDRIYEREREZG+9E5uv/rqK0yePBn16tWDXC6HIAjSMfV2vERERERE5qB3cvvxxx9j0aJFmDNnjjHiISIiIiKqML2XAnvw4AHefPNNY8RCRERERFQpeie3b775Jg4dOmSMWIiIiIiIKkXvaQnNmjXDvHnz8PPPP6NNmzZay39NmzbNYMEREREREelD7+R2zZo1cHBwQHx8POLj4zWOCYLA5JaIiIiIzEbvaQlpaWmlfv3xxx96Xev48eMICQmBh4cHBEHA7t27NY6LoojIyEh4eHjAzs4OPXv2xKVLlzTqFBYW4t1330W9evVQq1YtDBgwALdv39b3aRERGZRSJeJ06j18n5SO06n3oFSJ5g6JiOi5oHdya0iPHz/Giy++iFWrVpV4PDo6GsuXL8eqVatw9uxZyOVyBAYGIjc3V6ozY8YM7Nq1C9988w1OnjyJR48e4bXXXoNSqTTV0yAi0nDgYga6Lz2KYV/9jOnfJGHYVz+j+9KjOHAxw9yhERFZvApt4nD79m3s2bMHN2/eRFFRkcax5cuX63ydvn37om/fviUeE0URMTExmDt3LgYNGgQA2LBhA9zc3LB161ZMnDgR2dnZWLt2LTZt2oTevXsDADZv3gxPT08cPnwYffr0KfHahYWFKCwslB7n5OQAABQKBRQKRblxq+voUpfIENjnqo/Dl//Ee9uSIAKwtf67/MGjfMz473msGNIWvVu5mS0+XbHPkSmxv5EudO0feie3R44cwYABA+Dl5YWrV6/C19cX169fhyiKaN++vd6BliYtLQ2ZmZkICgqSymxtbfHKK68gISEBEydOxPnz56FQKDTqeHh4wNfXFwkJCaUmt0uWLEFUVJRW+aFDh2Bvb69zjHFxcXo8I6LKY5+rHpb6l36sKO089qeZLpbKYp8jU2J/o7Lk5eXpVE/v5DY8PByzZs3CggUL4OjoiO+++w6urq4YMWIEgoOD9Q60NJmZmQAANzfNEQ43NzfcuHFDqlOzZk3UqVNHq476/NKew8yZM6XHOTk58PT0RFBQEJycnMqNTaFQIC4uDoGBgVqrRRAZA/tc9XAm7T7GbThbbr11ozvB38vFBBFVHPscmRL7G+lC/Zf28uid3F6+fBn//e9/i0+uUQP5+flwcHDAggULEBoaismTJ+t7yTI9vb0vUDxd4dmyZ5VXx9bWFra2tlrlNjY2ev1Q6VufqLLY56q2u3lPUKgs+/NJXa+6vI/sc2RK7G9UFl37ht43lNWqVUuar+rh4YHU1FTp2N27d/W9XKnkcjkAaI3AZmVlSaO5crkcRUVFePDgQal1iIhMxdVRZtB6RESkP72T25deegmnTp0CAPTv3x+zZs3CokWLMG7cOLz00ksGC8zLywtyuVxj/k1RURHi4+PRtWtXAECHDh1gY2OjUScjIwMXL16U6hARmYq/lwvcnWUobexWAODuLKvyUxKIiKozvaclLF++HI8ePQIAREZG4tGjR9i2bRuaNWuGFStW6HWtR48eISUlRXqclpaGpKQkuLi4oFGjRpgxYwYWL16M5s2bo3nz5li8eDHs7e0xfPhwAICzszPGjx+PWbNmoW7dunBxccHs2bPRpk0bafUEIiJTsbYSEBHig8mbEyEAeHplW3XCGxHiA2ur8qcuEBFRxeid3Hp7e0vf29vb4/PPP69w4+fOnUNAQID0WH2T1+jRoxEbG4uwsDDk5+djypQpePDgATp37oxDhw7B0dFROmfFihWoUaMGBg8ejPz8fPTq1QuxsbGwtrbWao+IyNiCfd2xemR7RO1NRkZ2gVQud5YhIsQHwb7uZoyOiMjy6Z3cnj17FiqVCp07d9Yo/+WXX2BtbY2OHTvqfK2ePXtCFEvftUcQBERGRiIyMrLUOjKZDJ999hk+++wzndslIjKmYF93BPrIcSbtPrJyC+DqWDwVgSO2RETGp/ec26lTp+LWrVta5enp6Zg6dapBgiIiqu6srQR0aVoXoW0boEvTukxsiYhMRO/kNjk5ucTNGtq1a4fk5GSDBEVEREREVBF6J7e2trb4888/tcozMjJQo0aFdvMlIiIiIjIIvZPbwMBAhIeHIzs7Wyp7+PAhPvzwQwQGBho0OCIiIiIifeg91Lps2TL06NEDjRs3Rrt27QAASUlJcHNzw6ZNmwweIBERERGRrvRObhs0aIDffvsNW7Zswa+//go7OzuMHTsWw4YN45Z5RERERGRWFZokW6tWLUyYMMHQsRARERERVYpOye2ePXvQt29f2NjYYM+ePWXWHTBggEECIyIiIiLSl07J7cCBA5GZmQlXV1cMHDiw1HqCIECpVBoqNiIiIiIiveiU3KpUqhK/JyIiIiKqSnRaCszFxQV3794FAIwbNw65ublGDYqIiIiIqCJ0Sm6LioqQk5MDANiwYQMKCgqMGhQRERERUUXoNC2hS5cuGDhwIDp06ABRFDFt2jTY2dmVWHfdunUGDZCIiIiISFc6JbebN2/GihUrkJqaCkEQkJ2dzdFbIiIiIqpydEpu3dzc8MknnwAAvLy8sGnTJtStW9eogRERERER6UvvTRzS0tKMEQcRERERUaXplNz++9//xoQJEyCTyfDvf/+7zLrTpk0zSGBERERERPrSKbldsWIFRowYAZlMhhUrVpRaTxAEJrdEREREZDY6JbdPT0XgtAQiIiIiqqp0WueWiIiIiKg60PuGMqVSidjYWBw5cgRZWVla2/EePXrUYMEREREREelD7+R2+vTpiI2NRf/+/eHr6wtBEIwRFxERERGR3vRObr/55ht8++236NevnzHiISIiIiKqML3n3NasWRPNmjUzRixERERERJWid3I7a9YsrFy5EqIoGiMeIiIiIqIK03tawsmTJ3Hs2DH8+OOPaN26NWxsbDSO79y502DBERERERHpQ+/ktnbt2vjHP/5hjFiIiIiIiCpF7+R2/fr1xoiDiIiIiKjSuIkDEREREVkMvUdu27VrV+LatoIgQCaToVmzZhgzZgwCAgIMEiARERERka70HrkNDg7GH3/8gVq1aiEgIAA9e/aEg4MDUlNT0alTJ2RkZKB37974/vvvjREvEREREVGp9E5u7969i1mzZuHEiRNYtmwZli9fjuPHj2P27Nl4/PgxDh06hI8++ggLFy6sdHBPnjzBRx99BC8vL9jZ2cHb2xsLFizQ2PJXFEVERkbCw8MDdnZ26NmzJy5dulTptomIiIio+tE7uf32228xbNgwrfKhQ4fi22+/BQAMGzYMV69erXRwS5cuxRdffIFVq1bh8uXLiI6OxqefforPPvtMqhMdHY3ly5dj1apVOHv2LORyOQIDA5Gbm1vp9omIiIioetF7zq1MJkNCQoLWLmUJCQmQyWQAAJVKBVtb20oHd/r0aYSGhqJ///4AgCZNmuC///0vzp07B6B41DYmJgZz587FoEGDAAAbNmyAm5sbtm7diokTJ5Z43cLCQhQWFkqPc3JyAAAKhQIKhaLcuNR1dKlLZAjsc2Rq7HNkSuxvpAtd+4feye27776LSZMm4fz58+jUqRMEQcCZM2fw9ddf48MPPwQAHDx4EO3atdP30lq6d++OL774AteuXUOLFi3w66+/4uTJk4iJiQEApKWlITMzE0FBQdI5tra2eOWVV5CQkFBqcrtkyRJERUVplR86dAj29vY6xxcXF6ffEyKqJPY5MjX2OTIl9jcqS15enk71BLEC++hu2bIFq1atkqYetGzZEu+++y6GDx8OAMjPz5dWT6gMURTx4YcfYunSpbC2toZSqcSiRYsQHh4OoHi0uFu3bkhPT4eHh4d03oQJE3Djxg0cPHiwxOuWNHLr6emJu3fvwsnJqdy4FAoF4uLiEBgYqLVDG5ExsM+RqbHPkSmxv5EucnJyUK9ePWRnZ5eZr+k9cgsAI0aMwIgRI0o9bmdnV5HLatm2bRs2b96MrVu3onXr1khKSsKMGTPg4eGB0aNHS/WeXZpMFMUSlytTs7W1LXHahI2NjV4/VPrWJ6os9jnShVIl4kzafWTlFsDVUQZ/LxdYW5X+mVgW9jkyJfY3KouufaNCya2pvP/++/jggw8wdOhQAECbNm1w48YNLFmyBKNHj4ZcLgcAZGZmwt3dXTovKysLbm5uZomZiMicDlzMQNTeZGRkF0hl7s4yRIT4INjXvYwziYgsg06rJbi4uODu3bsAgDp16sDFxaXUL0PKy8uDlZVmiNbW1tJSYF5eXpDL5RpzdIqKihAfH4+uXbsaNBYioqruwMUMTN6cqJHYAkBmdgEmb07EgYsZZoqMiMh0dBq5XbFiBRwdHaXvy/qTvyGFhIRg0aJFaNSoEVq3bo0LFy5g+fLlGDduHIDi6QgzZszA4sWL0bx5czRv3hyLFy+Gvb29NP+XiOh5oFSJiNqbjJJuohABCACi9iYj0Ede4SkKRETVgU7J7dPzW8eMGWOsWLR89tlnmDdvHqZMmYKsrCx4eHhg4sSJmD9/vlQnLCwM+fn5mDJlCh48eIDOnTvj0KFDUjJORPQ8OJN2X2vE9mkigIzsApxJu48uTeuaLjAiIhPTe85tYmIibGxs0KZNGwDA999/j/Xr18PHxweRkZGoWbOmwYJzdHRETEyMtPRXSQRBQGRkJCIjIw3WLhFRdZOVW3piW5F6RETVld47lE2cOBHXrl0DAPzxxx8YMmQI7O3tsX37doSFhRk8QKLqTqkScTr1Hr5PSsfp1HtQqvRefY+oXK6Oui29qGs9IqLqSu+R22vXrqFt27YAgO3bt+OVV17B1q1bcerUKQwdOrTMUVai5w3vXCdT8fdygbuzDJnZBSXOuxUAyJ2LlwUjIrJkeo/ciqIorVZw+PBh9OvXDwCkTRCIqBjvXCdTsrYSEBHiA6A4kX2a+nFEiA9vJiMii6d3ctuxY0d8/PHH2LRpE+Lj49G/f38AxVvhcm1ZomLl3bkOFN+5zikKZEjBvu5YPbI95M6aUw/kzjKsHtmefy0goueC3tMSYmJiMGLECOzevRtz585Fs2bNAAA7duzg2rJE/493rpO5BPu6I9BHbrAdyoiIqhu9k1s/Pz/873//0yr/9NNPYW1tbZCgiKo73rlO5mRtJfCXJiJ6bhls+12ZjHfgEqnxznUiIiLz0HvOLRGVT33neml/CBZQvGoC71wnIiIyLCa3REbAO9eJiIjMg8ktkZHwznUiIiLTM9icWyLSxjvXiYiITEun5HbmzJk6X3D58uUVDobIEvHOdSIiItPRKbm9cOGCThcTBI5GEREREZH56JTcHjt2zNhxEBERERFVGm8oIyIiIiKLUaEbys6ePYvt27fj5s2bKCoq0ji2c+dOgwRGRERERKQvvUduv/nmG3Tr1g3JycnYtWsXFAoFkpOTcfToUTg7OxsjRiIiIiIineid3C5evBgrVqzAvn37ULNmTaxcuRKXL1/G4MGD0ahRI2PESERERESkE72T29TUVPTv3x8AYGtri8ePH0MQBLz33ntYs2aNwQMkIiIiItKV3smti4sLcnNzAQANGjTAxYsXAQAPHz5EXl6eYaMjIiIiItKD3jeUvfzyy4iLi0ObNm0wePBgTJ8+HUePHkVcXBx69epljBiJiIiIiHSid3K7atUqFBQUAADCw8NhY2ODkydPYtCgQZg3b57BAyQiIiIi0pVeye2TJ0+wd+9e9OnTBwBgZWWFsLAwhIWFGSU4IiIiIiJ96DXntkaNGpg8eTIKCwuNFQ8RERERUYXpfUNZ586dceHCBWPEQkRERERUKXrPuZ0yZQpmzZqF27dvo0OHDqhVq5bGcT8/P4MFR0RERESkD72T2yFDhgAApk2bJpUJggBRFCEIApRKpeGiIyIiIiLSg97JbVpamjHiICIiIiKqNL2T28aNGxsjDiIiIiKiStM7ud2zZ0+J5YIgQCaToVmzZvDy8qp0YERERERE+tI7uR04cKA0x/ZpT8+77d69O3bv3o06deoYLFAiIiIiovLovRRYXFwcOnXqhLi4OGRnZyM7OxtxcXHw9/fHvn37cPz4cdy7dw+zZ882SIDp6ekYOXIk6tatC3t7e7Rt2xbnz5+XjouiiMjISHh4eMDOzg49e/bEpUuXDNI2EREREVUveo/cTp8+HWvWrEHXrl2lsl69ekEmk2HChAm4dOkSYmJiMG7cuEoH9+DBA3Tr1g0BAQH48ccf4erqitTUVNSuXVuqEx0djeXLlyM2NhYtWrTAxx9/jMDAQFy9ehWOjo6VjoGIiIiIqg+9k9vU1FQ4OTlplTs5OeGPP/4AADRv3hx3796tdHBLly6Fp6cn1q9fL5U1adJE+l4URcTExGDu3LkYNGgQAGDDhg1wc3PD1q1bMXHixBKvW1hYqLHLWk5ODgBAoVBAoVCUG5e6ji51iQyBfY5MjX2OTIn9jXSha/8QxGcnz5aje/fucHR0xMaNG1G/fn0AwF9//YVRo0bh8ePHOH78OA4fPowpU6bg2rVr+kf+FB8fH/Tp0we3b99GfHw8GjRogClTpuDtt98GAPzxxx9o2rQpEhMT0a5dO+m80NBQ1K5dGxs2bCjxupGRkYiKitIq37p1K+zt7SsVMxEREREZXl5eHoYPH47s7OwSB1rV9B65Xbt2LUJDQ9GwYUN4enpCEATcvHkT3t7e+P777wEAjx49wrx58yoe/f/7448/sHr1asycORMffvghzpw5g2nTpsHW1hajRo1CZmYmAMDNzU3jPDc3N9y4caPU64aHh2PmzJnS45ycHHh6eiIoKKjMF0tNoVAgLi4OgYGBsLGxqeCzI9Id+xyZWkX7nFIl4vyNB7j7qBD1HGzRoXEdWFsJRoyULAE/40gX6r+0l0fv5LZly5a4fPkyDh48iGvXrkEURbzwwgsIDAyElVXx/WkDBw7U97IlUqlU6NixIxYvXgwAaNeuHS5duoTVq1dj1KhRUj1B0PzgVK/aUBpbW1vY2tpqldvY2Oj1Q6VvfaLKYp8jU9Onzx24mIGovcnIyC6QytydZYgI8UGwr7uxQiQLws84KouufUPv5BYoTiaDg4PRs2dP2NralplIVoa7uzt8fHw0ylq1aoXvvvsOACCXywEAmZmZcHf/+4MzKytLazSXiIiM58DFDEzenIhn57llZhdg8uZErB7ZngkuEZmE3kuBqVQqLFy4EA0aNICDg4O0He+8efOwdu1agwbXrVs3XL16VaPs2rVr0i5pXl5ekMvliIuLk44XFRUhPj5eYzUHIiIyHqVKRNTeZK3EFoBUFrU3GUqVXrd4EBFViN7J7ccff4zY2FhER0ejZs2aUnmbNm3w9ddfGzS49957Dz///DMWL16MlJQUbN26FWvWrMHUqVMBFI8gz5gxA4sXL8auXbtw8eJFjBkzBvb29hg+fLhBYyEiopKdSbuvMRXhWSKAjOwCnEm7b7qgiOi5pfe0hI0bN2LNmjXo1asXJk2aJJX7+fnhypUrBg2uU6dO2LVrF8LDw7FgwQJ4eXkhJiYGI0aMkOqEhYUhPz8fU6ZMwYMHD9C5c2ccOnSIa9wSEZlIVm7piW1F6hERVYbeyW16ejqaNWumVa5SqYyyPt1rr72G1157rdTjgiAgMjISkZGRBm+biIjK5+ooM2g9IqLK0HtaQuvWrXHixAmt8u3bt2usNUtERM8Hfy8XuDvLUNqtxQKKV03w93IxZVhE9JzSe+Q2IiICb731FtLT06FSqbBz505cvXoVGzduxL59+4wRIxERVWHWVgIiQnwweXMiBEDjxjJ1whsR4sP1bonIJPQeuQ0JCcG2bduwf/9+CIKA+fPn4/Lly9i7dy8CAwONESMREVVxwb7uWD2yPeTOmlMP5M4yLgNGRCZVoXVu+/Tpgz59+hg6FiIiqsaCfd0R6CPHmbT7yMotgKtj8VQEjtgSkSnpPXLr7e2Ne/fuaZU/fPgQ3t7eBgmKiIiqJ2srAV2a1kVo2wbo0rQuE1siMjm9k9vr169DqVRqlRcWFiI9Pd0gQRERERERVYTO0xL27NkjfX/w4EE4OztLj5VKJY4cOYImTZoYNDgiIiIiIn3onNwOHDgQQPG6sqNHj9Y4ZmNjgyZNmmDZsmUGDY6IiIiISB86J7cqlQoA4OXlhbNnz6JevXpGC4qIiIiIqCL0Xi0hLS3NGHEQEREREVVahZYCO3LkCI4cOYKsrCxpRFdt3bp1BgmMiIiIiEhfeie3UVFRWLBgATp27Ah3d3cIApd5ISIiIqKqQe/k9osvvkBsbCzeeustY8RDRERERFRheq9zW1RUhK5duxojFiIiIiKiStE7uf3nP/+JrVu3GiMWIiIiIqJK0XtaQkFBAdasWYPDhw/Dz88PNjY2GseXL19usOCIiIiIiPShd3L722+/oW3btgCAixcvahzjzWVEREREZE56J7fHjh0zRhxERERERJWm95xbIiIiIqKqSqeR20GDBiE2NhZOTk4YNGhQmXV37txpkMCIDEmpEnEm7T6ycgvg6iiDv5cLrK04jYaIiMjS6JTcOjs7S/NpnZ2djRoQkaEduJiBqL3JyMgukMrcnWWICPFBsK+7GSMjIiIiQ9MpuV2/fn2J3xNVdQcuZmDy5kSIz5RnZhdg8uZErB7ZngkuERGRBeGcW7JYSpWIqL3JWoktAKksam8ylKqSahAREVF1xOSWLNaZtPsaUxGeJQLIyC7AmbT7pguKiIiIjIrJLVmsrNzSE9uK1CMiIqKqj8ktWSxXR5lB6xEREVHVx+SWLJa/lwvcnWUobcEvAcWrJvh7uZgyLCIiIjIivXcoA4DHjx8jPj4eN2/eRFFRkcaxadOmGSQwosqythIQEeKDyZsTIQAaN5apE96IEB+ud0tERGRB9E5uL1y4gH79+iEvLw+PHz+Gi4sL7t69C3t7e7i6ujK5pSol2Ncdq0e211rnVs51bomIiCyS3snte++9h5CQEKxevRq1a9fGzz//DBsbG4wcORLTp083RoxElRLs645AHzl3KCN6DnA3QiLSO7lNSkrCl19+CWtra1hbW6OwsBDe3t6Ijo7G6NGjy92el8gcrK0EdGla19xhEJERcTdCIgIqcEOZjY2NtBWvm5sbbt68CaB4W17198ayZMkSCIKAGTNmSGWiKCIyMhIeHh6ws7NDz549cenSJaPGQUREVYt6N8Jn17ZW70Z44GKGmSIjIlPTO7lt164dzp07BwAICAjA/PnzsWXLFsyYMQNt2rQxeIBqZ8+exZo1a+Dn56dRHh0djeXLl2PVqlU4e/Ys5HI5AgMDkZuba7RYiIio6uBuhET0NL2T28WLF8PdvfjPOwsXLkTdunUxefJkZGVlYc2aNQYPEAAePXqEESNG4KuvvkKdOnWkclEUERMTg7lz52LQoEHw9fXFhg0bkJeXh61btxolFiIiqlq4GyERPU3vObcdO3aUvq9fvz72799v0IBKMnXqVPTv3x+9e/fGxx9/LJWnpaUhMzMTQUFBUpmtrS1eeeUVJCQkYOLEiSVer7CwEIWFhdLjnJwcAIBCoYBCoSg3HnUdXeoSGQL7HJladepzWdmPYWtd/qhsVvZjKBROJoiI9FWd+huZj679o0Lr3JrSN998g8TERJw9e1brWGZmJoDiub9Pc3Nzw40bN0q95pIlSxAVFaVVfujQIdjb2+scW1xcnM51iQyBfY5Mrbr0uWh/HSrduoD9ty4YPRaquOrS38g88vLydKqnc3L76quv6lTv6NGjul6yXLdu3cL06dNx6NAhyGSlb5GqvsFNTRRFrbKnhYeHY+bMmdLjnJwceHp6IigoCE5O5f9Wr1AoEBcXh8DAQNjY2OjwTAgonhd3/sYD3H1UiHoOtujQuA6X6NER+xyZWnXqc0qViD4xx/FnTkGJ824FAG5OMhyc0YOfOVVUdepvZD7qv7SXR+fk9qeffkLjxo3Rv39/k3W88+fPIysrCx06dJDKlEoljh8/jlWrVuHq1asAikdw1fOAASArK0trNPdptra2sLW11Sq3sbHR67npW/95xiV6DIN9jkytOvQ5GwDh/Vtj8uZEACXvRhjevzVktjVNHRrpqTr0NzIfXfuGzsntJ598gtjYWGzfvh0jRozAuHHj4OvrW+EAddGrVy/873//0ygbO3YsXnjhBcyZMwfe3t6Qy+WIi4tDu3btAABFRUWIj4/H0qVLjRob6U69RM+zIyrqJXpWj2zPBJeIKoW7ERKRms7JbVhYGMLCwnD69GmsW7cO3bp1Q8uWLTFu3DgMHz5cpz/n68vR0VErga5Vqxbq1q0rlc+YMQOLFy9G8+bN0bx5cyxevBj29vYYPny4weMh/ZW3RI+A4iV6An3k/HMhEVUKdyMkIqACN5R16dIFXbp0wcqVK7F9+3b85z//wezZs3Hnzh2jJLjlCQsLQ35+PqZMmYIHDx6gc+fOOHToEBwdHU0eiy7MuTWkOdrWZ4ke7iBGRJXF3QiJqMKrJSQmJiI+Ph6XL1+Gr6+vyebI/PTTTxqPBUFAZGQkIiMjTdJ+ZZhz3qm52s7KLT2xrUg9IiIiorLotYnDnTt3sHjxYrRo0QJvvPEGXFxc8Msvv+Dnn3+GnZ2dsWK0CObcGtKcbbs6lr7KRUXqEREREZVF5+S2X79+aNq0KX755Rd8+umnuH37Nv71r3/Bx8fHmPFZBHNuDWnubSn9vVzg7ixDaZMfBBSPIPt7uRilfSIiInq+6JzcHjhwAC4uLrh58yaioqLg7++P9u3ba32RNnNuDWnubSmtrQREhBT/AvRsgqt+HBHiwxs+iIiIyCB0nnMbERFhzDgsmjnnnVaFOa9cooeIiIhMhcmtCZhz3mlVmfPKJXqIiIjIFCq8WgLpTj3vNDO79K0h5Uaad2rOtp/FJXqIiIjI2PRaLYEqxpzzTjnnlYiIiJ4nTG5NRD3vVO6s+ed/ubPM6NvPmrNtIiIiIlPitAQTMue8U855JSIioueBQZPb+/fvw8WF65WWxZzzTjnnlYiIiCxdhacl+Pn5YerUqTh//jwA4Nq1a3jppZcMFhgRERERkb4qnNyOHj0aeXl5CAgIwD/+8Q907doVTZs2NWRsRERERER60XlagkqlAgBYWRXnw7NmzQIABAcHY9iwYXBwcMCWLVuMECIRERERkW50HrkdOnQovvzyS42yM2fO4O2330ZUVBS6d++ORYsWGTxAIiKi6kCpEnE69R6+T0rH6dR7UKpKWl2ciIxN55Hb+Ph4REVFSY8vX76M/v37Y+HChZg+fTpOnDiBkSNHYtmyZUYJlIiIqKo6cDFDa4txd24xTmQWOo/cPn78GNbW1gCAGzduoG/fvli6dCmmT58OAHB3d8fdu3eNEyUREVEVdeBiBiZvTtRIbAEgM7sAkzcn4sDFDDNFRvR80jm5bdu2LWbMmIGvv/4ar7zyCqZMmYJx48ZJxw8cOIBmzZoZJUgiIqKqSKkSEbU3ucTtzdVlUXuTOUWByIR0npYQExODIUOGIDo6Gm+88QY+/fRTODs7o23btjh+/DiioqKwfPlyY8ZKRERUpZxJu681Yvs0EUBGdgHOpN3nOuNEJqJzctuxY0ekpqZKj9u0aYPw8HBkZmbCzs4O06dPx4QJE4wSJBERUVWUlVt6YluRekRUeRXeoWz06NEYNWoUsrKyUKdOHdSsWdOQcREREVV5ro4yg9Yjosqr8CYOACAIAtzc3JjYEhHRc8nfywXuzjIIpRwXULxqgr8Xt6YnMpVKJbdERETPM2srAREhPgCgleCqH0eE+MDaqrT0l4gMjcktERFRJQT7umP1yPaQO2tOPZA7y7B6ZHuuc0tkYhWec0tERETFgn3dEegjx5m0+8jKLYCrY/FUBI7YEpkek1siIiIDsLYSuNwXURWgU3K7Z88e9O3bFzY2NtizZ0+ZdQcMGGCQwIiIiIiI9KVTcjtw4EBkZmbC1dUVAwcOLLWeIAhQKpWGio2IiIiISC86JbcqlarE74mIiIiIqhKDrZZw69YtjBs3zlCXIyIiIiLSm8GS2/v372PDhg2GuhwRERERkd64zi0RERERWYwqndwuWbIEnTp1gqOjo3Qz29WrVzXqiKKIyMhIeHh4wM7ODj179sSlS5fMFDERERERmVOVTm7j4+MxdepU/Pzzz4iLi8OTJ08QFBSEx48fS3Wio6OxfPlyrFq1CmfPnoVcLkdgYCByc3PNGDkRERERmYPOmzgMGjSozOMPHz6sbCxaDhw4oPF4/fr1cHV1xfnz59GjRw+IooiYmBjMnTtXim/Dhg1wc3PD1q1bMXHiRIPHRERERERVl87JrbOzc7nHR40aVemAypKdnQ0AcHFxAQCkpaUhMzMTQUFBUh1bW1u88sorSEhIKDW5LSwsRGFhofQ4JycHAKBQKKBQKMqNQ11Hl7pEhsA+R6bGPle9KFUizt94gLuPClHPwRYdGtepVlv/sr+RLnTtH4IoiqKRYzEIURQRGhqKBw8e4MSJEwCAhIQEdOvWDenp6fDw8JDqTpgwATdu3MDBgwdLvFZkZCSioqK0yrdu3Qp7e3vjPAEiIiIiqrC8vDwMHz4c2dnZcHJyKrWeziO35vbOO+/gt99+w8mTJ7WOCYLmb6eiKGqVPS08PBwzZ86UHufk5MDT0xNBQUFlvlhqCoUCcXFxCAwMhI2NjR7Pgqhi2OfI1NjnqofDl//Ee9uS8Owolfp/wBVD2qJ3KzdTh6U39jfShfov7eWpFsntu+++iz179uD48eNo2LChVC6XywEAmZmZcHd3l8qzsrLg5lb6D7OtrS1sbW21ym1sbPT6odK3PlFlsc+RqbHPVV1KlYgFP1xFgbLkwRwBwIIfriLIt0G1maLA/kZl0bVvVOnVEkRRxDvvvIOdO3fi6NGj8PLy0jju5eUFuVyOuLg4qayoqAjx8fHo2rWrqcMlIiIymTNp95GRXVDqcRFARnYBzqTdN11QRFVAlR65nTp1KrZu3Yrvv/8ejo6OyMzMBFB885qdnR0EQcCMGTOwePFiNG/eHM2bN8fixYthb2+P4cOHmzl6IiIi48nKLT2xrUg9IktRpZPb1atXAwB69uypUb5+/XqMGTMGABAWFob8/HxMmTIFDx48QOfOnXHo0CE4OjqaOFoiIiLTcXWUGbQekaWo0smtLgs5CIKAyMhIREZGGj8gIiKiKsLfywXuzjJkZhdo3VAGFM+5lTvL4O/lYurQiMyqSs+5JSIiopJZWwmICPEB8PfqCGrqxxEhPtXmZjIiQ2FyS0REVE0F+7pj9cj2kDtrTj2QO8uwemR7BPu6l3ImkeWq0tMSiIiIqGzBvu4I9JHjTNp9ZOUWwNWxeCoCR2zpecXkloiIqJqzthLQpWldc4dBVCVwWgIRERERWQwmt0RERERkMZjcEhEREZHF4JxbIiIiqjClSuTNbFSlMLklIiKiCjlwMQNRe5ORkf33Fr/uzjJEhPhwGTIyG05LICIiIr0duJiByZsTNRJbAMjMLsDkzYk4cDHDTJHR847JLREREelFqRIRtTe5xG1/1WVRe5OhVJVUg8i4mNwSERGRXs6k3dcasX2aCCAjuwBn0u6bLiii/8fkloiIiPSSlVt6YluRekSGxOSWiIiI9OLqKDNoPSJDYnJLREREevH3coG7swylLfgloHjVBH8vF1OGRQSAyS0RERHpydpKQESIDwBoJbjqxxEhPlzvlsyCyS0RERHpLdjXHatHtofcWXPqgdxZhtUj23OdWzIbbuJAREREFRLs645AH7lZdyjjDmn0LCa3REREVGHWVgK6NK1rlra5QxqVhNMSiIiIqNrhDmlUGia3REREVK1whzQqC5NbIiIiqla4QxqVhcktERERVSvcIY3KwuSWiIiIqhXukEZl4WoJREREVK2od0jLzC4ocd6tgOL1do2+Q5pKCdxIAB79CTi4AY27AlbWxm2TysXkloiIiKoV9Q5pkzcnQgA0ElyT7ZCWvAc4MAfIufN3mZMHELwU8BlgvHapXJyWQERERNWOWXdIS94DfDtKM7EFgJyM4vLkPcZrm8rFkVsiIiKqlsyyQ5pKWTxiW+pCZAJw4APghf7GnaLAKRGlYnJLRERE1ZbJd0i7kaA9YqtBBHLSi+t5vWycGDglokyclkBERESkq0d/GraevqrKlAiVEkg7AfxvR/G/KqVp2tUBR26JiIiIdOXgZth6+qgqUyKq+MixxYzcfv755/Dy8oJMJkOHDh1w4sQJc4dERERElqZx1+JEDqXN6xUApwbF9QxNnykRxlJVRo7LYBHJ7bZt2zBjxgzMnTsXFy5cwMsvv4y+ffvi5s2b5g6NiIiILImVdfEIJQDtBPf/Hwd/YpyRU3NPiSh35BjFI8dmnqJgEcnt8uXLMX78ePzzn/9Eq1atEBMTA09PT6xevdrcoREREZGl8RkADN4IOD2z3JiTR3G5sf40b84pEUDVGDnWQbWfc1tUVITz58/jgw8+0CgPCgpCQkLJL25hYSEKCwulxzk5OQAAhUIBhUJRbpvqOrrUJTIE9jkyNfY5MqVq2d+a9wWaBgG3zgCPs4BaroCnf/GIrbGeh0cnwNkLyM1EyaOnAuDoXlzPGDHk/AlY6bClcc6fRmlf1/4hiKJY0qtTbdy5cwcNGjTAqVOn0LXr3/NbFi9ejA0bNuDq1ata50RGRiIqKkqrfOvWrbC3tzdqvERERESkv7y8PAwfPhzZ2dlwcnIqtV61H7lVEwTNeS+iKGqVqYWHh2PmzJnS45ycHHh6eiIoKKjMF0tNoVAgLi4OgYGBsLGxqVzgRDpgnyNTY58jU2J/09OV/cDhCCA34+8yRw+gdyTwQj/jtatSAp+/VP7I8ZTTRplzrP5Le3mqfXJbr149WFtbIzMzU6M8KysLbm4lzzmxtbWFra2tVrmNjY1eP1T61ieqLPY5MjX2OTIl9jcdtQkFWr9mhh3KbIA+C4pXRQCgmeD+/4BinyjAVoepCxVpXce+Ue1vKKtZsyY6dOiAuLg4jfK4uDiNaQpEREREFsPKungHtDZvFP9rqq13zXUznR6q/cgtAMycORNvvfUWOnbsiC5dumDNmjW4efMmJk2aZO7QiIiIiCyLz4DijSJMPnKsG4tIbocMGYJ79+5hwYIFyMjIgK+vL/bv34/GjRubOzQiIiIiy6MeOa6CLCK5BYApU6ZgypQp5g6DiIiIiMyo2s+5JSIiIiJSY3JLRERERBaDyS0RERERWQwmt0RERERkMZjcEhEREZHFsJjVEipDFIt32NB1WzeFQoG8vDzk5ORwJxUyCfY5MjX2OTIl9jfShTpPU+dtpWFyCyA3NxcA4OnpaeZIiIiIiKgsubm5cHZ2LvW4IJaX/j4HVCoV7ty5A0dHRwiCIJV36tQJZ8+e1aqfk5MDT09P3Lp1C05OTqYM1WBKe27Voa3KXE/fc/Wpr0vd8upYap8zZX8zdHum7G/6nGPM/gawz5mzLX7Gsb+Zsr3KXstYfa6keqIoIjc3Fx4eHrCyKn1mLUduAVhZWaFhw4Za5dbW1mX+kDk5OVXLH0Kg/OdWlduqzPX0PVef+rrULa+OpfY5U/Y3Q7dnyv6mzzmm6G8A+5w52uJnHPubKdur7LWM1edKq1fWiK0abygrw9SpU80dgtGY8rkZuq3KXE/fc/Wpr0vd8upYap8z9fMyZHum7G/6nMP+VjZ+xhm+Pvtc6Z7Xz7iKnG/Iz7jScFpCBeTk5MDZ2RnZ2dnV8jdMqn7Y58jU2OfIlNjfyJA4clsBtra2iIiIgK2trblDoecE+xyZGvscmRL7GxkSR26JiIiIyGJw5JaIiIiILAaTWyIiIiKyGExuiYiIiMhiMLklIiIiIovB5JaIiIiILAaTWyO7desWevbsCR8fH/j5+WH79u3mDoks3D/+8Q/UqVMHb7zxhrlDIQu1b98+tGzZEs2bN8fXX39t7nDoOcDPNdIHlwIzsoyMDPz5559o27YtsrKy0L59e1y9ehW1atUyd2hkoY4dO4ZHjx5hw4YN2LFjh7nDIQvz5MkT+Pj44NixY3ByckL79u3xyy+/wMXFxdyhkQXj5xrpgyO3Rubu7o62bdsCAFxdXeHi4oL79++bNyiyaAEBAXB0dDR3GGShzpw5g9atW6NBgwZwdHREv379cPDgQXOHRRaOn2ukj+c+uT1+/DhCQkLg4eEBQRCwe/durTqff/45vLy8IJPJ0KFDB5w4caJCbZ07dw4qlQqenp6VjJqqK1P2N6KSVLYP3rlzBw0aNJAeN2zYEOnp6aYInaopfu6RqT33ye3jx4/x4osvYtWqVSUe37ZtG2bMmIG5c+fiwoULePnll9G3b1/cvHlTqtOhQwf4+vpqfd25c0eqc+/ePYwaNQpr1qwx+nOiqstU/Y2oNJXtgyXNZBMEwagxU/VmiM89Ir2IJAEg7tq1S6PM399fnDRpkkbZCy+8IH7wwQc6X7egoEB8+eWXxY0bNxoiTLIQxupvoiiKx44dE19//fXKhkgWriJ98NSpU+LAgQOlY9OmTRO3bNli9FjJMlTmc4+fa6Sr537ktixFRUU4f/48goKCNMqDgoKQkJCg0zVEUcSYMWPw6quv4q233jJGmGQhDNHfiCpDlz7o7++PixcvIj09Hbm5udi/fz/69OljjnDJAvBzj4yhhrkDqMru3r0LpVIJNzc3jXI3NzdkZmbqdI1Tp05h27Zt8PPzk+YZbdq0CW3atDF0uFTNGaK/AUCfPn2QmJiIx48fo2HDhti1axc6depk6HDJAunSB2vUqIFly5YhICAAKpUKYWFhqFu3rjnCJQug6+ceP9dIH0xudfDsfDJRFHWeY9a9e3eoVCpjhEUWqjL9DQDvXKdKK68PDhgwAAMGDDB1WGTByutz/FwjfXBaQhnq1asHa2trrVGzrKwsrd8yiSqL/Y3MjX2QTI19joyByW0ZatasiQ4dOiAuLk6jPC4uDl27djVTVGSp2N/I3NgHydTY58gYnvtpCY8ePUJKSor0OC0tDUlJSXBxcUGjRo0wc+ZMvPXWW+jYsSO6dOmCNWvW4ObNm5g0aZIZo6bqiv2NzI19kEyNfY5MzqxrNVQBx44dEwFofY0ePVqq85///Eds3LixWLNmTbF9+/ZifHy8+QKmao39jcyNfZBMjX2OTE0QxRJW5CYiIiIiqoY455aIiIiILAaTWyIiIiKyGExuiYiIiMhiMLklIiIiIovB5JaIiIiILAaTWyIiIiKyGExuiYiIiMhiMLklIiIiIovB5JaIiIiILAaTWyJ6LvTs2RMzZszQuf7169chCAKSkpKMFtPz7urVq5DL5cjNzTVJe1lZWahfvz7S09NN0h4RmQeTWyKqlsaMGQNBEDBp0iStY1OmTIEgCBgzZoxUtnPnTixcuFDn63t6eiIjIwO+vr6GCNckBEEo8eubb74xd2glmjt3LqZOnQpHR0fp/SzrqzTvvvsumjdvXuKx9PR0WFtbY+fOnXB1dcVbb72FiIgIYz0lIqoCBFEURXMHQUSkrzFjxuDo0aPIyclBRkYG7OzsAAAFBQVwd3eHk5MTAgICEBsba95ATUgQBKxfvx7BwcEa5bVr14ZMJtOqr1QqIQgCrKxMP85x+/ZteHt7448//kDDhg2RnZ2N/Px86bi7u7vWc5HL5SVeKykpCe3atcPx48fx8ssvaxxbtGgRVq5cifT0dNjY2OB///sf/P39cefOHdSpU8c4T46IzIojt0RUbbVv3x6NGjXCzp07pbKdO3fC09MT7dq106j77LSEJk2aYPHixRg3bhwcHR3RqFEjrFmzRjr+7LSEn376CYIg4ODBg2jXrh3s7Ozw6quvIisrCz/++CNatWoFJycnDBs2DHl5eRrtxMTEaMTStm1bREZGSo8FQcCXX36J1157Dfb29mjVqhVOnz6NlJQU9OzZE7Vq1UKXLl2Qmppa7mtSu3ZtyOVyjS91YhsbG4vatWtj37598PHxga2tLW7cuIGioiKEhYWhQYMGqFWrFjp37oyffvpJ47qnTp3CK6+8Ant7e9SpUwd9+vTBgwcPAACFhYWYNm0aXF1dIZPJ0L17d5w9e7bMOL/99lu8+OKLaNiwIQDA2dlZI+Znn4tSqcSQIUNQp04d1K1bF6Ghobh+/br0erZv3x7r1q3Taic2NhajRo2CjY0NAKBNmzaQy+XYtWtXua8lEVVPTG6JqFobO3Ys1q9fLz1et24dxo0bp9O5y5YtQ8eOHXHhwgVMmTIFkydPxpUrV8o8JzIyEqtWrUJCQgJu3bqFwYMHIyYmBlu3bsUPP/yAuLg4fPbZZ3o/j4ULF2LUqFFISkrCCy+8gOHDh2PixIkIDw/HuXPnAADvvPOO3td9Vl5eHpYsWYKvv/4aly5dgqurK8aOHYtTp07hm2++wW+//YY333wTwcHB+P333wEUj4z26tULrVu3xunTp3Hy5EmEhIRAqVQCAMLCwvDdd99hw4YNSExMRLNmzdCnTx/cv3+/1DiOHz+Ojh076hxzQEAAHBwccPz4cZw8eRIODg4IDg5GUVERAGD8+PHYvn07Hj16JJ0XHx+PlJQUrf7g7++PEydO6PW6EVE1IhIRVUOjR48WQ0NDxb/++ku0tbUV09LSxOvXr4symUz866+/xNDQUHH06NFS/VdeeUWcPn269Lhx48biyJEjpccqlUp0dXUVV69eLYqiKKalpYkAxAsXLoiiKIrHjh0TAYiHDx+WzlmyZIkIQExNTZXKJk6cKPbp00ejnRUrVmjE/uKLL4oRERHSYwDiRx99JD0+ffq0CEBcu3atVPbf//5XlMlkZb4mAESZTCbWqlVL40sd3/r160UAYlJSknROSkqKKAiCmJ6ernGtXr16ieHh4aIoiuKwYcPEbt26ldjmo0ePRBsbG3HLli1SWVFRkejh4SFGR0eXGuuLL74oLliwoMznsmvXLlEURXHt2rViy5YtRZVKJR0vLCwU7ezsxIMHD4qiKIoPHjwQZTKZuG7dOqnOqFGjxC5dumhd+7333hN79uxZattEVL3VMF9aTURUefXq1UP//v2xYcMGiKKI/v37o169ejqd6+fnJ30vCALkcjmysrJ0PsfNzQ329vbw9vbWKDtz5oyez0L7ukDxn9CfLisoKEBOTg6cnJxKvc6KFSvQu3dvjTJPT0/p+5o1a2q0lZiYCFEU0aJFC41zCgsLUbduXQDFI7dvvvlmie2lpqZCoVCgW7duUpmNjQ38/f1x+fLlUuPMz88vcR5wSc6fP4+UlBQ4OjpqlBcUFEhTNWrXro1BgwZh3bp1GDt2LHJzc/Hdd99pTQkBADs7O42pI0RkWZjcElG1N27cOOlP9v/5z390Pk89D1NNEASoVCqdzxEEodxrWFlZQXzmvl2FQlHudUsrKy8+uVyOZs2alXrczs5OY+UBlUoFa2trnD9/HtbW1hp1HRwcpHNKo35uz65mIIpimSsc1KtXT5qzWx6VSoUOHTpgy5YtWsfq168vfT9+/Hj06tULv//+O+Lj4wEAQ4YM0Trn/v37GucRkWXhnFsiqvbUcy+LiorQp08fc4ejoX79+sjIyJAe5+TkIC0tzYwRaWrXrh2USiWysrLQrFkzjS/1jV1+fn44cuRIiec3a9YMNWvWxMmTJ6UyhUKBc+fOoVWrVmW2m5ycrFOM7du3x++//w5XV1etGJ2dnaV6AQEB8Pb2RmxsLNatW4fBgwdrjfYCwMWLF7VuOCQiy8HkloiqPWtra1y+fBmXL1/WGn00t1dffRWbNm3CiRMncPHiRYwePdqoMT58+BCZmZkaX48fPy61fosWLTBixAiMGjUKO3fuRFpaGs6ePYulS5di//79AIDw8HCcPXsWU6ZMwW+//YYrV65g9erVuHv3LmrVqoXJkyfj/fffx4EDB5CcnIy3334beXl5GD9+fKnt9unTB6dPn5ZuSivLiBEjUK9ePYSGhuLEiRNIS0tDfHw8pk+fjtu3b0v1BEHA2LFjsXr1apw+fbrE9vPy8nD+/HkEBQWV2y4RVU9MbonIIjg5OZU5F9VcwsPD0aNHD7z22mvo168fBg4ciKZNmxqtvbFjx8Ld3V3jq7zVG9avX49Ro0Zh1qxZaNmyJQYMGIBffvlFmqvbokULHDp0CL/++iv8/f3RpUsXfP/996hRo3hm2yeffILXX38db731Ftq3b4+UlBQcPHiwzHVk+/XrBxsbGxw+fLjc52Rvb4/jx4+jUaNGGDRoEFq1aoVx48YhPz9f6z0fM2YMsrOz0bJlS415wGrff/89GjVqpLUeLhFZDm7iQEREZvH555/j+++/x8GDB03Wpr+/P2bMmIHhw4ebrE0iMi3eUEZERGYxYcIEPHjwALm5uSXOjTW0rKwsvPHGGxg2bJjR2yIi8+HILRERERFZDM65JSIiIiKLweSWiIiIiCwGk1siIiIishhMbomIiIjIYjC5JSIiIiKLweSWiIiIiCwGk1siIiIishhMbomIiIjIYjC5JSIiIiKL8X9ZALxV7WV4IAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8,4))\n",
+    "mask = integral_significance >= integral_significance_threshold\n",
+    "plt.scatter(erecobins[:-1][mask], integral_significance[mask], \n",
+    "            label=f'Significance >= {integral_significance_threshold} sigma')\n",
+    "plt.scatter(erecobins[:-1][~mask], integral_significance[~mask],\n",
+    "            label=f'Significance < {integral_significance_threshold} sigma')\n",
+    "plt.grid()\n",
+    "plt.xlabel('Minimum Ereco (TeV)')\n",
+    "plt.ylabel('Li & Ma integral significance')\n",
+    "plt.xscale('log')\n",
+    "\n",
+    "if mask.sum() > 0:\n",
+    "    print('*************************')\n",
+    "    print('Detection successful! :-D')\n",
+    "else:\n",
+    "    print('****************')\n",
+    "    print('No detection :-C')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04cd5759",
+   "metadata": {},
+   "source": [
+    "## Simulated SED from observation (Asimov dataset)\n",
+    "The fluxes & uncertainties are computed in the Ereco bins, and shamelessly placed at the same value in Etrue, and at the expected flux level. \n",
+    "In reality an energy unfolding process is needed... what is shown below is just a an estimate of what the spectrum will look like - but pretty accurate for the purpose of observation planning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "64661959",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "noff = total_bg_counts / alpha\n",
+    "non = total_signal_counts + total_bg_counts\n",
+    "# excess = non - alpha * noff (= total_signal_counts in Asimov dataset)\n",
+    "excess_error = (non + alpha**2 * noff)**0.5\n",
+    "relative_excess_error = excess_error / total_signal_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "4d56445f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SVqhx5XYRSspVKFaqUFKuQomy8lFcroK/oyW6tbQHANwtVuKD386j+N4+He7+hVn3Xke6dTJ+Sc7D4GcmAwDKVWrM/vEU5BIx5FIxTKSV/61a9neyQmQ7Z22On05eh0xyf7v8gf1tzGVwtzXX7ltYVgGZRAS5RAxR8u9A+n7AuxcQEN1Yf2xERESNjoWY6AGl5SrczC9FVl4pbt3OxeCqDRd3AO2G42puMd756SyKlSqU3iu6D349oYc35kYGAACy8kow8LN9j3yvsU95aQuxSq3BxoRr2m19pMe1/3dWaMRQ3DwKYLI24y+nMh/5uoM6uGgLsUqtwcwfTj5y334Bjvh6wv3x8EHvx6GsQo1o8WEsk/+vcuWRZfjc8T2IWkfjn/39tPuevZ4HhZkMLaxMYCqTPPI9iIiImjoWYjIaxcoK3MgrxY28UjgpTOHbwhIAcOlmAWb+cBI38kuRW6TU7h8tPozB8nsLmyYCUlNUOPTB7uRbj36Psgrt12ZyCWzMZTCXSWAmr3yYy6SVX8skCHS11u5raSrFnMjWMJNJYC6XoGV2FHA8DgAgFanhFRSu3VcmEeOdQYFQqtRQVtx73Pu6rEKNDu4K7b4qtQY9Wtlr9yt7YF9lhRo25rJq+ctVagBAN/EF7boKjRjmmYdxwCy02r7PrTyCgnvfr5WpFC2sTOBoZYIWVqbo6K7A5F4ttfum3iqEjbkctuYyiESiR/75ERERCYGFmAxCYVkFVGoNFGaVBS/zbgn+91cKbuSVICuv8oxvXkm5dv/pfX0x596ZXFOZBElZ+dptpjIxXBVmGKpJB6qupRNJgPQDcPQJx8cjO2iLa1W5NZdLYSaTwMbifsF0tDLFyXfuF9nHMZFKML1vqwfWTEKFdwuk7/kO3n3HwKvtkAfySfBiT59ava5cKsa6yU/Val8ASHovEkqVGrhQAWyLg0YkgRQq+HeLgpevl3Y/ZYUaCnMZyu6V64LSChSUViD1VuUfWGFpebVCPGTpQe1wDAfLquJc+WjvZoPnu3lq972ZXwq7a7sgyzjI4RpERNQoWIip2cgrKcfvZ7KQde8sb1Z+aWXhvVuKgrKKaiVXpdZg/bGMGq9hIZfAxcYMVqb3i6uzwhSrJ4TAxcYULtZmsDaTVp7FPJsLbPqlcieNCvDuCQsTKZ4J9miU71fjH4VzKRp4+Uc1yvsBlWXbVCYBOg0FTNdDlH4A8O6J3n8rpXKpGAfm9oNGo0F+aQVuFZQhu6AUtwrKcKugDC4KM+2+peUqyCSVZ4XLVRrtP1Cq3Awoq1aIP/rPh/hU/N/KhSPLsL7lR1D5R8HP0RJ+Tlaws5CDiIhIn1iIqcmoUKmRfrsI57MKcOFGPpJvFKCPfwuMDfUGABSUlmPeljOPfP6Dwx2cFaaYNcAPLgpTOCvM7v3XFNamshrPk0nECAtwrPmC/hH3vx652vjOVAZEP/F7FolEUJjJoDCToZWj5UP3MZVJcOKdcCgr1MgpLLtXnsu0JdrL/v5FfSVKFTprzmuXKzRiFF3cg38nVf4jpJefA76d1E27/fujGfCyN4efkyVaWJpwOAYREdUJC7GOYmNjERsbC5VKJXQUg5BXXI73fk3ChRv5uJRdCGWFutp2E5lEW4idrE0R1rqFtuBWPszgfK/sWprcP5xlEjFmDfDXX9AHyzHViVwqhquNGVxtzB65j5lcgjEvTAB+2KkdrqFoE4Z+5Y64lF0Afycr7b53i5V4Y+v9fyApzGT3ziJbopWjFYK9bNHRw6YBvyMiIjIULMQ6iomJQUxMDPLz86FQKJ78BCNXWq7CxZsFuHCjABfunfkNcLbGO4MDAQDmJhL8fOo6ylWaymW5BK2drRDgbI02Llbo9EChkUnEWD2xa+OFl1sAC/Ia7/0IACAK+Afw7P3hGqMCojHq3jaNRqPdr0ipwsBAJ6RkF+LK7SLklZQj/sodxF+5A6ByFo+qQlxUVoEFP5+Dn5Ml/Byt0MrREm42ZhCLeUaZiIhYiKkBqNUazPjhBJKy8pGeUwS1pvr2By9uk0nEeGdwWzhamaCNszXcbVlSCI8crvHgkAg3GzOsGhcMoPIfXmk5RbiUXYiUmwW4lF2IEB877b4p2YXVprUDADOZBL6OFvBztMKQjq73h81c2M75l4mIjAwLMeksr6QcyTcKkHwjH+dvFOBCVj6szWRYc+/srVgswrnMfKTlVM44YGchR8C9s74BLlYIdLGu9npjn/Kq8R5EujCVSdDGxRpt/nZsVbGzkOPVAf64lF2AlOxCpN6qvGHK2ev5OHs9H21drSsL8YXtwA/PQQUxJEeW4Wi3pXAPHQm3xwzzICKi5o+FmGpt7qbTOJCSg+t3S2psszKRQqPRaM/gzY8KgKlMggAXK17sRILzsDPHzAH3bypSoVIjI7e48oxydiG6+zpUbrj8FwBAAjUqNGKcOfgbRu+1g6vCFMHedgj2tsWANk6PHQdNRETNDwsxVXO3WImjabk4mpqLK7eL8NUDdzG7WVCqLcNuNmb3xvpaIcDFGgHOVtVeJ7ytM4iaKqlEjJYtLNGyhSUi2j6wwbcfcHwV1Pcu6LthGwzJbREy80rx86lM/HwqE07WptpCfOV2Ea7fLUEnDxuYy/lxSkTUXPET3MjlFilxLO02jqTm4kjqbSTfLMAD1y3h+t0S7a+L/9nPD9P7tkJrZyvtDTCIDEpANPDseojvXdD3VkA0Xi2rwKmrdxF/5Q6Op+ci2MtWu/uWxOv475+XIBWL0NbVGkFedgjxtkWQty0crUwF/EaIiEgXdS7EV69eRXp6OoqLi9GiRQu0bdsWJiYm+sxGDeBWQRlszGWQScQAgM/iLuLbI1eq7dPK0RLdfOzQraV9teIb9EARIDJYf7ugz8JEiu6tHNC9lUONXeVSMVwUpsjKK8Wpa3k4dS0PXx9MAwB42ZvjhylPVbtJCRERNU06FeIrV65gxYoVWL9+Pa5evVptCiS5XI5evXphypQpGDFiBMRisd7Dku5u5pfiSOrte8MgbuPyrSL8ODUUXe9dgR/qa49jabno1tIO3Xzs0dXHDi2s+A8botqICWuFmLBWuH63BPHpuTienov49DtIvlmA3EJltbPEC385h6u5xQj2rjyL3M5NAROpRMD0RERUpdaFeObMmVi9ejXCw8Px3nvvoWvXrnBzc4OZmRlyc3Nx9uxZ7N+/H2+//TYWLlyI1atXIyQk5MkvTHp38WYBvj6QhiOpt5F+u7jaNpGocntVIY5u74Lo9i5CxCQyGG42ZnDr5IahndwAVM7Ekp5TBMkDUwj+eT4bGbnF2HU+G0Dl2eWO7goEedmhm48dp30jIhJQrQuxXC7H5cuX0aJFixrbHB0d0a9fP/Tr1w/vvvsutm/fjitXrrAQN4KrucU4mpYLHwcL7ZCGorIK/HD8KgBALAICXa3Rzcce3Xzs0NXHDjbmciEjExk8hZmsxl3y/vtsJyTcG4eccOUOcgqVOJ5+B8fT72BPcna1ad80IglER5YBz65nKSYiagS1LsSLFy+u9YtGRxvuB7iQt27WaDTIyC3G0dRcHEm7jaOpudpZH57r6qEtxO3cFJjWxxch3rYI9rbjBXBETUBnT1t09rTF5F4todFokH67GPH3hlh42N0bZ3xv2jeRRoUKjRhxv25Edm4g+rdxhLutuYDpiYgMG2eZ0JFQt24uKqvAgE/3IiuvtNp6iViEDu4KtHK8P+2ZTCLGvKiARstGRLoRiUTwcbCAj4MFRgV73N9wb9o3FcSQitTYnOuDXT+fw7s/n0OAsxX6t3HE4I6uCHB++A1IiIiobnQuxMnJyVi/fj32799fbZaJzp07IyIiAiNGjOBsEw3AwkQKM7kEMokIHd1ttBfBBXnZwsKE/64hMgj3pn2TpB9All0wQko6Iv98NuKv5OLCjQJcuFEACxOpthCXlqugUmv4GUBEVE+1/hQ9ceIE5syZg/3796N79+7o2rUrnn766WoX1b355pv45z//iTlz5mDWrFksxnq2alwwXBVmMJPzynQig3Vv2jcXAFMBTO3jiztFSuy5mI1d57MRHuik3fWPszcwZ/NpdPe1R/82Tugf4Mi76BER1UGtC/HTTz+Nf/3rX9iwYQPs7Oweud/hw4fx2Wef4ZNPPsEbb7yhl5BUybeFpdARiEgAthZyDOvsjmGd3autT8y4A2WFGnuSb2FP8i28DSDQxRoD2jiifxsntHdTQCzmbdOJiJ6k1oX40qVLkMufPDtBaGgoQkNDoVQq6xWMiIgeb+GQthjzlBd2nb+JP89nIzHjDpKy8pGUlY/P/0rBsTf6w9G6ci5kjUYDkYjlmIjoYXSadk0Xuu5PRES6EYlE8Heygr+TFab3bYXbhWXYnXwLu5JuIq+kXFuGAeCltQnQaDSVQyvaOMLJmreWJiKqUqcrMa5duwYbGxtYWlb/FX55eTkOHz6M3r176yUcERHVnr2lCUYGuWNkkHu1O4kWlJZj78VslKs0+PNCNrAVaO+mQP82jhjQxgltXa159piIjJpO91fOyspC165d4eXlBRsbG4wfPx6FhYXa7bm5uQgLC9N7SCIi0s2DBdfSRIqfX+mJ18P90cnDBiIRcOZ6HpbsuoRB/zuAmT+crHzOxd/R7to6iC7+LlBqIiJh6HSGeN68eZBIJDh69Cju3r2L+fPno2/fvoiLi4OtbeVNIR48K0FERMITiURo42KNNi7WeKWfH24VlGH3hWzsOn8T+y/lVN7U5+wWSDdNhC8AbNyBS7dXolWvZ3jmmIiMgk6FeNeuXdi6dSuCg4MBAL169cLo0aPRr18//PnnnwDAD08ioiauhZUJngnxwDMhHigtV0Gt0QBxS7XbKzRi7N25FZOPOmJ4Z3cM7+IGDzveKY+IDJdOQyby8vK0Z4IBwMTEBJs2bYK3tzfCwsKQnZ2t94BERNRwTGUSmMullXfJA6C+d5e8E+J2uHK7GJ/tuoheH+/GM18cxo/Hr6JE2fi3rSciamg6FeKWLVvi9OnT1dZJpVJs3LgRLVu2xKBBg/QajoiIGklANCpGfYvUFgNRMepbLH5rHj59piN6tnKASAQcS8vFG1vPoFhZIXRSIiK902nIRFRUFFauXIkRI0ZUf5F7pXjEiBG4du2aXgMSEVHj0PhH4VyKBl7+UTCXSTG8izuGd3FH5t0SbDt5HTkFSthb3r8D6SvfJ8LN1gwjurjD38lKwORERPWjUyH+4IMPUFxc/PAXkkqxZcsWgy/EsbGxiI2NhUrFXxsSkXFwtTHD9L6tqq3LuF2MX09nAQC+2JuK9m4KjOjihiGd3GBnwXnoiah50WnIhFQqhbW19SO3SyQSeHl51TtUUxYTE4OkpCQcP35c6ChERIJxVphixZggDAx0glQswpnreVjwSxK6frALL62NR3x6rtARiYhqrU435tBoNNi0aRN2796N7OxsqNXqatu3bNmil3BERNQ0yaViRLZzRmQ7Z9wuLMMvpzKxOfE6zlzPQ1zSTQzq4ILge/uWq9SQikWchYiImqw6FeKZM2di5cqVCAsLg5OTEz/kiIiMmL2lCSb08MGEHj64eLMA205cR3igs3b7qv2p2JJ4HcO7uGFYZze4KMwETEtEVFOdCvF3332HLVu2IDo6Wt95iIioGfN3ssKcyIBq67afyUJKdiE+/iMZi3cko4evA0YEuSGirXPllG9ERAKr0yeRQqFAy5Yt9Z2FiIgM0PqXnsLvZ25gU+I1HEvLxYGUHBxIyYGF/CyGd3HH+0+3EzoiERk5nS6qq7JgwQIsXLgQJSUl+s5DREQGxspUhmdCPPDj1FDs+1cYZg3wg6edOYqUKuSXlgPKImCBAligQGHCj0LHJSIjVKczxKNGjcL69evh6OgIb29vyGSyatsTExP1Eo6IiAyLp705Zg3wx8z+foi/cgeWJlLg4g7tdstfXsKqxGx0ixqDDu42wgUlIqNSp0I8YcIEJCQkYMyYMbyojoiIdCYSiRDibVe5kHhIu75CI4boygEMWeqDYC9bvNjTB+GBTpBK6vQLTSKiWqlTIf7tt9+wY8cO9OzZU995iIjI2Pj2A46vAkQSSKGCyLsXZGkixF+5g/grd+BmY4bx3b0w9ilvmMklQqclIgNUp0Ls4eHx2Bt0EBER1VpANPDseiD9AODdE5MCojE4vxTfHbmC745m4PrdEnyxNxXjQr2FTkpEBqpOhfiTTz7BnDlzsGLFCnh7e+s5EhERGZ2A6MrHPY7Wppgd3hrTw1rh55OZqFBrYCqrPDusVmvw5razGNTBBd197Tlsj4jqrU6FeMyYMSguLoavry/Mzc1rXFSXm8tbdhIRUf2ZyiR4JsSj2rrdydlYfywD649loLWTFV7s6Y2hndy0hZmISFd1KsRLlizRcwwiIqLa8XO0wrhQL2yMv4bkmwWYu/kMPvojGWO6eWLMU15wtDYVOiIRNTN1KsTjx4/Xdw4iIqJa8bQ3x3tD2+G1ga2xIT4D3xy6gut3S/D5XylYvvcyfp/ZG60cLYWOSUTNSJ0K8fbt2yGRSBAREVFt/c6dO6FSqRAVFaWXcERERI+iMJdhSm9fvNjDBzuTbuLrA2koVqrg28JCu0/yjQK0crSERMxxxkT0aHWa2HHevHlQqVQ11qvVasybN6/eoYiIiGpLKhEjur0LNr3cHT9MfUp7kV1hWQVGLj+EPot348v9qZV3xSMieog6FeJLly4hMDCwxvqAgACkpKTUOxQREVFdWJvev8g7+UY+JBIRrt0pwb9/O4/ui/7Cwl/O4crtIgETElFTVKdCrFAokJqaWmN9SkoKLCwsHvIMIiKixhXkZYfD8/rjw2Ht0crREoVlFVh9MB19/7MHL62Nx+VbhUJHJKImok6FeMiQIZg1axYuX76sXZeSkoLXXnsNQ4YM0Vu4pig2NhaBgYEICQkROgoRET2BmVyC57t5Iu7V3lj7Ylf0bd0CGg3w5/mbkFfdDlpZBCxQVD7ObhE2MBEJok4X1S1evBiRkZEICAiAu7s7AODatWvo1asX/vOf/+g1YFMTExODmJgY5OfnQ6FQCB2HiIhqQSQSobd/C/T2b4GU7AIcTcuFh5155caLO+7vuGkiIDWtdpMQIjJ8dSrECoUChw4dQlxcHE6dOgUzMzN06NABvXv31nc+IiIivWrlaIVWjlba5fzkvbC+93UFxDh/8Fe0ahkBMzlv9EFkLOpUiIHKf22Hh4cjPDxcn3mIiIgalblfb+DMGgCAFGr897IzTi3ejZf7+OL5bp68Ax6REahzISYiIjIE0g4jALkZVGn7cVjVBhfOuePWnRK892sSVuy9jK/Gh6C9O4fIERkyFmIiIqKAaEgCotETwF+RamxKuIalf11CaYUaLVtw9iQiQ8dCTERE9AC5VIznu3liRJAbUrILYWFS+aNSo9FgyrcJ6BfgiJFB7pBJ6jRRExE1Qfy/mYiI6CFMpBK0db0/VCIu6Sbikm5i/pYz6PfJHvwYfxUVKrWACYlIX+pUiBMTE3HmzBnt8k8//YSnn34ab7zxBpRKpd7CERERNRW9/Vvg7UGBcLCU42puCeZsOo3+n+7F5oRrLMZEzVydCvHUqVNx8eJFAEBqaiqeffZZmJubY+PGjZgzZ45eAxIRETUFpjIJJvX0wb45YXgjOgD2FnJcuV2M1zaeQvhn+3Ajr1ToiERUR3UqxBcvXkSnTp0AABs3bkTv3r3x/fffY82aNdi8ebM+8xERETUp5nIppvT2xb45YZgbGQAbcxnM5BI4WZsIHY2I6qhOF9VpNBqo1ZW/Htq1axcGDRoEAPDw8EBOTo7+0hERETVRFiZSvNzXF2NDvXAzvxQikQgAUFhWgcnfHMf4UG9EtHWGWCwSOCkRPUmdCnFwcDD+/e9/Y8CAAdi7dy+WL18OAEhLS4OTk5NeAxIRETVlliZSWLaw1C5/d+QKjqTm4khqLtq4WGPWAD+EBzppCzMRNT11GjKxZMkSJCYm4pVXXsGbb76JVq1aAQA2bdqE7t276zUgERFRc/JcV0/M7O8HKxMpzmflY+q3CRi89AD+PH8TGo1G6HhE9BB1OkPcoUOHarNMVFm8eDEkEt7ikoiIjJfCTIZXB/pjYg9vfLk/DasPpuHs9XxM+iYenTxssGHqUzCR8mclUVOi13mITU1NIZPJAID/CiYiIqNmYy7H6xGtsX9uP7zc1xdmMgk87cxZhomaoFoX4jZt2uD7779/4jzDly5dwssvv4yPPvqo3uGIiIiaOzsLOeZGBmD/3DDMjw7Qrk/PKcK45buBBYrKx9ktAqYkMm61HjIRGxuLuXPnIiYmBuHh4QgODoarqytMTU1x584dJCUl4cCBA0hKSsIrr7yC6dOnN2RuIiKiZsXBsvq0bLG7U2B59U9Afm/FpomA1BQIiG78cERGrtaFuF+/fjh+/DgOHTqEDRs24Pvvv0d6ejpKSkrg4OCAzp07Y9y4cRgzZgxsbGwaMDIREVHz93pEa6TfzATuzVZaoRHjyK5taOPeH/aWnNOYqDHpfFFd9+7dOZMEERFRPTlZm8Kp7yBgU+UNraQiNdZkuuPof/ZgTkRrjA31FjYgkRHR60V1REREpAP/CO2Xl3r9D5lOYSgorUBO4eOv1yEi/arTtGtERESkB3ILYEEeAMAPwC9hGmw9cR3/aO+i3eVcZh5MpGK0crQSKCSR4WMhJiIiaiIkYhFGBrlrl9VqDeZuPo3zWQUY+5QXXh3gD4W5TMCERIaJQyaIiIiaqIKyCrgozKBSa7DmUDr6/mc3vj2cjgqVWuhoRAZFp0K8c+dOlJeXN1QWIiIieoDCTIZV44Lx3aRu8HeyxJ3icrz90zn84/MDOHApR+h4RAZDp0I8bdo0tGjRAqNHj8b333+Pu3fvNlAsIiIiqtLTzwHbZ/TC+0PbwsZchuSbBRjz1VEcTGEpJtIHnQpxamoq9u3bh/bt22PJkiVwdnZG//798fnnnyM9Pb2BIhIREZFUIsbYUG/seb0vJnT3RrCXLUJb2mu3azQaAdMRNW86jyHu0KED3nrrLRw7dgypqakYNWoU/vjjD7Rp0wYdO3bEO++8g/j4+IbISkREZPRszOVYMKQtfpjyFMRiEQCgqKwC//j8AH44lgGVmsWYSFf1uqjO1dUV06ZNw/bt25GTk4N33nkH6enpiIyMxIcffqivjERERPQ3Usn9H+HfHbmCpKx8zNtyBkOWHsDR1NsCJiNqfvQ2y4SFhQVGjBiBtWvXIjs7Gy+99JK+Xrrehg0bBltbW4wcOVKnbURERM3BxB4+eOsfbWBlKsW5zHyMXnkEMesScTW3WOhoRM1Cg0y7JhaL0aJFi4Z46TqZMWMG1q5dq/M2IiKi5kAuFWNyr5bY83pfPN/NE2IR8NuZLPT/dC8+2ZnM8cVET2AU8xCHhYXByurhd/h53DYiIqLmxN7SBB8Oa49f/9kLT7W0g7JCjfTbxRCJREJHI2rSBC/E+/btw+DBg+Hq6gqRSIRt27bV2GfZsmXw8fGBqakpgoKCsH///sYPSkRE1EwEulpj/UtPYcWYLpgXFaBdf/1uCU5k3BEwGVHTpNOtm2/duqX3oRBFRUXo2LEjJk6ciBEjRtTYvmHDBsyaNQvLli1Djx498MUXXyAqKgpJSUnw9PQEAAQFBaGsrKzGc3fu3AlXV1e95CwrK6v2Hvn5+QCA8vJy3qyEGkTVccXjixoLjznD07+1A4D7f6cf/HoO28/exNCOLvhXmBvcV/hXbv/XFUBu0ajZeLxRY6jt8aVTIXZzc8OQIUMwadIkREZG6uVXMFFRUYiKinrk9k8//RSTJk3C5MmTAQBLlizBjh07sHz5cixatAgAkJCQUO8cT7Jo0SIsXLiwxvqdO3fC3Ny8wd+fjFdcXJzQEcjI8JgzTCoNcPumGIAYP53Kwp4zqTglr9x28sePkWnXTZBcPN6oIRUX1+7CUp0K8TfffIPVq1dj8ODBcHZ2xsSJEzFhwgT4+vrWKeSTKJVKJCQkYN68edXWh4eH49ChQw3yno8yf/58zJ49W7ucn58PDw8PhIeHw9raulGzkHEoLy9HXFwcBg4cCJlMJnQcMgI85gzfYABnrufh39uT4XztsHZ9yJVYVHR9Chr/R5+g0jceb9QYqn6j/yQ6FeLnnnsOzz33HK5evYqvv/4a33zzDT788EP07t0bkydPxogRI2BqalqnwA+Tk5MDlUoFJyenauudnJxw48aNWr9OREQEEhMTUVRUBHd3d2zduhUhISFP3PYgExMTmJiY1Fgvk8n4PzI1KB5j1Nh4zBm2Lt4O2PyyPdLWfgekVa6r0IiRlhAHv7ZDGj0PjzdqSLU9tup0UZ2HhwfeffddpKamYufOnXBzc8OUKVPg4uKC6dOn1+UlH+vvQzM0Go1OwzV27NiBW7duobi4GNeuXatWeB+3jYiIyBCJRCK0DBqoXZaK1PDoEi5gIiJh1XuWif79++O7777D2rVrIRaL8cUXX+gjFwDAwcEBEomkxtng7OzsGmeNiYiISAf+Edovy57+CqbtBgMA1GoNPv7jAjLvlgiVjKjR1asQp6en491334W3tzdGjx6NLl26YN26dfrKBrlcjqCgoBoD7uPi4tC9e3e9vY8uYmNjERgYyDPJRERkMEwC748d/v5YBpbtuYyBn+7F2sPpUKt5Uw8yfDqNIQaA0tJSbNy4EatXr8a+ffvg5uaGCRMmYOLEifD29tY5QGFhIVJSUrTLaWlpOHnyJOzs7ODp6YnZs2dj7NixCA4ORmhoKFauXImMjAxMmzZN5/fSh5iYGMTExCA/Px8KhUKQDERERPUmtwAW5NVY/VRLOwR72SL+yh2889M5/HQyEx+NaI9WjryJFRkunQrxlClT8OOPP6K0tBRDhw7Fb7/9hvDw8HpNvxYfH4+wsDDtctVMDuPHj8eaNWswevRo3L59G++99x6ysrLQrl07bN++HV5eXnV+TyIiInq4Vo5W+HFqKL47egUf/X4BCVfuIPq/B/BKv1aY1scXcqng9/Qi0judCvGRI0ewcOFCjB07FnZ2dnoJ0Ldv3yfeY3369OkNcrEeERER1SQWizAu1Bv92zjhra1nsDv5Fj6Nu4iU7EJ8/lxnoeMR6Z1Ohfj06dPVlpVKJdLS0uDr6wupVOfRF0RERNSEudmY4esJIfj5VCbe//U8JvfyEToSUYOo0+89iouLMWnSJJibm6Nt27bIyMgAAMyYMQP/93//p9eAREREJByRSIShndxwYG4YOrjbaNd/e+QK9l28JVwwIj2qUyGeP38+Tp06hT179lS7EceAAQOwYcMGvYVrijjLBBERGSNTmUT79aWbBXjvl3MY9/UxvPbjKdwtVgqYjKj+6lSIt23bhqVLl6Jnz57VLqgLDAzE5cuX9RauKYqJiUFSUhKOHz8udBQiIiJBuNiY4YVuXhCJgM2J1zDg07349XTmE68JImqq6lSIb926BUdHxxrri4qK6jXjBBERETV9liZSLBjSFpumdYefoyVyCpV45fsTeGltPLLyeEMPan7qVIhDQkLw22+/aZerSvCqVasQGhqqn2RERETUpAV52eLXGT0xs78fZBIRdp3PxuD/HURpuUroaEQ60WlqiH79+mHLli1YtGgRIiMjkZSUhIqKCvz3v//FuXPncPjwYezdu7ehshIREVETYyKV4NWB/ohu74K5m08jqp1ztfHGRM2BTmeI9+zZA6VSie7du+PgwYMoLi6Gr68vdu7cCScnJxw+fBhBQUENlZWIiIiaqNbOVtj8cndM6nl/arb49FzE7k5BuUotYDKiJ6vz5MHt27fHN998o88szUJsbCxiY2OhUvHXQURERA+SiEUAKodRKivUmLv5NC7fKsKvp7Pw0Yj296dtUxZB9qErhgIoH3AFkNkIlJioks5jiAsKCpCfn//YhyHjLBNERERPJpOIEBPWCjbmMpzPysfTsQfxwW9JKFHyhBI1PTqfIfb393/kNo1GA5FIxLOnRERERk4kEmF4F3f09m+B935Jws+nMrFqfxp2nLuJj4b4gpfgU1OicyHetGkT7OzsGiILERERGRgHSxN8/lxnDO3kire2nUVGbjFeXHMc5+/d10uUEgd0HCVsSDJ6OhfiHj16PHQOYiIiIqJH6d/GCV197PDxH8lwzPgNyK1cL906GTCxAAKihQ1IRq1O8xATERER6crKVIb3n26HmJbZ2nUakQTxe3/m2GISlE6F2MvLCxIJ5xYkIiKiuhN799B+LdKosOKKKwYvPYBzmXkCpiJjplMhTktLg729fUNlISIiImPgH6H98kzoZzhl0R0p2YUYFnsIX+5PhVqtETAcGaNaF+LIyEgcOnToifsVFBTgo48+QmxsbL2CNVWxsbEIDAxESEiI0FGIiIiavYCew7FjVm8MaOMEpUqNf/92HuO+Poab+aVCRyMjUuuL6kaNGoVnnnkGVlZWGDJkCIKDg+Hq6gpTU1PcuXMHSUlJOHDgALZv345BgwZh8eLFDZlbMDExMYiJiUF+fj4UCoXQcYiIiJo9Ows5Vo0LwvfHMvD+r0k4kJKDyCX7sGpcMIK9ObMVNbxaF+JJkyZh7Nix2LRpEzZs2IBVq1bh7t27ACrnGgwMDERERAQSEhLQunXrhspLREREBkgkEuGFbl7o5mOPmT+cwM38Mng7WAgdi4yETtOuyeVyPP/883j++ecBAHl5eSgpKYG9vT1kMlmDBCQiIiLj0crRElun98CV20VwsDTRrr+aWwwPO3MBk5Ehq9e0awqFAs7OzizDREREVHtyC5S/mYOfOq8F5DXPAsulYvg5WWmXfzp5Hf0+2YMVey/zgjtqEJyHmIiIiJq0A5dyUK7S4P9+v4AXvjyKrLwSoSORgWEhJiIioibt45Ed8H/D28NMJsHh1NuIXLIfv5/JEjoWGRAWYiIiImrSRCIRnu3qid9m9ER7NwXySsrx8rpEzNl0CkVlFULHIwPAQqwjzkNMREQkjJYtLLH55e54ua8vRCLgx/hrOHn1rtCxyADoNMtEFY1Gg4SEBKSnp0MkEsHHxwedO3eGSCTSd74mh/MQExERCUcuFWNuZAB6+7VAwpVc9GjlIHQkMgA6F+Ldu3dj0qRJuHLlCjSayis9q0rx119/jd69e+s9JBEREdGDQn3tEeprr12+mluMhb+cw8Kh7eBmYyZgMmqOdBoykZKSgkGDBsHb2xtbtmzB+fPnkZSUhI0bN8Ld3R3R0dFITU1tqKxERERED/XWtrPYdT4bkUv24ZdTmULHoWZGp0K8ZMkSPPXUU/jrr78wdOhQtG7dGgEBARg+fDh2796Nbt264bPPPmuorEREREQPtXBIW3T0sEFBaQX+uf4EZv94EoWPuuBOWQQsUFQ+lEWNG5SaJJ0K8Z49ezBr1qyHbhOJRJg1axZ2796tj1xEREREtebtYIFN00Lxz36tIBYBWxKvI/q/+5GYcUfoaNQM6FSIMzIy0L59+0dub9euHa5cuVLvUERERES6kknEeC28NX6YEgo3GzNk5BZj1IrD2HfxltDRqInTqRAXFhbC3PzR9xE3NzdHcXFxvUMRERER1VVXHztsn9kLgzu6ws/REl197ISORE2czrNMJCUl4caNGw/dlpOTU+9ARERERPWlMJPh82c7oaCsAqYyCQCgQqXG/ks5CGtpIXA6amp0LsT9+/fXTrf2IJFIBI1GYxRzERMREVHTJxKJYG0q0y4v23MZn8ZdxOiOdvhIwFzU9OhUiNPS0hoqBxEREVGDEosqHz+fysRHpvdWXtwBtBsuaC4Snk6F2MvLq6FyNBuxsbGIjY2FSqUSOgoRERHp4JV+fgj1tcdP65YCynsrN02ERmoCUcA/BM1GwtLporqPP/4YJSUl2uV9+/ahrKxMu1xQUIDp06frL10TFBMTg6SkJBw/flzoKERERKSjIC87vNXurna5QiPGnh1bUax8xJzFZBR0KsTz589HQUGBdnnQoEG4fv26drm4uBhffPGF/tIRERER6Zm8ZU/t11KRGj/meOHKbc6SZcx0GjLx94vpHnZxHREREVGT5h+h/fJS7/8h2n4A2rhYCxiIhKbTGWIiIiIiQ+LXcwQGd3TVLp+8ehf/2ZEMlZon/YyJztOuERERERmiYmUFXv4uAVl5pTh59S4+f64z7CzkQseiRqBzIf7yyy9haWkJAKioqMCaNWvg4OAAANXGFxMRERE1J+ZyKeZHt8HcTadxICUHgz7fj2VjgtDJw0boaNTAdCrEnp6eWLVqlXbZ2dkZ3377bY19iIiIiJqjIR1dEeBshWnfJiA1pwjPrDiMd4cE4vmunrz5mAHTqRCnp6c3UAwiIiKipsHfyQo/vdIDr288hR3nbuLNrWdxIuMuPhzWHnIpL78yRPxbJSIiIvobK1MZVowJwtzIAIhFwN3ickjFPENsqHQqxEePHsXvv/9ebd3atWvh4+MDR0dHTJkypdqNOoiIiIiaHLkFsCCv8iG3eORuIpEIL/f1xfcvPYVPnukI8b1CzGlnDY9OhXjBggU4ffq0dvnMmTOYNGkSBgwYgHnz5uGXX37BokWL9B6SiIiISChPtbSHwkwGoLIMz/7xFJbsugg1p2YzGDqNIT558iTef/997fIPP/yAbt26aS+08/DwwLvvvosFCxboNSQRERFRU3AkNRdbT1TepffU1bv4bHQn2JjrMDWbsgj48N68x29kPvYMNTUenc4Q37lzB05OTtrlvXv3IjIyUrscEhKCq1ev6i8dERERURMS6muP/4zqCBOpGLuTb2Hw0gM4ez1P6FhUTzoVYicnJ6SlpQEAlEolEhMTERoaqt1eUFAAmUym34RNTGxsLAIDAxESEiJ0FCIiIhLAyCB3bJneHR52ZriaW4IRyw9hYzxPCDZnOhXiyMhIzJs3D/v378f8+fNhbm6OXr16abefPn0avr6+eg/ZlMTExCApKQnHjx8XOgoREREJpK2rAr++0gv9AhxRVqHGvzadxsd/XBA6FtWRToX43//+NyQSCfr06YNVq1Zh1apVkMvvj5v5+uuvER4erveQRERERE2NwlyGL8cFY/ZAf8gkIvT0cxA6EtWRThfVtWjRAvv370deXh4sLS0hkUiqbd+4caP2ts5EREREhk4sFmFGfz+MCHKHm42Zdv3dYqVuF9uRoOp0Yw6FQlGjDAOAnZ1dtTPGRERERMbgwTKckl2I3h/vxrI9KZyzuJngneqIiIiI9Oi301nIL63Ax38kY+q3CcgvLRc6Ej0BCzERERGRHs0c4IdFw9tDLhFjZ9JNDF16EMk3CoSORY/BQkxERESkZ8919cTGaaFwVZgiLacIT8cexE8nrwsdix6BhZiIiIioAXT0sMGvM3qhl58DSspVmPnDScQl3RQ6Fj0ECzERERFRA7GzkGPNxK54JawVunrboW/rFvc3XtwhXDCqRqdp14iIiIhINxKxCK9HtEa5Sg3Z+W33N2yaCEhNgYBowbJRJZ4hJiIiImoEMokYuHJIu1yhESMt/g8BE1EVFmIiIiKixuLVXfulVKTGB0kOiN3N+YqFxkJMRERE1Fj8I7RfbvR5H7vUQVi8IxmvbzwNZYVawGDGjYWYiIiISACjnpuE94e2hUQswubEaxj71VHcKVIKHcsosRATERERCWRsqDe+nhACKxMpjqbl4vkvj0Kl5vCJxsZCTERERCSgPv4tsHl6d3jamWPWAD9IxCKhIxkdTrtGREREJDB/JyvEze4NE6lEu+5OkRK2FnIBUxkPniEmIiIiagIeLMPX7hRj4Gf78H+/X4CaQygaHAsxERERUROzJ/kWcgrLsGLvZUxfl4gSpUroSAaNhVhHsbGxCAwMREhIiNBRiIiIyECNecoLnz7TEXKJGH+cu4HRKw8jO79U6FgGi4VYRzExMUhKSsLx48eFjkJEREQGbHgXd6x7qRtszWU4fS0PQ2MPIikzX+hYBomFmIiIiKiJCvG2w7aYHmjZwgJZeaUYueIQEjPuCB3L4LAQExERETVhXvYW2PpyD3T3tYePgwUCnK2EjmRwOO0aERERUROnMJfhmxe7Ir+kHObyyvqm0WigUmsglfD8Zn3xT5CIiIioGZBJxLC3NNEux+5OwYvfxCO/tFzAVIaBhZiIiIiomckuKMWyPZex7+ItjFx+CFdzi4WO1KyxEBMRERE1M45WpvhxaiicrE1w8WYhhi07yIvt6oGFmIiIiKgZauemwE8xPdHW1Ro5hUo8u/IIfj6VKXSsZomFmIiIiKixyC2ABXmVD7lFvV/OWVF5pnhAGycoK9SYsf4EYnen6CGocWEhJiIiImrGLEyk+GJsECb39AEA2FvIBU7U/HDaNSIiIqJmTiIW4a1BgYju4IIunrZCx2l2eIaYiIiIyEA8WIZzi5QY+9VRpGQXCpioeWAhJiIiIjJA7/1yDvsv5WD4soM4mJIjdJwmjYWYiIiIyAC9PSgQQV62yC+twPivj2H9sQyhIzVZLMREREREBsje0gTrJnfD0E6uqFBrMH/LGXy4/TxUao3Q0ZocFmIiIiIiA2Uqk2DJ6E54dYA/AGDlvlRM+y4BxcoKgZM1LSzERERERAZMJBJh5gA//PfZTpBLxbh4swBFZSqhYzUpnHaNiIiIyAgM7eQGd1sztLA0RQsrE6HjNCk8Q0xERERkJIK87OBpb65d/u10FlJvcVo2FmIiIiIiI7Tv4i3M+OEERq44jDPX8oSOIygWYiIiIiIjFOhqjTYuVsgtUuLZlYdxyIjnKmYhJiIiIjJCDpYmWP/SU+jua48ipQoTVh/H72eyhI4lCBZiIiIiIiNlZSrD1xNCENnWGUqVGtO/T8T3R43vBh4sxERERERGzFQmQewLXfBcVw9oNMAbW8/gSOptoWM1Kk67RkRERGTkJGIRPhzWHnYWctwpLkc3HzuhIzUqFmIiIiIigkgkwr8iAqDRaCASiQAApeUqSMQiyCSGPajAsL87IiIiItJJVRkuV6kxfV0ipn6bgBKlYd/ZzigK8bBhw2Bra4uRI0dWW3/16lX07dsXgYGB6NChAzZu3ChQQiIiIqKmJflGAQ5dzsFfF7Ix9qujyCsuFzpSgzGKQjxjxgysXbu2xnqpVIolS5YgKSkJu3btwquvvoqioiIBEhIRERE1Le3cFPhuUjdYm0oRf+UORq88jJv5pULHahBGUYjDwsJgZWVVY72Liws6deoEAHB0dISdnR1yc3MbOR0RERFR0xTsbYcfp4XC0coEF24UYMTyQ0jPMbyTh4IX4n379mHw4MFwdXWFSCTCtm3bauyzbNky+Pj4wNTUFEFBQdi/f7/ec8THx0OtVsPDw0Pvr01ERETUXAU4W2Pzy93hbW+Oa3dKMHLFIZy9bli3eha8EBcVFaFjx45YunTpQ7dv2LABs2bNwptvvokTJ06gV69eiIqKQkbG/Umjg4KC0K5duxqPzMzMWmW4ffs2xo0bh5UrV+rleyIiIiIyJB525tg4rTvaulqjWKlChVojdCS9EnzataioKERFRT1y+6effopJkyZh8uTJAIAlS5Zgx44dWL58ORYtWgQASEhIqPP7l5WVYdiwYZg/fz66d+/+2P3Kysq0y/n5+QCA8vJylJcb7iBzEk7VccXjixoLjzlqTDzemh8bUzG+nRiElFtFaOts0Sz+7mqbUfBC/DhKpRIJCQmYN29etfXh4eE4dOhQvV9fo9FgwoQJ6NevH8aOHfvYfRctWoSFCxfWWL9z506Ym5vXOwvRo8TFxQkdgYwMjzlqTDzemqesM5X/vVII3CgWoZtj0zxjXFxcXKv9mnQhzsnJgUqlgpOTU7X1Tk5OuHHjRq1fJyIiAomJiSgqKoK7uzu2bt2KkJAQHDx4EBs2bECHDh20Y5e//fZbtG/fvsZrzJ8/H7Nnz9Yu5+fnw8PDA+Hh4bC2tq7bN0j0GOXl5YiLi8PAgQMhk8mEjkNGgMccNSYeb83fjfxSLIg9jDvF5XDz9cOUXj5CR6qh6jf6T9KkC3GVqgmiqzx4B5Xa2LFjx0PX9+zZE2q1ulavYWJiAhMTkxrrZTIZ/0emBsVjjBobjzlqTDzemi93OymeCfHAF3tTsXjnJeSVqjA/KkCnjtbQantsCX5R3eM4ODhAIpHUOBucnZ1d46wxERERETUekUiE+VFt8EZ0AABg5b5UvL7xNCpUtTvZ2JQ06UIsl8sRFBRUY3xRXFzcYy+AIyIiIqLGMaW3LxaP7ACJWITNidcw7bsElJY3r1s9C16ICwsLcfLkSZw8eRIAkJaWhpMnT2qnVZs9eza+/PJLfP311zh//jxeffVVZGRkYNq0aYLkjY2NRWBgIEJCQgR5fyIiIqKmZlSwB1aMCYKJVIxd57Ox9K8UoSPpRPAxxPHx8QgLC9MuV124Nn78eKxZswajR4/G7du38d577yErKwvt2rXD9u3b4eXlJUjemJgYxMTEID8/HwqFQpAMRERERE3NwEAnrH2xK1bsvYyYsFZCx9GJ4IW4b9++0GgeP1XH9OnTMX369EZKRERERER10a2lPbq1tNcuazQa3C5SwsGy5sQETYngQyaIiIiIyDB9GncRUf/dj/NZtZv+TCgsxERERESkdyVKFeKSbuJWQRme+eIwjqXlCh3pkViIdcSL6oiIiIiezEwuwYapoQjxtkVBaQXGfnUUu5JuCh3roViIdRQTE4OkpCQcP35c6ChERERETZrCTIa1L3ZD/wBHlFWoMfW7BJy5lid0rBoEv6iOiIiIiAyXmVyCFWODMHfzaZhIxWjnZi10pBpYiImIiIioQckkYvxnZEdogCZ1a+cqLMRERERE1ODE4qZXhKtwDDERERERGTUWYiIiIiIyaizEOuK0a0RERESGhYVYR5x2jYiIiMiwsBATERERkVFjISYiIiIio8ZCTERERERGjYWYiIiIiIwaCzERERERGTUWYh1x2jUiIiIiw8JCrCNOu0ZERERkWFiIiYiIiMiosRATERERkVFjISYiIiIio8ZCTERERERGjYWYiIiIiIwaCzERERERGTUWYiIiIiIyaizEOuKNOYiIiIgMi1ToAM1NTEwMYmJikJeXBxsbG+Tn5wsdiQxUeXk5iouLkZ+fD5lMJnQcMgI85qgx8XijxlDV0zQazWP3YyGuo4KCAgCAh4eHwEmIiIiI6HEKCgqgUCgeuV2keVJlpodSq9XIzMyElZUVRCKR0HFqJSQkpNFuOa3v96rP69XlubV9Tm32e9I+j9qen58PDw8PXL16FdbW1k8O3cQ05vHWEO/XmMecLvvzmHu05nzM8TOu+R1vAH+uNsT++j7mNBoNCgoK4OrqCrH40SOFeYa4jsRiMdzd3YWOoROJRNJoHzr6fq/6vF5dnlvb59Rmvyft86Tt1tbWzfKHRWMebw3xfo15zOmyP4+5R2vOxxw/45rf8Qbw52pD7N8Qx9zjzgxX4UV1RiQmJqbZvld9Xq8uz63tc2qz35P2acy/l8bU2N9Xcz7mdNmfx9yjNedjjp9xzRN/rup/f6GOOQ6ZIGqi8vPzoVAokJeX12zPnlDzwmOOGhOPN2pKeIaYqIkyMTHBu+++CxMTE6GjkJHgMUeNiccbNSU8Q0xERERERo1niImIiIjIqLEQExEREZFRYyEmIiIiIqPGQkxERERERo2FmIiIiIiMGgsxkYEYNmwYbG1tMXLkSKGjkAH69ddf0bp1a/j5+eHLL78UOg4ZAX6mUWPitGtEBmL37t0oLCzEN998g02bNgkdhwxIRUUFAgMDsXv3blhbW6NLly44evQo7OzshI5GBoyfadSYeIaYyECEhYXByspK6BhkgI4dO4a2bdvCzc0NVlZWiI6Oxo4dO4SORQaOn2nUmFiIiRrBvn37MHjwYLi6ukIkEmHbtm019lm2bBl8fHxgamqKoKAg7N+/v/GDkkGq7/GXmZkJNzc37bK7uzuuX7/eGNGpmeJnHjU3LMREjaCoqAgdO3bE0qVLH7p9w4YNmDVrFt58802cOHECvXr1QlRUFDIyMrT7BAUFoV27djUemZmZjfVtUDNV3+PvYSPrRCJRg2am5k0fn3lEjUpDRI0KgGbr1q3V1nXt2lUzbdq0ausCAgI08+bN0+m1d+/erRkxYkR9I5IBq8vxd/DgQc3TTz+t3TZjxgzNunXrGjwrGYb6fObxM40aC88QEwlMqVQiISEB4eHh1daHh4fj0KFDAqUiY1Gb469r1644e/Ysrl+/joKCAmzfvh0RERFCxCUDwM88aoqkQgcgMnY5OTlQqVRwcnKqtt7JyQk3btyo9etEREQgMTERRUVFcHd3x9atWxESEqLvuGRganP8SaVSfPLJJwgLC4NarcacOXNgb28vRFwyALX9zONnGjUmFmKiJuLvYzI1Go1O4zR51T/Vx5OOvyFDhmDIkCGNHYsM2JOOOX6mUWPikAkigTk4OEAikdQ4G5ydnV3jDAqRvvH4o8bGY46aIhZiIoHJ5XIEBQUhLi6u2vq4uDh0795doFRkLHj8UWPjMUdNEYdMEDWCwsJCpKSkaJfT0tJw8uRJ2NnZwdPTE7Nnz8bYsWMRHByM0NBQrFy5EhkZGZg2bZqAqclQ8PijxsZjjpodgWe5IDIKu3fv1gCo8Rg/frx2n9jYWI2Xl5dGLpdrunTpotm7d69wgcmg8PijxsZjjpobkUbzkBnXiYiIiIiMBMcQExEREZFRYyEmIiIiIqPGQkxERERERo2FmIiIiIiMGgsxERERERk1FmIiIiIiMmosxERERERk1FiIiYiIiMiosRATERERkVFjISYiIiIio8ZCTETUzEyYMAEikajGIzIyUuhoRETNklToAEREpLvIyEisXr262joTE5MGez+lUgm5XN5gr09EJCSeISYiaoZMTEzg7Oxc7WFrawsAEIlE+PLLLzFs2DCYm5vDz88PP//8c7XnJyUlITo6GpaWlnBycsLYsWORk5Oj3d63b1+88sormD17NhwcHDBw4EAAwM8//ww/Pz+YmZkhLCwM33zzDUQiEe7evYuioiJYW1tj06ZN1d7rl19+gYWFBQoKChr4T4WIqG5YiImIDNDChQvxzDPP4PTp04iOjsYLL7yA3NxcAEBWVhb69OmDTp06IT4+Hn/88Qdu3ryJZ555ptprfPPNN5BKpTh48CC++OILpKenY+TIkXj66adx8uRJTJ06FW+++aZ2fwsLCzz77LM1zlyvXr0aI0eOhJWVVcN/40REdSDSaDQaoUMQEVHtTZgwAd999x1MTU2rrZ87dy7efvttiEQivPXWW3j//fcBAEVFRbCyssL27dsRGRmJd955B0ePHsWOHTu0z7127Ro8PDyQnJwMf39/9O3bF3l5eThx4oR2n3nz5uG3337DmTNntOveeustfPDBB7hz5w5sbGxw7NgxdO/eHRkZGXB1dUVOTg5cXV0RFxeHPn36NPCfDBFR3XAMMRFRMxQWFobly5dXW2dnZ6f9ukOHDtqvLSwsYGVlhezsbABAQkICdu/eDUtLyxqve/nyZfj7+wMAgoODq21LTk5GSEhItXVdu3atsdy2bVusXbsW8+bNw7fffgtPT0/07t27Dt8lEVHjYCEmImqGLCws0KpVq0dul8lk1ZZFIhHUajUAQK1WY/Dgwfjoo49qPM/FxaXaezxIo9FAJBLVWPd3kydPxtKlSzFv3jysXr0aEydOrPE8IqKmhIWYiMjIdOnSBZs3b4a3tzek0tr/GAgICMD27durrYuPj6+x35gxYzBnzhx8/vnnOHfuHMaPH1/vzEREDYkX1RERNUNlZWW4ceNGtceDs0Q8TkxMDHJzc/Hcc8/h2LFjSE1Nxc6dO/Hiiy9CpVI98nlTp07FhQsXMHfuXFy8eBE//vgj1qxZAwDVzgDb2tpi+PDh+Ne//oXw8HC4u7vX63slImpoLMRERM3QH3/8ARcXl2qPnj171uq5rq6uOHjwIFQqFSIiItCuXTvMnDkTCoUCYvGjfyz4+Phg06ZN2LJlCzp06IDly5drZ5n4+xzIkyZNglKpxIsvvlj3b5KIqJFwlgkiIqqzDz74ACtWrMDVq1errV+3bh1mzpyJzMxM3tCDiJo8jiEmIqJaW7ZsGUJCQmBvb4+DBw9i8eLFeOWVV7Tbi4uLkZaWhkWLFmHq1Kksw0TULHDIBBER1dqlS5cwdOhQBAYG4v3338drr72GBQsWaLd//PHH6NSpE5ycnDB//nzhghIR6YBDJoiIiIjIqPEMMREREREZNRZiIiIiIjJqLMREREREZNRYiImIiIjIqLEQExEREZFRYyEmIiIiIqPGQkxERERERo2FmIiIiIiM2v8DbwciWP8jaz0AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8,4))\n",
+    "SED = (erecobincenters*u.TeV)**2 * dFdE(erecobincenters*u.TeV)\n",
+    "SED_error = SED*relative_excess_error\n",
+    "intrinsic_SED = (erecobincenters*u.TeV)**2 * intrinsic_dFdE(erecobincenters*u.TeV)\n",
+    "\n",
+    "if redshift > 0:\n",
+    "    plt.plot(erecobincenters*u.TeV, intrinsic_SED, '--', color='lightgrey', label='intrinsic')\n",
+    "plt.plot(erecobincenters*u.TeV, SED, '--')\n",
+    "\n",
+    "plt.errorbar(erecobincenters[points_mask]*u.TeV, SED[points_mask], \n",
+    "             yerr=SED_error[points_mask], fmt='o', markersize=2, label='Observed')\n",
+    "\n",
+    "plt.yscale('log')\n",
+    "plt.xscale('log')\n",
+    "plt.xlabel('Energy')\n",
+    "plt.ylabel(f'SED ({SED.unit})')\n",
+    "plt.ylim(SED[points_mask].min().value*0.2, SED[points_mask].max().value*5)\n",
+    "plt.legend()\n",
+    "plt.grid()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}