diff --git a/pvdeg/degradation.py b/pvdeg/degradation.py
index 848fed9..5fce625 100644
--- a/pvdeg/degradation.py
+++ b/pvdeg/degradation.py
@@ -865,6 +865,7 @@ def _gJtoMJ(gJ):
 
 # new version of degradation
 # we can make this work with varying timedelta sizes
+# take C rather than K
 def degradation(
     spectra_df: pd.DataFrame,
     conditions_df: pd.DataFrame = None,
@@ -908,19 +909,19 @@ def degradation(
 
         - `index`: pd.DateTimeIndex identical to spectra_df.index
         - `columns`:  (required)
-            - "temperature" [°K]
+            - "temperature" [°C]
             - "relative_humidity" [%]
 
         Example::
 
             timestamp                 temperature  relative_humidity
-            2021-03-09 10:00:00         298.0               45.0
-            2021-03-09 11:00:00         301.0               50.0
-            2021-03-09 12:00:00         315.0               55.0
-            2021-03-09 13:00:00         150.0               60.0
+            2021-03-09 10:00:00         98.0               45.0
+            2021-03-09 11:00:00         31.0               50.0
+            2021-03-09 12:00:00         35.0               55.0
+            2021-03-09 13:00:00         50.0               60.0
 
     temp_module : pd.Series, optional
-        Module temperatures [°K]. Required if `conditions_df` is not provided. Time indexed same as spectra_df
+        Module temperatures [°C]. Required if `conditions_df` is not provided. Time indexed same as spectra_df
 
     rh_module : pd.Series, optional
         Relative humidity values [%]. Required if `conditions_df` is not provided. Time indexed same as spectra_df
@@ -961,6 +962,8 @@ def degradation(
         rh = rh_module.values
         temps = temp_module.values
 
+    temps += 273.15
+
     wavelengths = spectra_df.columns.values.astype(float)
     irr = spectra_df.values  # irradiance as array
     dt = (spectra_df.index[1] - spectra_df.index[0]).total_seconds() / 3600.0
diff --git a/testing-setpoints/standards_testing.ipynb b/testing-setpoints/standards_testing.ipynb
index 25527ec..87255f8 100644
--- a/testing-setpoints/standards_testing.ipynb
+++ b/testing-setpoints/standards_testing.ipynb
@@ -50,9 +50,142 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <div style=\"border:1px solid #ddd; border-radius: 5px; padding: 3px; margin-top: 5px;\">\n",
+       "            <h2>Chamber Simulation</h2>\n",
+       "\n",
+       "            <div id=\"setpoints_dataframe\" onclick=\"toggleVisibility('content_setpoints_dataframe')\" style=\"cursor: pointer; background-color: #000000; color: #FFFFFF; padding: 5px; border-radius: 3px; margin-bottom: 1px;\">\n",
+       "                <h4 style=\"font-family: monospace; margin: 0;\">\n",
+       "                    <span id=\"arrow_content_setpoints_dataframe\" style=\"color: #b676c2;\">►</span>\n",
+       "                     Setpoints Dataframe\n",
+       "                </h4>\n",
+       "            </div>\n",
+       "            <div id=\"content_setpoints_dataframe\" style=\"display:none; margin-left: 20px; padding: 5px;\">\n",
+       "                <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>setpoint_temperature</th>\n",
+       "      <th>setpoint_irradiance_280-400</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0 days 00:00:00</th>\n",
+       "      <td>60.0</td>\n",
+       "      <td>250.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0 days 00:01:00</th>\n",
+       "      <td>60.0</td>\n",
+       "      <td>250.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0 days 00:02:00</th>\n",
+       "      <td>60.0</td>\n",
+       "      <td>250.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0 days 00:03:00</th>\n",
+       "      <td>60.0</td>\n",
+       "      <td>250.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0 days 00:04:00</th>\n",
+       "      <td>60.0</td>\n",
+       "      <td>250.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2 days 11:55:00</th>\n",
+       "      <td>60.0</td>\n",
+       "      <td>250.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2 days 11:56:00</th>\n",
+       "      <td>60.0</td>\n",
+       "      <td>250.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2 days 11:57:00</th>\n",
+       "      <td>60.0</td>\n",
+       "      <td>250.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2 days 11:58:00</th>\n",
+       "      <td>60.0</td>\n",
+       "      <td>250.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2 days 11:59:00</th>\n",
+       "      <td>60.0</td>\n",
+       "      <td>250.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3600 rows × 2 columns</p>\n",
+       "</div>\n",
+       "            </div>\n",
+       "\n",
+       "            <div id=\"setpoints_plot\" onclick=\"toggleVisibility('content_setpoints_plot')\" style=\"cursor: pointer; background-color: #000000; color: #FFFFFF; padding: 5px; border-radius: 3px; margin-bottom: 1px;\">\n",
+       "                <h4 style=\"font-family: monospace; margin: 0;\">\n",
+       "                    <span id=\"arrow_content_setpoints_plot\" style=\"color: #b676c2;\">►</span>\n",
+       "                     Setpoints Plot\n",
+       "                </h4>\n",
+       "            </div>\n",
+       "            <div id=\"content_setpoints_plot\" style=\"display:none; margin-left: 20px; padding: 5px;\">\n",
+       "                <h3>Setpoints Plot</h3>\n",
+       "                <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAxAAAAGGCAYAAADrWWeKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABsXElEQVR4nO3deXxM1/8/8NdkmclkmYnsCZFEbLGFIppaY0ksVWuVKglBq5ailiq1b1UtrVK6haqltKgullgiaitaVKkPKpZK7BKxRJb37w+/uV8jE24kkcXr+XjM45F777nnvO+ZOzf3PffeMxoRERAREREREalgVdgBEBERERFR8cEEgoiIiIiIVGMCQUREREREqjGBICIiIiIi1ZhAEBERERGRakwgiIiIiIhINSYQRERERESkGhMIIiIiIiJSjQkEERERERGpxgSCiAAAEyZMgEajKewwcq1JkyZo0qTJU2tv5cqVcHFxQWpqaoG3lZCQAI1Gg0WLFhV4W0T56Wl/LoubRYsWQaPRICEhQfU6V69ehYODA3799deCC4xIJSYQRPnor7/+QufOneHn5wc7OzuULl0aLVq0wNy5c5+ovmXLlmHOnDn5G2QB2LVrFyZMmIAbN27kW53+/v7QaDTKy8PDAw0bNsSaNWvypf7bt29jwoQJiIuLU71OZmYmxo8fj0GDBsHR0VGZr9FoMHDgQIvrfP/999BoNLlq51F+/fVXTJgwIV/qMjH18YcffphtmelEZ//+/bmuN7f7RVRUlNl77ujoiHLlyqFz58744YcfkJWVlesYirtp06Zh7dq1BVJ3QXxui5oVK1ZAo9Fg4cKFFpf3798ftra2OHTokKr6unTpAo1Gg1GjRuVnmKq4urqiT58+eO+9955620TZCBHli507d4pWq5Xy5cvL5MmT5YsvvpBx48ZJeHi4BAYGPlGdbdq0ET8/v/wNNAfp6ely586dJ1r3gw8+EABy+vTpfIvHz89PatasKUuWLJElS5bI+++/L+XKlRMA8tlnnynlGjduLI0bN851/ZcvXxYAMn78eNXrrFmzRjQajZw/f95sPgAZMGCAxXVWrVolAGTbtm25jjErK0vu3LkjGRkZyrwBAwZIfh+6AQgA8fT0lFu3bpkti4mJEQCyb9++XNeb2/0iMjJSdDqd8p5//vnnMmbMGKlRo4YAkCZNmkhycnKu4yjOHBwcJDIyskDqLojPrcmTfi4LQsuWLcXZ2VmSkpLM5u/du1esrKxkxIgRqupJTk4WOzs78ff3F19fX8nKynrimDIyMuTOnTu5ruPo0aMCQLZs2fLEbRPlB5tCyVqISqCpU6fCaDRi3759cHZ2Nlt26dKlwgkqF2xsbGBjU7QOCaVLl8Zrr72mTPfs2RPly5fH7Nmz8cYbbzz1eGJiYlC/fn2ULl36qbSn0WhgZ2f3VNqqWbMmDh48iAULFmDYsGFPpU1LbGxszN5zAJgyZQpmzJiB0aNHo2/fvvjuu+8KKbqi7datW3BwcCjsMIqczz77DFWrVsXQoUOxbNkyAPevJr7++usoW7as6it6P/zwAzIzM/H111+jadOmiI+PR+PGjZ8oJmtra1hbW+d6vaCgIFSrVg2LFi1C06ZNn6htovzAW5iI8smpU6dQtWrVbMkDAHh4eGSb9+2336J27drQ6/VwcXFB165dce7cOWV5kyZN8Msvv+DMmTPKLR3+/v4AgLi4OGg0Gnz33Xd499134eXlBQcHB7z00ktmdZisWrVKacvNzQ2vvfYa/vvvP7Mylp6BMN2as3btWlSrVg06nQ5Vq1bFhg0bzNYbMWIEACAgIECJ1XRvb2xsLBo0aABnZ2c4OjqiUqVKePfdd1X16cO8vLwQFBSE06dPP7LcpUuXEB0dDU9PT9jZ2SE4OBiLFy9WlickJMDd3R0AMHHiRCXmR51I3L17Fxs2bEDz5s2fKPYHNWnSBNWqVcPRo0cRFhYGe3t7lC5dGjNnzjQr9/AzEFFRUZg3bx4AmN3qY7JixQrUrl0bTk5OMBgMqF69Oj7++GNVMdWvXx9NmzbFzJkzcefOnceW37p1Kxo2bAgHBwc4OzujXbt2OHbsmLL8cftFbr3zzjsIDw/HqlWr8L///c9s2fr165VYnJyc0KZNG/z9999mZaKiouDo6IizZ8/ixRdfhKOjI0qXLq30519//YWmTZvCwcEBfn5+yonmg/7991+8/PLLcHFxgb29PZ5//nn88ssvZmVMn82VK1di6tSpKFOmDOzs7NCsWTOcPHnSrOyJEyfQqVMneHl5wc7ODmXKlEHXrl2RnJwM4P57fOvWLSxevFjpv6ioKKV/NRoNjh49ildffRWlSpVCgwYNAACHDx9GVFQUypUrBzs7O3h5eaF37964evWq0raa9+dxxyiTzz//HIGBgdDr9QgJCcGOHTtyfB8flpGRgcmTJyMwMBA6nQ7+/v549913kZaWZlbO398fL774In777TeEhITAzs4O5cqVwzfffPPYNvz9/TFhwgQsX74csbGxAIBPPvkEBw8exGeffQZ7e3tVsS5duhQtWrRAWFgYgoKCsHTpUrPlIoKwsDC4u7ubfWl07949VK9eHYGBgbh16xYAy89A7N+/HxEREXBzc4Ner0dAQAB69+6dLY4WLVrgp59+goioipuoQBT2JRCikiI8PFycnJzkr7/+emzZKVOmiEajkVdeeUXmz58vEydOFDc3N/H395fr16+LiMimTZukZs2a4ubmptzSsWbNGhER2bZtmwCQ6tWrS40aNeSjjz6Sd955R+zs7KRixYpy+/ZtpS3TLSh169aV2bNnyzvvvCN6vd6sLRGR8ePHZ7s1BoAEBweLt7e3TJ48WebMmSPlypUTe3t7uXLlioiIHDp0SLp16yYAZPbs2UqsqampcuTIEdFqtVKnTh35+OOPZcGCBTJ8+HBp1KjRY/vIz89P2rRpYzbv3r174unpKV5eXsq8h2+VuH37tgQFBYmtra0MHTpUPvnkE2nYsKEAkDlz5oiISGpqqnz22WcCQDp06KDEfOjQoRzj+e233wSArFu3Ltsy5PIWpsaNG4uPj4/4+vrKW2+9JfPnz5emTZsKAPn111+VcqdPnxYAEhMTIyIiu3btkhYtWggAJeYlS5aIyP39BYA0a9ZM5s2bJ/PmzZOBAwfKyy+/nOM2PRx/fHy8AJAPP/xQWWbpFqbY2FixsbGRihUrysyZM5X9t1SpUsrtMI/aL3ISGRkpDg4OOS5fsmSJAJBPP/1UmffNN9+IRqORli1byty5c+X9998Xf39/cXZ2Nrs1JzIyUuzs7KRKlSryxhtvyLx58+SFF15Q+tfHx0dGjBghc+fOlapVq4q1tbX8+++/yvpJSUni6ekpTk5OMmbMGPnoo48kODhYrKysZPXq1Uo502ezVq1aUrt2bZk9e7ZMmDBB7O3tJSQkRCmXlpYmAQEB4uPjI1OmTJEvv/xSJk6cKHXr1pWEhARle3U6nTRs2FDpv127donI/31eq1SpIu3atZP58+fLvHnzRERk1qxZ0rBhQ5k0aZJ8/vnn8tZbb4ler5eQkBDllpnHvT9qjlEiIl9++aUAkBdeeEE++eQTGTJkiDg7O0u5cuVU3cIUGRkpAKRz584yb9486dmzpwCQ9u3bm5Xz8/OTSpUqiaenp7z77rvy6aefynPPPScajUaOHDny2HbS09MlODhYAgMD5cSJE+Lo6Chdu3Z97Hom//33n1hZWSmft0mTJkmpUqUkLS3NrNy///4rjo6O0qFDB2XeO++8IxqNRrZv367MM32uTPvoxYsXpVSpUlKxYkX54IMP5IsvvpAxY8ZIUFBQtli+/fZbAaDqfw1RQWECQZRPNm3aJNbW1mJtbS2hoaEycuRI2bhxo9y7d8+sXEJCglhbW8vUqVPN5v/1119iY2NjNj+nZyBMJymlS5eWlJQUZf7KlSsFgHz88ccicv+E28PDQ6pVq2b2fMPPP/8sAGTcuHHKvJwSCK1WKydPnlTmHTp0SADI3LlzlXk53Us9e/ZsASCXL1/Oqdty5OfnJ+Hh4XL58mW5fPmyHDp0SLp27SoAZNCgQUq5hxOIOXPmCAD59ttvlXn37t2T0NBQcXR0VPort89AmE6ULP3TfpIEAoB88803yry0tDTx8vKSTp06KfMeTiBEcn4G4q233hKDwWD2vIRaD8YfFhYmXl5eShJqKYGoWbOmeHh4yNWrV5V5hw4dEisrK+nZs6cy70megXhUAvHnn38KABk6dKiIiNy8eVOcnZ2lb9++ZuWSkpLEaDSazTedqE6bNk2Zd/36ddHr9aLRaGTFihXK/H/++SfbvjFkyBABIDt27FDm3bx5UwICAsTf318yMzNF5P8+m0FBQWYnlx9//LHZ/mPallWrVj2yT3J6BsL0ee3WrVu2ZQ9+gWCyfPlyASDx8fHKvJzeH7XHKNPxpWbNmmbb+vnnnwuAxyYQBw8eFADSp08fs/nDhw8XALJ161Zlnp+fX7b4L126JDqdTt5+++1HtmNieubBxcXF4jMRjzJr1izR6/XK8eN///ufAFC+1HnQwoULlWPQnj17xNraWoYMGWJW5uEEYs2aNaqfNdq1a5cAkO+++051/ET5jbcwEeWTFi1aYPfu3XjppZdw6NAhzJw5ExEREShdujTWrVunlFu9ejWysrLQpUsXXLlyRXl5eXmhQoUK2LZtm+o2e/bsCScnJ2W6c+fO8Pb2Vob5279/Py5duoQ333zT7F76Nm3aoHLlytluv7CkefPmCAwMVKZr1KgBg8GAf//997Hrmm7n+vHHH59oBJ1NmzbB3d0d7u7uCA4OxqpVq9CjRw+8//77Oa7z66+/wsvLC926dVPm2draYvDgwUhNTcX27dtzHQcA5faPUqVKPdH6D3N0dDS711+r1SIkJERVv1ri7OyMW7duKbdoPKkJEyYgKSkJCxYssLg8MTERBw8eRFRUFFxcXJT5NWrUQIsWLQp0iEnTyFc3b94EcP/2uBs3bqBbt25mnyVra2vUq1fP4mepT58+yt/Ozs6oVKkSHBwc0KVLF2V+pUqV4OzsbPZe/PrrrwgJCVFuEzLF069fPyQkJODo0aNm7fTq1QtarVaZbtiwIQAodRqNRgDAxo0bcfv27SfrEMDis0B6vV75++7du7hy5Qqef/55AMAff/zx2DrVHqNMx5c33njDbFujoqKU7XsU077y8DM3b7/9NgBkOz5VqVJF6UcAcHd3R6VKlVR/ZkJCQvDGG2/g2rVrmD59Ojw9PVWtB9y/falNmzbK8bZChQqoXbt2ttuYAKBfv36IiIjAoEGD0KNHDwQGBmLatGmPrN90rPz555+Rnp7+yLKmY9CVK1dUx0+U35hAEOWjunXrYvXq1bh+/Tp+//13jB49Gjdv3kTnzp2VE4wTJ05ARFChQgXl5Nj0OnbsWK4euK5QoYLZtEajQfny5ZX7as+cOQPg/gnRwypXrqwsf5SyZctmm1eqVClcv379seu+8sorqF+/Pvr06QNPT0907doVK1euVJ1M1KtXD7Gxsdi8eTN27dqFK1eu4JtvvjE7QXrYmTNnUKFCBVhZmR/egoKClOV5IU943/HDz5eUKVMm2zy1/WrJm2++iYoVK6JVq1YoU6YMevfubfasilqNGjVCWFhYjs9CPGqfCgoKwpUrV5T7vPOb6bc3TCdxJ06cAAA0bdo022dp06ZN2T5LdnZ2yrMvJkaj0eJ7YTQazd6LM2fO5LjNpuUPevhzYzrpM9UZEBCAYcOG4csvv4SbmxsiIiIwb9485fkHtQICArLNu3btGt566y14enpCr9fD3d1dKaemfrXHKNM2P3wcsrW1Rbly5R7bzpkzZ2BlZYXy5cubzffy8oKzs/Nj+xTI/Wembt26AIA6deqoXufYsWP4888/Ub9+fZw8eVJ5NWnSBD///DNSUlKyrfPVV1/h9u3bOHHiBBYtWvTIYxYANG7cGJ06dcLEiRPh5uaGdu3aISYmJtuzIMD/HYOK4+/2UMlRtIZcISohtFot6tati7p166JixYro1asXVq1ahfHjxyMrKwsajQbr16+3OArHg78vUBTkNFKImhNpvV6P+Ph4bNu2Db/88gs2bNiA7777Dk2bNsWmTZseOwqJm5tbvjy0nB9cXV0B3D8BLFOmjNkynU6X44PHpm+XHx5NKS/9aomHhwcOHjyIjRs3Yv369Vi/fj1iYmLQs2dPswfI1Rg/fjyaNGmChQsXWhwUoLAcOXIEAJQTTlMiumTJEnh5eWUr//CoYjn1eX6/F2rr/PDDDxEVFYUff/wRmzZtwuDBgzF9+nTs2bMn2z6WE0snpl26dMGuXbswYsQI1KxZE46OjsjKykLLli1VJe9P+xil9kS4IN4nNb799lsAwNChQzF06NBsy3/44Qf06tXLbF5cXJxy8v/XX38hNDT0kW1oNBp8//332LNnD3766Sds3LgRvXv3xocffog9e/aY9bkpYXJzc8vTdhHlBRMIogJm+qYrMTERABAYGAgRQUBAACpWrPjIdR/3j9X0DayJiODkyZOoUaMGAMDPzw8AcPz48WxD/h0/flxZnlePitPKygrNmjVDs2bN8NFHH2HatGkYM2YMtm3bViDJgZ+fHw4fPoysrCyzqxD//POPsvxxMVtSuXJlAMDp06dRvXr1bG0eP37c4nqm+U+jr7VaLdq2bYu2bdsiKysLb775JhYuXIj33nsv27e8j9K4cWM0adIE77//PsaNG2e27MF96mH//PMP3NzclKFE8/sb0iVLlkCj0aBFixYAoNxa5+HhUeCJZk7v8cP7VW5Vr14d1atXx9ixY7Fr1y7Ur18fCxYswJQpUwDkvg+vX7+OLVu2YOLEiWbv3cPHikfVrfYYZdrmEydOmB1f0tPTcfr0aQQHBz8yVj8/P2RlZeHEiRPKlRwAuHjxIm7cuJFvn5m8EBEsW7YMYWFhePPNN7Mtnzx5MpYuXWqWQCQmJmLQoEEIDw+HVqvF8OHDERERoWp7nn/+eTz//POYOnUqli1bhu7du2PFihVmt96ZRqF7sM+InjbewkSUT7Zt22bxmzDTfb6m2x86duwIa2trTJw4MVt5ETEbatHBweGRtxx88803yv3gwP1fPU5MTESrVq0A3E9ePDw8sGDBArNL4evXr8exY8fQpk2bJ9jS7EwnjA//ou21a9eyla1ZsyYAWLw0nx9at26NpKQks98KyMjIwNy5c+Ho6KiM224aulHtr/DWrl0bWq3W4i8yt27dGnv27MGBAwfM5t+4cQNLly5FzZo1LX5D/iRy6usH9xvgfuJmSiSfpK9Nz0J8/vnnZvO9vb1Rs2ZNLF682CyGI0eOYNOmTWjduvVjY30SM2bMwKZNm/DKK68ot8xERETAYDBg2rRpFu8bv3z5cp7bNWndujV+//137N69W5l369YtfP755/D390eVKlVyVV9KSgoyMjLM5lWvXh1WVlZm75eDg0Ou+s/0Lf3DxxZLv2if0/uj9hhVp04duLu7Y8GCBbh3755SZtGiRapiNu0rD8f20UcfAUC+HZ/yYufOnUhISECvXr3QuXPnbK9XXnkF27Ztw4ULF5R1+vbti6ysLHz11Vf4/PPPYWNjg+jo6EdeKbl+/Xq25TkdKw8cOACj0YiqVavm34YS5RKvQBDlk0GDBuH27dvo0KEDKleujHv37mHXrl347rvv4O/vr3xDFRgYiClTpmD06NFISEhA+/bt4eTkhNOnT2PNmjXo168fhg8fDuD+Set3332HYcOGoW7dunB0dETbtm2VNl1cXNCgQQP06tULFy9exJw5c1C+fHn07dsXwP17kd9//3306tULjRs3Rrdu3XDx4kV8/PHH8Pf3t3g5/knUrl0bADBmzBh07doVtra2aNu2LSZNmoT4+Hi0adMGfn5+uHTpEubPn48yZcqYPYyan/r164eFCxciKioKBw4cgL+/P77//nvs3LkTc+bMUe6f1+v1qFKlCr777jtUrFgRLi4uqFatGqpVq2axXjs7O4SHh2Pz5s2YNGmS2bJ33nkHq1atQqNGjfD666+jcuXKuHDhAhYtWoTExETExMTk2/aZ+nrw4MGIiIiAtbU1unbtij59+uDatWto2rQpypQpgzNnzmDu3LmoWbPmE31T2bhxYzRu3NjiQ+cffPABWrVqhdDQUERHR+POnTuYO3cujEaj2W9p5LRfPOrHzjIyMpRbRu7evYszZ85g3bp1OHz4MMLCwswSGoPBgM8++ww9evTAc889h65du8Ld3R1nz57FL7/8gvr16+PTTz/N9bZb8s4772D58uVo1aoVBg8eDBcXFyxevBinT5/GDz/8kO2Zm8fZunUrBg4ciJdffhkVK1ZERkYGlixZAmtra3Tq1EkpV7t2bWzevBkfffQRfHx8EBAQgHr16uVYr8FgQKNGjTBz5kykp6ejdOnS2LRpk8XfTsnp/VF7jLK1tcWUKVPw+uuvo2nTpnjllVdw+vRpxMTEqHoGIjg4GJGRkfj8889x48YNNG7cGL///jsWL16M9u3bIywsLFd9WhCWLl0Ka2vrHJOZl156CWPGjMGKFSswbNgwxMTE4JdffsGiRYuU29Dmzp2L1157DZ999pnFqxgAsHjxYsyfPx8dOnRAYGAgbt68iS+++AIGg8EsKQfuDx7Qtm1bPgNBheupjvlEVIKtX79eevfuLZUrVxZHR0fRarVSvnx5GTRokFy8eDFb+R9++EEaNGggDg4O4uDgIJUrV5YBAwbI8ePHlTKpqany6quvirOzswBQhnQ1DRW5fPlyGT16tHh4eIher5c2bdrImTNnsrX13XffSa1atUSn04mLi4t0795dzp8/b1Ymp2FcLQ1P6ufnl21oycmTJ0vp0qXFyspKGZ5wy5Yt0q5dO/Hx8RGtVis+Pj7SrVs3+d///vfY/rT0OxCWPDyMq8j9MdV79eolbm5uotVqpXr16mZDoZrs2rVLateuLVqtVtWQrqtXrxaNRiNnz57Ntuz8+fPSp08fKV26tNjY2IiLi4u8+OKLsmfPHosxV61aNdv8yMhIs2F7LQ3jmpGRIYMGDRJ3d3fRaDTKe/b9999LeHi4eHh4iFarlbJly8rrr78uiYmJj9wmkZzfZ9N+BgvDS27evFnq168ver1eDAaDtG3bVo4ePZqtDkv7RU5MQ62aXvb29uLv7y+dOnWS77//Xhkq1VKcERERYjQaxc7OTgIDAyUqKkr2799vVrelIWJzei8s7X+nTp2Szp07i7Ozs9jZ2UlISIj8/PPPFvvs4eFZH34v//33X+ndu7cEBgaKnZ2duLi4SFhYmGzevNlsvX/++UcaNWoker1eACifO9Pn1dIQyefPn5cOHTqIs7OzGI1Gefnll+XChQsW9/FHvT9qjlEiIvPnz5eAgADR6XRSp04diY+Pt/i5tCQ9PV0mTpwoAQEBYmtrK76+vjJ69Gi5e/euWbmcjgdq2zGxNCxxTu7duyeurq7SsGHDR5YLCAiQWrVqyblz58RoNErbtm2zlenQoYM4ODgovy3y8DCuf/zxh3Tr1k3Kli0rOp1OPDw85MUXXzTbh0VEjh07JgCy7SdET5tGhD9lSFTcxMXFISwsDKtWrULnzp0LO5xnRmZmJqpUqYIuXbpg8uTJhR0OET1jhgwZgvj4eBw4cIBXIKhQ8RkIIiKVrK2tMWnSJMybN08ZUpSI6Gm4evUqvvzyS0yZMoXJAxU6PgNBRJQLr7zyCl555ZXCDoOInjGurq784oKKDF6BICIiIiIi1fgMBBERERERqcYrEEREREREpBoTCCIiIiIiUo0PUT8kKysLFy5cgJOTE0c5ICIiIqISQURw8+ZN+Pj45PrHLx/GBOIhFy5cgK+vb2GHQURERESU786dO6f8UvqTYgLxECcnJwD3O9dgMBRyNEREREREeZeSkgJfX1/lXDcvmEA8xHTbksFgYAJBRERERCVKftyiz4eoiYiIiIhINSYQRERERESkGhMIIiIiIiJSjQkEERERERGpxgSCiIiIiIhUYwJBRERERESqMYEgIiIiIiLVikwCMX36dNStWxdOTk7w8PBA+/btcfz4cbMyTZo0gUajMXu98cYbZmXOnj2LNm3awN7eHh4eHhgxYgQyMjKe5qYQEREREZVYReaH5LZv344BAwagbt26yMjIwLvvvovw8HAcPXoUDg4OSrm+ffti0qRJyrS9vb3yd2ZmJtq0aQMvLy/s2rULiYmJ6NmzJ2xtbTFt2rSnuj1ERERERCWRRkSksIOw5PLly/Dw8MD27dvRqFEjAPevQNSsWRNz5syxuM769evx4osv4sKFC/D09AQALFiwAKNGjcLly5eh1Wof225KSgqMRiOSk5P5S9REREREVCLk5zlukbkC8bDk5GQAgIuLi9n8pUuX4ttvv4WXlxfatm2L9957T7kKsXv3blSvXl1JHgAgIiIC/fv3x99//41atWplayctLQ1paWnKdEpKyv0/5j8P2Fnn92YRERERET19dzPzraoimUBkZWVhyJAhqF+/PqpVq6bMf/XVV+Hn5wcfHx8cPnwYo0aNwvHjx7F69WoAQFJSklnyAECZTkpKstjW9OnTMXHixOwLks8DdzX5tEVERERERIUoLf9uOiqSCcSAAQNw5MgR/Pbbb2bz+/Xrp/xdvXp1eHt7o1mzZjh16hQCAwOfqK3Ro0dj2LBhynRKSgp8fX2Bnj8BTo5PtgFEREREREXJzVRgRpN8qarIJRADBw7Ezz//jPj4eJQpU+aRZevVqwcAOHnyJAIDA+Hl5YXff//drMzFixcBAF5eXhbr0Ol00Ol02ReUrgXwGQgiIiIiKglMt+nngyIzjKuIYODAgVizZg22bt2KgICAx65z8OBBAIC3tzcAIDQ0FH/99RcuXbqklImNjYXBYECVKlUKJG4iIiIiomdJkbkCMWDAACxbtgw//vgjnJyclGcWjEYj9Ho9Tp06hWXLlqF169ZwdXXF4cOHMXToUDRq1Ag1atQAAISHh6NKlSro0aMHZs6ciaSkJIwdOxYDBgywfJWBiIiIiIhypcgM46rRWH5gOSYmBlFRUTh37hxee+01HDlyBLdu3YKvry86dOiAsWPHmg1FdebMGfTv3x9xcXFwcHBAZGQkZsyYARsbdbkSh3ElIiIiopImP89xi0wCUVQwgSAiIiKikiY/z3GLzDMQRERERERU9DGBICIiIiIi1ZhAEBERERGRakwgiIiIiIhINSYQRERERESkGhMIIiIiIiJSjQkEERERERGpxgSCiIiIiIhUYwJBRERERESqMYEgIiIiIiLVmEAQEREREZFqTCCIiIiIiEg1JhBERERERKQaEwgiIiIiIlKNCQQREREREanGBIKIiIiIiFRjAkFERERERKoxgSAiIiIiItWYQBARERERkWpMIIiIiIiISDUmEEREREREpBoTCCIiIiIiUo0JBBERERERqcYEgoiIiIiIVGMCQUREREREqhWZBGL69OmoW7cunJyc4OHhgfbt2+P48ePK8mvXrmHQoEGoVKkS9Ho9ypYti8GDByM5OdmsHo1Gk+21YsWKp705REREREQlUpFJILZv344BAwZgz549iI2NRXp6OsLDw3Hr1i0AwIULF3DhwgXMmjULR44cwaJFi7BhwwZER0dnqysmJgaJiYnKq3379k95a4iIiIiISiaNiEhhB2HJ5cuX4eHhge3bt6NRo0YWy6xatQqvvfYabt26BRsbGwD3r0CsWbPmiZOGlJQUGI1GJCcnw2AwPGn4RERERERFRn6e4xaZKxAPM92a5OLi8sgyBoNBSR5MBgwYADc3N4SEhODrr79GEc2RiIiIiIiKHZvHF3n6srKyMGTIENSvXx/VqlWzWObKlSuYPHky+vXrZzZ/0qRJaNq0Kezt7bFp0ya8+eabSE1NxeDBgy3Wk5aWhrS0NGU6JSUl/zaEiIiIiKiEKZK3MPXv3x/r16/Hb7/9hjJlymRbnpKSghYtWsDFxQXr1q2Dra1tjnWNGzcOMTExOHfunMXlEyZMwMSJE7PN5y1MRERERFRSlOhbmAYOHIiff/4Z27Zts5g83Lx5Ey1btoSTkxPWrFnzyOQBAOrVq4fz58+bXWV40OjRo5GcnKy8cko0iIiIiIioCN3CJCIYNGgQ1qxZg7i4OAQEBGQrk5KSgoiICOh0Oqxbtw52dnaPrffgwYMoVaoUdDqdxeU6nS7HZUREREREZK7IJBADBgzAsmXL8OOPP8LJyQlJSUkAAKPRCL1ej5SUFISHh+P27dv49ttvkZKSojyv4O7uDmtra/z000+4ePEinn/+edjZ2SE2NhbTpk3D8OHDC3PTiIiIiIhKjCLzDIRGo7E4PyYmBlFRUYiLi0NYWJjFMqdPn4a/vz82bNiA0aNH4+TJkxARlC9fHv3790ffvn1hZaXubi0O40pEREREJU1+nuMWmQSiqGACQUREREQlTYl+iJqIiIiIiIouJhBERERERKQaEwgiIiIiIlKNCQQREREREanGBIKIiIiIiFRjAkFERERERKoxgSAiIiIiItWYQBARERERkWpMIIiIiIiISDUmEEREREREpBoTCCIiIiIiUo0JBBERERERqcYEgoiIiIiIVGMCQUREREREqjGBICIiIiIi1ZhAEBERERGRakwgiIiIiIhINSYQRERERESkGhMIIiIiIiJSjQkEERERERGpxgSCiIiIiIhUYwJBRERERESqMYEgIiIiIiLVmEAQEREREZFqTCCIiIiIiEi1IpNATJ8+HXXr1oWTkxM8PDzQvn17HD9+3KzM3bt3MWDAALi6usLR0RGdOnXCxYsXzcqcPXsWbdq0gb29PTw8PDBixAhkZGQ8zU0hIiIiIiqxikwCsX37dgwYMAB79uxBbGws0tPTER4ejlu3billhg4dip9++gmrVq3C9u3bceHCBXTs2FFZnpmZiTZt2uDevXvYtWsXFi9ejEWLFmHcuHGFsUlERERERCWORkSksIOw5PLly/Dw8MD27dvRqFEjJCcnw93dHcuWLUPnzp0BAP/88w+CgoKwe/duPP/881i/fj1efPFFXLhwAZ6engCABQsWYNSoUbh8+TK0Wu1j201JSYHRaERycjIMBkOBbiMRERER0dOQn+e4ReYKxMOSk5MBAC4uLgCAAwcOID09Hc2bN1fKVK5cGWXLlsXu3bsBALt370b16tWV5AEAIiIikJKSgr///vspRk9EREREVDLZFHYAlmRlZWHIkCGoX78+qlWrBgBISkqCVquFs7OzWVlPT08kJSUpZR5MHkzLTcssSUtLQ1pamjKdkpKSX5tBRERERFTiFMkrEAMGDMCRI0ewYsWKAm9r+vTpMBqNysvX17fA2yQiIiIiKq6KXAIxcOBA/Pzzz9i2bRvKlCmjzPfy8sK9e/dw48YNs/IXL16El5eXUubhUZlM06YyDxs9ejSSk5OV17lz5/Jxa4iIiIiISpYik0CICAYOHIg1a9Zg69atCAgIMFteu3Zt2NraYsuWLcq848eP4+zZswgNDQUAhIaG4q+//sKlS5eUMrGxsTAYDKhSpYrFdnU6HQwGg9mLiIiIiIgsKzLPQAwYMADLli3Djz/+CCcnJ+WZBaPRCL1eD6PRiOjoaAwbNgwuLi4wGAwYNGgQQkND8fzzzwMAwsPDUaVKFfTo0QMzZ85EUlISxo4diwEDBkCn0xXm5hERERERlQhFZhhXjUZjcX5MTAyioqIA3P8hubfffhvLly9HWloaIiIiMH/+fLPbk86cOYP+/fsjLi4ODg4OiIyMxIwZM2Bjoy5X4jCuRERERFTS5Oc5bpFJIIoKJhBEREREVNI8E78DQURERERERQ8TCCIiIiIiUo0JBBERERERqcYEgoiIiIiIVGMCQUREREREqjGBICIiIiIi1ZhAEBERERGRakwgiIiIiIhINSYQRERERESkGhMIIiIiIiJSjQkEERERERGpxgSCiIiIiIhUYwJBRERERESqMYEgIiIiIiLVmEAQEREREZFqTCCIiIiIiEg1JhBERERERKQaEwgiIiIiIlKNCQQREREREanGBIKIiIiIiFRjAkFERERERKoxgSAiIiIiItWYQBARERERkWpMIIiIiIiISDUmEEREREREpBoTCCIiIiIiUq1IJRDx8fFo27YtfHx8oNFosHbtWrPlGo3G4uuDDz5Qyvj7+2dbPmPGjKe8JUREREREJVORSiBu3bqF4OBgzJs3z+LyxMREs9fXX38NjUaDTp06mZWbNGmSWblBgwY9jfCJiIiIiEo8m8IO4EGtWrVCq1atclzu5eVlNv3jjz8iLCwM5cqVM5vv5OSUrSwREREREeVdkboCkRsXL17EL7/8gujo6GzLZsyYAVdXV9SqVQsffPABMjIycqwnLS0NKSkpZi8iIiIiIrKsSF2ByI3FixfDyckJHTt2NJs/ePBgPPfcc3BxccGuXbswevRoJCYm4qOPPrJYz/Tp0zFx4sSnETIRERERUbGnEREp7CAs0Wg0WLNmDdq3b29xeeXKldGiRQvMnTv3kfV8/fXXeP3115GamgqdTpdteVpaGtLS0pTplJQU+Pr6Ijk5GQaDIU/bQERERERUFKSkpMBoNObLOW6xvAKxY8cOHD9+HN99991jy9arVw8ZGRlISEhApUqVsi3X6XQWEwsiIiIiIsquWD4D8dVXX6F27doIDg5+bNmDBw/CysoKHh4eTyEyIiIiIqKSrUhdgUhNTcXJkyeV6dOnT+PgwYNwcXFB2bJlAdy//LJq1Sp8+OGH2dbfvXs39u7di7CwMDg5OWH37t0YOnQoXnvtNZQqVeqpbQcRERERUUlVpBKI/fv3IywsTJkeNmwYACAyMhKLFi0CAKxYsQIigm7dumVbX6fTYcWKFZgwYQLS0tIQEBCAoUOHKvUQERHR/8nMzER6enphh0FE+cTW1hbW1tYF3k6RfYi6sOTnAyZERERFkYggKSkJN27cKOxQiCifOTs7w8vLCxqNxmz+M/8QNRERET05U/Lg4eEBe3v7bCcaRFT8iAhu376NS5cuAQC8vb0LrC0mEERERM+QzMxMJXlwdXUt7HCIKB/p9XoAwKVLl+Dh4VFgtzMVy1GYiIiI6MmYnnmwt7cv5EiIqCCYPtsF+XwTEwgiIqJnEG9bIiqZnsZnmwkEERERERGpxgSCiIiIKA/i4uKg0Wg4qhU9M/IlgcjIyMDmzZuxcOFC3Lx5EwBw4cIFpKam5kf1RERERHmSkJAAjUaDgwcP5nvdL7zwAhITE2E0GlWvExUVhfbt26suX5DxF0X+/v6YM2dOYYdBOcjzKExnzpxBy5YtcfbsWaSlpaFFixZwcnLC+++/j7S0NCxYsCA/4iQiIiIqkrRaLby8vAo7jCJPRJCZmQkbm6c3COi9e/eg1WqfWnvPijxfgXjrrbdQp04dXL9+XRk6CgA6dOiALVu25LV6IiIiIsX333+P6tWrQ6/Xw9XVFc2bN8etW7cAAF9++SWCgoJgZ2eHypUrY/78+cp6AQEBAIBatWpBo9GgSZMmAP7vSsDEiRPh7u4Og8GAN954A/fu3VPWTUtLw+DBg+Hh4QE7Ozs0aNAA+/btU5Y/fAvTokWL4OzsjI0bNyIoKAiOjo5o2bIlEhMTAQATJkzA4sWL8eOPP0Kj0UCj0SAuLu6R251T/I/bbtOVi5UrV6Jhw4bQ6/WoW7cu/ve//2Hfvn2oU6cOHB0d0apVK1y+fFlZT02/ZGVlYfr06QgICIBer0dwcDC+//77bP2yfv161K5dGzqdDr/99htOnTqFdu3awdPTE46Ojqhbty42b96srNekSROcOXMGQ4cOVfrH1G81a9Y065c5c+bA398/W9xTp06Fj48PKlWqBAA4d+4cunTpAmdnZ7i4uKBdu3ZISEh4ZJ9TzvKcAu7YsQO7du3Klt35+/vjv//+y2v1REREVMBEBHfSMwulbb2ttepRYxITE9GtWzfMnDkTHTp0wM2bN7Fjxw6ICJYuXYpx48bh008/Ra1atfDnn3+ib9++cHBwQGRkJH7//XeEhIRg8+bNqFq1qtl5y5YtW2BnZ4e4uDgkJCSgV69ecHV1xdSpUwEAI0eOxA8//IDFixfDz88PM2fOREREBE6ePAkXFxeLsd6+fRuzZs3CkiVLYGVlhddeew3Dhw/H0qVLMXz4cBw7dgwpKSmIiYkBgBzrMckp/sdtt8n48eMxZ84clC1bFr1798arr74KJycnfPzxx7C3t0eXLl0wbtw4fPbZZ6r7Zfr06fj222+xYMECVKhQAfHx8Xjttdfg7u6Oxo0bK/W88847mDVrFsqVK4dSpUrh3LlzaN26NaZOnQqdTodvvvkGbdu2xfHjx1G2bFmsXr0awcHB6NevH/r27atq33jQli1bYDAYEBsbC+D+cKYREREIDQ3Fjh07YGNjgylTpqBly5Y4fPgwr1A8gTwnEFlZWcjMzH7QOX/+PJycnPJaPRERERWwO+mZqDJuY6G0fXRSBOy16k5HEhMTkZGRgY4dO8LPzw8AUL16dQD3T5A//PBDdOzYEcD9b+yPHj2KhQsXIjIyEu7u7gAAV1fXbLcbabVafP3117C3t0fVqlUxadIkjBgxApMnT8adO3fw2WefYdGiRWjVqhUA4IsvvkBsbCy++uorjBgxwmKs6enpWLBgAQIDAwEAAwcOxKRJkwAAjo6O0Ov1SEtLU33rU07xP267TYYPH46IiAgA9+8e6datG7Zs2YL69esDAKKjo7Fo0SLV/ZKeno5p06Zh8+bNCA0NBQCUK1cOv/32GxYuXGiWQEyaNAktWrRQpl1cXBAcHKxMT548GWvWrMG6deswcOBAuLi4wNraGk5OTk90a5iDgwO+/PJLJTH49ttvkZWVhS+//FJJVmNiYuDs7Iy4uDiEh4fnuo1nXZ4TiPDwcMyZMweff/45gPtjz6ampmL8+PFo3bp1ngMkIiIiAoDg4GA0a9YM1atXR0REBMLDw9G5c2dotVqcOnUK0dHRZt9YZ2RkqHqwOTg42OyH9UJDQ5Gamopz584hOTkZ6enpyok2ANja2iIkJATHjh3LsU57e3sleQAAb29vXLp0Kbeb/Ei3bt1Svd01atRQ/vb09ATwf8mXad7D8T2qX1JTU3H79m2zxAC4/8xBrVq1zObVqVPHbDo1NRUTJkzAL7/8oiSFd+7cwdmzZ3Oz+TmqXr262VWFQ4cO4eTJk9m+2L579y5OnTqVL20+a/KcQHz44YeIiIhAlSpVcPfuXbz66qs4ceIE3NzcsHz58vyIkYiIiAqQ3tYaRydFFFrballbWyM2Nha7du3Cpk2bMHfuXIwZMwY//fQTgPtXBurVq5dtncJga2trNq3RaCAi+dqGabRLNdv9YDymb+EfnpeVlZXrtn/55ReULl3abJlOpzObdnBwMJsePnw4YmNjMWvWLJQvXx56vR6dO3c2e77CEisrq2x9aOnXlh9uLzU1FbVr18bSpUuzlTVd2aHcyXMCUaZMGRw6dAgrVqzA4cOHkZqaiujoaHTv3t3soWoiIiIqmjQajerbiAqbRqNB/fr1Ub9+fYwbNw5+fn7YuXMnfHx88O+//6J79+4W1zN9I23ptutDhw7hzp07ynnLnj174OjoCF9fX7i5uUGr1WLnzp3KbVPp6enYt28fhgwZ8sTbodVqLcbyqPIPx+/p6fnY7c6LR/WLi4sLdDodzp49a3a7kho7d+5EVFQUOnToAOD+Cf7DDzRb6h93d3ckJSVBRJQkSM2wts899xy+++47eHh4wGAw5CpWsixfjhY2NjZ47bXX8qMqIiIiIov27t2LLVu2IDw8HB4eHti7dy8uX76MoKAgTJw4EYMHD4bRaETLli2RlpaG/fv34/r16xg2bBg8PDyg1+uxYcMGlClTBnZ2dsptPvfu3UN0dDTGjh2LhIQEjB8/HgMHDoSVlRUcHBzQv39/jBgxAi4uLihbtixmzpyJ27dvIzo6+om3xd/fHxs3bsTx48fh6uoKo9GY7arFg3KK/3HbnReP6hcnJycMHz4cQ4cORVZWFho0aIDk5GTs3LkTBoPB7PmLh1WoUAGrV69G27ZtodFo8N5772W7+uHv74/4+Hh07doVOp0Obm5uaNKkCS5fvoyZM2eic+fO2LBhA9avX//YpKB79+744IMP0K5dO0yaNAllypTBmTNnsHr1aowcORJlypTJUz89i/KcQHzzzTePXN6zZ8+8NkFEREQEg8GA+Ph4zJkzBykpKfDz88OHH36oPNxsb2+PDz74ACNGjICDgwOqV6+uXCWwsbHBJ598gkmTJmHcuHFo2LChMnRqs2bNUKFCBTRq1AhpaWno1q0bJkyYoLQ7Y8YMZGVloUePHrh58ybq1KmDjRs3olSpUk+8LX379kVcXBzq1KmD1NRUbNu2zWxo1oflFH+fPn0eud158bh+mTx5Mtzd3TF9+nT8+++/cHZ2xnPPPYd33333kfV+9NFH6N27N1544QW4ublh1KhRSElJMSszadIkvP766wgMDERaWhpEBEFBQZg/fz6mTZuGyZMno1OnThg+fLjyHG5O7O3tER8fj1GjRqFjx464efMmSpcujWbNmvGKxBPSSB5vyHv4w5Oeno7bt29Dq9XC3t4e165dy1OAT1tKSgqMRiOSk5O5UxERUYlz9+5dnD59GgEBAbCzsyvscApdVFQUbty4gbVr1xZ2KEUK+6X4yukznp/nuHn+Ibnr16+bvVJTU3H8+HE0aNCAD1ETEREREZUweU4gLKlQoQJmzJiBt956qyCqJyIiIipRpk2bBkdHR4sv0y1aREVFgQ25YGNjgwsXLhRU9URERER59vCPpxWWN954A126dLG4rDBGtSwq/UJFU54TiHXr1plNiwgSExPx6aefmv3oChERERFZ5uLiAhcXl8IOg0iVPCcQ7du3N5vWaDRwd3dH06ZN8eGHH+a1eiIiIiIiKkLynEDk5lcLiYiIiIioeCuQh6iJiIiIiKhkeqIrELn5ZcOPPvroSZogIiIiIqIi6ImuQPz555+qXgcPHsxVvfHx8Wjbti18fHyg0Wiy/XhJVFQUNBqN2atly5ZmZa5du4bu3bvDYDDA2dkZ0dHRSE1NfZLNJCIiIiKihzzRFYht27bldxwAgFu3biE4OBi9e/dGx44dLZZp2bIlYmJilGmdTme2vHv37khMTERsbCzS09PRq1cv9OvXD8uWLSuQmImIiOjZFhcXh7CwMFy/fh3Ozs4F0kZCQgICAgLw559/ombNmgXSxsMWLVqEIUOG4MaNGwCACRMmYO3atbn+gphKniL1DESrVq0wZcoUdOjQIccyOp0OXl5eyqtUqVLKsmPHjmHDhg348ssvUa9ePTRo0ABz587FihUr+JsUREREz7CEhARoNJoCOfl94YUXkJiYCKPRqHqdqKiobCNZPoqvry8SExNRrVq1J4gwfwwfPhxbtmwptPZzKyEhAdHR0QgICIBer0dgYCDGjx+Pe/fumZXbuHEjnn/+eTg5OcHd3R2dOnVCQkKCWZm4uDg899xz0Ol0KF++fK5/J+ONN96ARqPBnDlzzOaruXPm8OHDaNiwIezs7ODr64uZM2fmqu2CkC8JxP79+zFy5Eh07doVHTt2NHvlt7i4OHh4eKBSpUro378/rl69qizbvXs3nJ2dUadOHWVe8+bNYWVlhb179+Z7LERERERarRZeXl7QaDQF1oa1tTW8vLxgY2P55hERQUZGRrb5D58s54WjoyNcXV3zrb6C9s8//yArKwsLFy7E33//jdmzZ2PBggV49913lTKnT59Gu3bt0LRpUxw8eBAbN27ElStXzM5hT58+jTZt2iAsLAwHDx7EkCFD0KdPH2zcuFFVHGvWrMGePXvg4+OTbVn37t3x999/IzY2Fj///DPi4+PRr18/ZXlKSgrCw8Ph5+eHAwcO4IMPPsCECRPw+eef56Fn8oHk0fLly8XW1lZefPFF0Wq18uKLL0rFihXFaDRKVFTUE9cLQNasWZOtrR9//FEOHz4sa9askaCgIKlbt65kZGSIiMjUqVOlYsWK2epyd3eX+fPnW2zn7t27kpycrLzOnTsnACQ5OfmJYyciIiqq7ty5I0ePHpU7d+4UdihPZNWqVVKtWjWxs7MTFxcXadasmaSmpoqIyBdffCGVK1cWnU4nlSpVknnz5inrATB7NW7cWEREIiMjpV27djJhwgRxc3MTJycnef311yUtLU1Z9+7duzJo0CBxd3cXnU4n9evXl99//11Zvm3bNgEg169fFxGRmJgYMRqNsmHDBqlcubI4ODhIRESEXLhwQURExo8fny2ebdu2PXK7T58+LQDkzz//NGvz119/leeee05sbW1l27Zt0rhxYxkwYIC89dZb4urqKk2aNBERkQ8//FCqVasm9vb2UqZMGenfv7/cvHnTrI2YmBjx9fUVvV4v7du3l1mzZonRaFSWjx8/XoKDg5Xp33//XZo3by6urq5iMBikUaNGcuDAAbM6AcgXX3wh7du3F71eL+XLl5cff/zRrMyRI0ekTZs24uTkJI6OjtKgQQM5efKksvxR72tuzZw5UwICApTpVatWiY2NjWRmZirz1q1bJxqNRu7duyciIiNHjpSqVaua1fPKK69IRETEY9s7f/68lC5dWo4cOSJ+fn4ye/ZsZdnRo0cFgOzbt0+Zt379etFoNPLff/+JiMj8+fOlVKlSZvvjqFGjpFKlSjm2mdNnPDk5Od/OcfN8BWLatGmYPXs2fvrpJ2i1Wnz88cf4559/0KVLF5QtWzav1Zvp2rUrXnrpJVSvXh3t27fHzz//jH379iEuLu6J65w+fTqMRqPy8vX1zb+AiYiIigMR4N6twnmJqA4zMTER3bp1Q+/evXHs2DHExcWhY8eOEBEsXboU48aNw9SpU3Hs2DFMmzYN7733HhYvXgwA+P333wEAmzdvRmJiIlavXq3Uu2XLFqW+5cuXY/Xq1Zg4caKyfOTIkfjhhx+wePFi/PHHHyhfvjwiIiJw7dq1HGO9ffs2Zs2ahSVLliA+Ph5nz57F8OHDAdy/FahLly5o2bIlEhMTkZiYiBdeeCFXb5nJO++8gxkzZuDYsWOoUaMGAGDx4sXQarXYuXMnFixYAACwsrLCJ598gr///huLFy/G1q1bMXLkSKWevXv3Ijo6GgMHDsTBgwcRFhaGKVOmPLLtmzdvIjIyEr/99hv27NmDChUqoHXr1rh586ZZuYkTJ6JLly44fPgwWrduje7duyt9999//6FRo0bQ6XTYunUrDhw4gN69eytXUx73vuZWcnKy2S9+165dG1ZWVoiJiUFmZiaSk5OxZMkSNG/eHLa2tgDu3+HSvHlzs3oiIiKwe/fuR7aVlZWFHj16YMSIEahatWq25WrunNm9ezcaNWoErVZr1vbx48dx/fr13HdAfslrBmJvby+nT58WEREXFxc5fPiwiNzPqry8vJ64Xli4AmGJm5ubLFiwQEREvvrqK3F2djZbnp6eLtbW1rJ69WqL6/MKBBERPUssfjuZlioy3lA4r7RU1bEfOHBAAEhCQkK2ZYGBgbJs2TKzeZMnT5bQ0FARyf4NvklkZKS4uLjIrVu3lHmfffaZODo6SmZmpqSmpoqtra0sXbpUWX7v3j3x8fGRmTNniojlKxAAzL5Fnzdvnnh6epq1265dO9XbntMViLVr15qVa9y4sdSqVeux9a1atUpcXV2V6W7duknr1q3NyrzyyiuPvALxsMzMTHFycpKffvpJmQdAxo4dq0ynpqYKAFm/fr2IiIwePVoCAgKUb/sf9rj3NTdOnDghBoNBPv/8c7P5cXFx4uHhIdbW1gJAQkNDlfdSRKRChQoybdo0s3V++eUXASC3b9/Osb1p06ZJixYtJCsrS0Qk2xUINXfOtGjRQvr162e2/O+//xYAcvToUYvtFosrEKVKlVIyzdKlS+PIkSMAgBs3buD27dt5rf6Rzp8/j6tXr8Lb2xsAEBoaihs3buDAgQNKma1btyIrKwv16tWzWIdOp4PBYDB7ERERUdETHByMZs2aoXr16nj55ZfxxRdf4Pr167h16xZOnTqF6OhoODo6Kq8pU6bg1KlTquq1t7dXpkNDQ5Gamopz587h1KlTSE9PR/369ZXltra2CAkJwbFjx3Ks097eHoGBgcq0t7c3Ll269IRbnrMHv702qV27drZ5mzdvRrNmzVC6dGk4OTmhR48euHr1qnKuduzYsWznSqGhoY9s++LFi+jbty8qVKgAo9EIg8GA1NRUnD171qyc6coIADg4OMBgMCh9cfDgQTRs2FD5tv9BeX1fH/Tff/+hZcuWePnll9G3b19lflJSEvr27YvIyEjs27cP27dvh1arRefOnSEqr44tXbrULL4dO3bgwIED+Pjjj7Fo0aICfTamsDzRMK4AcOTIEVSrVg2NGjVCbGys8mF+6623sHXrVsTGxqJZs2a5qjM1NRUnT55Upk+fPo2DBw/CxcUFLi4umDhxIjp16gQvLy+cOnUKI0eOVC4jAkBQUBBatmyJvn37YsGCBUhPT8fAgQPRtWtXiw+uEBEREQBbe+DdQhqt0Nb+8WX+P2tra8TGxmLXrl3YtGkT5s6dizFjxuCnn34CAHzxxRfZToKtra3zNVy1Hj4h1mg0qk9Ic8PBweGx8xISEvDiiy+if//+mDp1KlxcXPDbb78hOjoa9+7dM0ueciMyMhJXr17Fxx9/DD8/P+h0OoSGhmZ7cNtSX2RlZQEA9Hp9jvWbRiPK6/t64cIFhIWF4YUXXsj28PG8efNgNBrNRjb69ttv4evri7179+L555+Hl5cXLl68aLbexYsXYTAYoNfr8dJLL5nFV7p0aSxcuBCXLl0yu50/MzMTb7/9NubMmYOEhAR4eXllSyozMjJw7do1eHl5AUCObZuWFZYnTiBq1KiBunXron379nj55ZcBAGPGjIGtrS127dqFTp06YezYsbmqc//+/QgLC1OmTb94HRkZic8++wyHDx/G4sWLcePGDfj4+CA8PByTJ082+y2IpUuXYuDAgWjWrBmsrKzQqVMnfPLJJ0+6mURERCWfRgNos5+IFkUajQb169dH/fr1MW7cOPj5+WHnzp3w8fHBv//+i+7du1tcz3QPeWZmZrZlhw4dwp07d5ST2T179sDR0RG+vr5wc3NTnifw8/MDAKSnp2Pfvn0YMmTIE2+HVqu1GEtBOHDgALKysvDhhx/Cyur+zScrV640KxMUFJRtxMo9e/Y8st6dO3di/vz5aN26NQDg3LlzuHLlSq5iq1GjBhYvXoz09PRsiYanp+dj39fH+e+//xAWFobatWsjJiZG2X6T27dvZ5tnSk5MSU5oaCh+/fVXszKxsbHKFRonJyc4OTmZLe/Ro4fF5yZ69OiBXr16KfWa7pwxXTV6+M6Z0NBQjBkzxqx/YmNjUalSJbOfMnjanjiB2L59O2JiYjB9+nRMnToVnTp1Qp8+ffDOO+88cTBNmjR5ZHauZrgsFxcX/mgcERFRCbR3715s2bIF4eHh8PDwwN69e3H58mUEBQVh4sSJGDx4MIxGI1q2bIm0tDTs378f169fx7Bhw+Dh4QG9Xo8NGzagTJkysLOzU3634d69e4iOjsbYsWORkJCA8ePHY+DAgbCysoKDgwP69++PESNGwMXFBWXLlsXMmTNx+/ZtREdHP/G2+Pv7Y+PGjTh+/DhcXV1hNBot3saTH8qXL4/09HTMnTsXbdu2NXu42mTw4MGoX78+Zs2ahXbt2mHjxo3YsGHDI+utUKEClixZgjp16iAlJQUjRox45BUFSwYOHIi5c+eia9euGD16NIxGI/bs2YOQkBBUqlTpse/ro/z3339o0qQJ/Pz8MGvWLFy+fFlZZvr2vk2bNpg9ezYmTZqEbt264ebNm3j33Xfh5+eHWrVqAbj/Gw6ffvopRo4cid69e2Pr1q1YuXIlfvnllxzbdnV1zTbkra2tLby8vFCpUiUA6u6cefXVVzFx4kRER0dj1KhROHLkCD7++GPMnj07V/2c7/L6EEVqaqp8/fXX0qhRI9FoNFKhQgWZMWOGJCYm5vkBjcKQnw+YEBERFTXFeRjXo0ePSkREhDKcasWKFWXu3LnK8qVLl0rNmjVFq9VKqVKlpFGjRmaDqHzxxRfi6+srVlZW2YZxHTdunLi6uoqjo6P07dtX7t69q6x3584dGTRokLi5ueVqGNcHrVmzRh487bp06ZK0aNFCHB0d8zSM64MP+4rcf4j6rbfeyrb+Rx99JN7e3qLX6yUiIkK++eabbOt/9dVXUqZMGdHr9dK2bdvHDuP6xx9/SJ06dcTOzk4qVKggq1atyvagMCwMimM0GiUmJkaZPnTokISHh4u9vb04OTlJw4YN5dSpU8ryx72vOTE9zG7p9aDly5dLrVq1xMHBQdzd3eWll16SY8eOmZXZtm2bEkO5cuXM4lfr4b4REbl69ap069ZNHB0dxWAwSK9evbINr3vo0CFp0KCB6HQ6KV26tMyYMeOR7TyNh6g1Ivl3Q97JkycRExODJUuWICkpCS1btsS6devyq/qnIiUlBUajEcnJyXygmoiISpy7d+/i9OnTCAgIgJ2dXWGHU+iioqJw48YNrF27trBDIcoXOX3G8/McN19+idqkfPnyePfddzF27Fg4OTk98tIOEREREREVP/mWQMTHxyMqKgpeXl4YMWIEOnbsiJ07d+ZX9UREREQl1rRp08yGAn3w1apVq8IOr0hinxWePN3CdOHCBSxatAiLFi3CyZMn8cILLyA6OhpdunSxOKxYccBbmIiIqCTjLUxF07Vr13L8ZWu9Xo/SpUs/5YiKPvaZZU/jFqYnHoWpVatW2Lx5M9zc3NCzZ0/07t1beaqciIiIiNQz/eYVqcc+KzxPnEDY2tri+++/x4svvlhoP9JCRERERERP1xMnEMVtdCUiIiL6P/k4CCMRFSFP47Odr6MwERERUdFm+rGy27dvF3IkRFQQTJ/tgvphQiAPVyCIiIio+LG2toazszMuXboEALC3t4dGoynkqIgor0QEt2/fxqVLl+Ds7FygjxgwgSAiInrGeHl5AYCSRBBRyeHs7Kx8xgsKEwgiIqJnjEajgbe3Nzw8PJCenl7Y4RBRPrG1tX0qgxsxgSAiInpGWVtbcyRFIso1PkRNRERERESqMYEgIiIiIiLVmEAQEREREZFqTCCIiIiIiEg1JhBERERERKQaEwgiIiIiIlKNCQQREREREanGBIKIiIiIiFRjAkFERERERKoxgSAiIiIiItWYQBARERERkWpMIIiIiIiISDUmEEREREREpFqRSiDi4+PRtm1b+Pj4QKPRYO3atcqy9PR0jBo1CtWrV4eDgwN8fHzQs2dPXLhwwawOf39/aDQas9eMGTOe8pYQEREREZVMRSqBuHXrFoKDgzFv3rxsy27fvo0//vgD7733Hv744w+sXr0ax48fx0svvZSt7KRJk5CYmKi8Bg0a9DTCJyIiIiIq8WwKO4AHtWrVCq1atbK4zGg0IjY21mzep59+ipCQEJw9exZly5ZV5js5OcHLy6tAYyUiIiIiehYVqSsQuZWcnAyNRgNnZ2ez+TNmzICrqytq1aqFDz74ABkZGTnWkZaWhpSUFLMXERERERFZVqSuQOTG3bt3MWrUKHTr1g0Gg0GZP3jwYDz33HNwcXHBrl27MHr0aCQmJuKjjz6yWM/06dMxceLEpxU2EREREVGxphERKewgLNFoNFizZg3at2+fbVl6ejo6deqE8+fPIy4uziyBeNjXX3+N119/HampqdDpdNmWp6WlIS0tTZlOSUmBr68vkpOTH1kvEREREVFxkZKSAqPRmC/nuMXuCkR6ejq6dOmCM2fOYOvWrY/tgHr16iEjIwMJCQmoVKlStuU6nc5iYkFERERERNkVqwTClDycOHEC27Ztg6ur62PXOXjwIKysrODh4fEUIiQiIiIiKtmKVAKRmpqKkydPKtOnT5/GwYMH4eLiAm9vb3Tu3Bl//PEHfv75Z2RmZiIpKQkA4OLiAq1Wi927d2Pv3r0ICwuDk5MTdu/ejaFDh+K1115DqVKlCmuziIiIiIhKjCL1DERcXBzCwsKyzY+MjMSECRMQEBBgcb1t27ahSZMm+OOPP/Dmm2/in3/+QVpaGgICAtCjRw8MGzZM9W1K+Xl/GBERERFRUZCf57hFKoEoCphAEBEREVFJk5/nuMX6dyCIiIiIiOjpYgJBRERERESqMYEgIiIiIiLVmEAQEREREZFqTCCIiIiIiEg1JhBERERERKQaEwgiIiIiIlKNCQQREREREanGBIKIiIiIiFRjAkFERERERKoxgSAiIiIiItWYQBARERERkWpMIIiIiIiISDUmEEREREREpBoTCCIiIiIiUo0JBBERERERqcYEgoiIiIiIVGMCQUREREREqjGBICIiIiIi1ZhAEBERERGRakwgiIiIiIhINSYQRERERESkGhMIIiIiIiJSjQkEERERERGpxgSCiIiIiIhUK1IJRHx8PNq2bQsfHx9oNBqsXbvWbLmIYNy4cfD29oZer0fz5s1x4sQJszLXrl1D9+7dYTAY4OzsjOjoaKSmpj7FrSAiIiIiKrmKVAJx69YtBAcHY968eRaXz5w5E5988gkWLFiAvXv3wsHBAREREbh7965Spnv37vj7778RGxuLn3/+GfHx8ejXr9/T2gQiIiIiohJNIyJS2EFYotFosGbNGrRv3x7A/asPPj4+ePvttzF8+HAAQHJyMjw9PbFo0SJ07doVx44dQ5UqVbBv3z7UqVMHALBhwwa0bt0a58+fh4+Pz2PbTUlJgdFoRHJyMgwGQ4FtHxERERHR05Kf57hF6grEo5w+fRpJSUlo3ry5Ms9oNKJevXrYvXs3AGD37t1wdnZWkgcAaN68OaysrLB3796nHjMRERERUUljU9gBqJWUlAQA8PT0NJvv6empLEtKSoKHh4fZchsbG7i4uChlHpaWloa0tDRlOiUlJT/DJiIiIiIqUYrNFYiCMn36dBiNRuXl6+tb2CERERERERVZxSaB8PLyAgBcvHjRbP7FixeVZV5eXrh06ZLZ8oyMDFy7dk0p87DRo0cjOTlZeZ07d64AoiciIiIiKhmKTQIREBAALy8vbNmyRZmXkpKCvXv3IjQ0FAAQGhqKGzdu4MCBA0qZrVu3IisrC/Xq1bNYr06ng8FgMHsREREREZFlReoZiNTUVJw8eVKZPn36NA4ePAgXFxeULVsWQ4YMwZQpU1ChQgUEBATgvffeg4+PjzJSU1BQEFq2bIm+fftiwYIFSE9Px8CBA9G1a1dVIzAREREREdGjFakEYv/+/QgLC1Omhw0bBgCIjIzEokWLMHLkSNy6dQv9+vXDjRs30KBBA2zYsAF2dnbKOkuXLsXAgQPRrFkzWFlZoVOnTvjkk0+e+rYQEREREZVERfZ3IAoLfweCiIiIiEqaZ/J3IIiIiIiIqPAxgSAiIiIiItWYQBARERERkWpMIIiIiIiISDUmEEREREREpBoTCCIiIiIiUo0JBBERERERqcYEgoiIiIiIVGMCQUREREREqjGBICIiIiIi1ZhAEBERERGRakwgiIiIiIhINSYQRERERESkGhMIIiIiIiJSjQkEERERERGpxgSCiIiIiIhUYwJBRERERESqMYEgIiIiIiLVmEAQEREREZFqTCCIiIiIiEg1JhBERERERKQaEwgiIiIiIlKNCQQREREREanGBIKIiIiIiFRjAkFERERERKoxgSAiIiIiItWKVQLh7+8PjUaT7TVgwAAAQJMmTbIte+ONNwo5aiIiIiKiksOmsAPIjX379iEzM1OZPnLkCFq0aIGXX35Zmde3b19MmjRJmba3t3+qMRIRERERlWTFKoFwd3c3m54xYwYCAwPRuHFjZZ69vT28vLyedmhERERERM+EYnUL04Pu3buHb7/9Fr1794ZGo1HmL126FG5ubqhWrRpGjx6N27dvP7KetLQ0pKSkmL2IiIiIiMiyYnUF4kFr167FjRs3EBUVpcx79dVX4efnBx8fHxw+fBijRo3C8ePHsXr16hzrmT59OiZOnPgUIiYiIiIiKv40IiKFHcSTiIiIgFarxU8//ZRjma1bt6JZs2Y4efIkAgMDLZZJS0tDWlqaMp2SkgJfX18kJyfDYDDke9xERERERE9bSkoKjEZjvpzjFssrEGfOnMHmzZsfeWUBAOrVqwcAj0wgdDoddDpdvsdIRERERFQSFctnIGJiYuDh4YE2bdo8stzBgwcBAN7e3k8hKiIiIiKikq/YXYHIyspCTEwMIiMjYWPzf+GfOnUKy5YtQ+vWreHq6orDhw9j6NChaNSoEWrUqFGIERMRERERlRzFLoHYvHkzzp49i969e5vN12q12Lx5M+bMmYNbt27B19cXnTp1wtixYwspUiIiIiKikqfYPkRdUPLzARMiIiIioqIgP89xi+UzEEREREREVDiYQBARERERkWpMIIiIiIiISDUmEEREREREpBoTCCIiIiIiUo0JBBERERERqcYEgoiIiIiIVGMCQUREREREqjGBICIiIiIi1ZhAEBERERGRakwgiIiIiIhINSYQRERERESkGhMIIiIiIiJSzaawAyiqTl1OhdNd5ldEREREVPzdvJmab3UxgchBu093wkpnX9hhEBERERHlWVba7XyriwlEDgx2NrC2sy3sMIiIiIiI8ixTk3+n/RoRkXyrrQRISUmB0WhEcnIyDAZDYYdDRERERJRn+XmOy5v8iYiIiIhINSYQRERERESkGhMIIiIiIiJSjQkEERERERGpxgSCiIiIiIhUYwJBRERERESqMYEgIiIiIiLVmEAQEREREZFqTCCIiIiIiEg1JhBERERERKQaEwgiIiIiIlLNprADKGpEBACQkpJSyJEQEREREeUP07mt6Vw3L5hAPOTq1asAAF9f30KOhIiIiIgof928eRNGozFPdTCBeIiLiwsA4OzZs3nu3GdVSkoKfH19ce7cORgMhsIOp1hiH+YN+y/v2Id5xz7MO/Zh3rD/8q4k9aGI4ObNm/Dx8clzXUwgHmJldf+xEKPRWOx3lMJmMBjYh3nEPswb9l/esQ/zjn2Yd+zDvGH/5V1J6cP8+nKcD1ETEREREZFqTCCIiIiIiEg1JhAP0el0GD9+PHQ6XWGHUmyxD/OOfZg37L+8Yx/mHfsw79iHecP+yzv2oWUayY+xnIiIiIiI6JnAKxBERERERKQaEwgiIiIiIlKNCQQREREREan2RAnEvHnz4O/vDzs7O9SrVw+///67suzu3bsYMGAAXF1d4ejoiE6dOuHixYuPrXPVqlWoXLky7OzsUL16dfz6669my0UE48aNg7e3N/R6PZo3b44TJ048tt64uDg899xz0Ol0KF++PBYtWpSr7SmoeOfNm4eyZcvC2toa1tbWcHR0RHR0NFJTU8360N7eHm5ubtDpdPD19cXMmTNzjMXW1hY6nQ5ardYsJlMsXl5esLGxgU6ng52dXbHuwwfbc3Nzg4ODA5ydnREdHY0rV66Y7YPNmzdHvXr1YGdnZ7EPv/jiCzRs2BAODg7K+1G+fHmzeKKioqDRaMxerq6uxbb/TG3mxz64aNGibH2j0WhK/D6Yn33YpEkTi33o5OSkxGRpH7S2ti7WfZibz7Gfnx+CgoJgY2OD9u3b57iNAQEB0Gg0sLKyQpkyZbLF8+mnn8LZ2VkpExISUmz7z9Smmn3Q1tYWRqMR1tbWFvtv9erVaNGiBdzd3aHX66HX67P9L5kwYUK2fdDKyqpY74P52YdxcXEWP8dBQUFmMc2bNw9+fn6wsbGBVqt9po6Fj+tDS8c5jUYDOzu7Er8fAsC1a9fQvXt3GAwG5ViYmppqFnu7du3g7e0NBwcH1KxZE0uXLi2QWB5LcmnFihWi1Wrl66+/lr///lv69u0rzs7OcvHiRREReeONN8TX11e2bNki+/fvl+eff15eeOGFR9a5c+dOsba2lpkzZ8rRo0dl7NixYmtrK3/99ZdSZsaMGWI0GmXt2rVy6NAheemllyQgIEDu3LmTY73//vuv2Nvby7Bhw+To0aMyd+5csba2lg0bNqjenoKI19RmtWrVpFKlStKuXTtxdHQUf39/6datm9KH69atExcXF3Fzc5OaNWvK8uXLRa/Xy8KFC7PF8uabb4q1tbU0a9ZMbGxspF+/fkpMplh69uwpTk5OUrduXSldurS8+OKLxbIPv/nmG6W9Bg0aiIuLizg5Ocm6deukfPnyUr58eWUfjIuLE1tbW3Fzc5MjR45Y7MNXX31V3n77bbG2tpbhw4dL+/btRafTmcUTGRkpFStWFIPBIDExMbJlyxZp1apVsey//N4HY2JixGAwyLp168Ta2lree+89iY+PN4uppO2D+d2HV69elcTERKUPhw0bJlZWVtK2bVslpsjISGnZsqWMGTNG2Q937NhRbI+Fufkc79ixQzw9PSUwMFAiIiKkXbt2FrdRp9OJRqORt99+W8aMGSMajUZsbGyUeFasWCHW1tai1+tl7ty50rFjR7GxsZGyZcsWu/7LzT74888/S6dOnaRcuXLi7Oxssf/eeustef/99+XLL78Ua2trCQsLExsbG+nTp48S0/jx46Vq1apm+2BcXFyx3Qfzuw+3bdsmAGT58uXKsXD79u0yZswYJSZTe507dxYnJydp0aKFODk5SUREBPtQRG7cuGF2LBwzZowYjUZp2LBhid4PTVq2bCnBwcGyZ88e2bFjh5QvX166deumLJ86daqMHTtWdu7cKSdPnpQ5c+aIlZWV/PTTT/key+PkOoEICQmRAQMGKNOZmZni4+Mj06dPlxs3boitra2sWrVKWX7s2DEBILt3786xzi5dukibNm3M5tWrV09ef/11ERHJysoSLy8v+eCDD5TlN27cEJ1OJ8uXL8+x3pEjR0rVqlXN5r3yyisSERGhansKKt6QkBB59dVXBYDs27dPabNXr14CQOnD+fPnS6lSpeTQoUNKH44aNUoqVaqULZYHYzLFUq9ePenXr594eXnJzJkzlZhMsXz11VfFsg8DAwNlwIABcvToUQEge/fuVdr7/vvvBYBycjZ//nwxGAxm++DDffhwPBkZGeLk5CSBgYFKPD179hSdTsd90MI+GBMTI0ajMceYSuI+mN99+HBMs2fPFicnJ0lNTVViioyMlJdeeqnEHAtz8zkW+b//Ja1bt7Z44jFy5EgxGAxm8bzyyitiNBqVeOrWrSv29vZKPJmZmeLl5SU2NjbFrv9ysw8+3IcNGzZUFVOVKlVk4sSJSkzjx4+XGjVqlJh9ML/70JRAdOjQIceYQkJC5M0331RiMrU3fvx49qGFmNasWSMajUYSEhJK9H4oIsqxcN++fUqZ9evXi0ajkf/++y/Htlu3bi29evXK11jUyNUtTPfu3cOBAwfQvHlzZZ7pstHu3btx4MABpKenmy2vXLkyypYti927dyvz/P39MWHCBGV69+7dZusAQEREhLLO6dOnkZSUZFbGaDSiXr16ZvU2adIEUVFRqut93PbkJC/x/vbbbzhw4ABKlSoFZ2dn1KlTR2nzypUrsLKyUvpw9+7daNSoEWrUqKH0YUREBI4fP46yZctiwoQJSiwPxmSKJSIiAvHx8UhKSkJQUJASkymWQ4cOFbs+rFu3Lv7991+lfmdnZ4SEhCjTpp+Zt7e3V9oJCwsz2wdNfThq1CiL8dy+fRvp6emoW7eusk5qairS0tIwbdo0VKpUCf3790dGRkax67+C2AdN/fPDDz/gt99+Q7t27fD3338rMZW0fbCg+vDBmL766it07doVDg4OZjFt27YNSUlJmDt3Lvr374+rV68Wy2Nhbj/HwP/9L7l06ZIy78H/Jbt370ZWVpZZWxEREbhz5w52796tbOPt27eVMlZWVggPD4fRaCxW/ZfbffDBPnRwcMD169ct9uGDMWVlZeHmzZtwcXExi+l///sfkpKSMHv2bHTv3h1nz54tlvtgQfbhunXrEB8fjxYtWmDnzp1KTLt27cKBAwdQvXp1JSZTe3/++Sf7MIdjYfPmzeHn51di90NTGdOxsE6dOkoZ0z6yd+/eHNtOTk6Gi4tLvsaiRq4SiCtXriAzMxOenp5m8z09PZGUlISkpCRotVo4OztbXG4SGBgINzc3ZTopKSnHOk3LTfMeVW/ZsmXh7e392HpTUlJw586dx25PTvIS79mzZ5GZmYmMjAx4eHiYLbt48SIcHBxgY2MDZ2dns3ZM9ZumfXx84ObmpszLqazp+ZOsrCyzmB4sU5z60NnZGSKilDX1oWn68uXLAICbN2+atfNg3aY6bW1tLcYzatQo+Pj44Pnnn1fWCQ4OBgB8//33eP/997F9+3a0atUKHh4exar/CmIfrFSpEr7++mtoNBoMHjwYWVlZeOGFF3D+/PkSuQ8WRB8+GNPNmzdx5MgR9OnTx2ydli1bYsyYMQCA8ePHK/ugaduLUx/m9nP8YN13795Vph/8X5KUlIQ7d+6YteXp6Yl79+4hKSkJV65cybYPmv4WkWLVf7ndBx9kZ2eXYx8+GNOsWbOQmpqKLl26KDHVq1dP2Qc/+OADnD59Gg0bNsTNmzeL3T5YEH3o7e2NBQsWQKPR4O2334avry+aNGmCP/74A56enkhMTERmZiasrKzMYnpWjoVq+vDBmLRaLdavX5/tWFjS9sMHyzzYhwBgY2MDFxeXHNteuXIl9u3bh169euVrLGrYqC6Zj7Zs2VIg9X7zzTcFUm9RFBMTg6CgIAwbNixf632W+rB79+7Z5s2YMQMrVqxAXFwcfvvtN2V+8+bNMX78eAQFBcHb2xs1atRAYGAg9Ho9vLy8lHLPUv+Z9kEACA0NRZ8+fRAUFIT33nsPQUFBWLhwodmBW61ntQ9N4uLiUL16dYSEhJjN79q1K3bt2gUAaNWqFZo0aYLAwEDExcVlq/dZ6UP+L8k7S324c+dOxMTE4McffzQ7oWnVqhWMRiPee+89hIWFoXXr1vDz88PKlSuz1fGs9mGlSpUwaNAgVKxYEePHj8epU6cwe/ZshIaG5rreZ7UPTeLj4+Hs7JztYWvuh/dt27YNvXr1whdffIGqVas+9fZzdQXCzc0N1tbW2UZVunjxIry8vODl5YV79+7hxo0bFpfnxMvLK8c6TctN8/KjXoPBAL1e/9jtKYh4TaMU2NjYmF2Kv3jxIjw9PXHr1i1kZGTgxo0bZu2Y6jdNP9jWw8senDZlmKZvPCyVKU59eOPGDWg0GqWsqQ9N0+7u7gAAJycns3YerPvhPjT9vWLFCsyYMQObNm1CjRo1HhlPuXLl4ObmhsTExGLVfwWxDz4ck62tLWrVqoWTJ0+WyH2wIPvQ09MTO3fuRHR09GNjMu2Dpn4uTn2Y28/xg3Xb2dnlGIterzdr6+LFi9BqtfDy8oKbm1u2fdD0t0ajKVb9l9t98EF3797NsQ8BwGAw4PPPP8fKlSuVWxxyisnZ2RkVK1YslvtgQfbhgzGFhIQo/ePt7Q1ra2vlStizdix8kJo+3LJlC3r06AGtVvvImIr7fvhgmQf7EAAyMjJw7dq1bG1v374dbdu2xezZs9GzZ898j0WNXCUQWq0WtWvXNssUs7KysGXLFoSGhqJ27dqwtbU1W378+HGcPXv2kdl3aGhotuwzNjZWWScgIEDZmUxSUlKwd+/ePNX7uO0piHgbNGiA2rVr4/r167hx4wYOHDigtOnu7o6srCylD0NDQxEfH48jR44ofRgbG4tKlSqhVKlSZrE8GJMpltjYWDRq1AheXl74559/lJhMsQQHBxe7Pty3bx/KlSun1H/jxg3s27dPmTYNd3bnzh2lnW3btpntgw/3IQCUKlUKv/76KzZs2KDcf/ioeM6fP48rV64gISGhWPVfQeyDD8eUmZmJv/76C97e3iVyHyzIPvT29kZ6ejpee+21x8Z0/vx55RmI4taHuf0cA//3v+ThS/wPxmJlZWXWVmxsLOzt7REaGqpso729vVImKysLsbGxSE5OLlb9l9t98ME+vHXrVrb9zmT58uW4fv06goOD0aZNm8fGlJqailOnTqFUqVLFbh8sqD58OKaDBw8qx8IXXngBtWvXxpEjR5SYTO3VqlWLffiAwMBAXLlyxezLlJK6H5rKmI6FBw4cUMps3boVWVlZqFevnjIvLi4Obdq0wfvvv49+/foVSCyqqH7c+v9bsWKF6HQ6WbRokRw9elT69esnzs7OkpSUJCL3h3EtW7asbN26Vfbv3y+hoaESGhpqVkfTpk1l7ty5yvTOnTvFxsZGZs2aJceOHZPx48dbHHLK2dlZfvzxRzl8+LC0a9cu25BTPXr0kHfeeUeZNg3XNWLECDl27JjMmzfP4nBdj9oeS/XmNV5Tm9WrV5egoCBp3769ODo6SkBAgDLkWdmyZZXhH93d3SU4OFhWrFgh9vb2snDhQqUPTbEMHDhQbGxspEWLFmJjYyP9+/c3G0LT2dlZGUIzJCQkxyE0i0MffvPNN0p7DRo0EFdXV2X4xwoVKkj58uWVfdA0jKu7u7scOXJE6cNKlSop++CMGTPExsZGrK2tZfz48RIfHy9vv/22Es/Nmzdl+PDh8uabb4rBYJBJkyZJUFCQODg4iL+/f7Hrv/zeBydOnCgbN26UlStXirW1tdSsWVO0Wq28+eabJXYfzO8+NKlRo4ZoNJpsMe3du1eGDx+ujODk6Ogo5cqVk7Jly0rbtm2LZR/m5nO8f/9+qVmzptSoUUPatm0rTZo0kT///FNCQkKU/jMN42plZSUjR46U9957L8dhXO3t7eXTTz+Vzp07WxzGtTj0X272wa1bt8rKlSulRo0aUqpUKaX//vzzT2UfXLp0qdjY2Mjbb78tNjY2yrFw1KhRSkxvv/22xMXFyahRo8TJyUmCg4PF2dlZWrZsWSz3wfzuw9mzZ8vatWvlu+++ExsbG2nYsKFoNBrp0aOH2TCuOp1OXn75ZTEYDBIeHi5OTk7sw4eOhRERERaPhSV1PzRp2bKl1KpVS/bu3Su//fabVKhQwWwY161bt4q9vb2MHj1aEhMTldfVq1fzPZbHyXUCISIyd+5cKVu2rGi1WgkJCZE9e/Yoy+7cuSNvvvmmlCpVSuzt7aVDhw6SmJhotr6fn5+MHz/ebN7KlSulYsWKotVqpWrVqvLLL7+YLc/KypL33ntPPD09RafTSbNmzeT48eNmZRo3biyRkZFm87Zt26ac0JQrV05iYmJytT051ZvXeOfOnStlypQRKysrsbKyEnt7e+nVq5fcvHnTrA/t7OzE1dVVtFqtlC5dWmbMmJGtD02x2NjYiFarFRsbG7OYTLF4eHiItbW1aLVa0Wq1xboPH2zP1dVV9Hq9GAwG6dWrl1y+fNlsH2zatKmEhISITqdT+vDB/vPz8xMA2V6vvvqqiIjcvn1bwsPDxc3NTXm/rK2tpWHDhsW2/0xt5sc+OGTIECV2o9EoDg4OYmtrW+L3wfzsQxGRf/75RwDI2LFjs8Vk2gfd3d3FxsZGjEaj6PX6Yn8szM3nWK/XW/ycPvi/ZNu2beLv768sK126dLZ4PvnkE2VoZ41GI3Xq1Cm2/WdqU80+qNFoLPafaR9s3LixxeXOzs5KTK+88op4e3uLra2tODk5iZ2d3TP1OX5cH77//vsSGBgodnZ24ujoKHq9Ptux0NSer6+vWFlZia2tLfvwgT4UuT+kqF6vl379+lmMqaTuhyL3fxOoW7du4ujoqBwLb968qSyPjIy02H+NGzfO91geRyMiov56BRERERERPcty9QwEERERERE925hAEBERERGRakwgiIiIiIhINSYQRERERESkGhMIIiIiIiJSjQkEERERERGpxgSCiIiIiIhUYwJBRERERESqMYEgIqJCo9FosHbt2sIOg4iIcoEJBBERPZGoqCi0b9++sMMgIqKnjAkEERERERGpxgSCiIjyrEmTJhg8eDBGjhwJFxcXeHl5YcKECWZlTpw4gUaNGsHOzg5VqlRBbGxstnrOnTuHLl26wNnZGS4uLmjXrh0SEhIAAP/88w/s7e2xbNkypfzKlSuh1+tx9OjRgtw8IiJ6ABMIIiLKF4sXL4aDgwP27t2LmTNnYtKkSUqSkJWVhY4dO0Kr1WLv3r1YsGABRo0aZbZ+eno6IiIi4OTkhB07dmDnzp1wdHREy5Ytce/ePVSuXBmzZs3Cm2++ibNnz+L8+fN444038P7776NKlSqFsclERM8kjYhIYQdBRETFT1RUFG7cuIG1a9eiSZMmyMzMxI4dO5TlISEhaNq0KWbMmIFNmzahTZs2OHPmDHx8fAAAGzZsQKtWrbBmzRq0b98e3377LaZMmYJjx45Bo9EAAO7duwdnZ2esXbsW4eHhAIAXX3wRKSkp0Gq1sLa2xoYNG5TyRERU8GwKOwAiIioZatSoYTbt7e2NS5cuAQCOHTsGX19fJXkAgNDQULPyhw4dwsmTJ+Hk5GQ2/+7duzh16pQy/fXXX6NixYqwsrLC33//zeSBiOgpYwJBRET5wtbW1mxao9EgKytL9fqpqamoXbs2li5dmm2Zu7u78vehQ4dw69YtWFlZITExEd7e3k8eNBER5RoTCCIiKnBBQUE4d+6c2Qn/nj17zMo899xz+O677+Dh4QGDwWCxnmvXriEqKgpjxoxBYmIiunfvjj/++AN6vb7At4GIiO7jQ9RERFTgmjdvjooVKyIyMhKHDh3Cjh07MGbMGLMy3bt3h5ubG9q1a4cdO3bg9OnTiIuLw+DBg3H+/HkAwBtvvAFfX1+MHTsWH330ETIzMzF8+PDC2CQiomcWEwgiIipwVlZWWLNmDe7cuYOQkBD06dMHU6dONStjb2+P+Ph4lC1bFh07dkRQUBCio6Nx9+5dGAwGfPPNN/j111+xZMkS2NjYwMHBAd9++y2++OILrF+/vpC2jIjo2cNRmIiIiIiISDVegSAiIiIiItWYQBARERERkWpMIIiIiIiISDUmEEREREREpBoTCCIiIiIiUo0JBBERERERqcYEgoiIiIiIVGMCQUREREREqjGBICIiIiIi1ZhAEBERERGRakwgiIiIiIhINSYQRERERESk2v8DM2lZiAq1Rq4AAAAASUVORK5CYII=\" alt=\"Setpoints Plot\" style=\"max-width:100%; height:auto;\">\n",
+       "            </div>\n",
+       "\n",
+       "        </div>\n",
+       "        <script>\n",
+       "            function toggleVisibility(id) {\n",
+       "                var content = document.getElementById(id);\n",
+       "                var arrow = document.getElementById('arrow_' + id);\n",
+       "                if (content.style.display === 'none') {\n",
+       "                    content.style.display = 'block';\n",
+       "                    arrow.innerHTML = '▼';\n",
+       "                } else {\n",
+       "                    content.style.display = 'none';\n",
+       "                    arrow.innerHTML = '►';\n",
+       "                }\n",
+       "            }\n",
+       "        </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "mqt_10"
    ]
@@ -337,9 +470,142 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <div style=\"border:1px solid #ddd; border-radius: 5px; padding: 3px; margin-top: 5px;\">\n",
+       "            <h2>Chamber Simulation</h2>\n",
+       "\n",
+       "            <div id=\"setpoints_dataframe\" onclick=\"toggleVisibility('content_setpoints_dataframe')\" style=\"cursor: pointer; background-color: #000000; color: #FFFFFF; padding: 5px; border-radius: 3px; margin-bottom: 1px;\">\n",
+       "                <h4 style=\"font-family: monospace; margin: 0;\">\n",
+       "                    <span id=\"arrow_content_setpoints_dataframe\" style=\"color: #b676c2;\">►</span>\n",
+       "                     Setpoints Dataframe\n",
+       "                </h4>\n",
+       "            </div>\n",
+       "            <div id=\"content_setpoints_dataframe\" style=\"display:none; margin-left: 20px; padding: 5px;\">\n",
+       "                <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>setpoint_temperature</th>\n",
+       "      <th>setpoint_irradiance_full</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0 days 00:00:00</th>\n",
+       "      <td>25.0</td>\n",
+       "      <td>1600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0 days 00:01:00</th>\n",
+       "      <td>25.0</td>\n",
+       "      <td>1600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0 days 00:02:00</th>\n",
+       "      <td>23.898305</td>\n",
+       "      <td>1600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0 days 00:03:00</th>\n",
+       "      <td>22.79661</td>\n",
+       "      <td>1600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0 days 00:04:00</th>\n",
+       "      <td>21.694915</td>\n",
+       "      <td>1600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0 days 05:26:00</th>\n",
+       "      <td>27.105263</td>\n",
+       "      <td>1600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0 days 05:27:00</th>\n",
+       "      <td>26.052632</td>\n",
+       "      <td>1600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0 days 05:28:00</th>\n",
+       "      <td>25.0</td>\n",
+       "      <td>1600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0 days 05:29:00</th>\n",
+       "      <td>25.0</td>\n",
+       "      <td>1600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0 days 05:30:00</th>\n",
+       "      <td>25.0</td>\n",
+       "      <td>1600</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>331 rows × 2 columns</p>\n",
+       "</div>\n",
+       "            </div>\n",
+       "\n",
+       "            <div id=\"setpoints_plot\" onclick=\"toggleVisibility('content_setpoints_plot')\" style=\"cursor: pointer; background-color: #000000; color: #FFFFFF; padding: 5px; border-radius: 3px; margin-bottom: 1px;\">\n",
+       "                <h4 style=\"font-family: monospace; margin: 0;\">\n",
+       "                    <span id=\"arrow_content_setpoints_plot\" style=\"color: #b676c2;\">►</span>\n",
+       "                     Setpoints Plot\n",
+       "                </h4>\n",
+       "            </div>\n",
+       "            <div id=\"content_setpoints_plot\" style=\"display:none; margin-left: 20px; padding: 5px;\">\n",
+       "                <h3>Setpoints Plot</h3>\n",
+       "                <img src=\"data:image/png;base64,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\" alt=\"Setpoints Plot\" style=\"max-width:100%; height:auto;\">\n",
+       "            </div>\n",
+       "\n",
+       "        </div>\n",
+       "        <script>\n",
+       "            function toggleVisibility(id) {\n",
+       "                var content = document.getElementById(id);\n",
+       "                var arrow = document.getElementById('arrow_' + id);\n",
+       "                if (content.style.display === 'none') {\n",
+       "                    content.style.display = 'block';\n",
+       "                    arrow.innerHTML = '▼';\n",
+       "                } else {\n",
+       "                    content.style.display = 'none';\n",
+       "                    arrow.innerHTML = '►';\n",
+       "                }\n",
+       "            }\n",
+       "        </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "tc_uv"
    ]
@@ -352,7 +618,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x28f1e74ac10>"
+       "<matplotlib.legend.Legend at 0x167e4027b20>"
       ]
      },
      "execution_count": 14,
@@ -1192,7 +1458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1226,7 +1492,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -1257,27 +1523,27 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 days 00:00:00</th>\n",
-       "      <td>358.150000</td>\n",
+       "      <td>85.000000</td>\n",
        "      <td>85.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0 days 00:01:00</th>\n",
-       "      <td>359.195333</td>\n",
+       "      <td>86.045333</td>\n",
        "      <td>82.493425</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0 days 00:02:00</th>\n",
-       "      <td>360.173250</td>\n",
+       "      <td>87.023250</td>\n",
        "      <td>79.730607</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0 days 00:03:00</th>\n",
-       "      <td>361.088098</td>\n",
+       "      <td>87.938098</td>\n",
        "      <td>77.033270</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0 days 00:04:00</th>\n",
-       "      <td>361.943944</td>\n",
+       "      <td>88.793944</td>\n",
        "      <td>74.522821</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1287,27 +1553,27 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50 days 06:55:00</th>\n",
-       "      <td>245.926548</td>\n",
+       "      <td>-27.223452</td>\n",
        "      <td>36.336043</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50 days 06:56:00</th>\n",
-       "      <td>245.984215</td>\n",
+       "      <td>-27.165785</td>\n",
        "      <td>36.261838</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50 days 06:57:00</th>\n",
-       "      <td>247.245000</td>\n",
+       "      <td>-25.905000</td>\n",
        "      <td>34.783481</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50 days 06:58:00</th>\n",
-       "      <td>248.601559</td>\n",
+       "      <td>-24.548441</td>\n",
        "      <td>33.286602</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50 days 06:59:00</th>\n",
-       "      <td>250.062289</td>\n",
+       "      <td>-23.087711</td>\n",
        "      <td>31.778878</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1317,22 +1583,22 @@
       ],
       "text/plain": [
        "                  temperature  relative_humidity\n",
-       "0 days 00:00:00    358.150000          85.000000\n",
-       "0 days 00:01:00    359.195333          82.493425\n",
-       "0 days 00:02:00    360.173250          79.730607\n",
-       "0 days 00:03:00    361.088098          77.033270\n",
-       "0 days 00:04:00    361.943944          74.522821\n",
+       "0 days 00:00:00     85.000000          85.000000\n",
+       "0 days 00:01:00     86.045333          82.493425\n",
+       "0 days 00:02:00     87.023250          79.730607\n",
+       "0 days 00:03:00     87.938098          77.033270\n",
+       "0 days 00:04:00     88.793944          74.522821\n",
        "...                       ...                ...\n",
-       "50 days 06:55:00   245.926548          36.336043\n",
-       "50 days 06:56:00   245.984215          36.261838\n",
-       "50 days 06:57:00   247.245000          34.783481\n",
-       "50 days 06:58:00   248.601559          33.286602\n",
-       "50 days 06:59:00   250.062289          31.778878\n",
+       "50 days 06:55:00   -27.223452          36.336043\n",
+       "50 days 06:56:00   -27.165785          36.261838\n",
+       "50 days 06:57:00   -25.905000          34.783481\n",
+       "50 days 06:58:00   -24.548441          33.286602\n",
+       "50 days 06:59:00   -23.087711          31.778878\n",
        "\n",
        "[72420 rows x 2 columns]"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1345,7 +1611,7 @@
     "conditions_df.columns = [\"temperature\", \"relative_humidity\"]\n",
     "\n",
     "# convert to kelvin\n",
-    "conditions_df[\"temperature\"] += 273.15\n",
+    "# conditions_df[\"temperature\"] += 273.15\n",
     "\n",
     "# uncomment to view dataframe\n",
     "conditions_df"
@@ -1353,7 +1619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -1362,7 +1628,7 @@
        "3.1961485899633e-06"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1395,7 +1661,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -1431,7 +1697,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1459,7 +1725,7 @@
        "Freq: h, Length: 8760, dtype: float64"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1469,7 +1735,8 @@
     "temp_module = pvdeg.temperature.temperature(\n",
     "    weather_df=weather_df,\n",
     "    meta=meta,\n",
-    "    cell_or_mod=\"module\"\n",
+    "    cell_or_mod=\"module\",\n",
+    "    conf = \"open_rack_glass_polymer\",\n",
     ")\n",
     "\n",
     "# fix index, tmy data has non-uniform timeseries data as a part of the way it is sampled, we want a consistently increasing index\n",
@@ -1481,7 +1748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -1631,7 +1898,7 @@
        "[8760 rows x 4 columns]"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1660,7 +1927,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1715,7 +1982,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -2072,7 +2339,7 @@
        "[10 rows x 122 columns]"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2092,7 +2359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -2101,7 +2368,7 @@
        "1.5815590853333936e-06"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2111,7 +2378,7 @@
     "miami_res = pvdeg.degradation.degradation(\n",
     "    spectra_df=miami_irr,\n",
     "    # we calculated these above the irradiance calculation and they are representative of the conditions on the module\n",
-    "    temp_module=temp_module + 273.15, # convert to K\n",
+    "    temp_module=temp_module, # open rack termperature [C]\n",
     "    rh_module=rh_module[\"RH_back_encap\"]\n",
     ")\n",
     "\n",
@@ -2120,7 +2387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -2129,7 +2396,7 @@
        "(3.1961485899633e-06, 1.5815590853333936e-06)"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2140,7 +2407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -2149,7 +2416,7 @@
        "2.020884720402052"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2159,13 +2426,6 @@
     "\n",
     "af"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {